system

Abstract

We provide the system. [Solution] A means of collecting data from online stores and physical retail stores and integrating this data, A means of performing demand forecasting using a generative AI model based on integrated data, A means for optimizing inventory across multiple sales channels based on demand forecast results, A means of distributing optimized inventory allocation instructions to each management terminal, A means of displaying inventory information and allocation instructions on a management terminal, A system that includes means for performing inventory management based on the instructions provided.

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 as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the retail industry, stockouts and overstocks due to the fragmentation of inventory management between online stores and physical stores are problems. Therefore, it is required to efficiently manage inventory without missing sales opportunities, improve customer satisfaction, and reduce business costs. Also, it is an important issue to grasp the inventory situation in real time and flexibly respond to fluctuations in demand. 【Means for Solving the Problems】 【0005】 The system collects data from online stores and physical retail locations and integrates this data. Furthermore, it uses an AI model based on the integrated data to forecast demand and optimize inventory across multiple sales channels based on the forecast results. By distributing optimized inventory allocation instructions to each management terminal, the system enables proper inventory management. In addition, it incorporates methods to automate in-store inventory counts using AI cameras and to detect missing or anomaly values ​​in the collected data and perform data cleaning, thereby enabling real-time inventory status monitoring and flexible responses. 【0006】 An "online store" is a virtual store where goods and services are sold and purchased via the internet. 【0007】 A "physical retail store" is a store located in a physical place where customers can visit in person to select and purchase products. 【0008】 "Means of collecting data" refers to the process and technology of gathering necessary information from various data sources, and in this invention, it means information from online stores and physical retail stores. 【0009】 "Means of integration" refer to technologies and methods for combining different datasets and information into a single, unified database. 【0010】 A "generative AI model" is a form of artificial intelligence that learns from large amounts of data and is used to predict future demand. 【0011】 "Demand forecasting" is the process of predicting future demand for products and services based on past data and trends. 【0012】 "Methods for optimizing inventory" refer to techniques and methods for adjusting the placement and quantity of inventory in order to meet demand while maintaining the minimum necessary inventory. 【0013】 "To deliver" refers to the act of sending specific information or data to a recipient. In this invention, it means sending optimized inventory allocation instructions to a terminal. 【0014】 A "management terminal" refers to a computer or device used to receive and display inventory information and instructions, and to perform necessary processing. 【0015】 "Automating inventory management" refers to a process that uses technologies such as AI cameras and sensors to mechanically measure and record inventory levels instead of manually. 【0016】 "Data cleaning" is the process of improving the accuracy and consistency of data by identifying missing or outlier values ​​in collected data and deleting or correcting them. [Brief explanation of the drawing] 【0017】 [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Modes for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the terms used in the following description will be explained. 【0020】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0021】 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. 【0022】 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. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 As shown in Figure 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. 【0028】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0029】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0030】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0031】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0032】 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. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 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. 【0035】 The 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. 【0036】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0037】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0038】 This invention is a system that integrates inventory management for online stores and physical retail stores, enabling real-time demand forecasting and inventory optimization. This system achieves efficient inventory management and improved customer satisfaction through the coordinated operation of a server, terminals, and users. 【0039】 Server Role 【0040】 The server is the core of the system, collecting inventory data from online stores and physical retail locations. This is done using APIs and database connections. The collected data is integrated on the server, where missing or outlier values ​​are detected and data cleaning is performed. Based on this cleaned and accurate data, the server uses a generative AI model to perform demand forecasting. The results of the demand forecast form the basis for optimally allocating inventory across each channel. Instructions based on the optimized inventory allocation are distributed to each management terminal. 【0041】 Terminal role 【0042】 The terminal receives information and instructions from the server and provides them to the user. The terminal screen visually displays real-time updated inventory information and specific allocation instructions, allowing users to accurately understand the current situation. Furthermore, the terminal is linked to the store's AI camera system, enabling automated inventory management. This allows for automatic, rather than manual, updates of inventory data, resulting in efficient and accurate inventory management. 【0043】 User roles 【0044】 Store staff and managers, who are users of the system, execute inventory information and allocation instructions received via terminals. Specifically, this includes ordering and receiving products, adjusting inventory, and transferring inventory between stores. They also perform tasks such as conducting inventory counts and confirming product placement based on instructions from the terminals. This maximizes sales opportunities and enables quick responses to customer demand. 【0045】 Specific example 【0046】 For example, when a user sells a new product through multiple channels, the server analyzes past sales data of similar products to predict demand for the new product. The server identifies stores and times when demand is concentrated and calculates the optimal inventory allocation accordingly. This optimization information is delivered to terminals, and users follow the instructions to move inventory to the necessary stores in a timely manner. In this way, the entire system works together to provide customers with a seamless purchasing experience and improve customer satisfaction. 【0047】 The following describes the processing flow. 【0048】 Step 1: 【0049】 The server collects inventory data from online stores and physical retail locations. It gathers information such as current inventory levels, expected arrival dates, and sales history through APIs and database connections. It also acquires physical store inventory data from AI cameras. 【0050】 Step 2: 【0051】 The server integrates the collected data and performs data cleaning to detect missing values ​​and outliers. This process builds an accurate and consistent dataset for analysis. 【0052】 Step 3: 【0053】 The server feeds cleaned data into an AI model to perform demand forecasting. This AI model analyzes historical data and trends to predict future inventory needs. 【0054】 Step 4: 【0055】 The server uses demand forecasts to perform calculations to optimally allocate inventory across multiple sales channels. This calculation takes into account the characteristics of each channel, the volume of demand, and the balance of inventory levels. 【0056】 Step 5: 【0057】 The server distributes optimized inventory allocation instructions to each management terminal. These instructions indicate which stores and channels to allocate inventory to at the appropriate time. 【0058】 Step 6: 【0059】 The terminal receives information and instructions from the server and displays inventory information and specific allocation instructions to the user. The terminal interface is designed to allow users to easily check information and take action. 【0060】 Step 7: 【0061】 Based on instructions provided by the user via their device, the system performs inventory management tasks such as inventory movement, ordering, and stocktaking. This enables rapid action to maximize sales opportunities and respond to customer needs. 【0062】 (Example 1) 【0063】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0064】 In modern sales systems, the disconnect between information processing equipment and physical stores reduces inventory management efficiency and creates a risk of missed sales opportunities due to demand uncertainty. Furthermore, inaccurate information, including missing or abnormal values, impairs the accuracy of supply and demand forecasts. As a result, resource allocation across sales channels is not optimized, potentially leading to decreased customer satisfaction. 【0065】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0066】 In this invention, the server includes means for aggregating information from an information processing device and physical stores and integrating this information, means for forecasting demand using an automatically generated model, and means for optimally allocating resources among multiple supply channels based on the forecast results. This ensures that information is accurately managed, improves the accuracy of supply and demand forecasting, and enables efficient resource allocation among sales channels. 【0067】 An "information processing device" is a computer system used to collect, process, and integrate data from online stores and physical stores. 【0068】 A "physical store" is a retail facility with physical locations where consumers can directly inspect and purchase products. 【0069】 "Information" refers to a collection of data related to sales activities, such as inventory status, sales history, and return history. 【0070】 "Integration" is the process of combining information obtained from multiple data sources into a form that can be centrally managed. 【0071】 An "automatically generated model" is a machine learning algorithm that learns from collected data and uses it to predict future demand and trends. 【0072】 "Demand forecasting" is the process of estimating how much of a product will sell in the future, based on past sales data. 【0073】 "Delivery channel" is a concept that refers to the distribution route through which goods and services are delivered to customers. 【0074】 "Optimal resource allocation" refers to a method of efficiently and effectively distributing product inventory across multiple distribution channels. 【0075】 "Resource management" refers to activities that control various elements in the supply chain, such as inventory retention, movement, and replenishment. 【0076】 In one embodiment of the invention, this system optimizes inventory management through the cooperation of an information processing device, a terminal, and a user. 【0077】 Server Processing 【0078】 The server functions as an information processing unit, aggregating and integrating data from online and physical stores. This involves retrieving data using APIs and database connections, and performing data cleaning using the Python Pandas library. Generative AI models are used to predict future demand from the integrated data. For example, the server analyzes sales trends for a specific product and predicts next month's demand using a prompt like this: "Predict next month's demand based on data from the past 12 months." 【0079】 Terminal processing 【0080】 The terminal receives optimized resource allocation instructions from the server and displays them in a user-friendly format. The terminal provides real-time updated inventory information, enabling users to quickly manage their inventory based on this data. The terminal also integrates with an AI camera system to automate physical store inventory counts and streamline inventory data updates. 【0081】 User actions 【0082】 Users, acting as store staff or managers, manage resources based on allocation instructions received via a terminal. Specific tasks include transferring inventory between stores, ordering products, and receiving deliveries. By following the instructions displayed on the terminal, users can maximize sales opportunities and respond quickly to customer demand. They also perform tasks such as verifying product placement based on inventory results. 【0083】 This system achieves consistent and efficient inventory management as a whole by having servers, terminals, and users each fulfill their respective roles. As a result, resource optimization is achieved across the entire supply chain, and customer satisfaction is improved. 【0084】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0085】 Step 1: 【0086】 The server first collects inventory data from online stores and physical stores. Inputs include information such as the stock quantity, sales history, and return history of each product, obtained through APIs and database connections. After the data is collected, the server integrates it into a single dataset, allowing it to understand the overall inventory situation. 【0087】 Step 2: 【0088】 The server performs data cleaning on the collected data. The input for this step is the previously integrated data. It removes inaccurate data and fills in missing values ​​with the mean or median. Specifically, it uses the Python Pandas library to perform data preparation and outputs the prepared data. 【0089】 Step 3: 【0090】 The server performs demand forecasting using the data after it has been cleaned. The input here is the cleaned data. The data is input into the generating AI model, and future demand is quantified. For example, it calculates the predicted sales volume of a specific product and outputs the result. An example of a prompt used in this process is, "Based on data from the past 12 months, please forecast the demand for next month." 【0091】 Step 4: 【0092】 The server optimizes inventory based on demand forecasts. The input is the forecasted demand data. Using the SciPy library, it calculates the optimal inventory allocation among stores and outputs the allocation plan. 【0093】 Step 5: 【0094】 The terminal displays inventory allocation instructions received from the server. The input is optimization instruction data sent by the server. The terminal displays this information in a user-friendly format in real time, allowing the user to understand what action to take next. 【0095】 Step 6: 【0096】 Users check instructions on a terminal and perform specific inventory management. The input is the allocation instructions displayed on the terminal. Based on this, users move inventory, order necessary products, and receive them. As a result, inventory levels are kept in an optimized state, and resource utilization efficiency is maximized. 【0097】 (Application Example 1) 【0098】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0099】 A system is needed to improve the inefficiencies in inventory management faced by online and physical stores, enabling real-time allocation of goods to meet demand. Furthermore, automating inventory counting in physical stores to prevent stockouts is a challenge. Additionally, store staff need to be able to quickly obtain accurate inventory allocation information, streamlining management. 【0100】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0101】 In this invention, the server includes means for collecting and integrating information from online stores and physical stores, means for performing demand forecasting using a generative AI model based on the integrated information, and means for optimizing items across multiple sales channels based on the results of the demand forecast. This enables real-time optimization of item allocation. Furthermore, by automatically monitoring the shelves of physical stores using AI cameras, the decrease in items can be detected immediately, enabling efficient inventory management. In addition, by utilizing smart devices to immediately provide store staff with item information and optimized allocation instructions, it is possible to improve the efficiency of management operations. 【0102】 An "online store" is a type of retail business that sells goods via the internet. 【0103】 A "physical store" is a retail store located in a physical place where consumers can actually visit and purchase goods. 【0104】 "Means of collecting information" refers to methods and devices for obtaining inventory and sales-related data from online stores and physical stores. 【0105】 "Means of integrating information" refers to the process of combining collected data into a single dataset, making it available for analysis and optimization. 【0106】 A "generative AI model" is an artificial intelligence model designed to make predictions and classifications based on specific data. 【0107】 "Methods for demand forecasting" refers to the process of using integrated data and AI models to predict future consumer demand. 【0108】 "Means for optimizing goods" refers to methods or devices for efficiently allocating inventory of goods across sales channels based on demand forecasts. 【0109】 A "control device" is an electronic device that receives and displays information and instructions about goods and transmits them to the user. 【0110】 "Item information" refers to important data about items, such as inventory levels, location, and price. 【0111】 An "AI camera" is a camera device equipped with artificial intelligence that has the function of automatically monitoring the presence and placement of objects. 【0112】 "Automated monitoring" is a process that uses machines or devices to continuously monitor a state without human intervention. 【0113】 A "smart device" is an electronic device that has communication capabilities and can be used with a variety of applications. 【0114】 This invention provides a system that efficiently integrates inventory management for online and physical stores, enabling real-time demand forecasting and inventory allocation. The system employs a configuration in which a server, terminals, and users work together in cooperation. 【0115】 The server's primary role is to collect information from online and physical stores. Information is typically collected via APIs or database connections. The collected data is integrated on the server and undergoes a data cleaning process to correct missing or outlier values. Subsequently, a generative AI model is used to forecast demand, and based on the forecast results, items across multiple sales channels are optimized. This calculates the optimal item allocation for each store and online store, and then transmits this information to the terminals. The AI ​​technologies used include TENSORFLOW® and similar machine learning frameworks. 【0116】 The terminal is a device that receives allocation information from the server and displays it in real time along with item information. This terminal is often composed of mobile devices such as smartphones and tablets, and is equipped with the function to automate inventory management in physical stores in conjunction with an AI camera system. 【0117】 Store staff, as users of this terminal, receive real-time inventory information and distribution instructions, enabling efficient management and movement of goods. This allows them to respond quickly to customer demand and prevent stockouts. For example, they can receive an immediate alert if the stock of a popular item in the store is running low, allowing them to replenish it at the appropriate time. 【0118】 An example of a prompt message is: "Based on data from the past six months, forecast the demand for T-shirts during the summer and suggest the optimal inventory allocation." 【0119】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0120】 Step 1: 【0121】 The server collects information from online and physical stores. It uses APIs and database connections from each store as input. This data includes inventory levels, sales figures, pricing, and more. The output generates an integrated raw dataset. 【0122】 Step 2: 【0123】 The server detects missing and outlier values ​​in the collected data and performs data cleaning. The input is the raw data collected in step 1. This process involves imputing missing values ​​and correcting outliers. The output is a cleaned and accurate dataset. 【0124】 Step 3: 【0125】 The server uses a generated AI model based on the cleaned data to perform demand forecasting. The input is the dataset obtained in step 2. A prompt is used to instruct the AI ​​model to make a prediction. The prompt used is: "Based on data from the past 6 months, forecast the demand for T-shirts in the summer and suggest the optimal inventory allocation." The output is the demand forecast result. 【0126】 Step 4: 【0127】 The server optimizes the distribution of goods across multiple sales channels based on the demand forecast results. The input is the demand forecast results from step 3. In this process, the optimal allocation of goods for each store and online store is calculated. Allocation instructions are generated as the output. 【0128】 Step 5: 【0129】 The server distributes calculated and optimized item distribution instructions to each terminal. The input is the distribution instructions from step 4. A real-time notification system over the network is used for this communication. The output is the distribution instructions displayed on each terminal. 【0130】 Step 6: 【0131】 The terminal displays the received item allocation instructions and the latest item information to the user. The input is the allocation instructions received in step 5. In terms of specific actions, the information is visually presented on the terminal screen. The output is a real-time information display that the user can check. 【0132】 Step 7: 【0133】 The user performs inventory management based on the presented item information and allocation instructions. The input is the information presented from the terminal in step 6. This action includes specific actions such as moving items, deciding on orders, and replenishing the store. As output, optimized inventory management is completed. 【0134】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0135】 This invention provides a system that integrates real-time inventory management for online stores and physical retail stores, and further enables inventory management that reflects user emotions. This system is centered around three components: a server, a terminal, and a user, and the integration of an emotion engine enables more flexible and human-centered inventory management. 【0136】 Server Role 【0137】 The server first collects inventory data from online stores and physical retail locations. This data is gathered via API or database connection. The server integrates the acquired data and performs data cleaning to detect and correct missing or outlier values. Based on accurate data, the server uses a generative AI model to forecast demand. This forecast helps in the efficient allocation of inventory. The server distributes optimized inventory allocation instructions to each management terminal. The server also receives the output of the emotion engine and adjusts inventory management suggestions according to the user's emotional state. 【0138】 Terminal role 【0139】 The terminal receives inventory information and instructions transmitted from the server and presents them visually to the user. This information includes inventory allocation instructions based on future demand forecasts, which the terminal updates in real time. Furthermore, the terminal has a built-in emotion engine that uses the terminal's camera and sensors to recognize the user's emotions. This analysis result works in conjunction with other functions of the system to customize instructions and suggestions for inventory management according to the user's emotions. 【0140】 User roles 【0141】 Users perform inventory management tasks based on inventory information and instructions viewed via their terminals. This includes placing orders, rearranging inventory layouts, and moving inventory between stores. User emotions are analyzed by an emotion engine, and information and suggestions are optimized to create the most user-friendly working environment. 【0142】 Specific example 【0143】 For example, when an inventory shortage occurs on-site, the server collects and analyzes the data, using an AI model to predict which products are needed. Simultaneously, the terminal uses an emotion engine to assess the user's stress level and suggests emergency support actions as needed, such as requesting assistance from other staff or initiating an automated ordering process. This reduces the user's burden while enabling efficient work execution. In this way, the entire system works together to achieve adaptive inventory management that responds to the user's emotions. 【0144】 The following describes the processing flow. 【0145】 Step 1: 【0146】 The server collects inventory data from online stores and physical retail locations via API or database connections. This includes comprehensive data such as inventory quantities, sales history, and expected arrival dates. 【0147】 Step 2: 【0148】 The server integrates the collected data into a unified database, detects missing or outlier values, and performs data cleaning. This process improves the accuracy and reliability of the analysis. 【0149】 Step 3: 【0150】 The server feeds the cleaned data into a generated AI model to perform demand forecasting. The model considers past trends and current market conditions to generate highly accurate demand forecast results. 【0151】 Step 4: 【0152】 The server calculates the optimal inventory allocation based on demand forecasts. This calculation takes into account the balance of demand and inventory levels across each sales channel. 【0153】 Step 5: 【0154】 The terminal receives optimized inventory allocation instructions delivered from the server and displays them visually to the user. The displayed information is presented in an easily understandable format using diagrams and graphs. 【0155】 Step 6: 【0156】 The device's built-in emotion engine uses cameras and sensors to recognize the user's emotions in real time. This allows it to evaluate the user's stress level and emotional state. 【0157】 Step 7: 【0158】 The server receives the results of the emotion engine's analysis and adjusts inventory management suggestions according to the user's emotional state. For example, it displays suggestions on the screen such as arranging urgent deliveries or staffing adjustments to reduce workload. 【0159】 Step 8: 【0160】 Users perform inventory management tasks such as moving inventory and placing orders based on information and suggestions provided by their terminals. This allows for efficient operations and quick responses to customer needs. 【0161】 Step 9: 【0162】 Based on user actions and feedback, the emotion engine continuously learns and improves the accuracy of future suggestions. This process contributes to enhancing the overall functionality of the system. 【0163】 (Example 2) 【0164】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0165】 Traditional inventory management systems manage online store and physical store inventory data separately, making it difficult to allocate and manage inventory appropriately. Furthermore, they lack the means to implement inventory management that considers employee emotions and stress levels, resulting in limitations in inventory management efficiency and improvements to the working environment. 【0166】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0167】 This invention includes a server that collects information from online stores and physical retail stores and integrates this information; a server that performs demand forecasting using a generation artificial intelligence model based on the integrated information; and a server that recognizes and analyzes the user's emotions using an emotion engine. This enables efficient management of inventory information from online stores and physical retail stores through integration, and optimizes inventory through demand forecasting. Furthermore, by enabling adjustments to inventory management that take user emotions into account, it also contributes to improving the working environment. 【0168】 An "online store" is a virtual marketplace for selling goods and services via the internet. 【0169】 A "physical retail store" is a retail location that provides goods and services to consumers in a physical place. 【0170】 "Collecting information" is the act of gathering specific data or knowledge and making it available for recording and analysis. 【0171】 "Integration" refers to the process of bringing together different pieces of information and combining them into a consistent system or dataset. 【0172】 A "generative artificial intelligence model" is an information processing system equipped with advanced algorithms that learn from data and make predictions and decisions. 【0173】 "Demand forecasting" is the process of predicting future consumer trends in advance using methods such as statistical analysis and machine learning. 【0174】 "Distribution channels" refer to the paths that goods and services take from producers to consumers. 【0175】 "Optimizing inventory" means efficiently adjusting the placement and quantity of inventory so that there is neither an excess nor a shortage of goods. 【0176】 "Allocation instructions" are specific instructions or guidelines for assigning goods or resources to specific locations or personnel. 【0177】 "Management equipment" is a general term for hardware and software used by companies and organizations to control and monitor their operations and processes. 【0178】 An "emotion engine" refers to a technological foundation for detecting and analyzing a user's emotions and psychological state. 【0179】 "Recognizing and analyzing emotions" is the process of detecting a user's psychological state and classifying and evaluating its meaning. 【0180】 "Adjusting a proposal" refers to the act of improving existing proposals or plans to their optimal form based on data and circumstances. 【0181】 This system integrates servers, terminals, and users to revolutionize inventory management for online stores and physical retail locations. The server first collects inventory data from each sales channel. This process uses APIs and database connections to systematically gather inventory information. The server uses data processing libraries such as Python and Pandas to perform data cleaning, detecting and correcting missing or outlier values ​​in the collected data. 【0182】 Next, the server uses a generative artificial intelligence model to perform demand forecasting. This forecasting utilizes AI libraries such as TensorFlow and PyTorch to generate demand scenarios that predict future demand from big data. Specifically, the server can use prompts to predict "which products will be needed and in what quantities next week." An example of such a prompt is, "Based on current inventory data and future demand forecasts, please list the products that will be needed next." 【0183】 Furthermore, the terminal has the ability to receive optimized inventory allocation instructions sent from the server and visualize them for the user. The terminal uses its built-in camera and sensors to run an emotion engine and recognize the user's emotions in real time. For example, if the user is experiencing high levels of stress, the terminal can suggest an optimal inventory management plan to the user. 【0184】 Users efficiently manage inventory based on inventory information and suggestions provided from their devices. This includes ordering products, rearranging inventory, and even transferring inventory between stores. To improve the user experience, tasks are optimized based on the results of an emotion engine analysis. This provides users with an environment where they can perform their tasks without stress. 【0185】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0186】 Step 1: 【0187】 The server collects inventory data from online stores and physical retail locations. Inputs include API calls and database queries to retrieve information such as product ID, quantity, and price. This information is imported into the server in JSON or CSV format. The output is a unified inventory data set, which is used in subsequent processing steps. 【0188】 Step 2: 【0189】 The server performs data cleaning based on the collected inventory data. The integrated dataset obtained in Step 1 is used as input. Missing values ​​are imputed and outliers are removed from this dataset. Specifically, the mean value is substituted for missing values, and obviously abnormal values ​​are removed based on a pre-set threshold. The output is cleaned and accurate inventory data. 【0190】 Step 3: 【0191】 The server inputs cleaned inventory data into a generated AI model to perform demand forecasting. The input is inventory information formatted as time-series data. The AI ​​model is built using TensorFlow and executes a forecasting algorithm. As output, for example, a forecast result is obtained that "the predicted sales of product A next week will be 200 units." This result is used to optimize inventory. 【0192】 Step 4: 【0193】 The server generates inventory optimization instructions based on the demand forecast results from the generated AI model. The input is the forecast results obtained in step 3, and based on this, it formulates an inventory allocation plan considering logistics costs and supply constraints. Optimization algorithms such as linear programming are used in this process. The output is inventory allocation instructions for each location and store. 【0194】 Step 5: 【0195】 The terminal displays inventory allocation instructions received from the server to the user. The input is inventory allocation data sent from the server. This data is displayed as a GUI on the terminal's screen, awaiting user confirmation and approval. The output provides the user with inventory management information in a visually easy-to-understand layout. 【0196】 Step 6: 【0197】 The device uses a built-in emotion engine to recognize the user's emotions. Inputs include audio and video data acquired from the device's camera and sensors. The emotion engine analyzes this data to evaluate the user's psychological state in real time. The output is information about the user's emotional state, representing stress, joy, fatigue, etc. 【0198】 Step 7: 【0199】 The server receives user emotional state information transmitted from the terminal and adjusts inventory management suggestions based on it. The input is the emotional state information obtained in step 6. Based on this information, specific suggestions are generated to reduce the user's burden. As output, optimized work instructions and support plans are provided and presented to the user. 【0200】 (Application Example 2) 【0201】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0202】 Traditional inventory management systems struggled to manage online platform and physical store inventory in real time and in an integrated manner. Furthermore, they failed to consider user emotional states, potentially leading to excessive workloads and inefficient management. They also lacked the means to accurately predict and quickly respond to inventory shortages and surpluses. 【0203】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0204】 In this invention, the server includes means for acquiring information from online platforms and physical stores and integrating this information, means for performing demand forecasting using generative AI technology based on the integrated information, and means for recognizing the user's emotions and adjusting inventory management suggestions according to their emotional state. This enables efficient inventory management in real time and that takes emotions into account. 【0205】 1. An "online platform" is a place where goods and services are sold in a digital environment, and is a form of sales that is accessible to consumers via the internet. 【0206】 2. A "physical store" is a sales facility that exists in a real space and where consumers can visit and purchase goods in person. 【0207】 3. "Information integration" is the process of centralizing data collected from online platforms and physical stores to ensure consistency and coherence. 【0208】 4. "Generative AI technology" is an artificial intelligence method used to recognize patterns from large amounts of data and predict future demand and behavior. 【0209】 5. "Demand forecasting" is the process of estimating future consumer demand based on market trends and historical data. 【0210】 6. "Emotion recognition" is a technology that analyzes and understands a user's emotional state from their facial expressions and gestures. 【0211】 7. "Inventory management" refers to the process of minimizing excesses and shortages of goods by properly receiving, storing, and shipping products. 【0212】 8. "Suggestions tailored to emotional state" means providing optimal action guidelines that take into account the user's current psychological state and emotions. 【0213】 To implement this invention, the system consists of three main components: a server, a terminal, and a user. 【0214】 The server collects inventory information from online platforms and physical stores via APIs and database connections. The collected data undergoes a data cleansing process to correct missing data and outliers, and then integrates. This process ensures the accuracy and consistency of the data. The integrated data is used for demand forecasting by generative AI technology, and inventory optimization is performed based on the forecast results. The server then sends optimized inventory allocation instructions to each management terminal, taking into account the emotional state of the users. 【0215】 The terminal visually displays inventory information and allocation instructions received from the server. Using built-in emotion recognition technology, the terminal analyzes the user's facial expressions and gestures to determine their emotional state. Based on this information, the terminal presents the user with the least burdensome inventory management suggestions. 【0216】 Users perform their daily tasks based on inventory information and suggestions provided by the device. For example, if an inventory shortage occurs, users check the inventory status through smart glasses and implement the measures suggested by the device. This reduces the user's workload while enabling efficient inventory management. 【0217】 For example, if customer traffic increases during a weekend sale, the terminal will detect a stock shortage and promptly suggest replenishment or automated ordering to the user. Since the suggestions are tailored to the user's stress level, the burden on the user is reduced. 【0218】 Examples of prompts for a generative AI model: 【0219】 "Design an application to optimize in-store inventory management and suggest countermeasures for stock shortages, in order to cope with increased customer traffic during weekend sales events." 【0220】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0221】 Step 1: 【0222】 The server collects inventory information from online platforms and physical stores. It uses APIs and database connections to obtain real-time inventory information. At this stage, the input is raw inventory data from online platforms and physical stores, and the output is raw data ready for integration. 【0223】 Step 2: 【0224】 The server processes the collected inventory data through a data cleansing process. It detects missing or outlier data and performs data reshaping to correct them. Specifically, this involves replacing invalid values ​​and recalculating them. The input to this process is the raw data from the previous step, and the output is data with maintained accuracy. 【0225】 Step 3: 【0226】 The server inputs the cleaned data into a generating AI model to perform demand forecasting. This model performs calculations to predict future demand based on past data patterns. The input at this stage is formatted inventory data, and the output is numerical data and graphs representing the results of the demand forecast. 【0227】 Step 4: 【0228】 The server optimizes inventory based on the prediction results of the generated AI model. It generates inventory allocation instructions for each store and sales channel according to the predicted demand. Specifically, this includes suggestions for inventory movements and additional orders. The input to this process is the prediction data, and the output is the optimized inventory allocation plan. 【0229】 Step 5: 【0230】 The terminal visually presents the user with optimized inventory information and allocation instructions sent from the server. The operation screen displays inventory status and allocation suggestions, preparing the user to take action based on this information. The input is optimized instruction data, and the output is the information displayed to the user. 【0231】 Step 6: 【0232】 The device analyzes the user's emotions using its built-in emotion recognition function. It utilizes cameras and sensors to evaluate the user's psychological state based on their facial expressions and movements. The input for this step is real-time visual data of the user, and the output is metrics representing the recognized emotional state. 【0233】 Step 7: 【0234】 The user performs inventory management based on the displayed information and suggestions from the terminal. They select specific actions in store operations, such as replenishing inventory or changing the layout. The input for this final step is suggested information from the terminal, and the output is the actual inventory management action. 【0235】 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. 【0236】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0237】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0238】 [Second Embodiment] 【0239】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0240】 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. 【0241】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0242】 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. 【0243】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0244】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0245】 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. 【0246】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0247】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0248】 The 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. 【0249】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0250】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0251】 This invention is a system that integrates inventory management for online stores and physical retail stores, enabling real-time demand forecasting and inventory optimization. This system achieves efficient inventory management and improved customer satisfaction through the coordinated operation of a server, terminals, and users. 【0252】 Server Role 【0253】 The server is the core of the system, collecting inventory data from online stores and physical retail locations. This is done using APIs and database connections. The collected data is integrated on the server, where missing or outlier values ​​are detected and data cleaning is performed. Based on this cleaned and accurate data, the server uses a generative AI model to perform demand forecasting. The results of the demand forecast form the basis for optimally allocating inventory across each channel. Instructions based on the optimized inventory allocation are distributed to each management terminal. 【0254】 Terminal role 【0255】 The terminal receives information and instructions from the server and provides them to the user. The terminal screen visually displays real-time updated inventory information and specific allocation instructions, allowing users to accurately understand the current situation. Furthermore, the terminal is linked to the store's AI camera system, enabling automated inventory management. This allows for automatic, rather than manual, updates of inventory data, resulting in efficient and accurate inventory management. 【0256】 User roles 【0257】 Store staff and managers, who are users of the system, execute inventory information and allocation instructions received via terminals. Specifically, this includes ordering and receiving products, adjusting inventory, and transferring inventory between stores. They also perform tasks such as conducting inventory counts and confirming product placement based on instructions from the terminals. This maximizes sales opportunities and enables quick responses to customer demand. 【0258】 Specific example 【0259】 For example, when a user sells a new product through multiple channels, the server analyzes past sales data of similar products to predict demand for the new product. The server identifies stores and times when demand is concentrated and calculates the optimal inventory allocation accordingly. This optimization information is delivered to terminals, and users follow the instructions to move inventory to the necessary stores in a timely manner. In this way, the entire system works together to provide customers with a seamless purchasing experience and improve customer satisfaction. 【0260】 The following describes the processing flow. 【0261】 Step 1: 【0262】 The server collects inventory data from online stores and physical retail locations. It gathers information such as current inventory levels, expected arrival dates, and sales history through APIs and database connections. It also acquires physical store inventory data from AI cameras. 【0263】 Step 2: 【0264】 The server integrates the collected data and performs data cleaning to detect missing values ​​and outliers. This process builds an accurate and consistent dataset for analysis. 【0265】 Step 3: 【0266】 The server feeds cleaned data into an AI model to perform demand forecasting. This AI model analyzes historical data and trends to predict future inventory needs. 【0267】 Step 4: 【0268】 The server uses demand forecasts to perform calculations to optimally allocate inventory across multiple sales channels. This calculation takes into account the characteristics of each channel, the volume of demand, and the balance of inventory levels. 【0269】 Step 5: 【0270】 The server distributes optimized inventory allocation instructions to each management terminal. These instructions indicate which stores and channels to allocate inventory to at the appropriate time. 【0271】 Step 6: 【0272】 The terminal receives information and instructions from the server and displays inventory information and specific allocation instructions to the user. The terminal interface is designed to allow users to easily check information and take action. 【0273】 Step 7: 【0274】 Based on instructions provided by the user via their device, the system performs inventory management tasks such as inventory movement, ordering, and stocktaking. This enables rapid action to maximize sales opportunities and respond to customer needs. 【0275】 (Example 1) 【0276】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0277】 In modern sales systems, the disconnect between information processing equipment and physical stores reduces inventory management efficiency and creates a risk of missed sales opportunities due to demand uncertainty. Furthermore, inaccurate information, including missing or abnormal values, impairs the accuracy of supply and demand forecasts. As a result, resource allocation across sales channels is not optimized, potentially leading to decreased customer satisfaction. 【0278】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0279】 In this invention, the server includes means for aggregating information from information processing devices and physical stores and integrating this information, means for predicting demand using an automatically generated model, and means for optimally allocating resources among a plurality of distribution channels based on the prediction results. As a result, information is accurately managed, the accuracy of supply and demand prediction is improved, and efficient resource allocation among sales channels becomes possible. 【0280】 An "information processing device" is a computer system for collecting, processing, and integrating data from online stores and physical stores. 【0281】 A "physical store" is a sales facility in a physical store where consumers can directly view and purchase goods. 【0282】 "Information" is a collection of data related to sales activities, such as inventory status, sales history, and return history. 【0283】 "Integration" is an operation of summarizing information obtained from multiple data sources in a form that can be managed centrally. 【0284】 An "automatically generated model" is a machine learning algorithm that learns collected data and predicts future demand and trends. 【0285】 "Demand prediction" is a process of estimating how many products will be sold in the future based on past sales data. 【0286】 A "distribution channel" is a concept indicating the distribution route until products and services reach customers. 【0287】 "Optimal resource allocation" is a method of efficiently and effectively allocating product inventory among multiple distribution channels. 【0288】 "Resource management" is an activity of controlling various elements in the supply chain, such as inventory holding, movement, and replenishment. 【0289】 In one embodiment of the invention, this system optimizes inventory management through the cooperation of an information processing device, a terminal, and a user. 【0290】 Server Processing 【0291】 The server functions as an information processing unit, aggregating and integrating data from online and physical stores. This involves retrieving data using APIs and database connections, and performing data cleaning using the Python Pandas library. Generative AI models are used to predict future demand from the integrated data. For example, the server analyzes sales trends for a specific product and predicts next month's demand using a prompt like this: "Predict next month's demand based on data from the past 12 months." 【0292】 Terminal processing 【0293】 The terminal receives optimized resource allocation instructions from the server and displays them in a user-friendly format. The terminal provides real-time updated inventory information, enabling users to quickly manage their inventory based on this data. The terminal also integrates with an AI camera system to automate physical store inventory counts and streamline inventory data updates. 【0294】 User actions 【0295】 Users, acting as store staff or managers, manage resources based on allocation instructions received via a terminal. Specific tasks include transferring inventory between stores, ordering products, and receiving deliveries. By following the instructions displayed on the terminal, users can maximize sales opportunities and respond quickly to customer demand. They also perform tasks such as verifying product placement based on inventory results. 【0296】 This system achieves consistent and efficient inventory management as a whole by having servers, terminals, and users each fulfill their respective roles. As a result, resource optimization is achieved across the entire supply chain, and customer satisfaction is improved. 【0297】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0298】 Step 1: 【0299】 The server first collects inventory data from online stores and physical stores. Inputs include information such as the stock quantity, sales history, and return history of each product, obtained through APIs and database connections. After the data is collected, the server integrates it into a single dataset, allowing it to understand the overall inventory situation. 【0300】 Step 2: 【0301】 The server performs data cleaning on the collected data. The input for this step is the previously integrated data. It removes inaccurate data and fills in missing values ​​with the mean or median. Specifically, it uses the Python Pandas library to perform data preparation and outputs the prepared data. 【0302】 Step 3: 【0303】 The server performs demand forecasting using the data after it has been cleaned. The input here is the cleaned data. The data is input into the generating AI model, and future demand is quantified. For example, it calculates the predicted sales volume of a specific product and outputs the result. An example of a prompt used in this process is, "Based on data from the past 12 months, please forecast the demand for next month." 【0304】 Step 4: 【0305】 The server optimizes inventory based on demand forecasts. The input is the forecasted demand data. Using the SciPy library, it calculates the optimal inventory allocation among stores and outputs the allocation plan. 【0306】 Step 5: 【0307】 The terminal displays the inventory allocation instructions received from the server. The input is the optimization instruction data transmitted by the server. The terminal displays this information in a user - friendly format in real - time so that the user can grasp the actions to be performed next. 【0308】 Step 6: 【0309】 The user checks the instructions on the terminal and executes specific inventory management. The input is the allocation instructions displayed on the terminal. Based on this, the user moves inventory, places orders for necessary goods, and receives them. As a result, the inventory situation is maintained in an optimized state, and the utilization efficiency of resources is maximized. 【0310】 (Application Example 1) 【0311】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0312】 There is a need for a system that can improve the inefficiencies in inventory management of online stores and physical stores and perform appropriate item allocation according to demand in real - time. Also, it is an issue to realize the automation of physical inventory in physical stores and prevent out - of - stock of items. Furthermore, it is also required that store staff can quickly obtain appropriate item allocation information and improve management efficiency. 【0313】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0314】 In this invention, the server includes means for collecting and integrating information from online stores and physical stores, means for performing demand forecasting using a generative AI model based on the integrated information, and means for optimizing items across multiple sales channels based on the results of the demand forecast. This enables real-time optimization of item allocation. Furthermore, by automatically monitoring the shelves of physical stores using AI cameras, the decrease in items can be detected immediately, enabling efficient inventory management. In addition, by utilizing smart devices to immediately provide store staff with item information and optimized allocation instructions, it is possible to improve the efficiency of management operations. 【0315】 An "online store" is a type of retail business that sells goods via the internet. 【0316】 A "physical store" is a retail store located in a physical place where consumers can actually visit and purchase goods. 【0317】 "Means of collecting information" refers to methods and devices for obtaining inventory and sales-related data from online stores and physical stores. 【0318】 "Means of integrating information" refers to the process of combining collected data into a single dataset, making it available for analysis and optimization. 【0319】 A "generative AI model" is an artificial intelligence model designed to make predictions and classifications based on specific data. 【0320】 "Methods for demand forecasting" refers to the process of using integrated data and AI models to predict future consumer demand. 【0321】 "Means for optimizing goods" refers to methods or devices for efficiently allocating inventory of goods across sales channels based on demand forecasts. 【0322】 A "control device" is an electronic device that receives and displays information and instructions about goods and transmits them to the user. 【0323】 "Item information" refers to important data about items, such as inventory levels, location, and price. 【0324】 An "AI camera" is a camera device equipped with artificial intelligence that has the function of automatically monitoring the presence and placement of objects. 【0325】 "Automated monitoring" is a process that uses machines or devices to continuously monitor a state without human intervention. 【0326】 A "smart device" is an electronic device that has communication capabilities and can be used with a variety of applications. 【0327】 This invention provides a system that efficiently integrates inventory management for online and physical stores, enabling real-time demand forecasting and inventory allocation. The system employs a configuration in which a server, terminals, and users work together in cooperation. 【0328】 The server's primary role is to collect information from online and physical stores. Information is typically collected via APIs or database connections. The collected data is integrated on the server and undergoes a data cleaning process to correct missing or outlier values. Subsequently, a generative AI model is used to forecast demand, and based on the forecast results, items across multiple sales channels are optimized. This calculates the optimal item allocation for each store and online store, and then sends this information to the terminals sequentially. The AI ​​technologies used include TensorFlow and similar machine learning frameworks. 【0329】 The terminal is a device that receives allocation information from the server and displays it in real time along with item information. This terminal is often composed of mobile devices such as smartphones and tablets, and is equipped with the function to automate inventory management in physical stores in conjunction with an AI camera system. 【0330】 Store staff, as users of this terminal, receive real-time inventory information and distribution instructions, enabling efficient management and movement of goods. This allows them to respond quickly to customer demand and prevent stockouts. For example, they can receive an immediate alert if the stock of a popular item in the store is running low, allowing them to replenish it at the appropriate time. 【0331】 An example of a prompt message is: "Based on data from the past six months, forecast the demand for T-shirts during the summer and suggest the optimal inventory allocation." 【0332】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0333】 Step 1: 【0334】 The server collects information from online and physical stores. It uses APIs and database connections from each store as input. This data includes inventory levels, sales figures, pricing, and more. The output generates an integrated raw dataset. 【0335】 Step 2: 【0336】 The server detects missing and outlier values ​​in the collected data and performs data cleaning. The input is the raw data collected in step 1. This process involves imputing missing values ​​and correcting outliers. The output is a cleaned and accurate dataset. 【0337】 Step 3: 【0338】 The server uses a generated AI model based on the cleaned data to perform demand forecasting. The input is the dataset obtained in step 2. A prompt is used to instruct the AI ​​model to make a prediction. The prompt used is: "Based on data from the past 6 months, forecast the demand for T-shirts in the summer and suggest the optimal inventory allocation." The output is the demand forecast result. 【0339】 Step 4: 【0340】 The server optimizes the distribution of goods across multiple sales channels based on the demand forecast results. The input is the demand forecast results from step 3. In this process, the optimal allocation of goods for each store and online store is calculated. Allocation instructions are generated as the output. 【0341】 Step 5: 【0342】 The server distributes calculated and optimized item distribution instructions to each terminal. The input is the distribution instructions from step 4. A real-time notification system over the network is used for this communication. The output is the distribution instructions displayed on each terminal. 【0343】 Step 6: 【0344】 The terminal displays the received item allocation instructions and the latest item information to the user. The input is the allocation instructions received in step 5. In terms of specific actions, the information is visually presented on the terminal screen. The output is a real-time information display that the user can check. 【0345】 Step 7: 【0346】 The user performs inventory management based on the presented item information and allocation instructions. The input is the information presented from the terminal in step 6. This action includes specific actions such as moving items, deciding on orders, and replenishing the store. As output, optimized inventory management is completed. 【0347】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0348】 This invention provides a system that integrates real-time inventory management for online stores and physical retail stores, and further enables inventory management that reflects user emotions. This system is centered around three components: a server, a terminal, and a user, and the integration of an emotion engine enables more flexible and human-centered inventory management. 【0349】 Server Role 【0350】 The server first collects inventory data from online stores and physical retail locations. This data is gathered via API or database connection. The server integrates the acquired data and performs data cleaning to detect and correct missing or outlier values. Based on accurate data, the server uses a generative AI model to forecast demand. This forecast helps in the efficient allocation of inventory. The server distributes optimized inventory allocation instructions to each management terminal. The server also receives the output of the emotion engine and adjusts inventory management suggestions according to the user's emotional state. 【0351】 Terminal role 【0352】 The terminal receives inventory information and instructions transmitted from the server and presents them visually to the user. This information includes inventory allocation instructions based on future demand forecasts, which the terminal updates in real time. Furthermore, the terminal has a built-in emotion engine that uses the terminal's camera and sensors to recognize the user's emotions. This analysis result works in conjunction with other functions of the system to customize instructions and suggestions for inventory management according to the user's emotions. 【0353】 User roles 【0354】 Users perform inventory management tasks based on inventory information and instructions viewed via their terminals. This includes placing orders, rearranging inventory layouts, and moving inventory between stores. User emotions are analyzed by an emotion engine, and information and suggestions are optimized to create the most user-friendly working environment. 【0355】 Specific example 【0356】 For example, when an inventory shortage occurs on-site, the server collects and analyzes the data, using an AI model to predict which products are needed. Simultaneously, the terminal uses an emotion engine to assess the user's stress level and suggests emergency support actions as needed, such as requesting assistance from other staff or initiating an automated ordering process. This reduces the user's burden while enabling efficient work execution. In this way, the entire system works together to achieve adaptive inventory management that responds to the user's emotions. 【0357】 The following describes the processing flow. 【0358】 Step 1: 【0359】 The server collects inventory data from online stores and physical retail locations via API or database connections. This includes comprehensive data such as inventory quantities, sales history, and expected arrival dates. 【0360】 Step 2: 【0361】 The server integrates the collected data into a unified database, detects missing or outlier values, and performs data cleaning. This process improves the accuracy and reliability of the analysis. 【0362】 Step 3: 【0363】 The server feeds the cleaned data into a generated AI model to perform demand forecasting. The model considers past trends and current market conditions to generate highly accurate demand forecast results. 【0364】 Step 4: 【0365】 The server calculates the optimal inventory allocation based on demand forecasts. This calculation takes into account the balance of demand and inventory levels across each sales channel. 【0366】 Step 5: 【0367】 The terminal receives optimized inventory allocation instructions delivered from the server and displays them visually to the user. The displayed information is presented in an easily understandable format using diagrams and graphs. 【0368】 Step 6: 【0369】 The device's built-in emotion engine uses cameras and sensors to recognize the user's emotions in real time. This allows it to evaluate the user's stress level and emotional state. 【0370】 Step 7: 【0371】 The server receives the results of the emotion engine's analysis and adjusts inventory management suggestions according to the user's emotional state. For example, it displays suggestions on the screen such as arranging urgent deliveries or staffing adjustments to reduce workload. 【0372】 Step 8: 【0373】 Users perform inventory management tasks such as moving inventory and placing orders based on information and suggestions provided by their terminals. This allows for efficient operations and quick responses to customer needs. 【0374】 Step 9: 【0375】 Based on user actions and feedback, the emotion engine continuously learns and improves the accuracy of future suggestions. This process contributes to enhancing the overall functionality of the system. 【0376】 (Example 2) 【0377】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0378】 Traditional inventory management systems manage online store and physical store inventory data separately, making it difficult to allocate and manage inventory appropriately. Furthermore, they lack the means to implement inventory management that considers employee emotions and stress levels, resulting in limitations in inventory management efficiency and improvements to the working environment. 【0379】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0380】 This invention includes a server that collects information from online stores and physical retail stores and integrates this information; a server that performs demand forecasting using a generation artificial intelligence model based on the integrated information; and a server that recognizes and analyzes the user's emotions using an emotion engine. This enables efficient management of inventory information from online stores and physical retail stores through integration, and optimizes inventory through demand forecasting. Furthermore, by enabling adjustments to inventory management that take user emotions into account, it also contributes to improving the working environment. 【0381】 An "online store" is a virtual marketplace for selling goods and services via the internet. 【0382】 A "physical retail store" is a retail location that provides goods and services to consumers in a physical place. 【0383】 "Collecting information" is the act of gathering specific data or knowledge and making it available for recording and analysis. 【0384】 "Integration" refers to the process of bringing together different pieces of information and combining them into a consistent system or dataset. 【0385】 A "generative artificial intelligence model" is an information processing system equipped with advanced algorithms that learn from data and make predictions and decisions. 【0386】 "Demand forecasting" is the process of predicting future consumer trends in advance using methods such as statistical analysis and machine learning. 【0387】 "Distribution channels" refer to the paths that goods and services take from producers to consumers. 【0388】 "Optimizing inventory" means efficiently adjusting the placement and quantity of inventory so that there is neither an excess nor a shortage of goods. 【0389】 "Allocation instructions" are specific instructions or guidelines for assigning goods or resources to specific locations or personnel. 【0390】 "Management equipment" is a general term for hardware and software used by companies and organizations to control and monitor their operations and processes. 【0391】 An "emotion engine" refers to a technological foundation for detecting and analyzing a user's emotions and psychological state. 【0392】 "Recognizing and analyzing emotions" is the process of detecting a user's psychological state and classifying and evaluating its meaning. 【0393】 "Adjusting a proposal" refers to the act of improving existing proposals or plans to their optimal form based on data and circumstances. 【0394】 This system integrates servers, terminals, and users to revolutionize inventory management for online stores and physical retail locations. The server first collects inventory data from each sales channel. This process uses APIs and database connections to systematically gather inventory information. The server uses data processing libraries such as Python and Pandas to perform data cleaning, detecting and correcting missing or outlier values ​​in the collected data. 【0395】 Next, the server uses a generative artificial intelligence model to perform demand forecasting. This forecasting utilizes AI libraries such as TensorFlow and PyTorch to generate demand scenarios that predict future demand from big data. Specifically, the server can use prompts to predict "which products will be needed and in what quantities next week." An example of such a prompt is, "Based on current inventory data and future demand forecasts, please list the products that will be needed next." 【0396】 Furthermore, the terminal has the ability to receive optimized inventory allocation instructions sent from the server and visualize them for the user. The terminal uses its built-in camera and sensors to run an emotion engine and recognize the user's emotions in real time. For example, if the user is experiencing high levels of stress, the terminal can suggest an optimal inventory management plan to the user. 【0397】 Users efficiently manage inventory based on inventory information and suggestions provided from their devices. This includes ordering products, rearranging inventory, and even transferring inventory between stores. To improve the user experience, tasks are optimized based on the results of an emotion engine analysis. This provides users with an environment where they can perform their tasks without stress. 【0398】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0399】 Step 1: 【0400】 The server collects inventory data from online stores and physical retail locations. Inputs include API calls and database queries to retrieve information such as product ID, quantity, and price. This information is imported into the server in JSON or CSV format. The output is a unified inventory data set, which is used in subsequent processing steps. 【0401】 Step 2: 【0402】 The server performs data cleaning based on the collected inventory data. The integrated dataset obtained in Step 1 is used as input. Missing values ​​are imputed and outliers are removed from this dataset. Specifically, the mean value is substituted for missing values, and obviously abnormal values ​​are removed based on a pre-set threshold. The output is cleaned and accurate inventory data. 【0403】 Step 3: 【0404】 The server inputs cleaned inventory data into a generated AI model to perform demand forecasting. The input is inventory information formatted as time-series data. The AI ​​model is built using TensorFlow and executes a forecasting algorithm. As output, for example, a forecast result is obtained that "the predicted sales of product A next week will be 200 units." This result is used to optimize inventory. 【0405】 Step 4: 【0406】 The server generates inventory optimization instructions based on the demand forecast results from the generated AI model. The input is the forecast results obtained in step 3, and based on this, it formulates an inventory allocation plan considering logistics costs and supply constraints. Optimization algorithms such as linear programming are used in this process. The output is inventory allocation instructions for each location and store. 【0407】 Step 5: 【0408】 The terminal displays inventory allocation instructions received from the server to the user. The input is inventory allocation data sent from the server. This data is displayed as a GUI on the terminal's screen, awaiting user confirmation and approval. The output provides the user with inventory management information in a visually easy-to-understand layout. 【0409】 Step 6: 【0410】 The device uses a built-in emotion engine to recognize the user's emotions. Inputs include audio and video data acquired from the device's camera and sensors. The emotion engine analyzes this data to evaluate the user's psychological state in real time. The output is information about the user's emotional state, representing stress, joy, fatigue, etc. 【0411】 Step 7: 【0412】 The server receives user emotional state information transmitted from the terminal and adjusts inventory management suggestions based on it. The input is the emotional state information obtained in step 6. Based on this information, specific suggestions are generated to reduce the user's burden. As output, optimized work instructions and support plans are provided and presented to the user. 【0413】 (Application Example 2) 【0414】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0415】 Traditional inventory management systems struggled to manage online platform and physical store inventory in real time and in an integrated manner. Furthermore, they failed to consider user emotional states, potentially leading to excessive workloads and inefficient management. They also lacked the means to accurately predict and quickly respond to inventory shortages and surpluses. 【0416】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0417】 In this invention, the server includes means for acquiring information from online platforms and physical stores and integrating this information, means for performing demand forecasting using generative AI technology based on the integrated information, and means for recognizing the user's emotions and adjusting inventory management suggestions according to their emotional state. This enables efficient inventory management in real time and that takes emotions into account. 【0418】 1. An "online platform" is a place where goods and services are sold in a digital environment, and is a form of sales that is accessible to consumers via the internet. 【0419】 2. A "physical store" is a sales facility that exists in a real space and where consumers can visit and purchase goods in person. 【0420】 3. "Information integration" is the process of centralizing data collected from online platforms and physical stores to ensure consistency and coherence. 【0421】 4. "Generative AI technology" is an artificial intelligence method used to recognize patterns from large amounts of data and predict future demand and behavior. 【0422】 5. "Demand forecasting" is the process of estimating future consumer demand based on market trends and historical data. 【0423】 6. "Emotion recognition" is a technology that analyzes and understands a user's emotional state from their facial expressions and gestures. 【0424】 7. "Inventory management" refers to the process of minimizing excesses and shortages of goods by properly receiving, storing, and shipping products. 【0425】 8. "Suggestions tailored to emotional state" means providing optimal action guidelines that take into account the user's current psychological state and emotions. 【0426】 To implement this invention, the system consists of three main components: a server, a terminal, and a user. 【0427】 The server collects inventory information from online platforms and physical stores via APIs and database connections. The collected data undergoes a data cleansing process to correct missing data and outliers, and then integrates. This process ensures the accuracy and consistency of the data. The integrated data is used for demand forecasting by generative AI technology, and inventory optimization is performed based on the forecast results. The server then sends optimized inventory allocation instructions to each management terminal, taking into account the emotional state of the users. 【0428】 The terminal visually displays inventory information and allocation instructions received from the server. Using built-in emotion recognition technology, the terminal analyzes the user's facial expressions and gestures to determine their emotional state. Based on this information, the terminal presents the user with the least burdensome inventory management suggestions. 【0429】 Users perform their daily tasks based on inventory information and suggestions provided by the device. For example, if an inventory shortage occurs, users check the inventory status through smart glasses and implement the measures suggested by the device. This reduces the user's workload while enabling efficient inventory management. 【0430】 For example, if customer traffic increases during a weekend sale, the terminal will detect a stock shortage and promptly suggest replenishment or automated ordering to the user. Since the suggestions are tailored to the user's stress level, the burden on the user is reduced. 【0431】 Examples of prompts for a generative AI model: 【0432】 "Design an application to optimize in-store inventory management and suggest countermeasures for stock shortages, in order to cope with increased customer traffic during weekend sales events." 【0433】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0434】 Step 1: 【0435】 The server collects inventory information from online platforms and physical stores. It uses APIs and database connections to obtain real-time inventory information. At this stage, the input is raw inventory data from online platforms and physical stores, and the output is raw data ready for integration. 【0436】 Step 2: 【0437】 The server processes the collected inventory data through a data cleansing process. It detects missing or outlier data and performs data reshaping to correct them. Specifically, this involves replacing invalid values ​​and recalculating them. The input to this process is the raw data from the previous step, and the output is data with maintained accuracy. 【0438】 Step 3: 【0439】 The server inputs the cleaned data into a generating AI model to perform demand forecasting. This model performs calculations to predict future demand based on past data patterns. The input at this stage is formatted inventory data, and the output is numerical data and graphs representing the results of the demand forecast. 【0440】 Step 4: 【0441】 The server optimizes inventory based on the prediction results of the generated AI model. It generates inventory allocation instructions for each store and sales channel according to the predicted demand. Specifically, this includes suggestions for inventory movements and additional orders. The input to this process is the prediction data, and the output is the optimized inventory allocation plan. 【0442】 Step 5: 【0443】 The terminal visually presents the user with optimized inventory information and allocation instructions sent from the server. The operation screen displays inventory status and allocation suggestions, preparing the user to take action based on this information. The input is optimized instruction data, and the output is the information displayed to the user. 【0444】 Step 6: 【0445】 The device analyzes the user's emotions using its built-in emotion recognition function. It utilizes cameras and sensors to evaluate the user's psychological state based on their facial expressions and movements. The input for this step is real-time visual data of the user, and the output is metrics representing the recognized emotional state. 【0446】 Step 7: 【0447】 The user performs inventory management based on the displayed information and suggestions from the terminal. They select specific actions in store operations, such as replenishing inventory or changing the layout. The input for this final step is suggested information from the terminal, and the output is the actual inventory management action. 【0448】 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. 【0449】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0450】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0451】 [Third Embodiment] 【0452】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0453】 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. 【0454】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0455】 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. 【0456】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0457】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0458】 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. 【0459】 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. 【0460】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0461】 The 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. 【0462】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0463】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0464】 This invention is a system that integrates inventory management for online stores and physical retail stores, enabling real-time demand forecasting and inventory optimization. This system achieves efficient inventory management and improved customer satisfaction through the coordinated operation of a server, terminals, and users. 【0465】 Server Role 【0466】 The server is the core of the system, collecting inventory data from online stores and physical retail locations. This is done using APIs and database connections. The collected data is integrated on the server, where missing or outlier values ​​are detected and data cleaning is performed. Based on this cleaned and accurate data, the server uses a generative AI model to perform demand forecasting. The results of the demand forecast form the basis for optimally allocating inventory across each channel. Instructions based on the optimized inventory allocation are distributed to each management terminal. 【0467】 Terminal role 【0468】 The terminal receives information and instructions from the server and provides them to the user. The terminal screen visually displays real-time updated inventory information and specific allocation instructions, allowing users to accurately understand the current situation. Furthermore, the terminal is linked to the store's AI camera system, enabling automated inventory management. This allows for automatic, rather than manual, updates of inventory data, resulting in efficient and accurate inventory management. 【0469】 User roles 【0470】 Store staff and managers, who are users of the system, execute inventory information and allocation instructions received via terminals. Specifically, this includes ordering and receiving products, adjusting inventory, and transferring inventory between stores. They also perform tasks such as conducting inventory counts and confirming product placement based on instructions from the terminals. This maximizes sales opportunities and enables quick responses to customer demand. 【0471】 Specific example 【0472】 For example, when a user sells a new product through multiple channels, the server analyzes past sales data of similar products to predict demand for the new product. The server identifies stores and times when demand is concentrated and calculates the optimal inventory allocation accordingly. This optimization information is delivered to terminals, and users follow the instructions to move inventory to the necessary stores in a timely manner. In this way, the entire system works together to provide customers with a seamless purchasing experience and improve customer satisfaction. 【0473】 The following describes the processing flow. 【0474】 Step 1: 【0475】 The server collects inventory data from online stores and physical retail locations. It gathers information such as current inventory levels, expected arrival dates, and sales history through APIs and database connections. It also acquires physical store inventory data from AI cameras. 【0476】 Step 2: 【0477】 The server integrates the collected data and performs data cleaning to detect missing values ​​and outliers. This process builds an accurate and consistent dataset for analysis. 【0478】 Step 3: 【0479】 The server feeds cleaned data into an AI model to perform demand forecasting. This AI model analyzes historical data and trends to predict future inventory needs. 【0480】 Step 4: 【0481】 The server uses demand forecasts to perform calculations to optimally allocate inventory across multiple sales channels. This calculation takes into account the characteristics of each channel, the volume of demand, and the balance of inventory levels. 【0482】 Step 5: 【0483】 The server distributes optimized inventory allocation instructions to each management terminal. These instructions indicate which stores and channels to allocate inventory to at the appropriate time. 【0484】 Step 6: 【0485】 The terminal receives information and instructions from the server and displays inventory information and specific allocation instructions to the user. The terminal interface is designed to allow users to easily check information and take action. 【0486】 Step 7: 【0487】 Based on instructions provided by the user via their device, the system performs inventory management tasks such as inventory movement, ordering, and stocktaking. This enables rapid action to maximize sales opportunities and respond to customer needs. 【0488】 (Example 1) 【0489】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0490】 In modern sales systems, the disconnect between information processing equipment and physical stores reduces inventory management efficiency and creates a risk of missed sales opportunities due to demand uncertainty. Furthermore, inaccurate information, including missing or abnormal values, impairs the accuracy of supply and demand forecasts. As a result, resource allocation across sales channels is not optimized, potentially leading to decreased customer satisfaction. 【0491】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0492】 In this invention, the server includes means for aggregating information from an information processing device and physical stores and integrating this information, means for forecasting demand using an automatically generated model, and means for optimally allocating resources among multiple supply channels based on the forecast results. This ensures that information is accurately managed, improves the accuracy of supply and demand forecasting, and enables efficient resource allocation among sales channels. 【0493】 An "information processing device" is a computer system used to collect, process, and integrate data from online stores and physical stores. 【0494】 A "physical store" is a retail facility with physical locations where consumers can directly inspect and purchase products. 【0495】 "Information" refers to a collection of data related to sales activities, such as inventory status, sales history, and return history. 【0496】 "Integration" is the process of combining information obtained from multiple data sources into a form that can be centrally managed. 【0497】 An "automatically generated model" is a machine learning algorithm that learns from collected data and uses it to predict future demand and trends. 【0498】 "Demand forecasting" is the process of estimating how much of a product will sell in the future, based on past sales data. 【0499】 "Delivery channel" is a concept that refers to the distribution route through which goods and services are delivered to customers. 【0500】 "Optimal resource allocation" refers to a method of efficiently and effectively distributing product inventory across multiple distribution channels. 【0501】 "Resource management" refers to activities that control various elements in the supply chain, such as inventory retention, movement, and replenishment. 【0502】 In one embodiment of the invention, this system optimizes inventory management through the cooperation of an information processing device, a terminal, and a user. 【0503】 Server Processing 【0504】 The server functions as an information processing unit, aggregating and integrating data from online and physical stores. This involves retrieving data using APIs and database connections, and performing data cleaning using the Python Pandas library. Generative AI models are used to predict future demand from the integrated data. For example, the server analyzes sales trends for a specific product and predicts next month's demand using a prompt like this: "Predict next month's demand based on data from the past 12 months." 【0505】 Terminal processing 【0506】 The terminal receives optimized resource allocation instructions from the server and displays them in a user-friendly format. The terminal provides real-time updated inventory information, enabling users to quickly manage their inventory based on this data. The terminal also integrates with an AI camera system to automate physical store inventory counts and streamline inventory data updates. 【0507】 User actions 【0508】 Users, acting as store staff or managers, manage resources based on allocation instructions received via a terminal. Specific tasks include transferring inventory between stores, ordering products, and receiving deliveries. By following the instructions displayed on the terminal, users can maximize sales opportunities and respond quickly to customer demand. They also perform tasks such as verifying product placement based on inventory results. 【0509】 This system achieves consistent and efficient inventory management as a whole by having servers, terminals, and users each fulfill their respective roles. As a result, resource optimization is achieved across the entire supply chain, and customer satisfaction is improved. 【0510】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0511】 Step 1: 【0512】 The server first collects inventory data from online stores and physical stores. Inputs include information such as the stock quantity, sales history, and return history of each product, obtained through APIs and database connections. After the data is collected, the server integrates it into a single dataset, allowing it to understand the overall inventory situation. 【0513】 Step 2: 【0514】 The server performs data cleaning on the collected data. The input for this step is the previously integrated data. It removes inaccurate data and fills in missing values ​​with the mean or median. Specifically, it uses the Python Pandas library to perform data preparation and outputs the prepared data. 【0515】 Step 3: 【0516】 The server performs demand forecasting using the data after it has been cleaned. The input here is the cleaned data. The data is input into the generating AI model, and future demand is quantified. For example, it calculates the predicted sales volume of a specific product and outputs the result. An example of a prompt used in this process is, "Based on data from the past 12 months, please forecast the demand for next month." 【0517】 Step 4: 【0518】 The server optimizes inventory based on demand forecasts. The input is the forecasted demand data. Using the SciPy library, it calculates the optimal inventory allocation among stores and outputs the allocation plan. 【0519】 Step 5: 【0520】 The terminal displays inventory allocation instructions received from the server. The input is optimization instruction data sent by the server. The terminal displays this information in a user-friendly format in real time, allowing the user to understand what action to take next. 【0521】 Step 6: 【0522】 Users check instructions on a terminal and perform specific inventory management. The input is the allocation instructions displayed on the terminal. Based on this, users move inventory, order necessary products, and receive them. As a result, inventory levels are kept in an optimized state, and resource utilization efficiency is maximized. 【0523】 (Application Example 1) 【0524】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0525】 A system is needed to improve the inefficiencies in inventory management faced by online and physical stores, enabling real-time allocation of goods to meet demand. Furthermore, automating inventory counting in physical stores to prevent stockouts is a challenge. Additionally, store staff need to be able to quickly obtain accurate inventory allocation information, streamlining management. 【0526】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0527】 In this invention, the server includes means for collecting and integrating information from online stores and physical stores, means for performing demand forecasting using a generative AI model based on the integrated information, and means for optimizing items across multiple sales channels based on the results of the demand forecast. This enables real-time optimization of item allocation. Furthermore, by automatically monitoring the shelves of physical stores using AI cameras, the decrease in items can be detected immediately, enabling efficient inventory management. In addition, by utilizing smart devices to immediately provide store staff with item information and optimized allocation instructions, it is possible to improve the efficiency of management operations. 【0528】 An "online store" is a type of retail business that sells goods via the internet. 【0529】 A "physical store" is a retail store located in a physical place where consumers can actually visit and purchase goods. 【0530】 "Means of collecting information" refers to methods and devices for obtaining inventory and sales-related data from online stores and physical stores. 【0531】 "Means of integrating information" refers to the process of combining collected data into a single dataset, making it available for analysis and optimization. 【0532】 A "generative AI model" is an artificial intelligence model designed to make predictions and classifications based on specific data. 【0533】 "Methods for demand forecasting" refers to the process of using integrated data and AI models to predict future consumer demand. 【0534】 "Means for optimizing goods" refers to methods or devices for efficiently allocating inventory of goods across sales channels based on demand forecasts. 【0535】 A "control device" is an electronic device that receives and displays information and instructions about goods and transmits them to the user. 【0536】 "Item information" refers to important data about items, such as inventory levels, location, and price. 【0537】 An "AI camera" is a camera device equipped with artificial intelligence that has the function of automatically monitoring the presence and placement of objects. 【0538】 "Automated monitoring" is a process that uses machines or devices to continuously monitor a state without human intervention. 【0539】 A "smart device" is an electronic device that has communication capabilities and can be used with a variety of applications. 【0540】 This invention provides a system that efficiently integrates inventory management for online and physical stores, enabling real-time demand forecasting and inventory allocation. The system employs a configuration in which a server, terminals, and users work together in cooperation. 【0541】 The server's primary role is to collect information from online and physical stores. Information is typically collected via APIs or database connections. The collected data is integrated on the server and undergoes a data cleaning process to correct missing or outlier values. Subsequently, a generative AI model is used to forecast demand, and based on the forecast results, items across multiple sales channels are optimized. This calculates the optimal item allocation for each store and online store, and then sends this information to the terminals sequentially. The AI ​​technologies used include TensorFlow and similar machine learning frameworks. 【0542】 The terminal is a device that receives allocation information from the server and displays it in real time along with item information. This terminal is often composed of mobile devices such as smartphones and tablets, and is equipped with the function to automate inventory management in physical stores in conjunction with an AI camera system. 【0543】 Store staff, as users of this terminal, receive real-time inventory information and distribution instructions, enabling efficient management and movement of goods. This allows them to respond quickly to customer demand and prevent stockouts. For example, they can receive an immediate alert if the stock of a popular item in the store is running low, allowing them to replenish it at the appropriate time. 【0544】 An example of a prompt message is: "Based on data from the past six months, forecast the demand for T-shirts during the summer and suggest the optimal inventory allocation." 【0545】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0546】 Step 1: 【0547】 The server collects information from online and physical stores. It uses APIs and database connections from each store as input. This data includes inventory levels, sales figures, pricing, and more. The output generates an integrated raw dataset. 【0548】 Step 2: 【0549】 The server detects missing and outlier values ​​in the collected data and performs data cleaning. The input is the raw data collected in step 1. This process involves imputing missing values ​​and correcting outliers. The output is a cleaned and accurate dataset. 【0550】 Step 3: 【0551】 The server uses a generated AI model based on the cleaned data to perform demand forecasting. The input is the dataset obtained in step 2. A prompt is used to instruct the AI ​​model to make a prediction. The prompt used is: "Based on data from the past 6 months, forecast the demand for T-shirts in the summer and suggest the optimal inventory allocation." The output is the demand forecast result. 【0552】 Step 4: 【0553】 The server optimizes the distribution of goods across multiple sales channels based on the demand forecast results. The input is the demand forecast results from step 3. In this process, the optimal allocation of goods for each store and online store is calculated. Allocation instructions are generated as the output. 【0554】 Step 5: 【0555】 The server distributes calculated and optimized item distribution instructions to each terminal. The input is the distribution instructions from step 4. A real-time notification system over the network is used for this communication. The output is the distribution instructions displayed on each terminal. 【0556】 Step 6: 【0557】 The terminal displays the received item allocation instructions and the latest item information to the user. The input is the allocation instructions received in step 5. In terms of specific actions, the information is visually presented on the terminal screen. The output is a real-time information display that the user can check. 【0558】 Step 7: 【0559】 The user performs inventory management based on the presented item information and allocation instructions. The input is the information presented from the terminal in step 6. This action includes specific actions such as moving items, deciding on orders, and replenishing the store. As output, optimized inventory management is completed. 【0560】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0561】 This invention provides a system that integrates real-time inventory management for online stores and physical retail stores, and further enables inventory management that reflects user emotions. This system is centered around three components: a server, a terminal, and a user, and the integration of an emotion engine enables more flexible and human-centered inventory management. 【0562】 Server Role 【0563】 The server first collects inventory data from online stores and physical retail locations. This data is gathered via API or database connection. The server integrates the acquired data and performs data cleaning to detect and correct missing or outlier values. Based on accurate data, the server uses a generative AI model to forecast demand. This forecast helps in the efficient allocation of inventory. The server distributes optimized inventory allocation instructions to each management terminal. The server also receives the output of the emotion engine and adjusts inventory management suggestions according to the user's emotional state. 【0564】 Terminal role 【0565】 The terminal receives inventory information and instructions transmitted from the server and presents them visually to the user. This information includes inventory allocation instructions based on future demand forecasts, which the terminal updates in real time. Furthermore, the terminal has a built-in emotion engine that uses the terminal's camera and sensors to recognize the user's emotions. This analysis result works in conjunction with other functions of the system to customize instructions and suggestions for inventory management according to the user's emotions. 【0566】 User roles 【0567】 Users perform inventory management tasks based on inventory information and instructions viewed via their terminals. This includes placing orders, rearranging inventory layouts, and moving inventory between stores. User emotions are analyzed by an emotion engine, and information and suggestions are optimized to create the most user-friendly working environment. 【0568】 Specific example 【0569】 For example, when an inventory shortage occurs on-site, the server collects and analyzes the data, using an AI model to predict which products are needed. Simultaneously, the terminal uses an emotion engine to assess the user's stress level and suggests emergency support actions as needed, such as requesting assistance from other staff or initiating an automated ordering process. This reduces the user's burden while enabling efficient work execution. In this way, the entire system works together to achieve adaptive inventory management that responds to the user's emotions. 【0570】 The following describes the processing flow. 【0571】 Step 1: 【0572】 The server collects inventory data from online stores and physical retail locations via API or database connections. This includes comprehensive data such as inventory quantities, sales history, and expected arrival dates. 【0573】 Step 2: 【0574】 The server integrates the collected data into a unified database, detects missing or outlier values, and performs data cleaning. This process improves the accuracy and reliability of the analysis. 【0575】 Step 3: 【0576】 The server feeds the cleaned data into a generated AI model to perform demand forecasting. The model considers past trends and current market conditions to generate highly accurate demand forecast results. 【0577】 Step 4: 【0578】 The server calculates the optimal inventory allocation based on demand forecasts. This calculation takes into account the balance of demand and inventory levels across each sales channel. 【0579】 Step 5: 【0580】 The terminal receives optimized inventory allocation instructions delivered from the server and displays them visually to the user. The displayed information is presented in an easily understandable format using diagrams and graphs. 【0581】 Step 6: 【0582】 The device's built-in emotion engine uses cameras and sensors to recognize the user's emotions in real time. This allows it to evaluate the user's stress level and emotional state. 【0583】 Step 7: 【0584】 The server receives the results of the emotion engine's analysis and adjusts inventory management suggestions according to the user's emotional state. For example, it displays suggestions on the screen such as arranging urgent deliveries or staffing adjustments to reduce workload. 【0585】 Step 8: 【0586】 Users perform inventory management tasks such as moving inventory and placing orders based on information and suggestions provided by their terminals. This allows for efficient operations and quick responses to customer needs. 【0587】 Step 9: 【0588】 Based on user actions and feedback, the emotion engine continuously learns and improves the accuracy of future suggestions. This process contributes to enhancing the overall functionality of the system. 【0589】 (Example 2) 【0590】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0591】 Traditional inventory management systems manage online store and physical store inventory data separately, making it difficult to allocate and manage inventory appropriately. Furthermore, they lack the means to implement inventory management that considers employee emotions and stress levels, resulting in limitations in inventory management efficiency and improvements to the working environment. 【0592】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0593】 This invention includes a server that collects information from online stores and physical retail stores and integrates this information; a server that performs demand forecasting using a generation artificial intelligence model based on the integrated information; and a server that recognizes and analyzes the user's emotions using an emotion engine. This enables efficient management of inventory information from online stores and physical retail stores through integration, and optimizes inventory through demand forecasting. Furthermore, by enabling adjustments to inventory management that take user emotions into account, it also contributes to improving the working environment. 【0594】 An "online store" is a virtual marketplace for selling goods and services via the internet. 【0595】 A "physical retail store" is a retail location that provides goods and services to consumers in a physical place. 【0596】 "Collecting information" is the act of gathering specific data or knowledge and making it available for recording and analysis. 【0597】 "Integration" refers to the process of bringing together different pieces of information and combining them into a consistent system or dataset. 【0598】 A "generative artificial intelligence model" is an information processing system equipped with advanced algorithms that learn from data and make predictions and decisions. 【0599】 "Demand forecasting" is the process of predicting future consumer trends in advance using methods such as statistical analysis and machine learning. 【0600】 "Distribution channels" refer to the paths that goods and services take from producers to consumers. 【0601】 "Optimizing inventory" means efficiently adjusting the placement and quantity of inventory so that there is neither an excess nor a shortage of goods. 【0602】 "Allocation instructions" are specific instructions or guidelines for assigning goods or resources to specific locations or personnel. 【0603】 "Management equipment" is a general term for hardware and software used by companies and organizations to control and monitor their operations and processes. 【0604】 An "emotion engine" refers to a technological foundation for detecting and analyzing a user's emotions and psychological state. 【0605】 "Recognizing and analyzing emotions" is the process of detecting a user's psychological state and classifying and evaluating its meaning. 【0606】 "Adjusting a proposal" refers to the act of improving existing proposals or plans to their optimal form based on data and circumstances. 【0607】 This system integrates servers, terminals, and users to revolutionize inventory management for online stores and physical retail locations. The server first collects inventory data from each sales channel. This process uses APIs and database connections to systematically gather inventory information. The server uses data processing libraries such as Python and Pandas to perform data cleaning, detecting and correcting missing or outlier values ​​in the collected data. 【0608】 Next, the server uses a generative artificial intelligence model to perform demand forecasting. This forecasting utilizes AI libraries such as TensorFlow and PyTorch to generate demand scenarios that predict future demand from big data. Specifically, the server can use prompts to predict "which products will be needed and in what quantities next week." An example of such a prompt is, "Based on current inventory data and future demand forecasts, please list the products that will be needed next." 【0609】 Furthermore, the terminal has the ability to receive optimized inventory allocation instructions sent from the server and visualize them for the user. The terminal uses its built-in camera and sensors to run an emotion engine and recognize the user's emotions in real time. For example, if the user is experiencing high levels of stress, the terminal can suggest an optimal inventory management plan to the user. 【0610】 Users efficiently manage inventory based on inventory information and suggestions provided from their devices. This includes ordering products, rearranging inventory, and even transferring inventory between stores. To improve the user experience, tasks are optimized based on the results of an emotion engine analysis. This provides users with an environment where they can perform their tasks without stress. 【0611】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0612】 Step 1: 【0613】 The server collects inventory data from online stores and physical retail locations. Inputs include API calls and database queries to retrieve information such as product ID, quantity, and price. This information is imported into the server in JSON or CSV format. The output is a unified inventory data set, which is used in subsequent processing steps. 【0614】 Step 2: 【0615】 The server performs data cleaning based on the collected inventory data. The integrated dataset obtained in Step 1 is used as input. Missing values ​​are imputed and outliers are removed from this dataset. Specifically, the mean value is substituted for missing values, and obviously abnormal values ​​are removed based on a pre-set threshold. The output is cleaned and accurate inventory data. 【0616】 Step 3: 【0617】 The server inputs cleaned inventory data into a generated AI model to perform demand forecasting. The input is inventory information formatted as time-series data. The AI ​​model is built using TensorFlow and executes a forecasting algorithm. As output, for example, a forecast result is obtained that "the predicted sales of product A next week will be 200 units." This result is used to optimize inventory. 【0618】 Step 4: 【0619】 The server generates inventory optimization instructions based on the demand forecast results from the generated AI model. The input is the forecast results obtained in step 3, and based on this, it formulates an inventory allocation plan considering logistics costs and supply constraints. Optimization algorithms such as linear programming are used in this process. The output is inventory allocation instructions for each location and store. 【0620】 Step 5: 【0621】 The terminal displays inventory allocation instructions received from the server to the user. The input is inventory allocation data sent from the server. This data is displayed as a GUI on the terminal's screen, awaiting user confirmation and approval. The output provides the user with inventory management information in a visually easy-to-understand layout. 【0622】 Step 6: 【0623】 The device uses a built-in emotion engine to recognize the user's emotions. Inputs include audio and video data acquired from the device's camera and sensors. The emotion engine analyzes this data to evaluate the user's psychological state in real time. The output is information about the user's emotional state, representing stress, joy, fatigue, etc. 【0624】 Step 7: 【0625】 The server receives user emotional state information transmitted from the terminal and adjusts inventory management suggestions based on it. The input is the emotional state information obtained in step 6. Based on this information, specific suggestions are generated to reduce the user's burden. As output, optimized work instructions and support plans are provided and presented to the user. 【0626】 (Application Example 2) 【0627】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0628】 Traditional inventory management systems struggled to manage online platform and physical store inventory in real time and in an integrated manner. Furthermore, they failed to consider user emotional states, potentially leading to excessive workloads and inefficient management. They also lacked the means to accurately predict and quickly respond to inventory shortages and surpluses. 【0629】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0630】 In this invention, the server includes means for acquiring information from online platforms and physical stores and integrating this information, means for performing demand forecasting using generative AI technology based on the integrated information, and means for recognizing the user's emotions and adjusting inventory management suggestions according to their emotional state. This enables efficient inventory management in real time and that takes emotions into account. 【0631】 1. An "online platform" is a place where goods and services are sold in a digital environment, and is a form of sales that is accessible to consumers via the internet. 【0632】 2. A "physical store" is a sales facility that exists in a real space and where consumers can visit and purchase goods in person. 【0633】 3. "Information integration" is the process of centralizing data collected from online platforms and physical stores to ensure consistency and coherence. 【0634】 4. "Generative AI technology" is an artificial intelligence method used to recognize patterns from large amounts of data and predict future demand and behavior. 【0635】 5. "Demand forecasting" is the process of estimating future consumer demand based on market trends and historical data. 【0636】 6. "Emotion recognition" is a technology that analyzes and understands a user's emotional state from their facial expressions and gestures. 【0637】 7. "Inventory management" refers to the process of minimizing excesses and shortages of goods by properly receiving, storing, and shipping products. 【0638】 8. "Suggestions tailored to emotional state" means providing optimal action guidelines that take into account the user's current psychological state and emotions. 【0639】 To implement this invention, the system consists of three main components: a server, a terminal, and a user. 【0640】 The server collects inventory information from online platforms and physical stores via APIs and database connections. The collected data undergoes a data cleansing process to correct missing data and outliers, and then integrates. This process ensures the accuracy and consistency of the data. The integrated data is used for demand forecasting by generative AI technology, and inventory optimization is performed based on the forecast results. The server then sends optimized inventory allocation instructions to each management terminal, taking into account the emotional state of the users. 【0641】 The terminal visually displays inventory information and allocation instructions received from the server. Using built-in emotion recognition technology, the terminal analyzes the user's facial expressions and gestures to determine their emotional state. Based on this information, the terminal presents the user with the least burdensome inventory management suggestions. 【0642】 Users perform their daily tasks based on inventory information and suggestions provided by the device. For example, if an inventory shortage occurs, users check the inventory status through smart glasses and implement the measures suggested by the device. This reduces the user's workload while enabling efficient inventory management. 【0643】 For example, if customer traffic increases during a weekend sale, the terminal will detect a stock shortage and promptly suggest replenishment or automated ordering to the user. Since the suggestions are tailored to the user's stress level, the burden on the user is reduced. 【0644】 Examples of prompts for a generative AI model: 【0645】 "Design an application to optimize in-store inventory management and suggest countermeasures for stock shortages, in order to cope with increased customer traffic during weekend sales events." 【0646】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0647】 Step 1: 【0648】 The server collects inventory information from online platforms and physical stores. It uses APIs and database connections to obtain real-time inventory information. At this stage, the input is raw inventory data from online platforms and physical stores, and the output is raw data ready for integration. 【0649】 Step 2: 【0650】 The server processes the collected inventory data through a data cleansing process. It detects missing or outlier data and performs data reshaping to correct them. Specifically, this involves replacing invalid values ​​and recalculating them. The input to this process is the raw data from the previous step, and the output is data with maintained accuracy. 【0651】 Step 3: 【0652】 The server inputs the cleaned data into a generating AI model to perform demand forecasting. This model performs calculations to predict future demand based on past data patterns. The input at this stage is formatted inventory data, and the output is numerical data and graphs representing the results of the demand forecast. 【0653】 Step 4: 【0654】 The server optimizes inventory based on the prediction results of the generated AI model. It generates inventory allocation instructions for each store and sales channel according to the predicted demand. Specifically, this includes suggestions for inventory movements and additional orders. The input to this process is the prediction data, and the output is the optimized inventory allocation plan. 【0655】 Step 5: 【0656】 The terminal visually presents the user with optimized inventory information and allocation instructions sent from the server. The operation screen displays inventory status and allocation suggestions, preparing the user to take action based on this information. The input is optimized instruction data, and the output is the information displayed to the user. 【0657】 Step 6: 【0658】 The device analyzes the user's emotions using its built-in emotion recognition function. It utilizes cameras and sensors to evaluate the user's psychological state based on their facial expressions and movements. The input for this step is real-time visual data of the user, and the output is metrics representing the recognized emotional state. 【0659】 Step 7: 【0660】 The user performs inventory management based on the displayed information and suggestions from the terminal. They select specific actions in store operations, such as replenishing inventory or changing the layout. The input for this final step is suggested information from the terminal, and the output is the actual inventory management action. 【0661】 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. 【0662】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0663】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0664】 [Fourth Embodiment] 【0665】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0666】 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. 【0667】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0668】 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. 【0669】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0670】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0671】 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. 【0672】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0673】 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. 【0674】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0675】 The 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. 【0676】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0677】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0678】 This invention is a system that integrates inventory management for online stores and physical retail stores, enabling real-time demand forecasting and inventory optimization. This system achieves efficient inventory management and improved customer satisfaction through the coordinated operation of a server, terminals, and users. 【0679】 Server Role 【0680】 The server is the core of the system, collecting inventory data from online stores and physical retail locations. This is done using APIs and database connections. The collected data is integrated on the server, where missing or outlier values ​​are detected and data cleaning is performed. Based on this cleaned and accurate data, the server uses a generative AI model to perform demand forecasting. The results of the demand forecast form the basis for optimally allocating inventory across each channel. Instructions based on the optimized inventory allocation are distributed to each management terminal. 【0681】 Terminal role 【0682】 The terminal receives information and instructions from the server and provides them to the user. The terminal screen visually displays real-time updated inventory information and specific allocation instructions, allowing users to accurately understand the current situation. Furthermore, the terminal is linked to the store's AI camera system, enabling automated inventory management. This allows for automatic, rather than manual, updates of inventory data, resulting in efficient and accurate inventory management. 【0683】 User roles 【0684】 Store staff and managers, who are users of the system, execute inventory information and allocation instructions received via terminals. Specifically, this includes ordering and receiving products, adjusting inventory, and transferring inventory between stores. They also perform tasks such as conducting inventory counts and confirming product placement based on instructions from the terminals. This maximizes sales opportunities and enables quick responses to customer demand. 【0685】 Specific example 【0686】 For example, when a user sells a new product through multiple channels, the server analyzes past sales data of similar products to predict demand for the new product. The server identifies stores and times when demand is concentrated and calculates the optimal inventory allocation accordingly. This optimization information is delivered to terminals, and users follow the instructions to move inventory to the necessary stores in a timely manner. In this way, the entire system works together to provide customers with a seamless purchasing experience and improve customer satisfaction. 【0687】 The following describes the processing flow. 【0688】 Step 1: 【0689】 The server collects inventory data from online stores and physical retail locations. It gathers information such as current inventory levels, expected arrival dates, and sales history through APIs and database connections. It also acquires physical store inventory data from AI cameras. 【0690】 Step 2: 【0691】 The server integrates the collected data and performs data cleaning to detect missing values ​​and outliers. This process builds an accurate and consistent dataset for analysis. 【0692】 Step 3: 【0693】 The server feeds cleaned data into an AI model to perform demand forecasting. This AI model analyzes historical data and trends to predict future inventory needs. 【0694】 Step 4: 【0695】 The server uses demand forecasts to perform calculations to optimally allocate inventory across multiple sales channels. This calculation takes into account the characteristics of each channel, the volume of demand, and the balance of inventory levels. 【0696】 Step 5: 【0697】 The server distributes optimized inventory allocation instructions to each management terminal. These instructions indicate which stores and channels to allocate inventory to at the appropriate time. 【0698】 Step 6: 【0699】 The terminal receives information and instructions from the server and displays inventory information and specific allocation instructions to the user. The terminal interface is designed to allow users to easily check information and take action. 【0700】 Step 7: 【0701】 Based on instructions provided by the user via their device, the system performs inventory management tasks such as inventory movement, ordering, and stocktaking. This enables rapid action to maximize sales opportunities and respond to customer needs. 【0702】 (Example 1) 【0703】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0704】 In modern sales systems, the disconnect between information processing equipment and physical stores reduces inventory management efficiency and creates a risk of missed sales opportunities due to demand uncertainty. Furthermore, inaccurate information, including missing or abnormal values, impairs the accuracy of supply and demand forecasts. As a result, resource allocation across sales channels is not optimized, potentially leading to decreased customer satisfaction. 【0705】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0706】 In this invention, the server includes means for aggregating information from an information processing device and physical stores and integrating this information, means for forecasting demand using an automatically generated model, and means for optimally allocating resources among multiple supply channels based on the forecast results. This ensures that information is accurately managed, improves the accuracy of supply and demand forecasting, and enables efficient resource allocation among sales channels. 【0707】 An "information processing device" is a computer system used to collect, process, and integrate data from online stores and physical stores. 【0708】 A "physical store" is a retail facility with physical locations where consumers can directly inspect and purchase products. 【0709】 "Information" refers to a collection of data related to sales activities, such as inventory status, sales history, and return history. 【0710】 "Integration" is the process of combining information obtained from multiple data sources into a form that can be centrally managed. 【0711】 An "automatically generated model" is a machine learning algorithm that learns from collected data and uses it to predict future demand and trends. 【0712】 "Demand forecasting" is the process of estimating how much of a product will sell in the future, based on past sales data. 【0713】 "Delivery channel" is a concept that refers to the distribution route through which goods and services are delivered to customers. 【0714】 "Optimal resource allocation" refers to a method of efficiently and effectively distributing product inventory across multiple distribution channels. 【0715】 "Resource management" refers to activities that control various elements in the supply chain, such as inventory retention, movement, and replenishment. 【0716】 In one embodiment of the invention, this system optimizes inventory management through the cooperation of an information processing device, a terminal, and a user. 【0717】 Server Processing 【0718】 The server functions as an information processing unit, aggregating and integrating data from online and physical stores. This involves retrieving data using APIs and database connections, and performing data cleaning using the Python Pandas library. Generative AI models are used to predict future demand from the integrated data. For example, the server analyzes sales trends for a specific product and predicts next month's demand using a prompt like this: "Predict next month's demand based on data from the past 12 months." 【0719】 Terminal processing 【0720】 The terminal receives optimized resource allocation instructions from the server and displays them in a user-friendly format. The terminal provides real-time updated inventory information, enabling users to quickly manage their inventory based on this data. The terminal also integrates with an AI camera system to automate physical store inventory counts and streamline inventory data updates. 【0721】 User actions 【0722】 Users, acting as store staff or managers, manage resources based on allocation instructions received via a terminal. Specific tasks include transferring inventory between stores, ordering products, and receiving deliveries. By following the instructions displayed on the terminal, users can maximize sales opportunities and respond quickly to customer demand. They also perform tasks such as verifying product placement based on inventory results. 【0723】 This system achieves consistent and efficient inventory management as a whole by having servers, terminals, and users each fulfill their respective roles. As a result, resource optimization is achieved across the entire supply chain, and customer satisfaction is improved. 【0724】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0725】 Step 1: 【0726】 The server first collects inventory data from online stores and physical stores. Inputs include information such as the stock quantity, sales history, and return history of each product, obtained through APIs and database connections. After the data is collected, the server integrates it into a single dataset, allowing it to understand the overall inventory situation. 【0727】 Step 2: 【0728】 The server performs data cleaning on the collected data. The input for this step is the previously integrated data. It removes inaccurate data and fills in missing values ​​with the mean or median. Specifically, it uses the Python Pandas library to perform data preparation and outputs the prepared data. 【0729】 Step 3: 【0730】 The server performs demand forecasting using the data after it has been cleaned. The input here is the cleaned data. The data is input into the generating AI model, and future demand is quantified. For example, it calculates the predicted sales volume of a specific product and outputs the result. An example of a prompt used in this process is, "Based on data from the past 12 months, please forecast the demand for next month." 【0731】 Step 4: 【0732】 The server optimizes inventory based on demand forecasts. The input is the forecasted demand data. Using the SciPy library, it calculates the optimal inventory allocation among stores and outputs the allocation plan. 【0733】 Step 5: 【0734】 The terminal displays inventory allocation instructions received from the server. The input is optimization instruction data sent by the server. The terminal displays this information in a user-friendly format in real time, allowing the user to understand what action to take next. 【0735】 Step 6: 【0736】 Users check instructions on a terminal and perform specific inventory management. The input is the allocation instructions displayed on the terminal. Based on this, users move inventory, order necessary products, and receive them. As a result, inventory levels are kept in an optimized state, and resource utilization efficiency is maximized. 【0737】 (Application Example 1) 【0738】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0739】 A system is needed to improve the inefficiencies in inventory management faced by online and physical stores, enabling real-time allocation of goods to meet demand. Furthermore, automating inventory counting in physical stores to prevent stockouts is a challenge. Additionally, store staff need to be able to quickly obtain accurate inventory allocation information, streamlining management. 【0740】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0741】 In this invention, the server includes means for collecting and integrating information from online stores and physical stores, means for performing demand forecasting using a generative AI model based on the integrated information, and means for optimizing items across multiple sales channels based on the results of the demand forecast. This enables real-time optimization of item allocation. Furthermore, by automatically monitoring the shelves of physical stores using AI cameras, the decrease in items can be detected immediately, enabling efficient inventory management. In addition, by utilizing smart devices to immediately provide store staff with item information and optimized allocation instructions, it is possible to improve the efficiency of management operations. 【0742】 An "online store" is a type of retail business that sells goods via the internet. 【0743】 A "physical store" is a retail store located in a physical place where consumers can actually visit and purchase goods. 【0744】 "Means of collecting information" refers to methods and devices for obtaining inventory and sales-related data from online stores and physical stores. 【0745】 "Means of integrating information" refers to the process of combining collected data into a single dataset, making it available for analysis and optimization. 【0746】 A "generative AI model" is an artificial intelligence model designed to make predictions and classifications based on specific data. 【0747】 "Methods for demand forecasting" refers to the process of using integrated data and AI models to predict future consumer demand. 【0748】 "Means for optimizing goods" refers to methods or devices for efficiently allocating inventory of goods across sales channels based on demand forecasts. 【0749】 A "control device" is an electronic device that receives and displays information and instructions about goods and transmits them to the user. 【0750】 "Item information" refers to important data about items, such as inventory levels, location, and price. 【0751】 An "AI camera" is a camera device equipped with artificial intelligence that has the function of automatically monitoring the presence and placement of objects. 【0752】 "Automated monitoring" is a process that uses machines or devices to continuously monitor a state without human intervention. 【0753】 A "smart device" is an electronic device that has communication capabilities and can be used with a variety of applications. 【0754】 This invention provides a system that efficiently integrates inventory management for online and physical stores, enabling real-time demand forecasting and inventory allocation. The system employs a configuration in which a server, terminals, and users work together in cooperation. 【0755】 The server's primary role is to collect information from online and physical stores. Information is typically collected via APIs or database connections. The collected data is integrated on the server and undergoes a data cleaning process to correct missing or outlier values. Subsequently, a generative AI model is used to forecast demand, and based on the forecast results, items across multiple sales channels are optimized. This calculates the optimal item allocation for each store and online store, and then sends this information to the terminals sequentially. The AI ​​technologies used include TensorFlow and similar machine learning frameworks. 【0756】 The terminal is a device that receives allocation information from the server and displays it in real time along with item information. This terminal is often composed of mobile devices such as smartphones and tablets, and is equipped with the function to automate inventory management in physical stores in conjunction with an AI camera system. 【0757】 Store staff, as users of this terminal, receive real-time inventory information and distribution instructions, enabling efficient management and movement of goods. This allows them to respond quickly to customer demand and prevent stockouts. For example, they can receive an immediate alert if the stock of a popular item in the store is running low, allowing them to replenish it at the appropriate time. 【0758】 An example of a prompt message is: "Based on data from the past six months, forecast the demand for T-shirts during the summer and suggest the optimal inventory allocation." 【0759】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0760】 Step 1: 【0761】 The server collects information from online and physical stores. It uses APIs and database connections from each store as input. This data includes inventory levels, sales figures, pricing, and more. The output generates an integrated raw dataset. 【0762】 Step 2: 【0763】 The server detects missing and outlier values ​​in the collected data and performs data cleaning. The input is the raw data collected in step 1. This process involves imputing missing values ​​and correcting outliers. The output is a cleaned and accurate dataset. 【0764】 Step 3: 【0765】 The server uses a generated AI model based on the cleaned data to perform demand forecasting. The input is the dataset obtained in step 2. A prompt is used to instruct the AI ​​model to make a prediction. The prompt used is: "Based on data from the past 6 months, forecast the demand for T-shirts in the summer and suggest the optimal inventory allocation." The output is the demand forecast result. 【0766】 Step 4: 【0767】 The server optimizes the distribution of goods across multiple sales channels based on the demand forecast results. The input is the demand forecast results from step 3. In this process, the optimal allocation of goods for each store and online store is calculated. Allocation instructions are generated as the output. 【0768】 Step 5: 【0769】 The server distributes calculated and optimized item distribution instructions to each terminal. The input is the distribution instructions from step 4. A real-time notification system over the network is used for this communication. The output is the distribution instructions displayed on each terminal. 【0770】 Step 6: 【0771】 The terminal displays the received item allocation instructions and the latest item information to the user. The input is the allocation instructions received in step 5. In terms of specific actions, the information is visually presented on the terminal screen. The output is a real-time information display that the user can check. 【0772】 Step 7: 【0773】 The user performs inventory management based on the presented item information and allocation instructions. The input is the information presented from the terminal in step 6. This action includes specific actions such as moving items, deciding on orders, and replenishing the store. As output, optimized inventory management is completed. 【0774】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0775】 This invention provides a system that integrates real-time inventory management for online stores and physical retail stores, and further enables inventory management that reflects user emotions. This system is centered around three components: a server, a terminal, and a user, and the integration of an emotion engine enables more flexible and human-centered inventory management. 【0776】 Server Role 【0777】 The server first collects inventory data from online stores and physical retail locations. This data is gathered via API or database connection. The server integrates the acquired data and performs data cleaning to detect and correct missing or outlier values. Based on accurate data, the server uses a generative AI model to forecast demand. This forecast helps in the efficient allocation of inventory. The server distributes optimized inventory allocation instructions to each management terminal. The server also receives the output of the emotion engine and adjusts inventory management suggestions according to the user's emotional state. 【0778】 Terminal role 【0779】 The terminal receives inventory information and instructions transmitted from the server and presents them visually to the user. This information includes inventory allocation instructions based on future demand forecasts, which the terminal updates in real time. Furthermore, the terminal has a built-in emotion engine that uses the terminal's camera and sensors to recognize the user's emotions. This analysis result works in conjunction with other functions of the system to customize instructions and suggestions for inventory management according to the user's emotions. 【0780】 User roles 【0781】 Users perform inventory management tasks based on inventory information and instructions viewed via their terminals. This includes placing orders, rearranging inventory layouts, and moving inventory between stores. User emotions are analyzed by an emotion engine, and information and suggestions are optimized to create the most user-friendly working environment. 【0782】 Specific example 【0783】 For example, when an inventory shortage occurs on-site, the server collects and analyzes the data, using an AI model to predict which products are needed. Simultaneously, the terminal uses an emotion engine to assess the user's stress level and suggests emergency support actions as needed, such as requesting assistance from other staff or initiating an automated ordering process. This reduces the user's burden while enabling efficient work execution. In this way, the entire system works together to achieve adaptive inventory management that responds to the user's emotions. 【0784】 The following describes the processing flow. 【0785】 Step 1: 【0786】 The server collects inventory data from online stores and physical retail locations via API or database connections. This includes comprehensive data such as inventory quantities, sales history, and expected arrival dates. 【0787】 Step 2: 【0788】 The server integrates the collected data into a unified database, detects missing or outlier values, and performs data cleaning. This process improves the accuracy and reliability of the analysis. 【0789】 Step 3: 【0790】 The server feeds the cleaned data into a generated AI model to perform demand forecasting. The model considers past trends and current market conditions to generate highly accurate demand forecast results. 【0791】 Step 4: 【0792】 The server calculates the optimal inventory allocation based on demand forecasts. This calculation takes into account the balance of demand and inventory levels across each sales channel. 【0793】 Step 5: 【0794】 The terminal receives optimized inventory allocation instructions delivered from the server and displays them visually to the user. The displayed information is presented in an easily understandable format using diagrams and graphs. 【0795】 Step 6: 【0796】 The device's built-in emotion engine uses cameras and sensors to recognize the user's emotions in real time. This allows it to evaluate the user's stress level and emotional state. 【0797】 Step 7: 【0798】 The server receives the results of the emotion engine's analysis and adjusts inventory management suggestions according to the user's emotional state. For example, it displays suggestions on the screen such as arranging urgent deliveries or staffing adjustments to reduce workload. 【0799】 Step 8: 【0800】 Users perform inventory management tasks such as moving inventory and placing orders based on information and suggestions provided by their terminals. This allows for efficient operations and quick responses to customer needs. 【0801】 Step 9: 【0802】 Based on user actions and feedback, the emotion engine continuously learns and improves the accuracy of future suggestions. This process contributes to enhancing the overall functionality of the system. 【0803】 (Example 2) 【0804】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0805】 Traditional inventory management systems manage online store and physical store inventory data separately, making it difficult to allocate and manage inventory appropriately. Furthermore, they lack the means to implement inventory management that considers employee emotions and stress levels, resulting in limitations in inventory management efficiency and improvements to the working environment. 【0806】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0807】 This invention includes a server that collects information from online stores and physical retail stores and integrates this information, a server that performs demand forecasting using a generation artificial intelligence model based on the integrated information, and a server that recognizes and analyzes the user's emotions using an emotion engine. This enables efficient management of inventory information from online stores and physical retail stores through integration, and optimizes inventory through demand forecasting. Furthermore, by enabling adjustments to inventory management that take user emotions into account, it also contributes to improving the working environment. 【0808】 An "online store" is a virtual marketplace for selling goods and services via the internet. 【0809】 A "physical retail store" is a retail location that provides goods and services to consumers in a physical place. 【0810】 "Collecting information" is the act of gathering specific data or knowledge and making it available for recording and analysis. 【0811】 "Integration" refers to the process of bringing together different pieces of information and combining them into a consistent system or dataset. 【0812】 A "generative artificial intelligence model" is an information processing system equipped with advanced algorithms that learn from data and make predictions and decisions. 【0813】 "Demand forecasting" is the process of predicting future consumer trends in advance using methods such as statistical analysis and machine learning. 【0814】 "Distribution channels" refer to the paths that goods and services take from producers to consumers. 【0815】 "Optimizing inventory" means efficiently adjusting the placement and quantity of inventory so that there is neither an excess nor a shortage of goods. 【0816】 "Allocation instructions" are specific instructions or guidelines for assigning goods or resources to specific locations or personnel. 【0817】 "Management equipment" is a general term for hardware and software used by companies and organizations to control and monitor their operations and processes. 【0818】 An "emotion engine" refers to a technological foundation for detecting and analyzing a user's emotions and psychological state. 【0819】 "Recognizing and analyzing emotions" is the process of detecting a user's psychological state and classifying and evaluating its meaning. 【0820】 "Adjusting a proposal" refers to the act of improving existing proposals or plans to their optimal form based on data and circumstances. 【0821】 This system integrates servers, terminals, and users to revolutionize inventory management for online stores and physical retail locations. The server first collects inventory data from each sales channel. This process uses APIs and database connections to systematically gather inventory information. The server uses data processing libraries such as Python and Pandas to perform data cleaning, detecting and correcting missing or outlier values ​​in the collected data. 【0822】 Next, the server uses a generative artificial intelligence model to perform demand forecasting. This forecasting utilizes AI libraries such as TensorFlow and PyTorch to generate demand scenarios that predict future demand from big data. Specifically, the server can use prompts to predict "which products will be needed and in what quantities next week." An example of such a prompt is, "Based on current inventory data and future demand forecasts, please list the products that will be needed next." 【0823】 Furthermore, the terminal has the ability to receive optimized inventory allocation instructions sent from the server and visualize them for the user. The terminal uses its built-in camera and sensors to run an emotion engine and recognize the user's emotions in real time. For example, if the user is experiencing high levels of stress, the terminal can suggest an optimal inventory management plan to the user. 【0824】 Users efficiently manage inventory based on inventory information and suggestions provided from their devices. This includes ordering products, rearranging inventory, and even transferring inventory between stores. To improve the user experience, tasks are optimized based on the results of an emotion engine analysis. This provides users with an environment where they can perform their tasks without stress. 【0825】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0826】 Step 1: 【0827】 The server collects inventory data from online stores and physical retail locations. Inputs include API calls and database queries to retrieve information such as product ID, quantity, and price. This information is imported into the server in JSON or CSV format. The output is a unified inventory data set, which is used in subsequent processing steps. 【0828】 Step 2: 【0829】 The server performs data cleaning based on the collected inventory data. The integrated dataset obtained in Step 1 is used as input. Missing values ​​are imputed and outliers are removed from this dataset. Specifically, the mean value is substituted for missing values, and obviously abnormal values ​​are removed based on a pre-set threshold. The output is cleaned and accurate inventory data. 【0830】 Step 3: 【0831】 The server inputs cleaned inventory data into a generated AI model to perform demand forecasting. The input is inventory information formatted as time-series data. The AI ​​model is built using TensorFlow and executes a forecasting algorithm. As output, for example, a forecast result is obtained that "the predicted sales of product A next week will be 200 units." This result is used to optimize inventory. 【0832】 Step 4: 【0833】 The server generates inventory optimization instructions based on the demand forecast results from the generated AI model. The input is the forecast results obtained in step 3, and based on this, it formulates an inventory allocation plan considering logistics costs and supply constraints. Optimization algorithms such as linear programming are used in this process. The output is inventory allocation instructions for each location and store. 【0834】 Step 5: 【0835】 The terminal displays inventory allocation instructions received from the server to the user. The input is inventory allocation data sent from the server. This data is displayed as a GUI on the terminal's screen, awaiting user confirmation and approval. The output provides the user with inventory management information in a visually easy-to-understand layout. 【0836】 Step 6: 【0837】 The device uses a built-in emotion engine to recognize the user's emotions. Inputs include audio and video data acquired from the device's camera and sensors. The emotion engine analyzes this data to evaluate the user's psychological state in real time. The output is information about the user's emotional state, representing stress, joy, fatigue, etc. 【0838】 Step 7: 【0839】 The server receives user emotional state information transmitted from the terminal and adjusts inventory management suggestions based on it. The input is the emotional state information obtained in step 6. Based on this information, specific suggestions are generated to reduce the user's burden. As output, optimized work instructions and support plans are provided and presented to the user. 【0840】 (Application Example 2) 【0841】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0842】 Traditional inventory management systems struggled to manage online platform and physical store inventory in real time and in an integrated manner. Furthermore, they failed to consider user emotional states, potentially leading to excessive workloads and inefficient management. They also lacked the means to accurately predict and quickly respond to inventory shortages and surpluses. 【0843】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0844】 In this invention, the server includes means for acquiring information from online platforms and physical stores and integrating this information, means for performing demand forecasting using generative AI technology based on the integrated information, and means for recognizing the user's emotions and adjusting inventory management suggestions according to their emotional state. This enables efficient inventory management in real time and that takes emotions into account. 【0845】 1. An "online platform" is a place where goods and services are sold in a digital environment, and is a form of sales that is accessible to consumers via the internet. 【0846】 2. A "physical store" is a sales facility that exists in a real space and where consumers can visit and purchase goods in person. 【0847】 3. "Information integration" is the process of centralizing data collected from online platforms and physical stores to ensure consistency and coherence. 【0848】 4. "Generative AI technology" is an artificial intelligence method used to recognize patterns from large amounts of data and predict future demand and behavior. 【0849】 5. "Demand forecasting" is the process of estimating future consumer demand based on market trends and historical data. 【0850】 6. "Emotion recognition" is a technology that analyzes and understands a user's emotional state from their facial expressions and gestures. 【0851】 7. "Inventory management" refers to the process of minimizing excesses and shortages of goods by properly receiving, storing, and shipping products. 【0852】 8. "Suggestions tailored to emotional state" means providing optimal action guidelines that take into account the user's current psychological state and emotions. 【0853】 To implement this invention, the system consists of three main components: a server, a terminal, and a user. 【0854】 The server collects inventory information from online platforms and physical stores via APIs and database connections. The collected data undergoes a data cleansing process to correct missing data and outliers, and then integrates. This process ensures the accuracy and consistency of the data. The integrated data is used for demand forecasting by generative AI technology, and inventory optimization is performed based on the forecast results. The server then sends optimized inventory allocation instructions to each management terminal, taking into account the emotional state of the users. 【0855】 The terminal visually displays inventory information and allocation instructions received from the server. Using built-in emotion recognition technology, the terminal analyzes the user's facial expressions and gestures to determine their emotional state. Based on this information, the terminal presents the user with the least burdensome inventory management suggestions. 【0856】 Users perform their daily tasks based on inventory information and suggestions provided by the device. For example, if an inventory shortage occurs, users check the inventory status through smart glasses and implement the measures suggested by the device. This reduces the user's workload while enabling efficient inventory management. 【0857】 For example, if customer traffic increases during a weekend sale, the terminal will detect a stock shortage and promptly suggest replenishment or automated ordering to the user. Since the suggestions are tailored to the user's stress level, the burden on the user is reduced. 【0858】 Examples of prompts for a generative AI model: 【0859】 "Design an application to optimize in-store inventory management and suggest countermeasures for stock shortages, in order to cope with increased customer traffic during weekend sales events." 【0860】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0861】 Step 1: 【0862】 The server collects inventory information from online platforms and physical stores. It uses APIs and database connections to obtain real-time inventory information. At this stage, the input is raw inventory data from online platforms and physical stores, and the output is raw data ready for integration. 【0863】 Step 2: 【0864】 The server processes the collected inventory data through a data cleansing process. It detects missing or outlier data and performs data reshaping to correct them. Specifically, this involves replacing invalid values ​​and recalculating them. The input to this process is the raw data from the previous step, and the output is data with maintained accuracy. 【0865】 Step 3: 【0866】 The server inputs the cleaned data into a generating AI model to perform demand forecasting. This model performs calculations to predict future demand based on past data patterns. The input at this stage is formatted inventory data, and the output is numerical data and graphs representing the results of the demand forecast. 【0867】 Step 4: 【0868】 The server optimizes inventory based on the prediction results of the generated AI model. It generates inventory allocation instructions for each store and sales channel according to the predicted demand. Specifically, this includes suggestions for inventory movements and additional orders. The input to this process is the prediction data, and the output is the optimized inventory allocation plan. 【0869】 Step 5: 【0870】 The terminal visually presents the user with optimized inventory information and allocation instructions sent from the server. The operation screen displays inventory status and allocation suggestions, preparing the user to take action based on this information. The input is optimized instruction data, and the output is the information displayed to the user. 【0871】 Step 6: 【0872】 The device analyzes the user's emotions using its built-in emotion recognition function. It utilizes cameras and sensors to evaluate the user's psychological state based on their facial expressions and movements. The input for this step is real-time visual data of the user, and the output is metrics representing the recognized emotional state. 【0873】 Step 7: 【0874】 The user performs inventory management based on the displayed information and suggestions from the terminal. They select specific actions in store operations, such as replenishing inventory or changing the layout. The input for this final step is suggested information from the terminal, and the output is the actual inventory management action. 【0875】 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. 【0876】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0877】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0878】 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. 【0879】 Figure 9 shows an 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. 【0880】 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. 【0881】 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. 【0882】 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, motorcycles, etc., 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, for example, based 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. 【0883】 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." 【0884】 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. 【0885】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0886】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0887】 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. 【0888】 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. 【0889】 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. 【0890】 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. 【0891】 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. 【0892】 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. 【0893】 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. 【0894】 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 the like 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. 【0895】 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 as being incorporated by reference. 【0896】 The following is further disclosed regarding the embodiments described above. 【0897】 (Claim 1) 【0898】 A means of collecting data from online stores and physical retail stores and integrating this data, 【0899】 A means of performing demand forecasting using a generative AI model based on integrated data, 【0900】 A means for optimizing inventory across multiple sales channels based on demand forecast results, 【0901】 A means of distributing optimized inventory allocation instructions to each management terminal, 【0902】 A means of displaying inventory information and allocation instructions on a management terminal, 【0903】 A system that includes means for performing inventory management based on the instructions provided. 【0904】 (Claim 2) 【0905】 The system according to claim 1, further comprising means for automating inventory taking of goods in a physical store using an AI camera. 【0906】 (Claim 3) 【0907】 The system according to claim 1, further comprising means for detecting missing or outlier values ​​in the collected data and performing data cleaning. 【0908】 "Example 1" 【0909】 (Claim 1) 【0910】 A means for aggregating information from information processing equipment and physical stores, and integrating this information, 【0911】 A means of forecasting demand using an automated generation model based on integrated information, 【0912】 A means for optimally allocating resources across multiple delivery channels based on prediction results, 【0913】 A means for distributing optimized resource allocation instructions to each control device, 【0914】 A means for displaying resource information and allocation instructions on a control device, 【0915】 A system that includes means for performing resource management based on displayed instructions. 【0916】 (Claim 2) 【0917】 The system according to claim 1, which automates the inventory of items in a physical store using image recognition technology. 【0918】 (Claim 3) 【0919】 The system according to claim 1, which detects missing or abnormal values ​​in aggregated information and performs information processing. 【0920】 "Application Example 1" 【0921】 (Claim 1) 【0922】 A means of collecting information from online stores and physical stores and integrating this information, 【0923】 A means of performing demand forecasting using a generative AI model based on integrated information, 【0924】 A means for optimizing goods across multiple sales channels based on the results of demand forecasting, 【0925】 A means for distributing optimized item distribution instructions to each control device, 【0926】 A means for displaying item information and distribution instructions on a control device, 【0927】 Means for carrying out inventory management based on the instructions provided, 【0928】 A system that includes a means to automatically monitor items on shelves in physical stores using AI cameras and to detect decreases in the number of items. 【0929】 (Claim 2) 【0930】 The system according to claim 1, further comprising means for detecting missing or abnormal values ​​in the collected information and performing information cleaning. 【0931】 (Claim 3) 【0932】 The system according to claim 1, which uses a smart device to provide real-time item information and optimized distribution instructions to employees in a physical store. 【0933】 "Example 2 of combining an emotion engine" 【0934】 (Claim 1) 【0935】 A means of collecting information from online stores and physical retail stores and integrating this information, 【0936】 A means of performing demand forecasting using a generative artificial intelligence model based on integrated information, 【0937】 A means of optimizing inventory across multiple distribution channels based on demand forecast results, 【0938】 A means for distributing optimized inventory allocation instructions to each management device, 【0939】 A means for displaying inventory information and allocation instructions on a management device, 【0940】 A means of carrying out inventory management based on the instructions provided, 【0941】 A means of recognizing and analyzing the user's emotions using an emotion engine, 【0942】 A system that includes means for adjusting inventory management suggestions based on the results of emotion analysis. 【0943】 (Claim 2) 【0944】 The system according to claim 1, which uses a visual recognition device to automate inventory taking of goods in a physical store. 【0945】 (Claim 3) 【0946】 The system according to claim 1, which detects missing or abnormal values ​​in the collected information and cleans up the information. 【0947】 "Application example 2 when combining with an emotional engine" 【0948】 (Claim 1) 【0949】 A means of acquiring information from online platforms and physical stores and integrating this information, 【0950】 A means of performing demand forecasting using generative AI technology based on integrated information, 【0951】 A means for optimizing inventory across multiple sales channels based on demand forecast results, 【0952】 A means of distributing optimized inventory allocation instructions to each management terminal, 【0953】 A means of displaying inventory information and allocation instructions on a management terminal, 【0954】 A means of performing inventory management based on the displayed instructions, 【0955】 A system that recognizes user emotions and includes means to adjust inventory management suggestions according to those emotional states. 【0956】 (Claim 2) 【0957】 The system according to claim 1, further comprising means for automating inventory taking of goods in a physical store using an artificial intelligence-equipped camera. 【0958】 (Claim 3) 【0959】 The system according to claim 1, further comprising means for detecting missing or abnormal values ​​in the acquired information and performing data cleaning. [Explanation of symbols] 【0960】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of collecting data from online stores and physical retail stores and integrating this data, A means of performing demand forecasting using a generative AI model based on integrated data, A means for optimizing inventory across multiple sales channels based on demand forecast results, A means of distributing optimized inventory allocation instructions to each management terminal, A means of displaying inventory information and allocation instructions on a management terminal, A system that includes means for performing inventory management based on the instructions provided. [Claim 2] The system according to claim 1, further comprising means for automating inventory taking of goods in a physical store using an AI camera. [Claim 3] The system according to claim 1, further comprising means for detecting missing or abnormal values ​​in the collected data and performing data cleaning.

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