system

Abstract

We provide the system. [Solution] The information processing device includes means for collecting distribution information, sales information, and inventory information. A means of analyzing collected information to optimize demand forecasting and inventory management, A means to track the status of the entire supply chain in real time and identify the risk of food waste, A food sharing network and means of sharing information to reduce the risk of food waste, A system that includes this.

Description

【Technical Field】 , , , , 【0004】 , , , , 【0005】 , , , , , 【0003】 , , 【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, including 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】 <00上記の課題を解決するため、本発明は、食品供給チェーンにおける食品廃棄物の増加に伴うコストや環境への負荷の問題を解決するためのシステムを提供する。本システムは、食品の需要と供給のバランスを適切に調整することができ、過剰在庫や期限切れによる廃棄を減少させることができる。また、食品が廃棄される前に適切なフードシェアリングが行われることができ、失われる食品の有効活用が行えるようになる。 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 The problem to be solved by the present invention is the problem of the cost and environmental burden associated with the increase in food waste in the modern food supply chain. The current system is difficult to appropriately adjust the balance between food demand and supply, and lacks effective means to reduce waste due to excess inventory and expiration. In addition, there is a problem that effective utilization of lost food cannot be achieved because appropriate food sharing is not performed before the food is discarded. 【Means for Solving the Problems】 [[ID=​This invention utilizes an information processing device to collect distribution, sales, and inventory information in real time, enabling demand forecasting and optimized inventory management based on this data. Furthermore, it tracks the entire supply chain in real time and identifies food waste risks, thereby preventing food waste. By sharing information with food sharing networks and proposing distribution destinations that ensure the effective use of food, it reduces food waste. In addition, by conducting analysis based on consumption trends and avoiding excess inventory, it is possible to build an efficient and sustainable food supply chain. 【0006】 An "information processing device" is a device that collects and analyzes distribution information, sales information, and inventory information in order to understand the overall picture of the supply chain. 【0007】 "Distribution information" refers to information at each stage of the supply chain from food production to consumer, and includes data on the movement and storage of goods. 【0008】 "Sales information" refers to data generated when food products are sold in stores or on online platforms, and includes information such as sales figures and consumer purchasing trends. 【0009】 "Inventory information" refers to data on inventory levels and product conditions at each point in the supply chain, including product expiration dates and deterioration status. 【0010】 "Demand forecasting" is an analytical method that predicts future consumer purchasing volume based on past sales data and market trends. 【0011】 "Inventory management" is the process of maintaining appropriate inventory levels to avoid excess or shortages. 【0012】 "Tracking the entire supply chain" is the process of monitoring the location and status of food at each stage as it moves through the supply chain. 【0013】 "Waste risk" is an indicator that shows the likelihood of food products expiring or being lost without being sold. 【0014】 A "food sharing network" is a community or platform built to provide surplus food to individuals or organizations in need. [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 Embodiment 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 with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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, a 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, a 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, etc. 【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】 In order to implement the present invention, it is necessary to construct a system centered on an information processing device and to collect and analyze various types of data across the entire food supply chain. The specific form of such a system is shown below. 【0037】 First, the server collects distribution, sales, and inventory information through terminals installed in stores and distribution centers. This information includes data from IoT sensors and POS systems. The server aggregates this information in real time and stores it in a unified database on the platform. The terminals also play a role in supplementing the information on-site and immediately uploading it to the cloud. 【0038】 Next, the server uses the collected data to perform analysis using an AI model. This analysis calculates consumption trends and unsold inventory risks for each product and forecasts demand. For example, a restaurant might use past sales data to predict demand during peak hours and on different days of the week, and then calculate the optimal inventory level. 【0039】 Furthermore, the server identifies food items at high risk of waste by monitoring each stage of the supply chain in real time. Based on this, it notifies users (stores and distributors) of the optimal distribution arrangement to prevent waste. 【0040】 Finally, to reduce food waste, the server shares information about food at risk of being discarded with food-sharing networks. For example, if food is nearing its expiration date, the server provides that information to local food banks and charities. Users then take appropriate action based on the shared information to ensure that food sharing is successful. 【0041】 In this way, the present invention makes it possible to build a sustainable system that reduces food waste and supports the optimization of supply chains by utilizing an information processing device. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The terminals are installed in each store and distribution center and collect distribution, sales, and inventory information. This includes real-time data obtained from IoT sensors attached to shelves and POS systems. The terminals aggregate this data and transmit it to a server via the internet. 【0045】 Step 2: 【0046】 The server receives data sent from terminals and stores it in a unified database. This database records the current inventory status, past sales history, and distribution channels for each product. Based on this information, the server prepares for analysis. 【0047】 Step 3: 【0048】 The server inputs the latest data into an AI model to perform demand forecasting. This AI model takes into account past sales data, seasonal variations, and external factors (e.g., weather, events). Based on the results of this analysis, recommendations for the next order quantity and adjustments to inventory levels are presented. 【0049】 Step 4: 【0050】 Store managers, who are users of the system, receive analysis results and suggestions from the server via their terminals. Based on this, they can review their store's inventory strategy and adjust the next order quantity to prevent excess inventory and stockouts. 【0051】 Step 5: 【0052】 The server monitors the entire supply chain and identifies products at high risk of being wasted. This includes forecasts of inventory expiring and distribution delays. Identified products are immediately notified to the food sharing network. 【0053】 Step 6: 【0054】 Users receive notifications about waste risks and prepare to participate in food sharing. For example, they can quickly contact and donate to a nearby food bank, preventing food waste while supporting the community. 【0055】 (Example 1) 【0056】 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." 【0057】 In the food supply chain, inefficiencies in distribution, sales, and inventory management lead to decreased productivity and increased food waste. Furthermore, the inability to properly manage products at high risk of disposal, resulting in their ineffective utilization, is a significant problem. Solving these challenges and realizing a sustainable supply chain is essential. 【0058】 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. 【0059】 This invention includes a server that collects distribution data, sales data, and inventory data via terminals installed at logistics centers and sales bases; a server that synchronizes the collected data and stores it in a unified database; and a server that analyzes the data using an AI model to predict demand trends and unsold inventory risks for each product and calculate the optimal inventory level. This enables efficient inventory management, reduction of waste risk, and the construction of a sustainable supply chain. 【0060】 An "information processing system" is a computer system that collects, analyzes, and stores data related to logistics, sales, and inventory, and then performs various management functions based on that data. 【0061】 A "terminal" is an input device and communication equipment installed at logistics centers and sales bases that works in conjunction with information processing devices to collect and transmit data. 【0062】 "Distribution data" refers to information about the quantity, timing, and route of a product as it moves through the supply chain. 【0063】 "Sales data" refers to information such as quantity, amount, and time related to product sales to consumers. 【0064】 "Inventory data" refers to information regarding the quantity and condition of goods held at distribution and sales locations. 【0065】 An "AI model" is a set of machine learning or artificial intelligence algorithms used for data analysis, enabling pattern recognition and predictive analytics. 【0066】 A "unified database" refers to a data management system that centrally manages various collected data, enabling efficient information utilization. 【0067】 A "supply chain" is a system that represents the entire process from the supply of raw materials to the manufacturing, distribution, and sale of products. 【0068】 "Waste risk" refers to the possibility that food or products may be discarded because they exceed their expiration date or quality standards. 【0069】 A "shared network" refers to an information and communication infrastructure that enables multiple organizations and entities to cooperate in exchanging and appropriately utilizing information. 【0070】 "Recipient" refers to a facility or organization that receives food that is at risk of being discarded from the supply chain and then utilizes or consumes it. 【0071】 The embodiments for carrying out the present invention are shown below. 【0072】 The server works in conjunction with terminals installed at logistics and sales locations to collect distribution data, sales data, and inventory data. IoT sensors and POS systems are used for this data collection. For example, terminals acquire inventory quantities in real time via shelf sensors and obtain sales information from POS systems. The obtained data is transmitted to the server via the cloud. 【0073】 The server stores data in a unified database and performs data analysis using a generative AI model. This analysis includes demand forecasting based on collected historical data, as well as predictions of consumption trends and unsold inventory risks for each product. The results of the analysis enable the prediction of the next supply volume and the calculation of optimal inventory levels. 【0074】 For example, in a restaurant located in a commuter area, an AI model can analyze past sales and weather data to determine if demand is high during weekday lunchtimes. If this reveals a high demand, the server can then recommend the necessary amount of ingredients based on that demand. This helps avoid excess inventory and minimize food waste. 【0075】 Furthermore, the server connects with a sharing network to provide information on food at risk of being wasted, notifying food banks and other charitable organizations. This allows users to take appropriate action based on the information, ensuring that food is used effectively within the community. 【0076】 An example of a prompt might be, "Based on sales data from the past three months, predict next week's demand and create a proposal to minimize waste risk." Based on this prompt, the AI ​​model performs a specific analysis and provides results that support the operation of a sustainable supply chain. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The terminals collect distribution data, sales data, and inventory data from IoT sensors and POS systems installed at logistics and sales bases. These terminals transmit the data to a server in real time via the cloud. The data entered includes, for example, the quantity of goods and sales records, and by aggregating this data, inventory status can be understood. 【0080】 Step 2: 【0081】 The server saves the data received from the terminal to a unified database. This saving process removes duplicate data, verifies data format consistency, and optimizes the database. After saving, the input data is managed in a clean state for the next analysis process. 【0082】 Step 3: 【0083】 The server performs analysis using a generated AI model based on stored data. This analysis process uses historical data as input to predict consumption trends and unsold inventory risks for each product. The output includes product demand forecasts and optimal inventory levels. Specifically, the AI ​​model performs pattern recognition to predict, for example, the next supply quantity. 【0084】 Step 4: 【0085】 Based on the analysis results, the server monitors the entire supply chain in real time and identifies products at high risk of being discarded. Specifically, it calculates the number of days until a particular food product reaches its expiration date and lists the products with high discard risk. The input is the analysis data obtained in the previous step, and the output is a detailed report on the discard risk. 【0086】 Step 5: 【0087】 The server transmits information about identified waste risks to a shared network and sends notifications to local food banks and charities. This step creates a log of the notification transmissions, recording which organizations received which information. The input is information about products at risk of waste, and the output is the notification transmission log. 【0088】 Step 6: 【0089】 Users manage food at risk of waste based on notifications from the server. Specifically, they arrange for food to be sent to appropriate recipients and engage in activities aimed at sustainable consumption. Receiving notifications enables actions toward the effective use of food. Input is notification information from the server, and output is delivery instructions and recipient lists. 【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】 In modern food supply chains, food waste resulting from fluctuations in demand and inadequate inventory management is a major challenge. Furthermore, there is a need to more accurately predict consumer behavior and make appropriate supply adjustments. This invention provides a method for reducing such food waste and improving the accuracy of demand forecasting. 【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 collecting distribution information, sales information, and inventory information; means for analyzing the user's past order history and suggesting the optimal inventory quantity; and means for notifying users of discounted sales using information about food products nearing their expiration date. This reduces food waste and enables appropriate inventory supply and information provision to consumers. 【0095】 An "information processing device" is a machine or device that collects and analyzes distribution information, sales information, and inventory information to support the optimization of the entire supply chain. 【0096】 "Distribution information" refers to data related to transportation and storage that is generated as a product passes through the supply chain. 【0097】 "Sales information" refers to data related to product sales at stores and online platforms. 【0098】 "Inventory information" refers to data that shows how many of a particular product are stored and where they are located. 【0099】 A "user" is a business entity or individual that uses this system to optimize inventory management and sales strategies. 【0100】 "Order history" refers to data that records the details of orders placed in the past. 【0101】 "Optimal inventory levels" refer to the ideal inventory quantity set to eliminate waste and prevent supply shortages, while taking into account fluctuations in demand. 【0102】 The "best before date" is the recommended date for consuming food, indicating the period during which the food can be safely consumed while maintaining its quality. 【0103】 A "discount sale notification" is a means of informing customers about discounts on products that are nearing their expiration date. 【0104】 The system based on this invention is designed to optimize the entire food supply chain and significantly reduce food waste. The server first collects information on distribution, sales, and inventory. Specifically, the server uses IoT sensors, POS systems, and other technologies to collect this data from physical stores and online platforms. 【0105】 The collected information is stored in a unified database and analyzed using a generative AI model. This analysis utilizes each user's smartphone as hardware and a cloud-based analytics platform as software to analyze their past order history and consumption trends. Based on the analysis results, the server suggests the optimal inventory levels for the next supply, and users are notified of discounts on products nearing their expiration date. 【0106】 By utilizing this information, users can optimize their product procurement and sales strategies. For example, the server can automatically adjust inventory levels for popular products that see a surge in orders during specific times on weekends, based on historical data, thereby preventing stockouts and supply shortages. 【0107】 Furthermore, an example of a prompt message could be, "Predict pizza demand this weekend based on past order history and generate inventory adjustment suggestions," which would instruct the generating AI model. This would lead to a reduction in food waste and improved supply efficiency. 【0108】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0109】 Step 1: 【0110】 The server collects information on distribution, sales, and inventory through IoT sensors and POS systems. This data is transmitted to the server in real time and stored in a unified database. The input to this process is data from sensors and POS systems, and the output is an integrated dataset. Specifically, sales quantities for each product, inventory status, and distribution channel information are collected. 【0111】 Step 2: 【0112】 The server uses a generative AI model to analyze the accumulated data. This allows users to analyze their past order history. The input for this step is an integrated dataset, and the output is demand forecasts and consumption trend patterns. Specifically, the analysis reveals peak hours, demand by day of the week, seasonal fluctuations, and more. 【0113】 Step 3: 【0114】 The server proposes the optimal inventory level required for the next supply based on demand forecasts. The inputs for this step are demand forecasts and current inventory data, and the output is the adjusted order quantity. Specifically, it indicates the exact quantity of goods to be delivered next and presents adjustment suggestions to prevent stockouts and excess inventory. 【0115】 Step 4: 【0116】 The user receives optimization suggestions from the server via their terminal and adjusts inventory orders and sales plans accordingly. The input for this step is the adjustment suggestions provided by the server, and the output is the revised order plan. Specifically, the user reviews the suggestions through the application and makes the necessary adjustments based on them. 【0117】 Step 5: 【0118】 The server analyzes information on products nearing their expiration date and sends users notifications about discounted sales. The inputs for this step are inventory information and expiration date information, and the output is a discount notification. Specifically, the server identifies products eligible for discounts and notifies the user via push notification or email. 【0119】 This processing flow enables the system to achieve efficient inventory management and supply adjustments in line with demand, thereby reducing food waste. 【0120】 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. 【0121】 To implement the present invention, it is necessary to construct a system that combines an information processing device and an emotion engine, and to provide an interface that takes into account the user's emotions, in addition to optimizing the entire food supply chain. The specific form of this system is shown below. 【0122】 First, the server collects distribution, sales, and inventory information from terminals, as in the conventional system. This data is stored in a unified database and analyzed by an AI model. This enables demand forecasting and inventory management at each point in the supply chain. 【0123】 Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and tone of voice. This emotion data is used to extract information about how the user is feeling, such as whether they are stressed or happy. 【0124】 Here, the server analyzes emotional data in combination with conventional sales and inventory data. This allows it to suggest improvements to operations or adjust the timing of inventory replenishment, for example, if store staff are experiencing stress. 【0125】 Users adjust their daily tasks based on feedback provided through their devices. Furthermore, the emotion engine responds to changes in the user's emotions, flexibly altering the interface's display and notification methods to improve usability. 【0126】 For example, if the emotion engine detects that staff at a restaurant are experiencing stress during a busy period, the server will suggest specific improvements to inventory allocation to alleviate the staff's burden. This can contribute not only to increased operational efficiency but also to improved staff satisfaction. 【0127】 In this way, the present invention, by combining an information processing device and an emotion engine, enables the construction of a system that not only optimizes processes but also takes into account the user's emotions. This makes it possible to reduce food waste, improve the efficiency of the supply chain, and provide a better user experience. 【0128】 The following describes the processing flow. 【0129】 Step 1: 【0130】 The terminals are installed in stores and distribution centers and collect distribution, sales, and inventory information through environmental sensors and POS systems. This data is transmitted to a server in real time. 【0131】 Step 2: 【0132】 The server stores data sent from the terminals in a database and performs analysis using an AI model. This analysis allows for the detection of abnormal inventory levels and demand forecasting, and the calculation of optimal inventory and order quantities. 【0133】 Step 3: 【0134】 The device utilizes an emotion engine to collect emotional data from the user's facial expressions and voice. For example, it tracks changes in facial expressions with the camera and analyzes voice tone with the microphone. 【0135】 Step 4: 【0136】 The server integrates and analyzes emotional data with sales and inventory data. If a user is experiencing stress, it identifies the cause and generates suggestions for improvements such as reviewing workflows and inventory placement. 【0137】 Step 5: 【0138】 Store managers, who are users of the system, receive analysis results and improvement suggestions through their terminals. By adjusting inventory placement and changing workflows based on these suggestions, they can improve operational efficiency and staff satisfaction. 【0139】 Step 6: 【0140】 The server tracks liquidity and inventory movements in real time down to the end of the supply chain, issuing early warnings to avoid unexpected stock shortages and waste risks. An emotional engine provides real-time feedback that motivates users and drives process improvement. 【0141】 In this way, by combining information processing equipment and an emotion engine, we can optimize the supply chain and enhance the user experience. 【0142】 (Example 2) 【0143】 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." 【0144】 Existing supply chain management systems often lack precision in demand forecasting and inventory management, leading to problems such as food waste and inadequate supply, particularly in the food industry. Furthermore, insufficient consideration has been given to improving operations that take into account employee emotions and stress levels, highlighting the need for more efficient operational management. These challenges are creating a complex web of problems, including increased food waste and decreased employee efficiency. 【0145】 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. 【0146】 In this invention, the server includes means for collecting distribution data, sales data, and inventory data; means for analyzing the collected data to optimize demand forecasting and inventory management; and means for acquiring user sentiment data and including it in the data analysis. This makes it possible to propose more precise supply chain management and business improvements while taking user sentiment into consideration. 【0147】 An "information processing device" is a device that collects and analyzes data and uses the results to support the optimization of various business operations. 【0148】 "Distribution data" refers to information about the flow of goods from supply to consumer. 【0149】 "Sales data" refers to information about the sales status and trends of a product at a specific point in time. 【0150】 "Inventory data" refers to information that shows the storage status of goods throughout the distribution process. 【0151】 "Emotional data" refers to information that quantifies or categorizes a user's emotional state. 【0152】 A "user" refers to a person who uses the system to perform their work. 【0153】 A "business improvement proposal" is a specific action plan or strategy proposed to improve business efficiency or solve problems. 【0154】 "Supply chain management" refers to management activities aimed at optimizing the entire process of product distribution. 【0155】 "Disposal" refers to goods that are disposed of without being used. 【0156】 An "interface" is a mechanism that facilitates the exchange of information between a user and an information processing device. 【0157】 To implement this invention, a system combining an information processing device and an emotion engine is used. Specifically, the system consists of a server and a terminal working in cooperation. 【0158】 First, the server collects distribution data, sales data, and inventory data from terminals. APIs and database technologies are used for this collection. The data stored on the server is analyzed using AI models. This analysis enables demand forecasting and optimization of inventory management. The server also analyzes comprehensive data, including sentiment data, to generate suggestions for business improvement. Data analysis on the server requires a processor with high computing power. 【0159】 Next, the device is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice tone via the camera and microphone to acquire emotion data. This emotion data is sent to a server in real time and analyzed in combination with other data. 【0160】 Users receive feedback through their devices and adjust their work accordingly. For example, the device interface can be adaptively changed based on the user's emotional data. This is expected to improve usability and reduce user stress. 【0161】 As a concrete example, if the staff at a restaurant are experiencing high levels of stress due to a busy schedule, the server can generate suggestions for improving inventory placement. This reduces the workload on the staff and enables more efficient business operations. 【0162】 An example of a prompt might be text such as, "Generate inventory management suggestions to reduce staff stress during busy periods." Using this prompt, the AI ​​model can generate effective suggestions. 【0163】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0164】 Step 1: 【0165】 The server collects distribution data, sales data, and inventory data from terminals. The input consists of data obtained from each terminal, which is collected in a unified format via an API and stored in a database to maintain data consistency. The output is an organized dataset. 【0166】 Step 2: 【0167】 The server performs analysis for demand forecasting and inventory management based on the collected data. Utilizing AI models, it analyzes past sales trends and current inventory levels to predict future demand. Historical data and models are required as input, and the output is the forecast result. This clarifies the required inventory levels and replenishment timing. 【0168】 Step 3: 【0169】 The device acquires user emotion data. This is done through facial expression analysis using the camera and voice tone analysis using the microphone. The input is real-time video and audio information of the user, and the output is quantified emotion data. This data is sent to a server and used for further analysis. 【0170】 Step 4: 【0171】 The server integrates and analyzes emotional data and demand forecast data to generate business improvement suggestions. Using a generative AI model, it creates suggestions that consider the impact of staff stress on work efficiency. Emotional data and analysis results are required as input, and the output is provided as a suggestion document. 【0172】 Step 5: 【0173】 Users receive suggested feedback through their devices and use it to adjust their work. Improvement suggestions from the server are presented via pop-up notifications on the device or email. By following these suggestions and selecting and executing specific actions, users can improve the efficiency of their work. 【0174】 Step 6: 【0175】 The device dynamically adjusts the interface display and notification mode in response to changes in the user's emotions. By changing screen colors and icons, and adjusting notification sounds, it provides a user-friendly environment. The input requires the user's latest emotional data, and the output is optimized user interface. 【0176】 (Application Example 2) 【0177】 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". 【0178】 In modern food supply chains, minimizing food waste risks, optimizing inventory management, and refining demand forecasting are critical challenges. However, in addition to these technical challenges, improving operational efficiency and streamlining operations while considering the stress levels of people involved throughout the supply chain are also necessary. Conventional systems rely purely on data analysis for optimization without considering stress levels, lacking integrated systems that incorporate human elements. 【0179】 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. 【0180】 In this invention, the server includes means for an information processing device to collect distribution information, sales information, and inventory information; means for analyzing the collected information and optimizing demand forecasting and inventory management; and means for an emotion analysis device to recognize the user's emotions and evaluate their stress level. This streamlines the management of the entire supply chain and enables the suggestion of operational improvements based on the user's stress level. As a result, not only is food waste minimized, but the job satisfaction of those involved is also improved. 【0181】 An "information processing device" is an electronic device used to collect and analyze distribution information, sales information, and inventory information. 【0182】 "Collected information" refers to data related to distribution, sales, and inventory, which is used as the basis for analysis. 【0183】 "Demand forecasting" is a process that enables efficient supply by estimating in advance the amount of goods or services that will be needed in the future. 【0184】 "Inventory management" is the process of optimizing the storage conditions of goods and replenishing or reducing them as needed. 【0185】 A "supply chain" is a network that includes a series of steps from the production and distribution of a product to its sale. 【0186】 An "emotion analysis device" is a device that analyzes data such as the user's facial expressions and voice to recognize their emotional state. 【0187】 "Stress" refers to a state of tension or burden that a user experiences due to environmental factors such as work. 【0188】 "Suggestions for operational improvements" are specific pieces of advice automatically provided by the system to improve the user's work efficiency. 【0189】 The server, acting as an information processing device, collects distribution, sales, and inventory information via the internet and stores it in a specific database. This database contains a large amount of data, and AI models are used for analysis. The AI ​​models perform demand forecasting and inventory management at each point in the supply chain, aiming to improve the efficiency of the entire supply chain. Furthermore, an emotion analysis device is installed in the terminal, which acquires the user's facial expressions and voice in real time and analyzes their emotions. This emotion data is transmitted from the terminal to the server and used in combination with the analysis results to generate optimal business improvement suggestions for the user. 【0190】 Emotion analysis utilizes software libraries such as OpenCV and TENSORFLOW®. The emotion analysis device uses a camera and microphone to recognize the user's face and voice tone, and analyzes the obtained data to evaluate their emotional state. If high stress levels are detected, the server sends the user specific suggestions for operational improvements. For example, during peak hours in a restaurant, suggestions for improving inventory placement based on the staff's stress levels might be provided. This system improves not only operational efficiency but also staff satisfaction. 【0191】 As a concrete example, consider a situation in a cafe where orders come in frequently during lunchtime. In this case, an emotion analysis device detects stress from the staff's faces and displays a suggestion on the terminal via a server, such as, "Let's change the inventory layout to handle orders more efficiently." This improves the operational efficiency of the physical store and enhances the user experience. Additionally, a prompt message such as, "Use the information processing device and emotion engine to generate suggestions for improving inventory management to reduce the busyness during lunchtime," is used by the generation AI model. 【0192】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0193】 Step 1: 【0194】 The server collects distribution, sales, and inventory information from terminals. This input data is stored in a unified database. This database contains detailed information such as inventory quantity, consumption rate, and price trends for each product. Daily batch processing is performed to maintain data consistency and integrity. 【0195】 Step 2: 【0196】 The server uses an AI model to forecast demand based on the collected data. The AI ​​model analyzes past sales data and trend data to predict demand for the following week and month. Specifically, it uses a time series analysis model to predict short-term demand fluctuations and processes the data for appropriate inventory management. The forecast results are generated in report format for administrators to review. 【0197】 Step 3: 【0198】 The device acquires emotional data in real time from the user's facial expressions and voice via an emotion analysis device. The input audio and image data are then feature-extracted using TensorFlow to evaluate the user's current emotional state. The emotional state is classified as "stress," "joy," etc., and the results are sent to the server. 【0199】 Step 4: 【0200】 The server integrates sentiment data and demand forecast results to generate feedback for business improvement. This process utilizes a generative AI model that considers multiple variables simultaneously to generate appropriate suggestions. For example, it might generate a prompt message containing a specific suggestion such as, "Staff assignments should be readjusted to reduce stress." This generated suggestion is immediately sent to the terminal. 【0201】 Step 5: 【0202】 The terminal receives suggestions generated from the server and notifies the user. The user can view the suggested business improvements on the terminal screen. Based on this, the user rearranges inventory or redistributes tasks. This operation is then analyzed again by the system for sentiment, and the effectiveness of the improvements after implementation is fed back to the server. 【0203】 This series of processes streamlines store operations, reduces the workload for users, and enables sustainable operations. 【0204】 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. 【0205】 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. 【0206】 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. 【0207】 [Second Embodiment] 【0208】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0209】 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. 【0210】 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). 【0211】 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. 【0212】 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. 【0213】 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). 【0214】 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. 【0215】 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. 【0216】 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. 【0217】 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. 【0218】 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. 【0219】 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". 【0220】 In order to implement the present invention, it is necessary to construct a system centered on an information processing device and to collect and analyze various types of data across the entire food supply chain. The specific form of such a system is shown below. 【0221】 First, the server collects distribution, sales, and inventory information through terminals installed in stores and distribution centers. This information includes data from IoT sensors and POS systems. The server aggregates this information in real time and stores it in a unified database on the platform. The terminals also play a role in supplementing the information on-site and immediately uploading it to the cloud. 【0222】 Next, the server uses the collected data to perform analysis using an AI model. This analysis calculates consumption trends and unsold inventory risks for each product and forecasts demand. For example, a restaurant might use past sales data to predict demand during peak hours and on different days of the week, and then calculate the optimal inventory level. 【0223】 Furthermore, the server identifies food items at high risk of waste by monitoring each stage of the supply chain in real time. Based on this, it notifies users (stores and distributors) of the optimal distribution arrangement to prevent waste. 【0224】 Finally, to reduce food waste, the server shares information about food at risk of being discarded with food-sharing networks. For example, if food is nearing its expiration date, the server provides that information to local food banks and charities. Users then take appropriate action based on the shared information to ensure that food sharing is successful. 【0225】 In this way, the present invention makes it possible to build a sustainable system that reduces food waste and supports the optimization of supply chains by utilizing an information processing device. 【0226】 The following describes the processing flow. 【0227】 Step 1: 【0228】 The terminals are installed in each store and distribution center and collect distribution, sales, and inventory information. This includes real-time data obtained from IoT sensors attached to shelves and POS systems. The terminals aggregate this data and transmit it to a server via the internet. 【0229】 Step 2: 【0230】 The server receives data sent from terminals and stores it in a unified database. This database records the current inventory status, past sales history, and distribution channels for each product. Based on this information, the server prepares for analysis. 【0231】 Step 3: 【0232】 The server inputs the latest data into an AI model to perform demand forecasting. This AI model takes into account past sales data, seasonal variations, and external factors (e.g., weather, events). Based on the results of this analysis, recommendations for the next order quantity and adjustments to inventory levels are presented. 【0233】 Step 4: 【0234】 Store managers, who are users of the system, receive analysis results and suggestions from the server via their terminals. Based on this, they can review their store's inventory strategy and adjust the next order quantity to prevent excess inventory and stockouts. 【0235】 Step 5: 【0236】 The server monitors the entire supply chain and identifies products at high risk of being wasted. This includes forecasts of inventory expiring and distribution delays. Identified products are immediately notified to the food sharing network. 【0237】 Step 6: 【0238】 Users receive notifications about waste risks and prepare to participate in food sharing. For example, they can quickly contact and donate to a nearby food bank, preventing food waste while supporting the community. 【0239】 (Example 1) 【0240】 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." 【0241】 In the food supply chain, inefficiencies in distribution, sales, and inventory management lead to decreased productivity and increased food waste. Furthermore, the inability to properly manage products at high risk of disposal, resulting in their ineffective utilization, is a significant problem. Solving these challenges and realizing a sustainable supply chain is essential. 【0242】 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. 【0243】 This invention includes a server that collects distribution data, sales data, and inventory data via terminals installed at logistics centers and sales bases; a server that synchronizes the collected data and stores it in a unified database; and a server that analyzes the data using an AI model to predict demand trends and unsold inventory risks for each product and calculate the optimal inventory level. This enables efficient inventory management, reduction of waste risk, and the construction of a sustainable supply chain. 【0244】 An "information processing system" is a computer system that collects, analyzes, and stores data related to logistics, sales, and inventory, and then performs various management functions based on that data. 【0245】 A "terminal" is an input device and communication equipment installed at logistics centers and sales bases that works in conjunction with information processing devices to collect and transmit data. 【0246】 "Distribution data" refers to information about the quantity, timing, and route of a product as it moves through the supply chain. 【0247】 "Sales data" refers to information such as quantity, amount, and time related to product sales to consumers. 【0248】 "Inventory data" refers to information regarding the quantity and condition of goods held at distribution and sales locations. 【0249】 An "AI model" is a set of machine learning or artificial intelligence algorithms used for data analysis, enabling pattern recognition and predictive analytics. 【0250】 A "unified database" refers to a data management system that centrally manages various collected data, enabling efficient information utilization. 【0251】 A "supply chain" is a system that represents the entire process from the supply of raw materials to the manufacturing, distribution, and sale of products. 【0252】 "Waste risk" refers to the possibility that food or products may be discarded because they exceed their expiration date or quality standards. 【0253】 A "shared network" refers to an information and communication infrastructure that enables multiple organizations and entities to cooperate in exchanging and appropriately utilizing information. 【0254】 "Recipient" refers to a facility or organization that receives food that is at risk of being discarded from the supply chain and then utilizes or consumes it. 【0255】 The embodiments for carrying out the present invention are shown below. 【0256】 The server works in conjunction with terminals installed at logistics and sales locations to collect distribution data, sales data, and inventory data. IoT sensors and POS systems are used for this data collection. For example, terminals acquire inventory quantities in real time via shelf sensors and obtain sales information from POS systems. The obtained data is transmitted to the server via the cloud. 【0257】 The server stores data in a unified database and performs data analysis using a generative AI model. This analysis includes demand forecasting based on collected historical data, as well as predictions of consumption trends and unsold inventory risks for each product. The results of the analysis enable the prediction of the next supply volume and the calculation of optimal inventory levels. 【0258】 For example, in a restaurant located in a commuter area, an AI model can analyze past sales and weather data to determine if demand is high during weekday lunchtimes. If this reveals a high demand, the server can then recommend the necessary amount of ingredients based on that demand. This helps avoid excess inventory and minimize food waste. 【0259】 Furthermore, the server connects with a sharing network to provide information on food at risk of being wasted, notifying food banks and other charitable organizations. This allows users to take appropriate action based on the information, ensuring that food is used effectively within the community. 【0260】 An example of a prompt might be, "Based on sales data from the past three months, predict next week's demand and create a proposal to minimize waste risk." Based on this prompt, the AI ​​model performs a specific analysis and provides results that support the operation of a sustainable supply chain. 【0261】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0262】 Step 1: 【0263】 The terminals collect distribution data, sales data, and inventory data from IoT sensors and POS systems installed at logistics and sales bases. These terminals transmit the data to a server in real time via the cloud. The data entered includes, for example, the quantity of goods and sales records, and by aggregating this data, inventory status can be understood. 【0264】 Step 2: 【0265】 The server saves the data received from the terminal to a unified database. This saving process removes duplicate data, verifies data format consistency, and optimizes the database. After saving, the input data is managed in a clean state for the next analysis process. 【0266】 Step 3: 【0267】 The server performs analysis using a generated AI model based on stored data. This analysis process uses historical data as input to predict consumption trends and unsold inventory risks for each product. The output includes product demand forecasts and optimal inventory levels. Specifically, the AI ​​model performs pattern recognition to predict, for example, the next supply quantity. 【0268】 Step 4: 【0269】 Based on the analysis results, the server monitors the entire supply chain in real time and identifies products at high risk of being discarded. Specifically, it calculates the number of days until a particular food product reaches its expiration date and lists the products with high discard risk. The input is the analysis data obtained in the previous step, and the output is a detailed report on the discard risk. 【0270】 Step 5: 【0271】 The server transmits information about identified waste risks to a shared network and sends notifications to local food banks and charities. This step creates a log of the notification transmissions, recording which organizations received which information. The input is information about products at risk of waste, and the output is the notification transmission log. 【0272】 Step 6: 【0273】 Users manage food at risk of waste based on notifications from the server. Specifically, they arrange for food to be sent to appropriate recipients and engage in activities aimed at sustainable consumption. Receiving notifications enables actions toward the effective use of food. Input is notification information from the server, and output is delivery instructions and recipient lists. 【0274】 (Application Example 1) 【0275】 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 glasses 214 will be referred to as the "terminal." 【0276】 In modern food supply chains, food waste resulting from fluctuations in demand and inadequate inventory management is a major challenge. Furthermore, there is a need to more accurately predict consumer behavior and make appropriate supply adjustments. This invention provides a method for reducing such food waste and improving the accuracy of demand forecasting. 【0277】 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. 【0278】 In this invention, the server includes means for collecting distribution information, sales information, and inventory information; means for analyzing the user's past order history and suggesting the optimal inventory quantity; and means for notifying users of discounted sales using information about food products nearing their expiration date. This reduces food waste and enables appropriate inventory supply and information provision to consumers. 【0279】 An "information processing device" is a machine or device that collects and analyzes distribution information, sales information, and inventory information to support the optimization of the entire supply chain. 【0280】 "Distribution information" refers to data related to transportation and storage that is generated as a product passes through the supply chain. 【0281】 "Sales information" refers to data related to product sales at stores and online platforms. 【0282】 "Inventory information" refers to data that shows how many of a particular product are stored and where they are located. 【0283】 A "user" is a business entity or individual that uses this system to optimize inventory management and sales strategies. 【0284】 The "order history" is data that records the details of orders placed in the past. 【0285】 The "optimal inventory level" is an ideal inventory quantity set to eliminate waste while taking into account demand fluctuations and prevent supply shortages. 【0286】 The "expiration date" is the date recommended as the consumption deadline of food, indicating the period during which it can be safely consumed while maintaining quality. 【0287】 The "discount sale notice" is a means to inform customers of discount information regarding products with approaching expiration dates. 【0288】 The system based on this invention is designed to optimize the entire food supply chain and significantly reduce food waste. First, the server collects information on distribution, sales, and inventory. Specifically, the server uses IoT sensors, POS systems, etc. to collect this data from physical stores and online platforms. 【0289】 The collected information is stored in a unified database and analyzed using a generative AI model. In this analysis, each user's smartphone is used as hardware to analyze the user's past order history and consumption trends, and a cloud-based analysis platform is used as software. Based on the analysis results, the server presents the optimal inventory level for the next supply, and discount information regarding products with approaching expiration dates is notified to the user. 【0290】 By leveraging this information, users can optimize the procurement and sales strategies of products. As a specific example, the server can prevent out-of-stock situations and supply shortages by automatically adjusting the inventory of popular products with a sudden increase in orders during specific time periods on weekends based on past data. 【0291】 Furthermore, an example of a prompt message could be, "Predict pizza demand this weekend based on past order history and generate inventory adjustment suggestions," which would instruct the generating AI model. This would lead to a reduction in food waste and improved supply efficiency. 【0292】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0293】 Step 1: 【0294】 The server collects information on distribution, sales, and inventory through IoT sensors and POS systems. This data is transmitted to the server in real time and stored in a unified database. The input to this process is data from sensors and POS systems, and the output is an integrated dataset. Specifically, sales quantities for each product, inventory status, and distribution channel information are collected. 【0295】 Step 2: 【0296】 The server uses a generative AI model to analyze the accumulated data. This allows users to analyze their past order history. The input for this step is an integrated dataset, and the output is demand forecasts and consumption trend patterns. Specifically, the analysis reveals peak hours, demand by day of the week, seasonal fluctuations, and more. 【0297】 Step 3: 【0298】 The server proposes the optimal inventory level required for the next supply based on demand forecasts. The inputs for this step are demand forecasts and current inventory data, and the output is the adjusted order quantity. Specifically, it indicates the exact quantity of goods to be delivered next and presents adjustment suggestions to prevent stockouts and excess inventory. 【0299】 Step 4: 【0300】 The user receives optimization suggestions from the server via their terminal and adjusts inventory orders and sales plans accordingly. The input for this step is the adjustment suggestions provided by the server, and the output is the revised order plan. Specifically, the user reviews the suggestions through the application and makes the necessary adjustments based on them. 【0301】 Step 5: 【0302】 The server analyzes information on products nearing their expiration date and sends users notifications about discounted sales. The inputs for this step are inventory information and expiration date information, and the output is a discount notification. Specifically, the server identifies products eligible for discounts and notifies the user via push notification or email. 【0303】 This processing flow enables the system to achieve efficient inventory management and supply adjustments in line with demand, thereby reducing food waste. 【0304】 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. 【0305】 To implement the present invention, it is necessary to construct a system that combines an information processing device and an emotion engine, and to provide an interface that takes into account the user's emotions, in addition to optimizing the entire food supply chain. The specific form of this system is shown below. 【0306】 First, the server collects distribution, sales, and inventory information from terminals, as in the conventional system. This data is stored in a unified database and analyzed by an AI model. This enables demand forecasting and inventory management at each point in the supply chain. 【0307】 Furthermore, the terminal is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice tones, and the like. This emotion data is used to extract information on how the user is feeling, for example, whether they are stressed or happy. 【0308】 Here, the server combines the emotion data with conventional sales and inventory data for analysis. As a result, for example, when the store staff is stressed, it becomes possible to propose adjustments to improve operations and the timing of inventory replenishment. 【0309】 The user adjusts their daily work based on the feedback provided through the terminal. Also, the emotion engine flexibly changes the display content and notification method of the interface in response to the user's emotional changes, improving usability. 【0310】 As a specific example, when the emotion engine recognizes that the staff at a certain restaurant is stressed during a busy period, the server presents a specific improvement plan for inventory placement to reduce the staff's burden. This can contribute not only to improving business efficiency but also to enhancing staff satisfaction. 【0311】 In this way, the present invention enables the construction of a system that not only optimizes processes but also takes into account the user's emotions by combining an information processing device and an emotion engine. This realizes the reduction of food waste and the improvement of the efficiency of the supply chain, and can provide a better user experience. 【0312】 The following describes the processing flow. 【0313】 Step 1: 【0314】 The terminal is installed in a store or a logistics center and collects distribution information, sales information, and inventory information through environmental sensors and a POS system. These data are transmitted to the server in real time. 【0315】 Step 2: 【0316】 The server stores data sent from the terminals in a database and performs analysis using an AI model. This analysis allows for the detection of abnormal inventory levels and demand forecasting, and the calculation of optimal inventory and order quantities. 【0317】 Step 3: 【0318】 The device utilizes an emotion engine to collect emotional data from the user's facial expressions and voice. For example, it tracks changes in facial expressions with the camera and analyzes voice tone with the microphone. 【0319】 Step 4: 【0320】 The server integrates and analyzes emotional data with sales and inventory data. If a user is experiencing stress, it identifies the cause and generates suggestions for improvements such as reviewing workflows and inventory placement. 【0321】 Step 5: 【0322】 Store managers, who are users of the system, receive analysis results and improvement suggestions through their terminals. By adjusting inventory placement and changing workflows based on these suggestions, they can improve operational efficiency and staff satisfaction. 【0323】 Step 6: 【0324】 The server tracks liquidity and inventory movements in real time down to the end of the supply chain, issuing early warnings to avoid unexpected stock shortages and waste risks. An emotional engine provides real-time feedback that motivates users and drives process improvement. 【0325】 In this way, by combining information processing equipment and an emotion engine, we can optimize the supply chain and enhance the user experience. 【0326】 (Example 2) 【0327】 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". 【0328】 Existing supply chain management systems often lack precision in demand forecasting and inventory management, leading to problems such as food waste and inadequate supply, particularly in the food industry. Furthermore, insufficient consideration has been given to improving operations that take into account employee emotions and stress levels, highlighting the need for more efficient operational management. These challenges are creating a complex web of problems, including increased food waste and decreased employee efficiency. 【0329】 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. 【0330】 In this invention, the server includes means for collecting distribution data, sales data, and inventory data; means for analyzing the collected data to optimize demand forecasting and inventory management; and means for acquiring user sentiment data and including it in the data analysis. This makes it possible to propose more precise supply chain management and business improvements while taking user sentiment into consideration. 【0331】 An "information processing device" is a device that collects and analyzes data and uses the results to support the optimization of various business operations. 【0332】 "Distribution data" refers to information about the flow of goods from supply to consumer. 【0333】 "Sales data" refers to information about the sales status and trends of a product at a specific point in time. 【0334】 "Inventory data" refers to information that shows the storage status of goods throughout the distribution process. 【0335】 "Emotional data" refers to information that quantifies or categorizes a user's emotional state. 【0336】 A "user" refers to a person who uses the system to perform their work. 【0337】 A "business improvement proposal" is a specific action plan or strategy proposed to improve business efficiency or solve problems. 【0338】 "Supply chain management" refers to management activities aimed at optimizing the entire process of product distribution. 【0339】 "Disposal" refers to goods that are disposed of without being used. 【0340】 An "interface" is a mechanism that facilitates the exchange of information between a user and an information processing device. 【0341】 To implement this invention, a system combining an information processing device and an emotion engine is used. Specifically, the system consists of a server and a terminal working in cooperation. 【0342】 First, the server collects distribution data, sales data, and inventory data from terminals. APIs and database technologies are used for this collection. The data stored on the server is analyzed using AI models. This analysis enables demand forecasting and optimization of inventory management. The server also analyzes comprehensive data, including sentiment data, to generate suggestions for business improvement. Data analysis on the server requires a processor with high computing power. 【0343】 Next, the device is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice tone via the camera and microphone to acquire emotion data. This emotion data is sent to a server in real time and analyzed in combination with other data. 【0344】 Users receive feedback through their devices and adjust their work accordingly. For example, the device interface can be adaptively changed based on the user's emotional data. This is expected to improve usability and reduce user stress. 【0345】 As a concrete example, if the staff at a restaurant are experiencing high levels of stress due to a busy schedule, the server can generate suggestions for improving inventory placement. This reduces the workload on the staff and enables more efficient business operations. 【0346】 An example of a prompt might be text such as, "Generate inventory management suggestions to reduce staff stress during busy periods." Using this prompt, the AI ​​model can generate effective suggestions. 【0347】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0348】 Step 1: 【0349】 The server collects distribution data, sales data, and inventory data from terminals. The input consists of data obtained from each terminal, which is collected in a unified format via an API and stored in a database to maintain data consistency. The output is an organized dataset. 【0350】 Step 2: 【0351】 The server performs analysis for demand forecasting and inventory management based on the collected data. Utilizing AI models, it analyzes past sales trends and current inventory levels to predict future demand. Historical data and models are required as input, and the output is the forecast result. This clarifies the required inventory levels and replenishment timing. 【0352】 Step 3: 【0353】 The device acquires user emotion data. This is done through facial expression analysis using the camera and voice tone analysis using the microphone. The input is real-time video and audio information of the user, and the output is quantified emotion data. This data is sent to a server and used for further analysis. 【0354】 Step 4: 【0355】 The server integrates and analyzes emotional data and demand forecast data to generate business improvement suggestions. Using a generative AI model, it creates suggestions that consider the impact of staff stress on work efficiency. Emotional data and analysis results are required as input, and the output is provided as a suggestion document. 【0356】 Step 5: 【0357】 Users receive suggested feedback through their devices and use it to adjust their work. Improvement suggestions from the server are presented via pop-up notifications on the device or email. By following these suggestions and selecting and executing specific actions, users can improve the efficiency of their work. 【0358】 Step 6: 【0359】 The device dynamically adjusts the interface display and notification mode in response to changes in the user's emotions. By changing screen colors and icons, and adjusting notification sounds, it provides a user-friendly environment. The input requires the user's latest emotional data, and the output is optimized user interface. 【0360】 (Application Example 2) 【0361】 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." 【0362】 In modern food supply chains, minimizing food waste risks, optimizing inventory management, and refining demand forecasting are critical challenges. However, in addition to these technical challenges, improving operational efficiency and streamlining operations while considering the stress levels of people involved throughout the supply chain are also necessary. Conventional systems rely purely on data analysis for optimization without considering stress levels, lacking integrated systems that incorporate human elements. 【0363】 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. 【0364】 In this invention, the server includes means for an information processing device to collect distribution information, sales information, and inventory information; means for analyzing the collected information and optimizing demand forecasting and inventory management; and means for an emotion analysis device to recognize the user's emotions and evaluate their stress level. This streamlines the management of the entire supply chain and enables the suggestion of operational improvements based on the user's stress level. As a result, not only is food waste minimized, but the job satisfaction of those involved is also improved. 【0365】 An "information processing device" is an electronic device used to collect and analyze distribution information, sales information, and inventory information. 【0366】 "Collected information" refers to data related to distribution, sales, and inventory, which is used as the basis for analysis. 【0367】 "Demand forecasting" is a process that enables efficient supply by estimating in advance the amount of goods or services that will be needed in the future. 【0368】 "Inventory management" is the process of optimizing the storage conditions of goods and replenishing or reducing them as needed. 【0369】 A "supply chain" is a network that includes a series of steps from the production and distribution of a product to its sale. 【0370】 An "emotion analysis device" is a device that analyzes data such as the user's facial expressions and voice to recognize their emotional state. 【0371】 "Stress" refers to a state of tension or burden that a user experiences due to environmental factors such as work. 【0372】 "Suggestions for operational improvements" are specific pieces of advice automatically provided by the system to improve the user's work efficiency. 【0373】 The server, acting as an information processing device, collects distribution, sales, and inventory information via the internet and stores it in a specific database. This database contains a large amount of data, and AI models are used for analysis. The AI ​​models perform demand forecasting and inventory management at each point in the supply chain, aiming to improve the efficiency of the entire supply chain. Furthermore, an emotion analysis device is installed in the terminal, which acquires the user's facial expressions and voice in real time and analyzes their emotions. This emotion data is transmitted from the terminal to the server and used in combination with the analysis results to generate optimal business improvement suggestions for the user. 【0374】 Emotion analysis utilizes software libraries such as OpenCV and TensorFlow. The emotion analysis device uses a camera and microphone to recognize the user's face and voice tone, and analyzes the obtained data to evaluate their emotional state. If high stress levels are detected, the server sends the user specific suggestions for operational improvements. For example, during peak hours in a restaurant, suggestions for improving inventory placement based on the staff's stress levels might be provided. This system improves not only operational efficiency but also staff satisfaction. 【0375】 As a concrete example, consider a situation in a cafe where orders come in frequently during lunchtime. In this case, an emotion analysis device detects stress from the staff's faces and displays a suggestion on the terminal via a server, such as, "Let's change the inventory layout to handle orders more efficiently." This improves the operational efficiency of the physical store and enhances the user experience. Additionally, a prompt message such as, "Use the information processing device and emotion engine to generate suggestions for improving inventory management to reduce the busyness during lunchtime," is used by the generation AI model. 【0376】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0377】 Step 1: 【0378】 The server collects distribution, sales, and inventory information from terminals. This input data is stored in a unified database. This database contains detailed information such as inventory quantity, consumption rate, and price trends for each product. Daily batch processing is performed to maintain data consistency and integrity. 【0379】 Step 2: 【0380】 The server uses an AI model to forecast demand based on the collected data. The AI ​​model analyzes past sales data and trend data to predict demand for the following week and month. Specifically, it uses a time series analysis model to predict short-term demand fluctuations and processes the data for appropriate inventory management. The forecast results are generated in report format for administrators to review. 【0381】 Step 3: 【0382】 The device acquires emotional data in real time from the user's facial expressions and voice via an emotion analysis device. The input audio and image data are then feature-extracted using TensorFlow to evaluate the user's current emotional state. The emotional state is classified as "stress," "joy," etc., and the results are sent to the server. 【0383】 Step 4: 【0384】 The server integrates sentiment data and demand forecast results to generate feedback for business improvement. This process utilizes a generative AI model that considers multiple variables simultaneously to generate appropriate suggestions. For example, it might generate a prompt message containing a specific suggestion such as, "Staff assignments should be readjusted to reduce stress." This generated suggestion is immediately sent to the terminal. 【0385】 Step 5: 【0386】 The terminal receives suggestions generated from the server and notifies the user. The user can view the suggested business improvements on the terminal screen. Based on this, the user rearranges inventory or redistributes tasks. This operation is then analyzed again by the system for sentiment, and the effectiveness of the improvements after implementation is fed back to the server. 【0387】 This series of processes streamlines store operations, reduces the workload for users, and enables sustainable operations. 【0388】 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. 【0389】 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. 【0390】 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. 【0391】 [Third Embodiment] 【0392】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0393】 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. 【0394】 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). 【0395】 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. 【0396】 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. 【0397】 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). 【0398】 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. 【0399】 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. 【0400】 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. 【0401】 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. 【0402】 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. 【0403】 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". 【0404】 In order to implement the present invention, it is necessary to construct a system centered on an information processing device and to collect and analyze various types of data across the entire food supply chain. The specific form of such a system is shown below. 【0405】 First, the server collects distribution, sales, and inventory information through terminals installed in stores and distribution centers. This information includes data from IoT sensors and POS systems. The server aggregates this information in real time and stores it in a unified database on the platform. The terminals also play a role in supplementing the information on-site and immediately uploading it to the cloud. 【0406】 Next, the server uses the collected data to perform analysis using an AI model. This analysis calculates consumption trends and unsold inventory risks for each product and forecasts demand. For example, a restaurant might use past sales data to predict demand during peak hours and on different days of the week, and then calculate the optimal inventory level. 【0407】 Furthermore, the server identifies food items at high risk of waste by monitoring each stage of the supply chain in real time. Based on this, it notifies users (stores and distributors) of the optimal distribution arrangement to prevent waste. 【0408】 Finally, to reduce food waste, the server shares information about food at risk of being discarded with food-sharing networks. For example, if food is nearing its expiration date, the server provides that information to local food banks and charities. Users then take appropriate action based on the shared information to ensure that food sharing is successful. 【0409】 In this way, the present invention makes it possible to build a sustainable system that reduces food waste and supports the optimization of supply chains by utilizing an information processing device. 【0410】 The following describes the processing flow. 【0411】 Step 1: 【0412】 The terminals are installed in each store and distribution center and collect distribution, sales, and inventory information. This includes real-time data obtained from IoT sensors attached to shelves and POS systems. The terminals aggregate this data and transmit it to a server via the internet. 【0413】 Step 2: 【0414】 The server receives data sent from terminals and stores it in a unified database. This database records the current inventory status, past sales history, and distribution channels for each product. Based on this information, the server prepares for analysis. 【0415】 Step 3: 【0416】 The server inputs the latest data into an AI model to perform demand forecasting. This AI model takes into account past sales data, seasonal variations, and external factors (e.g., weather, events). Based on the results of this analysis, recommendations for the next order quantity and adjustments to inventory levels are presented. 【0417】 Step 4: 【0418】 Store managers, who are users of the system, receive analysis results and suggestions from the server via their terminals. Based on this, they can review their store's inventory strategy and adjust the next order quantity to prevent excess inventory and stockouts. 【0419】 Step 5: 【0420】 The server monitors the entire supply chain and identifies products at high risk of being wasted. This includes forecasts of inventory expiring and distribution delays. Identified products are immediately notified to the food sharing network. 【0421】 Step 6: 【0422】 Users receive notifications about waste risks and prepare to participate in food sharing. For example, they can quickly contact and donate to a nearby food bank, preventing food waste while supporting the community. 【0423】 (Example 1) 【0424】 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." 【0425】 In the food supply chain, inefficiencies in distribution, sales, and inventory management lead to decreased productivity and increased food waste. Furthermore, the inability to properly manage products at high risk of disposal, resulting in their ineffective utilization, is a significant problem. Solving these challenges and realizing a sustainable supply chain is essential. 【0426】 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. 【0427】 This invention includes a server that collects distribution data, sales data, and inventory data via terminals installed at logistics centers and sales bases; a server that synchronizes the collected data and stores it in a unified database; and a server that analyzes the data using an AI model to predict demand trends and unsold inventory risks for each product and calculate the optimal inventory level. This enables efficient inventory management, reduction of waste risk, and the construction of a sustainable supply chain. 【0428】 An "information processing system" is a computer system that collects, analyzes, and stores data related to logistics, sales, and inventory, and then performs various management functions based on that data. 【0429】 A "terminal" is an input device and communication equipment installed at logistics centers and sales bases that works in conjunction with information processing devices to collect and transmit data. 【0430】 "Distribution data" refers to information about the quantity, timing, and route of a product as it moves through the supply chain. 【0431】 "Sales data" refers to information such as quantity, amount, and time related to product sales to consumers. 【0432】 "Inventory data" refers to information regarding the quantity and condition of goods held at distribution and sales locations. 【0433】 An "AI model" is a set of machine learning or artificial intelligence algorithms used for data analysis, enabling pattern recognition and predictive analytics. 【0434】 A "unified database" refers to a data management system that centrally manages various collected data, enabling efficient information utilization. 【0435】 A "supply chain" is a system that represents the entire process from the supply of raw materials to the manufacturing, distribution, and sale of products. 【0436】 "Waste risk" refers to the possibility that food or products may be discarded because they exceed their expiration date or quality standards. 【0437】 A "shared network" refers to an information and communication infrastructure that enables multiple organizations and entities to cooperate in exchanging and appropriately utilizing information. 【0438】 "Recipient" refers to a facility or organization that receives food that is at risk of being discarded from the supply chain and then utilizes or consumes it. 【0439】 The embodiments for carrying out the present invention are shown below. 【0440】 The server works in conjunction with terminals installed at logistics and sales locations to collect distribution data, sales data, and inventory data. IoT sensors and POS systems are used for this data collection. For example, terminals acquire inventory quantities in real time via shelf sensors and obtain sales information from POS systems. The obtained data is transmitted to the server via the cloud. 【0441】 The server stores data in a unified database and performs data analysis using a generative AI model. This analysis includes demand forecasting based on collected historical data, as well as predictions of consumption trends and unsold inventory risks for each product. The results of the analysis enable the prediction of the next supply volume and the calculation of optimal inventory levels. 【0442】 For example, in a restaurant located in a commuter area, an AI model can analyze past sales and weather data to determine if demand is high during weekday lunchtimes. If this reveals a high demand, the server can then recommend the necessary amount of ingredients based on that demand. This helps avoid excess inventory and minimize food waste. 【0443】 Furthermore, the server connects with a sharing network to provide information on food at risk of being wasted, notifying food banks and other charitable organizations. This allows users to take appropriate action based on the information, ensuring that food is used effectively within the community. 【0444】 An example of a prompt might be, "Based on sales data from the past three months, predict next week's demand and create a proposal to minimize waste risk." Based on this prompt, the AI ​​model performs a specific analysis and provides results that support the operation of a sustainable supply chain. 【0445】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0446】 Step 1: 【0447】 The terminals collect distribution data, sales data, and inventory data from IoT sensors and POS systems installed at logistics and sales bases. These terminals transmit the data to a server in real time via the cloud. The data entered includes, for example, the quantity of goods and sales records, and by aggregating this data, inventory status can be understood. 【0448】 Step 2: 【0449】 The server saves the data received from the terminal to a unified database. This saving process removes duplicate data, verifies data format consistency, and optimizes the database. After saving, the input data is managed in a clean state for the next analysis process. 【0450】 Step 3: 【0451】 The server performs analysis using a generated AI model based on stored data. This analysis process uses historical data as input to predict consumption trends and unsold inventory risks for each product. The output includes product demand forecasts and optimal inventory levels. Specifically, the AI ​​model performs pattern recognition to predict, for example, the next supply quantity. 【0452】 Step 4: 【0453】 Based on the analysis results, the server monitors the entire supply chain in real time and identifies products at high risk of being discarded. Specifically, it calculates the number of days until a particular food product reaches its expiration date and lists the products with high discard risk. The input is the analysis data obtained in the previous step, and the output is a detailed report on the discard risk. 【0454】 Step 5: 【0455】 The server transmits information about identified waste risks to a shared network and sends notifications to local food banks and charities. This step creates a log of the notification transmissions, recording which organizations received which information. The input is information about products at risk of waste, and the output is the notification transmission log. 【0456】 Step 6: 【0457】 Users manage food at risk of waste based on notifications from the server. Specifically, they arrange for food to be sent to appropriate recipients and engage in activities aimed at sustainable consumption. Receiving notifications enables actions toward the effective use of food. Input is notification information from the server, and output is delivery instructions and recipient lists. 【0458】 (Application Example 1) 【0459】 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." 【0460】 In modern food supply chains, food waste resulting from fluctuations in demand and inadequate inventory management is a major challenge. Furthermore, there is a need to more accurately predict consumer behavior and make appropriate supply adjustments. This invention provides a method for reducing such food waste and improving the accuracy of demand forecasting. 【0461】 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. 【0462】 In this invention, the server includes means for collecting distribution information, sales information, and inventory information; means for analyzing the user's past order history and suggesting the optimal inventory quantity; and means for notifying users of discounted sales using information about food products nearing their expiration date. This reduces food waste and enables appropriate inventory supply and information provision to consumers. 【0463】 An "information processing device" is a machine or device that collects and analyzes distribution information, sales information, and inventory information to support the optimization of the entire supply chain. 【0464】 "Distribution information" refers to data related to transportation and storage that is generated as a product passes through the supply chain. 【0465】 "Sales information" refers to data related to product sales at stores and online platforms. 【0466】 "Inventory information" refers to data that shows how many of a particular product are stored and where they are located. 【0467】 A "user" is a business entity or individual that uses this system to optimize inventory management and sales strategies. 【0468】 "Order history" refers to data that records the details of orders placed in the past. 【0469】 "Optimal inventory levels" refer to the ideal inventory quantity set to eliminate waste and prevent supply shortages, while taking into account fluctuations in demand. 【0470】 The "best before date" is the recommended date for consuming food, indicating the period during which the food can be safely consumed while maintaining its quality. 【0471】 A "discount sale notification" is a means of informing customers about discounts on products that are nearing their expiration date. 【0472】 The system based on this invention is designed to optimize the entire food supply chain and significantly reduce food waste. The server first collects information on distribution, sales, and inventory. Specifically, the server uses IoT sensors, POS systems, and other technologies to collect this data from physical stores and online platforms. 【0473】 The collected information is stored in a unified database and analyzed using a generative AI model. This analysis utilizes each user's smartphone as hardware and a cloud-based analytics platform as software to analyze their past order history and consumption trends. Based on the analysis results, the server suggests the optimal inventory levels for the next supply, and users are notified of discounts on products nearing their expiration date. 【0474】 By utilizing this information, users can optimize their product procurement and sales strategies. For example, the server can automatically adjust inventory levels for popular products that see a surge in orders during specific times on weekends, based on historical data, thereby preventing stockouts and supply shortages. 【0475】 Furthermore, an example of a prompt message could be, "Predict pizza demand this weekend based on past order history and generate inventory adjustment suggestions," which would instruct the generating AI model. This would lead to a reduction in food waste and improved supply efficiency. 【0476】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0477】 Step 1: 【0478】 The server collects information on distribution, sales, and inventory through IoT sensors and POS systems. This data is transmitted to the server in real time and stored in a unified database. The input to this process is data from sensors and POS systems, and the output is an integrated dataset. Specifically, sales quantities for each product, inventory status, and distribution channel information are collected. 【0479】 Step 2: 【0480】 The server uses a generative AI model to analyze the accumulated data. This allows users to analyze their past order history. The input for this step is an integrated dataset, and the output is demand forecasts and consumption trend patterns. Specifically, the analysis reveals peak hours, demand by day of the week, seasonal fluctuations, and more. 【0481】 Step 3: 【0482】 The server proposes the optimal inventory level required for the next supply based on demand forecasts. The inputs for this step are demand forecasts and current inventory data, and the output is the adjusted order quantity. Specifically, it indicates the exact quantity of goods to be delivered next and presents adjustment suggestions to prevent stockouts and excess inventory. 【0483】 Step 4: 【0484】 The user receives optimization suggestions from the server via their terminal and adjusts inventory orders and sales plans accordingly. The input for this step is the adjustment suggestions provided by the server, and the output is the revised order plan. Specifically, the user reviews the suggestions through the application and makes the necessary adjustments based on them. 【0485】 Step 5: 【0486】 The server analyzes information on products nearing their expiration date and sends users notifications about discounted sales. The inputs for this step are inventory information and expiration date information, and the output is a discount notification. Specifically, the server identifies products eligible for discounts and notifies the user via push notification or email. 【0487】 This processing flow enables the system to achieve efficient inventory management and supply adjustments in line with demand, thereby reducing food waste. 【0488】 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. 【0489】 To implement the present invention, it is necessary to construct a system that combines an information processing device and an emotion engine, and to provide an interface that takes into account the user's emotions, in addition to optimizing the entire food supply chain. The specific form of this system is shown below. 【0490】 First, the server collects distribution, sales, and inventory information from terminals, as in the conventional system. This data is stored in a unified database and analyzed by an AI model. This enables demand forecasting and inventory management at each point in the supply chain. 【0491】 Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and tone of voice. This emotion data is used to extract information about how the user is feeling, such as whether they are stressed or happy. 【0492】 Here, the server analyzes emotional data in combination with conventional sales and inventory data. This allows it to suggest improvements to operations or adjust the timing of inventory replenishment, for example, if store staff are experiencing stress. 【0493】 Users adjust their daily tasks based on feedback provided through their devices. Furthermore, the emotion engine responds to changes in the user's emotions, flexibly altering the interface's display and notification methods to improve usability. 【0494】 For example, if the emotion engine detects that staff at a restaurant are experiencing stress during a busy period, the server will suggest specific improvements to inventory allocation to alleviate the staff's burden. This can contribute not only to increased operational efficiency but also to improved staff satisfaction. 【0495】 In this way, the present invention, by combining an information processing device and an emotion engine, enables the construction of a system that not only optimizes processes but also takes into account the user's emotions. This makes it possible to reduce food waste, improve the efficiency of the supply chain, and provide a better user experience. 【0496】 The following describes the processing flow. 【0497】 Step 1: 【0498】 The terminals are installed in stores and distribution centers and collect distribution, sales, and inventory information through environmental sensors and POS systems. This data is transmitted to a server in real time. 【0499】 Step 2: 【0500】 The server stores data sent from the terminals in a database and performs analysis using an AI model. This analysis allows for the detection of abnormal inventory levels and demand forecasting, and the calculation of optimal inventory and order quantities. 【0501】 Step 3: 【0502】 The device utilizes an emotion engine to collect emotional data from the user's facial expressions and voice. For example, it tracks changes in facial expressions with the camera and analyzes voice tone with the microphone. 【0503】 Step 4: 【0504】 The server integrates and analyzes emotional data with sales and inventory data. If a user is experiencing stress, it identifies the cause and generates suggestions for improvements such as reviewing workflows and inventory placement. 【0505】 Step 5: 【0506】 Store managers, who are users of the system, receive analysis results and improvement suggestions through their terminals. By adjusting inventory placement and changing workflows based on these suggestions, they can improve operational efficiency and staff satisfaction. 【0507】 Step 6: 【0508】 The server tracks liquidity and inventory movements in real time down to the end of the supply chain, issuing early warnings to avoid unexpected stock shortages and waste risks. An emotional engine provides real-time feedback that motivates users and drives process improvement. 【0509】 In this way, by combining information processing equipment and an emotion engine, we can optimize the supply chain and enhance the user experience. 【0510】 (Example 2) 【0511】 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." 【0512】 Existing supply chain management systems often lack precision in demand forecasting and inventory management, leading to problems such as food waste and inadequate supply, particularly in the food industry. Furthermore, insufficient consideration has been given to improving operations that take into account employee emotions and stress levels, highlighting the need for more efficient operational management. These challenges are creating a complex web of problems, including increased food waste and decreased employee efficiency. 【0513】 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. 【0514】 In this invention, the server includes means for collecting distribution data, sales data, and inventory data; means for analyzing the collected data to optimize demand forecasting and inventory management; and means for acquiring user sentiment data and including it in the data analysis. This makes it possible to propose more precise supply chain management and business improvements while taking user sentiment into consideration. 【0515】 An "information processing device" is a device that collects and analyzes data and uses the results to support the optimization of various business operations. 【0516】 "Distribution data" refers to information about the flow of goods from supply to consumer. 【0517】 "Sales data" refers to information about the sales status and trends of a product at a specific point in time. 【0518】 "Inventory data" refers to information that shows the storage status of goods throughout the distribution process. 【0519】 "Emotional data" refers to information that quantifies or categorizes a user's emotional state. 【0520】 A "user" refers to a person who uses the system to perform their work. 【0521】 A "business improvement proposal" is a specific action plan or strategy proposed to improve business efficiency or solve problems. 【0522】 "Supply chain management" refers to management activities aimed at optimizing the entire process of product distribution. 【0523】 "Disposal" refers to goods that are disposed of without being used. 【0524】 An "interface" is a mechanism that facilitates the exchange of information between a user and an information processing device. 【0525】 To implement this invention, a system combining an information processing device and an emotion engine is used. Specifically, the system consists of a server and a terminal working in cooperation. 【0526】 First, the server collects distribution data, sales data, and inventory data from terminals. APIs and database technologies are used for this collection. The data stored on the server is analyzed using AI models. This analysis enables demand forecasting and optimization of inventory management. The server also analyzes comprehensive data, including sentiment data, to generate suggestions for business improvement. Data analysis on the server requires a processor with high computing power. 【0527】 Next, the device is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice tone via the camera and microphone to acquire emotion data. This emotion data is sent to a server in real time and analyzed in combination with other data. 【0528】 Users receive feedback through their devices and adjust their work accordingly. For example, the device interface can be adaptively changed based on the user's emotional data. This is expected to improve usability and reduce user stress. 【0529】 As a concrete example, if the staff at a restaurant are experiencing high levels of stress due to a busy schedule, the server can generate suggestions for improving inventory placement. This reduces the workload on the staff and enables more efficient business operations. 【0530】 An example of a prompt might be text such as, "Generate inventory management suggestions to reduce staff stress during busy periods." Using this prompt, the AI ​​model can generate effective suggestions. 【0531】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0532】 Step 1: 【0533】 The server collects distribution data, sales data, and inventory data from terminals. The input consists of data obtained from each terminal, which is collected in a unified format via an API and stored in a database to maintain data consistency. The output is an organized dataset. 【0534】 Step 2: 【0535】 The server performs analysis for demand forecasting and inventory management based on the collected data. Utilizing AI models, it analyzes past sales trends and current inventory levels to predict future demand. Historical data and models are required as input, and the output is the forecast result. This clarifies the required inventory levels and replenishment timing. 【0536】 Step 3: 【0537】 The device acquires user emotion data. This is done through facial expression analysis using the camera and voice tone analysis using the microphone. The input is real-time video and audio information of the user, and the output is quantified emotion data. This data is sent to a server and used for further analysis. 【0538】 Step 4: 【0539】 The server integrates and analyzes emotional data and demand forecast data to generate business improvement suggestions. Using a generative AI model, it creates suggestions that consider the impact of staff stress on work efficiency. Emotional data and analysis results are required as input, and the output is provided as a suggestion document. 【0540】 Step 5: 【0541】 Users receive suggested feedback through their devices and use it to adjust their work. Improvement suggestions from the server are presented via pop-up notifications on the device or email. By following these suggestions and selecting and executing specific actions, users can improve the efficiency of their work. 【0542】 Step 6: 【0543】 The device dynamically adjusts the interface display and notification mode in response to changes in the user's emotions. By changing screen colors and icons, and adjusting notification sounds, it provides a user-friendly environment. The input requires the user's latest emotional data, and the output is optimized user interface. 【0544】 (Application Example 2) 【0545】 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." 【0546】 In modern food supply chains, minimizing food waste risks, optimizing inventory management, and refining demand forecasting are critical challenges. However, in addition to these technical challenges, improving operational efficiency and streamlining operations while considering the stress levels of people involved throughout the supply chain are also necessary. Conventional systems rely purely on data analysis for optimization without considering stress levels, lacking integrated systems that incorporate human elements. 【0547】 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. 【0548】 In this invention, the server includes means for an information processing device to collect distribution information, sales information, and inventory information; means for analyzing the collected information and optimizing demand forecasting and inventory management; and means for an emotion analysis device to recognize the user's emotions and evaluate their stress level. This streamlines the management of the entire supply chain and enables the suggestion of operational improvements based on the user's stress level. As a result, not only is food waste minimized, but the job satisfaction of those involved is also improved. 【0549】 An "information processing device" is an electronic device used to collect and analyze distribution information, sales information, and inventory information. 【0550】 "Collected information" refers to data related to distribution, sales, and inventory, which is used as the basis for analysis. 【0551】 "Demand forecasting" is a process that enables efficient supply by estimating in advance the amount of goods or services that will be needed in the future. 【0552】 "Inventory management" is the process of optimizing the storage conditions of goods and replenishing or reducing them as needed. 【0553】 A "supply chain" is a network that includes a series of steps from the production and distribution of a product to its sale. 【0554】 An "emotion analysis device" is a device that analyzes data such as the user's facial expressions and voice to recognize their emotional state. 【0555】 "Stress" refers to a state of tension or burden that a user experiences due to environmental factors such as work. 【0556】 "Suggestions for operational improvements" are specific pieces of advice automatically provided by the system to improve the user's work efficiency. 【0557】 The server, acting as an information processing device, collects distribution, sales, and inventory information via the internet and stores it in a specific database. This database contains a large amount of data, and AI models are used for analysis. The AI ​​models perform demand forecasting and inventory management at each point in the supply chain, aiming to improve the efficiency of the entire supply chain. Furthermore, an emotion analysis device is installed in the terminal, which acquires the user's facial expressions and voice in real time and analyzes their emotions. This emotion data is transmitted from the terminal to the server and used in combination with the analysis results to generate optimal business improvement suggestions for the user. 【0558】 Emotion analysis utilizes software libraries such as OpenCV and TensorFlow. The emotion analysis device uses a camera and microphone to recognize the user's face and voice tone, and analyzes the obtained data to evaluate their emotional state. If high stress levels are detected, the server sends the user specific suggestions for operational improvements. For example, during peak hours in a restaurant, suggestions for improving inventory placement based on the staff's stress levels might be provided. This system improves not only operational efficiency but also staff satisfaction. 【0559】 As a concrete example, consider a situation in a cafe where orders come in frequently during lunchtime. In this case, an emotion analysis device detects stress from the staff's faces and displays a suggestion on the terminal via a server, such as, "Let's change the inventory layout to handle orders more efficiently." This improves the operational efficiency of the physical store and enhances the user experience. Additionally, a prompt message such as, "Use the information processing device and emotion engine to generate suggestions for improving inventory management to reduce the busyness during lunchtime," is used by the generation AI model. 【0560】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0561】 Step 1: 【0562】 The server collects distribution, sales, and inventory information from terminals. This input data is stored in a unified database. This database contains detailed information such as inventory quantity, consumption rate, and price trends for each product. Daily batch processing is performed to maintain data consistency and integrity. 【0563】 Step 2: 【0564】 The server uses an AI model to forecast demand based on the collected data. The AI ​​model analyzes past sales data and trend data to predict demand for the following week and month. Specifically, it uses a time series analysis model to predict short-term demand fluctuations and processes the data for appropriate inventory management. The forecast results are generated in report format for administrators to review. 【0565】 Step 3: 【0566】 The device acquires emotional data in real time from the user's facial expressions and voice via an emotion analysis device. The input audio and image data are then feature-extracted using TensorFlow to evaluate the user's current emotional state. The emotional state is classified as "stress," "joy," etc., and the results are sent to the server. 【0567】 Step 4: 【0568】 The server integrates sentiment data and demand forecast results to generate feedback for business improvement. This process utilizes a generative AI model that considers multiple variables simultaneously to generate appropriate suggestions. For example, it might generate a prompt message containing a specific suggestion such as, "Staff assignments should be readjusted to reduce stress." This generated suggestion is immediately sent to the terminal. 【0569】 Step 5: 【0570】 The terminal receives suggestions generated from the server and notifies the user. The user can view the suggested business improvements on the terminal screen. Based on this, the user rearranges inventory or redistributes tasks. This operation is then analyzed again by the system for sentiment, and the effectiveness of the improvements after implementation is fed back to the server. 【0571】 This series of processes streamlines store operations, reduces the workload for users, and enables sustainable operations. 【0572】 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. 【0573】 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. 【0574】 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. 【0575】 [Fourth Embodiment] 【0576】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0577】 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. 【0578】 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). 【0579】 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. 【0580】 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. 【0581】 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). 【0582】 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. 【0583】 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. 【0584】 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. 【0585】 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. 【0586】 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. 【0587】 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. 【0588】 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". 【0589】 In order to implement the present invention, it is necessary to construct a system centered on an information processing device and to collect and analyze various types of data across the entire food supply chain. The specific form of such a system is shown below. 【0590】 First, the server collects distribution, sales, and inventory information through terminals installed in stores and distribution centers. This information includes data from IoT sensors and POS systems. The server aggregates this information in real time and stores it in a unified database on the platform. The terminals also play a role in supplementing the information on-site and immediately uploading it to the cloud. 【0591】 Next, the server uses the collected data to perform analysis using an AI model. This analysis calculates consumption trends and unsold inventory risks for each product and forecasts demand. For example, a restaurant might use past sales data to predict demand during peak hours and on different days of the week, and then calculate the optimal inventory level. 【0592】 Furthermore, the server identifies food items at high risk of waste by monitoring each stage of the supply chain in real time. Based on this, it notifies users (stores and distributors) of the optimal distribution arrangement to prevent waste. 【0593】 Finally, to reduce food waste, the server shares information about food at risk of being discarded with food-sharing networks. For example, if food is nearing its expiration date, the server provides that information to local food banks and charities. Users then take appropriate action based on the shared information to ensure that food sharing is successful. 【0594】 In this way, the present invention makes it possible to build a sustainable system that reduces food waste and supports the optimization of supply chains by utilizing an information processing device. 【0595】 The following describes the processing flow. 【0596】 Step 1: 【0597】 The terminals are installed in each store and distribution center and collect distribution, sales, and inventory information. This includes real-time data obtained from IoT sensors attached to shelves and POS systems. The terminals aggregate this data and transmit it to a server via the internet. 【0598】 Step 2: 【0599】 The server receives data sent from terminals and stores it in a unified database. This database records the current inventory status, past sales history, and distribution channels for each product. Based on this information, the server prepares for analysis. 【0600】 Step 3: 【0601】 The server inputs the latest data into an AI model to perform demand forecasting. This AI model takes into account past sales data, seasonal variations, and external factors (e.g., weather, events). Based on the results of this analysis, recommendations for the next order quantity and adjustments to inventory levels are presented. 【0602】 Step 4: 【0603】 Store managers, who are users of the system, receive analysis results and suggestions from the server via their terminals. Based on this, they can review their store's inventory strategy and adjust the next order quantity to prevent excess inventory and stockouts. 【0604】 Step 5: 【0605】 The server monitors the entire supply chain and identifies products at high risk of being wasted. This includes forecasts of inventory expiring and distribution delays. Identified products are immediately notified to the food sharing network. 【0606】 Step 6: 【0607】 Users receive notifications about waste risks and prepare to participate in food sharing. For example, they can quickly contact and donate to a nearby food bank, preventing food waste while supporting the community. 【0608】 (Example 1) 【0609】 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". 【0610】 In the food supply chain, inefficiencies in distribution, sales, and inventory management lead to decreased productivity and increased food waste. Furthermore, the inability to properly manage products at high risk of disposal, resulting in their ineffective utilization, is a significant problem. Solving these challenges and realizing a sustainable supply chain is essential. 【0611】 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. 【0612】 This invention includes a server that collects distribution data, sales data, and inventory data via terminals installed at logistics centers and sales bases; a server that synchronizes the collected data and stores it in a unified database; and a server that analyzes the data using an AI model to predict demand trends and unsold inventory risks for each product and calculate the optimal inventory level. This enables efficient inventory management, reduction of waste risk, and the construction of a sustainable supply chain. 【0613】 An "information processing system" is a computer system that collects, analyzes, and stores data related to logistics, sales, and inventory, and then performs various management functions based on that data. 【0614】 A "terminal" is an input device and communication equipment installed at logistics centers and sales bases that works in conjunction with information processing devices to collect and transmit data. 【0615】 "Distribution data" refers to information about the quantity, timing, and route of a product as it moves through the supply chain. 【0616】 "Sales data" refers to information such as quantity, amount, and time related to product sales to consumers. 【0617】 "Inventory data" refers to information regarding the quantity and condition of goods held at distribution and sales locations. 【0618】 An "AI model" is a set of machine learning or artificial intelligence algorithms used for data analysis, enabling pattern recognition and predictive analytics. 【0619】 A "unified database" refers to a data management system that centrally manages various collected data, enabling efficient information utilization. 【0620】 A "supply chain" is a system that represents the entire process from the supply of raw materials to the manufacturing, distribution, and sale of products. 【0621】 "Waste risk" refers to the possibility that food or products may be discarded because they exceed their expiration date or quality standards. 【0622】 A "shared network" refers to an information and communication infrastructure that enables multiple organizations and entities to cooperate in exchanging and appropriately utilizing information. 【0623】 "Recipient" refers to a facility or organization that receives food that is at risk of being discarded from the supply chain and then utilizes or consumes it. 【0624】 The embodiments for carrying out the present invention are shown below. 【0625】 The server works in conjunction with terminals installed at logistics and sales locations to collect distribution data, sales data, and inventory data. IoT sensors and POS systems are used for this data collection. For example, terminals acquire inventory quantities in real time via shelf sensors and obtain sales information from POS systems. The obtained data is transmitted to the server via the cloud. 【0626】 The server stores data in a unified database and performs data analysis using a generative AI model. This analysis includes demand forecasting based on collected historical data, as well as predictions of consumption trends and unsold inventory risks for each product. The results of the analysis enable the prediction of the next supply volume and the calculation of optimal inventory levels. 【0627】 For example, in a restaurant located in a commuter area, an AI model can analyze past sales and weather data to determine if demand is high during weekday lunchtimes. If this reveals a high demand, the server can then recommend the necessary amount of ingredients based on that demand. This helps avoid excess inventory and minimize food waste. 【0628】 Furthermore, the server connects with a sharing network to provide information on food at risk of being wasted, notifying food banks and other charitable organizations. This allows users to take appropriate action based on the information, ensuring that food is used effectively within the community. 【0629】 An example of a prompt might be, "Based on sales data from the past three months, predict next week's demand and create a proposal to minimize waste risk." Based on this prompt, the AI ​​model performs a specific analysis and provides results that support the operation of a sustainable supply chain. 【0630】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0631】 Step 1: 【0632】 The terminals collect distribution data, sales data, and inventory data from IoT sensors and POS systems installed at logistics and sales bases. These terminals transmit the data to a server in real time via the cloud. The data entered includes, for example, the quantity of goods and sales records, and by aggregating this data, inventory status can be understood. 【0633】 Step 2: 【0634】 The server saves the data received from the terminal to a unified database. This saving process removes duplicate data, verifies data format consistency, and optimizes the database. After saving, the input data is managed in a clean state for the next analysis process. 【0635】 Step 3: 【0636】 The server performs analysis using a generated AI model based on stored data. This analysis process uses historical data as input to predict consumption trends and unsold inventory risks for each product. The output includes product demand forecasts and optimal inventory levels. Specifically, the AI ​​model performs pattern recognition to predict, for example, the next supply quantity. 【0637】 Step 4: 【0638】 Based on the analysis results, the server monitors the entire supply chain in real time and identifies products at high risk of being discarded. Specifically, it calculates the number of days until a particular food product reaches its expiration date and lists the products with high discard risk. The input is the analysis data obtained in the previous step, and the output is a detailed report on the discard risk. 【0639】 Step 5: 【0640】 The server transmits information about identified waste risks to a shared network and sends notifications to local food banks and charities. This step creates a log of the notification transmissions, recording which organizations received which information. The input is information about products at risk of waste, and the output is the notification transmission log. 【0641】 Step 6: 【0642】 Users manage food at risk of waste based on notifications from the server. Specifically, they arrange for food to be sent to appropriate recipients and engage in activities aimed at sustainable consumption. Receiving notifications enables actions toward the effective use of food. Input is notification information from the server, and output is delivery instructions and recipient lists. 【0643】 (Application Example 1) 【0644】 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". 【0645】 In modern food supply chains, food waste resulting from fluctuations in demand and inadequate inventory management is a major challenge. Furthermore, there is a need to more accurately predict consumer behavior and make appropriate supply adjustments. This invention provides a method for reducing such food waste and improving the accuracy of demand forecasting. 【0646】 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. 【0647】 In this invention, the server includes means for collecting distribution information, sales information, and inventory information; means for analyzing the user's past order history and suggesting the optimal inventory quantity; and means for notifying users of discounted sales using information about food products nearing their expiration date. This reduces food waste and enables appropriate inventory supply and information provision to consumers. 【0648】 An "information processing device" is a machine or device that collects and analyzes distribution information, sales information, and inventory information to support the optimization of the entire supply chain. 【0649】 "Distribution information" refers to data related to transportation and storage that is generated as a product passes through the supply chain. 【0650】 "Sales information" refers to data related to product sales at stores and online platforms. 【0651】 "Inventory information" refers to data that shows how many of a particular product are stored and where they are located. 【0652】 A "user" is a business entity or individual that uses this system to optimize inventory management and sales strategies. 【0653】 "Order history" refers to data that records the details of orders placed in the past. 【0654】 "Optimal inventory levels" refer to the ideal inventory quantity set to eliminate waste and prevent supply shortages, while taking into account fluctuations in demand. 【0655】 The "best before date" is the recommended date for consuming food, indicating the period during which the food can be safely consumed while maintaining its quality. 【0656】 A "discount sale notification" is a means of informing customers about discounts on products that are nearing their expiration date. 【0657】 The system based on this invention is designed to optimize the entire food supply chain and significantly reduce food waste. The server first collects information on distribution, sales, and inventory. Specifically, the server uses IoT sensors, POS systems, and other technologies to collect this data from physical stores and online platforms. 【0658】 The collected information is stored in a unified database and analyzed using a generative AI model. This analysis utilizes each user's smartphone as hardware and a cloud-based analytics platform as software to analyze their past order history and consumption trends. Based on the analysis results, the server suggests the optimal inventory levels for the next supply, and users are notified of discounts on products nearing their expiration date. 【0659】 By utilizing this information, users can optimize their product procurement and sales strategies. For example, the server can automatically adjust inventory levels for popular products that see a surge in orders during specific times on weekends, based on historical data, thereby preventing stockouts and supply shortages. 【0660】 Furthermore, an example of a prompt message could be, "Predict pizza demand this weekend based on past order history and generate inventory adjustment suggestions," which would instruct the generating AI model. This would lead to a reduction in food waste and improved supply efficiency. 【0661】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0662】 Step 1: 【0663】 The server collects information on distribution, sales, and inventory through IoT sensors and POS systems. This data is transmitted to the server in real time and stored in a unified database. The input to this process is data from sensors and POS systems, and the output is an integrated dataset. Specifically, sales quantities for each product, inventory status, and distribution channel information are collected. 【0664】 Step 2: 【0665】 The server uses a generative AI model to analyze the accumulated data. This allows users to analyze their past order history. The input for this step is an integrated dataset, and the output is demand forecasts and consumption trend patterns. Specifically, the analysis reveals peak hours, demand by day of the week, seasonal fluctuations, and more. 【0666】 Step 3: 【0667】 The server proposes the optimal inventory level required for the next supply based on demand forecasts. The inputs for this step are demand forecasts and current inventory data, and the output is the adjusted order quantity. Specifically, it indicates the exact quantity of goods to be delivered next and presents adjustment suggestions to prevent stockouts and excess inventory. 【0668】 Step 4: 【0669】 The user receives optimization suggestions from the server via their terminal and adjusts inventory orders and sales plans accordingly. The input for this step is the adjustment suggestions provided by the server, and the output is the revised order plan. Specifically, the user reviews the suggestions through the application and makes the necessary adjustments based on them. 【0670】 Step 5: 【0671】 The server analyzes information on products nearing their expiration date and sends users notifications about discounted sales. The inputs for this step are inventory information and expiration date information, and the output is a discount notification. Specifically, the server identifies products eligible for discounts and notifies the user via push notification or email. 【0672】 This processing flow enables the system to achieve efficient inventory management and supply adjustments in line with demand, thereby reducing food waste. 【0673】 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. 【0674】 To implement the present invention, it is necessary to construct a system that combines an information processing device and an emotion engine, and to provide an interface that takes into account the user's emotions, in addition to optimizing the entire food supply chain. The specific form of this system is shown below. 【0675】 First, the server collects distribution, sales, and inventory information from terminals, as in the conventional system. This data is stored in a unified database and analyzed by an AI model. This enables demand forecasting and inventory management at each point in the supply chain. 【0676】 Furthermore, the device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and tone of voice. This emotion data is used to extract information about how the user is feeling, such as whether they are stressed or happy. 【0677】 Here, the server analyzes emotional data in combination with conventional sales and inventory data. This allows it to suggest improvements to operations or adjust the timing of inventory replenishment, for example, if store staff are experiencing stress. 【0678】 Users adjust their daily tasks based on feedback provided through their devices. Furthermore, the emotion engine responds to changes in the user's emotions, flexibly altering the interface's display and notification methods to improve usability. 【0679】 For example, if the emotion engine detects that staff at a restaurant are experiencing stress during a busy period, the server will suggest specific improvements to inventory allocation to alleviate the staff's burden. This can contribute not only to increased operational efficiency but also to improved staff satisfaction. 【0680】 In this way, the present invention, by combining an information processing device and an emotion engine, enables the construction of a system that not only optimizes processes but also takes into account the user's emotions. This makes it possible to reduce food waste, improve the efficiency of the supply chain, and provide a better user experience. 【0681】 The following describes the processing flow. 【0682】 Step 1: 【0683】 The terminals are installed in stores and distribution centers and collect distribution, sales, and inventory information through environmental sensors and POS systems. This data is transmitted to a server in real time. 【0684】 Step 2: 【0685】 The server stores data sent from the terminals in a database and performs analysis using an AI model. This analysis allows for the detection of abnormal inventory levels and demand forecasting, and the calculation of optimal inventory and order quantities. 【0686】 Step 3: 【0687】 The device utilizes an emotion engine to collect emotional data from the user's facial expressions and voice. For example, it tracks changes in facial expressions with the camera and analyzes voice tone with the microphone. 【0688】 Step 4: 【0689】 The server integrates and analyzes emotional data with sales and inventory data. If a user is experiencing stress, it identifies the cause and generates suggestions for improvements such as reviewing workflows and inventory placement. 【0690】 Step 5: 【0691】 Store managers, who are users of the system, receive analysis results and improvement suggestions through their terminals. By adjusting inventory placement and changing workflows based on these suggestions, they can improve operational efficiency and staff satisfaction. 【0692】 Step 6: 【0693】 The server tracks liquidity and inventory movements in real time down to the end of the supply chain, issuing early warnings to avoid unexpected stock shortages and waste risks. An emotional engine provides real-time feedback that motivates users and drives process improvement. 【0694】 In this way, by combining information processing equipment and an emotion engine, we can optimize the supply chain and enhance the user experience. 【0695】 (Example 2) 【0696】 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". 【0697】 Existing supply chain management systems often lack precision in demand forecasting and inventory management, leading to problems such as food waste and inadequate supply, particularly in the food industry. Furthermore, insufficient consideration has been given to improving operations that take into account employee emotions and stress levels, highlighting the need for more efficient operational management. These challenges are creating a complex web of problems, including increased food waste and decreased employee efficiency. 【0698】 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. 【0699】 In this invention, the server includes means for collecting distribution data, sales data, and inventory data; means for analyzing the collected data to optimize demand forecasting and inventory management; and means for acquiring user sentiment data and including it in the data analysis. This makes it possible to propose more precise supply chain management and business improvements while taking user sentiment into consideration. 【0700】 An "information processing device" is a device that collects and analyzes data and uses the results to support the optimization of various business operations. 【0701】 "Distribution data" refers to information about the flow of goods from supply to consumer. 【0702】 "Sales data" refers to information about the sales status and trends of a product at a specific point in time. 【0703】 "Inventory data" refers to information that shows the storage status of goods throughout the distribution process. 【0704】 "Emotional data" refers to information that quantifies or categorizes a user's emotional state. 【0705】 A "user" refers to a person who uses the system to perform their work. 【0706】 A "business improvement proposal" is a specific action plan or strategy proposed to improve business efficiency or solve problems. 【0707】 "Supply chain management" refers to management activities aimed at optimizing the entire process of product distribution. 【0708】 "Disposal" refers to goods that are disposed of without being used. 【0709】 An "interface" is a mechanism that facilitates the exchange of information between a user and an information processing device. 【0710】 To implement this invention, a system combining an information processing device and an emotion engine is used. Specifically, the system consists of a server and a terminal working in cooperation. 【0711】 First, the server collects distribution data, sales data, and inventory data from terminals. APIs and database technologies are used for this collection. The data stored on the server is analyzed using AI models. This analysis enables demand forecasting and optimization of inventory management. The server also analyzes comprehensive data, including sentiment data, to generate suggestions for business improvement. Data analysis on the server requires a processor with high computing power. 【0712】 Next, the device is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice tone via the camera and microphone to acquire emotion data. This emotion data is sent to a server in real time and analyzed in combination with other data. 【0713】 Users receive feedback through their devices and adjust their work accordingly. For example, the device interface can be adaptively changed based on the user's emotional data. This is expected to improve usability and reduce user stress. 【0714】 As a concrete example, if the staff at a restaurant are experiencing high levels of stress due to a busy schedule, the server can generate suggestions for improving inventory placement. This reduces the workload on the staff and enables more efficient business operations. 【0715】 An example of a prompt might be text such as, "Generate inventory management suggestions to reduce staff stress during busy periods." Using this prompt, the AI ​​model can generate effective suggestions. 【0716】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0717】 Step 1: 【0718】 The server collects distribution data, sales data, and inventory data from terminals. The input consists of data obtained from each terminal, which is collected in a unified format via an API and stored in a database to maintain data consistency. The output is an organized dataset. 【0719】 Step 2: 【0720】 The server performs analysis for demand forecasting and inventory management based on the collected data. Utilizing AI models, it analyzes past sales trends and current inventory levels to predict future demand. Historical data and models are required as input, and the output is the forecast result. This clarifies the required inventory levels and replenishment timing. 【0721】 Step 3: 【0722】 The device acquires user emotion data. This is done through facial expression analysis using the camera and voice tone analysis using the microphone. The input is real-time video and audio information of the user, and the output is quantified emotion data. This data is sent to a server and used for further analysis. 【0723】 Step 4: 【0724】 The server integrates and analyzes emotional data and demand forecast data to generate business improvement suggestions. Using a generative AI model, it creates suggestions that consider the impact of staff stress on work efficiency. Emotional data and analysis results are required as input, and the output is provided as a suggestion document. 【0725】 Step 5: 【0726】 Users receive suggested feedback through their devices and use it to adjust their work. Improvement suggestions from the server are presented via pop-up notifications on the device or email. By following these suggestions and selecting and executing specific actions, users can improve the efficiency of their work. 【0727】 Step 6: 【0728】 The device dynamically adjusts the interface display and notification mode in response to changes in the user's emotions. By changing screen colors and icons, and adjusting notification sounds, it provides a user-friendly environment. The input requires the user's latest emotional data, and the output is optimized user interface. 【0729】 (Application Example 2) 【0730】 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". 【0731】 In modern food supply chains, minimizing food waste risks, optimizing inventory management, and refining demand forecasting are critical challenges. However, in addition to these technical challenges, improving operational efficiency and streamlining operations while considering the stress levels of people involved throughout the supply chain are also necessary. Conventional systems rely purely on data analysis for optimization without considering stress levels, lacking integrated systems that incorporate human elements. 【0732】 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. 【0733】 In this invention, the server includes means for an information processing device to collect distribution information, sales information, and inventory information; means for analyzing the collected information and optimizing demand forecasting and inventory management; and means for an emotion analysis device to recognize the user's emotions and evaluate their stress level. This streamlines the management of the entire supply chain and enables the suggestion of operational improvements based on the user's stress level. As a result, not only is food waste minimized, but the job satisfaction of those involved is also improved. 【0734】 An "information processing device" is an electronic device used to collect and analyze distribution information, sales information, and inventory information. 【0735】 "Collected information" refers to data related to distribution, sales, and inventory, which is used as the basis for analysis. 【0736】 "Demand forecasting" is a process that enables efficient supply by estimating in advance the amount of goods or services that will be needed in the future. 【0737】 "Inventory management" is the process of optimizing the storage conditions of goods and replenishing or reducing them as needed. 【0738】 A "supply chain" is a network that includes a series of steps from the production and distribution of a product to its sale. 【0739】 An "emotion analysis device" is a device that analyzes data such as the user's facial expressions and voice to recognize their emotional state. 【0740】 "Stress" refers to a state of tension or burden that a user experiences due to environmental factors such as work. 【0741】 "Suggestions for operational improvements" are specific pieces of advice automatically provided by the system to improve the user's work efficiency. 【0742】 The server, acting as an information processing device, collects distribution, sales, and inventory information via the internet and stores it in a specific database. This database contains a large amount of data, and AI models are used for analysis. The AI ​​models perform demand forecasting and inventory management at each point in the supply chain, aiming to improve the efficiency of the entire supply chain. Furthermore, an emotion analysis device is installed in the terminal, which acquires the user's facial expressions and voice in real time and analyzes their emotions. This emotion data is transmitted from the terminal to the server and used in combination with the analysis results to generate optimal business improvement suggestions for the user. 【0743】 Emotion analysis utilizes software libraries such as OpenCV and TensorFlow. The emotion analysis device uses a camera and microphone to recognize the user's face and voice tone, and analyzes the obtained data to evaluate their emotional state. If high stress levels are detected, the server sends the user specific suggestions for operational improvements. For example, during peak hours in a restaurant, suggestions for improving inventory placement based on the staff's stress levels might be provided. This system improves not only operational efficiency but also staff satisfaction. 【0744】 As a concrete example, consider a situation in a cafe where orders come in frequently during lunchtime. In this case, an emotion analysis device detects stress from the staff's faces and displays a suggestion on the terminal via a server, such as, "Let's change the inventory layout to handle orders more efficiently." This improves the operational efficiency of the physical store and enhances the user experience. Additionally, a prompt message such as, "Use the information processing device and emotion engine to generate suggestions for improving inventory management to reduce the busyness during lunchtime," is used by the generation AI model. 【0745】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0746】 Step 1: 【0747】 The server collects distribution, sales, and inventory information from terminals. This input data is stored in a unified database. This database contains detailed information such as inventory quantity, consumption rate, and price trends for each product. Daily batch processing is performed to maintain data consistency and integrity. 【0748】 Step 2: 【0749】 The server uses an AI model to forecast demand based on the collected data. The AI ​​model analyzes past sales data and trend data to predict demand for the following week and month. Specifically, it uses a time series analysis model to predict short-term demand fluctuations and processes the data for appropriate inventory management. The forecast results are generated in report format for administrators to review. 【0750】 Step 3: 【0751】 The device acquires emotional data in real time from the user's facial expressions and voice via an emotion analysis device. The input audio and image data are then feature-extracted using TensorFlow to evaluate the user's current emotional state. The emotional state is classified as "stress," "joy," etc., and the results are sent to the server. 【0752】 Step 4: 【0753】 The server integrates sentiment data and demand forecast results to generate feedback for business improvement. This process utilizes a generative AI model that considers multiple variables simultaneously to generate appropriate suggestions. For example, it might generate a prompt message containing a specific suggestion such as, "Staff assignments should be readjusted to reduce stress." This generated suggestion is immediately sent to the terminal. 【0754】 Step 5: 【0755】 The terminal receives suggestions generated from the server and notifies the user. The user can view the suggested business improvements on the terminal screen. Based on this, the user rearranges inventory or redistributes tasks. This operation is then analyzed again by the system for sentiment, and the effectiveness of the improvements after implementation is fed back to the server. 【0756】 This series of processes streamlines store operations, reduces the workload for users, and enables sustainable operations. 【0757】 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. 【0758】 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. 【0759】 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. 【0760】 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. 【0761】 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. 【0762】 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. 【0763】 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. 【0764】 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. 【0765】 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." 【0766】 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. 【0767】 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. 【0768】 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. 【0769】 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. 【0770】 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. 【0771】 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. 【0772】 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. 【0773】 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. 【0774】 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. 【0775】 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. 【0776】 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. 【0777】 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. 【0778】 The following is further disclosed regarding the embodiments described above. 【0779】 (Claim 1) 【0780】 The information processing device includes means for collecting distribution information, sales information, and inventory information. 【0781】 A means of analyzing collected information to optimize demand forecasting and inventory management, 【0782】 A means to track the status of the entire supply chain in real time and identify the risk of food waste, 【0783】 A food sharing network and means of sharing information to reduce the risk of food waste, 【0784】 A system that includes this. 【0785】 (Claim 2) 【0786】 The system according to claim 1, further comprising means for avoiding excess inventory by an information processing device analyzing consumption trends in real time and predicting the next supply quantity. 【0787】 (Claim 3) 【0788】 The system according to claim 1, further comprising an information processing device for recommending appropriate distribution arrangements based on the expiration date and consumption trends of food products, and for determining appropriate destinations for food products before they are discarded. 【0789】 "Example 1" 【0790】 (Claim 1) 【0791】 The information processing device is a means for collecting distribution data, sales data, and inventory data via terminals installed at logistics centers and sales bases. 【0792】 A means of synchronizing the collected data and storing it in a unified database, 【0793】 A method for analyzing data using AI models to predict demand trends and unsold inventory risks for each product, and to calculate the optimal inventory level. 【0794】 A means to monitor all stages of the supply chain in real time and identify products at risk of being discarded, 【0795】 A means of transferring information about food products at risk of being wasted to a shared network and notifying appropriate local recipients, 【0796】 A system that includes this. 【0797】 (Claim 2) 【0798】 The system according to claim 1, further comprising means for preventing excess inventory by analyzing consumption trends based on real-time collected data and appropriately predicting the next supply quantity. 【0799】 (Claim 3) 【0800】 The system according to claim 1, further comprising means for proposing an optimal distribution setting based on the product's expiration date and consumption patterns, and determining the recipient in order to prevent food waste. 【0801】 "Application Example 1" 【0802】 (Claim 1) 【0803】 The information processing device includes means for collecting distribution information, sales information, and inventory information. 【0804】 A means of analyzing collected information to optimize demand forecasting and inventory management, 【0805】 A means to track the status of the entire supply chain in real time and identify the risk of food waste, 【0806】 A food sharing network and means of sharing information to reduce the risk of food waste, 【0807】 A method to analyze the user's past order history and suggest the optimal inventory level, 【0808】 A means of notifying customers of discounted sales using information about food products nearing their expiration date, 【0809】 A system that includes this. 【0810】 (Claim 2) 【0811】 The system according to claim 1, further comprising means for avoiding excess inventory by an information processing device analyzing consumption trends in real time and predicting the next supply quantity. 【0812】 (Claim 3) 【0813】 The system according to claim 1, further comprising an information processing device for recommending appropriate distribution arrangements based on the expiration date and consumption trends of food products, and for determining appropriate destinations for food products before they are discarded. 【0814】 "Example 2 of combining an emotion engine" 【0815】 (Claim 1) 【0816】 The information processing device includes means for collecting distribution data, sales data, and inventory data. 【0817】 A means of analyzing collected data to optimize demand forecasting and inventory management, 【0818】 A means of acquiring user sentiment data and including it in data analysis, 【0819】 A method for integrating and analyzing emotional data, sales data, and inventory data to generate business improvement suggestions based on stress levels, 【0820】 A means by which the interface optimizes usability in response to changes in the user's emotions, 【0821】 A system that includes this. 【0822】 (Claim 2) 【0823】 The system according to claim 1 further includes means for avoiding excess inventory by analyzing consumption trends in real time and predicting the next supply quantity. 【0824】 (Claim 3) 【0825】 The system according to claim 1, further comprising means for recommending appropriate distribution arrangements based on the expiration date and consumption trends of food products, and for determining appropriate destinations for food products before they are discarded. 【0826】 "Application example 2 when combining with an emotional engine" 【0827】 (Claim 1) 【0828】 The information processing device includes means for collecting distribution information, sales information, and inventory information. 【0829】 A means of analyzing collected information to optimize demand forecasting and inventory management, 【0830】 A means to track the status of the entire supply chain in real time and identify the risk of food waste, 【0831】 A means of providing shared information to reduce the risk of food waste, 【0832】 The emotion analysis device is a means of recognizing the user's emotions and evaluating their stress level. 【0833】 A means of suggesting operational improvements based on user stress, 【0834】 A system that includes this. 【0835】 (Claim 2) 【0836】 The system according to claim 1, further comprising means for avoiding excess inventory by an information processing device analyzing consumption trends in real time and predicting the next supply quantity. 【0837】 (Claim 3) 【0838】 The system according to claim 1, further comprising an information processing device for recommending appropriate distribution arrangements based on the expiration date and consumption trends of food products, and for determining appropriate destinations for food products before they are discarded. [Explanation of symbols] 【0839】 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] The information processing device includes means for collecting distribution information, sales information, and inventory information. A means of analyzing collected information to optimize demand forecasting and inventory management, A means to track the status of the entire supply chain in real time and identify the risk of food waste, A food sharing network and means of sharing information to reduce the risk of food waste, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for avoiding excess inventory by an information processing device analyzing consumption trends in real time and predicting the next supply quantity. [Claim 3] The system according to claim 1, further comprising an information processing device for recommending appropriate distribution arrangements based on the expiration date and consumption trends of food products, and for determining appropriate destinations for food products before they are discarded.

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