system

Abstract

To provide a system for reducing food loss.SOLUTION: This system is provided with a means for collecting food data, a means for predicting a demand based on the data, a means for optimizing a stock level based on the obtained prediction result, a means for using an RFID tag or a sensor for acquiring use-by date data, and a means for managing the use-by date data. A system comprising: means for notifying of impending food products; means for re-evaluating food appearance criteria; means for optimizing efficient logistics and distribution; means for providing educational programs to suppliers, retailers, and consumers on the importance and resolution of food losses; and means for contributing excess inventory and impending food products to food banks and destinations.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, food waste is a serious problem that hinders the realization of a sustainable society. Food waste occurs due to multiple factors, including inadequate demand forecasting and inventory management, excessive consumer standards for appearance, and logistics issues. Therefore, there is a need for technology that can effectively reduce food waste by providing efficient systems and facilitating information sharing among relevant stakeholders. [Means for solving the problem]

[0005] The present invention provides a system for reducing food waste, which specifically includes the following means:

[0006] 1. Providing a means to collect food data and consolidate sales data and demand fluctuation patterns across the industry.

[0007] 2. Provide a means for forecasting demand based on the data, and optimize inventory levels based on the forecast results.

[0008] 3. Providing a means to use RFID tags or sensors to capture expiration date data, manage said expiration date data, and provide notifications about foods approaching their expiration date.

[0009] 4. Provide a means to reassess food appearance standards and reduce waste of aesthetically unsightly food that is of good quality.

[0010] 5. Providing a means for efficient logistics and delivery optimization, minimizing damage to food during handling and transportation.

[0011] 6. Providing avenues for educational programs and awareness-raising activities for suppliers, retailers and consumers on the importance of and solutions to food waste.

[0012] 7. Promote sustainable food use by providing a means for people to donate excess or near-expiry food to food banks and charitable organizations.

[0013] By implementing these measures, the food waste problem can be effectively solved.

[0014] "Food data" refers to information related to food sales information, demand fluctuation patterns, expiration dates, quality status, etc.

[0015] "Demand forecasting" refers to predicting future food demand based on past sales data and market trends.

[0016] "Inventory level" is an indicator that indicates the appropriate amount of inventory for a specific period of time.

[0017] An "RFID tag" is an identification device that uses radio frequencies to read and manage information.

[0018] A "sensor" is a device that measures a specific physical or chemical property, converts it into data, and outputs it.

[0019] "Best before date data" is information about the expiration date of a food product.

[0020] "Appearance standards" refer to the quality standards for the appearance of food.

[0021] "Logistics" is the process that includes the series of transportation, storage, and handling of a product from production to consumption.

[0022] "Delivery" is the act of transporting a product from one location to another.

[0023] A "food bank" is an organization that collects surplus food and distributes it to people and organizations in need.

[0024] A "donation" is the act of providing goods or funds for a specific purpose. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0026] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0027] First, the terms used in the following description will be explained.

[0028] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0029] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0030] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0031] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0032] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 1, a 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.

[0036] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0037] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0038] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0039] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0040] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0043] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0044] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0045] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0046] The present invention relates to a system for solving the food waste problem, and specific embodiments thereof are described below. The system collects food data, performs demand forecasting, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations.

[0047] 1. Demand forecasting and inventory management

[0048] Program processing overview

[0049] server

[0050] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer. For example, the server analyzes sales data from the past year to predict demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[0051] 2. Introduction of expiration date management

[0052] Program processing overview

[0053] Terminal

[0054] The terminal uses RFID tags and sensors to obtain expiration date data for each food item as it arrives. The obtained data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods whose expiration dates are approaching and sends a warning message. For example, the terminal scans newly arrived products, obtains their expiration dates, and then sends them to the server. The server generates a list of products with expiration dates within one week and notifies the retailer, allowing them to set priorities for sales or donations.

[0055] 3. Review of food appearance standards

[0056] Program processing overview

[0057] User (supplier)

[0058] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have slight appearance issues. These identification results are registered on a server. The server stores this information in a database and provides it to higher-ranking retailers. This reduces the amount of food that is discarded based solely on appearance. For example, a supplier can register blemished but safe apples on a server, and the server can notify retailers of this information, preventing the apples from being wasted.

[0059] 4. Improved logistics and delivery

[0060] Program processing overview

[0061] server

[0062] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions. As a specific example, the server analyzes temperature data during transport and discovers that food quality is maintained within a specific temperature range. It then notifies this information to the logistics company, allowing it to set optimal transport conditions.

[0063] 5. Education and Awareness

[0064] Program processing overview

[0065] Terminal

[0066] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal. After the user watches the program, they send their understanding and impressions to the server via a feedback form. The server analyzes the collected feedback data and improves the educational programs. As a specific example, the terminal plays an educational video about food waste to consumers, who then submit their feedback, and the server analyzes that data to generate better content.

[0067] 6. Building food banks and donation networks

[0068] Program processing overview

[0069] User (retailer)

[0070] Retailers register excess inventory and food items approaching their expiration date on a server. The server analyzes this information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to receive the food. As a specific example, retailers register food items approaching their expiration date on a server, and the server notifies food banks of this data, which then accepts the food, thereby reducing food waste.

[0071] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[0072] The processing flow will be explained below.

[0073] 1. Demand forecasting and inventory management

[0074] Step 1: Data collection

[0075] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[0076] Step 2: Data Preprocessing

[0077] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[0078] Step 3: Demand forecast

[0079] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[0080] Step 4: Optimize inventory levels

[0081] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[0082] Step 5: Notification

[0083] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[0084] 2. Introduction of expiration date management

[0085] Step 1: Obtaining expiration date data

[0086] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[0087] Step 2: Send data

[0088] Terminal: Sends the acquired expiration date data to the server.

[0089] Step 3: Update the database

[0090] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[0091] Step 4: Notification

[0092] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[0093] 3. Review of food appearance standards

[0094] Step 1: Reassess the criteria

[0095] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[0096] Step 2: Registration

[0097] User (provider): Registers the identified results on the server.

[0098] Step 3: Data Management

[0099] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[0100] Step 4: Notification

[0101] Server: Sends proposals for relaxing standards to the retailer's terminal.

[0102] 4. Improved logistics and delivery

[0103] Step 1: Data collection

[0104] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[0105] Step 2: Send data

[0106] Terminal: Sends acquired data to the server in real time.

[0107] Step 3: Data analysis

[0108] Server: Analyzes the received data and identifies factors that affect quality.

[0109] Step 4: Improvement proposals

[0110] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[0111] 5. Education and Awareness

[0112] Step 1: Content Delivery

[0113] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[0114] Step 2: Listening and Feedback

[0115] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[0116] Step 3: Feedback analysis

[0117] Server: Analyzes collected feedback and improves educational programs.

[0118] 6. Building food banks and donation networks

[0119] Step 1: Register excess inventory

[0120] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[0121] Step 2: Find the best donation destination

[0122] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[0123] Step 3: Donation Notification

[0124] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[0125] Step 4: Donate

[0126] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[0127] The above are the specific processing steps for carrying out the invention, which will realize a system that effectively solves the problem of food waste.

[0128] Example 1

[0129] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] Food waste has become a serious problem in modern society. Large amounts of food are wasted at each stage of food production, distribution, and consumption, resulting in increased resource waste and environmental impact. Food waste is also caused by a wide range of factors, including inaccurate demand forecasts, improper inventory management, frequent expired food, and inefficient logistics. Therefore, a system that can comprehensively and efficiently solve these problems is needed.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0132] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs to suppliers, retailers, and consumers, and means for donating excess inventory and foods approaching their expiration date. This enables systematic reduction of food waste, efficient use of resources, and realization of a sustainable society.

[0133] "Food data" refers to various information related to food production, distribution, and sales, including past sales history, demand fluctuations, seasonal factors, and other data.

[0134] "Demand forecasting" refers to predicting future demand based on collected data, using algorithms such as generative AI models.

[0135] "Inventory level" refers to the quantity of goods held for sale or consumption. Optimizing inventory levels is done to prevent overstocking or understocking.

[0136] "Tag" refers to a device for reading information such as RFID tags. Specifically, it is used to obtain data such as expiration dates from tags attached to each food item.

[0137] "Sensor" refers to a device that collects environmental data such as temperature, vibration, and time. It is used to monitor logistics and storage conditions.

[0138] "Best before date data" refers to information about the expiration date of each food product, which is captured using tags and sensors and stored in a database.

[0139] "Appearance standards" refer to the evaluation criteria for the appearance of food. If there are any problems with appearance that do not affect quality, these standards will be reevaluated.

[0140] "Logistics" refers to the entire process of transporting and storing goods. Efficient logistics is important to maintain food quality while reducing waste.

[0141] "Distribution" refers to the process of moving goods to consumers and retailers. Efficient distribution prevents food from spoiling and allows for quick sale.

[0142] "Educational Program" refers to a series of educational content to educate people about the importance of food waste and solutions, including videos and interactive materials.

[0143] "Donation destination" refers to a place or organization that accepts excess or near-expiration food, such as a food bank or charity.

[0144] "Generative AI models" refer to artificial intelligence algorithms that analyze massive amounts of data to perform tasks such as demand forecasting. These include time series analysis models.

[0145] MODE FOR CARRYING OUT THE INVENTION

[0146] This invention relates to a system that solves the food waste problem by collecting food data, forecasting demand, optimizing inventory, managing expiration date data, reevaluating food appearance standards, streamlining logistics and delivery, providing educational programs, and promoting food donations.

[0147] A specific implementation of this system will be described below.

[0148] 1. Basic system configuration

[0149] The system consists of a server, terminals, and users (suppliers, retailers, and consumers). The server plays a central role in collecting, analyzing, and managing data. The terminals are used to obtain expiration date data and provide educational programs. Users are involved in providing food data and reevaluating appearance standards.

[0150] 2. Hardware and Software Used

[0151] The system uses the following hardware and software:

[0152] Server: Performs data collection, cleaning, demand forecasting, inventory optimization, expiration date management, logistics data analysis, educational program delivery, and donation destination search. Specifically, it uses a cloud-based database, generative AI models, and various algorithms.

[0153] Terminals: RFID tag readers and sensors for acquiring expiration date data, and devices (smartphones, tablets, PCs) for providing educational programs are used.

[0154] Users: Involved in food appearance standard review, data provision and feedback collection.

[0155] 3. Example of a system

[0156] 3.1 Demand Forecasting and Inventory Management

[0157] The server collects sales data provided by retailers and the food industry through APIs, including past sales history, demand fluctuations, and seasonal factors.

[0158] The data is stored in a database and cleaned of missing values ​​and outliers. A generative AI model (e.g., a time series analysis model) is then used to forecast demand. For example, the server analyzes sales data from the past year to forecast demand for the next month.

[0159] 3.2 Best before date management

[0160] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives. For example, the terminal scans the barcode of a newly arrived product to obtain its expiration date.

[0161] The acquired data is sent in real time to a server, which stores it in a database, and the server prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[0162] 3.3 Review of food appearance standards

[0163] The user (supplier) reviews the appearance criteria of the food and identifies foods that are of good quality but have minor cosmetic issues. For example, a supplier may select apples that are blemished but still safe.

[0164] Suppliers register the information of the identified food products on the server, which stores this information in a database and notifies the upper level retailers.

[0165] 3.4 Improving logistics and delivery

[0166] The server analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and identifies factors that affect food quality based on the analysis results.

[0167] The server then proposes optimal transport conditions and notifies the logistics company of these conditions, for example, notifying them that a certain temperature range is optimal for maintaining food quality.

[0168] 3.5 Education and Awareness

[0169] The server generates educational programs about the importance of food waste and solutions, including videos and interactive learning materials, and distributes them to devices.

[0170] The terminal provides educational content to suppliers, retailers, and consumers, and then collects their feedback. Users transmit their understanding and impressions after viewing the content to a server.

[0171] 3.6 Building food banks and donation networks

[0172] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[0173] The server then searches for the most suitable recipient (food bank or charity) based on this information and notifies them of the relevant information. The recipient then receives the notification and prepares to receive the donation.

[0174] Prompt Sentence Examples

[0175] Here are some example prompts used by generative AI models:

[0176] "Please predict next month's demand based on the sales data from the past year."

[0177] "Generate a list of food items that are close to expiry and notify retailers."

[0178] The above is a specific embodiment for carrying out the present invention. This system can systematically reduce food waste and contribute to the realization of a sustainable society.

[0179] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0180] Demand forecasting and inventory management

[0181] Step 1:

[0182] The server collects sales data from retailers and the food industry via APIs, including past sales history, demand fluctuations, and seasonal factors.

[0183] Input: Sales data from retailers and the food industry

[0184] Specific operation: The server temporarily stores the data collected using the API.

[0185] Step 2:

[0186] The server stores the collected data in a database and performs data cleaning if there are missing or outliers.

[0187] Input: Sales data collected in Step 1

[0188] Data processing: Imputing missing values ​​and removing outliers

[0189] Output: Cleaned sales data

[0190] Specific operation: After storing in the database, an algorithm is used to fill in missing data and remove abnormal data.

[0191] Step 3:

[0192] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the cleaned data.

[0193] Input: Cleaned sales data

[0194] Data Computing: Time Series Analysis with Generative AI Models

[0195] Output: Forecasted sales for the next month or period

[0196] Specific operation: Sales data is input into the generative AI model, and the prediction results are compiled in report format.

[0197] Step 4:

[0198] The server runs an algorithm to calculate optimal inventory levels and order quantities based on the results of the demand forecast and notifies the retailer.

[0199] Input: Demand forecast results

[0200] Data processing: Optimizing inventory levels and order quantities

[0201] Output: Notification message to retailer

[0202] What it does: It uses algorithms to calculate stock levels and order quantities, then notifies retailers using automated notifications.

[0203] Best before date management

[0204] Step 1:

[0205] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives.

[0206] Input: RFID tag and sensor information of received food

[0207] How it works: The device scans the expiration date of each food item and collects the data.

[0208] Step 2:

[0209] The terminal transmits the acquired expiration date data to the server in real time.

[0210] Input: Retrieved expiration date data

[0211] Output: Sending expiration date data to the server

[0212] Specific operation: The terminal immediately transfers the expiration date data to the server via the network.

[0213] Step 3:

[0214] The server stores expiration date data in a database, prioritizes the list of foods approaching their expiration date, and sends warning messages.

[0215] Input: Best before date data sent from the terminal

[0216] Data processing: Identifying and listing foods that are close to expiry date

[0217] Output: Warning message

[0218] What happens: The server stores the expiration date data in a database and sends a warning message using an alert system.

[0219] Review of food appearance standards

[0220] Step 1:

[0221] The user (supplier) reassessed the food appearance standards and identified foods that had no quality issues but had minor appearance issues.

[0222] Input: Food appearance information

[0223] What it does: Suppliers manually sort out food that looks unsightly.

[0224] Step 2:

[0225] The user (supplier) registers the information of the identified food on the server.

[0226] Input: Reevaluated food information

[0227] Output: Register food information on the server

[0228] Specific operation: The supplier inputs and transmits food information to the server using a dedicated interface.

[0229] Step 3:

[0230] The server stores the registered information in a database and provides the information to top retailers.

[0231] Input: Food information registered by the supplier

[0232] Data processing: storing registration information and providing it to retailers

[0233] Output: Notification message to retailer

[0234] What it does: The server stores the registration information and notifies the retailer using an automatic notification function.

[0235] Improved logistics and delivery

[0236] Step 1:

[0237] The server collects data obtained from various sensors during logistics (temperature, vibration, time, etc.).

[0238] Input: Logistics data from sensors

[0239] Specific operation: The server automatically collects data from sensors installed in trucks and warehouses.

[0240] Step 2:

[0241] The server analyzes the collected data and identifies factors that affect food quality.

[0242] Input: Logistics data

[0243] Data Computation: Identifying Factors Affecting Quality

[0244] Output: Quality Risk Identification Report

[0245] Specific Actions: Identify quality risk factors using data analysis algorithms.

[0246] Step 3:

[0247] The server generates a proposal for the optimal shipping conditions and notifies the logistics company.

[0248] Input: Quality Risk Identification Report

[0249] Data processing: Generation of optimization proposals for transport conditions

[0250] Output: Notification of proposed transport conditions

[0251] Specific operation: The server calculates the optimal transportation conditions based on the analysis results and sends proposals to logistics companies using a notification system.

[0252] Education and Awareness

[0253] Step 1:

[0254] The server generates an educational program about the importance of food waste and solutions, and distributes it to the device.

[0255] Input: Educational content material

[0256] Data processing: Creation of educational programs

[0257] Output: Educational program distribution to terminals

[0258] Specific operation: The server creates educational content and sends it to the terminal based on the distribution schedule.

[0259] Step 2:

[0260] The terminal provides educational content to suppliers, retailers and consumers.

[0261] Input: Educational program distributed from the server

[0262] Output: Providing education to suppliers, retailers and consumers

[0263] Specific operation: The device plays videos and interactive learning materials and provides them to the user.

[0264] Step 3:

[0265] The terminal collects the user's understanding and impressions after viewing the program in a feedback form and transmits the collected information to the server.

[0266] Input: User feedback

[0267] Output: Send feedback to the server

[0268] Specific operation: The terminal displays a feedback form and sends it to the server after completion.

[0269] Step 4:

[0270] The server analyzes the collected feedback and improves the educational program.

[0271] Input: Feedback sent from the device

[0272] Data Computation: Feedback Analysis

[0273] Output: Improved educational programs

[0274] Specific operation: The server analyzes the feedback and improves the next educational content based on the analysis results.

[0275] Building food banks and donation networks

[0276] Step 1:

[0277] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[0278] Input: Information on excess stock and food items with approaching expiration dates

[0279] Output: Register food information on the server

[0280] How it works: Retailers use a dedicated interface to input and send food information to the server.

[0281] Step 2:

[0282] The server searches for the most suitable donation recipient (food bank or charity) based on the registered data.

[0283] Input: Food information registered by retailers

[0284] Data calculations: Finding the best donation destination

[0285] Output: Generate a donation list

[0286] Specific operation: The server searches a database of donation recipients and creates a list of the most suitable donation recipients.

[0287] Step 3:

[0288] The server generates a list of donation recipients and notifies each recipient of relevant information.

[0289] Input: Donation list

[0290] Output: Notification message to the donation recipient

[0291] Specific operation: The server sends a notification message to the recipient of the donation, prompting them to prepare for collection.

[0292] These are the specific processing steps of the system, which makes it possible to efficiently reduce food waste.

[0293] (Application example 1)

[0294] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] Food waste is a serious problem in modern society, and how retailers and consumers can reduce food waste is an important issue. Conventional systems are inadequate in demand forecasting, making it difficult to optimize inventory management. Food waste also occurs due to inadequate best-before date management and inefficient logistics and delivery. Furthermore, a lack of education and awareness about food waste has prevented people from raising awareness. For this reason, there is a need for a system that can more accurately forecast demand and manage inventory, streamline best-before date management, optimize logistics and delivery, and raise awareness about reducing food waste.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0297] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using wireless tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of food waste and solutions, means for donating excess inventory and food approaching its expiration date to a charity, means for suggesting optimal food inventory levels based on the demand forecast, means for offering foods with appearance issues but no quality issues as value options to customers, means for presenting users with educational videos and campaign information about reducing food waste, and means for providing optimized delivery routes and temperature and quality control information. This improves the accuracy of demand forecasts and inventory management, streamlines expiration date management, enables optimization of logistics and delivery, promotes awareness activities for reducing food waste, and realizes a sustainable food supply chain.

[0298] "Food data" refers to data provided by retailers and the food industry, including information on sales history, demand fluctuations, seasonal factors, etc.

[0299] "Demand forecasting" is the act of predicting future demand based on past data, which helps optimize inventory levels.

[0300] "Inventory level" refers to the appropriate amount of inventory, adjusted based on demand forecasts.

[0301] A "wireless tag" is a device that exchanges data using wireless communication, like an RFID tag, and is used to obtain information such as expiration dates.

[0302] A "sensor" is a device used to measure a physical variable and capture it as digital data.

[0303] "Best before date data" refers to data that includes information about the expiration date of food products, and is collected and managed.

[0304] "Appearance standards" refer to the evaluation criteria for food appearance, but may be reevaluated based on the quality of the ingredients.

[0305] "Logistics" refers to all activities related to the distribution process of goods, and is an area that requires efficiency.

[0306] "Delivery" refers to the process of receiving goods from a specific point and transporting them to their destination.

[0307] "Educational Program" refers to awareness-raising and educational content on food waste provided to suppliers, retailers and consumers.

[0308] "Donation recipients" refer to the institutions and organizations that receive excess inventory and food items nearing their expiration date.

[0309] A "value option" is a food option that offers customers food that has cosmetic issues but is of the same quality at a lower price than usual.

[0310] A "delivery route" is a route for efficiently delivering products, and is an optimized and proposed route.

[0311] "Temperature and quality control information" refers to information that includes environmental data such as temperature necessary to maintain food quality.

[0312] The present invention relates to a system for solving the food waste problem. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations. Specific embodiments of the system are described below.

[0313] 1. Demand forecasting and inventory management

[0314] The server collects sales data provided by retailers and the food industry and stores it in a database. The collected data includes past sales history, demand fluctuations, seasonal factors, etc. Missing values ​​and outliers are processed through data cleaning. Demand forecasts are made using the preprocessed data using an AI model (e.g., a time series analysis model run on Amazon Sagemaker). Optimal inventory levels and order quantities are calculated based on the results and notified to retailers.

[0315] (Specific examples)

[0316] The server analyzes sales data from the past year and predicts demand for the next month. As a result, if a particular product is expected to sell well, it will be optimized to order more of it.

[0317] 2. Introduction of expiration date management

[0318] The terminal uses wireless tags and sensors to acquire expiration date data for each food item when it arrives. The acquired data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[0319] (Specific examples)

[0320] The terminal scans newly arrived products, obtains their expiration dates, and sends them to a server, which generates a list of products with expiration dates within a week and notifies retailers, allowing them to prioritize them for sale or donation.

[0321] 3. Review of food appearance standards

[0322] The supplier reassessed the food's appearance standards and identified foods that had no quality issues but had minor visual issues. The results of this identification were registered on the server, which then stored this information in a database and provided it to higher-ranking retailers.

[0323] (Specific examples)

[0324] Suppliers register damaged but safe apples on a server, and the server notifies retailers of this information, preventing the apples from being wasted.

[0325] 4. Improved logistics and delivery

[0326] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[0327] (Specific examples)

[0328] The server analyzes temperature data during transport, discovers that food quality is maintained within a specific temperature range, and notifies the logistics company of this information, allowing them to set optimal transport conditions.

[0329] 5. Education and Awareness

[0330] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[0331] (Specific examples)

[0332] The device plays an educational video about food waste to consumers, who then provide feedback. The server analyzes the data and generates better content.

[0333] 6. Building food banks and donation networks

[0334] Retailers register excess inventory and food items nearing their expiration date on the server, which analyzes the information and generates a list of the most suitable recipients for donation. This ensures food is provided where it is needed and reduces waste.

[0335] (Specific examples)

[0336] Retailers register food items that are nearing their expiration date on a server, which then notifies the recipient of that data. By receiving the food, the recipient can reduce food waste.

[0337] Prompt Sentence Examples

[0338] "Create an application that uses past order data to forecast next week's demand and suggest optimal inventory levels."

[0339] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[0340] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0341] Step 1:

[0342] The server collects sales data provided by retailers and the food industry. Input data includes past sales history, demand fluctuations, seasonal factors, etc. This data is then stored in a database. Specifically, the server retrieves and stores the data via provided CSV files or APIs. This provides the data necessary for the predictive model.

[0343] Step 2:

[0344] The server preprocesses the collected data. It receives sales data as input and performs data cleaning. It processes missing values ​​and outliers to improve the quality of the data. Specifically, it extracts the necessary data from the database, fills in missing values, and removes outliers. This results in a clean dataset.

[0345] Step 3:

[0346] The server performs demand forecasting based on the preprocessed data. It uses the clean dataset as input and analyzes it using an AI model (Amazon Sagemaker's time series analysis model). The model performs demand forecasting and generates future demand forecast data as output. Specifically, it inputs data into the AI ​​model and stores the results in a database. This enables optimal inventory management in anticipation of future demand.

[0347] Step 4:

[0348] The terminal uses wireless tags and sensors to obtain expiration date data for each food item when it arrives. It reads the information on the tag attached to the food as input and obtains the expiration date data. The obtained data is sent to the server in real time. Specifically, it uses an RFID reader or barcode scanner to obtain data and sends it to the server. This ensures that expiration date data is managed reliably.

[0349] Step 5:

[0350] The server prioritizes and lists foods approaching their expiration date and sends a warning message to retailers. It uses the expiration date data acquired as input to extract foods that are close to their expiration date from the database. It generates and notifies warning messages and lists as output. Specifically, it periodically scans the database, extracts data that matches the conditions, and issues warnings via email or a notification system. This enables effective inventory management.

[0351] Step 6:

[0352] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have minor visual issues. Food appearance data is acquired as input and registered on a server. A list of reevaluated foods is generated as output. Specifically, visual inspections are performed manually or using image recognition technology, and the identification results are entered into the server. This reduces food waste.

[0353] Step 7:

[0354] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). It receives sensor data as input and performs analysis. It identifies factors that affect quality as output and proposes optimal transport conditions. Specifically, it receives sensor data in real time, processes the data using an analysis engine, and notifies the logistics company of the results. This allows quality to be maintained even during transport.

[0355] Step 8:

[0356] The device provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. It receives and displays educational content distributed from the server as input. It collects user viewing results and feedback as output. Specifically, it provides video playback functions and a questionnaire form, and sends viewing history and feedback information to the server. This aims to raise awareness of food waste.

[0357] Step 9:

[0358] Retailers register excess inventory and food items approaching their expiration date on the server. Inventory data is acquired as input and uploaded to the server. Based on this information, the server searches for the most suitable donation recipients and generates a list. Specifically, food data for donation is entered, and the server creates a list of potential donation recipients and notifies the recipient. This allows surplus food to be used effectively and reduces food waste.

[0359] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0360] The present invention relates to a system for solving the food waste problem, and in particular, it combines an emotion engine that recognizes user emotions and optimizes educational programs, etc. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations, and further uses the emotion engine to improve the user's educational experience.

[0361] 1. Demand forecasting and inventory management

[0362] Program processing overview

[0363] server:

[0364] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer.

[0365] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[0366] 2. Introduction of expiration date management

[0367] Program processing overview

[0368] Device:

[0369] The terminal uses RFID tags and sensors to capture expiration date data for each food item as it arrives. The data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[0370] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[0371] 3. Review of food appearance standards

[0372] Program processing overview

[0373] User (supplier):

[0374] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[0375] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[0376] 4. Improved logistics and delivery

[0377] Program processing overview

[0378] server:

[0379] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[0380] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[0381] 5. Education and Awareness

[0382] Program processing overview

[0383] Device:

[0384] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[0385] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[0386] server:

[0387] Analyze the feedback collected and improve your education program.

[0388] 6. Building food banks and donation networks

[0389] Program processing overview

[0390] User (retailer):

[0391] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to pick up the items.

[0392] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[0393] 7. Introducing the Emotion Engine

[0394] Program processing overview

[0395] Device:

[0396] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[0397] server:

[0398] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, optimizing it to provide more detailed explanations or supplementary materials if the user is confused.

[0399] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[0400] The above is a specific embodiment of the present invention. This system can systematically reduce the problem of food waste, improve the educational experience of users, and contribute to the realization of a sustainable society.

[0401] The processing flow will be explained below.

[0402] 1. Demand forecasting and inventory management

[0403] Step 1: Data collection

[0404] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[0405] Step 2: Data Preprocessing

[0406] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[0407] Step 3: Demand forecast

[0408] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[0409] Step 4: Optimize inventory levels

[0410] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[0411] Step 5: Notification

[0412] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[0413] 2. Introduction of expiration date management

[0414] Step 1: Obtaining expiration date data

[0415] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[0416] Step 2: Send data

[0417] Terminal: Sends the acquired expiration date data to the server.

[0418] Step 3: Update the database

[0419] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[0420] Step 4: Notification

[0421] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[0422] 3. Review of food appearance standards

[0423] Step 1: Reassess the criteria

[0424] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[0425] Step 2: Registration

[0426] User (provider): Registers the identified results on the server.

[0427] Step 3: Data Management

[0428] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[0429] Step 4: Notification

[0430] Server: Sends proposals for relaxing standards to the retailer's terminal.

[0431] 4. Improved logistics and delivery

[0432] Step 1: Data collection

[0433] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[0434] Step 2: Send data

[0435] Terminal: Sends acquired data to the server in real time.

[0436] Step 3: Data analysis

[0437] Server: Analyzes the received data and identifies factors that affect quality.

[0438] Step 4: Improvement proposals

[0439] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[0440] 5. Education and Awareness

[0441] Step 1: Content Delivery

[0442] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[0443] Step 2: Listening and Feedback

[0444] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[0445] Step 3: Feedback analysis

[0446] Server: Analyze the collected feedback and improve the education program.

[0447] 6. Building food banks and donation networks

[0448] Step 1: Register excess inventory

[0449] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[0450] Step 2: Find the best donation destination

[0451] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[0452] Step 3: Donation Notification

[0453] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[0454] Step 4: Donate

[0455] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[0456] 7. Introducing the Emotion Engine

[0457] Step 1: Collecting Emotional Data

[0458] Terminal: Recognizes emotions from the user's facial expressions, voice, text input, etc., and collects emotional data.

[0459] Step 2: Send data

[0460] Terminal: Sends collected emotion data to the server in real time.

[0461] Step 3: Sentiment Data Analysis

[0462] Server: Analyzes the received emotion data and evaluates the user's emotional state.

[0463] Step 4: Optimize your education program

[0464] Server: Dynamically adjusts the content of the user's educational program based on emotional data. For example, if the user is confused, the server will provide more detailed explanations or supplementary materials.

[0465] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[0466] The above are the specific processing steps for implementing the present invention. This system can systematically reduce the food waste problem, improve the educational experience for users, and contribute to the realization of a sustainable society.

[0467] Example 2

[0468] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0469] Food waste is a social problem that not only increases the environmental burden but also causes economic losses. The causes of this problem include inaccurate demand forecasts, inadequate inventory management, lax expiration date management, and wasteful disposal due to food appearance standards. Furthermore, the lack of effective educational programs and systems for the effective use of excess inventory exacerbates this issue. The present invention aims to comprehensively solve these problems.

[0470] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0471] In this invention, the server includes means for collecting food data, means for performing demand forecasting, means for optimizing inventory levels based on the forecast results, means for using an identification device to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs, means for donating excess inventory or foods approaching their expiration date, and means for recognizing user emotions and dynamically adjusting the content of educational programs based on the emotions, thereby enabling food waste reduction, efficient logistics, effective provision of educational programs, and promotion of donations.

[0472] "Food data" refers to information on food types, sales volumes, expiration dates, storage conditions, etc.

[0473] "Demand forecasting" refers to the process of predicting future demand based on past sales data and market trends.

[0474] "Stock levels" refers to the quantity of goods stored at a retailer or warehouse.

[0475] "RFID tag" refers to a device used to identify an item using wireless communication.

[0476] A "sensor" refers to a device that senses physical information (temperature, vibration, position, etc.) and converts it into data.

[0477] "Best before date data" refers to information about the date by which a food product can be safely consumed.

[0478] "Appearance standards" refer to quality standards regarding the appearance of food.

[0479] "Logistics" refers to the entire process of delivering goods from production to consumers.

[0480] "Delivery optimization" refers to the process of delivering goods efficiently while reducing costs and maintaining quality.

[0481] An "educational program" refers to a collection of materials and activities designed to provide learners with knowledge on a particular topic.

[0482] A "food bank" is a facility that collects and distributes food to people and organizations in need.

[0483] "Recipient" refers to the organization or individual that receives the donated goods or funds.

[0484] An "emotion engine" refers to technology that recognizes emotions from people's facial expressions, voices, text, etc., and adjusts the system's behavior based on that.

[0485] "Dynamic adjustment" refers to changing the contents of a system or program based on real-time conditions and data.

[0486] The present invention relates to a comprehensive management system for solving the food waste problem, including food data collection, demand forecasting, inventory management, expiration date management, review of appearance standards, optimization of logistics and delivery, provision of educational programs, donation of excess inventory, and optimization of user educational experience using an emotion engine.

[0487] 1. Demand forecasting and inventory management

[0488] Program processing overview

[0489] Server: The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, etc. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Demand forecasts are made using the preprocessed data with a time series analysis model (e.g., ARIMA model). Based on the forecasted demand, optimal inventory levels and order quantities are calculated and notified to the retailer.

[0490] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, so it optimizes the order to order more of it.

[0491] 2. Introduction of expiration date management

[0492] Program processing overview

[0493] Terminal: The terminal uses RFID tags and sensors to capture expiration date data for each food item when it arrives. The captured data is sent in real time to the server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[0494] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[0495] 3. Review of food appearance standards

[0496] Program processing overview

[0497] User (supplier): The supplier reevaluates food appearance standards and identifies foods that have visual issues but are not of high quality. The supplier registers the results of this identification on the server. The server stores this information in a database and proposes relaxing the standards to retailers.

[0498] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[0499] 4. Improved logistics and delivery

[0500] Program processing overview

[0501] Server: The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[0502] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[0503] 5. Education and Awareness

[0504] Program processing overview

[0505] Terminal: The terminal provides educational programs to suppliers, retailers, and consumers about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal.

[0506] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[0507] Server: Analyze the collected feedback and improve the educational program.

[0508] 6. Building food banks and donation networks

[0509] Program processing overview

[0510] User (retailer): Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[0511] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[0512] 7. Introducing the Emotion Engine

[0513] Program processing overview

[0514] Device: The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to the server in real time.

[0515] Server: The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state. For example, if the user is confused, the server optimizes the content by providing more detailed explanations and supplementary materials.

[0516] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[0517] The system of the present invention enables efficient inventory management and proper management of expiration dates, effectively reducing food waste. It also makes it possible to fundamentally solve the food waste problem by optimizing logistics and providing educational programs. Furthermore, the use of an emotion engine improves the user's educational experience and raises awareness of reducing food waste.

[0518] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0519] 1. Demand forecasting and inventory management

[0520] Step 1: Data collection

[0521] Server: Automatically collects data from retailers and the food industry, such as past sales data, demand fluctuations, and seasonal factors, via API.

[0522] Inputs: Historical sales data, demand fluctuations, seasonal factors, etc.

[0523] Output: The collected data is stored in a database.

[0524] Specific operation: The server synchronizes data with the sales systems of each retailer and acquires the specified data.

[0525] Step 2: Data cleaning

[0526] Server: Processes the collected data to remove missing or outlier values.

[0527] Input: Raw data collected.

[0528] Output: The cleaned dataset.

[0529] Specific operation: The server uses Python and the pandas library to fill in NaN values ​​with the median and remove obviously anomalous data.

[0530] Step 3: Demand forecast

[0531] Server: Using the preprocessed data, perform demand forecasting using a time series analysis model (e.g., ARIMA model).

[0532] Input: The cleaned dataset.

[0533] Output: Forecasted demand data.

[0534] Specific operation: The server uses the scikit-learn library to build an ARIMA model and fit it to past data. The results are saved in the database as predicted values.

[0535] Step 4: Optimize inventory levels

[0536] Server: Runs algorithms that calculate optimal inventory levels and order quantities based on predicted demand.

[0537] Input: Forecasted demand data.

[0538] Output: Optimal inventory levels and order quantities.

[0539] Specific operation: The server uses linear programming to optimize the balance between supply and demand and calculate the order quantity.

[0540] Step 5: Notification

[0541] Server: Notifies the retailer of the calculation results.

[0542] Inputs: Optimal stock levels and order quantities.

[0543] Output: Notification email.

[0544] Specific operation: The server sends an email notification using the SMTP protocol.

[0545] 2. Introduction of expiration date management

[0546] Step 1: Obtaining expiration date data

[0547] Terminal: Obtains expiration date data from RFID tags and sensors attached to incoming food products.

[0548] Input: RFID tag, sensor signal.

[0549] Output: Best before date data.

[0550] Specific operation: The terminal uses an RFID scanner to read the tag and obtain the expiration date information.

[0551] Step 2: Send data

[0552] Terminal: Sends the acquired expiration date data to the server in real time.

[0553] Input: Best before date data.

[0554] Output: The data sent to the server.

[0555] Specific operation: The device captures the signal and sends the information to the server using an HTTP request.

[0556] Step 3: Save data

[0557] Server: Stores the received data in a database.

[0558] Input: Best before date data.

[0559] Output: Data stored in the database.

[0560] Specific operation: The server uses a NoSQL database (e.g., MongoDB) to store data.

[0561] Step 4: Generate a warning message

[0562] Server: Lists food items that are nearing their expiration date and generates warning messages.

[0563] Input: Best before date data stored in the database.

[0564] Output: A warning message.

[0565] What it does: The server periodically queries, generates a list of expiration dates, and sends notifications to each retailer.

[0566] 3. Review of food appearance standards

[0567] Step 1: Reassess appearance criteria

[0568] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[0569] Input: Image data of the actual item.

[0570] Output: Appearance-based classification results.

[0571] How it works: Suppliers use image recognition apps to scan food and determine if it meets standards.

[0572] Step 2: Registering the identification results

[0573] User (provider): Registers the identification results on the server.

[0574] Input: Identification result data.

[0575] Output: Data registered on the server.

[0576] Specific operation: The supplier uses a dedicated app to input the identification results and send them to the server.

[0577] Step 3: Notification

[0578] Server: Stores the re-evaluated criteria information in a database and notifies the retailer.

[0579] Input: Identification result data.

[0580] Output: Notification message to retailer.

[0581] Specific operation: The server uses AWS SNS (Simple Notification Service) to notify the baseline information.

[0582] 4. Improved logistics and delivery

[0583] Step 1: Receiving data

[0584] Server: Receives data from various sensors during logistics (temperature, vibration, time, etc.).

[0585] Input: Sensor data (temperature, vibration, time, etc.).

[0586] Output: Sensor data stored in a database.

[0587] Specific operation: The sensor sends data to the server via the IoT network.

[0588] Step 2: Data analysis

[0589] Server: Analyzes the received data and identifies factors that affect quality.

[0590] Input: Sensor data.

[0591] Output: Identification of factors that affect quality.

[0592] Specific operation: The server uses a data analysis tool (e.g., MATLAB) to detect abnormal values ​​in temperature and vibration data.

[0593] Step 3: Optimization suggestions

[0594] Server: Based on the analysis results, proposes optimal transport conditions.

[0595] Input: Identification of factors affecting quality.

[0596] Output: Proposal of optimal transport conditions.

[0597] Specific operation: The server uses an AI algorithm to calculate the optimal transportation conditions and notify the logistics company.

[0598] 5. Education and Awareness

[0599] Step 1: Educational content distribution

[0600] Terminal: Presents the educational program delivered from the server to the user.

[0601] Input: Educational content data.

[0602] Output: Presenting educational content to the user.

[0603] What it does: The device uses a web app to play videos and infographics.

[0604] Step 2: Send your feedback

[0605] User: Sends the knowledge and impressions gained through the educational program as feedback via the terminal.

[0606] Input: User feedback data.

[0607] Output: Feedback data sent to the server.

[0608] Specific operation: The user fills out a questionnaire form, and the terminal sends the data to the server.

[0609] Step 3: Feedback analysis

[0610] Server: Analyze the collected feedback and improve the educational program.

[0611] Input: Feedback data.

[0612] Output: Improved educational content data.

[0613] Specific operation: The server analyzes the feedback using data analysis tools and generates new educational content.

[0614] 6. Building food banks and donation networks

[0615] Step 1: Register excess inventory

[0616] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[0617] Input: Excess stock data, expiration date data.

[0618] Output: Inventory data registered on the server.

[0619] Specific operation: Retailers use a dedicated app to enter inventory information and send it to the server.

[0620] Step 2: Find the best donation destination

[0621] Server: Analyzes registered information and searches for the most suitable food bank or donation destination.

[0622] Input: Inventory data, expiration date data.

[0623] Output: A list of optimal donation recipients.

[0624] What it does: The server uses the Google Maps API to geographically search for the best donation destination.

[0625] Step 3: Donation notification

[0626] Server: Based on the generated list, notifications are sent to the recipients, instructing them to prepare for collection.

[0627] Input: A list of optimal donation destinations.

[0628] Output: Notification message to the donation recipient.

[0629] Specific operation: The server sends a notification to the donation recipient via email or SMS.

[0630] 7. Introducing the Emotion Engine

[0631] Step 1: Collecting Emotional Data

[0632] Device: Data is acquired using an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc.

[0633] Input: facial expression data, voice data, text data.

[0634] Output: Emotion data.

[0635] Specific operation: The device uses the camera and microphone to read the user's emotions using image recognition and voice analysis technology.

[0636] Step 2: Send data

[0637] Terminal: Sends collected emotion data to the server in real time.

[0638] Input: Emotion data.

[0639] Output: Emotion data sent to the server.

[0640] Specific operation: Emotion data is sent to the server via API.

[0641] Step 3: Data analysis

[0642] Server: Analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state.

[0643] Input: Emotion data.

[0644] Output: Tailored educational content.

[0645] Specific operation: The server uses a machine learning model to analyze the emotional data and select appropriate educational content.

[0646] (Application example 2)

[0647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0648] Food waste has become a serious problem in the modern food industry. In particular, large amounts of food are often discarded due to insufficient management of expiration dates and difficulties in maintaining appropriate inventory. The problem is also exacerbated by a lack of education on food waste among suppliers, retailers, and consumers. Furthermore, inefficient logistics and inadequate optimization of delivery can lead to deterioration in food quality and waste. In addition, educational programs that ignore customer feelings make it difficult to effectively raise awareness. There is a need for a system that can solve these problems, reduce food waste, and contribute to the realization of a sustainable society.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting food data, means for performing demand forecasting based on the data, means for optimizing inventory levels based on the obtained forecast results, means for using identification tags or sensors to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of and solutions to food waste, means for donating excess inventory and food approaching its expiration date to welfare facilities or charity recipients, means for notifying inventory status and expiration date information in real time using smart devices, and means for recognizing and analyzing customer emotion data and dynamically adjusting educational content. This enables effective inventory management and expiration date management, efficient logistics and delivery, and education based on customer emotion, all aimed at reducing food waste.

[0650] "Food data" is a general term for various data related to food, such as food sales history, inventory, expiration date, and transportation status.

[0651] "Demand forecasting" is a technology that predicts the amount of inventory that will be needed in the future based on past sales data and market trends.

[0652] "Inventory level" is a standard for managing the number of products required within a specific period of time.

[0653] An "identification tag" is a device used to identify an item, such as an RFID tag or a barcode.

[0654] A "sensor" is a device that detects physical or chemical properties and collects data.

[0655] "Best before date data" is information that indicates the period of time during which food can be safely consumed.

[0656] "Food appearance standards" are quality standards regarding the appearance of food.

[0657] "Logistics" is the set of processes that move goods from supplier to consumer.

[0658] "Delivery" is the process of delivering goods to their destination.

[0659] An "educational program" is an educational tool that allows students to acquire specific knowledge and skills.

[0660] A "welfare facility" is a facility for providing social welfare.

[0661] "Recipient" refers to the institution or organization that will receive the surplus supplies or assistance.

[0662] "Smart devices" are electronic devices that have built-in communication functions and various sensors and can be operated intelligently.

[0663] "Stock status" is information indicating the stock status at a specific point in time.

[0664] "Customer emotion data" refers to emotion information analyzed from the user's facial expressions and voice using emotion recognition technology.

[0665] "Educational Content" is a general term for information and materials provided for educational purposes.

[0666] This invention is a comprehensive system for solving the food waste problem, and in particular, it improves the user experience by using automated data collection, demand forecasting, inventory management, expiration date management, reevaluation of appearance standards, logistics and delivery optimization, educational programs, and emotion recognition. The system aims to increase efficiency and sustainability in the food industry.

[0667] 1. Demand forecasting and inventory management

[0668] Program processing overview

[0669] server:

[0670] The server collects sales data provided by the food industry and stores it in a database. The data includes past sales history, seasonal factors, and demand fluctuations. The collected data is preprocessed to remove missing values ​​and outliers. Demand forecasts are then made using a generative AI model (e.g., a time series analysis model) using the processed data. Optimal inventory levels and order quantities are calculated based on the forecast results.

[0671] Example: For example, a server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, and notifies the retailer to order more of it.

[0672] 2. Introduction of expiration date management

[0673] Program processing overview

[0674] Device:

[0675] The terminal uses identification tags or sensors to capture expiration date data for each food item upon arrival. The captured data is sent to a server in real time and stored in a database. Food items approaching their expiration date are automatically listed and warning messages are sent to retailers and consumers via the terminal or smart device.

[0676] Example: A terminal scans newly arrived products for their identification tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies retailers so they can prioritize them for sale or donation.

[0677] 3. Review of food appearance standards

[0678] Program processing overview

[0679] User (supplier):

[0680] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[0681] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[0682] 4. Improved logistics and delivery

[0683] Program processing overview

[0684] server:

[0685] The server receives and analyzes data collected from various sensors during logistics (such as temperature, vibration, and time). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[0686] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a certain temperature range, and notifies the logistics company of this information.

[0687] 5. Education and Awareness

[0688] Program processing overview

[0689] Device:

[0690] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[0691] server:

[0692] The server analyzes the collected feedback and improves the educational program.

[0693] Example: A device plays an educational video about food waste to consumers, and the consumers provide feedback. The server analyzes the data and generates better content.

[0694] 6. Building food banks and donation networks

[0695] Program processing overview

[0696] User (retailer):

[0697] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable welfare facilities and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[0698] Example: Retailers register food products that are nearing their expiration date on a server, and the server notifies the food bank of this data, allowing the food bank to receive the food.

[0699] 7. Introducing the Emotion Engine

[0700] Program processing overview

[0701] Device:

[0702] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[0703] server:

[0704] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, providing more detailed explanations or supplementary materials if the user is confused.

[0705] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[0706] Prompt Sentence Examples

[0707] "Predict next month's product demand based on current sales data and historical trends, and calculate inventory levels to match demand. Also, develop a system that notifies smart glasses in real time when products are about to expire, and recognize customer sentiment to deliver appropriate educational content."

[0708] This will reduce overall food waste, as well as enable effective inventory management, expiration date management, improved logistics, and education, all of which will be concrete steps towards a sustainable society.

[0709] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0710] Step 1:

[0711] Food data collection

[0712] server:

[0713] The server collects sales data, inventory data, and transportation data provided by the food industry, including past sales history, inventory quantity, expiration date, transportation status, etc. This data is stored in a database.

[0714] Input: Various data provided by the food industry

[0715] Output: Organized food data is saved in a database

[0716] Step 2:

[0717] Data Preprocessing

[0718] server:

[0719] The server cleans the collected data, removing missing values ​​and outliers, and classifies the data by time, region, product category, etc.

[0720] Input: Raw data collected

[0721] Output: Preprocessed data

[0722] Specific operations: Apply data cleaning algorithms to remove invalid data, and use classification algorithms to structure the data.

[0723] Step 3:

[0724] Demand forecasting

[0725] server:

[0726] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the pre-processed data.

[0727] Input: Preprocessed data

[0728] Output: Demand forecast results

[0729] Specific operation: Apply a time series analysis model (e.g., Prophet) to calculate future demand.

[0730] Step 4:

[0731] Inventory Optimization

[0732] server:

[0733] Based on the results of the demand forecast, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are communicated to the retailer.

[0734] Input: Demand forecast results

[0735] Output: Recommendations for optimal inventory levels and order quantities

[0736] Specific behavior: Runs inventory optimization algorithm and generates notification messages

[0737] Step 5:

[0738] Obtaining expiration date data

[0739] Device:

[0740] The terminal uses an identification tag or sensor to obtain product expiration date data and transmits it to a server in real time.

[0741] Input: Information from an identification tag or sensor

[0742] Output: Expiration date data obtained

[0743] Specific operation: Scan the expiration date of the product with the identifier and send the data to the server

[0744] Step 6:

[0745] Best before date management

[0746] server:

[0747] The server stores the acquired expiration date data in a database, lists foods whose expiration dates are approaching, and sends notifications.

[0748] Input: Retrieved expiration date data

[0749] Output: Notification message and listed data

[0750] Specific operation: Applying the expiration date management algorithm and generating a notification message

[0751] Step 7:

[0752] Reevaluation of Appearance Standards

[0753] User (supplier):

[0754] The supplier identifies food products that have quality issues but have appearance issues and registers the information on the server.

[0755] Input: Supplier evaluation results

[0756] Output: Proposal to revise appearance standards

[0757] Specific operation: Enter the rating information into the server and save it in the database.

[0758] Step 8:

[0759] Improved logistics and delivery

[0760] server:

[0761] The server analyzes data obtained from various sensors during logistics (temperature, vibration, time, etc.) and proposes optimal transportation conditions.

[0762] Input: Sensor data during logistics

[0763] Output: Optimal transport proposal

[0764] Specific behavior: Applying data analysis algorithms to generate recommendations

[0765] Step 9:

[0766] Providing educational programs

[0767] Device:

[0768] The terminal provides educational programs to suppliers, retailers, and consumers. The contents of the programs are distributed from the server.

[0769] Input: Program content sent from the server

[0770] Output: Displayed educational program

[0771] Specific behavior: Play educational content and collect user feedback

[0772] Step 10:

[0773] Building a donation network

[0774] User (retailer):

[0775] Retailers register excess stock and food items with approaching expiration dates on a server, which then notifies the most appropriate welfare facilities or donation recipients.

[0776] Input: Retailer registration information

[0777] Output: Notification message and donation list

[0778] Specific operation: Analyzes registration information and sends notification to the donation recipient

[0779] Step 11:

[0780] Introducing the Emotion Engine

[0781] Device:

[0782] The device recognizes emotions from the user's facial expressions, voice, and text input and transmits them to the server in real time.

[0783] Input: User emotion data

[0784] Output: Parsed emotion data

[0785] Specific behavior: Runs emotion recognition algorithm and sends data to server

[0786] server:

[0787] The server dynamically adjusts the content of the educational program based on the received emotional data, for example, providing detailed explanations if the user is confused.

[0788] Input: Emotion data sent from the device

[0789] Output: Adjusted educational program content

[0790] Specific actions: Analyzing emotional data and adjusting educational programs

[0791] This will enable a comprehensive process that covers everything from collecting food data to analyzing it, educating people, managing logistics, and adjusting based on their emotions.

[0792] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0793] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0794] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0795] [Second embodiment]

[0796] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0797] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0798] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0799] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0800] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0801] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0802] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0803] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0804] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0805] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0806] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0807] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0808] The present invention relates to a system for solving the food waste problem, and specific embodiments thereof are described below. The system collects food data, performs demand forecasting, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations.

[0809] 1. Demand forecasting and inventory management

[0810] Program processing overview

[0811] server

[0812] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer. For example, the server analyzes sales data from the past year to predict demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[0813] 2. Introduction of expiration date management

[0814] Program processing overview

[0815] Terminal

[0816] The terminal uses RFID tags and sensors to obtain expiration date data for each food item as it arrives. The obtained data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods whose expiration dates are approaching and sends a warning message. For example, the terminal scans newly arrived products, obtains their expiration dates, and then sends them to the server. The server generates a list of products with expiration dates within one week and notifies the retailer, allowing them to set priorities for sales or donations.

[0817] 3. Review of food appearance standards

[0818] Program processing overview

[0819] User (supplier)

[0820] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have slight appearance issues. These identification results are registered on a server. The server stores this information in a database and provides it to higher-ranking retailers. This reduces the amount of food that is discarded based solely on appearance. For example, a supplier can register blemished but safe apples on a server, and the server can notify retailers of this information, preventing the apples from being wasted.

[0821] 4. Improved logistics and delivery

[0822] Program processing overview

[0823] server

[0824] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions. As a specific example, the server analyzes temperature data during transport and discovers that food quality is maintained within a specific temperature range. It then notifies this information to the logistics company, allowing it to set optimal transport conditions.

[0825] 5. Education and Awareness

[0826] Program processing overview

[0827] Terminal

[0828] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal. After the user watches the program, they send their understanding and impressions to the server via a feedback form. The server analyzes the collected feedback data and improves the educational programs. As a specific example, the terminal plays an educational video about food waste to consumers, who then submit their feedback, and the server analyzes that data to generate better content.

[0829] 6. Building food banks and donation networks

[0830] Program processing overview

[0831] User (retailer)

[0832] Retailers register excess inventory and food items approaching their expiration date on a server. The server analyzes this information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to receive the food. As a specific example, retailers register food items approaching their expiration date on a server, and the server notifies food banks of this data, which then accepts the food, thereby reducing food waste.

[0833] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[0834] The processing flow will be explained below.

[0835] 1. Demand forecasting and inventory management

[0836] Step 1: Data collection

[0837] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[0838] Step 2: Data Preprocessing

[0839] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[0840] Step 3: Demand forecast

[0841] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[0842] Step 4: Optimize inventory levels

[0843] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[0844] Step 5: Notification

[0845] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[0846] 2. Introduction of expiration date management

[0847] Step 1: Obtaining expiration date data

[0848] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[0849] Step 2: Send data

[0850] Terminal: Sends the acquired expiration date data to the server.

[0851] Step 3: Update the database

[0852] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[0853] Step 4: Notification

[0854] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[0855] 3. Review of food appearance standards

[0856] Step 1: Reassess the criteria

[0857] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[0858] Step 2: Registration

[0859] User (provider): Registers the identified results on the server.

[0860] Step 3: Data Management

[0861] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[0862] Step 4: Notification

[0863] Server: Sends proposals for relaxing standards to the retailer's terminal.

[0864] 4. Improved logistics and delivery

[0865] Step 1: Data collection

[0866] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[0867] Step 2: Send data

[0868] Terminal: Sends acquired data to the server in real time.

[0869] Step 3: Data analysis

[0870] Server: Analyzes the received data and identifies factors that affect quality.

[0871] Step 4: Improvement proposals

[0872] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[0873] 5. Education and Awareness

[0874] Step 1: Content Delivery

[0875] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[0876] Step 2: Listening and Feedback

[0877] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[0878] Step 3: Feedback analysis

[0879] Server: Analyzes collected feedback and improves educational programs.

[0880] 6. Building food banks and donation networks

[0881] Step 1: Register excess inventory

[0882] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[0883] Step 2: Find the best donation destination

[0884] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[0885] Step 3: Donation Notification

[0886] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[0887] Step 4: Donate

[0888] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[0889] The above are the specific processing steps for carrying out the invention, which will realize a system that effectively solves the problem of food waste.

[0890] Example 1

[0891] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0892] Food waste has become a serious problem in modern society. Large amounts of food are wasted at each stage of food production, distribution, and consumption, resulting in increased resource waste and environmental impact. Food waste is also caused by a wide range of factors, including inaccurate demand forecasts, improper inventory management, frequent expired food, and inefficient logistics. Therefore, a system that can comprehensively and efficiently solve these problems is needed.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0894] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs to suppliers, retailers, and consumers, and means for donating excess inventory and foods approaching their expiration date. This enables systematic reduction of food waste, efficient use of resources, and realization of a sustainable society.

[0895] "Food data" refers to various information related to food production, distribution, and sales, including past sales history, demand fluctuations, seasonal factors, and other data.

[0896] "Demand forecasting" refers to predicting future demand based on collected data, using algorithms such as generative AI models.

[0897] "Inventory level" refers to the quantity of goods held for sale or consumption. Optimizing inventory levels is done to prevent overstocking or understocking.

[0898] "Tag" refers to a device for reading information such as RFID tags. Specifically, it is used to obtain data such as expiration dates from tags attached to each food item.

[0899] "Sensor" refers to a device that collects environmental data such as temperature, vibration, and time. It is used to monitor logistics and storage conditions.

[0900] "Best before date data" refers to information about the expiration date of each food product, which is captured using tags and sensors and stored in a database.

[0901] "Appearance standards" refer to the evaluation criteria for the appearance of food. If there are any problems with appearance that do not affect quality, these standards will be reevaluated.

[0902] "Logistics" refers to the entire process of transporting and storing goods. Efficient logistics is important to maintain food quality while reducing waste.

[0903] "Distribution" refers to the process of moving goods to consumers and retailers. Efficient distribution prevents food from spoiling and allows for quick sale.

[0904] "Educational Program" refers to a series of educational content to educate people about the importance of food waste and solutions, including videos and interactive materials.

[0905] "Donation destination" refers to a place or organization that accepts excess or near-expiration food, such as a food bank or charity.

[0906] "Generative AI models" refer to artificial intelligence algorithms that analyze massive amounts of data to perform tasks such as demand forecasting. These include time series analysis models.

[0907] MODE FOR CARRYING OUT THE INVENTION

[0908] This invention relates to a system that solves the food waste problem by collecting food data, forecasting demand, optimizing inventory, managing expiration date data, reevaluating food appearance standards, streamlining logistics and delivery, providing educational programs, and promoting food donations.

[0909] A specific implementation of this system will be described below.

[0910] 1. Basic system configuration

[0911] The system consists of a server, terminals, and users (suppliers, retailers, and consumers). The server plays a central role in collecting, analyzing, and managing data. The terminals are used to obtain expiration date data and provide educational programs. Users are involved in providing food data and reevaluating appearance standards.

[0912] 2. Hardware and Software Used

[0913] The system uses the following hardware and software:

[0914] Server: Performs data collection, cleaning, demand forecasting, inventory optimization, expiration date management, logistics data analysis, educational program delivery, and donation destination search. Specifically, it uses a cloud-based database, generative AI models, and various algorithms.

[0915] Terminals: RFID tag readers and sensors for acquiring expiration date data, and devices (smartphones, tablets, PCs) for providing educational programs are used.

[0916] Users: Involved in food appearance standard review, data provision and feedback collection.

[0917] 3. Example of a system

[0918] 3.1 Demand Forecasting and Inventory Management

[0919] The server collects sales data provided by retailers and the food industry through APIs, including past sales history, demand fluctuations, and seasonal factors.

[0920] The data is stored in a database and cleaned of missing values ​​and outliers. A generative AI model (e.g., a time series analysis model) is then used to forecast demand. For example, the server analyzes sales data from the past year to forecast demand for the next month.

[0921] 3.2 Best before date management

[0922] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives. For example, the terminal scans the barcode of a newly arrived product to obtain its expiration date.

[0923] The acquired data is sent in real time to a server, which stores it in a database, and the server prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[0924] 3.3 Review of food appearance standards

[0925] The user (supplier) reviews the appearance criteria of the food and identifies foods that are of good quality but have minor cosmetic issues. For example, a supplier may select apples that are blemished but still safe.

[0926] Suppliers register the information of the identified food products on the server, which stores this information in a database and notifies the upper level retailers.

[0927] 3.4 Improving logistics and delivery

[0928] The server analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and identifies factors that affect food quality based on the analysis results.

[0929] The server then proposes optimal transport conditions and notifies the logistics company of these conditions, for example, notifying them that a certain temperature range is optimal for maintaining food quality.

[0930] 3.5 Education and Awareness

[0931] The server generates educational programs about the importance of food waste and solutions, including videos and interactive learning materials, and distributes them to devices.

[0932] The terminal provides educational content to suppliers, retailers, and consumers, and then collects their feedback. Users transmit their understanding and impressions after viewing the content to a server.

[0933] 3.6 Building food banks and donation networks

[0934] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[0935] The server then searches for the most suitable recipient (food bank or charity) based on this information and notifies them of the relevant information. The recipient then receives the notification and prepares to receive the donation.

[0936] Prompt Sentence Examples

[0937] Here are some example prompts used by generative AI models:

[0938] "Please predict next month's demand based on the sales data from the past year."

[0939] "Generate a list of food items that are close to expiry and notify retailers."

[0940] The above is a specific embodiment for carrying out the present invention. This system can systematically reduce food waste and contribute to the realization of a sustainable society.

[0941] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0942] Demand forecasting and inventory management

[0943] Step 1:

[0944] The server collects sales data from retailers and the food industry via APIs, including past sales history, demand fluctuations, and seasonal factors.

[0945] Input: Sales data from retailers and the food industry

[0946] Specific operation: The server temporarily stores the data collected using the API.

[0947] Step 2:

[0948] The server stores the collected data in a database and performs data cleaning if there are missing or outliers.

[0949] Input: Sales data collected in Step 1

[0950] Data processing: Imputing missing values ​​and removing outliers

[0951] Output: Cleaned sales data

[0952] Specific operation: After storing in the database, an algorithm is used to fill in missing data and remove abnormal data.

[0953] Step 3:

[0954] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the cleaned data.

[0955] Input: Cleaned sales data

[0956] Data Computing: Time Series Analysis with Generative AI Models

[0957] Output: Forecasted sales for the next month or period

[0958] Specific operation: Sales data is input into the generative AI model, and the prediction results are compiled in report format.

[0959] Step 4:

[0960] The server runs an algorithm to calculate optimal inventory levels and order quantities based on the results of the demand forecast and notifies the retailer.

[0961] Input: Demand forecast results

[0962] Data processing: Optimizing inventory levels and order quantities

[0963] Output: Notification message to retailer

[0964] What it does: It uses algorithms to calculate stock levels and order quantities, then notifies retailers using automated notifications.

[0965] Best before date management

[0966] Step 1:

[0967] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives.

[0968] Input: RFID tag and sensor information of received food

[0969] How it works: The device scans the expiration date of each food item and collects the data.

[0970] Step 2:

[0971] The terminal transmits the acquired expiration date data to the server in real time.

[0972] Input: Retrieved expiration date data

[0973] Output: Sending expiration date data to the server

[0974] Specific operation: The terminal immediately transfers the expiration date data to the server via the network.

[0975] Step 3:

[0976] The server stores expiration date data in a database, prioritizes the list of foods approaching their expiration date, and sends warning messages.

[0977] Input: Best before date data sent from the terminal

[0978] Data processing: Identifying and listing foods that are close to expiry date

[0979] Output: Warning message

[0980] What happens: The server stores the expiration date data in a database and sends a warning message using an alert system.

[0981] Review of food appearance standards

[0982] Step 1:

[0983] The user (supplier) reassessed the food appearance standards and identified foods that had no quality issues but had minor appearance issues.

[0984] Input: Food appearance information

[0985] What it does: Suppliers manually sort out food that looks unsightly.

[0986] Step 2:

[0987] The user (supplier) registers the information of the identified food on the server.

[0988] Input: Reevaluated food information

[0989] Output: Register food information on the server

[0990] Specific operation: The supplier inputs and transmits food information to the server using a dedicated interface.

[0991] Step 3:

[0992] The server stores the registered information in a database and provides the information to top retailers.

[0993] Input: Food information registered by the supplier

[0994] Data processing: storing registration information and providing it to retailers

[0995] Output: Notification message to retailer

[0996] What it does: The server stores the registration information and notifies the retailer using an automatic notification function.

[0997] Improved logistics and delivery

[0998] Step 1:

[0999] The server collects data obtained from various sensors during logistics (temperature, vibration, time, etc.).

[1000] Input: Logistics data from sensors

[1001] Specific operation: The server automatically collects data from sensors installed in trucks and warehouses.

[1002] Step 2:

[1003] The server analyzes the collected data and identifies factors that affect food quality.

[1004] Input: Logistics data

[1005] Data Computation: Identifying Factors Affecting Quality

[1006] Output: Quality Risk Identification Report

[1007] Specific Actions: Identify quality risk factors using data analysis algorithms.

[1008] Step 3:

[1009] The server generates a proposal for the optimal shipping conditions and notifies the logistics company.

[1010] Input: Quality Risk Identification Report

[1011] Data processing: Generation of optimization proposals for transport conditions

[1012] Output: Notification of proposed transport conditions

[1013] Specific operation: The server calculates the optimal transportation conditions based on the analysis results and sends proposals to logistics companies using a notification system.

[1014] Education and Awareness

[1015] Step 1:

[1016] The server generates an educational program about the importance of food waste and solutions, and distributes it to the device.

[1017] Input: Educational content material

[1018] Data processing: Creation of educational programs

[1019] Output: Educational program distribution to terminals

[1020] Specific operation: The server creates educational content and sends it to the terminal based on the distribution schedule.

[1021] Step 2:

[1022] The terminal provides educational content to suppliers, retailers and consumers.

[1023] Input: Educational program distributed from the server

[1024] Output: Providing education to suppliers, retailers and consumers

[1025] Specific operation: The device plays videos and interactive learning materials and provides them to the user.

[1026] Step 3:

[1027] The terminal collects the user's understanding and impressions after viewing the program in a feedback form and transmits the collected information to the server.

[1028] Input: User feedback

[1029] Output: Send feedback to the server

[1030] Specific operation: The terminal displays a feedback form and sends it to the server after completion.

[1031] Step 4:

[1032] The server analyzes the collected feedback and improves the educational program.

[1033] Input: Feedback sent from the device

[1034] Data Computation: Feedback Analysis

[1035] Output: Improved educational programs

[1036] Specific operation: The server analyzes the feedback and improves the next educational content based on the analysis results.

[1037] Building food banks and donation networks

[1038] Step 1:

[1039] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[1040] Input: Information on excess stock and food items with approaching expiration dates

[1041] Output: Register food information on the server

[1042] How it works: Retailers use a dedicated interface to input and send food information to the server.

[1043] Step 2:

[1044] The server searches for the most suitable donation recipient (food bank or charity) based on the registered data.

[1045] Input: Food information registered by retailers

[1046] Data calculations: Finding the best donation destination

[1047] Output: Generate a donation list

[1048] Specific operation: The server searches a database of donation recipients and creates a list of the most suitable donation recipients.

[1049] Step 3:

[1050] The server generates a list of donation recipients and notifies each recipient of relevant information.

[1051] Input: Donation list

[1052] Output: Notification message to the donation recipient

[1053] Specific operation: The server sends a notification message to the recipient of the donation, prompting them to prepare for collection.

[1054] These are the specific processing steps of the system, which makes it possible to efficiently reduce food waste.

[1055] (Application example 1)

[1056] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1057] Food waste is a serious problem in modern society, and how retailers and consumers can reduce food waste is an important issue. Conventional systems are inadequate in demand forecasting, making it difficult to optimize inventory management. Food waste also occurs due to inadequate best-before date management and inefficient logistics and delivery. Furthermore, a lack of education and awareness about food waste has prevented people from raising awareness. For this reason, there is a need for a system that can more accurately forecast demand and manage inventory, streamline best-before date management, optimize logistics and delivery, and raise awareness about reducing food waste.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1059] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using wireless tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of food waste and solutions, means for donating excess inventory and food approaching its expiration date to a charity, means for suggesting optimal food inventory levels based on the demand forecast, means for offering foods with appearance issues but no quality issues as value options to customers, means for presenting users with educational videos and campaign information about reducing food waste, and means for providing optimized delivery routes and temperature and quality control information. This improves the accuracy of demand forecasts and inventory management, streamlines expiration date management, enables optimization of logistics and delivery, promotes awareness activities for reducing food waste, and realizes a sustainable food supply chain.

[1060] "Food data" refers to data provided by retailers and the food industry, including information on sales history, demand fluctuations, seasonal factors, etc.

[1061] "Demand forecasting" is the act of predicting future demand based on past data, which helps optimize inventory levels.

[1062] "Inventory level" refers to the appropriate amount of inventory, adjusted based on demand forecasts.

[1063] A "wireless tag" is a device that exchanges data using wireless communication, like an RFID tag, and is used to obtain information such as expiration dates.

[1064] A "sensor" is a device used to measure a physical variable and capture it as digital data.

[1065] "Best before date data" refers to data that includes information about the expiration date of food products, and is collected and managed.

[1066] "Appearance standards" refer to the evaluation criteria for food appearance, but may be reevaluated based on the quality of the ingredients.

[1067] "Logistics" refers to all activities related to the distribution process of goods, and is an area that requires efficiency.

[1068] "Delivery" refers to the process of receiving goods from a specific point and transporting them to their destination.

[1069] "Educational Program" refers to awareness-raising and educational content on food waste provided to suppliers, retailers and consumers.

[1070] "Donation recipients" refer to the institutions and organizations that receive excess inventory and food items nearing their expiration date.

[1071] A "value option" is a food option that offers customers food that has cosmetic issues but is of the same quality at a lower price than usual.

[1072] A "delivery route" is a route for efficiently delivering products, and is an optimized and proposed route.

[1073] "Temperature and quality control information" refers to information that includes environmental data such as temperature necessary to maintain food quality.

[1074] The present invention relates to a system for solving the food waste problem. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations. Specific embodiments of the system are described below.

[1075] 1. Demand forecasting and inventory management

[1076] The server collects sales data provided by retailers and the food industry and stores it in a database. The collected data includes past sales history, demand fluctuations, seasonal factors, etc. Missing values ​​and outliers are processed through data cleaning. Demand forecasts are made using the preprocessed data using an AI model (e.g., a time series analysis model run on Amazon Sagemaker). Optimal inventory levels and order quantities are calculated based on the results and notified to retailers.

[1077] (Specific examples)

[1078] The server analyzes sales data from the past year and predicts demand for the next month. As a result, if a particular product is expected to sell well, it will be optimized to order more of it.

[1079] 2. Introduction of expiration date management

[1080] The terminal uses wireless tags and sensors to acquire expiration date data for each food item when it arrives. The acquired data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1081] (Specific examples)

[1082] The terminal scans newly arrived products, obtains their expiration dates, and sends them to a server, which generates a list of products with expiration dates within a week and notifies retailers, allowing them to prioritize them for sale or donation.

[1083] 3. Review of food appearance standards

[1084] The supplier reassessed the food's appearance standards and identified foods that had no quality issues but had minor visual issues. The results of this identification were registered on the server, which then stored this information in a database and provided it to higher-ranking retailers.

[1085] (Specific examples)

[1086] Suppliers register damaged but safe apples on a server, and the server notifies retailers of this information, preventing the apples from being wasted.

[1087] 4. Improved logistics and delivery

[1088] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[1089] (Specific examples)

[1090] The server analyzes temperature data during transport, discovers that food quality is maintained within a specific temperature range, and notifies the logistics company of this information, allowing them to set optimal transport conditions.

[1091] 5. Education and Awareness

[1092] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[1093] (Specific examples)

[1094] The device plays an educational video about food waste to consumers, who then provide feedback. The server analyzes the data and generates better content.

[1095] 6. Building food banks and donation networks

[1096] Retailers register excess inventory and food items nearing their expiration date on the server, which analyzes the information and generates a list of the most suitable recipients for donation. This ensures food is provided where it is needed and reduces waste.

[1097] (Specific examples)

[1098] Retailers register food items that are nearing their expiration date on a server, which then notifies the recipient of that data. By receiving the food, the recipient can reduce food waste.

[1099] Prompt Sentence Examples

[1100] "Create an application that uses past order data to forecast next week's demand and suggest optimal inventory levels."

[1101] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[1102] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1103] Step 1:

[1104] The server collects sales data provided by retailers and the food industry. Input data includes past sales history, demand fluctuations, seasonal factors, etc. This data is then stored in a database. Specifically, the server retrieves and stores the data via provided CSV files or APIs. This provides the data necessary for the predictive model.

[1105] Step 2:

[1106] The server preprocesses the collected data. It receives sales data as input and performs data cleaning. It processes missing values ​​and outliers to improve the quality of the data. Specifically, it extracts the necessary data from the database, fills in missing values, and removes outliers. This results in a clean dataset.

[1107] Step 3:

[1108] The server performs demand forecasting based on the preprocessed data. It uses the clean dataset as input and analyzes it using an AI model (Amazon Sagemaker's time series analysis model). The model performs demand forecasting and generates future demand forecast data as output. Specifically, it inputs data into the AI ​​model and stores the results in a database. This enables optimal inventory management in anticipation of future demand.

[1109] Step 4:

[1110] The terminal uses wireless tags and sensors to obtain expiration date data for each food item when it arrives. It reads the information on the tag attached to the food as input and obtains the expiration date data. The obtained data is sent to the server in real time. Specifically, it uses an RFID reader or barcode scanner to obtain data and sends it to the server. This ensures that expiration date data is managed reliably.

[1111] Step 5:

[1112] The server prioritizes and lists foods approaching their expiration date and sends a warning message to retailers. It uses the expiration date data acquired as input to extract foods that are close to their expiration date from the database. It generates and notifies warning messages and lists as output. Specifically, it periodically scans the database, extracts data that matches the conditions, and issues warnings via email or a notification system. This enables effective inventory management.

[1113] Step 6:

[1114] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have minor visual issues. Food appearance data is acquired as input and registered on a server. A list of reevaluated foods is generated as output. Specifically, visual inspections are performed manually or using image recognition technology, and the identification results are entered into the server. This reduces food waste.

[1115] Step 7:

[1116] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). It receives sensor data as input and performs analysis. It identifies factors that affect quality as output and proposes optimal transport conditions. Specifically, it receives sensor data in real time, processes the data using an analysis engine, and notifies the logistics company of the results. This allows quality to be maintained even during transport.

[1117] Step 8:

[1118] The device provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. It receives and displays educational content distributed from the server as input. It collects user viewing results and feedback as output. Specifically, it provides video playback functions and a questionnaire form, and sends viewing history and feedback information to the server. This aims to raise awareness of food waste.

[1119] Step 9:

[1120] Retailers register excess inventory and food items approaching their expiration date on the server. Inventory data is acquired as input and uploaded to the server. Based on this information, the server searches for the most suitable donation recipients and generates a list. Specifically, food data for donation is entered, and the server creates a list of potential donation recipients and notifies the recipient. This allows surplus food to be used effectively and reduces food waste.

[1121] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1122] The present invention relates to a system for solving the food waste problem, and in particular, it combines an emotion engine that recognizes user emotions and optimizes educational programs, etc. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations, and further uses the emotion engine to improve the user's educational experience.

[1123] 1. Demand forecasting and inventory management

[1124] Program processing overview

[1125] server:

[1126] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer.

[1127] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[1128] 2. Introduction of expiration date management

[1129] Program processing overview

[1130] Device:

[1131] The terminal uses RFID tags and sensors to capture expiration date data for each food item as it arrives. The data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1132] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[1133] 3. Review of food appearance standards

[1134] Program processing overview

[1135] User (supplier):

[1136] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[1137] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[1138] 4. Improved logistics and delivery

[1139] Program processing overview

[1140] server:

[1141] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[1142] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[1143] 5. Education and Awareness

[1144] Program processing overview

[1145] Device:

[1146] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[1147] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[1148] server:

[1149] Analyze the feedback collected and improve your education program.

[1150] 6. Building food banks and donation networks

[1151] Program processing overview

[1152] User (retailer):

[1153] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to pick up the items.

[1154] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[1155] 7. Introducing the Emotion Engine

[1156] Program processing overview

[1157] Device:

[1158] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[1159] server:

[1160] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, optimizing it to provide more detailed explanations or supplementary materials if the user is confused.

[1161] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1162] The above is a specific embodiment of the present invention. This system can systematically reduce the problem of food waste, improve the educational experience of users, and contribute to the realization of a sustainable society.

[1163] The processing flow will be explained below.

[1164] 1. Demand forecasting and inventory management

[1165] Step 1: Data collection

[1166] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[1167] Step 2: Data Preprocessing

[1168] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[1169] Step 3: Demand forecast

[1170] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[1171] Step 4: Optimize inventory levels

[1172] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[1173] Step 5: Notification

[1174] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[1175] 2. Introduction of expiration date management

[1176] Step 1: Obtaining expiration date data

[1177] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[1178] Step 2: Send data

[1179] Terminal: Sends the acquired expiration date data to the server.

[1180] Step 3: Update the database

[1181] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[1182] Step 4: Notification

[1183] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[1184] 3. Review of food appearance standards

[1185] Step 1: Reassess the criteria

[1186] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[1187] Step 2: Registration

[1188] User (provider): Registers the identified results on the server.

[1189] Step 3: Data Management

[1190] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[1191] Step 4: Notification

[1192] Server: Sends proposals for relaxing standards to the retailer's terminal.

[1193] 4. Improved logistics and delivery

[1194] Step 1: Data collection

[1195] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[1196] Step 2: Send data

[1197] Terminal: Sends acquired data to the server in real time.

[1198] Step 3: Data analysis

[1199] Server: Analyzes the received data and identifies factors that affect quality.

[1200] Step 4: Improvement proposals

[1201] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[1202] 5. Education and Awareness

[1203] Step 1: Content Delivery

[1204] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[1205] Step 2: Listening and Feedback

[1206] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[1207] Step 3: Feedback analysis

[1208] Server: Analyze the collected feedback and improve the education program.

[1209] 6. Building food banks and donation networks

[1210] Step 1: Register excess inventory

[1211] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[1212] Step 2: Find the best donation destination

[1213] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[1214] Step 3: Donation Notification

[1215] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[1216] Step 4: Donate

[1217] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[1218] 7. Introducing the Emotion Engine

[1219] Step 1: Collecting Emotional Data

[1220] Terminal: Recognizes emotions from the user's facial expressions, voice, text input, etc., and collects emotional data.

[1221] Step 2: Send data

[1222] Terminal: Sends collected emotion data to the server in real time.

[1223] Step 3: Sentiment Data Analysis

[1224] Server: Analyzes the received emotion data and evaluates the user's emotional state.

[1225] Step 4: Optimize your education program

[1226] Server: Dynamically adjusts the content of the user's educational program based on emotional data. For example, if the user is confused, the server will provide more detailed explanations or supplementary materials.

[1227] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1228] The above are the specific processing steps for implementing the present invention. This system can systematically reduce the food waste problem, improve the educational experience for users, and contribute to the realization of a sustainable society.

[1229] Example 2

[1230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1231] Food waste is a social problem that not only increases the environmental burden but also causes economic losses. The causes of this problem include inaccurate demand forecasts, inadequate inventory management, lax expiration date management, and wasteful disposal due to food appearance standards. Furthermore, the lack of effective educational programs and systems for the effective use of excess inventory exacerbates this issue. The present invention aims to comprehensively solve these problems.

[1232] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1233] In this invention, the server includes means for collecting food data, means for performing demand forecasting, means for optimizing inventory levels based on the forecast results, means for using an identification device to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs, means for donating excess inventory or foods approaching their expiration date, and means for recognizing user emotions and dynamically adjusting the content of educational programs based on the emotions, thereby enabling food waste reduction, efficient logistics, effective provision of educational programs, and promotion of donations.

[1234] "Food data" refers to information on food types, sales volumes, expiration dates, storage conditions, etc.

[1235] "Demand forecasting" refers to the process of predicting future demand based on past sales data and market trends.

[1236] "Stock levels" refers to the quantity of goods stored at a retailer or warehouse.

[1237] "RFID tag" refers to a device used to identify an item using wireless communication.

[1238] A "sensor" refers to a device that senses physical information (temperature, vibration, position, etc.) and converts it into data.

[1239] "Best before date data" refers to information about the date by which a food product can be safely consumed.

[1240] "Appearance standards" refer to quality standards regarding the appearance of food.

[1241] "Logistics" refers to the entire process of delivering goods from production to consumers.

[1242] "Delivery optimization" refers to the process of delivering goods efficiently while reducing costs and maintaining quality.

[1243] An "educational program" refers to a collection of materials and activities designed to provide learners with knowledge on a particular topic.

[1244] A "food bank" is a facility that collects and distributes food to people and organizations in need.

[1245] "Recipient" refers to the organization or individual that receives the donated goods or funds.

[1246] An "emotion engine" refers to technology that recognizes emotions from people's facial expressions, voices, text, etc., and adjusts the system's behavior based on that.

[1247] "Dynamic adjustment" refers to changing the contents of a system or program based on real-time conditions and data.

[1248] The present invention relates to a comprehensive management system for solving the food waste problem, including food data collection, demand forecasting, inventory management, expiration date management, review of appearance standards, optimization of logistics and delivery, provision of educational programs, donation of excess inventory, and optimization of user educational experience using an emotion engine.

[1249] 1. Demand forecasting and inventory management

[1250] Program processing overview

[1251] Server: The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, etc. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Demand forecasts are made using the preprocessed data with a time series analysis model (e.g., ARIMA model). Based on the forecasted demand, optimal inventory levels and order quantities are calculated and notified to the retailer.

[1252] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, so it optimizes the order to order more of it.

[1253] 2. Introduction of expiration date management

[1254] Program processing overview

[1255] Terminal: The terminal uses RFID tags and sensors to capture expiration date data for each food item when it arrives. The captured data is sent in real time to the server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1256] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[1257] 3. Review of food appearance standards

[1258] Program processing overview

[1259] User (supplier): The supplier reevaluates food appearance standards and identifies foods that have visual issues but are not of high quality. The supplier registers the results of this identification on the server. The server stores this information in a database and proposes relaxing the standards to retailers.

[1260] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[1261] 4. Improved logistics and delivery

[1262] Program processing overview

[1263] Server: The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[1264] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[1265] 5. Education and Awareness

[1266] Program processing overview

[1267] Terminal: The terminal provides educational programs to suppliers, retailers, and consumers about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal.

[1268] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[1269] Server: Analyze the collected feedback and improve the educational program.

[1270] 6. Building food banks and donation networks

[1271] Program processing overview

[1272] User (retailer): Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[1273] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[1274] 7. Introducing the Emotion Engine

[1275] Program processing overview

[1276] Device: The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to the server in real time.

[1277] Server: The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state. For example, if the user is confused, the server optimizes the content by providing more detailed explanations and supplementary materials.

[1278] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1279] The system of the present invention enables efficient inventory management and proper management of expiration dates, effectively reducing food waste. It also makes it possible to fundamentally solve the food waste problem by optimizing logistics and providing educational programs. Furthermore, the use of an emotion engine improves the user's educational experience and raises awareness of reducing food waste.

[1280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1281] 1. Demand forecasting and inventory management

[1282] Step 1: Data collection

[1283] Server: Automatically collects data from retailers and the food industry, such as past sales data, demand fluctuations, and seasonal factors, via API.

[1284] Inputs: Historical sales data, demand fluctuations, seasonal factors, etc.

[1285] Output: The collected data is stored in a database.

[1286] Specific operation: The server synchronizes data with the sales systems of each retailer and acquires the specified data.

[1287] Step 2: Data cleaning

[1288] Server: Processes the collected data to remove missing or outlier values.

[1289] Input: Raw data collected.

[1290] Output: The cleaned dataset.

[1291] Specific operation: The server uses Python and the pandas library to fill in NaN values ​​with the median and remove obviously anomalous data.

[1292] Step 3: Demand forecast

[1293] Server: Using the preprocessed data, perform demand forecasting using a time series analysis model (e.g., ARIMA model).

[1294] Input: The cleaned dataset.

[1295] Output: Forecasted demand data.

[1296] Specific operation: The server uses the scikit-learn library to build an ARIMA model and fit it to past data. The results are saved in the database as predicted values.

[1297] Step 4: Optimize inventory levels

[1298] Server: Runs algorithms that calculate optimal inventory levels and order quantities based on predicted demand.

[1299] Input: Forecasted demand data.

[1300] Output: Optimal inventory levels and order quantities.

[1301] Specific operation: The server uses linear programming to optimize the balance between supply and demand and calculate the order quantity.

[1302] Step 5: Notification

[1303] Server: Notifies the retailer of the calculation results.

[1304] Inputs: Optimal stock levels and order quantities.

[1305] Output: Notification email.

[1306] Specific operation: The server sends an email notification using the SMTP protocol.

[1307] 2. Introduction of expiration date management

[1308] Step 1: Obtaining expiration date data

[1309] Terminal: Obtains expiration date data from RFID tags and sensors attached to incoming food products.

[1310] Input: RFID tag, sensor signal.

[1311] Output: Best before date data.

[1312] Specific operation: The terminal uses an RFID scanner to read the tag and obtain the expiration date information.

[1313] Step 2: Send data

[1314] Terminal: Sends the acquired expiration date data to the server in real time.

[1315] Input: Best before date data.

[1316] Output: The data sent to the server.

[1317] Specific operation: The device captures the signal and sends the information to the server using an HTTP request.

[1318] Step 3: Save data

[1319] Server: Stores the received data in a database.

[1320] Input: Best before date data.

[1321] Output: Data stored in the database.

[1322] Specific operation: The server uses a NoSQL database (e.g., MongoDB) to store data.

[1323] Step 4: Generate a warning message

[1324] Server: Lists food items that are nearing their expiration date and generates warning messages.

[1325] Input: Best before date data stored in the database.

[1326] Output: A warning message.

[1327] What it does: The server periodically queries, generates a list of expiration dates, and sends notifications to each retailer.

[1328] 3. Review of food appearance standards

[1329] Step 1: Reassess appearance criteria

[1330] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[1331] Input: Image data of the actual item.

[1332] Output: Appearance-based classification results.

[1333] How it works: Suppliers use image recognition apps to scan food and determine if it meets standards.

[1334] Step 2: Registering the identification results

[1335] User (provider): Registers the identification results on the server.

[1336] Input: Identification result data.

[1337] Output: Data registered on the server.

[1338] Specific operation: The supplier uses a dedicated app to input the identification results and send them to the server.

[1339] Step 3: Notification

[1340] Server: Stores the re-evaluated criteria information in a database and notifies the retailer.

[1341] Input: Identification result data.

[1342] Output: Notification message to retailer.

[1343] Specific operation: The server uses AWS SNS (Simple Notification Service) to notify the baseline information.

[1344] 4. Improved logistics and delivery

[1345] Step 1: Receiving data

[1346] Server: Receives data from various sensors during logistics (temperature, vibration, time, etc.).

[1347] Input: Sensor data (temperature, vibration, time, etc.).

[1348] Output: Sensor data stored in a database.

[1349] Specific operation: The sensor sends data to the server via the IoT network.

[1350] Step 2: Data analysis

[1351] Server: Analyzes the received data and identifies factors that affect quality.

[1352] Input: Sensor data.

[1353] Output: Identification of factors that affect quality.

[1354] Specific operation: The server uses a data analysis tool (e.g., MATLAB) to detect abnormal values ​​in temperature and vibration data.

[1355] Step 3: Optimization suggestions

[1356] Server: Based on the analysis results, proposes optimal transport conditions.

[1357] Input: Identification of factors affecting quality.

[1358] Output: Proposal of optimal transport conditions.

[1359] Specific operation: The server uses an AI algorithm to calculate the optimal transportation conditions and notify the logistics company.

[1360] 5. Education and Awareness

[1361] Step 1: Educational content distribution

[1362] Terminal: Presents the educational program delivered from the server to the user.

[1363] Input: Educational content data.

[1364] Output: Presenting educational content to the user.

[1365] What it does: The device uses a web app to play videos and infographics.

[1366] Step 2: Send your feedback

[1367] User: Sends the knowledge and impressions gained through the educational program as feedback via the terminal.

[1368] Input: User feedback data.

[1369] Output: Feedback data sent to the server.

[1370] Specific operation: The user fills out a questionnaire form, and the terminal sends the data to the server.

[1371] Step 3: Feedback analysis

[1372] Server: Analyze the collected feedback and improve the educational program.

[1373] Input: Feedback data.

[1374] Output: Improved educational content data.

[1375] Specific operation: The server analyzes the feedback using data analysis tools and generates new educational content.

[1376] 6. Building food banks and donation networks

[1377] Step 1: Register excess inventory

[1378] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[1379] Input: Excess stock data, expiration date data.

[1380] Output: Inventory data registered on the server.

[1381] Specific operation: Retailers use a dedicated app to enter inventory information and send it to the server.

[1382] Step 2: Find the best donation destination

[1383] Server: Analyzes registered information and searches for the most suitable food bank or donation destination.

[1384] Input: Inventory data, expiration date data.

[1385] Output: A list of optimal donation recipients.

[1386] What it does: The server uses the Google Maps API to geographically search for the best donation destination.

[1387] Step 3: Donation notification

[1388] Server: Based on the generated list, notifications are sent to the recipients, instructing them to prepare for collection.

[1389] Input: A list of optimal donation destinations.

[1390] Output: Notification message to the donation recipient.

[1391] Specific operation: The server sends a notification to the donation recipient via email or SMS.

[1392] 7. Introducing the Emotion Engine

[1393] Step 1: Collecting Emotional Data

[1394] Device: Data is acquired using an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc.

[1395] Input: facial expression data, voice data, text data.

[1396] Output: Emotion data.

[1397] Specific operation: The device uses the camera and microphone to read the user's emotions using image recognition and voice analysis technology.

[1398] Step 2: Send data

[1399] Terminal: Sends collected emotion data to the server in real time.

[1400] Input: Emotion data.

[1401] Output: Emotion data sent to the server.

[1402] Specific operation: Emotion data is sent to the server via API.

[1403] Step 3: Data analysis

[1404] Server: Analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state.

[1405] Input: Emotion data.

[1406] Output: Tailored educational content.

[1407] Specific operation: The server uses a machine learning model to analyze the emotional data and select appropriate educational content.

[1408] (Application example 2)

[1409] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1410] Food waste has become a serious problem in the modern food industry. In particular, large amounts of food are often discarded due to insufficient management of expiration dates and difficulties in maintaining appropriate inventory. The problem is also exacerbated by a lack of education on food waste among suppliers, retailers, and consumers. Furthermore, inefficient logistics and inadequate optimization of delivery can lead to deterioration in food quality and waste. In addition, educational programs that ignore customer feelings make it difficult to effectively raise awareness. There is a need for a system that can solve these problems, reduce food waste, and contribute to the realization of a sustainable society.

[1411] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting food data, means for performing demand forecasting based on the data, means for optimizing inventory levels based on the obtained forecast results, means for using identification tags or sensors to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of and solutions to food waste, means for donating excess inventory and food approaching its expiration date to welfare facilities or charity recipients, means for notifying inventory status and expiration date information in real time using smart devices, and means for recognizing and analyzing customer emotion data and dynamically adjusting educational content. This enables effective inventory management and expiration date management, efficient logistics and delivery, and education based on customer emotion, all aimed at reducing food waste.

[1412] "Food data" is a general term for various data related to food, such as food sales history, inventory, expiration date, and transportation status.

[1413] "Demand forecasting" is a technology that predicts the amount of inventory that will be needed in the future based on past sales data and market trends.

[1414] "Inventory level" is a standard for managing the number of products required within a specific period of time.

[1415] An "identification tag" is a device used to identify an item, such as an RFID tag or a barcode.

[1416] A "sensor" is a device that detects physical or chemical properties and collects data.

[1417] "Best before date data" is information that indicates the period of time during which food can be safely consumed.

[1418] "Food appearance standards" are quality standards regarding the appearance of food.

[1419] "Logistics" is the set of processes that move goods from supplier to consumer.

[1420] "Delivery" is the process of delivering goods to their destination.

[1421] An "educational program" is an educational tool that allows students to acquire specific knowledge and skills.

[1422] A "welfare facility" is a facility for providing social welfare.

[1423] "Recipient" refers to the institution or organization that will receive the surplus supplies or assistance.

[1424] "Smart devices" are electronic devices that have built-in communication functions and various sensors and can be operated intelligently.

[1425] "Stock status" is information indicating the stock status at a specific point in time.

[1426] "Customer emotion data" refers to emotion information analyzed from the user's facial expressions and voice using emotion recognition technology.

[1427] "Educational Content" is a general term for information and materials provided for educational purposes.

[1428] This invention is a comprehensive system for solving the food waste problem, and in particular, it improves the user experience by using automated data collection, demand forecasting, inventory management, expiration date management, reevaluation of appearance standards, logistics and delivery optimization, educational programs, and emotion recognition. The system aims to increase efficiency and sustainability in the food industry.

[1429] 1. Demand forecasting and inventory management

[1430] Program processing overview

[1431] server:

[1432] The server collects sales data provided by the food industry and stores it in a database. The data includes past sales history, seasonal factors, and demand fluctuations. The collected data is preprocessed to remove missing values ​​and outliers. Demand forecasts are then made using a generative AI model (e.g., a time series analysis model) using the processed data. Optimal inventory levels and order quantities are calculated based on the forecast results.

[1433] Example: For example, a server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, and notifies the retailer to order more of it.

[1434] 2. Introduction of expiration date management

[1435] Program processing overview

[1436] Device:

[1437] The terminal uses identification tags or sensors to capture expiration date data for each food item upon arrival. The captured data is sent to a server in real time and stored in a database. Food items approaching their expiration date are automatically listed and warning messages are sent to retailers and consumers via the terminal or smart device.

[1438] Example: A terminal scans newly arrived products for their identification tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies retailers so they can prioritize them for sale or donation.

[1439] 3. Review of food appearance standards

[1440] Program processing overview

[1441] User (supplier):

[1442] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[1443] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[1444] 4. Improved logistics and delivery

[1445] Program processing overview

[1446] server:

[1447] The server receives and analyzes data collected from various sensors during logistics (such as temperature, vibration, and time). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[1448] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a certain temperature range, and notifies the logistics company of this information.

[1449] 5. Education and Awareness

[1450] Program processing overview

[1451] Device:

[1452] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[1453] server:

[1454] The server analyzes the collected feedback and improves the educational program.

[1455] Example: A device plays an educational video about food waste to consumers, and the consumers provide feedback. The server analyzes the data and generates better content.

[1456] 6. Building food banks and donation networks

[1457] Program processing overview

[1458] User (retailer):

[1459] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable welfare facilities and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[1460] Example: Retailers register food products that are nearing their expiration date on a server, and the server notifies the food bank of this data, allowing the food bank to receive the food.

[1461] 7. Introducing the Emotion Engine

[1462] Program processing overview

[1463] Device:

[1464] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[1465] server:

[1466] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, providing more detailed explanations or supplementary materials if the user is confused.

[1467] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1468] Prompt Sentence Examples

[1469] "Predict next month's product demand based on current sales data and historical trends, and calculate inventory levels to match demand. Also, develop a system that notifies smart glasses in real time when products are about to expire, and recognize customer sentiment to deliver appropriate educational content."

[1470] This will reduce overall food waste, as well as enable effective inventory management, expiration date management, improved logistics, and education, all of which will be concrete steps towards a sustainable society.

[1471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1472] Step 1:

[1473] Food data collection

[1474] server:

[1475] The server collects sales data, inventory data, and transportation data provided by the food industry, including past sales history, inventory quantity, expiration date, transportation status, etc. This data is stored in a database.

[1476] Input: Various data provided by the food industry

[1477] Output: Organized food data is saved in a database

[1478] Step 2:

[1479] Data Preprocessing

[1480] server:

[1481] The server cleans the collected data, removing missing values ​​and outliers, and classifies the data by time, region, product category, etc.

[1482] Input: Raw data collected

[1483] Output: Preprocessed data

[1484] Specific operations: Apply data cleaning algorithms to remove invalid data, and use classification algorithms to structure the data.

[1485] Step 3:

[1486] Demand forecasting

[1487] server:

[1488] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the pre-processed data.

[1489] Input: Preprocessed data

[1490] Output: Demand forecast results

[1491] Specific operation: Apply a time series analysis model (e.g., Prophet) to calculate future demand.

[1492] Step 4:

[1493] Inventory Optimization

[1494] server:

[1495] Based on the results of the demand forecast, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are communicated to the retailer.

[1496] Input: Demand forecast results

[1497] Output: Recommendations for optimal inventory levels and order quantities

[1498] Specific behavior: Runs inventory optimization algorithm and generates notification messages

[1499] Step 5:

[1500] Obtaining expiration date data

[1501] Device:

[1502] The terminal uses an identification tag or sensor to obtain product expiration date data and transmits it to a server in real time.

[1503] Input: Information from an identification tag or sensor

[1504] Output: Expiration date data obtained

[1505] Specific operation: Scan the expiration date of the product with the identifier and send the data to the server

[1506] Step 6:

[1507] Best before date management

[1508] server:

[1509] The server stores the acquired expiration date data in a database, lists foods whose expiration dates are approaching, and sends notifications.

[1510] Input: Retrieved expiration date data

[1511] Output: Notification message and listed data

[1512] Specific operation: Applying the expiration date management algorithm and generating a notification message

[1513] Step 7:

[1514] Reevaluation of Appearance Standards

[1515] User (supplier):

[1516] The supplier identifies food products that have quality issues but have appearance issues and registers the information on the server.

[1517] Input: Supplier evaluation results

[1518] Output: Proposal to revise appearance standards

[1519] Specific operation: Enter the rating information into the server and save it in the database.

[1520] Step 8:

[1521] Improved logistics and delivery

[1522] server:

[1523] The server analyzes data obtained from various sensors during logistics (temperature, vibration, time, etc.) and proposes optimal transportation conditions.

[1524] Input: Sensor data during logistics

[1525] Output: Optimal transport proposal

[1526] Specific behavior: Applying data analysis algorithms to generate recommendations

[1527] Step 9:

[1528] Providing educational programs

[1529] Device:

[1530] The terminal provides educational programs to suppliers, retailers, and consumers. The contents of the programs are distributed from the server.

[1531] Input: Program content sent from the server

[1532] Output: Displayed educational program

[1533] Specific behavior: Play educational content and collect user feedback

[1534] Step 10:

[1535] Building a donation network

[1536] User (retailer):

[1537] Retailers register excess stock and food items with approaching expiration dates on a server, which then notifies the most appropriate welfare facilities or donation recipients.

[1538] Input: Retailer registration information

[1539] Output: Notification message and donation list

[1540] Specific operation: Analyzes registration information and sends notification to the donation recipient

[1541] Step 11:

[1542] Introducing the Emotion Engine

[1543] Device:

[1544] The device recognizes emotions from the user's facial expressions, voice, and text input and transmits them to the server in real time.

[1545] Input: User emotion data

[1546] Output: Parsed emotion data

[1547] Specific behavior: Runs emotion recognition algorithm and sends data to server

[1548] server:

[1549] The server dynamically adjusts the content of the educational program based on the received emotional data, for example, providing detailed explanations if the user is confused.

[1550] Input: Emotion data sent from the device

[1551] Output: Adjusted educational program content

[1552] Specific actions: Analyzing emotional data and adjusting educational programs

[1553] This will enable a comprehensive process that covers everything from collecting food data to analyzing it, educating people, managing logistics, and adjusting based on their emotions.

[1554] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1555] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1556] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1557] [Third embodiment]

[1558] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1559] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1560] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1561] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1562] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1563] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1564] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1565] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1566] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1567] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1568] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1569] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1570] The present invention relates to a system for solving the food waste problem, and specific embodiments thereof are described below. The system collects food data, performs demand forecasting, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations.

[1571] 1. Demand forecasting and inventory management

[1572] Program processing overview

[1573] server

[1574] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer. For example, the server analyzes sales data from the past year to predict demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[1575] 2. Introduction of expiration date management

[1576] Program processing overview

[1577] Terminal

[1578] The terminal uses RFID tags and sensors to obtain expiration date data for each food item as it arrives. The obtained data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods whose expiration dates are approaching and sends a warning message. For example, the terminal scans newly arrived products, obtains their expiration dates, and then sends them to the server. The server generates a list of products with expiration dates within one week and notifies the retailer, allowing them to set priorities for sales or donations.

[1579] 3. Review of food appearance standards

[1580] Program processing overview

[1581] User (supplier)

[1582] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have slight appearance issues. These identification results are registered on a server. The server stores this information in a database and provides it to higher-ranking retailers. This reduces the amount of food that is discarded based solely on appearance. For example, a supplier can register blemished but safe apples on a server, and the server can notify retailers of this information, preventing the apples from being wasted.

[1583] 4. Improved logistics and delivery

[1584] Program processing overview

[1585] server

[1586] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions. As a specific example, the server analyzes temperature data during transport and discovers that food quality is maintained within a specific temperature range. It then notifies this information to the logistics company, allowing it to set optimal transport conditions.

[1587] 5. Education and Awareness

[1588] Program processing overview

[1589] Terminal

[1590] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal. After the user watches the program, they send their understanding and impressions to the server via a feedback form. The server analyzes the collected feedback data and improves the educational programs. As a specific example, the terminal plays an educational video about food waste to consumers, who then submit their feedback, and the server analyzes that data to generate better content.

[1591] 6. Building food banks and donation networks

[1592] Program processing overview

[1593] User (retailer)

[1594] Retailers register excess inventory and food items approaching their expiration date on a server. The server analyzes this information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to receive the food. As a specific example, retailers register food items approaching their expiration date on a server, and the server notifies food banks of this data, which then accepts the food, thereby reducing food waste.

[1595] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[1596] The processing flow will be explained below.

[1597] 1. Demand forecasting and inventory management

[1598] Step 1: Data collection

[1599] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[1600] Step 2: Data Preprocessing

[1601] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[1602] Step 3: Demand forecast

[1603] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[1604] Step 4: Optimize inventory levels

[1605] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[1606] Step 5: Notification

[1607] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[1608] 2. Introduction of expiration date management

[1609] Step 1: Obtaining expiration date data

[1610] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[1611] Step 2: Send data

[1612] Terminal: Sends the acquired expiration date data to the server.

[1613] Step 3: Update the database

[1614] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[1615] Step 4: Notification

[1616] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[1617] 3. Review of food appearance standards

[1618] Step 1: Reassess the criteria

[1619] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[1620] Step 2: Registration

[1621] User (provider): Registers the identified results on the server.

[1622] Step 3: Data Management

[1623] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[1624] Step 4: Notification

[1625] Server: Sends proposals for relaxing standards to the retailer's terminal.

[1626] 4. Improved logistics and delivery

[1627] Step 1: Data collection

[1628] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[1629] Step 2: Send data

[1630] Terminal: Sends acquired data to the server in real time.

[1631] Step 3: Data analysis

[1632] Server: Analyzes the received data and identifies factors that affect quality.

[1633] Step 4: Improvement proposals

[1634] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[1635] 5. Education and Awareness

[1636] Step 1: Content Delivery

[1637] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[1638] Step 2: Listening and Feedback

[1639] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[1640] Step 3: Feedback analysis

[1641] Server: Analyzes collected feedback and improves educational programs.

[1642] 6. Building food banks and donation networks

[1643] Step 1: Register excess inventory

[1644] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[1645] Step 2: Find the best donation destination

[1646] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[1647] Step 3: Donation Notification

[1648] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[1649] Step 4: Donate

[1650] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[1651] The above are the specific processing steps for carrying out the invention, which will realize a system that effectively solves the problem of food waste.

[1652] Example 1

[1653] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1654] Food waste has become a serious problem in modern society. Large amounts of food are wasted at each stage of food production, distribution, and consumption, resulting in increased resource waste and environmental impact. Food waste is also caused by a wide range of factors, including inaccurate demand forecasts, improper inventory management, frequent expired food, and inefficient logistics. Therefore, a system that can comprehensively and efficiently solve these problems is needed.

[1655] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1656] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs to suppliers, retailers, and consumers, and means for donating excess inventory and foods approaching their expiration date. This enables systematic reduction of food waste, efficient use of resources, and realization of a sustainable society.

[1657] "Food data" refers to various information related to food production, distribution, and sales, including past sales history, demand fluctuations, seasonal factors, and other data.

[1658] "Demand forecasting" refers to predicting future demand based on collected data, using algorithms such as generative AI models.

[1659] "Inventory level" refers to the quantity of goods held for sale or consumption. Optimizing inventory levels is done to prevent overstocking or understocking.

[1660] "Tag" refers to a device for reading information such as RFID tags. Specifically, it is used to obtain data such as expiration dates from tags attached to each food item.

[1661] "Sensor" refers to a device that collects environmental data such as temperature, vibration, and time. It is used to monitor logistics and storage conditions.

[1662] "Best before date data" refers to information about the expiration date of each food product, which is captured using tags and sensors and stored in a database.

[1663] "Appearance standards" refer to the evaluation criteria for the appearance of food. If there are any problems with appearance that do not affect quality, these standards will be reevaluated.

[1664] "Logistics" refers to the entire process of transporting and storing goods. Efficient logistics is important to maintain food quality while reducing waste.

[1665] "Distribution" refers to the process of moving goods to consumers and retailers. Efficient distribution prevents food from spoiling and allows for quick sale.

[1666] "Educational Program" refers to a series of educational content to educate people about the importance of food waste and solutions, including videos and interactive materials.

[1667] "Donation destination" refers to a place or organization that accepts excess or near-expiration food, such as a food bank or charity.

[1668] "Generative AI models" refer to artificial intelligence algorithms that analyze massive amounts of data to perform tasks such as demand forecasting. These include time series analysis models.

[1669] MODE FOR CARRYING OUT THE INVENTION

[1670] This invention relates to a system that solves the food waste problem by collecting food data, forecasting demand, optimizing inventory, managing expiration date data, reevaluating food appearance standards, streamlining logistics and delivery, providing educational programs, and promoting food donations.

[1671] A specific implementation of this system will be described below.

[1672] 1. Basic system configuration

[1673] The system consists of a server, terminals, and users (suppliers, retailers, and consumers). The server plays a central role in collecting, analyzing, and managing data. The terminals are used to obtain expiration date data and provide educational programs. Users are involved in providing food data and reevaluating appearance standards.

[1674] 2. Hardware and Software Used

[1675] The system uses the following hardware and software:

[1676] Server: Performs data collection, cleaning, demand forecasting, inventory optimization, expiration date management, logistics data analysis, educational program delivery, and donation destination search. Specifically, it uses a cloud-based database, generative AI models, and various algorithms.

[1677] Terminals: RFID tag readers and sensors for acquiring expiration date data, and devices (smartphones, tablets, PCs) for providing educational programs are used.

[1678] Users: Involved in food appearance standard review, data provision and feedback collection.

[1679] 3. Example of a system

[1680] 3.1 Demand Forecasting and Inventory Management

[1681] The server collects sales data provided by retailers and the food industry through APIs, including past sales history, demand fluctuations, and seasonal factors.

[1682] The data is stored in a database and cleaned of missing values ​​and outliers. A generative AI model (e.g., a time series analysis model) is then used to forecast demand. For example, the server analyzes sales data from the past year to forecast demand for the next month.

[1683] 3.2 Best before date management

[1684] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives. For example, the terminal scans the barcode of a newly arrived product to obtain its expiration date.

[1685] The acquired data is sent in real time to a server, which stores it in a database, and the server prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1686] 3.3 Review of food appearance standards

[1687] The user (supplier) reviews the appearance criteria of the food and identifies foods that are of good quality but have minor cosmetic issues. For example, a supplier may select apples that are blemished but still safe.

[1688] Suppliers register the information of the identified food products on the server, which stores this information in a database and notifies the upper level retailers.

[1689] 3.4 Improving logistics and delivery

[1690] The server analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and identifies factors that affect food quality based on the analysis results.

[1691] The server then proposes optimal transport conditions and notifies the logistics company of these conditions, for example, notifying them that a certain temperature range is optimal for maintaining food quality.

[1692] 3.5 Education and Awareness

[1693] The server generates educational programs about the importance of food waste and solutions, including videos and interactive learning materials, and distributes them to devices.

[1694] The terminal provides educational content to suppliers, retailers, and consumers, and then collects their feedback. Users transmit their understanding and impressions after viewing the content to a server.

[1695] 3.6 Building food banks and donation networks

[1696] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[1697] The server then searches for the most suitable recipient (food bank or charity) based on this information and notifies them of the relevant information. The recipient then receives the notification and prepares to receive the donation.

[1698] Prompt Sentence Examples

[1699] Here are some example prompts used by generative AI models:

[1700] "Please predict next month's demand based on the sales data from the past year."

[1701] "Generate a list of food items that are close to expiry and notify retailers."

[1702] The above is a specific embodiment for carrying out the present invention. This system can systematically reduce food waste and contribute to the realization of a sustainable society.

[1703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1704] Demand forecasting and inventory management

[1705] Step 1:

[1706] The server collects sales data from retailers and the food industry via APIs, including past sales history, demand fluctuations, and seasonal factors.

[1707] Input: Sales data from retailers and the food industry

[1708] Specific operation: The server temporarily stores the data collected using the API.

[1709] Step 2:

[1710] The server stores the collected data in a database and performs data cleaning if there are missing or outliers.

[1711] Input: Sales data collected in Step 1

[1712] Data processing: Imputing missing values ​​and removing outliers

[1713] Output: Cleaned sales data

[1714] Specific operation: After storing in the database, an algorithm is used to fill in missing data and remove abnormal data.

[1715] Step 3:

[1716] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the cleaned data.

[1717] Input: Cleaned sales data

[1718] Data Computing: Time Series Analysis with Generative AI Models

[1719] Output: Forecasted sales for the next month or period

[1720] Specific operation: Sales data is input into the generative AI model, and the prediction results are compiled in report format.

[1721] Step 4:

[1722] The server runs an algorithm to calculate optimal inventory levels and order quantities based on the results of the demand forecast and notifies the retailer.

[1723] Input: Demand forecast results

[1724] Data processing: Optimizing inventory levels and order quantities

[1725] Output: Notification message to retailer

[1726] What it does: It uses algorithms to calculate stock levels and order quantities, then notifies retailers using automated notifications.

[1727] Best before date management

[1728] Step 1:

[1729] The terminal uses RFID tags and sensors to obtain expiration date data for each food item when it arrives.

[1730] Input: RFID tag and sensor information of received food

[1731] How it works: The device scans the expiration date of each food item and collects the data.

[1732] Step 2:

[1733] The terminal transmits the acquired expiration date data to the server in real time.

[1734] Input: Retrieved expiration date data

[1735] Output: Sending expiration date data to the server

[1736] Specific operation: The terminal immediately transfers the expiration date data to the server via the network.

[1737] Step 3:

[1738] The server stores expiration date data in a database, prioritizes the list of foods approaching their expiration date, and sends warning messages.

[1739] Input: Best before date data sent from the terminal

[1740] Data processing: Identifying and listing foods that are close to expiry date

[1741] Output: Warning message

[1742] What happens: The server stores the expiration date data in a database and sends a warning message using an alert system.

[1743] Review of food appearance standards

[1744] Step 1:

[1745] The user (supplier) reassessed the food appearance standards and identified foods that had no quality issues but had minor appearance issues.

[1746] Input: Food appearance information

[1747] What it does: Suppliers manually sort out food that looks unsightly.

[1748] Step 2:

[1749] The user (supplier) registers the information of the identified food on the server.

[1750] Input: Reevaluated food information

[1751] Output: Register food information on the server

[1752] Specific operation: The supplier inputs and transmits food information to the server using a dedicated interface.

[1753] Step 3:

[1754] The server stores the registered information in a database and provides the information to top retailers.

[1755] Input: Food information registered by the supplier

[1756] Data processing: storing registration information and providing it to retailers

[1757] Output: Notification message to retailer

[1758] What it does: The server stores the registration information and notifies the retailer using an automatic notification function.

[1759] Improved logistics and delivery

[1760] Step 1:

[1761] The server collects data obtained from various sensors during logistics (temperature, vibration, time, etc.).

[1762] Input: Logistics data from sensors

[1763] Specific operation: The server automatically collects data from sensors installed in trucks and warehouses.

[1764] Step 2:

[1765] The server analyzes the collected data and identifies factors that affect food quality.

[1766] Input: Logistics data

[1767] Data Computation: Identifying Factors Affecting Quality

[1768] Output: Quality Risk Identification Report

[1769] Specific Actions: Identify quality risk factors using data analysis algorithms.

[1770] Step 3:

[1771] The server generates a proposal for the optimal shipping conditions and notifies the logistics company.

[1772] Input: Quality Risk Identification Report

[1773] Data processing: Generation of optimization proposals for transport conditions

[1774] Output: Notification of proposed transport conditions

[1775] Specific operation: The server calculates the optimal transportation conditions based on the analysis results and sends proposals to logistics companies using a notification system.

[1776] Education and Awareness

[1777] Step 1:

[1778] The server generates an educational program about the importance of food waste and solutions, and distributes it to the device.

[1779] Input: Educational content material

[1780] Data processing: Creation of educational programs

[1781] Output: Educational program distribution to terminals

[1782] Specific operation: The server creates educational content and sends it to the terminal based on the distribution schedule.

[1783] Step 2:

[1784] The terminal provides educational content to suppliers, retailers and consumers.

[1785] Input: Educational program distributed from the server

[1786] Output: Providing education to suppliers, retailers and consumers

[1787] Specific operation: The device plays videos and interactive learning materials and provides them to the user.

[1788] Step 3:

[1789] The terminal collects the user's understanding and impressions after viewing the program in a feedback form and transmits the collected information to the server.

[1790] Input: User feedback

[1791] Output: Send feedback to the server

[1792] Specific operation: The terminal displays a feedback form and sends it to the server after completion.

[1793] Step 4:

[1794] The server analyzes the collected feedback and improves the educational program.

[1795] Input: Feedback sent from the device

[1796] Data Computation: Feedback Analysis

[1797] Output: Improved educational programs

[1798] Specific operation: The server analyzes the feedback and improves the next educational content based on the analysis results.

[1799] Building food banks and donation networks

[1800] Step 1:

[1801] Users (retailers) register excess stock and food items approaching their expiration date on the server.

[1802] Input: Information on excess stock and food items with approaching expiration dates

[1803] Output: Register food information on the server

[1804] How it works: Retailers use a dedicated interface to input and send food information to the server.

[1805] Step 2:

[1806] The server searches for the most suitable donation recipient (food bank or charity) based on the registered data.

[1807] Input: Food information registered by retailers

[1808] Data calculations: Finding the best donation destination

[1809] Output: Generate a donation list

[1810] Specific operation: The server searches a database of donation recipients and creates a list of the most suitable donation recipients.

[1811] Step 3:

[1812] The server generates a list of donation recipients and notifies each recipient of relevant information.

[1813] Input: Donation list

[1814] Output: Notification message to the donation recipient

[1815] Specific operation: The server sends a notification message to the recipient of the donation, prompting them to prepare for collection.

[1816] These are the specific processing steps of the system, which makes it possible to efficiently reduce food waste.

[1817] (Application example 1)

[1818] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1819] Food waste is a serious problem in modern society, and how retailers and consumers can reduce food waste is an important issue. Conventional systems are inadequate in demand forecasting, making it difficult to optimize inventory management. Food waste also occurs due to inadequate best-before date management and inefficient logistics and delivery. Furthermore, a lack of education and awareness about food waste has prevented people from raising awareness. For this reason, there is a need for a system that can more accurately forecast demand and manage inventory, streamline best-before date management, optimize logistics and delivery, and raise awareness about reducing food waste.

[1820] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1821] In this invention, the server includes means for collecting food data, means for forecasting demand based on the data, means for optimizing inventory levels based on the forecast results, means for using wireless tags or sensors to acquire expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of food waste and solutions, means for donating excess inventory and food approaching its expiration date to a charity, means for suggesting optimal food inventory levels based on the demand forecast, means for offering foods with appearance issues but no quality issues as value options to customers, means for presenting users with educational videos and campaign information about reducing food waste, and means for providing optimized delivery routes and temperature and quality control information. This improves the accuracy of demand forecasts and inventory management, streamlines expiration date management, enables optimization of logistics and delivery, promotes awareness activities for reducing food waste, and realizes a sustainable food supply chain.

[1822] "Food data" refers to data provided by retailers and the food industry, including information on sales history, demand fluctuations, seasonal factors, etc.

[1823] "Demand forecasting" is the act of predicting future demand based on past data, which helps optimize inventory levels.

[1824] "Inventory level" refers to the appropriate amount of inventory, adjusted based on demand forecasts.

[1825] A "wireless tag" is a device that exchanges data using wireless communication, like an RFID tag, and is used to obtain information such as expiration dates.

[1826] A "sensor" is a device used to measure a physical variable and capture it as digital data.

[1827] "Best before date data" refers to data that includes information about the expiration date of food products, and is collected and managed.

[1828] "Appearance standards" refer to the evaluation criteria for food appearance, but may be reevaluated based on the quality of the ingredients.

[1829] "Logistics" refers to all activities related to the distribution process of goods, and is an area that requires efficiency.

[1830] "Delivery" refers to the process of receiving goods from a specific point and transporting them to their destination.

[1831] "Educational Program" refers to awareness-raising and educational content on food waste provided to suppliers, retailers and consumers.

[1832] "Donation recipients" refer to the institutions and organizations that receive excess inventory and food items nearing their expiration date.

[1833] A "value option" is a food option that offers customers food that has cosmetic issues but is of the same quality at a lower price than usual.

[1834] A "delivery route" is a route for efficiently delivering products, and is an optimized and proposed route.

[1835] "Temperature and quality control information" refers to information that includes environmental data such as temperature necessary to maintain food quality.

[1836] The present invention relates to a system for solving the food waste problem. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations. Specific embodiments of the system are described below.

[1837] 1. Demand forecasting and inventory management

[1838] The server collects sales data provided by retailers and the food industry and stores it in a database. The collected data includes past sales history, demand fluctuations, seasonal factors, etc. Missing values ​​and outliers are processed through data cleaning. Demand forecasts are made using the preprocessed data using an AI model (e.g., a time series analysis model run on Amazon Sagemaker). Optimal inventory levels and order quantities are calculated based on the results and notified to retailers.

[1839] (Specific examples)

[1840] The server analyzes sales data from the past year and predicts demand for the next month. As a result, if a particular product is expected to sell well, it will be optimized to order more of it.

[1841] 2. Introduction of expiration date management

[1842] The terminal uses wireless tags and sensors to acquire expiration date data for each food item when it arrives. The acquired data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1843] (Specific examples)

[1844] The terminal scans newly arrived products, obtains their expiration dates, and sends them to a server, which generates a list of products with expiration dates within a week and notifies retailers, allowing them to prioritize them for sale or donation.

[1845] 3. Review of food appearance standards

[1846] The supplier reassessed the food's appearance standards and identified foods that had no quality issues but had minor visual issues. The results of this identification were registered on the server, which then stored this information in a database and provided it to higher-ranking retailers.

[1847] (Specific examples)

[1848] Suppliers register damaged but safe apples on a server, and the server notifies retailers of this information, preventing the apples from being wasted.

[1849] 4. Improved logistics and delivery

[1850] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[1851] (Specific examples)

[1852] The server analyzes temperature data during transport, discovers that food quality is maintained within a specific temperature range, and notifies the logistics company of this information, allowing them to set optimal transport conditions.

[1853] 5. Education and Awareness

[1854] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[1855] (Specific examples)

[1856] The device plays an educational video about food waste to consumers, who then provide feedback. The server analyzes the data and generates better content.

[1857] 6. Building food banks and donation networks

[1858] Retailers register excess inventory and food items nearing their expiration date on the server, which analyzes the information and generates a list of the most suitable recipients for donation. This ensures food is provided where it is needed and reduces waste.

[1859] (Specific examples)

[1860] Retailers register food items that are nearing their expiration date on a server, which then notifies the recipient of that data. By receiving the food, the recipient can reduce food waste.

[1861] Prompt Sentence Examples

[1862] "Create an application that uses past order data to forecast next week's demand and suggest optimal inventory levels."

[1863] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[1864] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1865] Step 1:

[1866] The server collects sales data provided by retailers and the food industry. Input data includes past sales history, demand fluctuations, seasonal factors, etc. This data is then stored in a database. Specifically, the server retrieves and stores the data via provided CSV files or APIs. This provides the data necessary for the predictive model.

[1867] Step 2:

[1868] The server preprocesses the collected data. It receives sales data as input and performs data cleaning. It processes missing values ​​and outliers to improve the quality of the data. Specifically, it extracts the necessary data from the database, fills in missing values, and removes outliers. This results in a clean dataset.

[1869] Step 3:

[1870] The server performs demand forecasting based on the preprocessed data. It uses the clean dataset as input and analyzes it using an AI model (Amazon Sagemaker's time series analysis model). The model performs demand forecasting and generates future demand forecast data as output. Specifically, it inputs data into the AI ​​model and stores the results in a database. This enables optimal inventory management in anticipation of future demand.

[1871] Step 4:

[1872] The terminal uses wireless tags and sensors to obtain expiration date data for each food item when it arrives. It reads the information on the tag attached to the food as input and obtains the expiration date data. The obtained data is sent to the server in real time. Specifically, it uses an RFID reader or barcode scanner to obtain data and sends it to the server. This ensures that expiration date data is managed reliably.

[1873] Step 5:

[1874] The server prioritizes and lists foods approaching their expiration date and sends a warning message to retailers. It uses the expiration date data acquired as input to extract foods that are close to their expiration date from the database. It generates and notifies warning messages and lists as output. Specifically, it periodically scans the database, extracts data that matches the conditions, and issues warnings via email or a notification system. This enables effective inventory management.

[1875] Step 6:

[1876] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have minor visual issues. Food appearance data is acquired as input and registered on a server. A list of reevaluated foods is generated as output. Specifically, visual inspections are performed manually or using image recognition technology, and the identification results are entered into the server. This reduces food waste.

[1877] Step 7:

[1878] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). It receives sensor data as input and performs analysis. It identifies factors that affect quality as output and proposes optimal transport conditions. Specifically, it receives sensor data in real time, processes the data using an analysis engine, and notifies the logistics company of the results. This allows quality to be maintained even during transport.

[1879] Step 8:

[1880] The device provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. It receives and displays educational content distributed from the server as input. It collects user viewing results and feedback as output. Specifically, it provides video playback functions and a questionnaire form, and sends viewing history and feedback information to the server. This aims to raise awareness of food waste.

[1881] Step 9:

[1882] Retailers register excess inventory and food items approaching their expiration date on the server. Inventory data is acquired as input and uploaded to the server. Based on this information, the server searches for the most suitable donation recipients and generates a list. Specifically, food data for donation is entered, and the server creates a list of potential donation recipients and notifies the recipient. This allows surplus food to be used effectively and reduces food waste.

[1883] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1884] The present invention relates to a system for solving the food waste problem, and in particular, it combines an emotion engine that recognizes user emotions and optimizes educational programs, etc. This system collects food data, forecasts demand, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations, and further uses the emotion engine to improve the user's educational experience.

[1885] 1. Demand forecasting and inventory management

[1886] Program processing overview

[1887] server:

[1888] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer.

[1889] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[1890] 2. Introduction of expiration date management

[1891] Program processing overview

[1892] Device:

[1893] The terminal uses RFID tags and sensors to capture expiration date data for each food item as it arrives. The data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[1894] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[1895] 3. Review of food appearance standards

[1896] Program processing overview

[1897] User (supplier):

[1898] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[1899] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[1900] 4. Improved logistics and delivery

[1901] Program processing overview

[1902] server:

[1903] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.) and, based on the analysis results, identifies factors that affect quality and proposes optimal transport conditions.

[1904] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[1905] 5. Education and Awareness

[1906] Program processing overview

[1907] Device:

[1908] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[1909] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[1910] server:

[1911] Analyze the feedback collected and improve your education program.

[1912] 6. Building food banks and donation networks

[1913] Program processing overview

[1914] User (retailer):

[1915] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to pick up the items.

[1916] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[1917] 7. Introducing the Emotion Engine

[1918] Program processing overview

[1919] Device:

[1920] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[1921] server:

[1922] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, optimizing it to provide more detailed explanations or supplementary materials if the user is confused.

[1923] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1924] The above is a specific embodiment of the present invention. This system can systematically reduce the problem of food waste, improve the educational experience of users, and contribute to the realization of a sustainable society.

[1925] The processing flow will be explained below.

[1926] 1. Demand forecasting and inventory management

[1927] Step 1: Data collection

[1928] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[1929] Step 2: Data Preprocessing

[1930] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[1931] Step 3: Demand forecast

[1932] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[1933] Step 4: Optimize inventory levels

[1934] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[1935] Step 5: Notification

[1936] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[1937] 2. Introduction of expiration date management

[1938] Step 1: Obtaining expiration date data

[1939] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[1940] Step 2: Send data

[1941] Terminal: Sends the acquired expiration date data to the server.

[1942] Step 3: Update the database

[1943] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[1944] Step 4: Notification

[1945] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[1946] 3. Review of food appearance standards

[1947] Step 1: Reassess the criteria

[1948] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[1949] Step 2: Registration

[1950] User (provider): Registers the identified results on the server.

[1951] Step 3: Data Management

[1952] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[1953] Step 4: Notification

[1954] Server: Sends proposals for relaxing standards to the retailer's terminal.

[1955] 4. Improved logistics and delivery

[1956] Step 1: Data collection

[1957] Terminal: Data such as temperature, vibration, and time during transportation is collected from various sensors.

[1958] Step 2: Send data

[1959] Terminal: Sends acquired data to the server in real time.

[1960] Step 3: Data analysis

[1961] Server: Analyzes the received data and identifies factors that affect quality.

[1962] Step 4: Improvement proposals

[1963] Server: Generates recommendations for optimal shipping conditions and sends notifications to retailers and logistics providers.

[1964] 5. Education and Awareness

[1965] Step 1: Content Delivery

[1966] Server: Generates and distributes educational programs and awareness materials to suppliers, retailers, and consumers.

[1967] Step 2: Listening and Feedback

[1968] Terminal: The user watches the educational program and sends their understanding and impressions to the server via a feedback form.

[1969] Step 3: Feedback analysis

[1970] Server: Analyze the collected feedback and improve the education program.

[1971] 6. Building food banks and donation networks

[1972] Step 1: Register excess inventory

[1973] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[1974] Step 2: Find the best donation destination

[1975] Server: Analyzes registered food data and searches for the most suitable food banks and donation destinations.

[1976] Step 3: Donation Notification

[1977] Server: Generates a list of foods that can be donated to food banks and donation recipients and notifies them.

[1978] Step 4: Donate

[1979] User (retailer): Receives notification from the server, dispatches the relevant food to the food bank, and reports the donation completion.

[1980] 7. Introducing the Emotion Engine

[1981] Step 1: Collecting Emotional Data

[1982] Terminal: Recognizes emotions from the user's facial expressions, voice, text input, etc., and collects emotional data.

[1983] Step 2: Send data

[1984] Terminal: Sends collected emotion data to the server in real time.

[1985] Step 3: Sentiment Data Analysis

[1986] Server: Analyzes the received emotion data and evaluates the user's emotional state.

[1987] Step 4: Optimize your education program

[1988] Server: Dynamically adjusts the content of the user's educational program based on emotional data. For example, if the user is confused, the server will provide more detailed explanations or supplementary materials.

[1989] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[1990] The above are the specific processing steps for implementing the present invention. This system can systematically reduce the food waste problem, improve the educational experience for users, and contribute to the realization of a sustainable society.

[1991] Example 2

[1992] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1993] Food waste is a social problem that not only increases the environmental burden but also causes economic losses. The causes of this problem include inaccurate demand forecasts, inadequate inventory management, lax expiration date management, and wasteful disposal due to food appearance standards. Furthermore, the lack of effective educational programs and systems for the effective use of excess inventory exacerbates this issue. The present invention aims to comprehensively solve these problems.

[1994] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1995] In this invention, the server includes means for collecting food data, means for performing demand forecasting, means for optimizing inventory levels based on the forecast results, means for using an identification device to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration date, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs, means for donating excess inventory or foods approaching their expiration date, and means for recognizing user emotions and dynamically adjusting the content of educational programs based on the emotions, thereby enabling food waste reduction, efficient logistics, effective provision of educational programs, and promotion of donations.

[1996] "Food data" refers to information on food types, sales volumes, expiration dates, storage conditions, etc.

[1997] "Demand forecasting" refers to the process of predicting future demand based on past sales data and market trends.

[1998] "Stock levels" refers to the quantity of goods stored at a retailer or warehouse.

[1999] "RFID tag" refers to a device used to identify an item using wireless communication.

[2000] A "sensor" refers to a device that senses physical information (temperature, vibration, position, etc.) and converts it into data.

[2001] "Best before date data" refers to information about the date by which a food product can be safely consumed.

[2002] "Appearance standards" refer to quality standards regarding the appearance of food.

[2003] "Logistics" refers to the entire process of delivering goods from production to consumers.

[2004] "Delivery optimization" refers to the process of delivering goods efficiently while reducing costs and maintaining quality.

[2005] An "educational program" refers to a collection of materials and activities designed to provide learners with knowledge on a particular topic.

[2006] A "food bank" is a facility that collects and distributes food to people and organizations in need.

[2007] "Recipient" refers to the organization or individual that receives the donated goods or funds.

[2008] An "emotion engine" refers to technology that recognizes emotions from people's facial expressions, voices, text, etc., and adjusts the system's behavior based on that.

[2009] "Dynamic adjustment" refers to changing the contents of a system or program based on real-time conditions and data.

[2010] The present invention relates to a comprehensive management system for solving the food waste problem, including food data collection, demand forecasting, inventory management, expiration date management, review of appearance standards, optimization of logistics and delivery, provision of educational programs, donation of excess inventory, and optimization of user educational experience using an emotion engine.

[2011] 1. Demand forecasting and inventory management

[2012] Program processing overview

[2013] Server: The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, etc. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Demand forecasts are made using the preprocessed data with a time series analysis model (e.g., ARIMA model). Based on the forecasted demand, optimal inventory levels and order quantities are calculated and notified to the retailer.

[2014] Example: The server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, so it optimizes the order to order more of it.

[2015] 2. Introduction of expiration date management

[2016] Program processing overview

[2017] Terminal: The terminal uses RFID tags and sensors to capture expiration date data for each food item when it arrives. The captured data is sent in real time to the server, which stores it in a database. The server then prioritizes and lists foods that are approaching their expiration date and sends a warning message.

[2018] Example: A terminal scans newly arrived products for RFID tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies the retailer, allowing them to prioritize sales or donations.

[2019] 3. Review of food appearance standards

[2020] Program processing overview

[2021] User (supplier): The supplier reevaluates food appearance standards and identifies foods that have visual issues but are not of high quality. The supplier registers the results of this identification on the server. The server stores this information in a database and proposes relaxing the standards to retailers.

[2022] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[2023] 4. Improved logistics and delivery

[2024] Program processing overview

[2025] Server: The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[2026] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a specific temperature range, and notifies logistics companies of this information to set optimal transportation conditions.

[2027] 5. Education and Awareness

[2028] Program processing overview

[2029] Terminal: The terminal provides educational programs to suppliers, retailers, and consumers about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal.

[2030] Example: A device plays an educational video about food waste to consumers, consumers provide feedback, and the server analyzes the data to generate better content.

[2031] Server: Analyze the collected feedback and improve the educational program.

[2032] 6. Building food banks and donation networks

[2033] Program processing overview

[2034] User (retailer): Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[2035] Example: Retailers register food products that are nearing their expiration date on a server, which then notifies the food bank of that data, which then accepts the food, thereby reducing food waste.

[2036] 7. Introducing the Emotion Engine

[2037] Program processing overview

[2038] Device: The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to the server in real time.

[2039] Server: The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state. For example, if the user is confused, the server optimizes the content by providing more detailed explanations and supplementary materials.

[2040] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[2041] The system of the present invention enables efficient inventory management and proper management of expiration dates, effectively reducing food waste. It also makes it possible to fundamentally solve the food waste problem by optimizing logistics and providing educational programs. Furthermore, the use of an emotion engine improves the user's educational experience and raises awareness of reducing food waste.

[2042] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2043] 1. Demand forecasting and inventory management

[2044] Step 1: Data collection

[2045] Server: Automatically collects data from retailers and the food industry, such as past sales data, demand fluctuations, and seasonal factors, via API.

[2046] Inputs: Historical sales data, demand fluctuations, seasonal factors, etc.

[2047] Output: The collected data is stored in a database.

[2048] Specific operation: The server synchronizes data with the sales systems of each retailer and acquires the specified data.

[2049] Step 2: Data cleaning

[2050] Server: Processes the collected data to remove missing or outlier values.

[2051] Input: Raw data collected.

[2052] Output: The cleaned dataset.

[2053] Specific operation: The server uses Python and the pandas library to fill in NaN values ​​with the median and remove obviously anomalous data.

[2054] Step 3: Demand forecast

[2055] Server: Using the preprocessed data, perform demand forecasting using a time series analysis model (e.g., ARIMA model).

[2056] Input: The cleaned dataset.

[2057] Output: Forecasted demand data.

[2058] Specific operation: The server uses the scikit-learn library to build an ARIMA model and fit it to past data. The results are saved in the database as predicted values.

[2059] Step 4: Optimize inventory levels

[2060] Server: Runs algorithms that calculate optimal inventory levels and order quantities based on predicted demand.

[2061] Input: Forecasted demand data.

[2062] Output: Optimal inventory levels and order quantities.

[2063] Specific operation: The server uses linear programming to optimize the balance between supply and demand and calculate the order quantity.

[2064] Step 5: Notification

[2065] Server: Notifies the retailer of the calculation results.

[2066] Inputs: Optimal stock levels and order quantities.

[2067] Output: Notification email.

[2068] Specific operation: The server sends an email notification using the SMTP protocol.

[2069] 2. Introduction of expiration date management

[2070] Step 1: Obtaining expiration date data

[2071] Terminal: Obtains expiration date data from RFID tags and sensors attached to incoming food products.

[2072] Input: RFID tag, sensor signal.

[2073] Output: Best before date data.

[2074] Specific operation: The terminal uses an RFID scanner to read the tag and obtain the expiration date information.

[2075] Step 2: Send data

[2076] Terminal: Sends the acquired expiration date data to the server in real time.

[2077] Input: Best before date data.

[2078] Output: The data sent to the server.

[2079] Specific operation: The device captures the signal and sends the information to the server using an HTTP request.

[2080] Step 3: Save data

[2081] Server: Stores the received data in a database.

[2082] Input: Best before date data.

[2083] Output: Data stored in the database.

[2084] Specific operation: The server uses a NoSQL database (e.g., MongoDB) to store data.

[2085] Step 4: Generate a warning message

[2086] Server: Lists food items that are nearing their expiration date and generates warning messages.

[2087] Input: Best before date data stored in the database.

[2088] Output: A warning message.

[2089] What it does: The server periodically queries, generates a list of expiration dates, and sends notifications to each retailer.

[2090] 3. Review of food appearance standards

[2091] Step 1: Reassess appearance criteria

[2092] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[2093] Input: Image data of the actual item.

[2094] Output: Appearance-based classification results.

[2095] How it works: Suppliers use image recognition apps to scan food and determine if it meets standards.

[2096] Step 2: Registering the identification results

[2097] User (provider): Registers the identification results on the server.

[2098] Input: Identification result data.

[2099] Output: Data registered on the server.

[2100] Specific operation: The supplier uses a dedicated app to input the identification results and send them to the server.

[2101] Step 3: Notification

[2102] Server: Stores the re-evaluated criteria information in a database and notifies the retailer.

[2103] Input: Identification result data.

[2104] Output: Notification message to retailer.

[2105] Specific operation: The server uses AWS SNS (Simple Notification Service) to notify the baseline information.

[2106] 4. Improved logistics and delivery

[2107] Step 1: Receiving data

[2108] Server: Receives data from various sensors during logistics (temperature, vibration, time, etc.).

[2109] Input: Sensor data (temperature, vibration, time, etc.).

[2110] Output: Sensor data stored in a database.

[2111] Specific operation: The sensor sends data to the server via the IoT network.

[2112] Step 2: Data analysis

[2113] Server: Analyzes the received data and identifies factors that affect quality.

[2114] Input: Sensor data.

[2115] Output: Identification of factors that affect quality.

[2116] Specific operation: The server uses a data analysis tool (e.g., MATLAB) to detect abnormal values ​​in temperature and vibration data.

[2117] Step 3: Optimization suggestions

[2118] Server: Based on the analysis results, proposes optimal transport conditions.

[2119] Input: Identification of factors affecting quality.

[2120] Output: Proposal of optimal transport conditions.

[2121] Specific operation: The server uses an AI algorithm to calculate the optimal transportation conditions and notify the logistics company.

[2122] 5. Education and Awareness

[2123] Step 1: Educational content distribution

[2124] Terminal: Presents the educational program delivered from the server to the user.

[2125] Input: Educational content data.

[2126] Output: Presenting educational content to the user.

[2127] What it does: The device uses a web app to play videos and infographics.

[2128] Step 2: Send your feedback

[2129] User: Sends the knowledge and impressions gained through the educational program as feedback via the terminal.

[2130] Input: User feedback data.

[2131] Output: Feedback data sent to the server.

[2132] Specific operation: The user fills out a questionnaire form, and the terminal sends the data to the server.

[2133] Step 3: Feedback analysis

[2134] Server: Analyze the collected feedback and improve the educational program.

[2135] Input: Feedback data.

[2136] Output: Improved educational content data.

[2137] Specific operation: The server analyzes the feedback using data analysis tools and generates new educational content.

[2138] 6. Building food banks and donation networks

[2139] Step 1: Register excess inventory

[2140] User (retailer): Registers excess stock and food items approaching their expiration date on the server.

[2141] Input: Excess stock data, expiration date data.

[2142] Output: Inventory data registered on the server.

[2143] Specific operation: Retailers use a dedicated app to enter inventory information and send it to the server.

[2144] Step 2: Find the best donation destination

[2145] Server: Analyzes registered information and searches for the most suitable food bank or donation destination.

[2146] Input: Inventory data, expiration date data.

[2147] Output: A list of optimal donation recipients.

[2148] What it does: The server uses the Google Maps API to geographically search for the best donation destination.

[2149] Step 3: Donation notification

[2150] Server: Based on the generated list, notifications are sent to the recipients, instructing them to prepare for collection.

[2151] Input: A list of optimal donation destinations.

[2152] Output: Notification message to the donation recipient.

[2153] Specific operation: The server sends a notification to the donation recipient via email or SMS.

[2154] 7. Introducing the Emotion Engine

[2155] Step 1: Collecting Emotional Data

[2156] Device: Data is acquired using an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc.

[2157] Input: facial expression data, voice data, text data.

[2158] Output: Emotion data.

[2159] Specific operation: The device uses the camera and microphone to read the user's emotions using image recognition and voice analysis technology.

[2160] Step 2: Send data

[2161] Terminal: Sends collected emotion data to the server in real time.

[2162] Input: Emotion data.

[2163] Output: Emotion data sent to the server.

[2164] Specific operation: Emotion data is sent to the server via API.

[2165] Step 3: Data analysis

[2166] Server: Analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state.

[2167] Input: Emotion data.

[2168] Output: Tailored educational content.

[2169] Specific operation: The server uses a machine learning model to analyze the emotional data and select appropriate educational content.

[2170] (Application example 2)

[2171] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2172] Food waste has become a serious problem in the modern food industry. In particular, large amounts of food are often discarded due to insufficient management of expiration dates and difficulties in maintaining appropriate inventory. The problem is also exacerbated by a lack of education on food waste among suppliers, retailers, and consumers. Furthermore, inefficient logistics and inadequate optimization of delivery can lead to deterioration in food quality and waste. In addition, educational programs that ignore customer feelings make it difficult to effectively raise awareness. There is a need for a system that can solve these problems, reduce food waste, and contribute to the realization of a sustainable society.

[2173] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting food data, means for performing demand forecasting based on the data, means for optimizing inventory levels based on the obtained forecast results, means for using identification tags or sensors to obtain expiration date data, means for managing the expiration date data and notifying about foods approaching their expiration dates, means for reevaluating food appearance standards, means for optimizing efficient logistics and delivery, means for providing educational programs for suppliers, retailers, and consumers about the importance of and solutions to food waste, means for donating excess inventory and food approaching its expiration date to welfare facilities or charity recipients, means for notifying inventory status and expiration date information in real time using smart devices, and means for recognizing and analyzing customer emotion data and dynamically adjusting educational content. This enables effective inventory management and expiration date management, efficient logistics and delivery, and education based on customer emotion, all aimed at reducing food waste.

[2174] "Food data" is a general term for various data related to food, such as food sales history, inventory, expiration date, and transportation status.

[2175] "Demand forecasting" is a technology that predicts the amount of inventory that will be needed in the future based on past sales data and market trends.

[2176] "Inventory level" is a standard for managing the number of products required within a specific period of time.

[2177] An "identification tag" is a device used to identify an item, such as an RFID tag or a barcode.

[2178] A "sensor" is a device that detects physical or chemical properties and collects data.

[2179] "Best before date data" is information that indicates the period of time during which food can be safely consumed.

[2180] "Food appearance standards" are quality standards regarding the appearance of food.

[2181] "Logistics" is the set of processes that move goods from supplier to consumer.

[2182] "Delivery" is the process of delivering goods to their destination.

[2183] An "educational program" is an educational tool that allows students to acquire specific knowledge and skills.

[2184] A "welfare facility" is a facility for providing social welfare.

[2185] "Recipient" refers to the institution or organization that will receive the surplus supplies or assistance.

[2186] "Smart devices" are electronic devices that have built-in communication functions and various sensors and can be operated intelligently.

[2187] "Stock status" is information indicating the stock status at a specific point in time.

[2188] "Customer emotion data" refers to emotion information analyzed from the user's facial expressions and voice using emotion recognition technology.

[2189] "Educational Content" is a general term for information and materials provided for educational purposes.

[2190] This invention is a comprehensive system for solving the food waste problem, and in particular, it improves the user experience by using automated data collection, demand forecasting, inventory management, expiration date management, reevaluation of appearance standards, logistics and delivery optimization, educational programs, and emotion recognition. The system aims to increase efficiency and sustainability in the food industry.

[2191] 1. Demand forecasting and inventory management

[2192] Program processing overview

[2193] server:

[2194] The server collects sales data provided by the food industry and stores it in a database. The data includes past sales history, seasonal factors, and demand fluctuations. The collected data is preprocessed to remove missing values ​​and outliers. Demand forecasts are then made using a generative AI model (e.g., a time series analysis model) using the processed data. Optimal inventory levels and order quantities are calculated based on the forecast results.

[2195] Example: For example, a server analyzes sales data from the past year and predicts demand for the next month. As a result, it predicts that a particular product will sell well, and notifies the retailer to order more of it.

[2196] 2. Introduction of expiration date management

[2197] Program processing overview

[2198] Device:

[2199] The terminal uses identification tags or sensors to capture expiration date data for each food item upon arrival. The captured data is sent to a server in real time and stored in a database. Food items approaching their expiration date are automatically listed and warning messages are sent to retailers and consumers via the terminal or smart device.

[2200] Example: A terminal scans newly arrived products for their identification tags, obtains expiration date data, and sends it to a server. The server generates a list of products with expiration dates within a week and notifies retailers so they can prioritize them for sale or donation.

[2201] 3. Review of food appearance standards

[2202] Program processing overview

[2203] User (supplier):

[2204] Suppliers reassess food appearance standards and identify foods that have visual issues but are still of good quality. The results of this identification are registered on a server, which stores this information in a database and proposes relaxing standards to retailers.

[2205] Example: A supplier registers blemished but safe apples on a server, and the server notifies the retailer of this information, preventing the apples from being wasted.

[2206] 4. Improved logistics and delivery

[2207] Program processing overview

[2208] server:

[2209] The server receives and analyzes data collected from various sensors during logistics (such as temperature, vibration, and time). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions.

[2210] Example: A server analyzes temperature data during transportation, discovers that food quality is maintained within a certain temperature range, and notifies the logistics company of this information.

[2211] 5. Education and Awareness

[2212] Program processing overview

[2213] Device:

[2214] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to users by the terminal.

[2215] server:

[2216] The server analyzes the collected feedback and improves the educational program.

[2217] Example: A device plays an educational video about food waste to consumers, and the consumers provide feedback. The server analyzes the data and generates better content.

[2218] 6. Building food banks and donation networks

[2219] Program processing overview

[2220] User (retailer):

[2221] Retailers register excess inventory and food items approaching their expiration date on the server. The server analyzes the information and searches for the most suitable welfare facilities and donation recipients. Based on the list generated by the server, the recipients are notified and prompted to prepare for collection.

[2222] Example: Retailers register food products that are nearing their expiration date on a server, and the server notifies the food bank of this data, allowing the food bank to receive the food.

[2223] 7. Introducing the Emotion Engine

[2224] Program processing overview

[2225] Device:

[2226] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, voice, text input, etc. The collected emotion data is sent to a server in real time.

[2227] server:

[2228] The server analyzes the received emotional data and dynamically adjusts the content of the educational program based on the user's emotional state, for example, providing more detailed explanations or supplementary materials if the user is confused.

[2229] Example: The device reads the user's stress from their facial expression, the server analyzes it, and provides relaxation content and simplified explanations to reduce stress.

[2230] Prompt Sentence Examples

[2231] "Predict next month's product demand based on current sales data and historical trends, and calculate inventory levels to match demand. Also, develop a system that notifies smart glasses in real time when products are about to expire, and recognize customer sentiment to deliver appropriate educational content."

[2232] This will reduce overall food waste, as well as enable effective inventory management, expiration date management, improved logistics, and education, all of which will be concrete steps towards a sustainable society.

[2233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2234] Step 1:

[2235] Food data collection

[2236] server:

[2237] The server collects sales data, inventory data, and transportation data provided by the food industry, including past sales history, inventory quantity, expiration date, transportation status, etc. This data is stored in a database.

[2238] Input: Various data provided by the food industry

[2239] Output: Organized food data is saved in a database

[2240] Step 2:

[2241] Data Preprocessing

[2242] server:

[2243] The server cleans the collected data, removing missing values ​​and outliers, and classifies the data by time, region, product category, etc.

[2244] Input: Raw data collected

[2245] Output: Preprocessed data

[2246] Specific operations: Apply data cleaning algorithms to remove invalid data, and use classification algorithms to structure the data.

[2247] Step 3:

[2248] Demand forecasting

[2249] server:

[2250] The server uses a generative AI model (e.g., a time series analysis model) to make demand forecasts based on the pre-processed data.

[2251] Input: Preprocessed data

[2252] Output: Demand forecast results

[2253] Specific operation: Apply a time series analysis model (e.g., Prophet) to calculate future demand.

[2254] Step 4:

[2255] Inventory Optimization

[2256] server:

[2257] Based on the results of the demand forecast, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are communicated to the retailer.

[2258] Input: Demand forecast results

[2259] Output: Recommendations for optimal inventory levels and order quantities

[2260] Specific behavior: Runs inventory optimization algorithm and generates notification messages

[2261] Step 5:

[2262] Obtaining expiration date data

[2263] Device:

[2264] The terminal uses an identification tag or sensor to obtain product expiration date data and transmits it to a server in real time.

[2265] Input: Information from an identification tag or sensor

[2266] Output: Expiration date data obtained

[2267] Specific operation: Scan the expiration date of the product with the identifier and send the data to the server

[2268] Step 6:

[2269] Best before date management

[2270] server:

[2271] The server stores the acquired expiration date data in a database, lists foods whose expiration dates are approaching, and sends notifications.

[2272] Input: Retrieved expiration date data

[2273] Output: Notification message and listed data

[2274] Specific operation: Applying the expiration date management algorithm and generating a notification message

[2275] Step 7:

[2276] Reevaluation of Appearance Standards

[2277] User (supplier):

[2278] The supplier identifies food products that have quality issues but have appearance issues and registers the information on the server.

[2279] Input: Supplier evaluation results

[2280] Output: Proposal to revise appearance standards

[2281] Specific operation: Enter the rating information into the server and save it in the database.

[2282] Step 8:

[2283] Improved logistics and delivery

[2284] server:

[2285] The server analyzes data obtained from various sensors during logistics (temperature, vibration, time, etc.) and proposes optimal transportation conditions.

[2286] Input: Sensor data during logistics

[2287] Output: Optimal transport proposal

[2288] Specific behavior: Applying data analysis algorithms to generate recommendations

[2289] Step 9:

[2290] Providing educational programs

[2291] Device:

[2292] The terminal provides educational programs to suppliers, retailers, and consumers. The contents of the programs are distributed from the server.

[2293] Input: Program content sent from the server

[2294] Output: Displayed educational program

[2295] Specific behavior: Play educational content and collect user feedback

[2296] Step 10:

[2297] Building a donation network

[2298] User (retailer):

[2299] Retailers register excess stock and food items with approaching expiration dates on a server, which then notifies the most appropriate welfare facilities or donation recipients.

[2300] Input: Retailer registration information

[2301] Output: Notification message and donation list

[2302] Specific operation: Analyzes registration information and sends notification to the donation recipient

[2303] Step 11:

[2304] Introducing the Emotion Engine

[2305] Device:

[2306] The device recognizes emotions from the user's facial expressions, voice, and text input and transmits them to the server in real time.

[2307] Input: User emotion data

[2308] Output: Parsed emotion data

[2309] Specific behavior: Runs emotion recognition algorithm and sends data to server

[2310] server:

[2311] The server dynamically adjusts the content of the educational program based on the received emotional data, for example, providing detailed explanations if the user is confused.

[2312] Input: Emotion data sent from the device

[2313] Output: Adjusted educational program content

[2314] Specific actions: Analyzing emotional data and adjusting educational programs

[2315] This will enable a comprehensive process that covers everything from collecting food data to analyzing it, educating people, managing logistics, and adjusting based on their emotions.

[2316] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2317] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2318] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2319] [Fourth embodiment]

[2320] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2321] 7, a 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.

[2322] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2323] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2324] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2325] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2326] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2327] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2328] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2329] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[2330] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2331] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2332] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2333] The present invention relates to a system for solving the food waste problem, and specific embodiments thereof are described below. The system collects food data, performs demand forecasting, optimizes inventory, manages expiration date data, reevaluates appearance standards, streamlines logistics and delivery, provides educational programs, and promotes food donations.

[2334] 1. Demand forecasting and inventory management

[2335] Program processing overview

[2336] server

[2337] The server collects sales data provided by retailers and the food industry. This data includes past sales history, demand fluctuations, seasonal factors, and more. The collected data is stored in a database, and missing values ​​and outliers are cleaned. Next, demand forecasts are made using an AI model (e.g., a time series analysis model) using the preprocessed data. Based on the predicted demand, an algorithm is run to calculate optimal inventory levels and order quantities, and the results are notified to the retailer. For example, the server analyzes sales data from the past year to predict demand for the next month. As a result, it is optimized to order more of a particular product because it is expected to sell well.

[2338] 2. Introduction of expiration date management

[2339] Program processing overview

[2340] Terminal

[2341] The terminal uses RFID tags and sensors to obtain expiration date data for each food item as it arrives. The obtained data is sent in real time to a server, which stores it in a database. The server then prioritizes and lists foods whose expiration dates are approaching and sends a warning message. For example, the terminal scans newly arrived products, obtains their expiration dates, and then sends them to the server. The server generates a list of products with expiration dates within one week and notifies the retailer, allowing them to set priorities for sales or donations.

[2342] 3. Review of food appearance standards

[2343] Program processing overview

[2344] User (supplier)

[2345] Suppliers reevaluate food appearance standards and identify foods that are of good quality but have slight appearance issues. These identification results are registered on a server. The server stores this information in a database and provides it to higher-ranking retailers. This reduces the amount of food that is discarded based solely on appearance. For example, a supplier can register blemished but safe apples on a server, and the server can notify retailers of this information, preventing the apples from being wasted.

[2346] 4. Improved logistics and delivery

[2347] Program processing overview

[2348] server

[2349] The server receives and analyzes data collected from various sensors during logistics (temperature, vibration, time, etc.). Based on the analysis results, it identifies factors that affect quality and proposes optimal transport conditions. As a specific example, the server analyzes temperature data during transport and discovers that food quality is maintained within a specific temperature range. It then notifies this information to the logistics company, allowing it to set optimal transport conditions.

[2350] 5. Education and Awareness

[2351] Program processing overview

[2352] Terminal

[2353] The terminal provides suppliers, retailers, and consumers with educational programs about the importance of food waste and solutions. The educational programs are distributed from the server and presented to the user by the terminal. After the user watches the program, they send their understanding and impressions to the server via a feedback form. The server analyzes the collected feedback data and improves the educational programs. As a specific example, the terminal plays an educational video about food waste to consumers, who then submit their feedback, and the server analyzes that data to generate better content.

[2354] 6. Building food banks and donation networks

[2355] Program processing overview

[2356] User (retailer)

[2357] Retailers register excess inventory and food items approaching their expiration date on a server. The server analyzes this information and searches for the most suitable food banks and donation recipients. Based on the list generated by the server, it sends notifications to donation recipients and guides them in preparing to receive the food. As a specific example, retailers register food items approaching their expiration date on a server, and the server notifies food banks of this data, which then accepts the food, thereby reducing food waste.

[2358] The above is a specific embodiment for carrying out the present invention, which can systematically reduce food waste and contribute to the realization of a sustainable society.

[2359] The processing flow will be explained below.

[2360] 1. Demand forecasting and inventory management

[2361] Step 1: Data collection

[2362] Server: Requests and collects historical sales data from retailers and the food industry. The data is then stored in a database.

[2363] Step 2: Data Preprocessing

[2364] Server: Cleans the collected data for missing values ​​and outliers, and classifies it by time, region, product category, etc.

[2365] Step 3: Demand forecast

[2366] Server: Uses an AI model (e.g., a time series analysis model) to predict demand for the next month based on pre-processed data.

[2367] Step 4: Optimize inventory levels

[2368] Server: Runs algorithms that calculate optimal order quantities and inventory levels based on predicted demand data.

[2369] Step 5: Notification

[2370] Server: Sends optimal order quantity and stock level information to the retailer's terminal.

[2371] 2. Introduction of expiration date management

[2372] Step 1: Obtaining expiration date data

[2373] Terminal: Using RFID tags and sensors, each food item is scanned and its expiration date is captured upon arrival.

[2374] Step 2: Send data

[2375] Terminal: Sends the acquired expiration date data to the server.

[2376] Step 3: Update the database

[2377] Server: Registers the received expiration date data in a database and periodically updates the list of foods approaching their expiration date.

[2378] Step 4: Notification

[2379] Server: Prioritizes and lists products that are approaching their expiration date and sends a warning message to the retailer's terminal.

[2380] 3. Review of food appearance standards

[2381] Step 1: Reassess the criteria

[2382] User (supplier): Reassess food appearance standards and identify foods that have cosmetic issues but are not of quality.

[2383] Step 2: Registration

[2384] User (provider): Registers the identified results on the server.

[2385] Step 3: Data Management

[2386] Server: Stores the registered appearance standard data in a database and generates a message to the retailer proposing relaxation of the standard.

[2387] Step 4: Notification

[2388] Server: Sends proposals for relaxing standards to the retailer's terminal.

[2389] 4. Improved logistics and delivery

[2390] Step 1: Data collection

[2391] Terminal: Data such as temperature, vibration, and time during transportat...

Claims

1. a means of collecting food data; A means for making a demand forecast based on the data; A means for optimizing inventory levels based on the obtained prediction results; a means for using RFID tags or sensors to obtain expiration date data; A means for managing the expiration date data and notifying about foods whose expiration date is approaching; measures to reassess food appearance standards; Efficient logistics and delivery optimization measures; A means to provide educational programs for suppliers, retailers and consumers about the importance of and solutions to food waste; A way to donate excess inventory or food nearing its expiration date to food banks and charitable organizations. A system including:

2. A means of handling missing values ​​and outliers based on the collected data; means for classifying the data by time, region, product category, etc.; a means for performing a demand forecast using a time series analysis model using the preprocessed data; The system of claim 1 .

3. A means of analyzing received data such as temperature, vibration, and time to identify factors that affect quality; means for generating recommendations regarding efficient transportation conditions; means for notifying the logistics company of the proposal; The system of claim 1 .

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