system

Abstract

We provide the system. [Solution] Means for obtaining information in real time for demand forecasting, A means of optimizing inventory management by analyzing acquired information, A means of predicting the generation of food waste and suggesting appropriate distribution channels, Means for building community-based food sharing networks, A means of automatically generating reports based on activity data, A means for logistics facility managers to check information and display information on excess inventory and products nearing their expiration date, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern food supply chains, the increasing amount of food waste has become a serious social and environmental problem. This not only causes waste of food resources and environmental burden, but also economic losses. In addition, the inaccuracy of demand forecasting and the inappropriateness of inventory management have led to inefficiencies in the entire supply chain. Furthermore, due to insufficient cooperation in local communities, the sharing and recycling of surplus food are not carried out sufficiently. In response to such problems, it is required to realize a sustainable food supply chain while improving transparency and optimization.

Means for Solving the Problems

[0005] This invention provides a means for collecting data in real time for demand forecasting. Specifically, it aggregates data generated at each stage of the supply chain using an API that integrates information from sensors and terminals. It also provides a means for analyzing the collected data to optimize inventory management, and uses machine learning algorithms to predict excess inventory and stockouts. Furthermore, it includes a means for predicting the generation of food waste and suggesting appropriate distribution channels, aiming to prevent waste generation. In addition, it provides a means for building a community-based food sharing network, promoting food exchange and recycling within the region. As a result, by using a means for automatically generating reports based on activity data, the achievements of food waste reduction can be visualized, supporting the realization of a sustainable food supply chain.

[0006] "Demand forecasting" is the process of analyzing market and consumer trends to predict future consumption and demand for products and services.

[0007] "Real-time data" refers to dynamic data that is processed or analyzed immediately as soon as it is generated.

[0008] "Means of data collection" refers to methods or techniques for gathering and structuring data from various sources and providing it for analysis and processing.

[0009] "Analysis" is the process of examining collected data and deriving meaningful patterns or conclusions.

[0010] "Inventory management" refers to management activities aimed at maintaining appropriate inventory levels of products and materials, improving supply efficiency, and minimizing costs.

[0011] "Optimization" is the process of adjusting or improving a system to maximize performance or minimize costs under specific goals and constraints.

[0012] "Food waste" refers to food that is discarded despite being suitable for human consumption or use.

[0013] "Means of indicating distribution channels" refers to methods of showing pathways or options for delivering products or services to appropriate destinations or beneficiaries.

[0014] A "community-based food sharing network" is a network that establishes mechanisms for sharing food within a local community and promotes the exchange and distribution of surplus food.

[0015] "Methods for automatically generating reports" refer to technologies and methods that create analytical results and statistical information as documents based on certain conditions and algorithms, without human intervention. [Brief explanation of the drawing]

[0016] [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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the attached drawings.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system implementing this invention involves the interaction of server, terminal, and user components to optimize the food supply chain and reduce food waste. Specifically, the server collects and analyzes data in real time. The terminal functions as a data input interface in the field, enabling users to input and verify data.

[0038] The server collects real-time data from producers, distributors, and consumers via sensors and the internet. This data includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is managed in a central database and analyzed using machine learning algorithms.

[0039] Based on this analysis, the server forecasts demand and calculates the optimal inventory level. This allows for the development of strategies to prevent excess inventory and stockouts. The server also identifies food items that may be discarded in the future and suggests alternative distribution channels. For example, if there are products nearing their expiration date, the server may suggest donating them to food banks or community facilities.

[0040] The terminal is a device responsible for on-site data verification and input. Users can check inventory status through the terminal and modify data as needed. The terminal also receives demand forecasts and distribution suggestions from the server, supporting decision-making.

[0041] Users can leverage this system to take quick and appropriate action based on the generated suggestions. For example, they can respond to a suggested food-sharing initiative and encourage actions such as donating surplus food to their local community. As a result, food waste is reduced, and sustainable supply chain management is supported.

[0042] As a concrete example, in a retail store, if the server detects an excess of inventory, it notifies the user via a terminal. Upon receiving this notification, the user can take appropriate action immediately, preventing unnecessary waste and economic losses. In this way, the system effectively achieves waste reduction and supply chain optimization.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects real-time data from the entire supply chain via sensor and user input. This data includes production volume, consumption volume, and inventory information. This data is sent to the server via an API.

[0046] Step 2:

[0047] The server stores the collected data in a central database. Next, machine learning algorithms are used to analyze the data and forecast demand. This analysis identifies which products are at risk of excess inventory or stockouts.

[0048] Step 3:

[0049] The terminal provides users with demand forecasts and inventory information obtained from the server. Through the terminal, users can make business decisions based on inventory and forecasts.

[0050] Step 4:

[0051] The server identifies food items nearing their expiration date or those at risk of being discarded due to excessive demand, and determines appropriate distribution channels. For example, it might suggest donations to local food banks or community facilities.

[0052] Step 5:

[0053] The user decides to provide food to the designated distribution destination using the terminal. Relevant transaction information is then transmitted to the server via the terminal.

[0054] Step 6:

[0055] The server records all activities and automatically generates reports based on them. These reports provide users with insights into food waste reduction and supply chain performance, and suggest areas for further improvement.

[0056] (Example 1)

[0057] 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."

[0058] In modern food supply chains, increasing food waste and inefficient inventory management are major problems. This raises concerns about resource waste and negative environmental impacts. Traditional methods make real-time data collection and analysis difficult, hindering accurate demand forecasting and rapid decision-making. Therefore, new technologies are needed to effectively optimize food supply chains and reduce food waste.

[0059] 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.

[0060] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for predicting the generation of food waste and suggesting appropriate supply destinations. This enables increased efficiency throughout the supply chain and a reduction in food waste.

[0061] "Demand forecasting" is the process of predicting future consumption and supply of goods and services in order to create appropriate supply plans.

[0062] "Means of collecting information in real time" refers to technologies and processes for instantly obtaining current information, thereby enabling immediate decision-making.

[0063] "Methods for optimizing inventory control" refer to methods that prevent inventory shortages and surpluses by properly managing inventory levels, thereby achieving efficient supply chain operations.

[0064] "Means for predicting the generation of food waste and suggesting appropriate recipients" refers to a system for identifying food that is likely to be discarded in advance and supplying it to appropriate recipients who need it.

[0065] "Means of building a food sharing infrastructure using digital networks" refers to the process of creating a foundation that facilitates food sharing and donation by utilizing online platforms and digital technologies.

[0066] "Methods for automatically generating reports based on behavioral data" refers to methods for creating necessary reports based on collected behavioral information without manual intervention.

[0067] "Means for users to view and modify information via a terminal" refers to a system that allows users to view displayed information using electronic devices and update or modify data as needed.

[0068] A description of the embodiment for carrying out the invention will be provided.

[0069] This invention is a system aimed at optimizing the food supply chain and reducing food waste. The system mainly consists of three components: a server, a terminal, and a user.

[0070] The server plays a central role in data processing. It collects real-time information from food producers, distributors, and consumers via sensors and the internet. This information includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is stored in a central database and analyzed using knowledge acquisition algorithms. Based on the analysis, the server forecasts demand and creates optimal inventory control plans. It also suggests appropriate recipients for items at risk of being wasted, such as food nearing their expiration date, including food banks and community facilities.

[0071] The terminal functions as an interface between the user and the server. Users can check inventory information and modify data through the terminal. It also displays demand forecasts and supplier suggestions sent from the server, supporting decision-making.

[0072] Users can take swift and appropriate action based on the information provided. For example, they can immediately arrange for the donation of food nearing its expiration date to a food bank, thereby preventing unnecessary waste and fulfilling their social responsibility.

[0073] As a concrete example, if a server in a supermarket detects an oversupply trend, that data is immediately sent to a terminal and notified to the user. Based on this information, the user can quickly take action such as adjusting inventory or promoting food sharing.

[0074] An example of a prompt message is, "List excess inventory items from the current inventory data and suggest items that can be donated to a food bank." This prompt allows the system to efficiently coordinate food distribution and reduce waste.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects data in real time from sensors and the internet. Inputs include information such as production volume, consumption volume, expiration date, and inventory levels. The server stores this data in a central database, forming an initial dataset. This enables immediate data processing.

[0078] Step 2:

[0079] The server performs data analysis using a generative AI model. The input is the data collected in Step 1. Through data processing and calculations, it obtains an output that predicts demand across the entire supply chain. This allows for the calculation of optimal inventory levels, leading to more efficient inventory management.

[0080] Step 3:

[0081] The server identifies food items that are likely to be wasted based on the analysis results and suggests appropriate destinations. The input is the demand forecast results from step 2. Utilizing the analysis results, it generates concrete proposals for waste reduction and outputs the proposed content. This promotes the efficient use of food.

[0082] Step 4:

[0083] The terminal displays demand forecasts and supplier suggestions sent from the server to the user. The input is suggestion data from the server. The terminal displays this information in an easy-to-understand format and outputs it to support the user's decision-making. This enables the user to respond quickly.

[0084] Step 5:

[0085] Users review the information displayed on the terminal and correct the data as needed. The input is the information displayed on the terminal; users select the data to be corrected and perform the necessary actions. The output includes updated inventory information and a list of donations to food banks. This enables proper inventory management and waste reduction.

[0086] (Application Example 1)

[0087] 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."

[0088] In the food supply chain, excess inventory and shortages of food frequently occur, leading to problems such as environmental damage due to food waste and economic losses. Furthermore, food loss occurs when products nearing their expiration date do not reach their distribution channels at the appropriate time. Therefore, an efficient system is needed to solve these problems and achieve sustainable supply management.

[0089] 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.

[0090] In this invention, the server includes means for acquiring information in real time for demand forecasting, means for analyzing the acquired information to optimize inventory management, means for predicting the generation of food waste and suggesting appropriate distribution destinations, and means for logistics facility managers to check the information and display information on excess inventory and products nearing their expiration date. This makes it possible to prevent excess inventory and stockouts and to distribute products nearing their expiration date appropriately.

[0091] "Demand forecasting" is a method used to estimate market demand in advance and to adjust inventory and optimize distribution.

[0092] "Real-time information" refers to instantly acquiring and providing readily available information that is currently in progress and subject to frequent changes.

[0093] "Inventory management" refers to a series of business processes aimed at maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0094] "Food waste" is a general term for food that is discarded without being consumed, and it causes environmental burden and economic loss.

[0095] "Distribution destination" refers to the intermediate points or final destinations in the distribution process until a product reaches the consumer.

[0096] A "logistics facility" is a facility established for carrying out logistics operations such as storing, packaging, and shipping goods.

[0097] The system for implementing this invention involves the organic collaboration of a server, terminals, and users. The server acquires information in real time and analyzes the data using machine learning algorithms to optimize demand forecasting and inventory management. The acquired information includes inventory data from logistics facilities and demand fluctuation information from the market. This makes it possible to prevent excess inventory and stockouts.

[0098] The server communicates with information terminals to provide logistics facility managers with information on excess inventory and products nearing their expiration dates. This allows managers to take prompt action, contributing to the reduction of food waste. The server also supports the creation of community-based food sharing networks by suggesting appropriate distribution channels for products nearing their expiration dates.

[0099] This system allows users to access information in real time and take necessary actions quickly using information terminals such as smartphones and tablets. Furthermore, Node.js and Python are used as the backend, and the Scikit-learn library is particularly effective for machine learning.

[0100] As a concrete example, a logistics facility manager uses a smartphone app to check inventory levels and receives warnings about excess inventory sent from a server. Based on this, the manager can take action such as donating surplus food to a local food bank. Such actions can help prevent food waste.

[0101] An example of a prompt for a generated AI model might be: "Design an app that supports logistics center managers in checking inventory levels in real time, obtaining information on excess inventory and products nearing their expiration date, and providing them to food banks."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server collects data in real time from sensors and information terminals. This data includes inventory levels, consumption, and expiration dates. This data is entered into the server in JSON format and stored in a central database. This data collection allows for an accurate understanding of the current state of the supply chain.

[0105] Step 2:

[0106] The server uses the Python Scikit-learn library to analyze the collected data. Specifically, it uses machine learning algorithms to forecast demand. This analysis allows for real-time prediction of demand fluctuations and calculation of optimal inventory levels. As a result of the analysis, it identifies the possibility of excess inventory or stockouts.

[0107] Step 3:

[0108] The server notifies the administrator based on the analysis results. Information about excess inventory and products nearing their expiration date is sent from the server as a notification in JSON format. This notification is triggered when a predefined threshold is exceeded and is provided to the administrator's terminal in real time.

[0109] Step 4:

[0110] The terminal analyzes notifications received from the server and displays them to the administrator in an easy-to-understand visualization. The application on the terminal uses React Native to build the user interface and displays the notification content in an instantly editable format. This allows administrators to make quick decisions.

[0111] Step 5:

[0112] Through an interface on the terminal, users decide how to dispose of excess inventory and where to donate products nearing their expiration date. Specific actions include choosing to donate to local food banks. User actions are fed back from the terminal to the server and used to improve the overall system.

[0113] 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.

[0114] The system implementing this invention consists of a server, terminals, users, and an emotion engine. The server plays a central role in data collection, analysis, and result delivery. Specifically, it collects real-time data from sensors and terminals and analyzes it to forecast demand. Furthermore, it optimizes inventory management using machine learning algorithms and aims to reduce food waste.

[0115] The emotion engine analyzes the user's emotional state and interacts with other components of the system. By analyzing the feedback and reactions the user provides through their device, the emotion engine estimates the user's emotions. This emotional data is sent to the server and used to improve the accuracy of demand forecasts and suggest distribution channels.

[0116] In a concrete example, a food retailer's inventory management process could be enhanced by emotional data. Users provide emotional feedback about food trends and demand. For instance, information such as whether a product is popular or selling poorly is conveyed to the server through the user's emotional expressions. The server integrates this information and uses it to adjust demand and improve inventory strategies.

[0117] The terminal functions as an interface for interacting with the user. The emotion engine analyzes the data provided by the user, and the analysis results support the decision-making process throughout the supply chain. Furthermore, based on the emotion data, the server generates more appropriate food waste distribution proposals for local communities, providing guidance for users to choose their actions.

[0118] This allows the system to reduce food waste and improve the efficiency of the supply chain, while also optimizing the overall system by utilizing emotional data.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server collects data from sensors and devices in real time via APIs and stores it in a database. This data includes inventory status, consumption trends, and expiration date information for items.

[0122] Step 2:

[0123] Users use their devices to input information about sales status and inventory. This data is sent to the sentiment engine as user opinions and feedback.

[0124] Step 3:

[0125] The emotion engine analyzes user input and estimates their emotional state. For example, it generates emotional data such as whether the user is satisfied with the product or feels that sales are poor.

[0126] Step 4:

[0127] The server integrates and analyzes collected real-time data and sentiment data. It uses machine learning algorithms to forecast demand and develops an optimal inventory management strategy that reflects user sentiment data.

[0128] Step 5:

[0129] Based on the analysis results, the server identifies food items nearing their expiration date or those in excess inventory, and suggests appropriate distribution channels (e.g., food banks or local communities).

[0130] Step 6:

[0131] Users verify the provision of food to the suggested distribution channels on their terminals and send the results to the server. This process supports sustainable choices to reduce food waste.

[0132] Step 7:

[0133] The server integrates all activity data and automatically generates reports demonstrating the results of food waste reduction and supply chain efficiency improvements. These reports are provided to users to help develop further improvement strategies.

[0134] (Example 2)

[0135] 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 as the "terminal".

[0136] Uncertainty in demand forecasting within the food industry leads to excess inventory and food waste. Furthermore, inventory management that doesn't consider consumer sentiment can hinder optimal demand forecasting. There is a need for efficient and automated methods to address these challenges.

[0137] 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.

[0138] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for analyzing emotional states to improve the accuracy of demand forecasting. This enables a reduction in food waste and efficient inventory management.

[0139] "Demand forecasting" is the process of analyzing market and consumer trends to estimate the future demand for products and services.

[0140] "Real-time" refers to a state where data and information are collected, processed, and updated instantly, with virtually no time delay.

[0141] "Information gathering" refers to the activity of collecting necessary data and information from various data sources.

[0142] "Inventory control" is the process of managing the purchasing, storage, sales, and use of inventory, with the aim of maintaining optimal inventory levels.

[0143] "Emotional state" refers to the emotional state and psychological tendencies of users and consumers, encompassing a wide range of emotions, including whether they are positive or negative.

[0144] "Improving accuracy" refers to the efforts and processes to reduce errors in predictions and measurements, and to bring the results closer to the actual outcome.

[0145] "Food waste" refers to food that is discarded during the production, distribution, or consumption of food, even though it is still edible or in a marketable condition.

[0146] "Reduction" means to make something smaller in quantity, size, or degree, or to remove unnecessary parts.

[0147] A "generative AI model" refers to a structured algorithm or system that uses artificial intelligence technology to generate, analyze, and predict data.

[0148] This invention is configured as a system that improves the accuracy of demand forecasting and inventory control. The server plays a central role in collecting information in real time from observation devices and user terminals and analyzing that information. Specifically, it uses Python's Pandas library and the Scikit-learn library, which is specialized for machine learning, for data preprocessing and analysis. This enables the rapid and efficient processing of sales information, inventory status, and other data.

[0149] The device receives feedback from the user and sends it to the server as emotion data. As the user inputs ratings and opinions, the emotion engine analyzes the feedback and classifies it as positive or negative emotion. This analysis utilizes a generative AI model and employs natural language processing techniques to accurately assess the emotional state.

[0150] Users can utilize the inventory management suggestions provided by the server and choose appropriate actions. By referring to inventory replenishment and sales strategies based on demand forecasts, they can expect to reduce food waste and gain economic benefits.

[0151] As a concrete example, if a retail store implements this system, it can maintain an appropriate inventory level according to the store's demand. The system analyzes user feedback such as "This new product is well-received," reflects this as an emotional state in the server, and optimizes the timing of the next purchase. An example of a prompt is, "User feedback: 'This new product is well-received.' How does the emotion engine analyze this feedback and communicate it to the server?" Using this prompt, the process of performing emotion analysis using a generative AI model and providing feedback to the server can be understood. In this way, the system of the present invention contributes to reducing food waste and improving operational efficiency.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects information in real time from observation devices and terminals. Sales information, inventory data, and user feedback are sent as input. This data is processed using the Python Pandas library, and cleansed to output an analyzable dataset. Specifically, it removes missing and outlier values.

[0155] Step 2:

[0156] The device sends user feedback to the emotion engine. The input consists of ratings and opinions entered by the user through the device. This is analyzed by a generative AI model, which then analyzes the emotional state to output a positive or negative label. Specifically, natural language processing techniques are used to classify the emotion of the text.

[0157] Step 3:

[0158] The server integrates analyzed sentiment data with collected market data to forecast demand. It accepts labeled sentiment data and organized market data as input. It applies machine learning algorithms to predict future demand and outputs the results. Specifically, it uses a Scikit-learn regression model to calculate demand values.

[0159] Step 4:

[0160] The server optimizes inventory management based on demand forecasts. It uses forecasted demand data as input to determine inventory replenishment and sales strategies. It outputs optimal inventory levels and distribution plans using mathematical optimization algorithms. Specifically, it calculates order quantities to prevent excess inventory.

[0161] Step 5:

[0162] The user reviews inventory management suggestions provided by the server and makes appropriate business decisions. As input, they receive suggestions and reports from the server and incorporate them into their actual operations. As output, business policies and product procurement plans are determined. Specific actions include placing product orders and changing product arrangements based on the suggestions.

[0163] (Application Example 2)

[0164] 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".

[0165] In today's commercial environment, inventory management and food waste reduction are critical issues, particularly in the food distribution industry. Improper inventory management leads to increased waste and difficulty in meeting supply demand. Furthermore, delays in quickly understanding customer sentiment and responding accordingly with inventory adjustments and promotions can result in missed business opportunities. A new system is needed to comprehensively address these challenges.

[0166] 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.

[0167] In this invention, the server includes means for collecting data in real time for demand forecasting, means for detecting emotional states and adjusting inventory status and promotions, and means for automatically generating reports based on activity data. This enables optimization of inventory management and reduction of food waste.

[0168] "Demand forecasting" is an analytical method used to predict consumer preferences and market fluctuations in order to develop appropriate supply plans.

[0169] "Methods for collecting data in real time" refer to technological methods that allow for the immediate acquisition and utilization of information as it is being processed.

[0170] "Methods for optimizing inventory management" refer to techniques for maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0171] "Means for predicting the generation of food waste and suggesting appropriate distribution channels" refers to a technological process for optimizing the sales destinations or donation destinations for food nearing its expiration date, thereby reducing waste.

[0172] "Means of building community-based food sharing networks" refers to technologies that create systems in which local communities share food with each other and supply it as needed.

[0173] "Means of detecting emotional states and adjusting inventory levels and promotions" refers to technologies that analyze customer emotions and modify inventory and sales promotion strategies based on the results.

[0174] "Methods for automatically generating reports based on activity data" refers to technologies that automatically compile analysis results into reports based on accumulated data.

[0175] In the system that realizes this invention, a server plays a central role. The server first collects diverse data in real time. This includes data obtained from sensing devices placed in the store and terminals carried by users. The collected data is integrated using hardware such as sensor devices and cameras, and software such as data collection APIs.

[0176] The collected data is analyzed by a server. Here, generative AI models are used to analyze diverse data, including users' emotional states, to optimize demand forecasting and inventory management. The analysis results are processed by machine learning models on the server and output in real time.

[0177] Furthermore, the terminal functions as an interface for direct interaction with the user. Through smart glasses or other wearable devices carried by the user, the server provides the user with promotional information and adaptive inventory information. In this process, the content and timing of promotions are dynamically adjusted based on sentiment data.

[0178] As a concrete example, when a user is looking at a product that interests them in a store, promotional information for that product is displayed on their smart glasses. This feedback loop enables immediate inventory adjustments based on consumer behavior.

[0179] When utilizing generative AI models, an example prompt message is used: "Please propose a method to estimate the emotional state of a user based on their facial image input, and use this to forecast demand." This improves analysis accuracy and optimizes the user experience.

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The server collects real-time data from sensing devices placed within the store and from user terminals. This collection process encompasses visual and environmental information obtained through sensor devices and cameras, and uses a data collection API to create an integrated dataset. The input is raw data from the devices, and the output is an integrated data stream.

[0183] Step 2:

[0184] The server analyzes the collected data using a generative AI model. This analysis uses the prompt message, "Propose a method to estimate the user's emotional state using a user's facial image as input, and use it to forecast demand." The input is an integrated data stream, and the output is emotional data and demand forecast results. Here, demand trends are predicted by analyzing the user's facial expressions and behavioral patterns.

[0185] Step 3:

[0186] The server optimizes inventory and promotions based on generated sentiment data and demand forecasts. Specifically, it uses machine learning models to analyze inventory levels and derive appropriate promotional strategies. The inputs are sentiment data and demand forecasts, and the outputs are optimized inventory plans and promotion plans. The server also identifies products that need replenishment and products that can be offered at a discount.

[0187] Step 4:

[0188] The terminal displays promotional information provided by the server to the user. The information is displayed in real time through the user's smart glasses or wearable device, delivering promotions at a time that captures the user's interest. The input is the promotional plan transmitted from the server, and the output is the visual information displayed on the glasses or device. User responses are collected again as data and used for subsequent analysis.

[0189] Step 5:

[0190] Users make purchasing decisions based on the displayed promotional information. User feedback and purchase data are also collected and sent to the server. The input is the user's purchasing decision, and the output is stored as feedback data for the entire system. This feedback allows the system to obtain information for further optimization.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] The system implementing this invention involves the interaction of server, terminal, and user components to optimize the food supply chain and reduce food waste. Specifically, the server collects and analyzes data in real time. The terminal functions as a data input interface in the field, enabling users to input and verify data.

[0208] The server collects real-time data from producers, distributors, and consumers via sensors and the internet. This data includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is managed in a central database and analyzed using machine learning algorithms.

[0209] Based on this analysis, the server forecasts demand and calculates the optimal inventory level. This allows for the development of strategies to prevent excess inventory and stockouts. The server also identifies food items that may be discarded in the future and suggests alternative distribution channels. For example, if there are products nearing their expiration date, the server may suggest donating them to food banks or community facilities.

[0210] The terminal is a device responsible for on-site data verification and input. Users can check inventory status through the terminal and modify data as needed. The terminal also receives demand forecasts and distribution suggestions from the server, supporting decision-making.

[0211] Users can leverage this system to take quick and appropriate action based on the generated suggestions. For example, they can respond to a suggested food-sharing initiative and encourage actions such as donating surplus food to their local community. As a result, food waste is reduced, and sustainable supply chain management is supported.

[0212] As a concrete example, in a retail store, if the server detects an excess of inventory, it notifies the user via a terminal. Upon receiving this notification, the user can take appropriate action immediately, preventing unnecessary waste and economic losses. In this way, the system effectively achieves waste reduction and supply chain optimization.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The server collects real-time data from the entire supply chain via sensor and user input. This data includes production volume, consumption volume, and inventory information. This data is sent to the server via an API.

[0216] Step 2:

[0217] The server stores the collected data in a central database. Next, machine learning algorithms are used to analyze the data and forecast demand. This analysis identifies which products are at risk of excess inventory or stockouts.

[0218] Step 3:

[0219] The terminal provides users with demand forecasts and inventory information obtained from the server. Through the terminal, users can make business decisions based on inventory and forecasts.

[0220] Step 4:

[0221] The server identifies food items nearing their expiration date or those at risk of being discarded due to excessive demand, and determines appropriate distribution channels. For example, it might suggest donations to local food banks or community facilities.

[0222] Step 5:

[0223] The user decides to provide food to the designated distribution destination using the terminal. Relevant transaction information is then transmitted to the server via the terminal.

[0224] Step 6:

[0225] The server records all activities and automatically generates reports based on them. These reports provide users with insights into food waste reduction and supply chain performance, and suggest areas for further improvement.

[0226] (Example 1)

[0227] 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."

[0228] In modern food supply chains, increasing food waste and inefficient inventory management are major problems. This raises concerns about resource waste and negative environmental impacts. Traditional methods make real-time data collection and analysis difficult, hindering accurate demand forecasting and rapid decision-making. Therefore, new technologies are needed to effectively optimize food supply chains and reduce food waste.

[0229] 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.

[0230] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for predicting the generation of food waste and suggesting appropriate supply destinations. This enables increased efficiency throughout the supply chain and a reduction in food waste.

[0231] "Demand forecasting" is the process of predicting future consumption and supply of goods and services in order to create appropriate supply plans.

[0232] "Means of collecting information in real time" refers to technologies and processes for instantly obtaining current information, thereby enabling immediate decision-making.

[0233] "Methods for optimizing inventory control" refer to methods that prevent inventory shortages and surpluses by properly managing inventory levels, thereby achieving efficient supply chain operations.

[0234] "Means for predicting the generation of food waste and suggesting appropriate recipients" refers to a system for identifying food that is likely to be discarded in advance and supplying it to appropriate recipients who need it.

[0235] "Means of building a food sharing infrastructure using digital networks" refers to the process of creating a foundation that facilitates food sharing and donation by utilizing online platforms and digital technologies.

[0236] "Methods for automatically generating reports based on behavioral data" refers to methods for creating necessary reports based on collected behavioral information without manual intervention.

[0237] "Means for users to view and modify information via a terminal" refers to a system that allows users to view displayed information using electronic devices and update or modify data as needed.

[0238] A description of the embodiment for carrying out the invention will be provided.

[0239] This invention is a system aimed at optimizing the food supply chain and reducing food waste. The system mainly consists of three components: a server, a terminal, and a user.

[0240] The server plays a central role in data processing. It collects real-time information from food producers, distributors, and consumers via sensors and the internet. This information includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is stored in a central database and analyzed using knowledge acquisition algorithms. Based on the analysis, the server forecasts demand and creates optimal inventory control plans. It also suggests appropriate recipients for items at risk of being wasted, such as food nearing their expiration date, including food banks and community facilities.

[0241] The terminal functions as an interface between the user and the server. Users can check inventory information and modify data through the terminal. It also displays demand forecasts and supplier suggestions sent from the server, supporting decision-making.

[0242] Users can take swift and appropriate action based on the information provided. For example, they can immediately arrange for the donation of food nearing its expiration date to a food bank, thereby preventing unnecessary waste and fulfilling their social responsibility.

[0243] As a concrete example, if a server in a supermarket detects an oversupply trend, that data is immediately sent to a terminal and notified to the user. Based on this information, the user can quickly take action such as adjusting inventory or promoting food sharing.

[0244] An example of a prompt message is, "List excess inventory items from the current inventory data and suggest items that can be donated to a food bank." This prompt allows the system to efficiently coordinate food distribution and reduce waste.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The server collects data in real time from sensors and the internet. Inputs include information such as production volume, consumption volume, expiration date, and inventory levels. The server stores this data in a central database, forming an initial dataset. This enables immediate data processing.

[0248] Step 2:

[0249] The server performs data analysis using a generative AI model. The input is the data collected in Step 1. Through data processing and calculations, it obtains an output that predicts demand across the entire supply chain. This allows for the calculation of optimal inventory levels, leading to more efficient inventory management.

[0250] Step 3:

[0251] The server identifies food items that are likely to be wasted based on the analysis results and suggests appropriate destinations. The input is the demand forecast results from step 2. Utilizing the analysis results, it generates concrete proposals for waste reduction and outputs the proposed content. This promotes the efficient use of food.

[0252] Step 4:

[0253] The terminal displays demand forecasts and supplier suggestions sent from the server to the user. The input is suggestion data from the server. The terminal displays this information in an easy-to-understand format and outputs it to support the user's decision-making. This enables the user to respond quickly.

[0254] Step 5:

[0255] Users review the information displayed on the terminal and correct the data as needed. The input is the information displayed on the terminal; users select the data to be corrected and perform the necessary actions. The output includes updated inventory information and a list of donations to food banks. This enables proper inventory management and waste reduction.

[0256] (Application Example 1)

[0257] 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."

[0258] In the food supply chain, excess inventory and shortages of food frequently occur, leading to problems such as environmental damage due to food waste and economic losses. Furthermore, food loss occurs when products nearing their expiration date do not reach their distribution channels at the appropriate time. Therefore, an efficient system is needed to solve these problems and achieve sustainable supply management.

[0259] 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.

[0260] In this invention, the server includes means for acquiring information in real time for demand forecasting, means for analyzing the acquired information to optimize inventory management, means for predicting the generation of food waste and suggesting appropriate distribution destinations, and means for logistics facility managers to check the information and display information on excess inventory and products nearing their expiration date. This makes it possible to prevent excess inventory and stockouts and to distribute products nearing their expiration date appropriately.

[0261] "Demand forecasting" is a method used to estimate market demand in advance and to adjust inventory and optimize distribution.

[0262] "Real-time information" refers to instantly acquiring and providing readily available information that is currently in progress and subject to frequent changes.

[0263] "Inventory management" refers to a series of business processes aimed at maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0264] "Food waste" is a general term for food that is discarded without being consumed, and it causes environmental burden and economic loss.

[0265] "Distribution destination" refers to the intermediate points or final destinations in the distribution process until a product reaches the consumer.

[0266] A "logistics facility" is a facility established for carrying out logistics operations such as storing, packaging, and shipping goods.

[0267] The system for implementing this invention involves the organic collaboration of a server, terminals, and users. The server acquires information in real time and analyzes the data using machine learning algorithms to optimize demand forecasting and inventory management. The acquired information includes inventory data from logistics facilities and demand fluctuation information from the market. This makes it possible to prevent excess inventory and stockouts.

[0268] The server communicates with information terminals to provide logistics facility managers with information on excess inventory and products nearing their expiration dates. This allows managers to take prompt action, contributing to the reduction of food waste. The server also supports the creation of community-based food sharing networks by suggesting appropriate distribution channels for products nearing their expiration dates.

[0269] This system allows users to access information in real time and take necessary actions quickly using information terminals such as smartphones and tablets. Furthermore, Node.js and Python are used as the backend, and the Scikit-learn library is particularly effective for machine learning.

[0270] As a concrete example, a logistics facility manager uses a smartphone app to check inventory levels and receives warnings about excess inventory sent from a server. Based on this, the manager can take action such as donating surplus food to a local food bank. Such actions can help prevent food waste.

[0271] An example of a prompt for a generated AI model might be: "Design an app that supports logistics center managers in checking inventory levels in real time, obtaining information on excess inventory and products nearing their expiration date, and providing them to food banks."

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The server collects data in real time from sensors and information terminals. This data includes inventory levels, consumption, and expiration dates. This data is entered into the server in JSON format and stored in a central database. This data collection allows for an accurate understanding of the current state of the supply chain.

[0275] Step 2:

[0276] The server uses the Python Scikit-learn library to analyze the collected data. Specifically, it uses machine learning algorithms to forecast demand. This analysis allows for real-time prediction of demand fluctuations and calculation of optimal inventory levels. As a result of the analysis, it identifies the possibility of excess inventory or stockouts.

[0277] Step 3:

[0278] The server notifies the administrator based on the analysis results. Information about excess inventory and products nearing their expiration date is sent from the server as a notification in JSON format. This notification is triggered when a predefined threshold is exceeded and is provided to the administrator's terminal in real time.

[0279] Step 4:

[0280] The terminal analyzes notifications received from the server and displays them to the administrator in an easy-to-understand visualization. The application on the terminal uses React Native to build the user interface and displays the notification content in an instantly editable format. This allows administrators to make quick decisions.

[0281] Step 5:

[0282] Through an interface on the terminal, users decide how to dispose of excess inventory and where to donate products nearing their expiration date. Specific actions include choosing to donate to local food banks. User actions are fed back from the terminal to the server and used to improve the overall system.

[0283] 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.

[0284] The system for implementing this invention consists of a server, terminals, users, and an emotion engine. The server plays a central role in data collection, analysis, and result provision. Specifically, it collects real-time data from sensors and terminals, analyzes it to predict demand, and further optimizes inventory management using machine learning algorithms to reduce food waste.

[0285] The emotion engine analyzes the user's emotional state and collaborates with other components of the system. By analyzing the feedback and reactions provided by the user through the terminal, the emotion engine estimates the user's emotions. This emotional data is transmitted to the server and reflected in improving the accuracy of demand prediction and proposing distribution destinations.

[0286] In a specific example, in a certain food retail store, the inventory management process may be enhanced by emotional data. The user provides emotional feedback on food trends and demand. For example, information such as a certain product being popular or having poor sales is transmitted from the user's emotional expression to the server. The server integrates this information and reflects it in adjusting demand and inventory strategies.

[0287] The terminal functions as an interface for interacting with the user. The emotion engine analyzes the data provided by the user, and the analysis results assist the decision-making process throughout the supply chain. Furthermore, based on the emotional data, the server generates more appropriate food waste distribution proposals for the local community and provides guidelines for the user to select actions.

[0288] Thereby, the system realizes the reduction of food waste and the efficiency improvement of the supply chain, and at the same time, aims for overall optimization by leveraging emotional data.

[0289] The following describes the processing flow.

[0290] Step 1:

[0291] The server collects data from sensors and devices in real time via APIs and stores it in a database. This data includes inventory status, consumption trends, and expiration date information for items.

[0292] Step 2:

[0293] Users use their devices to input information about sales status and inventory. This data is sent to the sentiment engine as user opinions and feedback.

[0294] Step 3:

[0295] The emotion engine analyzes user input and estimates their emotional state. For example, it generates emotional data such as whether the user is satisfied with the product or feels that sales are poor.

[0296] Step 4:

[0297] The server integrates and analyzes collected real-time data and sentiment data. It uses machine learning algorithms to forecast demand and develops an optimal inventory management strategy that reflects user sentiment data.

[0298] Step 5:

[0299] Based on the analysis results, the server identifies food items nearing their expiration date or those in excess inventory, and suggests appropriate distribution channels (e.g., food banks or local communities).

[0300] Step 6:

[0301] Users verify the provision of food to the suggested distribution channels on their terminals and send the results to the server. This process supports sustainable choices to reduce food waste.

[0302] Step 7:

[0303] The server integrates all activity data and automatically generates a report showing the results of food waste reduction and supply chain efficiency improvement. This report is provided to users and contributes to the development of strategies that can be further improved.

[0304] (Example 2)

[0305] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] The uncertainty in demand forecasting in the food industry is a cause of excess inventory and food waste. Also, inventory management that does not consider the emotional state of consumers may prevent optimal demand forecasting. Furthermore, an efficient and automatic method for solving these problems is required.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0308] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for analyzing the emotional state to improve the accuracy of demand forecasting. Thereby, reduction of food waste and efficient inventory management become possible.

[0309] "Demand forecasting" means analyzing market and consumer trends and estimating the future demand volume of products and services.

[0310] "Real time" refers to the state where data and information are collected, processed, and updated immediately, indicating a state with almost no time delay.

[0311] "Information collection" refers to the activity of collecting necessary data and information from various data sources.

[0312] "Inventory control" is an operation aimed at managing the processes of purchasing, storing, selling, and using inventory and maintaining an optimal inventory level.

[0313] "Emotional state" refers to the emotional state and psychological tendencies of users and consumers, encompassing a wide range of emotions, including whether they are positive or negative.

[0314] "Improving accuracy" refers to the efforts and processes to reduce errors in predictions and measurements, and to bring the results closer to the actual outcome.

[0315] "Food waste" refers to food that is discarded during the production, distribution, or consumption of food, even though it is still edible or in a marketable condition.

[0316] "Reduction" means to make something smaller in quantity, size, or degree, or to remove unnecessary parts.

[0317] A "generative AI model" refers to a structured algorithm or system that uses artificial intelligence technology to generate, analyze, and predict data.

[0318] This invention is configured as a system that improves the accuracy of demand forecasting and inventory control. The server plays a central role in collecting information in real time from observation devices and user terminals and analyzing that information. Specifically, it uses Python's Pandas library and the Scikit-learn library, which is specialized for machine learning, for data preprocessing and analysis. This enables the rapid and efficient processing of sales information, inventory status, and other data.

[0319] The device receives feedback from the user and sends it to the server as emotion data. As the user inputs ratings and opinions, the emotion engine analyzes the feedback and classifies it as positive or negative emotion. This analysis utilizes a generative AI model and employs natural language processing techniques to accurately assess the emotional state.

[0320] Users can utilize the inventory management suggestions provided by the server and choose appropriate actions. By referring to inventory replenishment and sales strategies based on demand forecasts, they can expect to reduce food waste and gain economic benefits.

[0321] As a concrete example, if a retail store implements this system, it can maintain an appropriate inventory level according to the store's demand. The system analyzes user feedback such as "This new product is well-received," reflects this as an emotional state in the server, and optimizes the timing of the next purchase. An example of a prompt is, "User feedback: 'This new product is well-received.' How does the emotion engine analyze this feedback and communicate it to the server?" Using this prompt, the process of performing emotion analysis using a generative AI model and providing feedback to the server can be understood. In this way, the system of the present invention contributes to reducing food waste and improving operational efficiency.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] The server collects information in real time from observation devices and terminals. Sales information, inventory data, and user feedback are sent as input. This data is processed using the Python Pandas library, and cleansed to output an analyzable dataset. Specifically, it removes missing and outlier values.

[0325] Step 2:

[0326] The device sends user feedback to the emotion engine. The input consists of ratings and opinions entered by the user through the device. This is analyzed by a generative AI model, which then analyzes the emotional state to output a positive or negative label. Specifically, natural language processing techniques are used to classify the emotion of the text.

[0327] Step 3:

[0328] The server integrates analyzed sentiment data with collected market data to forecast demand. It accepts labeled sentiment data and organized market data as input. It applies machine learning algorithms to predict future demand and outputs the results. Specifically, it uses a Scikit-learn regression model to calculate demand values.

[0329] Step 4:

[0330] The server optimizes inventory management based on demand forecasts. It uses forecasted demand data as input to determine inventory replenishment and sales strategies. It outputs optimal inventory levels and distribution plans using mathematical optimization algorithms. Specifically, it calculates order quantities to prevent excess inventory.

[0331] Step 5:

[0332] The user reviews inventory management suggestions provided by the server and makes appropriate business decisions. As input, they receive suggestions and reports from the server and incorporate them into their actual operations. As output, business policies and product procurement plans are determined. Specific actions include placing product orders and changing product arrangements based on the suggestions.

[0333] (Application Example 2)

[0334] 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."

[0335] In today's commercial environment, inventory management and food waste reduction are critical issues, particularly in the food distribution industry. Improper inventory management leads to increased waste and difficulty in meeting supply demand. Furthermore, delays in quickly understanding customer sentiment and responding accordingly with inventory adjustments and promotions can result in missed business opportunities. A new system is needed to comprehensively address these challenges.

[0336] 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.

[0337] In this invention, the server includes means for collecting data in real time for demand forecasting, means for detecting emotional states and adjusting inventory status and promotions, and means for automatically generating reports based on activity data. This enables optimization of inventory management and reduction of food waste.

[0338] "Demand forecasting" is an analytical method used to predict consumer preferences and market fluctuations in order to develop appropriate supply plans.

[0339] "Methods for collecting data in real time" refer to technological methods that allow for the immediate acquisition and utilization of information as it is being processed.

[0340] "Methods for optimizing inventory management" refer to techniques for maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0341] "Means for predicting the generation of food waste and suggesting appropriate distribution channels" refers to a technological process for optimizing the sales destinations or donation destinations for food nearing its expiration date, thereby reducing waste.

[0342] "Means of building community-based food sharing networks" refers to technologies that create systems in which local communities share food with each other and supply it as needed.

[0343] "Means of detecting emotional states and adjusting inventory levels and promotions" refers to technologies that analyze customer emotions and modify inventory and sales promotion strategies based on the results.

[0344] "Methods for automatically generating reports based on activity data" refers to technologies that automatically compile analysis results into reports based on accumulated data.

[0345] In the system that realizes this invention, a server plays a central role. The server first collects diverse data in real time. This includes data obtained from sensing devices placed in the store and terminals carried by users. The collected data is integrated using hardware such as sensor devices and cameras, and software such as data collection APIs.

[0346] The collected data is analyzed by a server. Here, generative AI models are used to analyze diverse data, including users' emotional states, to optimize demand forecasting and inventory management. The analysis results are processed by machine learning models on the server and output in real time.

[0347] Furthermore, the terminal functions as an interface for direct interaction with the user. Through smart glasses or other wearable devices carried by the user, the server provides the user with promotional information and adaptive inventory information. In this process, the content and timing of promotions are dynamically adjusted based on sentiment data.

[0348] As a concrete example, when a user is looking at a product that interests them in a store, promotional information for that product is displayed on their smart glasses. This feedback loop enables immediate inventory adjustments based on consumer behavior.

[0349] When utilizing generative AI models, an example prompt message is used: "Please propose a method to estimate the emotional state of a user based on their facial image input, and use this to forecast demand." This improves analysis accuracy and optimizes the user experience.

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The server collects real-time data from sensing devices placed within the store and from user terminals. This collection process encompasses visual and environmental information obtained through sensor devices and cameras, and uses a data collection API to create an integrated dataset. The input is raw data from the devices, and the output is an integrated data stream.

[0353] Step 2:

[0354] The server analyzes the collected data using a generative AI model. This analysis uses the prompt message, "Propose a method to estimate the user's emotional state using a user's facial image as input, and use it to forecast demand." The input is an integrated data stream, and the output is emotional data and demand forecast results. Here, demand trends are predicted by analyzing the user's facial expressions and behavioral patterns.

[0355] Step 3:

[0356] The server optimizes inventory and promotions based on generated sentiment data and demand forecasts. Specifically, it uses machine learning models to analyze inventory levels and derive appropriate promotional strategies. The inputs are sentiment data and demand forecasts, and the outputs are optimized inventory plans and promotion plans. The server also identifies products that need replenishment and products that can be offered at a discount.

[0357] Step 4:

[0358] The terminal displays promotional information provided by the server to the user. The information is displayed in real time through the user's smart glasses or wearable device, delivering promotions at a time that captures the user's interest. The input is the promotional plan transmitted from the server, and the output is the visual information displayed on the glasses or device. User responses are collected again as data and used for subsequent analysis.

[0359] Step 5:

[0360] Users make purchasing decisions based on the displayed promotional information. User feedback and purchase data are also collected and sent to the server. The input is the user's purchasing decision, and the output is stored as feedback data for the entire system. This feedback allows the system to obtain information for further optimization.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] [Third Embodiment]

[0365] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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".

[0377] The system implementing this invention involves the interaction of server, terminal, and user components to optimize the food supply chain and reduce food waste. Specifically, the server collects and analyzes data in real time. The terminal functions as a data input interface in the field, enabling users to input and verify data.

[0378] The server collects real-time data from producers, distributors, and consumers via sensors and the internet. This data includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is managed in a central database and analyzed using machine learning algorithms.

[0379] Based on this analysis, the server forecasts demand and calculates the optimal inventory level. This allows for the development of strategies to prevent excess inventory and stockouts. The server also identifies food items that may be discarded in the future and suggests alternative distribution channels. For example, if there are products nearing their expiration date, the server may suggest donating them to food banks or community facilities.

[0380] The terminal is a device responsible for on-site data verification and input. Users can check inventory status through the terminal and modify data as needed. The terminal also receives demand forecasts and distribution suggestions from the server, supporting decision-making.

[0381] Users can leverage this system to take quick and appropriate action based on the generated suggestions. For example, they can respond to a suggested food-sharing initiative and encourage actions such as donating surplus food to their local community. As a result, food waste is reduced, and sustainable supply chain management is supported.

[0382] As a concrete example, in a retail store, if the server detects an excess of inventory, it notifies the user via a terminal. Upon receiving this notification, the user can take appropriate action immediately, preventing unnecessary waste and economic losses. In this way, the system effectively achieves waste reduction and supply chain optimization.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The server collects real-time data from the entire supply chain via sensor and user input. This data includes production volume, consumption volume, and inventory information. This data is sent to the server via an API.

[0386] Step 2:

[0387] The server stores the collected data in a central database. Next, machine learning algorithms are used to analyze the data and forecast demand. This analysis identifies which products are at risk of excess inventory or stockouts.

[0388] Step 3:

[0389] The terminal provides users with demand forecasts and inventory information obtained from the server. Through the terminal, users can make business decisions based on inventory and forecasts.

[0390] Step 4:

[0391] The server identifies food items nearing their expiration date or those at risk of being discarded due to excessive demand, and determines appropriate distribution channels. For example, it might suggest donations to local food banks or community facilities.

[0392] Step 5:

[0393] The user decides to provide food to the designated distribution destination using the terminal. Relevant transaction information is then transmitted to the server via the terminal.

[0394] Step 6:

[0395] The server records all activities and automatically generates reports based on them. These reports provide users with insights into food waste reduction and supply chain performance, and suggest areas for further improvement.

[0396] (Example 1)

[0397] 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."

[0398] In modern food supply chains, increasing food waste and inefficient inventory management are major problems. This raises concerns about resource waste and negative environmental impacts. Traditional methods make real-time data collection and analysis difficult, hindering accurate demand forecasting and rapid decision-making. Therefore, new technologies are needed to effectively optimize food supply chains and reduce food waste.

[0399] 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.

[0400] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for predicting the generation of food waste and suggesting appropriate supply destinations. This enables increased efficiency throughout the supply chain and a reduction in food waste.

[0401] "Demand forecasting" is the process of predicting future consumption and supply of goods and services in order to create appropriate supply plans.

[0402] "Means of collecting information in real time" refers to technologies and processes for instantly obtaining current information, thereby enabling immediate decision-making.

[0403] "Methods for optimizing inventory control" refer to methods that prevent inventory shortages and surpluses by properly managing inventory levels, thereby achieving efficient supply chain operations.

[0404] "Means for predicting the generation of food waste and suggesting appropriate recipients" refers to a system for identifying food that is likely to be discarded in advance and supplying it to appropriate recipients who need it.

[0405] "Means of building a food sharing infrastructure using digital networks" refers to the process of creating a foundation that facilitates food sharing and donation by utilizing online platforms and digital technologies.

[0406] "Methods for automatically generating reports based on behavioral data" refers to methods for creating necessary reports based on collected behavioral information without manual intervention.

[0407] "Means for users to view and modify information via a terminal" refers to a system that allows users to view displayed information using electronic devices and update or modify data as needed.

[0408] A description of the embodiment for carrying out the invention will be provided.

[0409] This invention is a system aimed at optimizing the food supply chain and reducing food waste. The system mainly consists of three components: a server, a terminal, and a user.

[0410] The server plays a central role in data processing. It collects real-time information from food producers, distributors, and consumers via sensors and the internet. This information includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is stored in a central database and analyzed using knowledge acquisition algorithms. Based on the analysis, the server forecasts demand and creates optimal inventory control plans. It also suggests appropriate recipients for items at risk of being wasted, such as food nearing their expiration date, including food banks and community facilities.

[0411] The terminal functions as an interface between the user and the server. Users can check inventory information and modify data through the terminal. It also displays demand forecasts and supplier suggestions sent from the server, supporting decision-making.

[0412] Users can take swift and appropriate action based on the information provided. For example, they can immediately arrange for the donation of food nearing its expiration date to a food bank, thereby preventing unnecessary waste and fulfilling their social responsibility.

[0413] As a concrete example, if a server in a supermarket detects an oversupply trend, that data is immediately sent to a terminal and notified to the user. Based on this information, the user can quickly take action such as adjusting inventory or promoting food sharing.

[0414] An example of a prompt message is, "List excess inventory items from the current inventory data and suggest items that can be donated to a food bank." This prompt allows the system to efficiently coordinate food distribution and reduce waste.

[0415] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0416] Step 1:

[0417] The server collects data in real time from sensors and the internet. Inputs include information such as production volume, consumption volume, expiration date, and inventory levels. The server stores this data in a central database, forming an initial dataset. This enables immediate data processing.

[0418] Step 2:

[0419] The server performs data analysis using a generative AI model. The input is the data collected in Step 1. Through data processing and calculations, it obtains an output that predicts demand across the entire supply chain. This allows for the calculation of optimal inventory levels, leading to more efficient inventory management.

[0420] Step 3:

[0421] The server identifies food items that are likely to be wasted based on the analysis results and suggests appropriate destinations. The input is the demand forecast results from step 2. Utilizing the analysis results, it generates concrete proposals for waste reduction and outputs the proposed content. This promotes the efficient use of food.

[0422] Step 4:

[0423] The terminal displays demand forecasts and supplier suggestions sent from the server to the user. The input is suggestion data from the server. The terminal displays this information in an easy-to-understand format and outputs it to support the user's decision-making. This enables the user to respond quickly.

[0424] Step 5:

[0425] Users review the information displayed on the terminal and correct the data as needed. The input is the information displayed on the terminal; users select the data to be corrected and perform the necessary actions. The output includes updated inventory information and a list of donations to food banks. This enables proper inventory management and waste reduction.

[0426] (Application Example 1)

[0427] 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."

[0428] In the food supply chain, excess inventory and shortages of food frequently occur, leading to problems such as environmental damage due to food waste and economic losses. Furthermore, food loss occurs when products nearing their expiration date do not reach their distribution channels at the appropriate time. Therefore, an efficient system is needed to solve these problems and achieve sustainable supply management.

[0429] 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.

[0430] In this invention, the server includes means for acquiring information in real time for demand forecasting, means for analyzing the acquired information to optimize inventory management, means for predicting the generation of food waste and suggesting appropriate distribution destinations, and means for logistics facility managers to check the information and display information on excess inventory and products nearing their expiration date. This makes it possible to prevent excess inventory and stockouts and to distribute products nearing their expiration date appropriately.

[0431] "Demand forecasting" is a method used to estimate market demand in advance and to adjust inventory and optimize distribution.

[0432] "Real-time information" refers to instantly acquiring and providing readily available information that is currently in progress and subject to frequent changes.

[0433] "Inventory management" refers to a series of business processes aimed at maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0434] "Food waste" is a general term for food that is discarded without being consumed, and it causes environmental burden and economic loss.

[0435] "Distribution destination" refers to the intermediate points or final destinations in the distribution process until a product reaches the consumer.

[0436] A "logistics facility" is a facility established for carrying out logistics operations such as storing, packaging, and shipping goods.

[0437] The system for implementing this invention involves the organic collaboration of a server, terminals, and users. The server acquires information in real time and analyzes the data using machine learning algorithms to optimize demand forecasting and inventory management. The acquired information includes inventory data from logistics facilities and demand fluctuation information from the market. This makes it possible to prevent excess inventory and stockouts.

[0438] The server communicates with information terminals to provide logistics facility managers with information on excess inventory and products nearing their expiration dates. This allows managers to take prompt action, contributing to the reduction of food waste. The server also supports the creation of community-based food sharing networks by suggesting appropriate distribution channels for products nearing their expiration dates.

[0439] This system allows users to access information in real time and take necessary actions quickly using information terminals such as smartphones and tablets. Furthermore, Node.js and Python are used as the backend, and the Scikit-learn library is particularly effective for machine learning.

[0440] As a concrete example, a logistics facility manager uses a smartphone app to check inventory levels and receives warnings about excess inventory sent from a server. Based on this, the manager can take action such as donating surplus food to a local food bank. Such actions can help prevent food waste.

[0441] An example of a prompt for a generated AI model might be: "Design an app that supports logistics center managers in checking inventory levels in real time, obtaining information on excess inventory and products nearing their expiration date, and providing them to food banks."

[0442] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0443] Step 1:

[0444] The server collects data in real time from sensors and information terminals. This data includes inventory levels, consumption, and expiration dates. This data is entered into the server in JSON format and stored in a central database. This data collection allows for an accurate understanding of the current state of the supply chain.

[0445] Step 2:

[0446] The server uses the Python Scikit-learn library to analyze the collected data. Specifically, it uses machine learning algorithms to forecast demand. This analysis allows for real-time prediction of demand fluctuations and calculation of optimal inventory levels. As a result of the analysis, it identifies the possibility of excess inventory or stockouts.

[0447] Step 3:

[0448] The server notifies the administrator based on the analysis results. Information about excess inventory and products nearing their expiration date is sent from the server as a notification in JSON format. This notification is triggered when a predefined threshold is exceeded and is provided to the administrator's terminal in real time.

[0449] Step 4:

[0450] The terminal analyzes notifications received from the server and displays them to the administrator in an easy-to-understand visualization. The application on the terminal uses React Native to build the user interface and displays the notification content in an instantly editable format. This allows administrators to make quick decisions.

[0451] Step 5:

[0452] Through an interface on the terminal, users decide how to dispose of excess inventory and where to donate products nearing their expiration date. Specific actions include choosing to donate to local food banks. User actions are fed back from the terminal to the server and used to improve the overall system.

[0453] 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.

[0454] The system implementing this invention consists of a server, terminals, users, and an emotion engine. The server plays a central role in data collection, analysis, and result delivery. Specifically, it collects real-time data from sensors and terminals and analyzes it to forecast demand. Furthermore, it optimizes inventory management using machine learning algorithms and aims to reduce food waste.

[0455] The emotion engine analyzes the user's emotional state and interacts with other components of the system. By analyzing the feedback and reactions the user provides through their device, the emotion engine estimates the user's emotions. This emotional data is sent to the server and used to improve the accuracy of demand forecasts and suggest distribution channels.

[0456] In a concrete example, a food retailer's inventory management process could be enhanced by emotional data. Users provide emotional feedback about food trends and demand. For instance, information such as whether a product is popular or selling poorly is conveyed to the server through the user's emotional expressions. The server integrates this information and uses it to adjust demand and improve inventory strategies.

[0457] The terminal functions as an interface for interacting with the user. The emotion engine analyzes the data provided by the user, and the analysis results support the decision-making process throughout the supply chain. Furthermore, based on the emotion data, the server generates more appropriate food waste distribution proposals for local communities, providing guidance for users to choose their actions.

[0458] This allows the system to reduce food waste and improve the efficiency of the supply chain, while also optimizing the overall system by utilizing emotional data.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The server collects data from sensors and devices in real time via APIs and stores it in a database. This data includes inventory status, consumption trends, and expiration date information for items.

[0462] Step 2:

[0463] Users use their devices to input information about sales status and inventory. This data is sent to the sentiment engine as user opinions and feedback.

[0464] Step 3:

[0465] The emotion engine analyzes user input and estimates their emotional state. For example, it generates emotional data such as whether the user is satisfied with the product or feels that sales are poor.

[0466] Step 4:

[0467] The server integrates and analyzes collected real-time data and sentiment data. It uses machine learning algorithms to forecast demand and develops an optimal inventory management strategy that reflects user sentiment data.

[0468] Step 5:

[0469] Based on the analysis results, the server identifies food items nearing their expiration date or those in excess inventory, and suggests appropriate distribution channels (e.g., food banks or local communities).

[0470] Step 6:

[0471] Users verify the provision of food to the suggested distribution channels on their terminals and send the results to the server. This process supports sustainable choices to reduce food waste.

[0472] Step 7:

[0473] The server integrates all activity data and automatically generates reports demonstrating the results of food waste reduction and supply chain efficiency improvements. These reports are provided to users to help develop further improvement strategies.

[0474] (Example 2)

[0475] 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."

[0476] Uncertainty in demand forecasting within the food industry leads to excess inventory and food waste. Furthermore, inventory management that doesn't consider consumer sentiment can hinder optimal demand forecasting. There is a need for efficient and automated methods to address these challenges.

[0477] 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.

[0478] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for analyzing emotional states to improve the accuracy of demand forecasting. This enables a reduction in food waste and efficient inventory management.

[0479] "Demand forecasting" is the process of analyzing market and consumer trends to estimate the future demand for products and services.

[0480] "Real-time" refers to a state where data and information are collected, processed, and updated instantly, with virtually no time delay.

[0481] "Information gathering" refers to the activity of collecting necessary data and information from various data sources.

[0482] "Inventory control" is the process of managing the purchasing, storage, sales, and use of inventory, with the aim of maintaining optimal inventory levels.

[0483] "Emotional state" refers to the emotional state and psychological tendencies of users and consumers, encompassing a wide range of emotions, including whether they are positive or negative.

[0484] "Improving accuracy" refers to the efforts and processes to reduce errors in predictions and measurements, and to bring the results closer to the actual outcome.

[0485] "Food waste" refers to food that is discarded during the production, distribution, or consumption of food, even though it is still edible or in a marketable condition.

[0486] "Reduction" means to make something smaller in quantity, size, or degree, or to remove unnecessary parts.

[0487] A "generative AI model" refers to a structured algorithm or system that uses artificial intelligence technology to generate, analyze, and predict data.

[0488] This invention is configured as a system that improves the accuracy of demand forecasting and inventory control. The server plays a central role in collecting information in real time from observation devices and user terminals and analyzing that information. Specifically, it uses Python's Pandas library and the Scikit-learn library, which is specialized for machine learning, for data preprocessing and analysis. This enables the rapid and efficient processing of sales information, inventory status, and other data.

[0489] The device receives feedback from the user and sends it to the server as emotion data. As the user inputs ratings and opinions, the emotion engine analyzes the feedback and classifies it as positive or negative emotion. This analysis utilizes a generative AI model and employs natural language processing techniques to accurately assess the emotional state.

[0490] Users can utilize the inventory management suggestions provided by the server and choose appropriate actions. By referring to inventory replenishment and sales strategies based on demand forecasts, they can expect to reduce food waste and gain economic benefits.

[0491] As a concrete example, if a retail store implements this system, it can maintain an appropriate inventory level according to the store's demand. The system analyzes user feedback such as "This new product is well-received," reflects this as an emotional state in the server, and optimizes the timing of the next purchase. An example of a prompt is, "User feedback: 'This new product is well-received.' How does the emotion engine analyze this feedback and communicate it to the server?" Using this prompt, the process of performing emotion analysis using a generative AI model and providing feedback to the server can be understood. In this way, the system of the present invention contributes to reducing food waste and improving operational efficiency.

[0492] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0493] Step 1:

[0494] The server collects information in real time from observation devices and terminals. Sales information, inventory data, and user feedback are sent as input. This data is processed using the Python Pandas library, and cleansed to output an analyzable dataset. Specifically, it removes missing and outlier values.

[0495] Step 2:

[0496] The device sends user feedback to the emotion engine. The input consists of ratings and opinions entered by the user through the device. This is analyzed by a generative AI model, which then analyzes the emotional state to output a positive or negative label. Specifically, natural language processing techniques are used to classify the emotion of the text.

[0497] Step 3:

[0498] The server integrates analyzed sentiment data with collected market data to forecast demand. It accepts labeled sentiment data and organized market data as input. It applies machine learning algorithms to predict future demand and outputs the results. Specifically, it uses a Scikit-learn regression model to calculate demand values.

[0499] Step 4:

[0500] The server optimizes inventory management based on demand forecasts. It uses forecasted demand data as input to determine inventory replenishment and sales strategies. It outputs optimal inventory levels and distribution plans using mathematical optimization algorithms. Specifically, it calculates order quantities to prevent excess inventory.

[0501] Step 5:

[0502] The user reviews inventory management suggestions provided by the server and makes appropriate business decisions. As input, they receive suggestions and reports from the server and incorporate them into their actual operations. As output, business policies and product procurement plans are determined. Specific actions include placing product orders and changing product arrangements based on the suggestions.

[0503] (Application Example 2)

[0504] 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."

[0505] In today's commercial environment, inventory management and food waste reduction are critical issues, particularly in the food distribution industry. Improper inventory management leads to increased waste and difficulty in meeting supply demand. Furthermore, delays in quickly understanding customer sentiment and responding accordingly with inventory adjustments and promotions can result in missed business opportunities. A new system is needed to comprehensively address these challenges.

[0506] 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.

[0507] In this invention, the server includes means for collecting data in real time for demand forecasting, means for detecting emotional states and adjusting inventory status and promotions, and means for automatically generating reports based on activity data. This enables optimization of inventory management and reduction of food waste.

[0508] "Demand forecasting" is an analytical method used to predict consumer preferences and market fluctuations in order to develop appropriate supply plans.

[0509] "Methods for collecting data in real time" refer to technological methods that allow for the immediate acquisition and utilization of information as it is being processed.

[0510] "Methods for optimizing inventory management" refer to techniques for maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0511] "Means for predicting the generation of food waste and suggesting appropriate distribution channels" refers to a technological process for optimizing the sales destinations or donation destinations for food nearing its expiration date, thereby reducing waste.

[0512] "Means of building community-based food sharing networks" refers to technologies that create systems in which local communities share food with each other and supply it as needed.

[0513] "Means of detecting emotional states and adjusting inventory levels and promotions" refers to technologies that analyze customer emotions and modify inventory and sales promotion strategies based on the results.

[0514] "Methods for automatically generating reports based on activity data" refers to technologies that automatically compile analysis results into reports based on accumulated data.

[0515] In the system that realizes this invention, a server plays a central role. The server first collects diverse data in real time. This includes data obtained from sensing devices placed in the store and terminals carried by users. The collected data is integrated using hardware such as sensor devices and cameras, and software such as data collection APIs.

[0516] The collected data is analyzed by a server. Here, generative AI models are used to analyze diverse data, including users' emotional states, to optimize demand forecasting and inventory management. The analysis results are processed by machine learning models on the server and output in real time.

[0517] Furthermore, the terminal functions as an interface for direct interaction with the user. Through smart glasses or other wearable devices carried by the user, the server provides the user with promotional information and adaptive inventory information. In this process, the content and timing of promotions are dynamically adjusted based on sentiment data.

[0518] As a concrete example, when a user is looking at a product that interests them in a store, promotional information for that product is displayed on their smart glasses. This feedback loop enables immediate inventory adjustments based on consumer behavior.

[0519] When utilizing generative AI models, an example prompt message is used: "Please propose a method to estimate the emotional state of a user based on their facial image input, and use this to forecast demand." This improves analysis accuracy and optimizes the user experience.

[0520] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0521] Step 1:

[0522] The server collects real-time data from sensing devices placed within the store and from user terminals. This collection process encompasses visual and environmental information obtained through sensor devices and cameras, and uses a data collection API to create an integrated dataset. The input is raw data from the devices, and the output is an integrated data stream.

[0523] Step 2:

[0524] The server analyzes the collected data using a generative AI model. This analysis uses the prompt message, "Propose a method to estimate the user's emotional state using a user's facial image as input, and use it to forecast demand." The input is an integrated data stream, and the output is emotional data and demand forecast results. Here, demand trends are predicted by analyzing the user's facial expressions and behavioral patterns.

[0525] Step 3:

[0526] The server optimizes inventory and promotions based on generated sentiment data and demand forecasts. Specifically, it uses machine learning models to analyze inventory levels and derive appropriate promotional strategies. The inputs are sentiment data and demand forecasts, and the outputs are optimized inventory plans and promotion plans. The server also identifies products that need replenishment and products that can be offered at a discount.

[0527] Step 4:

[0528] The terminal displays promotional information provided by the server to the user. The information is displayed in real time through the user's smart glasses or wearable device, delivering promotions at a time that captures the user's interest. The input is the promotional plan transmitted from the server, and the output is the visual information displayed on the glasses or device. User responses are collected again as data and used for subsequent analysis.

[0529] Step 5:

[0530] Users make purchasing decisions based on the displayed promotional information. User feedback and purchase data are also collected and sent to the server. The input is the user's purchasing decision, and the output is stored as feedback data for the entire system. This feedback allows the system to obtain information for further optimization.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] [Fourth Embodiment]

[0535] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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).

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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".

[0548] The system implementing this invention involves the interaction of server, terminal, and user components to optimize the food supply chain and reduce food waste. Specifically, the server collects and analyzes data in real time. The terminal functions as a data input interface in the field, enabling users to input and verify data.

[0549] The server collects real-time data from producers, distributors, and consumers via sensors and the internet. This data includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is managed in a central database and analyzed using machine learning algorithms.

[0550] Based on this analysis, the server forecasts demand and calculates the optimal inventory level. This allows for the development of strategies to prevent excess inventory and stockouts. The server also identifies food items that may be discarded in the future and suggests alternative distribution channels. For example, if there are products nearing their expiration date, the server may suggest donating them to food banks or community facilities.

[0551] The terminal is a device responsible for on-site data verification and input. Users can check inventory status through the terminal and modify data as needed. The terminal also receives demand forecasts and distribution suggestions from the server, supporting decision-making.

[0552] Users can leverage this system to take quick and appropriate action based on the generated suggestions. For example, they can respond to a suggested food-sharing initiative and encourage actions such as donating surplus food to their local community. As a result, food waste is reduced, and sustainable supply chain management is supported.

[0553] As a concrete example, in a retail store, if the server detects an excess of inventory, it notifies the user via a terminal. Upon receiving this notification, the user can take appropriate action immediately, preventing unnecessary waste and economic losses. In this way, the system effectively achieves waste reduction and supply chain optimization.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The server collects real-time data from the entire supply chain via sensor and user input. This data includes production volume, consumption volume, and inventory information. This data is sent to the server via an API.

[0557] Step 2:

[0558] The server stores the collected data in a central database. Next, machine learning algorithms are used to analyze the data and forecast demand. This analysis identifies which products are at risk of excess inventory or stockouts.

[0559] Step 3:

[0560] The terminal provides users with demand forecasts and inventory information obtained from the server. Through the terminal, users can make business decisions based on inventory and forecasts.

[0561] Step 4:

[0562] The server identifies food items nearing their expiration date or those at risk of being discarded due to excessive demand, and determines appropriate distribution channels. For example, it might suggest donations to local food banks or community facilities.

[0563] Step 5:

[0564] The user decides to provide food to the designated distribution destination using the terminal. Relevant transaction information is then transmitted to the server via the terminal.

[0565] Step 6:

[0566] The server records all activities and automatically generates reports based on them. These reports provide users with insights into food waste reduction and supply chain performance, and suggest areas for further improvement.

[0567] (Example 1)

[0568] 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".

[0569] In modern food supply chains, increasing food waste and inefficient inventory management are major problems. This raises concerns about resource waste and negative environmental impacts. Traditional methods make real-time data collection and analysis difficult, hindering accurate demand forecasting and rapid decision-making. Therefore, new technologies are needed to effectively optimize food supply chains and reduce food waste.

[0570] 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.

[0571] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for predicting the generation of food waste and suggesting appropriate supply destinations. This enables increased efficiency throughout the supply chain and a reduction in food waste.

[0572] "Demand forecasting" is the process of predicting future consumption and supply of goods and services in order to create appropriate supply plans.

[0573] "Means of collecting information in real time" refers to technologies and processes for instantly obtaining current information, thereby enabling immediate decision-making.

[0574] "Methods for optimizing inventory control" refer to methods that prevent inventory shortages and surpluses by properly managing inventory levels, thereby achieving efficient supply chain operations.

[0575] "Means for predicting the generation of food waste and suggesting appropriate recipients" refers to a system for identifying food that is likely to be discarded in advance and supplying it to appropriate recipients who need it.

[0576] "Means of building a food sharing infrastructure using digital networks" refers to the process of creating a foundation that facilitates food sharing and donation by utilizing online platforms and digital technologies.

[0577] "Methods for automatically generating reports based on behavioral data" refers to methods for creating necessary reports based on collected behavioral information without manual intervention.

[0578] "Means for users to view and modify information via a terminal" refers to a system that allows users to view displayed information using electronic devices and update or modify data as needed.

[0579] A description of the embodiment for carrying out the invention will be provided.

[0580] This invention is a system aimed at optimizing the food supply chain and reducing food waste. The system mainly consists of three components: a server, a terminal, and a user.

[0581] The server plays a central role in data processing. It collects real-time information from food producers, distributors, and consumers via sensors and the internet. This information includes production volume, consumption volume, expiration dates, and inventory levels. The collected data is stored in a central database and analyzed using knowledge acquisition algorithms. Based on the analysis, the server forecasts demand and creates optimal inventory control plans. It also suggests appropriate recipients for items at risk of being wasted, such as food nearing their expiration date, including food banks and community facilities.

[0582] The terminal functions as an interface between the user and the server. Users can check inventory information and modify data through the terminal. It also displays demand forecasts and supplier suggestions sent from the server, supporting decision-making.

[0583] Users can take swift and appropriate action based on the information provided. For example, they can immediately arrange for the donation of food nearing its expiration date to a food bank, thereby preventing unnecessary waste and fulfilling their social responsibility.

[0584] As a concrete example, if a server in a supermarket detects an oversupply trend, that data is immediately sent to a terminal and notified to the user. Based on this information, the user can quickly take action such as adjusting inventory or promoting food sharing.

[0585] An example of a prompt message is, "List excess inventory items from the current inventory data and suggest items that can be donated to a food bank." This prompt allows the system to efficiently coordinate food distribution and reduce waste.

[0586] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0587] Step 1:

[0588] The server collects data in real time from sensors and the internet. Inputs include information such as production volume, consumption volume, expiration date, and inventory levels. The server stores this data in a central database, forming an initial dataset. This enables immediate data processing.

[0589] Step 2:

[0590] The server performs data analysis using a generative AI model. The input is the data collected in Step 1. Through data processing and calculations, it obtains an output that predicts demand across the entire supply chain. This allows for the calculation of optimal inventory levels, leading to more efficient inventory management.

[0591] Step 3:

[0592] The server identifies food items that are likely to be wasted based on the analysis results and suggests appropriate destinations. The input is the demand forecast results from step 2. Utilizing the analysis results, it generates concrete proposals for waste reduction and outputs the proposed content. This promotes the efficient use of food.

[0593] Step 4:

[0594] The terminal displays demand forecasts and supplier suggestions sent from the server to the user. The input is suggestion data from the server. The terminal displays this information in an easy-to-understand format and outputs it to support the user's decision-making. This enables the user to respond quickly.

[0595] Step 5:

[0596] Users review the information displayed on the terminal and correct the data as needed. The input is the information displayed on the terminal; users select the data to be corrected and perform the necessary actions. The output includes updated inventory information and a list of donations to food banks. This enables proper inventory management and waste reduction.

[0597] (Application Example 1)

[0598] 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".

[0599] In the food supply chain, excess inventory and shortages of food frequently occur, leading to problems such as environmental damage due to food waste and economic losses. Furthermore, food loss occurs when products nearing their expiration date do not reach their distribution channels at the appropriate time. Therefore, an efficient system is needed to solve these problems and achieve sustainable supply management.

[0600] 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.

[0601] In this invention, the server includes means for acquiring information in real time for demand forecasting, means for analyzing the acquired information to optimize inventory management, means for predicting the generation of food waste and suggesting appropriate distribution destinations, and means for logistics facility managers to check the information and display information on excess inventory and products nearing their expiration date. This makes it possible to prevent excess inventory and stockouts and to distribute products nearing their expiration date appropriately.

[0602] "Demand forecasting" is a method used to estimate market demand in advance and to adjust inventory and optimize distribution.

[0603] "Real-time information" refers to instantly acquiring and providing readily available information that is currently in progress and subject to frequent changes.

[0604] "Inventory management" refers to a series of business processes aimed at maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0605] "Food waste" is a general term for food that is discarded without being consumed, and it causes environmental burden and economic loss.

[0606] "Distribution destination" refers to the intermediate points or final destinations in the distribution process until a product reaches the consumer.

[0607] A "logistics facility" is a facility established for carrying out logistics operations such as storing, packaging, and shipping goods.

[0608] The system for implementing this invention involves the organic collaboration of a server, terminals, and users. The server acquires information in real time and analyzes the data using machine learning algorithms to optimize demand forecasting and inventory management. The acquired information includes inventory data from logistics facilities and demand fluctuation information from the market. This makes it possible to prevent excess inventory and stockouts.

[0609] The server communicates with information terminals to provide logistics facility managers with information on excess inventory and products nearing their expiration dates. This allows managers to take prompt action, contributing to the reduction of food waste. The server also supports the creation of community-based food sharing networks by suggesting appropriate distribution channels for products nearing their expiration dates.

[0610] This system allows users to access information in real time and take necessary actions quickly using information terminals such as smartphones and tablets. Furthermore, Node.js and Python are used as the backend, and the Scikit-learn library is particularly effective for machine learning.

[0611] As a concrete example, a logistics facility manager uses a smartphone app to check inventory levels and receives warnings about excess inventory sent from a server. Based on this, the manager can take action such as donating surplus food to a local food bank. Such actions can help prevent food waste.

[0612] An example of a prompt for a generated AI model might be: "Design an app that supports logistics center managers in checking inventory levels in real time, obtaining information on excess inventory and products nearing their expiration date, and providing them to food banks."

[0613] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0614] Step 1:

[0615] The server collects data in real time from sensors and information terminals. This data includes inventory levels, consumption, and expiration dates. This data is entered into the server in JSON format and stored in a central database. This data collection allows for an accurate understanding of the current state of the supply chain.

[0616] Step 2:

[0617] The server uses the Python Scikit-learn library to analyze the collected data. Specifically, it uses machine learning algorithms to forecast demand. This analysis allows for real-time prediction of demand fluctuations and calculation of optimal inventory levels. As a result of the analysis, it identifies the possibility of excess inventory or stockouts.

[0618] Step 3:

[0619] The server notifies the administrator based on the analysis results. Information about excess inventory and products nearing their expiration date is sent from the server as a notification in JSON format. This notification is triggered when a predefined threshold is exceeded and is provided to the administrator's terminal in real time.

[0620] Step 4:

[0621] The terminal analyzes notifications received from the server and displays them to the administrator in an easy-to-understand visualization. The application on the terminal uses React Native to build the user interface and displays the notification content in an instantly editable format. This allows administrators to make quick decisions.

[0622] Step 5:

[0623] Through an interface on the terminal, users decide how to dispose of excess inventory and where to donate products nearing their expiration date. Specific actions include choosing to donate to local food banks. User actions are fed back from the terminal to the server and used to improve the overall system.

[0624] 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.

[0625] The system implementing this invention consists of a server, terminals, users, and an emotion engine. The server plays a central role in data collection, analysis, and result delivery. Specifically, it collects real-time data from sensors and terminals and analyzes it to forecast demand. Furthermore, it optimizes inventory management using machine learning algorithms and aims to reduce food waste.

[0626] The emotion engine analyzes the user's emotional state and interacts with other components of the system. By analyzing the feedback and reactions the user provides through their device, the emotion engine estimates the user's emotions. This emotional data is sent to the server and used to improve the accuracy of demand forecasts and suggest distribution channels.

[0627] In a concrete example, a food retailer's inventory management process could be enhanced by emotional data. Users provide emotional feedback about food trends and demand. For instance, information such as whether a product is popular or selling poorly is conveyed to the server through the user's emotional expressions. The server integrates this information and uses it to adjust demand and improve inventory strategies.

[0628] The terminal functions as an interface for interacting with the user. The emotion engine analyzes the data provided by the user, and the analysis results support the decision-making process throughout the supply chain. Furthermore, based on the emotion data, the server generates more appropriate food waste distribution proposals for local communities, providing guidance for users to choose their actions.

[0629] This allows the system to reduce food waste and improve the efficiency of the supply chain, while also optimizing the overall system by utilizing emotional data.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The server collects data from sensors and devices in real time via APIs and stores it in a database. This data includes inventory status, consumption trends, and expiration date information for items.

[0633] Step 2:

[0634] Users use their devices to input information about sales status and inventory. This data is sent to the sentiment engine as user opinions and feedback.

[0635] Step 3:

[0636] The emotion engine analyzes user input and estimates their emotional state. For example, it generates emotional data such as whether the user is satisfied with the product or feels that sales are poor.

[0637] Step 4:

[0638] The server integrates and analyzes collected real-time data and sentiment data. It uses machine learning algorithms to forecast demand and develops an optimal inventory management strategy that reflects user sentiment data.

[0639] Step 5:

[0640] Based on the analysis results, the server identifies food items nearing their expiration date or those in excess inventory, and suggests appropriate distribution channels (e.g., food banks or local communities).

[0641] Step 6:

[0642] Users verify the provision of food to the suggested distribution channels on their terminals and send the results to the server. This process supports sustainable choices to reduce food waste.

[0643] Step 7:

[0644] The server integrates all activity data and automatically generates reports demonstrating the results of food waste reduction and supply chain efficiency improvements. These reports are provided to users to help develop further improvement strategies.

[0645] (Example 2)

[0646] 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".

[0647] Uncertainty in demand forecasting within the food industry leads to excess inventory and food waste. Furthermore, inventory management that doesn't consider consumer sentiment can hinder optimal demand forecasting. There is a need for efficient and automated methods to address these challenges.

[0648] 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.

[0649] In this invention, the server includes means for collecting information in real time for demand forecasting, means for analyzing the collected information to optimize inventory control, and means for analyzing emotional states to improve the accuracy of demand forecasting. This enables a reduction in food waste and efficient inventory management.

[0650] "Demand forecasting" is the process of analyzing market and consumer trends to estimate the future demand for products and services.

[0651] "Real-time" refers to a state where data and information are collected, processed, and updated instantly, with virtually no time delay.

[0652] "Information gathering" refers to the activity of collecting necessary data and information from various data sources.

[0653] "Inventory control" is the process of managing the purchasing, storage, sales, and use of inventory, with the aim of maintaining optimal inventory levels.

[0654] "Emotional state" refers to the emotional state and psychological tendencies of users and consumers, encompassing a wide range of emotions, including whether they are positive or negative.

[0655] "Improving accuracy" refers to the efforts and processes to reduce errors in predictions and measurements, and to bring the results closer to the actual outcome.

[0656] "Food waste" refers to food that is discarded during the production, distribution, or consumption of food, even though it is still edible or in a marketable condition.

[0657] "Reduction" means to make something smaller in quantity, size, or degree, or to remove unnecessary parts.

[0658] A "generative AI model" refers to a structured algorithm or system that uses artificial intelligence technology to generate, analyze, and predict data.

[0659] This invention is configured as a system that improves the accuracy of demand forecasting and inventory control. The server plays a central role in collecting information in real time from observation devices and user terminals and analyzing that information. Specifically, it uses Python's Pandas library and the Scikit-learn library, which is specialized for machine learning, for data preprocessing and analysis. This enables the rapid and efficient processing of sales information, inventory status, and other data.

[0660] The device receives feedback from the user and sends it to the server as emotion data. As the user inputs ratings and opinions, the emotion engine analyzes the feedback and classifies it as positive or negative emotion. This analysis utilizes a generative AI model and employs natural language processing techniques to accurately assess the emotional state.

[0661] Users can utilize the inventory management suggestions provided by the server and choose appropriate actions. By referring to inventory replenishment and sales strategies based on demand forecasts, they can expect to reduce food waste and gain economic benefits.

[0662] As a concrete example, if a retail store implements this system, it can maintain an appropriate inventory level according to the store's demand. The system analyzes user feedback such as "This new product is well-received," reflects this as an emotional state in the server, and optimizes the timing of the next purchase. An example of a prompt is, "User feedback: 'This new product is well-received.' How does the emotion engine analyze this feedback and communicate it to the server?" Using this prompt, the process of performing emotion analysis using a generative AI model and providing feedback to the server can be understood. In this way, the system of the present invention contributes to reducing food waste and improving operational efficiency.

[0663] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0664] Step 1:

[0665] The server collects information in real time from observation devices and terminals. Sales information, inventory data, and user feedback are sent as input. This data is processed using the Python Pandas library, and cleansed to output an analyzable dataset. Specifically, it removes missing and outlier values.

[0666] Step 2:

[0667] The device sends user feedback to the emotion engine. The input consists of ratings and opinions entered by the user through the device. This is analyzed by a generative AI model, which then analyzes the emotional state to output a positive or negative label. Specifically, natural language processing techniques are used to classify the emotion of the text.

[0668] Step 3:

[0669] The server integrates analyzed sentiment data with collected market data to forecast demand. It accepts labeled sentiment data and organized market data as input. It applies machine learning algorithms to predict future demand and outputs the results. Specifically, it uses a Scikit-learn regression model to calculate demand values.

[0670] Step 4:

[0671] The server optimizes inventory management based on demand forecasts. It uses forecasted demand data as input to determine inventory replenishment and sales strategies. It outputs optimal inventory levels and distribution plans using mathematical optimization algorithms. Specifically, it calculates order quantities to prevent excess inventory.

[0672] Step 5:

[0673] The user reviews inventory management suggestions provided by the server and makes appropriate business decisions. As input, they receive suggestions and reports from the server and incorporate them into their actual operations. As output, business policies and product procurement plans are determined. Specific actions include placing product orders and changing product arrangements based on the suggestions.

[0674] (Application Example 2)

[0675] 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".

[0676] In today's commercial environment, inventory management and food waste reduction are critical issues, particularly in the food distribution industry. Improper inventory management leads to increased waste and difficulty in meeting supply demand. Furthermore, delays in quickly understanding customer sentiment and responding accordingly with inventory adjustments and promotions can result in missed business opportunities. A new system is needed to comprehensively address these challenges.

[0677] 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.

[0678] In this invention, the server includes means for collecting data in real time for demand forecasting, means for detecting emotional states and adjusting inventory status and promotions, and means for automatically generating reports based on activity data. This enables optimization of inventory management and reduction of food waste.

[0679] "Demand forecasting" is an analytical method used to predict consumer preferences and market fluctuations in order to develop appropriate supply plans.

[0680] "Methods for collecting data in real time" refer to technological methods that allow for the immediate acquisition and utilization of information as it is being processed.

[0681] "Methods for optimizing inventory management" refer to techniques for maintaining optimal inventory levels and preventing excess inventory and stockouts.

[0682] "Means for predicting the generation of food waste and suggesting appropriate distribution channels" refers to a technological process for optimizing the sales destinations or donation destinations for food nearing its expiration date, thereby reducing waste.

[0683] "Means of building community-based food sharing networks" refers to technologies that create systems in which local communities share food with each other and supply it as needed.

[0684] "Means of detecting emotional states and adjusting inventory levels and promotions" refers to technologies that analyze customer emotions and modify inventory and sales promotion strategies based on the results.

[0685] "Methods for automatically generating reports based on activity data" refers to technologies that automatically compile analysis results into reports based on accumulated data.

[0686] In the system that realizes this invention, a server plays a central role. The server first collects diverse data in real time. This includes data obtained from sensing devices placed in the store and terminals carried by users. The collected data is integrated using hardware such as sensor devices and cameras, and software such as data collection APIs.

[0687] The collected data is analyzed by a server. Here, generative AI models are used to analyze diverse data, including users' emotional states, to optimize demand forecasting and inventory management. The analysis results are processed by machine learning models on the server and output in real time.

[0688] Furthermore, the terminal functions as an interface for direct interaction with the user. Through smart glasses or other wearable devices carried by the user, the server provides the user with promotional information and adaptive inventory information. In this process, the content and timing of promotions are dynamically adjusted based on sentiment data.

[0689] As a concrete example, when a user is looking at a product that interests them in a store, promotional information for that product is displayed on their smart glasses. This feedback loop enables immediate inventory adjustments based on consumer behavior.

[0690] When utilizing generative AI models, an example prompt message is used: "Please propose a method to estimate the emotional state of a user based on their facial image input, and use this to forecast demand." This improves analysis accuracy and optimizes the user experience.

[0691] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0692] Step 1:

[0693] The server collects real-time data from sensing devices placed within the store and from user terminals. This collection process encompasses visual and environmental information obtained through sensor devices and cameras, and uses a data collection API to create an integrated dataset. The input is raw data from the devices, and the output is an integrated data stream.

[0694] Step 2:

[0695] The server analyzes the collected data using a generative AI model. This analysis uses the prompt message, "Propose a method to estimate the user's emotional state using a user's facial image as input, and use it to forecast demand." The input is an integrated data stream, and the output is emotional data and demand forecast results. Here, demand trends are predicted by analyzing the user's facial expressions and behavioral patterns.

[0696] Step 3:

[0697] The server optimizes inventory and promotions based on generated sentiment data and demand forecasts. Specifically, it uses machine learning models to analyze inventory levels and derive appropriate promotional strategies. The inputs are sentiment data and demand forecasts, and the outputs are optimized inventory plans and promotion plans. The server also identifies products that need replenishment and products that can be offered at a discount.

[0698] Step 4:

[0699] The terminal displays promotional information provided by the server to the user. The information is displayed in real time through the user's smart glasses or wearable device, delivering promotions at a time that captures the user's interest. The input is the promotional plan transmitted from the server, and the output is the visual information displayed on the glasses or device. User responses are collected again as data and used for subsequent analysis.

[0700] Step 5:

[0701] Users make purchasing decisions based on the displayed promotional information. User feedback and purchase data are also collected and sent to the server. The input is the user's purchasing decision, and the output is stored as feedback data for the entire system. This feedback allows the system to obtain information for further optimization.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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."

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0723] The following is further disclosed regarding the embodiments described above.

[0724] (Claim 1)

[0725] A means of collecting data in real time for demand forecasting,

[0726] A means of optimizing inventory management by analyzing collected data,

[0727] A means of predicting the generation of food waste and suggesting appropriate distribution channels,

[0728] Means for building community-based food sharing networks,

[0729] A means of automatically generating reports based on activity data,

[0730] A system that includes this.

[0731] (Claim 2)

[0732] The system according to claim 1, characterized in that the real-time data collection means uses an API that integrates information from sensors and terminals.

[0733] (Claim 3)

[0734] The system according to claim 1, characterized in that the means for predicting the generation of food waste and indicating distribution destinations uses a machine learning algorithm.

[0735] "Example 1"

[0736] (Claim 1)

[0737] Means for collecting information in real time for demand forecasting,

[0738] A means for optimizing inventory control by analyzing the collected information,

[0739] A means of predicting the generation of food waste and suggesting appropriate supply destinations,

[0740] Means for building a food sharing infrastructure using digital networks,

[0741] A means of automatically generating reports based on behavioral data,

[0742] A means for users to view and modify information through their devices,

[0743] A system that includes this.

[0744] (Claim 2)

[0745] The system according to claim 1, characterized in that the real-time information collection means uses a technique for integrating information from an information acquisition device and an input / output device.

[0746] (Claim 3)

[0747] The system according to claim 1, characterized in that the means for predicting the generation of food waste and suggesting supply destinations uses a knowledge acquisition algorithm.

[0748] "Application Example 1"

[0749] (Claim 1)

[0750] Means for obtaining information in real time for demand forecasting,

[0751] A means of optimizing inventory management by analyzing acquired information,

[0752] A means of predicting the generation of food waste and suggesting appropriate distribution channels,

[0753] Means for building community-based food sharing networks,

[0754] A means of automatically generating reports based on activity data,

[0755] A means for logistics facility managers to check information and display information on excess inventory and products nearing their expiration date,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, characterized in that the means for acquiring real-time information uses an API that integrates information from sensors and information terminals.

[0759] (Claim 3)

[0760] The system according to claim 1, characterized in that the means for predicting the generation of food waste and indicating distribution destinations uses a machine learning algorithm.

[0761] "Example 2 of combining an emotion engine"

[0762] (Claim 1)

[0763] Means for collecting information in real time for demand forecasting,

[0764] A means for optimizing inventory control by analyzing the collected information,

[0765] A means of improving the accuracy of demand forecasting by analyzing emotional states,

[0766] A means of proposing a method for providing food waste reduction based on predicted demand,

[0767] A means of analyzing feedback using a generative AI model,

[0768] A means of automatically generating reports based on activity information,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, characterized in that the real-time information gathering means uses an interface that integrates information from observation devices and terminals.

[0772] (Claim 3)

[0773] The system according to claim 1, characterized in that the means for analyzing the emotional state classifies the emotional data using natural language processing techniques.

[0774] "Application example 2 when combining with an emotional engine"

[0775] (Claim 1)

[0776] A means of collecting data in real time for demand forecasting,

[0777] A means of optimizing inventory management by analyzing collected data,

[0778] A means of predicting the generation of food waste and suggesting appropriate distribution channels,

[0779] Means for building community-based food sharing networks,

[0780] A means of detecting emotional states and adjusting inventory levels and promotions accordingly,

[0781] A means of automatically generating reports based on activity data,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, characterized in that the real-time data collection means uses a data linkage method that integrates information from sensing devices and terminals.

[0785] (Claim 3)

[0786] The system according to claim 1, characterized in that the means for predicting the generation of food waste and indicating distribution destinations uses machine learning technology. [Explanation of Symbols]

[0787] 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

1. Means for obtaining information in real time for demand forecasting, A means of optimizing inventory management by analyzing acquired information, A means of predicting the generation of food waste and suggesting appropriate distribution channels, Means for building community-based food sharing networks, A means of automatically generating reports based on activity data, A means for logistics facility managers to check information and display information on excess inventory and products nearing their expiration date, A system that includes this.

2. The system according to claim 1, characterized in that the means for acquiring real-time information uses an API that integrates information from sensors and information terminals.

3. The system according to claim 1, characterized in that the means for predicting the generation of food waste and indicating distribution destinations uses a machine learning algorithm.

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