system

Abstract

We provide the system. [Solution] A method using artificial intelligence algorithms to predict regional demand based on sales data, seasonal information, and weather information, A communication device that monitors the inventory of each store in real time and automatically orders the necessary items, A means of using a database to adjust inventory between stores with excess inventory and stores with insufficient inventory, A processing unit for analyzing customer order history and generating personalized promotions for each customer, A system that integrates sales data from all stores and includes a server for analyzing trends.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the food service industry, when operating multiple stores, it is required to efficiently manage different demands and inventory situations for each individual store. However, in the conventional system, it has been difficult to comprehensively perform demand forecasting according to regional characteristics, real-time inventory monitoring, automatic ordering, and inventory sharing among other stores. There has also been a problem that means for providing personalized promotions based on customer order history are not sufficiently developed to meet diverse consumer needs. 【Means for Solving the Problems】 【0005】 This invention solves these problems by providing a system that includes an artificial intelligence algorithm for forecasting regional demand based on sales data, seasonal information, and weather information; a communication device that monitors the inventory of each store in real time and places automatic orders; and a means for utilizing a database that efficiently adjusts inventory between stores with excess and insufficient stock. Furthermore, by providing a processing device that analyzes customer order history and generates personalized promotions, and a server that integrates sales data from all stores and analyzes trends, it becomes possible to respond quickly and flexibly to consumer needs. 【0006】 "Sales data" refers to information related to the sales of each store in the restaurant industry, specifically the sales performance of each menu item on a daily basis. 【0007】 "Seasonal information" refers to data related to specific periods throughout the year, and is used to predict seasonal fluctuations in demand. 【0008】 "Weather information" refers to data on meteorological conditions, which is used in demand forecasting as a factor in sales fluctuations. 【0009】 An "artificial intelligence algorithm" refers to a computational method that learns patterns from past data and makes predictions based on machine learning and data analysis techniques. 【0010】 A "communication device" is hardware or a system used for sending and receiving information, and is used to monitor inventory status and place necessary orders. 【0011】 A "database" refers to a collection of digital data that is organized to store information systematically and allows for efficient access and management. 【0012】 "Personalized promotions" are promotional activities tailored to individual customers' order history and preferences, with the aim of improving the customer experience. 【0013】 A "processing device" is a device or computer program that receives data and performs calculations, and is used for promotional generation and data analysis. 【0014】 A "server" is a computer system that provides data and services over a network, playing a central role in integrating sales data and performing trend analysis. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one 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. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0021】 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). 【0022】 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." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 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. 【0026】 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). 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 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. 【0033】 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. 【0034】 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. 【0035】 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". 【0036】 This invention is intended as a system for efficient inventory management and sales expansion for chain restaurants in the food service industry. The system is based on an artificial intelligence algorithm that utilizes sales data, seasonal information, and weather information. This allows for demand forecasting that varies by region and optimizes store inventory management. 【0037】 First, the server collects sales data from each store and performs demand forecasting based on regional characteristics. Through analysis of past sales history, seasonal trends, and weather data, it predicts when specific menu items will be consumed in large quantities. In this process, artificial intelligence-based data analysis plays a crucial role. 【0038】 Furthermore, the terminal monitors the inventory status of each store in real time and sends the information to the server. The server automatically places orders based on this information. For example, if a store's beef inventory falls below a minimum level, the server automatically places an order to prevent stockouts. 【0039】 Furthermore, through its inventory sharing function, the server matches stores with excess inventory with stores that are short on inventory, efficiently reallocating stock. This provides a framework that minimizes food waste and promotes the efficient use of resources. 【0040】 Furthermore, the server analyzes customer order history to plan personalized promotions. For example, it can improve customer satisfaction by offering discounts on menu items frequently ordered by certain customers or recommending new menu items. Users receive these promotions, making it easier for them to receive offers that match their preferences. 【0041】 Finally, by integrating and analyzing sales data from all stores, the server identifies trends and leverages successful menus and promotions in other stores, contributing to overall sales improvement. Implementing this system can improve operational efficiency and sustainability in the food service industry. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The server collects sales data from each store. This data includes the number of units sold for each menu item, the date and time, and factors such as whether it was a weekday or weekend. 【0045】 Step 2: 【0046】 The server retrieves weather data in real time from a weather information service and stores it in a database. This information is then used for demand forecasting. 【0047】 Step 3: 【0048】 The server inputs sales data, seasonal trends, and weather information into an AI algorithm to build a demand forecasting model tailored to regional characteristics. The model then calculates projected sales for the following week or month. 【0049】 Step 4: 【0050】 The terminal monitors the inventory levels of each store in real time via IoT sensors and sends the information to the server at regular intervals. 【0051】 Step 5: 【0052】 The server analyzes the received inventory level information and detects when an order is needed. If the level falls below a threshold, the automated ordering system is activated. 【0053】 Step 6: 【0054】 The server aggregates inventory data from other stores and identifies stores with excess or insufficient inventory. Based on this information, it proposes the optimal inventory movement and notifies the relevant stores. 【0055】 Step 7: 【0056】 The server analyzes each customer's order history and generates personalized promotions based on their preferences. 【0057】 Step 8: 【0058】 The device sends personalized promotional information to each customer and offers special offers that will pique their interest. 【0059】 Step 9: 【0060】 The server analyzes the latest sales data from all stores to identify trends. It then creates suggestions for deploying successful menus and promotions to other stores. 【0061】 (Example 1) 【0062】 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." 【0063】 In the modern restaurant industry, efficient inventory management and sales maximization are critical challenges. Diverse restaurants each have different demand patterns, leading to risks of excess or shortages of inventory. Furthermore, providing personalized service to individual customers is difficult. These challenges stem from inaccurate demand forecasting and inefficient inventory management. Additionally, ineffective inventory adjustments between stores are a significant problem. 【0064】 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. 【0065】 In this invention, the server includes means for using data processing technology to predict regional demand based on sales information, seasonal data, and weather data; a communication network for monitoring inventory at each business location in real time and automatically replenishing necessary items; and information recording means for adjusting inventory between business locations with surplus inventory and those with insufficient inventory. This enables efficient demand forecasting and inventory adjustment, prevents inventory surpluses and shortages, and allows for the provision of services tailored to the needs of each customer. 【0066】 "Sales information" refers to information such as the number of products sold, sales amount, and sales date and time at each business location. 【0067】 "Seasonal data" refers to information related to specific seasons or time periods, and is used to understand seasonal fluctuations in sales. 【0068】 "Weather data" refers to information about weather conditions and is a factor that influences demand forecasts for each region. 【0069】 "Data processing technology" refers to algorithms and methods for analyzing large amounts of data and extracting useful information. 【0070】 A "communication network" refers to a system for exchanging information in real time between different business locations. 【0071】 "Information recording means" refers to databases and systems for recording and managing information regarding excess or insufficient inventory. 【0072】 An "information processing device" refers to an electronic device used to input data and perform analysis and processing based on that data. 【0073】 "Demand trends" refer to changes in demand and trends in the market for goods and services. 【0074】 This invention is implemented using a system consisting of a server and terminals. The server collects sales information, seasonal data, and weather data from each business location, and uses a generated AI model based on this data to perform demand forecasting. Specifically, a database management system and a machine learning framework are used in combination to construct a forecasting model. By inputting past sales data into this model, it is possible to predict future demand with high accuracy. 【0075】 Terminals are installed at each business location to monitor inventory status in real time. These terminals utilize barcode scanners to automatically update information when inventory is received or shipped. This information is sent to a server, and automatic orders are placed based on the results. 【0076】 As a concrete example, consider a scenario where a business's beef inventory falls below a minimum standard. In this case, the server automatically sends an order to the supplier, arranging delivery within a few days. This improves the efficiency of inventory management. 【0077】 The server also analyzes purchase history and plans personalized promotions for each customer. The generating AI model receives an example prompt message such as, "Generate promotional suggestions for products that are likely to be purchased under specific conditions," to generate promotional ideas for products that a particular customer frequently purchases. 【0078】 This system enables efficient inventory management and customized service delivery, making sustainable operations for the entire business possible. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The server collects sales information, seasonal data, and weather data from each business location. Specifically, each terminal is linked to a POS system and automatically sends daily sales data to the server. The input includes the number of items sold, sales amount, and date and time information, which the server stores in a database. The stored data is used for subsequent demand forecasting. 【0082】 Step 2: 【0083】 The server performs demand forecasting based on the collected data. Using a generative AI model, it analyzes sales information, seasonal data, and weather data to predict future demand for a specific product. At this time, the server inputs a prompt message into the model: "Predict the consumption pattern of the product under specific conditions." As output, demand forecast data for the product is generated. This forecast data is used in the next phase of inventory management. 【0084】 Step 3: 【0085】 The terminal monitors inventory status at each business location in real time. A barcode scanner is connected to the terminal, and it updates inventory data by reading product barcodes when inventory is received or shipped. This allows the current inventory level to be determined, and the updated information is sent to the server. Inputs are barcode information and quantity, and output is the latest inventory status data. 【0086】 Step 4: 【0087】 The server automatically places orders based on inventory data and demand forecast data received from terminals. Specifically, when inventory falls below a set threshold, the server automatically sends an order to the supplier. Inputs include the latest inventory data and demand forecast data, and output is an order form. This prevents stockouts and maintains appropriate inventory levels. 【0088】 Step 5: 【0089】 The server analyzes purchase history and generates promotions tailored to each customer. Using a generation AI model, it analyzes customer attributes and purchase history, and receives a prompt message: "Generate promotional suggestions that propose products the customer might be interested in." The output is a personalized promotional suggestion. This suggestion is delivered to the user, contributing to improved customer satisfaction. 【0090】 (Application Example 1) 【0091】 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." 【0092】 To respond promptly to the diverse needs of consumers, efficient inventory management and demand forecasting are essential. However, conventional technologies have made it difficult to accurately forecast demand and reallocate inventory based on real-time data, resulting in inventory shortages or excesses and wasted resources. Furthermore, personalized proposals to individual consumers have been insufficient, limiting the improvement of customer satisfaction. To overcome these challenges, a new system is needed that can adapt to consumer needs and achieve efficient resource utilization. 【0093】 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. 【0094】 In this invention, the server includes means for using an inference algorithm to predict regional demand based on sales data, seasonal information, and weather information; means including a computation algorithm for acquiring information from external sources and optimizing product suggestions and delivery schedules; and information processing means for adjusting inventory between facilities with excess inventory and facilities with insufficient inventory. This enables a rapid and accurate response to the diverse needs of consumers, as well as efficient inventory management. 【0095】 An "inference algorithm" is a computational method that analyzes sales data, seasonal information, and weather information to predict demand patterns for each region. 【0096】 An "information and communication device" is a device that monitors inventory information at each facility in real time and automatically places orders for necessary resources. 【0097】 "Information processing means" refers to a means for managing data to adjust inventory between facilities with excess inventory and facilities with insufficient inventory, and for performing optimal reallocation. 【0098】 A "calculating device" is a device that analyzes users' historical information and generates personalized suggestions for each user. 【0099】 A "calculation unit" is a device that integrates sales data from all facilities and analyzes trends to support effective sales strategies. 【0100】 A "computational algorithm" is a computational method used to acquire information from external sources and optimize product recommendations and delivery schedules. 【0101】 The system of the present invention is designed to achieve efficient inventory management and demand forecasting for food delivery. This enables appropriate recommendations to consumers and efficient resource utilization. The main components include a server, information and communication equipment, information processing means, a computer, and a computing device. 【0102】 The server collects sales data, seasonal information, and weather information, and uses inference algorithms to predict demand for each region. This allows for the prediction of demand at specific times of day and under specific weather conditions, enabling appropriate inventory management. In addition, it monitors the inventory status of each facility in real time via information and communication devices and places automatic orders as needed. The information processing system adjusts inventory between facilities with excess and insufficient stock, and the computing device generates personalized suggestions based on the user's history information. 【0103】 The computing unit integrates and analyzes sales data from all facilities to understand sales trends. This allows for the identification of successful sales strategies and product trends, providing information that can be used at other facilities. Furthermore, the computing algorithm optimizes product recommendations and delivery schedules by referencing external information. 【0104】 For example, during rainy evenings, it can be predicted that hot soups will be in high demand. The server compares past data with current weather to predict demand and prepares potentially scarce items in advance, allowing for a quick response to customer orders. Such a system is expected to improve customer satisfaction and enable efficient use of resources. 【0105】 An example of a prompt for the generating AI model would be: "Considering the current weather and time of day, predict which menu items are likely to be in the highest demand in this area over the next three hours." Based on this prompt, the AI ​​model analyzes the data and provides a list of menu items that are likely to be in high demand. Based on the information obtained in this way, each facility can appropriately adjust its inventory and promotional activities. 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server collects sales data, seasonal information, and weather information from each facility. Inputs include sales databases and external weather information APIs, and output is a dataset for demand forecasting. This dataset is used to prepare the necessary information for demand forecasting in the next step. 【0109】 Step 2: 【0110】 The server uses the collected dataset to run an inference algorithm and perform demand forecasts for each region. The input is the dataset obtained in the previous step, and the output is a demand forecast value. This demand forecast value indicates the expected sales for each time period and weather condition. 【0111】 Step 3: 【0112】 The terminal reads real-time inventory information from each facility and sends the information to the server. The input is the inventory management system of each facility, and the output is real-time inventory data that is sent to the server. This data is useful for automatic ordering to prevent inventory shortages. 【0113】 Step 4: 【0114】 The server compares demand forecasts with real-time inventory data and automatically places orders for the necessary resources. The inputs are the demand forecast values ​​and inventory data obtained in steps 2 and 3, and the output is an order instruction sent to the purchasing system. This ensures that necessary resources are replenished in a timely manner. 【0115】 Step 5: 【0116】 The server matches facilities with excess inventory with those with shortages and proposes the optimal inventory reallocation. The input is real-time inventory data, and the output is an inventory movement plan. This ensures efficient inventory distribution between facilities. 【0117】 Step 6: 【0118】 Users place orders based on suggested products and promotions. The input is generated promotional information, and the output is consumer order data. This process enables personalized product recommendations that take into account the user's history. 【0119】 Step 7: 【0120】 The computing unit uses collected history and trend information to generate personalized promotions for individual customers. Inputs are past consumer order history and overall sales trend data, while output is optimized promotional information. Prompt messages are used to collect information and help identify specific demand patterns. 【0121】 Step 8: 【0122】 The server integrates and analyzes sales data from all facilities and recommends effective sales strategies to other facilities. The input is integrated sales data from each facility, and the output is an improved sales strategy plan. As a result, successful models and techniques are utilized across all facilities, contributing to overall efficiency improvements. 【0123】 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. 【0124】 This invention is implemented as a system for providing flexible service that takes into account the emotions of users in restaurant chains. This system has an artificial intelligence algorithm at its core that performs advanced demand forecasting using sales data, seasonal information, and weather information, and combines this with an emotion engine to further improve customer satisfaction. 【0125】 First, the server collects and analyzes sales data and trend information from each store to predict demand in each region. Furthermore, seasonal factors and weather data that affect sales are also incorporated into this prediction. This allows stores to prepare inventory according to demand. 【0126】 Next, the terminal monitors the inventory of each store in real time and places automatic orders based on revised demand forecasts as needed. The server, through the automated ordering system, prevents food shortages and supports efficient inventory management. 【0127】 The emotion engine works in conjunction with devices that analyze the user's real-time emotional state and collect data. For example, the terminal processes feedback, facial expressions, and tone of voice entered by the user through a device installed in the store to identify the user's emotions. Based on this, it suggests the most suitable menu and promotions for each user. 【0128】 For example, if a user visits a particular store on the weekend and the system determines they are feeling relaxed, the device can suggest health-conscious menu options and seating with lounge music. On the other hand, if the emotional engine determines the user is tired, it can prompt them for quick service and menu items suitable for energy replenishment. 【0129】 Finally, sales data and emotional feedback from all stores are integrated and analyzed by a server to improve the quality of the customer experience at each store. This enhances the overall service level of the brand and helps build long-term customer relationships. 【0130】 In this way, the present invention supports each store in providing inventory and services that meet customer needs, maintaining high customer satisfaction, and pursuing a sustainable business model. 【0131】 The following describes the processing flow. 【0132】 Step 1: 【0133】 The server periodically collects sales data from terminals in each store. This data includes sales volume, time of sale, and date for each item, as well as seasonal information and weather data. 【0134】 Step 2: 【0135】 The server runs an AI algorithm that uses sales data, seasonal information, and weather data to build a demand forecasting model. This creates demand forecasts for each region and determines the amount of inventory needed for the following week or month. 【0136】 Step 3: 【0137】 The terminal monitors the inventory of each store in real time and sends inventory level information to the server sequentially via sensors. When inventory falls below a set threshold, it is registered as a situation requiring replenishment. 【0138】 Step 4: 【0139】 The server initiates an automated ordering process based on real-time inventory information and demand forecasts. It places orders for the necessary ingredients in the specified quantities with the appropriate suppliers. 【0140】 Step 5: 【0141】 The device works in conjunction with an emotion engine to recognize the user's emotions and collects the user's facial expressions and tone of voice from devices within the store. This data is then used to analyze each user's emotional state. 【0142】 Step 6: 【0143】 The server analyzes emotional data and generates personalized promotions for each user. Menu recommendations and promotional information tailored to the user's emotions are sent to their device. 【0144】 Step 7: 【0145】 The device displays the generated promotions to the user, offering special offers and messages to attract the user's interest. 【0146】 Step 8: 【0147】 The server aggregates sales data from all stores and user sentiment feedback, and performs multivariate analysis to identify trends and areas for improvement. 【0148】 Step 9: 【0149】 The server provides feedback to each store based on the analysis results, optimizing services and operations. This allows each store to continuously improve the customer experience. 【0150】 (Example 2) 【0151】 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." 【0152】 In modern restaurant chains, accurately predicting demand fluctuations and efficiently managing inventory are major challenges. Furthermore, it is essential to appropriately analyze customer emotional states and provide services based on that analysis. Therefore, it is crucial to enhance customer satisfaction not only by utilizing sales information and weather data, but also by analyzing customer emotions. 【0153】 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. 【0154】 In this invention, the server includes means for using a machine learning algorithm to predict demand for each area based on sales information, cycle information, and weather information; communication equipment for instantly monitoring inventory at each facility and automatically ordering necessary products; and means including an emotion engine for analyzing the user's real-time emotions and providing appropriate services. This enables efficient inventory management in response to demand and optimal service proposals based on customer emotions. 【0155】 "Sales information" refers to information related to product sales data and customer purchase history at stores. 【0156】 "Periodic information" refers to information necessary to account for seasonal and time-dependent fluctuations, and examples include data on specific events and holiday periods throughout the year. 【0157】 "Weather information" refers to data about weather conditions, specifically information such as temperature, precipitation, and humidity. 【0158】 A "machine learning algorithm" is a type of artificial intelligence technology used to analyze large amounts of data and identify patterns and trends. 【0159】 "Communication equipment" refers to devices used to send and receive data, and plays a role in exchanging information over a network. 【0160】 A "data storage device" refers to equipment and technology for storing information and making it available for retrieval as needed. 【0161】 An "information processing device" refers to a computer or program used to generate useful data by calculating and analyzing information. 【0162】 A "central processing unit" refers to a central computer or server that manages and processes information for the entire system in one place. 【0163】 An "emotion engine" refers to a system or software that analyzes a user's emotional state and provides appropriate responses or suggestions based on that analysis. 【0164】 This invention is a system aimed at efficient inventory management and improved customer satisfaction in restaurant chain stores. This system functions by combining a machine learning algorithm that predicts demand using sales information, cycle information, and weather information, with an emotion engine that analyzes the real-time emotional state of customers. 【0165】 The server aggregates sales information, cycle information, and weather information collected from each facility and uses machine learning algorithms to predict demand for each area. Specifically, it performs data analysis using Python's pandas and scikit-learn to generate demand forecasts. Based on this forecast data, it sends instructions to terminals within the facilities via communication devices. 【0166】 The terminal monitors the facility's inventory in real time and automatically orders necessary products based on predicted demand. IoT devices are used for this purpose, enabling immediate tracking of inventory fluctuations. Furthermore, the terminal collects user emotional data through devices installed within the facility. Specifically, it identifies the user's emotional state using facial recognition and voice recognition software. 【0167】 When users visit a store, they can provide their emotions and feedback through a terminal, and based on this, they can receive appropriate suggestions. For example, a user who is in a relaxed mood will be suggested menus and seating that allow them to spend their time leisurely. This system aims to improve customer satisfaction by providing personalized services based on the analysis of the user's emotions. 【0168】 As a concrete example, the system generates suggestions to the user using the following prompt: 【0169】 "Design a demand forecasting system for local branches of a restaurant chain. It should propose optimal inventory management methods using artificial intelligence, incorporating sales data, seasonal information, and weather information. Furthermore, it should include specific measures aimed at improving the customer experience through sentiment analysis." 【0170】 By implementing this system, restaurant chains will be able to manage inventory efficiently and provide flexible services tailored to customer needs. 【0171】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0172】 Step 1: 【0173】 The server periodically collects sales information, cycle information, and weather information from each facility. It takes data from store sales databases and external weather information APIs as input. This data is preprocessed using Python, and a cleaned dataset is output. This output data is then ready to be fed into the demand forecasting model. 【0174】 Step 2: 【0175】 The server uses a machine learning algorithm to forecast demand based on the collected data. The cleaned dataset generated in Step 1 is used as input. The server uses the generated AI model to predict future demand at each facility. The output is the demand forecast data, which is used in the next step. 【0176】 Step 3: 【0177】 The terminal monitors inventory within the facility in real time using demand forecast data received from the server. It uses inventory sensor and inbound / outbound data as input to compare predicted demand with current inventory levels. This allows the terminal to identify shortages or excess inventory and automatically initiate communication for ordering. The output is instruction data regarding inventory adjustments and ordering. 【0178】 Step 4: 【0179】 Users provide feedback and input emotional data through terminals within the facility. This input includes the user's tone of voice and facial expressions, which are analyzed by the terminal's emotional engine. Based on the analysis, the terminal suggests the most suitable services and menus for the user. The output of this process is a customized service suggestion that meets the user's expectations. 【0180】 Step 5: 【0181】 The server performs continuous trend analysis based on sales information and sentiment data aggregated from all facilities. This data, along with the demand forecast results from Step 2, is used as input. The server processes the data using statistical methods and analytical tools to gain insights for service improvement at each facility. The output is strategic recommendations for service improvement. 【0182】 (Application Example 2) 【0183】 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". 【0184】 The problem that this invention aims to solve is to enable flexible service provision that takes into account the emotions of users in restaurant chain stores, thereby improving customer satisfaction and building a sustainable business model by streamlining inventory management. 【0185】 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. 【0186】 In this invention, the server includes means for using a machine learning algorithm to predict regional demand based on sales data, seasonal information, and weather information; a data communication device for monitoring the inventory of each store in real time and automatically ordering necessary products; means for using an information management device to adjust inventory between stores with excess inventory and stores with insufficient inventory; a sensing device for dynamically suggesting menus and seating by analyzing the user's emotions; an information processing device for analyzing customer order history and generating sales promotions tailored to each customer; and a computing server for integrating sales data from all stores and analyzing market trends. This enables the provision of services that respond to the user's emotions and efficient inventory management. 【0187】 A "machine learning algorithm" is a computational method that makes it possible to predict demand based on data. 【0188】 A "data communication device" is a device that monitors the inventory of each store in real time and transmits necessary information to other systems. 【0189】 An "information management device" is a device used to manage and process information necessary for coordinating inventory across multiple stores. 【0190】 A "sensing device" is a device used to collect and analyze data necessary to analyze a user's emotions. 【0191】 An "information processing device" is a device that analyzes customer order history and processes data to generate personalized promotions. 【0192】 A "computation server" is a server that integrates sales data from all stores and is responsible for processing calculations to analyze market trends. 【0193】 "Inventory" refers to the total amount of goods and materials held in a store for sale. 【0194】 "Demand forecasting" is a predictive activity that estimates future demand and takes the necessary steps to prepare for it. 【0195】 "User emotions" refers to the emotional state of customers when using a store, and is information used to optimize services based on that state. 【0196】 To realize this invention, several key elements are combined. First, the server utilizes machine learning algorithms to predict regional demand based on sales data, seasonal information, and weather information. This allows each store to efficiently manage its inventory. Inventory information is updated in real time via data communication devices, and automatic product ordering is performed as needed. This is a crucial element in preventing inventory shortages or surpluses in stores. 【0197】 Next, sensing devices are used to analyze the user's emotions. These devices use cameras and microphones to capture the user's facial expressions and voice, and analyze this data to identify their emotional state. Based on these analysis results, appropriate menus and seating are suggested to the customer. For example, if the user is relaxed, health-conscious menus are suggested; if they are tired, menus suitable for energy replenishment are suggested. 【0198】 The information management system is configured to adjust inventory across multiple stores. Based on inventory movement history, it determines the optimal method of inventory transfer and efficiently adjusts inventory. This enables individual stores to conduct sustainable business operations. 【0199】 Customer order history is analyzed by an information processing system, and personalized promotions are generated. These promotions are an important tool for boosting sales. 【0200】 Finally, the computing server integrates sales data from all stores and analyzes market trends. This data is extremely useful when formulating long-term business strategies. 【0201】 For example, if a user visits a cafe and sensing equipment determines that they are relaxed, the server will present a health-conscious menu. The user can also request more detailed services using prompts. An example of a prompt might be, "Please suggest menu items and seating that are suitable for a user who is relaxed." 【0202】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0203】 Step 1: 【0204】 The server receives sales data, seasonal information, and weather information as input, and uses machine learning algorithms to forecast demand for each region based on this data. The demand forecast calculation process outputs a demand outlook for each region, which serves as an indicator for inventory management. 【0205】 Step 2: 【0206】 The terminal acquires inventory information from each store in real time and transmits it to the server via a data communication device. A system that constantly monitors inventory status operates, using the inventory information as input to make automatic ordering decisions. As a result, if an inventory shortage is predicted, additional orders are automatically placed. 【0207】 Step 3: 【0208】 Users undergo emotion analysis using sensing devices within the store. The system analyzes the user's facial expressions and voice as input, and outputs their emotional state based on the results. Based on this output, the terminal generates prompt messages to suggest the most suitable menu items and seating for the user. 【0209】 Step 4: 【0210】 The information management system collects inventory information from multiple stores and selects the most suitable method of relocation when inventory adjustments are necessary. It receives inventory data from all stores as input and presents efficient inventory relocation methods as output. Based on past relocation history, the selected relocation method is then implemented. 【0211】 Step 5: 【0212】 The server generates personalized promotional content for each customer based on their order history. It processes customer purchase data as input and outputs customized promotions accordingly. These promotions are crucial for encouraging customer return visits and additional purchases. 【0213】 Step 6: 【0214】 The computing server integrates sales data from all stores and analyzes market trends. It uses the integrated data as input to perform market analysis and outputs the results. These analysis results are used as information to help formulate management strategies and optimize business operations. 【0215】 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. 【0216】 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. 【0217】 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. 【0218】 [Second Embodiment] 【0219】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0220】 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. 【0221】 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). 【0222】 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. 【0223】 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. 【0224】 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). 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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. 【0229】 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. 【0230】 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". 【0231】 This invention is intended as a system for efficient inventory management and sales expansion for chain restaurants in the food service industry. The system is based on an artificial intelligence algorithm that utilizes sales data, seasonal information, and weather information. This allows for demand forecasting that varies by region and optimizes store inventory management. 【0232】 First, the server collects sales data from each store and performs demand forecasting based on regional characteristics. Through analysis of past sales history, seasonal trends, and weather data, it predicts when specific menu items will be consumed in large quantities. In this process, artificial intelligence-based data analysis plays a crucial role. 【0233】 Furthermore, the terminal monitors the inventory status of each store in real time and sends the information to the server. The server automatically places orders based on this information. For example, if a store's beef inventory falls below a minimum level, the server automatically places an order to prevent stockouts. 【0234】 Furthermore, through its inventory sharing function, the server matches stores with excess inventory with stores that are short on inventory, efficiently reallocating stock. This provides a framework that minimizes food waste and promotes the efficient use of resources. 【0235】 Furthermore, the server analyzes customer order history to plan personalized promotions. For example, it can improve customer satisfaction by offering discounts on menu items frequently ordered by certain customers or recommending new menu items. Users receive these promotions, making it easier for them to receive offers that match their preferences. 【0236】 Finally, by integrating and analyzing sales data from all stores, the server identifies trends and leverages successful menus and promotions in other stores, contributing to overall sales improvement. Implementing this system can improve operational efficiency and sustainability in the food service industry. 【0237】 The following describes the processing flow. 【0238】 Step 1: 【0239】 The server collects sales data from each store. This data includes the number of units sold for each menu item, the date and time, and factors such as whether it was a weekday or weekend. 【0240】 Step 2: 【0241】 The server retrieves weather data in real time from a weather information service and stores it in a database. This information is then used for demand forecasting. 【0242】 Step 3: 【0243】 The server inputs sales data, seasonal trends, and weather information into an AI algorithm to build a demand forecasting model tailored to regional characteristics. The model then calculates projected sales for the following week or month. 【0244】 Step 4: 【0245】 The terminal monitors the inventory levels of each store in real time via IoT sensors and sends the information to the server at regular intervals. 【0246】 Step 5: 【0247】 The server analyzes the received inventory level information and detects when an order is needed. If the level falls below a threshold, the automated ordering system is activated. 【0248】 Step 6: 【0249】 The server aggregates inventory data from other stores and identifies stores with excess or insufficient inventory. Based on this information, it proposes the optimal inventory movement and notifies the relevant stores. 【0250】 Step 7: 【0251】 The server analyzes each customer's order history and generates personalized promotions based on their preferences. 【0252】 Step 8: 【0253】 The device sends personalized promotional information to each customer and offers special offers that will pique their interest. 【0254】 Step 9: 【0255】 The server analyzes the latest sales data from all stores to identify trends. It then creates suggestions for deploying successful menus and promotions to other stores. 【0256】 (Example 1) 【0257】 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." 【0258】 In the modern restaurant industry, efficient inventory management and sales maximization are critical challenges. Diverse restaurants each have different demand patterns, leading to risks of excess or shortages of inventory. Furthermore, providing personalized service to individual customers is difficult. These challenges stem from inaccurate demand forecasting and inefficient inventory management. Additionally, ineffective inventory adjustments between stores are a significant problem. 【0259】 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. 【0260】 In this invention, the server includes means for using data processing technology to predict regional demand based on sales information, seasonal data, and weather data; a communication network for monitoring inventory at each business location in real time and automatically replenishing necessary items; and information recording means for adjusting inventory between business locations with surplus inventory and those with insufficient inventory. This enables efficient demand forecasting and inventory adjustment, prevents inventory surpluses and shortages, and allows for the provision of services tailored to the needs of each customer. 【0261】 "Sales information" refers to information such as the number of products sold, sales amount, and sales date and time at each business location. 【0262】 "Seasonal data" refers to information related to specific seasons or time periods, and is used to understand seasonal fluctuations in sales. 【0263】 "Weather data" refers to information about weather conditions and is a factor that influences demand forecasts for each region. 【0264】 "Data processing technology" refers to algorithms and methods for analyzing large amounts of data and extracting useful information. 【0265】 A "communication network" refers to a system for exchanging information in real time between different business locations. 【0266】 "Information recording means" refers to databases and systems for recording and managing information regarding excess or insufficient inventory. 【0267】 An "information processing device" refers to an electronic device used to input data and perform analysis and processing based on that data. 【0268】 "Demand trends" refer to changes in demand and trends in the market for goods and services. 【0269】 This invention is implemented using a system consisting of a server and terminals. The server collects sales information, seasonal data, and weather data from each business location, and uses a generated AI model based on this data to perform demand forecasting. Specifically, a database management system and a machine learning framework are used in combination to construct a forecasting model. By inputting past sales data into this model, it is possible to predict future demand with high accuracy. 【0270】 Terminals are installed at each business location to monitor inventory status in real time. These terminals utilize barcode scanners to automatically update information when inventory is received or shipped. This information is sent to a server, and automatic orders are placed based on the results. 【0271】 As a concrete example, consider a scenario where a business's beef inventory falls below a minimum standard. In this case, the server automatically sends an order to the supplier, arranging delivery within a few days. This improves the efficiency of inventory management. 【0272】 The server also analyzes purchase history and plans personalized promotions for each customer. The generating AI model receives an example prompt message such as, "Generate promotional suggestions for products that are likely to be purchased under specific conditions," to generate promotional ideas for products that a particular customer frequently purchases. 【0273】 This system enables efficient inventory management and customized service delivery, making sustainable operations for the entire business possible. 【0274】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0275】 Step 1: 【0276】 The server collects sales information, seasonal data, and weather data from each business location. Specifically, each terminal is linked to a POS system and automatically sends daily sales data to the server. The input includes the number of items sold, sales amount, and date and time information, which the server stores in a database. The stored data is used for subsequent demand forecasting. 【0277】 Step 2: 【0278】 The server performs demand forecasting based on the collected data. Using a generative AI model, it analyzes sales information, seasonal data, and weather data to predict future demand for a specific product. At this time, the server inputs a prompt message into the model: "Predict the consumption pattern of the product under specific conditions." As output, demand forecast data for the product is generated. This forecast data is used in the next phase of inventory management. 【0279】 Step 3: 【0280】 The terminal monitors the inventory status at each business location in real time. A barcode scanner is connected to the terminal, which reads the barcode of the product when receiving or shipping inventory and updates the inventory data. This enables the current inventory quantity to be grasped, and the updated information is sent to the server. The input is barcode information and quantity, and the output is the latest inventory status data. 【0281】 Step 4: 【0282】 Based on the inventory data and demand forecast data received from the terminal, the server places automatic orders. Specifically, when the inventory falls below the set threshold, the server automatically sends an order instruction to the supplier. The inputs are the latest inventory data and demand forecast data, and the output is the purchase order. This prevents out-of-stock situations and maintains an appropriate inventory level. 【0283】 Step 5: 【0284】 The server analyzes the purchase history and generates promotions suitable for customers. Using a generation AI model, it analyzes customer attributes and purchase history and inputs the prompt sentence "Generate a promotion plan that proposes products that the customer is likely to be interested in." The output is a personalized promotion plan. This plan is distributed to users and contributes to improving customer satisfaction. 【0285】 (Application Example 1) 【0286】 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". 【0287】 To respond promptly to the diverse needs of consumers, efficient inventory management and demand forecasting are essential. However, conventional technologies have made it difficult to accurately forecast demand and reallocate inventory based on real-time data, resulting in inventory shortages or excesses and wasted resources. Furthermore, personalized proposals to individual consumers have been insufficient, limiting the improvement of customer satisfaction. To overcome these challenges, a new system is needed that can adapt to consumer needs and achieve efficient resource utilization. 【0288】 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. 【0289】 In this invention, the server includes means for using an inference algorithm to predict regional demand based on sales data, seasonal information, and weather information; means including a computation algorithm for acquiring information from external sources and optimizing product suggestions and delivery schedules; and information processing means for adjusting inventory between facilities with excess inventory and facilities with insufficient inventory. This enables a rapid and accurate response to the diverse needs of consumers, as well as efficient inventory management. 【0290】 An "inference algorithm" is a computational method that analyzes sales data, seasonal information, and weather information to predict demand patterns for each region. 【0291】 An "information and communication device" is a device that monitors inventory information at each facility in real time and automatically places orders for necessary resources. 【0292】 "Information processing means" refers to a means for managing data to adjust inventory between facilities with excess inventory and facilities with insufficient inventory, and for performing optimal reallocation. 【0293】 A "calculating device" is a device that analyzes users' historical information and generates personalized suggestions for each user. 【0294】 A "calculation unit" is a device that integrates sales data from all facilities and analyzes trends to support effective sales strategies. 【0295】 A "computational algorithm" is a computational method used to acquire information from external sources and optimize product recommendations and delivery schedules. 【0296】 The system of the present invention is designed to achieve efficient inventory management and demand forecasting for food delivery. This enables appropriate recommendations to consumers and efficient resource utilization. The main components include a server, information and communication equipment, information processing means, a computer, and a computing device. 【0297】 The server collects sales data, seasonal information, and weather information, and uses inference algorithms to predict demand for each region. This allows for the prediction of demand at specific times of day and under specific weather conditions, enabling appropriate inventory management. In addition, it monitors the inventory status of each facility in real time via information and communication devices and places automatic orders as needed. The information processing system adjusts inventory between facilities with excess and insufficient stock, and the computing device generates personalized suggestions based on the user's history information. 【0298】 The computing unit integrates and analyzes sales data from all facilities to understand sales trends. This allows for the identification of successful sales strategies and product trends, providing information that can be used at other facilities. Furthermore, the computing algorithm optimizes product recommendations and delivery schedules by referencing external information. 【0299】 For example, during rainy evenings, it can be predicted that hot soups will be in high demand. The server compares past data with current weather to predict demand and prepares potentially scarce items in advance, allowing for a quick response to customer orders. Such a system is expected to improve customer satisfaction and enable efficient use of resources. 【0300】 As an example of a prompt sentence for the generated AI model, it is in the form of "Please predict the menu that is most likely to have increased demand in this area in the next 3 hours considering the current weather and time zone." Based on this prompt sentence, the AI model performs data analysis and provides a list of menus with increased demand. Based on the information obtained in this way, each facility can appropriately adjust its inventory and promotional activities. 【0301】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0302】 Step 1: 【0303】 The server collects sales data, season information, and weather information from each facility. The input is a sales database or an external weather information API, and a dataset for demand prediction is obtained as the output. Based on this dataset, materials for predicting demand are prepared in the next step. 【0304】 Step 2: 【0305】 The server executes an inference algorithm using the collected dataset to predict demand for each region. The input is the dataset obtained in the previous step, and a demand prediction value is obtained as the output. This demand prediction value indicates the expected sales for each time zone and weather condition. 【0306】 Step 3: 【0307】 The terminal reads the real-time inventory information of the facility and transmits the information to the server. The input is the inventory management system of each facility, and real-time inventory data transmitted to the server is obtained as the output. This data is useful for automatic ordering to prevent stock shortages. 【0308】 Step 4: 【0309】 The server compares demand forecasts with real-time inventory data and automatically places orders for the necessary resources. The inputs are the demand forecast values ​​and inventory data obtained in steps 2 and 3, and the output is an order instruction sent to the purchasing system. This ensures that necessary resources are replenished in a timely manner. 【0310】 Step 5: 【0311】 The server matches facilities with excess inventory with those with shortages and proposes the optimal inventory reallocation. The input is real-time inventory data, and the output is an inventory movement plan. This ensures efficient inventory distribution between facilities. 【0312】 Step 6: 【0313】 Users place orders based on suggested products and promotions. The input is generated promotional information, and the output is consumer order data. This process enables personalized product recommendations that take into account the user's history. 【0314】 Step 7: 【0315】 The computing unit uses collected history and trend information to generate personalized promotions for individual customers. Inputs are past consumer order history and overall sales trend data, while output is optimized promotional information. Prompt messages are used to collect information and help identify specific demand patterns. 【0316】 Step 8: 【0317】 The server integrates and analyzes sales data from all facilities and recommends effective sales strategies to other facilities. The input is integrated sales data from each facility, and the output is an improved sales strategy plan. As a result, successful models and techniques are utilized across all facilities, contributing to overall efficiency improvements. 【0318】 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. 【0319】 This invention is implemented as a system for providing flexible service that takes into account the emotions of users in restaurant chains. This system has an artificial intelligence algorithm at its core that performs advanced demand forecasting using sales data, seasonal information, and weather information, and combines this with an emotion engine to further improve customer satisfaction. 【0320】 First, the server collects and analyzes sales data and trend information from each store to predict demand in each region. Furthermore, seasonal factors and weather data that affect sales are also incorporated into this prediction. This allows stores to prepare inventory according to demand. 【0321】 Next, the terminal monitors the inventory of each store in real time and places automatic orders based on revised demand forecasts as needed. The server, through the automated ordering system, prevents food shortages and supports efficient inventory management. 【0322】 The emotion engine works in conjunction with devices that analyze the user's real-time emotional state and collect data. For example, the terminal processes feedback, facial expressions, and tone of voice entered by the user through a device installed in the store to identify the user's emotions. Based on this, it suggests the most suitable menu and promotions for each user. 【0323】 For example, if a user visits a particular store on the weekend and the system determines they are feeling relaxed, the device can suggest health-conscious menu options and seating with lounge music. On the other hand, if the emotional engine determines the user is tired, it can prompt them for quick service and menu items suitable for energy replenishment. 【0324】 Finally, sales data and emotional feedback from all stores are integrated and analyzed by a server to improve the quality of the customer experience at each store. This enhances the overall service level of the brand and helps build long-term customer relationships. 【0325】 In this way, the present invention supports each store in providing inventory and services that meet customer needs, maintaining high customer satisfaction, and pursuing a sustainable business model. 【0326】 The following describes the processing flow. 【0327】 Step 1: 【0328】 The server periodically collects sales data from terminals in each store. This data includes sales volume, time of sale, and date for each item, as well as seasonal information and weather data. 【0329】 Step 2: 【0330】 The server runs an AI algorithm that uses sales data, seasonal information, and weather data to build a demand forecasting model. This creates demand forecasts for each region and determines the amount of inventory needed for the following week or month. 【0331】 Step 3: 【0332】 The terminal monitors the inventory of each store in real time and sends inventory level information to the server sequentially via sensors. When inventory falls below a set threshold, it is registered as a situation requiring replenishment. 【0333】 Step 4: 【0334】 The server initiates an automated ordering process based on real-time inventory information and demand forecasts. It places orders for the necessary ingredients in the specified quantities with the appropriate suppliers. 【0335】 Step 5: 【0336】 The device works in conjunction with an emotion engine to recognize the user's emotions and collects the user's facial expressions and tone of voice from devices within the store. This data is then used to analyze each user's emotional state. 【0337】 Step 6: 【0338】 The server analyzes emotional data and generates personalized promotions for each user. Menu recommendations and promotional information tailored to the user's emotions are sent to their device. 【0339】 Step 7: 【0340】 The device displays the generated promotions to the user, offering special offers and messages to attract the user's interest. 【0341】 Step 8: 【0342】 The server aggregates sales data from all stores and user sentiment feedback, and performs multivariate analysis to identify trends and areas for improvement. 【0343】 Step 9: 【0344】 The server provides feedback to each store based on the analysis results, optimizing services and operations. This allows each store to continuously improve the customer experience. 【0345】 (Example 2) 【0346】 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". 【0347】 In modern restaurant chains, accurately predicting demand fluctuations and efficiently managing inventory are major challenges. Furthermore, it is essential to appropriately analyze customer emotional states and provide services based on that analysis. Therefore, it is crucial to enhance customer satisfaction not only by utilizing sales information and weather data, but also by analyzing customer emotions. 【0348】 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. 【0349】 In this invention, the server includes means for using a machine learning algorithm to predict demand for each area based on sales information, cycle information, and weather information; communication equipment for instantly monitoring inventory at each facility and automatically ordering necessary products; and means including an emotion engine for analyzing the user's real-time emotions and providing appropriate services. This enables efficient inventory management in response to demand and optimal service proposals based on customer emotions. 【0350】 "Sales information" refers to information related to product sales data and customer purchase history at stores. 【0351】 "Periodic information" refers to information necessary to account for seasonal and time-dependent fluctuations, and examples include data on specific events and holiday periods throughout the year. 【0352】 "Weather information" refers to data about weather conditions, specifically information such as temperature, precipitation, and humidity. 【0353】 A "machine learning algorithm" is a type of artificial intelligence technology used to analyze large amounts of data and identify patterns and trends. 【0354】 "Communication equipment" refers to devices used to send and receive data, and plays a role in exchanging information over a network. 【0355】 A "data storage device" refers to equipment and technology for storing information and making it available for retrieval as needed. 【0356】 An "information processing device" refers to a computer or program used to generate useful data by calculating and analyzing information. 【0357】 A "central processing unit" refers to a central computer or server that manages and processes information for the entire system in one place. 【0358】 An "emotion engine" refers to a system or software that analyzes a user's emotional state and provides appropriate responses or suggestions based on that analysis. 【0359】 This invention is a system aimed at efficient inventory management and improved customer satisfaction in restaurant chain stores. This system functions by combining a machine learning algorithm that predicts demand using sales information, cycle information, and weather information, with an emotion engine that analyzes the real-time emotional state of customers. 【0360】 The server aggregates sales information, cycle information, and weather information collected from each facility and uses machine learning algorithms to predict demand for each area. Specifically, it performs data analysis using Python's pandas and scikit-learn to generate demand forecasts. Based on this forecast data, it sends instructions to terminals within the facilities via communication devices. 【0361】 The terminal monitors the facility's inventory in real time and automatically orders necessary products based on predicted demand. IoT devices are used for this purpose, enabling immediate tracking of inventory fluctuations. Furthermore, the terminal collects user emotional data through devices installed within the facility. Specifically, it identifies the user's emotional state using facial recognition and voice recognition software. 【0362】 When users visit a store, they can provide their emotions and feedback through a terminal, and based on this, they can receive appropriate suggestions. For example, a user who is in a relaxed mood will be suggested menus and seating that allow them to spend their time leisurely. This system aims to improve customer satisfaction by providing personalized services based on the analysis of the user's emotions. 【0363】 As a concrete example, the system generates suggestions to the user using the following prompt: 【0364】 "Design a demand forecasting system for local branches of a restaurant chain. It should propose optimal inventory management methods using artificial intelligence, incorporating sales data, seasonal information, and weather information. Furthermore, it should include specific measures aimed at improving the customer experience through sentiment analysis." 【0365】 By implementing this system, restaurant chains will be able to manage inventory efficiently and provide flexible services tailored to customer needs. 【0366】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0367】 Step 1: 【0368】 The server periodically collects sales information, cycle information, and weather information from each facility. It takes data from store sales databases and external weather information APIs as input. This data is preprocessed using Python, and a cleaned dataset is output. This output data is then ready to be fed into the demand forecasting model. 【0369】 Step 2: 【0370】 The server uses a machine learning algorithm to forecast demand based on the collected data. The cleaned dataset generated in Step 1 is used as input. The server uses the generated AI model to predict future demand at each facility. The output is the demand forecast data, which is used in the next step. 【0371】 Step 3: 【0372】 The terminal monitors inventory within the facility in real time using demand forecast data received from the server. It uses inventory sensor and inbound / outbound data as input to compare predicted demand with current inventory levels. This allows the terminal to identify shortages or excess inventory and automatically initiate communication for ordering. The output is instruction data regarding inventory adjustments and ordering. 【0373】 Step 4: 【0374】 Users provide feedback and input emotional data through terminals within the facility. This input includes the user's tone of voice and facial expressions, which are analyzed by the terminal's emotional engine. Based on the analysis, the terminal suggests the most suitable services and menus for the user. The output of this process is a customized service suggestion that meets the user's expectations. 【0375】 Step 5: 【0376】 The server performs continuous trend analysis based on sales information and sentiment data aggregated from all facilities. This data, along with the demand forecast results from Step 2, is used as input. The server processes the data using statistical methods and analytical tools to gain insights for service improvement at each facility. The output is strategic recommendations for service improvement. 【0377】 (Application Example 2) 【0378】 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." 【0379】 The problem that this invention aims to solve is to enable flexible service provision that takes into account the emotions of users in restaurant chain stores, thereby improving customer satisfaction and building a sustainable business model by streamlining inventory management. 【0380】 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. 【0381】 In this invention, the server includes means for using a machine learning algorithm to predict regional demand based on sales data, seasonal information, and weather information; a data communication device for monitoring the inventory of each store in real time and automatically ordering necessary products; means for using an information management device to adjust inventory between stores with excess inventory and stores with insufficient inventory; a sensing device for dynamically suggesting menus and seating by analyzing the user's emotions; an information processing device for analyzing customer order history and generating sales promotions tailored to each customer; and a computing server for integrating sales data from all stores and analyzing market trends. This enables the provision of services that respond to the user's emotions and efficient inventory management. 【0382】 A "machine learning algorithm" is a computational method that makes it possible to predict demand based on data. 【0383】 A "data communication device" is a device that monitors the inventory of each store in real time and transmits necessary information to other systems. 【0384】 An "information management device" is a device used to manage and process information necessary for coordinating inventory across multiple stores. 【0385】 A "sensing device" is a device used to collect and analyze data necessary to analyze a user's emotions. 【0386】 An "information processing device" is a device that analyzes customer order history and processes data to generate personalized promotions. 【0387】 A "computation server" is a server that integrates sales data from all stores and is responsible for processing calculations to analyze market trends. 【0388】 "Inventory" refers to the total amount of goods and materials held in a store for sale. 【0389】 "Demand forecasting" is a predictive activity that estimates future demand and takes the necessary steps to prepare for it. 【0390】 "User emotions" refers to the emotional state of customers when using a store, and is information used to optimize services based on that state. 【0391】 To realize this invention, several key elements are combined. First, the server utilizes machine learning algorithms to predict regional demand based on sales data, seasonal information, and weather information. This allows each store to efficiently manage its inventory. Inventory information is updated in real time via data communication devices, and automatic product ordering is performed as needed. This is a crucial element in preventing inventory shortages or surpluses in stores. 【0392】 Next, sensing devices are used to analyze the user's emotions. These devices use cameras and microphones to capture the user's facial expressions and voice, and analyze this data to identify their emotional state. Based on these analysis results, appropriate menus and seating are suggested to the customer. For example, if the user is relaxed, health-conscious menus are suggested; if they are tired, menus suitable for energy replenishment are suggested. 【0393】 The information management system is configured to adjust inventory across multiple stores. Based on inventory movement history, it determines the optimal method of inventory transfer and efficiently adjusts inventory. This enables individual stores to conduct sustainable business operations. 【0394】 Customer order history is analyzed by an information processing system, and personalized promotions are generated. These promotions are an important tool for boosting sales. 【0395】 Finally, the computing server integrates sales data from all stores and analyzes market trends. This data is extremely useful when formulating long-term business strategies. 【0396】 For example, if a user visits a cafe and sensing equipment determines that they are relaxed, the server will present a health-conscious menu. The user can also request more detailed services using prompts. An example of a prompt might be, "Please suggest menu items and seating that are suitable for a user who is relaxed." 【0397】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0398】 Step 1: 【0399】 The server receives sales data, seasonal information, and weather information as input, and uses machine learning algorithms to forecast demand for each region based on this data. The demand forecast calculation process outputs a demand outlook for each region, which serves as an indicator for inventory management. 【0400】 Step 2: 【0401】 The terminal acquires inventory information from each store in real time and transmits it to the server via a data communication device. A system that constantly monitors inventory status operates, using the inventory information as input to make automatic ordering decisions. As a result, if an inventory shortage is predicted, additional orders are automatically placed. 【0402】 Step 3: 【0403】 Users undergo emotion analysis using sensing devices within the store. The system analyzes the user's facial expressions and voice as input, and outputs their emotional state based on the results. Based on this output, the terminal generates prompt messages to suggest the most suitable menu items and seating for the user. 【0404】 Step 4: 【0405】 The information management system collects inventory information from multiple stores and selects the most suitable method of relocation when inventory adjustments are necessary. It receives inventory data from all stores as input and presents efficient inventory relocation methods as output. Based on past relocation history, the selected relocation method is then implemented. 【0406】 Step 5: 【0407】 The server generates personalized promotional content for each customer based on their order history. It processes customer purchase data as input and outputs customized promotions accordingly. These promotions are crucial for encouraging customer return visits and additional purchases. 【0408】 Step 6: 【0409】 The computing server integrates sales data from all stores and analyzes market trends. It uses the integrated data as input to perform market analysis and outputs the results. These analysis results are used as information to help formulate management strategies and optimize business operations. 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 [Third Embodiment] 【0414】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0415】 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. 【0416】 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). 【0417】 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. 【0418】 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. 【0419】 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). 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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". 【0426】 This invention is intended as a system for efficient inventory management and sales expansion for chain restaurants in the food service industry. The system is based on an artificial intelligence algorithm that utilizes sales data, seasonal information, and weather information. This allows for demand forecasting that varies by region and optimizes store inventory management. 【0427】 First, the server collects sales data from each store and performs demand forecasting based on regional characteristics. Through analysis of past sales history, seasonal trends, and weather data, it predicts when specific menu items will be consumed in large quantities. In this process, artificial intelligence-based data analysis plays a crucial role. 【0428】 Furthermore, the terminal monitors the inventory status of each store in real time and sends the information to the server. The server automatically places orders based on this information. For example, if a store's beef inventory falls below a minimum level, the server automatically places an order to prevent stockouts. 【0429】 Furthermore, through its inventory sharing function, the server matches stores with excess inventory with stores that are short on inventory, efficiently reallocating stock. This provides a framework that minimizes food waste and promotes the efficient use of resources. 【0430】 Furthermore, the server analyzes customer order history to plan personalized promotions. For example, it can improve customer satisfaction by offering discounts on menu items frequently ordered by certain customers or recommending new menu items. Users receive these promotions, making it easier for them to receive offers that match their preferences. 【0431】 Finally, by integrating and analyzing sales data from all stores, the server identifies trends and leverages successful menus and promotions in other stores, contributing to overall sales improvement. Implementing this system can improve operational efficiency and sustainability in the food service industry. 【0432】 The following describes the processing flow. 【0433】 Step 1: 【0434】 The server collects sales data from each store. This data includes the number of units sold for each menu item, the date and time, and factors such as whether it was a weekday or weekend. 【0435】 Step 2: 【0436】 The server retrieves weather data in real time from a weather information service and stores it in a database. This information is then used for demand forecasting. 【0437】 Step 3: 【0438】 The server inputs sales data, seasonal trends, and weather information into an AI algorithm to build a demand forecasting model tailored to regional characteristics. The model then calculates projected sales for the following week or month. 【0439】 Step 4: 【0440】 The terminal monitors the inventory levels of each store in real time via IoT sensors and sends the information to the server at regular intervals. 【0441】 Step 5: 【0442】 The server analyzes the received inventory level information and detects when an order is needed. If the level falls below a threshold, the automated ordering system is activated. 【0443】 Step 6: 【0444】 The server aggregates inventory data from other stores and identifies stores with excess or insufficient inventory. Based on this information, it proposes the optimal inventory movement and notifies the relevant stores. 【0445】 Step 7: 【0446】 The server analyzes each customer's order history and generates personalized promotions based on their preferences. 【0447】 Step 8: 【0448】 The device sends personalized promotional information to each customer and offers special offers that will pique their interest. 【0449】 Step 9: 【0450】 The server analyzes the latest sales data from all stores to identify trends. It then creates suggestions for deploying successful menus and promotions to other stores. 【0451】 (Example 1) 【0452】 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." 【0453】 In the modern restaurant industry, efficient inventory management and sales maximization are critical challenges. Diverse restaurants each have different demand patterns, leading to risks of excess or shortages of inventory. Furthermore, providing personalized service to individual customers is difficult. These challenges stem from inaccurate demand forecasting and inefficient inventory management. Additionally, ineffective inventory adjustments between stores are a significant problem. 【0454】 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. 【0455】 In this invention, the server includes means for using data processing technology to predict regional demand based on sales information, seasonal data, and weather data; a communication network for monitoring inventory at each business location in real time and automatically replenishing necessary items; and information recording means for adjusting inventory between business locations with surplus inventory and those with insufficient inventory. This enables efficient demand forecasting and inventory adjustment, prevents inventory surpluses and shortages, and allows for the provision of services tailored to the needs of each customer. 【0456】 "Sales information" refers to information such as the number of products sold, sales amount, and sales date and time at each business location. 【0457】 "Seasonal data" refers to information related to specific seasons or time periods, and is used to understand seasonal fluctuations in sales. 【0458】 "Weather data" refers to information about weather conditions and is a factor that influences demand forecasts for each region. 【0459】 "Data processing technology" refers to algorithms and methods for analyzing large amounts of data and extracting useful information. 【0460】 A "communication network" refers to a system for exchanging information in real time between different business locations. 【0461】 "Information recording means" refers to databases and systems for recording and managing information regarding excess or insufficient inventory. 【0462】 An "information processing device" refers to an electronic device used to input data and perform analysis and processing based on that data. 【0463】 "Demand trends" refer to changes in demand and trends in the market for goods and services. 【0464】 This invention is implemented using a system consisting of a server and terminals. The server collects sales information, seasonal data, and weather data from each business location, and uses a generated AI model based on this data to perform demand forecasting. Specifically, a database management system and a machine learning framework are used in combination to construct a forecasting model. By inputting past sales data into this model, it is possible to predict future demand with high accuracy. 【0465】 Terminals are installed at each business location to monitor inventory status in real time. These terminals utilize barcode scanners to automatically update information when inventory is received or shipped. This information is sent to a server, and automatic orders are placed based on the results. 【0466】 As a concrete example, consider a scenario where a business's beef inventory falls below a minimum standard. In this case, the server automatically sends an order to the supplier, arranging delivery within a few days. This improves the efficiency of inventory management. 【0467】 The server also analyzes purchase history and plans personalized promotions for each customer. The generating AI model receives an example prompt message such as, "Generate promotional suggestions for products that are likely to be purchased under specific conditions," to generate promotional ideas for products that a particular customer frequently purchases. 【0468】 This system enables efficient inventory management and customized service delivery, making sustainable operations for the entire business possible. 【0469】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0470】 Step 1: 【0471】 The server collects sales information, seasonal data, and weather data from each business location. Specifically, each terminal is linked to a POS system and automatically sends daily sales data to the server. The input includes the number of items sold, sales amount, and date and time information, which the server stores in a database. The stored data is used for subsequent demand forecasting. 【0472】 Step 2: 【0473】 The server performs demand forecasting based on the collected data. Using a generative AI model, it analyzes sales information, seasonal data, and weather data to predict future demand for a specific product. At this time, the server inputs a prompt message into the model: "Predict the consumption pattern of the product under specific conditions." As output, demand forecast data for the product is generated. This forecast data is used in the next phase of inventory management. 【0474】 Step 3: 【0475】 The terminal monitors inventory status at each business location in real time. A barcode scanner is connected to the terminal, and it updates inventory data by reading product barcodes when inventory is received or shipped. This allows the current inventory level to be determined, and the updated information is sent to the server. Inputs are barcode information and quantity, and output is the latest inventory status data. 【0476】 Step 4: 【0477】 The server automatically places orders based on inventory data and demand forecast data received from terminals. Specifically, when inventory falls below a set threshold, the server automatically sends an order to the supplier. Inputs include the latest inventory data and demand forecast data, and output is an order form. This prevents stockouts and maintains appropriate inventory levels. 【0478】 Step 5: 【0479】 The server analyzes purchase history and generates promotions tailored to each customer. Using a generation AI model, it analyzes customer attributes and purchase history, and receives a prompt message: "Generate promotional suggestions that propose products the customer might be interested in." The output is a personalized promotional suggestion. This suggestion is delivered to the user, contributing to improved customer satisfaction. 【0480】 (Application Example 1) 【0481】 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." 【0482】 To respond promptly to the diverse needs of consumers, efficient inventory management and demand forecasting are essential. However, conventional technologies have made it difficult to accurately forecast demand and reallocate inventory based on real-time data, resulting in inventory shortages or excesses and wasted resources. Furthermore, personalized proposals to individual consumers have been insufficient, limiting the improvement of customer satisfaction. To overcome these challenges, a new system is needed that can adapt to consumer needs and achieve efficient resource utilization. 【0483】 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. 【0484】 In this invention, the server includes means for using an inference algorithm to predict regional demand based on sales data, seasonal information, and weather information; means including a computation algorithm for acquiring information from external sources and optimizing product suggestions and delivery schedules; and information processing means for adjusting inventory between facilities with excess inventory and facilities with insufficient inventory. This enables a rapid and accurate response to the diverse needs of consumers, as well as efficient inventory management. 【0485】 An "inference algorithm" is a computational method that analyzes sales data, seasonal information, and weather information to predict demand patterns for each region. 【0486】 An "information and communication device" is a device that monitors inventory information at each facility in real time and automatically places orders for necessary resources. 【0487】 "Information processing means" refers to a means for managing data to adjust inventory between facilities with excess inventory and facilities with insufficient inventory, and for performing optimal reallocation. 【0488】 A "calculating device" is a device that analyzes users' historical information and generates personalized suggestions for each user. 【0489】 A "calculation unit" is a device that integrates sales data from all facilities and analyzes trends to support effective sales strategies. 【0490】 A "computational algorithm" is a computational method used to acquire information from external sources and optimize product recommendations and delivery schedules. 【0491】 The system of the present invention is designed to achieve efficient inventory management and demand forecasting for food delivery. This enables appropriate recommendations to consumers and efficient resource utilization. The main components include a server, information and communication equipment, information processing means, a computer, and a computing device. 【0492】 The server collects sales data, seasonal information, and weather information, and uses inference algorithms to predict demand for each region. This allows for the prediction of demand at specific times of day and under specific weather conditions, enabling appropriate inventory management. In addition, it monitors the inventory status of each facility in real time via information and communication devices and places automatic orders as needed. The information processing system adjusts inventory between facilities with excess and insufficient stock, and the computing device generates personalized suggestions based on the user's history information. 【0493】 The computing unit integrates and analyzes sales data from all facilities to understand sales trends. This allows for the identification of successful sales strategies and product trends, providing information that can be used at other facilities. Furthermore, the computing algorithm optimizes product recommendations and delivery schedules by referencing external information. 【0494】 For example, during rainy evenings, it can be predicted that hot soups will be in high demand. The server compares past data with current weather to predict demand and prepares potentially scarce items in advance, allowing for a quick response to customer orders. Such a system is expected to improve customer satisfaction and enable efficient use of resources. 【0495】 An example of a prompt for the generating AI model would be: "Considering the current weather and time of day, predict which menu items are likely to be in the highest demand in this area over the next three hours." Based on this prompt, the AI ​​model analyzes the data and provides a list of menu items that are likely to be in high demand. Based on the information obtained in this way, each facility can appropriately adjust its inventory and promotional activities. 【0496】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0497】 Step 1: 【0498】 The server collects sales data, seasonal information, and weather information from each facility. Inputs include sales databases and external weather information APIs, and output is a dataset for demand forecasting. This dataset is used to prepare the necessary information for demand forecasting in the next step. 【0499】 Step 2: 【0500】 The server uses the collected dataset to run an inference algorithm and perform demand forecasts for each region. The input is the dataset obtained in the previous step, and the output is a demand forecast value. This demand forecast value indicates the expected sales for each time period and weather condition. 【0501】 Step 3: 【0502】 The terminal reads real-time inventory information from each facility and sends the information to the server. The input is the inventory management system of each facility, and the output is real-time inventory data that is sent to the server. This data is useful for automatic ordering to prevent inventory shortages. 【0503】 Step 4: 【0504】 The server compares demand forecasts with real-time inventory data and automatically places orders for the necessary resources. The inputs are the demand forecast values ​​and inventory data obtained in steps 2 and 3, and the output is an order instruction sent to the purchasing system. This ensures that necessary resources are replenished in a timely manner. 【0505】 Step 5: 【0506】 The server matches facilities with excess inventory with those with shortages and proposes the optimal inventory reallocation. The input is real-time inventory data, and the output is an inventory movement plan. This ensures efficient inventory distribution between facilities. 【0507】 Step 6: 【0508】 Users place orders based on suggested products and promotions. The input is generated promotional information, and the output is consumer order data. This process enables personalized product recommendations that take into account the user's history. 【0509】 Step 7: 【0510】 The computing unit uses collected history and trend information to generate personalized promotions for individual customers. Inputs are past consumer order history and overall sales trend data, while output is optimized promotional information. Prompt messages are used to collect information and help identify specific demand patterns. 【0511】 Step 8: 【0512】 The server integrates and analyzes sales data from all facilities and recommends effective sales strategies to other facilities. The input is integrated sales data from each facility, and the output is an improved sales strategy plan. As a result, successful models and techniques are utilized across all facilities, contributing to overall efficiency improvements. 【0513】 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. 【0514】 This invention is implemented as a system for providing flexible service that takes into account the emotions of users in restaurant chains. This system has an artificial intelligence algorithm at its core that performs advanced demand forecasting using sales data, seasonal information, and weather information, and combines this with an emotion engine to further improve customer satisfaction. 【0515】 First, the server collects and analyzes sales data and trend information from each store to predict demand in each region. Furthermore, seasonal factors and weather data that affect sales are also incorporated into this prediction. This allows stores to prepare inventory according to demand. 【0516】 Next, the terminal monitors the inventory of each store in real time and places automatic orders based on revised demand forecasts as needed. The server, through the automated ordering system, prevents food shortages and supports efficient inventory management. 【0517】 The emotion engine works in conjunction with devices that analyze the user's real-time emotional state and collect data. For example, the terminal processes feedback, facial expressions, and tone of voice entered by the user through a device installed in the store to identify the user's emotions. Based on this, it suggests the most suitable menu and promotions for each user. 【0518】 For example, if a user visits a particular store on the weekend and the system determines they are feeling relaxed, the device can suggest health-conscious menu options and seating with lounge music. On the other hand, if the emotional engine determines the user is tired, it can prompt them for quick service and menu items suitable for energy replenishment. 【0519】 Finally, sales data and emotional feedback from all stores are integrated and analyzed by a server to improve the quality of the customer experience at each store. This enhances the overall service level of the brand and helps build long-term customer relationships. 【0520】 In this way, the present invention supports each store in providing inventory and services that meet customer needs, maintaining high customer satisfaction, and pursuing a sustainable business model. 【0521】 The following describes the processing flow. 【0522】 Step 1: 【0523】 The server periodically collects sales data from terminals in each store. This data includes sales volume, time of sale, and date for each item, as well as seasonal information and weather data. 【0524】 Step 2: 【0525】 The server runs an AI algorithm that uses sales data, seasonal information, and weather data to build a demand forecasting model. This creates demand forecasts for each region and determines the amount of inventory needed for the following week or month. 【0526】 Step 3: 【0527】 The terminal monitors the inventory of each store in real time and sends inventory level information to the server sequentially via sensors. When inventory falls below a set threshold, it is registered as a situation requiring replenishment. 【0528】 Step 4: 【0529】 The server initiates an automated ordering process based on real-time inventory information and demand forecasts. It places orders for the necessary ingredients in the specified quantities with the appropriate suppliers. 【0530】 Step 5: 【0531】 The device works in conjunction with an emotion engine to recognize the user's emotions and collects the user's facial expressions and tone of voice from devices within the store. This data is then used to analyze each user's emotional state. 【0532】 Step 6: 【0533】 The server analyzes emotional data and generates personalized promotions for each user. Menu recommendations and promotional information tailored to the user's emotions are sent to their device. 【0534】 Step 7: 【0535】 The device displays the generated promotions to the user, offering special offers and messages to attract the user's interest. 【0536】 Step 8: 【0537】 The server aggregates sales data from all stores and user sentiment feedback, and performs multivariate analysis to identify trends and areas for improvement. 【0538】 Step 9: 【0539】 The server provides feedback to each store based on the analysis results, optimizing services and operations. This allows each store to continuously improve the customer experience. 【0540】 (Example 2) 【0541】 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." 【0542】 In modern restaurant chains, accurately predicting demand fluctuations and efficiently managing inventory are major challenges. Furthermore, it is essential to appropriately analyze customer emotional states and provide services based on that analysis. Therefore, it is crucial to enhance customer satisfaction not only by utilizing sales information and weather data, but also by analyzing customer emotions. 【0543】 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. 【0544】 In this invention, the server includes means for using a machine learning algorithm to predict demand for each area based on sales information, cycle information, and weather information; communication equipment for instantly monitoring inventory at each facility and automatically ordering necessary products; and means including an emotion engine for analyzing the user's real-time emotions and providing appropriate services. This enables efficient inventory management in response to demand and optimal service proposals based on customer emotions. 【0545】 "Sales information" refers to information related to product sales data and customer purchase history at stores. 【0546】 "Periodic information" refers to information necessary to account for seasonal and time-dependent fluctuations, and examples include data on specific events and holiday periods throughout the year. 【0547】 "Weather information" refers to data about weather conditions, specifically information such as temperature, precipitation, and humidity. 【0548】 A "machine learning algorithm" is a type of artificial intelligence technology used to analyze large amounts of data and identify patterns and trends. 【0549】 "Communication equipment" refers to devices used to send and receive data, and plays a role in exchanging information over a network. 【0550】 A "data storage device" refers to equipment and technology for storing information and making it available for retrieval as needed. 【0551】 An "information processing device" refers to a computer or program used to generate useful data by calculating and analyzing information. 【0552】 A "central processing unit" refers to a central computer or server that manages and processes information for the entire system in one place. 【0553】 An "emotion engine" refers to a system or software that analyzes a user's emotional state and provides appropriate responses or suggestions based on that analysis. 【0554】 This invention is a system aimed at efficient inventory management and improved customer satisfaction in restaurant chain stores. This system functions by combining a machine learning algorithm that predicts demand using sales information, cycle information, and weather information, with an emotion engine that analyzes the real-time emotional state of customers. 【0555】 The server aggregates sales information, cycle information, and weather information collected from each facility and uses machine learning algorithms to predict demand for each area. Specifically, it performs data analysis using Python's pandas and scikit-learn to generate demand forecasts. Based on this forecast data, it sends instructions to terminals within the facilities via communication devices. 【0556】 The terminal monitors the facility's inventory in real time and automatically orders necessary products based on predicted demand. IoT devices are used for this purpose, enabling immediate tracking of inventory fluctuations. Furthermore, the terminal collects user emotional data through devices installed within the facility. Specifically, it identifies the user's emotional state using facial recognition and voice recognition software. 【0557】 When users visit a store, they can provide their emotions and feedback through a terminal, and based on this, they can receive appropriate suggestions. For example, a user who is in a relaxed mood will be suggested menus and seating that allow them to spend their time leisurely. This system aims to improve customer satisfaction by providing personalized services based on the analysis of the user's emotions. 【0558】 As a concrete example, the system generates suggestions to the user using the following prompt: 【0559】 "Design a demand forecasting system for local branches of a restaurant chain. It should propose optimal inventory management methods using artificial intelligence, incorporating sales data, seasonal information, and weather information. Furthermore, it should include specific measures aimed at improving the customer experience through sentiment analysis." 【0560】 By implementing this system, restaurant chains will be able to manage inventory efficiently and provide flexible services tailored to customer needs. 【0561】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0562】 Step 1: 【0563】 The server periodically collects sales information, cycle information, and weather information from each facility. It takes data from store sales databases and external weather information APIs as input. This data is preprocessed using Python, and a cleaned dataset is output. This output data is then ready to be fed into the demand forecasting model. 【0564】 Step 2: 【0565】 The server uses a machine learning algorithm to forecast demand based on the collected data. The cleaned dataset generated in Step 1 is used as input. The server uses the generated AI model to predict future demand at each facility. The output is the demand forecast data, which is used in the next step. 【0566】 Step 3: 【0567】 The terminal monitors inventory within the facility in real time using demand forecast data received from the server. It uses inventory sensor and inbound / outbound data as input to compare predicted demand with current inventory levels. This allows the terminal to identify shortages or excess inventory and automatically initiate communication for ordering. The output is instruction data regarding inventory adjustments and ordering. 【0568】 Step 4: 【0569】 Users provide feedback and input emotional data through terminals within the facility. This input includes the user's tone of voice and facial expressions, which are analyzed by the terminal's emotional engine. Based on the analysis, the terminal suggests the most suitable services and menus for the user. The output of this process is a customized service suggestion that meets the user's expectations. 【0570】 Step 5: 【0571】 The server performs continuous trend analysis based on sales information and sentiment data aggregated from all facilities. This data, along with the demand forecast results from Step 2, is used as input. The server processes the data using statistical methods and analytical tools to gain insights for service improvement at each facility. The output is strategic recommendations for service improvement. 【0572】 (Application Example 2) 【0573】 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." 【0574】 The problem that this invention aims to solve is to enable flexible service provision that takes into account the emotions of users in restaurant chain stores, thereby improving customer satisfaction and building a sustainable business model by streamlining inventory management. 【0575】 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. 【0576】 In this invention, the server includes means for using a machine learning algorithm to predict regional demand based on sales data, seasonal information, and weather information; a data communication device for monitoring the inventory of each store in real time and automatically ordering necessary products; means for using an information management device to adjust inventory between stores with excess inventory and stores with insufficient inventory; a sensing device for dynamically suggesting menus and seating by analyzing the user's emotions; an information processing device for analyzing customer order history and generating sales promotions tailored to each customer; and a computing server for integrating sales data from all stores and analyzing market trends. This enables the provision of services that respond to the user's emotions and efficient inventory management. 【0577】 A "machine learning algorithm" is a computational method that makes it possible to predict demand based on data. 【0578】 A "data communication device" is a device that monitors the inventory of each store in real time and transmits necessary information to other systems. 【0579】 An "information management device" is a device used to manage and process information necessary for coordinating inventory across multiple stores. 【0580】 A "sensing device" is a device used to collect and analyze data necessary to analyze a user's emotions. 【0581】 An "information processing device" is a device that analyzes customer order history and processes data to generate personalized promotions. 【0582】 A "computation server" is a server that integrates sales data from all stores and is responsible for processing calculations to analyze market trends. 【0583】 "Inventory" refers to the total amount of goods and materials held in a store for sale. 【0584】 "Demand forecasting" is a predictive activity that estimates future demand and takes the necessary steps to prepare for it. 【0585】 "User emotions" refers to the emotional state of customers when using a store, and is information used to optimize services based on that state. 【0586】 To realize this invention, several key elements are combined. First, the server utilizes machine learning algorithms to predict regional demand based on sales data, seasonal information, and weather information. This allows each store to efficiently manage its inventory. Inventory information is updated in real time via data communication devices, and automatic product ordering is performed as needed. This is a crucial element in preventing inventory shortages or surpluses in stores. 【0587】 Next, sensing devices are used to analyze the user's emotions. These devices use cameras and microphones to capture the user's facial expressions and voice, and analyze this data to identify their emotional state. Based on these analysis results, appropriate menus and seating are suggested to the customer. For example, if the user is relaxed, health-conscious menus are suggested; if they are tired, menus suitable for energy replenishment are suggested. 【0588】 The information management system is configured to adjust inventory across multiple stores. Based on inventory movement history, it determines the optimal method of inventory transfer and efficiently adjusts inventory. This enables individual stores to conduct sustainable business operations. 【0589】 Customer order history is analyzed by an information processing system, and personalized promotions are generated. These promotions are an important tool for boosting sales. 【0590】 Finally, the computing server integrates sales data from all stores and analyzes market trends. This data is extremely useful when formulating long-term business strategies. 【0591】 For example, if a user visits a cafe and sensing equipment determines that they are relaxed, the server will present a health-conscious menu. The user can also request more detailed services using prompts. An example of a prompt might be, "Please suggest menu items and seating that are suitable for a user who is relaxed." 【0592】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0593】 Step 1: 【0594】 The server receives sales data, seasonal information, and weather information as input, and uses machine learning algorithms to forecast demand for each region based on this data. The demand forecast calculation process outputs a demand outlook for each region, which serves as an indicator for inventory management. 【0595】 Step 2: 【0596】 The terminal acquires inventory information from each store in real time and transmits it to the server via a data communication device. A system that constantly monitors inventory status operates, using the inventory information as input to make automatic ordering decisions. As a result, if an inventory shortage is predicted, additional orders are automatically placed. 【0597】 Step 3: 【0598】 Users undergo emotion analysis using sensing devices within the store. The system analyzes the user's facial expressions and voice as input, and outputs their emotional state based on the results. Based on this output, the terminal generates prompt messages to suggest the most suitable menu items and seating for the user. 【0599】 Step 4: 【0600】 The information management system collects inventory information from multiple stores and selects the most suitable method of relocation when inventory adjustments are necessary. It receives inventory data from all stores as input and presents efficient inventory relocation methods as output. Based on past relocation history, the selected relocation method is then implemented. 【0601】 Step 5: 【0602】 The server generates personalized promotional content for each customer based on their order history. It processes customer purchase data as input and outputs customized promotions accordingly. These promotions are crucial for encouraging customer return visits and additional purchases. 【0603】 Step 6: 【0604】 The computing server integrates sales data from all stores and analyzes market trends. It uses the integrated data as input to perform market analysis and outputs the results. These analysis results are used as information to help formulate management strategies and optimize business operations. 【0605】 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. 【0606】 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. 【0607】 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. 【0608】 [Fourth Embodiment] 【0609】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0610】 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. 【0611】 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). 【0612】 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. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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". 【0622】 This invention is intended as a system for efficient inventory management and sales expansion for chain restaurants in the food service industry. The system is based on an artificial intelligence algorithm that utilizes sales data, seasonal information, and weather information. This allows for demand forecasting that varies by region and optimizes store inventory management. 【0623】 First, the server collects sales data from each store and performs demand forecasting based on regional characteristics. Through analysis of past sales history, seasonal trends, and weather data, it predicts when specific menu items will be consumed in large quantities. In this process, artificial intelligence-based data analysis plays a crucial role. 【0624】 Furthermore, the terminal monitors the inventory status of each store in real time and sends the information to the server. The server automatically places orders based on this information. For example, if a store's beef inventory falls below a minimum level, the server automatically places an order to prevent stockouts. 【0625】 Furthermore, through its inventory sharing function, the server matches stores with excess inventory with stores that are short on inventory, efficiently reallocating stock. This provides a framework that minimizes food waste and promotes the efficient use of resources. 【0626】 Furthermore, the server analyzes customer order history to plan personalized promotions. For example, it can improve customer satisfaction by offering discounts on menu items frequently ordered by certain customers or recommending new menu items. Users receive these promotions, making it easier for them to receive offers that match their preferences. 【0627】 Finally, by integrating and analyzing sales data from all stores, the server identifies trends and leverages successful menus and promotions in other stores, contributing to overall sales improvement. Implementing this system can improve operational efficiency and sustainability in the food service industry. 【0628】 The following describes the processing flow. 【0629】 Step 1: 【0630】 The server collects sales data from each store. This data includes the number of units sold for each menu item, the date and time, and factors such as whether it was a weekday or weekend. 【0631】 Step 2: 【0632】 The server retrieves weather data in real time from a weather information service and stores it in a database. This information is then used for demand forecasting. 【0633】 Step 3: 【0634】 The server inputs sales data, seasonal trends, and weather information into an AI algorithm to build a demand forecasting model tailored to regional characteristics. The model then calculates projected sales for the following week or month. 【0635】 Step 4: 【0636】 The terminal monitors the inventory levels of each store in real time via IoT sensors and sends the information to the server at regular intervals. 【0637】 Step 5: 【0638】 The server analyzes the received inventory level information and detects when an order is needed. If the level falls below a threshold, the automated ordering system is activated. 【0639】 Step 6: 【0640】 The server aggregates inventory data from other stores and identifies stores with excess or insufficient inventory. Based on this information, it proposes the optimal inventory movement and notifies the relevant stores. 【0641】 Step 7: 【0642】 The server analyzes each customer's order history and generates personalized promotions based on their preferences. 【0643】 Step 8: 【0644】 The device sends personalized promotional information to each customer and offers special offers that will pique their interest. 【0645】 Step 9: 【0646】 The server analyzes the latest sales data from all stores to identify trends. It then creates suggestions for deploying successful menus and promotions to other stores. 【0647】 (Example 1) 【0648】 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". 【0649】 In the modern restaurant industry, efficient inventory management and sales maximization are critical challenges. Diverse restaurants each have different demand patterns, leading to risks of excess or shortages of inventory. Furthermore, providing personalized service to individual customers is difficult. These challenges stem from inaccurate demand forecasting and inefficient inventory management. Additionally, ineffective inventory adjustments between stores are a significant problem. 【0650】 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. 【0651】 In this invention, the server includes means for using data processing technology to predict regional demand based on sales information, seasonal data, and weather data; a communication network for monitoring inventory at each business location in real time and automatically replenishing necessary items; and information recording means for adjusting inventory between business locations with surplus inventory and those with insufficient inventory. This enables efficient demand forecasting and inventory adjustment, prevents inventory surpluses and shortages, and allows for the provision of services tailored to the needs of each customer. 【0652】 "Sales information" refers to information such as the number of products sold, sales amount, and sales date and time at each business location. 【0653】 "Seasonal data" refers to information related to specific seasons or time periods, and is used to understand seasonal fluctuations in sales. 【0654】 "Weather data" refers to information about weather conditions and is a factor that influences demand forecasts for each region. 【0655】 "Data processing technology" refers to algorithms and methods for analyzing large amounts of data and extracting useful information. 【0656】 A "communication network" refers to a system for exchanging information in real time between different business locations. 【0657】 "Information recording means" refers to databases and systems for recording and managing information regarding excess or insufficient inventory. 【0658】 An "information processing device" refers to an electronic device used to input data and perform analysis and processing based on that data. 【0659】 "Demand trends" refer to changes in demand and trends in the market for goods and services. 【0660】 This invention is implemented using a system consisting of a server and terminals. The server collects sales information, seasonal data, and weather data from each business location, and uses a generated AI model based on this data to perform demand forecasting. Specifically, a database management system and a machine learning framework are used in combination to construct a forecasting model. By inputting past sales data into this model, it is possible to predict future demand with high accuracy. 【0661】 Terminals are installed at each business location to monitor inventory status in real time. These terminals utilize barcode scanners to automatically update information when inventory is received or shipped. This information is sent to a server, and automatic orders are placed based on the results. 【0662】 As a concrete example, consider a scenario where a business's beef inventory falls below a minimum standard. In this case, the server automatically sends an order to the supplier, arranging delivery within a few days. This improves the efficiency of inventory management. 【0663】 The server also analyzes purchase history and plans personalized promotions for each customer. The generating AI model receives an example prompt message such as, "Generate promotional suggestions for products that are likely to be purchased under specific conditions," to generate promotional ideas for products that a particular customer frequently purchases. 【0664】 This system enables efficient inventory management and customized service delivery, making sustainable operations for the entire business possible. 【0665】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0666】 Step 1: 【0667】 The server collects sales information, seasonal data, and weather data from each business location. Specifically, each terminal is linked to a POS system and automatically sends daily sales data to the server. The input includes the number of items sold, sales amount, and date and time information, which the server stores in a database. The stored data is used for subsequent demand forecasting. 【0668】 Step 2: 【0669】 The server performs demand forecasting based on the collected data. Using a generative AI model, it analyzes sales information, seasonal data, and weather data to predict future demand for a specific product. At this time, the server inputs a prompt message into the model: "Predict the consumption pattern of the product under specific conditions." As output, demand forecast data for the product is generated. This forecast data is used in the next phase of inventory management. 【0670】 Step 3: 【0671】 The terminal monitors inventory status at each business location in real time. A barcode scanner is connected to the terminal, and it updates inventory data by reading product barcodes when inventory is received or shipped. This allows the current inventory level to be determined, and the updated information is sent to the server. Inputs are barcode information and quantity, and output is the latest inventory status data. 【0672】 Step 4: 【0673】 The server automatically places orders based on inventory data and demand forecast data received from terminals. Specifically, when inventory falls below a set threshold, the server automatically sends an order to the supplier. Inputs include the latest inventory data and demand forecast data, and output is an order form. This prevents stockouts and maintains appropriate inventory levels. 【0674】 Step 5: 【0675】 The server analyzes purchase history and generates promotions tailored to each customer. Using a generation AI model, it analyzes customer attributes and purchase history, and receives a prompt message: "Generate promotional suggestions that propose products the customer might be interested in." The output is a personalized promotional suggestion. This suggestion is delivered to the user, contributing to improved customer satisfaction. 【0676】 (Application Example 1) 【0677】 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". 【0678】 To respond promptly to the diverse needs of consumers, efficient inventory management and demand forecasting are essential. However, conventional technologies have made it difficult to accurately forecast demand and reallocate inventory based on real-time data, resulting in inventory shortages or excesses and wasted resources. Furthermore, personalized proposals to individual consumers have been insufficient, limiting the improvement of customer satisfaction. To overcome these challenges, a new system is needed that can adapt to consumer needs and achieve efficient resource utilization. 【0679】 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. 【0680】 In this invention, the server includes means for using an inference algorithm to predict regional demand based on sales data, seasonal information, and weather information; means including a computation algorithm for acquiring information from external sources and optimizing product suggestions and delivery schedules; and information processing means for adjusting inventory between facilities with excess inventory and facilities with insufficient inventory. This enables a rapid and accurate response to the diverse needs of consumers, as well as efficient inventory management. 【0681】 An "inference algorithm" is a computational method that analyzes sales data, seasonal information, and weather information to predict demand patterns for each region. 【0682】 An "information and communication device" is a device that monitors inventory information at each facility in real time and automatically places orders for necessary resources. 【0683】 "Information processing means" refers to a means for managing data to adjust inventory between facilities with excess inventory and facilities with insufficient inventory, and for performing optimal reallocation. 【0684】 A "calculating device" is a device that analyzes users' historical information and generates personalized suggestions for each user. 【0685】 A "calculation unit" is a device that integrates sales data from all facilities and analyzes trends to support effective sales strategies. 【0686】 A "computational algorithm" is a computational method used to acquire information from external sources and optimize product recommendations and delivery schedules. 【0687】 The system of the present invention is designed to achieve efficient inventory management and demand forecasting for food delivery. This enables appropriate recommendations to consumers and efficient resource utilization. The main components include a server, information and communication equipment, information processing means, a computer, and a computing device. 【0688】 The server collects sales data, seasonal information, and weather information, and uses inference algorithms to predict demand for each region. This allows for the prediction of demand at specific times of day and under specific weather conditions, enabling appropriate inventory management. In addition, it monitors the inventory status of each facility in real time via information and communication devices and places automatic orders as needed. The information processing system adjusts inventory between facilities with excess and insufficient stock, and the computing device generates personalized suggestions based on the user's history information. 【0689】 The computing unit integrates and analyzes sales data from all facilities to understand sales trends. This allows for the identification of successful sales strategies and product trends, providing information that can be used at other facilities. Furthermore, the computing algorithm optimizes product recommendations and delivery schedules by referencing external information. 【0690】 For example, during rainy evenings, it can be predicted that hot soups will be in high demand. The server compares past data with current weather to predict demand and prepares potentially scarce items in advance, allowing for a quick response to customer orders. Such a system is expected to improve customer satisfaction and enable efficient use of resources. 【0691】 An example of a prompt for the generating AI model would be: "Considering the current weather and time of day, predict which menu items are likely to be in the highest demand in this area over the next three hours." Based on this prompt, the AI ​​model analyzes the data and provides a list of menu items that are likely to be in high demand. Based on the information obtained in this way, each facility can appropriately adjust its inventory and promotional activities. 【0692】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0693】 Step 1: 【0694】 The server collects sales data, seasonal information, and weather information from each facility. Inputs include sales databases and external weather information APIs, and output is a dataset for demand forecasting. This dataset is used to prepare the necessary information for demand forecasting in the next step. 【0695】 Step 2: 【0696】 The server uses the collected dataset to run an inference algorithm and perform demand forecasts for each region. The input is the dataset obtained in the previous step, and the output is a demand forecast value. This demand forecast value indicates the expected sales for each time period and weather condition. 【0697】 Step 3: 【0698】 The terminal reads real-time inventory information from each facility and sends the information to the server. The input is the inventory management system of each facility, and the output is real-time inventory data that is sent to the server. This data is useful for automatic ordering to prevent inventory shortages. 【0699】 Step 4: 【0700】 The server compares demand forecasts with real-time inventory data and automatically places orders for the necessary resources. The inputs are the demand forecast values ​​and inventory data obtained in steps 2 and 3, and the output is an order instruction sent to the purchasing system. This ensures that necessary resources are replenished in a timely manner. 【0701】 Step 5: 【0702】 The server matches facilities with excess inventory with those with shortages and proposes the optimal inventory reallocation. The input is real-time inventory data, and the output is an inventory movement plan. This ensures efficient inventory distribution between facilities. 【0703】 Step 6: 【0704】 Users place orders based on suggested products and promotions. The input is generated promotional information, and the output is consumer order data. This process enables personalized product recommendations that take into account the user's history. 【0705】 Step 7: 【0706】 The computing unit uses collected history and trend information to generate personalized promotions for individual customers. Inputs are past consumer order history and overall sales trend data, while output is optimized promotional information. Prompt messages are used to collect information and help identify specific demand patterns. 【0707】 Step 8: 【0708】 The server integrates and analyzes sales data from all facilities and recommends effective sales strategies to other facilities. The input is integrated sales data from each facility, and the output is an improved sales strategy plan. As a result, successful models and techniques are utilized across all facilities, contributing to overall efficiency improvements. 【0709】 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. 【0710】 This invention is implemented as a system for providing flexible service that takes into account the emotions of users in restaurant chains. This system has an artificial intelligence algorithm at its core that performs advanced demand forecasting using sales data, seasonal information, and weather information, and combines this with an emotion engine to further improve customer satisfaction. 【0711】 First, the server collects and analyzes sales data and trend information from each store to predict demand in each region. Furthermore, seasonal factors and weather data that affect sales are also incorporated into this prediction. This allows stores to prepare inventory according to demand. 【0712】 Next, the terminal monitors the inventory of each store in real time and places automatic orders based on revised demand forecasts as needed. The server, through the automated ordering system, prevents food shortages and supports efficient inventory management. 【0713】 The emotion engine works in conjunction with devices that analyze the user's real-time emotional state and collect data. For example, the terminal processes feedback, facial expressions, and tone of voice entered by the user through a device installed in the store to identify the user's emotions. Based on this, it suggests the most suitable menu and promotions for each user. 【0714】 For example, if a user visits a particular store on the weekend and the system determines they are feeling relaxed, the device can suggest health-conscious menu options and seating with lounge music. On the other hand, if the emotional engine determines the user is tired, it can prompt them for quick service and menu items suitable for energy replenishment. 【0715】 Finally, sales data and emotional feedback from all stores are integrated and analyzed by a server to improve the quality of the customer experience at each store. This enhances the overall service level of the brand and helps build long-term customer relationships. 【0716】 In this way, the present invention supports each store in providing inventory and services that meet customer needs, maintaining high customer satisfaction, and pursuing a sustainable business model. 【0717】 The following describes the processing flow. 【0718】 Step 1: 【0719】 The server periodically collects sales data from terminals in each store. This data includes sales volume, time of sale, and date for each item, as well as seasonal information and weather data. 【0720】 Step 2: 【0721】 The server runs an AI algorithm that uses sales data, seasonal information, and weather data to build a demand forecasting model. This creates demand forecasts for each region and determines the amount of inventory needed for the following week or month. 【0722】 Step 3: 【0723】 The terminal monitors the inventory of each store in real time and sends inventory level information to the server sequentially via sensors. When inventory falls below a set threshold, it is registered as a situation requiring replenishment. 【0724】 Step 4: 【0725】 The server initiates an automated ordering process based on real-time inventory information and demand forecasts. It places orders for the necessary ingredients in the specified quantities with the appropriate suppliers. 【0726】 Step 5: 【0727】 The device works in conjunction with an emotion engine to recognize the user's emotions and collects the user's facial expressions and tone of voice from devices within the store. This data is then used to analyze each user's emotional state. 【0728】 Step 6: 【0729】 The server analyzes emotional data and generates personalized promotions for each user. Menu recommendations and promotional information tailored to the user's emotions are sent to their device. 【0730】 Step 7: 【0731】 The device displays the generated promotions to the user, offering special offers and messages to attract the user's interest. 【0732】 Step 8: 【0733】 The server aggregates sales data from all stores and user sentiment feedback, and performs multivariate analysis to identify trends and areas for improvement. 【0734】 Step 9: 【0735】 The server provides feedback to each store based on the analysis results, optimizing services and operations. This allows each store to continuously improve the customer experience. 【0736】 (Example 2) 【0737】 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". 【0738】 In modern restaurant chains, accurately predicting demand fluctuations and efficiently managing inventory are major challenges. Furthermore, it is essential to appropriately analyze customer emotional states and provide services based on that analysis. Therefore, it is crucial to enhance customer satisfaction not only by utilizing sales information and weather data, but also by analyzing customer emotions. 【0739】 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. 【0740】 In this invention, the server includes means for using a machine learning algorithm to predict demand for each area based on sales information, cycle information, and weather information; communication equipment for instantly monitoring inventory at each facility and automatically ordering necessary products; and means including an emotion engine for analyzing the user's real-time emotions and providing appropriate services. This enables efficient inventory management in response to demand and optimal service proposals based on customer emotions. 【0741】 "Sales information" refers to information related to product sales data and customer purchase history at stores. 【0742】 "Periodic information" refers to information necessary to account for seasonal and time-dependent fluctuations, and examples include data on specific events and holiday periods throughout the year. 【0743】 "Weather information" refers to data about weather conditions, specifically information such as temperature, precipitation, and humidity. 【0744】 A "machine learning algorithm" is a type of artificial intelligence technology used to analyze large amounts of data and identify patterns and trends. 【0745】 "Communication equipment" refers to devices used to send and receive data, and plays a role in exchanging information over a network. 【0746】 A "data storage device" refers to equipment and technology for storing information and making it available for retrieval as needed. 【0747】 An "information processing device" refers to a computer or program used to generate useful data by calculating and analyzing information. 【0748】 A "central processing unit" refers to a central computer or server that manages and processes information for the entire system in one place. 【0749】 An "emotion engine" refers to a system or software that analyzes a user's emotional state and provides appropriate responses or suggestions based on that analysis. 【0750】 This invention is a system aimed at efficient inventory management and improved customer satisfaction in restaurant chain stores. This system functions by combining a machine learning algorithm that predicts demand using sales information, cycle information, and weather information, with an emotion engine that analyzes the real-time emotional state of customers. 【0751】 The server aggregates sales information, cycle information, and weather information collected from each facility and uses machine learning algorithms to predict demand for each area. Specifically, it performs data analysis using Python's pandas and scikit-learn to generate demand forecasts. Based on this forecast data, it sends instructions to terminals within the facilities via communication devices. 【0752】 The terminal monitors the facility's inventory in real time and automatically orders necessary products based on predicted demand. IoT devices are used for this purpose, enabling immediate tracking of inventory fluctuations. Furthermore, the terminal collects user emotional data through devices installed within the facility. Specifically, it identifies the user's emotional state using facial recognition and voice recognition software. 【0753】 When users visit a store, they can provide their emotions and feedback through a terminal, and based on this, they can receive appropriate suggestions. For example, a user who is in a relaxed mood will be suggested menus and seating that allow them to spend their time leisurely. This system aims to improve customer satisfaction by providing personalized services based on the analysis of the user's emotions. 【0754】 As a concrete example, the system generates suggestions to the user using the following prompt: 【0755】 "Design a demand forecasting system for local branches of a restaurant chain. It should propose optimal inventory management methods using artificial intelligence, incorporating sales data, seasonal information, and weather information. Furthermore, it should include specific measures aimed at improving the customer experience through sentiment analysis." 【0756】 By implementing this system, restaurant chains will be able to manage inventory efficiently and provide flexible services tailored to customer needs. 【0757】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0758】 Step 1: 【0759】 The server periodically collects sales information, cycle information, and weather information from each facility. It takes data from store sales databases and external weather information APIs as input. This data is preprocessed using Python, and a cleaned dataset is output. This output data is then ready to be fed into the demand forecasting model. 【0760】 Step 2: 【0761】 The server uses a machine learning algorithm to forecast demand based on the collected data. The cleaned dataset generated in Step 1 is used as input. The server uses the generated AI model to predict future demand at each facility. The output is the demand forecast data, which is used in the next step. 【0762】 Step 3: 【0763】 The terminal monitors inventory within the facility in real time using demand forecast data received from the server. It uses inventory sensor and inbound / outbound data as input to compare predicted demand with current inventory levels. This allows the terminal to identify shortages or excess inventory and automatically initiate communication for ordering. The output is instruction data regarding inventory adjustments and ordering. 【0764】 Step 4: 【0765】 Users provide feedback and input emotional data through terminals within the facility. This input includes the user's tone of voice and facial expressions, which are analyzed by the terminal's emotional engine. Based on the analysis, the terminal suggests the most suitable services and menus for the user. The output of this process is a customized service suggestion that meets the user's expectations. 【0766】 Step 5: 【0767】 The server performs continuous trend analysis based on sales information and sentiment data aggregated from all facilities. This data, along with the demand forecast results from Step 2, is used as input. The server processes the data using statistical methods and analytical tools to gain insights for service improvement at each facility. The output is strategic recommendations for service improvement. 【0768】 (Application Example 2) 【0769】 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". 【0770】 The problem that this invention aims to solve is to enable flexible service provision that takes into account the emotions of users in restaurant chain stores, thereby improving customer satisfaction and building a sustainable business model by streamlining inventory management. 【0771】 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. 【0772】 In this invention, the server includes means for using a machine learning algorithm to predict regional demand based on sales data, seasonal information, and weather information; a data communication device for monitoring the inventory of each store in real time and automatically ordering necessary products; means for using an information management device to adjust inventory between stores with excess inventory and stores with insufficient inventory; a sensing device for dynamically suggesting menus and seating by analyzing the user's emotions; an information processing device for analyzing customer order history and generating sales promotions tailored to each customer; and a computing server for integrating sales data from all stores and analyzing market trends. This enables the provision of services that respond to the user's emotions and efficient inventory management. 【0773】 A "machine learning algorithm" is a computational method that makes it possible to predict demand based on data. 【0774】 A "data communication device" is a device that monitors the inventory of each store in real time and transmits necessary information to other systems. 【0775】 An "information management device" is a device used to manage and process information necessary for coordinating inventory across multiple stores. 【0776】 A "sensing device" is a device used to collect and analyze data necessary to analyze a user's emotions. 【0777】 An "information processing device" is a device that analyzes customer order history and processes data to generate personalized promotions. 【0778】 A "computation server" is a server that integrates sales data from all stores and is responsible for processing calculations to analyze market trends. 【0779】 "Inventory" refers to the total amount of goods and materials held in a store for sale. 【0780】 "Demand forecasting" is a predictive activity that estimates future demand and takes the necessary steps to prepare for it. 【0781】 "User emotions" refers to the emotional state of customers when using a store, and is information used to optimize services based on that state. 【0782】 To realize this invention, several key elements are combined. First, the server utilizes machine learning algorithms to predict regional demand based on sales data, seasonal information, and weather information. This allows each store to efficiently manage its inventory. Inventory information is updated in real time via data communication devices, and automatic product ordering is performed as needed. This is a crucial element in preventing inventory shortages or surpluses in stores. 【0783】 Next, sensing devices are used to analyze the user's emotions. These devices use cameras and microphones to capture the user's facial expressions and voice, and analyze this data to identify their emotional state. Based on these analysis results, appropriate menus and seating are suggested to the customer. For example, if the user is relaxed, health-conscious menus are suggested; if they are tired, menus suitable for energy replenishment are suggested. 【0784】 The information management system is configured to adjust inventory across multiple stores. Based on inventory movement history, it determines the optimal method of inventory transfer and efficiently adjusts inventory. This enables individual stores to conduct sustainable business operations. 【0785】 Customer order history is analyzed by an information processing system, and personalized promotions are generated. These promotions are an important tool for boosting sales. 【0786】 Finally, the computing server integrates sales data from all stores and analyzes market trends. This data is extremely useful when formulating long-term business strategies. 【0787】 For example, if a user visits a cafe and sensing equipment determines that they are relaxed, the server will present a health-conscious menu. The user can also request more detailed services using prompts. An example of a prompt might be, "Please suggest menu items and seating that are suitable for a user who is relaxed." 【0788】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0789】 Step 1: 【0790】 The server receives sales data, seasonal information, and weather information as input, and uses machine learning algorithms to forecast demand for each region based on this data. The demand forecast calculation process outputs a demand outlook for each region, which serves as an indicator for inventory management. 【0791】 Step 2: 【0792】 The terminal acquires inventory information from each store in real time and transmits it to the server via a data communication device. A system that constantly monitors inventory status operates, using the inventory information as input to make automatic ordering decisions. As a result, if an inventory shortage is predicted, additional orders are automatically placed. 【0793】 Step 3: 【0794】 Users undergo emotion analysis using sensing devices within the store. The system analyzes the user's facial expressions and voice as input, and outputs their emotional state based on the results. Based on this output, the terminal generates prompt messages to suggest the most suitable menu items and seating for the user. 【0795】 Step 4: 【0796】 The information management system collects inventory information from multiple stores and selects the most suitable method of relocation when inventory adjustments are necessary. It receives inventory data from all stores as input and presents efficient inventory relocation methods as output. Based on past relocation history, the selected relocation method is then implemented. 【0797】 Step 5: 【0798】 The server generates personalized promotional content for each customer based on their order history. It processes customer purchase data as input and outputs customized promotions accordingly. These promotions are crucial for encouraging customer return visits and additional purchases. 【0799】 Step 6: 【0800】 The computing server integrates sales data from all stores and analyzes market trends. It uses the integrated data as input to perform market analysis and outputs the results. These analysis results are used as information to help formulate management strategies and optimize business operations. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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." 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 The following is further disclosed regarding the embodiments described above. 【0823】 (Claim 1) 【0824】 A method using artificial intelligence algorithms to predict regional demand based on sales data, seasonal information, and weather information, 【0825】 A communication device that monitors the inventory of each store in real time and automatically orders the necessary items, 【0826】 A means of using a database to adjust inventory between stores with excess inventory and stores with insufficient inventory, 【0827】 A processing unit for analyzing customer order history and generating personalized promotions for each customer, 【0828】 A system that integrates sales data from all stores and includes a server for analyzing trends. 【0829】 (Claim 2) 【0830】 The system according to claim 1, comprising an algorithm that optimizes automatic ordering based on inventory level information acquired in real time. 【0831】 (Claim 3) 【0832】 The system according to claim 1, which includes a program that selects the most efficient method of inventory movement based on past inventory movement history when proposing inventory adjustments between stores. 【0833】 "Example 1" 【0834】 (Claim 1) 【0835】 A method using data processing technology to predict regional demand based on sales information, seasonal data, and weather data, 【0836】 A communication network to monitor inventory at each business location in real time and automatically replenish necessary items, 【0837】 Information recording means for adjusting inventory between business establishments with surplus inventory and business establishments with inventory shortages, 【0838】 An information processing device for analyzing purchase history and generating sales promotions tailored to the customer, 【0839】 A system that integrates sales information from all business locations and includes a processing unit for analyzing demand trends. 【0840】 (Claim 2) 【0841】 The system according to claim 1, comprising an algorithm that optimizes automatic replenishment based on inventory status information acquired in real time. 【0842】 (Claim 3) 【0843】 The system according to claim 1, which includes a program that selects the most efficient movement strategy based on past inventory movement history when proposing inventory adjustments between business locations. 【0844】 "Application Example 1" 【0845】 (Claim 1) 【0846】 A method using inference algorithms to predict regional demand based on sales data, seasonal information, and weather information, 【0847】 An information and communication device for monitoring the inventory of each facility in real time and automatically ordering the necessary resources, 【0848】 Information processing means for adjusting inventory between facilities with excess inventory and facilities with insufficient inventory, 【0849】 A computing device for analyzing user history information and generating personalized suggestions for each user, 【0850】 A computing device for integrating sales data from all facilities and analyzing trends, 【0851】 A means including a computational algorithm for acquiring external information and optimizing product suggestions and delivery schedules, 【0852】 A system that includes this. 【0853】 (Claim 2) 【0854】 The system according to claim 1, comprising an inference algorithm that optimizes automatic ordering based on inventory level information acquired in real time. 【0855】 (Claim 3) 【0856】 The system according to claim 1, which includes a program that selects the most efficient method of movement based on past inventory movement history when proposing inventory adjustments between facilities. 【0857】 "Example 2 of combining an emotion engine" 【0858】 (Claim 1) 【0859】 A method using machine learning algorithms to predict demand by area based on sales information, cycle information, and weather information, 【0860】 Communication equipment to instantly monitor inventory at each facility and automatically order necessary products, 【0861】 Means for using a data storage device to adjust inventory between facilities with excess inventory and facilities with insufficient inventory, 【0862】 An information processing device for analyzing customer emotions and generating personalized suggestions for each user, 【0863】 A central processing unit aggregates sales information from all facilities and analyzes trends, 【0864】 A means including an emotion engine to analyze users' real-time emotions and provide appropriate services, 【0865】 A system that includes this. 【0866】 (Claim 2) 【0867】 The system according to claim 1, which includes an algorithm that makes optimal service suggestions based on employee sentiment data. 【0868】 (Claim 3) 【0869】 The system according to claim 1, comprising a program for selecting the most efficient method of inventory movement based on past inventory movement history when proposing inventory adjustments between facilities, and a program for optimizing inventory placement by taking into account user sentiment data. 【0870】 "Application example 2 when combining with an emotional engine" 【0871】 (Claim 1) 【0872】 A method using machine learning algorithms to predict regional demand based on sales data, seasonal information, and weather information, 【0873】 A data communication device that monitors the inventory of each store in real time and automatically orders the necessary products, 【0874】 A means of using an information management device to adjust inventory between stores with excess inventory and stores with insufficient inventory, 【0875】 A sensing device that dynamically suggests menus and seating arrangements by analyzing user emotions, 【0876】 An information processing device for analyzing customer order history and generating sales promotions tailored to each customer, 【0877】 A system that integrates sales data from all stores and includes a computing server for analyzing market trends. 【0878】 (Claim 2) 【0879】 The system according to claim 1, comprising a calculation method for optimizing automatic ordering based on inventory level information acquired in real time. 【0880】 (Claim 3) 【0881】 The system according to claim 1, which includes a step of selecting the most efficient means of transport based on past inventory movement history when proposing inventory adjustments between stores. [Explanation of Symbols] 【0882】 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 method using artificial intelligence algorithms to predict regional demand based on sales data, seasonal information, and weather information, A communication device that monitors the inventory of each store in real time and automatically orders the necessary items, A means of using a database to adjust inventory between stores with excess inventory and stores with insufficient inventory, A processing unit for analyzing customer order history and generating personalized promotions for each customer, A system that integrates sales data from all stores and includes a server for analyzing trends. [Claim 2] The system according to claim 1, comprising an algorithm that optimizes automatic ordering based on inventory level information acquired in real time. [Claim 3] The system according to claim 1, which includes a program that selects the most efficient method of inventory movement based on past inventory movement history when proposing inventory adjustments between stores.

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