System

Abstract

A system is provided.SOLUTION: A system includes a means for collecting data from a local government and a food provider, a means for analyzing supply and demand based on the collected data, a means for generating an optimal food delivery strategy based on the analysis result, a means for notifying the local government and the food provider of the generated strategy, a means for instructing food delivery based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback.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 recent years, the issue of food waste has attracted significant social attention. Food businesses are throwing away a large amount of food without consumption, and some local governments are experiencing food shortages in some areas. To resolve this imbalance and reduce food waste, local governments and food businesses need to work together to efficiently distribute food. However, determining the appropriate balance of food supply and demand and establishing a system to provide food in the right place is not easy. Traditional manual adjustment methods require time and effort, and an effective solution is needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for collecting data from local governments and food suppliers, a means for analyzing supply and demand based on the collected data using generative AI, a means for generating an optimal food delivery strategy based on the analysis results, a means for notifying the local government and food suppliers of the generated strategy, a means for issuing food delivery instructions based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback. This enables local governments and food suppliers to work together to efficiently distribute food and effectively reduce food waste. In particular, the generated strategy is optimized taking into account food expiration dates, and food delivery instructions are automatically transmitted to delivery companies, achieving fast and efficient delivery.

[0006] "Local government" refers to local governments and their related organizations, which are responsible for local administrative services and public works.

[0007] "Food business" refers to a business or organization that produces, processes, sells or distributes food.

[0008] "Data collection means" refers to the methods and devices used to collect the necessary information from local governments and food businesses.

[0009] "Generative AI" refers to systems and algorithms that use artificial intelligence to analyze data and generate optimal strategies.

[0010] "Analysis tools" refers to methods and devices for using collected data to assess and analyze the balance between supply and demand.

[0011] "Food delivery strategy" refers to policies and methods for formulating optimal food delivery plans based on collected and analyzed data.

[0012] "Notification means" refers to the methods and devices used to communicate the generated food delivery strategy to relevant parties (local governments and food businesses).

[0013] "Instruction means" refers to a method or device for instructing specific actions (such as food delivery) based on the notified strategy.

[0014] "Feedback means" refers to a method or device for collecting the results of actions taken and reflecting them in the next strategy generation. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The purpose of this system is to efficiently distribute food through cooperation between local governments and food businesses, thereby reducing food waste. Specific embodiments for carrying out the present invention are described below.

[0037] Data collection

[0038] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[0039] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[0040] Data analysis

[0041] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0042] Optimal strategy generation and notification

[0043] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[0044] Server: The generated strategy is notified to local governments and food businesses through notification means, such as email or push notification.

[0045] Execution instructions

[0046] Users: Local government officials and food businesses review the notified strategy and take specific actions after approval. For example, food businesses arrange for the delivery of designated food to designated areas.

[0047] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation.

[0048] Specific examples

[0049] Example 1: Supplying food to areas with food shortages

[0050] Terminal: A local government official enters food shortage information for Area A into the system.

[0051] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[0052] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0053] User: A food supplier delivers food from region B to region A.

[0054] Example 2: Redistribution of food nearing its expiry date

[0055] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0056] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0057] Server: Notifies food businesses and relevant local governments of the proposal.

[0058] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0059] This system will strengthen cooperation between local governments and food businesses, effectively reducing food waste.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date, etc.) into the system. This data is updated regularly.

[0063] Step 2:

[0064] Terminal: Local government officials input local demand data (type of food needed, quantity, deadline for request, etc.) into the system. This data is also updated regularly.

[0065] Step 3:

[0066] Server: Stores collected inventory and demand data in a database. The database is updated with the latest data.

[0067] Step 4:

[0068] Server: Passes the data stored in the database to the generative AI, which evaluates and analyzes the balance between supply and demand.

[0069] Step 5:

[0070] Server: Generative AI generates optimal food delivery strategies, such as determining which foods should be delivered to which areas, delivery priorities, schedules, etc.

[0071] Step 6:

[0072] Server: The generated food delivery strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[0073] Step 7:

[0074] Users: Local government officials and food businesses review and approve the communicated strategy.

[0075] Step 8:

[0076] User: After approval, the food supplier will arrange for the delivery of the specified food, arrange for a delivery company, and coordinate the delivery destination and delivery date.

[0077] Step 9:

[0078] Terminal: After completing a delivery, the delivery company's representative reports the status to the system, including the time of delivery completion and the recipient's confirmation of receipt.

[0079] Step 10:

[0080] Server: Receives delivery completion data and updates the database. This information is used to generate future delivery strategies.

[0081] Step 11:

[0082] Server: Based on the results of the actions taken, it provides feedback to the generative AI to improve the accuracy of the next food delivery strategy, thereby improving the accuracy and effectiveness of the entire system.

[0083] In this way, a series of steps are taken: data collection, analysis, notification, action, and feedback. By repeating this process, supply optimization and food waste reduction are achieved.

[0084] Example 1

[0085] 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."

[0086] Currently, when local governments and food suppliers work together to supply food, efficient inventory management and demand forecasting are difficult, resulting in food waste. It is also difficult to create optimal delivery plans that take food expiration dates into account, resulting in the risk of expired food being delivered. Furthermore, the delivery strategies generated are notified manually, which can hinder rapid implementation. These issues need to be resolved.

[0087] 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.

[0088] In this invention, the server includes: a means for acquiring information from local governments and food suppliers; a means for using artificial intelligence to analyze supply and demand based on the acquired information; a means for generating an optimal food delivery strategy based on the analysis results; a means for notifying the local government and food suppliers of the generated strategy via email, push notification, or other means; a means for issuing food delivery instructions based on the notified strategy; and a means for collecting data after delivery is completed and providing feedback for generating the next strategy. This enables the optimization of the balance between food supply and demand and the reduction of food waste. Furthermore, by automatically generating an optimal delivery plan that takes expiration dates into account, the risk of expiration is reduced, and the automated notification function promotes rapid implementation.

[0089] A "municipality" is a local government entity that provides administrative services in a specific area.

[0090] A "food business" is a company or individual that produces, processes, distributes, sells, etc. food.

[0091] "Means of acquiring information" refers to input devices, software, and associated interfaces for collecting data.

[0092] "Artificial intelligence" is a technology that uses data analysis and predictive models to make advanced decisions.

[0093] A "generator" is an algorithm or system that automatically creates the plans and strategies needed to achieve a specific goal.

[0094] "Means of notification" refers to a system for conveying information to the target person using email, push notifications, etc.

[0095] The "means for issuing food delivery instructions" is a function for issuing instructions for executing a specific delivery plan based on the generated strategy.

[0096] The "means for collecting data after delivery completion and providing feedback" is a function for collecting result data after delivery completion and using it to help generate the next strategy.

[0097] A "food delivery strategy" is a plan that optimizes which foods to deliver to which areas and how.

[0098] "Optimization" refers to achieving the most efficient and effective state under certain conditions.

[0099] The "best before" date is the date by which food is at its most delicious. Food is still edible after this date, but the quality may deteriorate.

[0100] "Analysis results" are information obtained as a result of processing collected data.

[0101] "Data" is a collection of numbers, text, etc. that are collected to represent specific information or phenomena.

[0102] This invention is a system in which local governments and food businesses work together to efficiently distribute food and reduce food waste.

[0103] Hardware and Software

[0104] Terminals: The terminals for this system are general devices such as PCs, tablets, and smartphones used by food service providers and local government officials. These terminals are capable of connecting to the Internet and provide a user interface for inputting and transmitting the necessary data.

[0105] Server: The server runs on a cloud platform, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP). This server performs the functions of data collection, storage, analysis, and notification.

[0106] Software: Various programming languages ​​(e.g., Python, Node.js) and database systems (e.g., MySQL (registered trademark), PostgreSQL) are used to control the entire system. Machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch are used to implement generative AI models.

[0107] Data collection

[0108] Terminal: Food supplier personnel enter inventory data (food type, quantity, expiration date, etc.) through a dedicated food inventory management app. Local government personnel log in to a browser-based demand management portal and enter demand data for each region (type of food needed, quantity, required deadline, etc.).

[0109] Server: Data entered from the terminal is sent to the server via SOAP or REST API and stored in a database.

[0110] Data analysis

[0111] Server: The server uses periodic cron jobs or trigger events to collect up-to-date inventory and demand data from the database, which is then pre-processed and converted into a format that can be fed into the generative AI model.

[0112] Generative AI model: The generative AI model evaluates the balance between supply and demand based on collected data and generates an optimal food supply strategy, including which food to deliver to which areas, delivery priorities, and schedules.

[0113] Optimal strategy generation and notification

[0114] Server: Receives the output from the generative AI model and reconstructs it into a specific delivery strategy. This strategy is then communicated to local governments and food providers via email and push notifications.

[0115] Execution instructions

[0116] Users: Local government officials and food service providers receive notifications. They review the notification content and, after approval, execute specific food delivery plans. For example, food service delivery personnel use the workflow management system to create delivery tasks, allocate resources, and adjust schedules.

[0117] Server: After delivery is completed, the delivery data is collected again and stored in the database. This data is used to generate the next strategy, contributing to improving the accuracy and efficiency of the system.

[0118] Specific examples

[0119] Example 1: Supplying food to areas with food shortages

[0120] Terminal: A local government official enters food shortage information for Area A into the system.

[0121] Server: The generative AI model analyzes demand in region A and generates proposals to procure excess inventory in region B based on food supplier supply data.

[0122] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0123] User: A food supplier delivers food from region B to region A.

[0124] Example 2: Redistribution of food nearing its expiry date

[0125] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0126] Server: A generative AI model analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0127] Server: Notifies food businesses and relevant local governments of the proposal.

[0128] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0129] Prompt Sentence Examples

[0130] "Please enter food shortage information for Area A."

[0131] "Please enter information about inventory that is close to expiry into the system."

[0132] "Generate optimal food delivery strategies"

[0133] In this way, local governments and food businesses can work together to create an effective system for minimizing food waste.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] Terminal: A food supplier uses a food inventory management app to input inventory data (food type, quantity, expiration date, etc.). This data is compiled in JSON format.

[0137] Input: Inventory data such as food type, quantity, expiration date, etc.

[0138] Output: Inventory data in JSON format

[0139] Specific operation: Open the app from your device, enter inventory information into the input form, and click the "Submit" button. The submitted data is sent to the server via the API endpoint.

[0140] Step 2:

[0141] Terminal: A local government official logs into the demand management portal and enters demand data for each region (type of food needed, quantity, deadline for request, etc.). This data is also compiled in JSON format.

[0142] Input: Demand data by region

[0143] Output: Demand data in JSON format

[0144] Specific operations: Open a browser, access the demand management portal, and log in. Enter demand information and click the "Submit" button. The submitted data is sent to the server via the REST API.

[0145] Step 3:

[0146] Server: Receives the inventory and demand data and stores it in a database, for example, using a database such as MySQL or PostgreSQL.

[0147] Input: Inventory and demand data in JSON format

[0148] Output: Inventory and demand data stored in a database

[0149] What happens: The server parses the JSON data received by the API, maps it to the appropriate fields, and executes a SQL query to store it in the database.

[0150] Step 4:

[0151] Server: Collects and pre-processes the stored data using a cron job or trigger events. Pre-processing includes cleaning and shaping the data, for example using the Pandas or NumPy libraries.

[0152] Input: Inventory and demand data stored in a database

[0153] Output: Preprocessed data for generative AI models

[0154] What it does: A scheduled job extracts the latest data from the database, which is then cleaned and formatted by a Python script.

[0155] Step 5:

[0156] Server: The preprocessed data is input into a generative AI model to generate an optimal food supply strategy that evaluates the balance between supply and demand. For example, using the TensorFlow or PyTorch libraries, the AI ​​model then performs inference processing and outputs the optimal distribution plan.

[0157] Input: Preprocessed data for generative AI models

[0158] Output: Optimal food supply strategy

[0159] Specific operation: The preprocessed data is input into the AI ​​model and the inference results are obtained. These results are returned to the server in JSON format.

[0160] Step 6:

[0161] Server: Receives the results returned by the generative AI model and reconstructs them as a specific delivery strategy. The generated strategy is notified to local governments and food businesses via email or push notifications. This notification is sent using services such as Amazon SES and Firebase Cloud Messaging.

[0162] Input: Optimal food supply strategy

[0163] Output: Notifications sent to local governments and food businesses

[0164] Specific Actions: Analyze the results from the generative AI model and send information to relevant personnel via email or notification services, including specific actions to take.

[0165] Step 7:

[0166] Users: Local government officials and food service providers receive notifications, review the details, and, after providing approval as necessary, execute specific food delivery plans. Food service delivery personnel create delivery tasks in the workflow management system and allocate and schedule the necessary resources.

[0167] Input: Informed Food Supply Strategy

[0168] Output: Executed delivery tasks

[0169] Specific actions: Open the notification email or push notification and follow the instructions to carry out the delivery plan. The delivery person then uses the workflow tool to create specific tasks and allocate resources.

[0170] Step 8:

[0171] Server: After the delivery is completed, the delivery completion data is collected again and stored in the database. This data is used to generate the next strategy.

[0172] Input: Delivery completion data

[0173] Output: Delivery completion data stored in the database

[0174] How it works: The delivery person enters a completion report into the system, and the server collects and stores the report in a database. The stored data is used for subsequent analysis and training of the AI ​​model.

[0175] In this way, the system strengthens cooperation between local governments and food businesses and effectively reduces food waste.

[0176] (Application example 1)

[0177] 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."

[0178] In recent years, the problem of food waste has become more serious, and there is a need for food businesses and local governments to work together to distribute food efficiently. However, many processes, such as food inventory management, regional demand surveys, formulating optimal supply strategies, and arranging deliveries, rely on manual labor, making it difficult to operate efficiently. This makes food waste more likely to occur, and has become a social problem.

[0179] 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.

[0180] In this invention, the server includes means for collecting data from local governments and food suppliers, means for analyzing supply and demand based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for issuing food delivery instructions based on the notified strategy, means for collecting data after delivery is completed and providing feedback, means for food suppliers and local government officials to input and check data via mobile devices, and means for the generative model to generate an optimal supply strategy in real time. This makes it possible to efficiently operate the entire process from data collection to delivery instructions and effectively reduce food waste.

[0181] "Data collection means" refers to devices and systems for collecting inventory data, demand data, etc. from food businesses and local government officials.

[0182] "Analysis means" refers to a device or system that analyzes the collected data to evaluate the balance between supply and demand and generate an appropriate supply strategy based on the collected data.

[0183] The "generation means" is a device or system that generates an optimal food delivery strategy based on the analyzed data.

[0184] "Notification means" refers to a device or system for communicating the generated food delivery strategy to local governments and food businesses.

[0185] The "instruction means" is a device or system for issuing specific food delivery instructions based on the notified strategy.

[0186] A "feedback means" is a device or system that collects data after delivery is completed and provides information that is useful for generating the next strategy.

[0187] A "mobile terminal" is an information terminal device such as a smartphone or tablet that can be operated while on the move.

[0188] A "real-time generative model" is an algorithm or system that instantly analyzes collected data and generates an optimal supply strategy based on the results.

[0189] "Push notifications" are a technology that sends information directly to users' mobile devices in real time.

[0190] This invention is a system that enables local governments and food businesses to work together to efficiently distribute food and reduce food waste. The system aims to minimize food waste by automating the processes of data collection, analysis, generation of optimal strategies, notification, execution instructions, and feedback.

[0191] Specifically, the following hardware and software configuration is used.

[0192] Hardware

[0193] Server: Responsible for data aggregation, analysis, and running generative AI models. Specific examples include EC2 instances from AWS (Amazon Web Services) and Compute Engine from Google Cloud Platform.

[0194] Mobile devices: Used by food businesses and local government officials to input data and check notifications. Specifically, this applies to smartphones and tablets.

[0195] software

[0196] Mobile application: An application for entering data, receiving notifications, checking delivery instructions, etc. It runs on the ANDROID (registered trademark) and iOS platforms.

[0197] Generative AI model: Analyzes collected data, evaluates the balance between supply and demand, and generates an optimal food delivery strategy. Specifically, OpenAI's GPT-3 (registered trademark) and other latest generative models are used.

[0198] Notification service: A system for communicating the generated strategies to food businesses and local governments. Firebase Cloud Messaging and AWS SNS are used.

[0199] Data collection and analysis

[0200] First, food suppliers and local government officials use their mobile devices to input inventory data and local demand data. This data is sent in real time to a server, which collects the data and stores it in a database. A generative AI model analyzes the stored data and evaluates the balance between supply and demand.

[0201] Optimal strategy generation and notification

[0202] The generative AI model generates an optimal food delivery strategy based on the analyzed data. This strategy includes which food to deliver to which area, priorities, schedules, etc. The generated strategy is pushed to food businesses and local government officials via a notification service.

[0203] Instructions and feedback

[0204] Food vendors and local government officials use their mobile devices to check the notified strategies and take specific actions after approval. For example, food vendors arrange for the delivery of designated food to designated areas. Data after delivery is completed is sent back to the server and used as feedback to help generate the next strategy.

[0205] Prompt Sentence Examples

[0206] Examples of prompts that an AI model might use to create a generative strategy include:

[0207] "Suggest a redistribution of food items nearing their expiration date. Data: {'food_item': 'bread', 'expiry_date': '2023-10-31', 'quantity': 50}. Demand region: {'region_A': 30, 'region_B': 20}"

[0208] In this way, local governments and food businesses can work together to develop and implement optimal food delivery strategies in real time, creating a system that can effectively reduce food waste.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Users use their mobile devices to input food inventory data and local demand data. Specifically, food vendors input information such as inventory levels and expiration dates, while local government officials input demand data such as the type, quantity, and location of food needed. This input data is then sent from the device to a server.

[0212] Input: Inventory data (food type, quantity, expiration date), demand data (food required, quantity, location)

[0213] Output: Collected data stored on the server

[0214] Step 2:

[0215] The server stores the submitted inventory and demand data in a database. This data is then analyzed by the generative AI model. Specifically, the server passes the collected data set to the generative AI model to evaluate the balance between supply and demand.

[0216] Input: Collected data (stock and demand data stored in a database)

[0217] Output: Analysis results (demand and supply balance evaluation)

[0218] Step 3:

[0219] The generative AI model generates an optimal food delivery strategy based on the analysis results, including which food items should be delivered to which areas and when, as well as delivery priorities and schedules. The server receives this generated strategy and converts it into a format for notification.

[0220] Input: Analysis results (demand and supply balance evaluation)

[0221] Output: Optimal delivery strategy (food delivery strategy)

[0222] Step 4:

[0223] The server then sends the generated food delivery strategy to food businesses and local government officials as push notifications, using notification services such as Firebase Cloud Messaging and AWS SNS to deliver real-time notifications to mobile devices.

[0224] Input: Optimal delivery strategy (food delivery strategy)

[0225] Output: Notification (push notification to mobile device)

[0226] Step 5:

[0227] The user checks the notification on their mobile device and approves the strategy. The food business then makes specific arrangements to deliver the food to the designated area based on the approved strategy. Specifically, the business issues instructions to delivery companies and arranges the necessary logistics work.

[0228] Input: Notification (food delivery strategy information)

[0229] Output: Delivery arrangements (instructions to delivery companies, logistics arrangements)

[0230] Step 6:

[0231] After the food delivery is completed, the delivery company sends the delivery completion data to the server, which collects the data and stores it in a database. The collected delivery completion data is used as feedback for generating the next optimal strategy.

[0232] Input: Delivery completion data (reported data from delivery company)

[0233] Output: Feedback data (used for next strategy generation)

[0234] In this way, through a series of processing steps, local governments and food businesses work together to create a system that efficiently distributes food and reduces food waste.

[0235] 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.

[0236] The purpose of this system is to efficiently distribute food through collaboration between local governments and food businesses, thereby reducing food waste. Furthermore, by adding an emotion engine, it is possible to provide more appropriate strategies that take into account the emotions of users. Specific embodiments for implementing the present invention are described below.

[0237] Data collection

[0238] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[0239] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[0240] Data analysis

[0241] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0242] Optimal strategy generation and notification

[0243] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[0244] Server: The generated strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[0245] Emotion recognition by emotion engine

[0246] Terminal: The emotion engine recognizes the user's emotions based on the user's input data (for example, comments and behavioral patterns when entering demand data or inventory data).

[0247] Server: The emotion engine analyzes the user's emotional data and customizes the notification content based on this. For example, a user with high stress levels will receive a simple and intuitive notification.

[0248] Execution instructions

[0249] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, food businesses can arrange for the delivery of designated food to designated areas.

[0250] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[0251] Specific examples

[0252] Example 1: Supplying food to areas with food shortages

[0253] Terminal: A local government official enters food shortage information for Area A into the system.

[0254] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[0255] Server: The emotion engine analyzes the emotional data of local government officials and customizes the notification content to ensure that the proposal is appropriately accepted.

[0256] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0257] User: A food supplier delivers food from region B to region A.

[0258] Example 2: Redistribution of food nearing its expiry date

[0259] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0260] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0261] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes the notification content to ensure that the proposal is appropriately accepted.

[0262] Server: Notifies food businesses and relevant local governments of the proposal.

[0263] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0264] This system will strengthen cooperation between local governments and food businesses, and effectively reduce food waste. Furthermore, the emotion engine function will take the user's emotions into consideration when issuing notifications and instructions, improving the system's acceptability and effectiveness.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] Terminal: A food vendor enters food inventory data (food type, quantity, expiration date, etc.) into the system. This data is periodically sent from the terminal to the system.

[0268] Step 2:

[0269] Terminal: Local government officials enter local demand data (type of food needed, quantity, deadline, etc.) into the system. This data is also periodically sent from the terminal to the system.

[0270] Step 3:

[0271] Server: Stores the input inventory and demand data in a database. The database is updated with the latest information in real time.

[0272] Step 4:

[0273] Server: Passes inventory and demand data stored in the database to the generative AI, which analyzes this data and evaluates the balance between supply and demand.

[0274] Step 5:

[0275] Server: Generative AI generates optimal food delivery strategies, including details of which foods should be delivered to which areas and when.

[0276] Step 6:

[0277] Server: Notifies the local government and food businesses of the generated food delivery strategy through notification means. This notification is sent via email or push notification.

[0278] Step 7:

[0279] Device: The emotion engine analyzes the user's input data and behavioral patterns to recognize the user's emotions. For example, if the input data indicates fatigue, it will recognize that as an emotion.

[0280] Step 8:

[0281] Server: The emotion engine analyzes the emotional data and customizes the notification content and interface. For example, a user who is feeling stressed will receive a simple and intuitive notification.

[0282] Step 9:

[0283] Users: Local government officials and food businesses review the notification strategy and customized notification content and approve it as needed.

[0284] Step 10:

[0285] User: After approval, the food supplier will arrange for the specified food to be delivered to the specified area. Specifically, the supplier will arrange for a delivery company and coordinate the delivery destination and delivery date and time.

[0286] Step 11:

[0287] Terminal: After completing delivery, the delivery company's staff reports the status to the system, including the time of delivery completion and confirmation of the recipient.

[0288] Step 12:

[0289] Server: Receives delivery completion data and updates the database. This ensures that the information required for the next strategy generation is up to date.

[0290] Step 13:

[0291] Server: The emotion engine analyzes the actual delivery results and user feedback to reflect in the next strategy generation. For example, it improves the notification method based on emotion data.

[0292] This process strengthens cooperation between local governments and food businesses, leading to efficient food distribution and reduced food waste. The emotion engine also enables flexible responses that take user emotions into account, improving the acceptability and effectiveness of the entire system.

[0293] Example 2

[0294] 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."

[0295] Food waste is a very serious problem in modern times, and there is a need to build an efficient supply chain. It is particularly difficult to accurately understand the management of food that is approaching its expiration date and fluctuations in food demand in each region, thereby minimizing waste. Furthermore, rapid and accurate information sharing is essential for smooth collaboration among the multiple parties involved (municipalities, food businesses, etc.). Furthermore, it is necessary to ensure that efforts to reduce food waste do not become a stress or burden for users.

[0296] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for periodically collecting data from local governments and food suppliers, means for analyzing supply and demand using a generative AI model based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for analyzing user emotion data using an emotion engine and customizing the notification content, means for issuing food delivery instructions based on the notified strategy, and means for collecting data and emotion data after delivery is completed and providing feedback. This enables an efficient food supply system that takes emotions into consideration.

[0297] A "local government" is a government body that manages a particular area and provides public services.

[0298] A "food business" is a business or individual that produces, processes, distributes, or sells food.

[0299] "Means for periodic data collection" refers to a system that automatically obtains necessary information from local governments and food businesses at specific time intervals.

[0300] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and predicts and evaluates supply and demand.

[0301] "Demand and supply analysis tools" are methods for calculating food needs and supplies in a specific area or period based on collected data.

[0302] The "means for generating an optimal food delivery strategy" is a method for creating a plan for efficiently distributing food based on the analysis results.

[0303] "Means of notification" refers to the mechanisms used to communicate generated strategies and information to stakeholders, including email and push notifications.

[0304] The "emotion engine" is a system that analyzes emotions from user input data and behavioral patterns and responds appropriately.

[0305] "Means for customizing notification content" refers to a method for providing information to users in the most optimal form based on analyzed emotional data.

[0306] The "means for issuing food delivery instructions" refers to the procedure for initiating and managing specific delivery operations based on the generated strategy.

[0307] "Means for collecting data and emotional data after delivery is completed and providing feedback" is a mechanism for collecting the situation after delivery is completed and the user's reactions, and using this information to generate the next strategy.

[0308] This invention relates to a system that enables local governments and food businesses to efficiently distribute food and reduce food waste. The system utilizes a generative AI model and has the ability to customize notification content based on user sentiment. Specific embodiments are described below.

[0309] 1. Data Collection

[0310] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date) into the terminal through a dedicated application. Similarly, local government officials enter demand data for each region (food type, quantity, required deadline) into the terminal. This data is converted into JSON format and sent to the server.

[0311] 2. Data Analysis

[0312] Server: The server receives the data sent from the device and stores it in a database. A generative AI model (e.g., GPT-3) is used to analyze the received inventory and demand data. The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0313] 3. Generating and notifying optimal strategies

[0314] Server: The generative AI model generates an optimal food delivery strategy based on the analysis results. This strategy includes which food to deliver to which area, delivery priorities, and schedules. The generated strategy is notified to local governments and food businesses via email, push notifications, etc.

[0315] 4. Emotion Recognition by Emotion Engine

[0316] Terminal: An emotion engine is used to analyze user comments and behavioral patterns. For example, emotional data of users is collected from comments made during data entry and operation logs.

[0317] Server: Analyzes the collected emotional data and customizes the notification content. For example, it sends a simple and intuitive notification to a user who is highly stressed, and generates appropriate notification content according to the user's state.

[0318] 5. Execution Instructions

[0319] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[0320] Server: Collects data after delivery is completed and provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[0321] Specific examples

[0322] Example 1: Supplying food to areas with food shortages

[0323] Terminal: A local government official enters food shortage information for Area A into the system.

[0324] Server: Generative AI analyzes demand data from region A and generates a proposal to procure from surplus inventory in region B based on supply data from food suppliers.

[0325] Server: The emotion engine analyzes the emotion data of local government officials and customizes notifications to ensure that the proposals are appropriately accepted.

[0326] Server: Notifies the local government and food vendors of the proposal and issues delivery instructions.

[0327] User: A food supplier delivers food from region B to region A.

[0328] Example 2: Redistribution of food nearing its expiry date

[0329] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0330] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0331] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes notifications to ensure that the proposals are appropriately accepted.

[0332] Server: Notifies food businesses and relevant local governments of the proposal.

[0333] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0334] Prompt Sentence Examples

[0335] To suggest an appropriate delivery strategy for food-insecure areas:

[0336] Area A is short of food. Area B has excess stock. Please suggest how to supply it.

[0337] If you are proposing a redistribution of food that is nearing its expiry date:

[0338] I have some food that is nearing its expiration date. Please suggest areas or facilities that can distribute it quickly.

[0339] This system enables local governments and food businesses to work together efficiently and reduce food waste. The emotion engine provides notifications that take into account the user's emotions, improving the system's acceptability and effectiveness.

[0340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0341] Step 1: Data collection

[0342] Terminal: Food vendors use a dedicated application to input food inventory data (food type, quantity, expiration date) into the terminal. Similarly, local government officials input demand data for each region (type of food needed, quantity, required deadline) into the terminal. The input data is converted into JSON format and sent to the server.

[0343] Input: Inventory data (food type, quantity, expiration date), Demand data (food type, quantity, required deadline)

[0344] Output: JSON format data

[0345] Specific operations: The person in charge opens a dedicated application on a tablet or PC, enters the necessary data, and clicks the send button.

[0346] Step 2: Receiving and storing data

[0347] Server: The server receives the JSON format data sent from the device and stores it in a MySQL database.

[0348] Input: JSON format data sent from the terminal

[0349] Output: Inventory and demand data stored in a database

[0350] What happens: A server-side receiving script receives the JSON data and inserts it into a database using an SQL query.

[0351] Step 3: Data analysis

[0352] Server: The server retrieves inventory and demand data from the database and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0353] Input: Inventory and demand data retrieved from a database

[0354] Output: Optimal food supply strategy based on the balance between supply and demand

[0355] Specific operation: The server retrieves data using an SQL query, sends the data to the generative AI model's API, and receives the analysis results.

[0356] Step 4: Generate and communicate optimal strategies

[0357] Server: Generates an optimal food delivery strategy based on the analysis results generated by the generative AI model. Prepares notification content based on the generated strategy and notifies local governments and food businesses via email and push notifications.

[0358] Input: Analysis results of the generative AI model

[0359] Output: Notification content sent to local governments and food businesses

[0360] What it does: The server receives the results from the generative AI model, inserts them into a prefixed notification template, and sends it using an SMTP server or push notification service.

[0361] Step 5: Emotion Recognition and Notification Customization

[0362] Terminal: When a user enters comments or behavioral patterns during data entry, the emotion engine recognizes them and generates emotion data.

[0363] Input: User comments and behavior patterns

[0364] Output: Emotion data

[0365] Specific operation: The device's sensors and log acquisition system analyze the user's actions and collect emotion-related data.

[0366] Server: Analyzes the emotion data generated by the emotion engine and customizes the notification content. For example, it generates a simple and intuitive notification for users with high stress levels.

[0367] Input: Emotion data

[0368] Output: Customized notification content

[0369] Specific operation: The server generates notification content based on the analysis results of the emotion engine and sends it to the user in an appropriate format.

[0370] Step 6: Execution instructions

[0371] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[0372] Input: Customized notification content, strategic information

[0373] Output: Execution instructions after approval

[0374] Specific actions: The person in charge checks the notification and clicks the approval button. The food supplier prepares the delivery and issues delivery instructions.

[0375] Step 7: Gather feedback

[0376] Server: After the delivery is completed, the server collects the delivery completion data and the user's emotion data again and provides feedback for the next strategy generation.

[0377] Input: Delivery completion data, emotion data

[0378] Output: Feedback data for next strategy generation

[0379] Specific operation: After delivery is completed, the server automatically collects the data, stores it in a database, and reuses it as input data for the generative AI model.

[0380] Through these steps, the system can efficiently and effectively manage food supply and demand, thereby reducing food waste. Furthermore, by using an emotion engine, notifications and instructions that take into account the user's emotions can be provided, increasing the system's acceptability and effectiveness.

[0381] (Application example 2)

[0382] 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."

[0383] Food waste not only causes economic losses but also has a serious impact on the environment, necessitating the development of efficient food distribution systems. However, existing systems struggle to optimize the balance between supply and demand, and lack the ability to respond to individual user needs based on user emotions. In particular, in the food delivery field, there is a lack of effective customization of notifications based on emotion recognition for delivery routes and urgent food distribution.

[0384] 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 a means for collecting data from local governments and food businesses, a means for analyzing supply and demand based on the collected data, a means for generating an optimal food delivery strategy based on the analysis results, a means for notifying the local government and food businesses of the generated strategy, an emotion engine means for recognizing the user's emotions and customizing the notification content, a means for issuing food delivery instructions based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback. This not only enables efficient balancing of supply and demand, but also enables flexible responses tailored to the user's emotions.

[0385] A "local government" is a public institution that is responsible for local administration and provides administrative services related to the lives of local residents.

[0386] "Food businesses" are companies and organizations that produce, process, and sell food, and play an important role in food distribution.

[0387] "Data collection means" refers to a method or device for acquiring and organizing information, and in this invention, it is used to collect inventory and demand information provided by local governments and food suppliers.

[0388] "Means for supply and demand analysis" refers to methods and mechanisms that use collected data to assess and analyze where and how much food is needed and where and how much is in stock.

[0389] The "means for generating an optimal food delivery strategy" is a method or device for determining, based on the analysis results, which food items should be delivered to which areas as a priority.

[0390] "Means for notifying the generated strategy" refers to methods or devices for informing relevant parties (local governments and food businesses) of the generated delivery strategy, and includes email and push notifications.

[0391] An "emotion engine" is a system or device that analyzes the user's input data, recognizes their emotional state at the time, and presents the optimal response.

[0392] The "means for issuing instructions for food delivery" refers to a method or device for issuing instructions to relevant parties to actually deliver food based on an optimal delivery strategy.

[0393] The "means for providing feedback" refers to a method or device that collects data when delivery is completed and provides complementary information to be used in generating the next strategy.

[0394] The present invention provides a system for efficiently distributing food and reducing food waste through collaboration between local governments and food businesses. The system includes a data collection means, a data analysis means, a means for generating an optimal food delivery strategy, an emotion engine means, a notification means, a means for issuing food delivery instructions, and a means for providing feedback.

[0395] First, food vendors use terminals to input food inventory data (e.g., food type, quantity, expiration date, etc.) and local governments input demand data for each region (e.g., food type, quantity, required deadline, etc.) into the system. The server then collects the data and stores it in a database. This data is periodically collected by a data collection module using Python.

[0396] Next, using a generative AI model, the server analyzes the collected inventory and demand data to assess the balance between supply and demand. Based on the analysis results, it generates an optimal food delivery strategy, which includes which food items to deliver to which areas, delivery priorities, and schedules.

[0397] The server notifies the local government and food suppliers of the generated strategy via notification means (email, push notification, etc.). At this time, the emotion engine recognizes the user's emotions and customizes the notification content based on, for example, the user's input data and behavioral patterns. Specifically, if the user is feeling stressed, a simple and intuitive notification is sent, and if not, a detailed notification is sent.

[0398] For example, by inputting a prompt such as, "Compare the data from region A with next month's demand, have the generative AI model generate suggestions on how to efficiently distribute food that is nearing its expiration date, and use the emotion engine to customize the notification content," into the generative AI model, analysis and notification customization are performed.

[0399] Local government officials and food vendors then review the strategy and take specific action after approval. For example, a food vendor arranges for the delivery of a specified food item to a specified area. After the delivery is completed, the server collects data about the delivery again and uses it as feedback for generating the next strategy. This feedback also includes emotional data.

[0400] This will strengthen cooperation between local governments and food businesses, effectively reducing food waste, and improve the system's acceptability and effectiveness by providing appropriate notifications and instructions that take into account the user's emotions through the emotion engine.

[0401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0402] Step 1:

[0403] The terminal receives data input from food businesses and local governments. Specifically, food businesses input food inventory data (food type, quantity, expiration date, etc.), and local governments input demand data for each region (type of food needed, quantity, required deadline, etc.). The input data is sent to a server and stored in a database. The input data is then processed by a data collection module and converted into a unified format.

[0404] Step 2:

[0405] The server retrieves inventory and demand data stored in the database. Based on this data, it uses a generative AI model to analyze the data. Specifically, it evaluates the balance between supply and demand and analyzes which foods should be prioritized for distribution to which areas. The generative AI model used here uses algorithms that forecast demand and optimize supply based on the input data.

[0406] Step 3:

[0407] The server generates an optimal food delivery strategy based on the analysis results of the generative AI model. This strategy includes information such as which food to deliver to which area, delivery priorities, and schedules. The generated strategy includes specific instructions such as food distribution routes and allocation amounts. This strategy is then saved back into the database.

[0408] Step 4:

[0409] The server notifies the local government and food suppliers of the generated delivery strategy via a notification mechanism. Here, the emotion engine recognizes the user's emotions and customizes the notification content. It analyzes the user's input data and behavioral patterns to determine whether the user is under stress and sends notifications according to their state. For example, it sends a simple notification to users who are highly stressed, and a detailed notification to users who are not.

[0410] Step 5:

[0411] Users (municipal government officials and food businesses) review and approve the notified strategy. After approval, they take specific actions. Specifically, food businesses arrange for the delivery of designated food to designated areas and begin actual delivery work on site.

[0412] Step 6:

[0413] The server collects information after delivery is complete. The data after delivery includes information on delivery time, delivery volume, and delivery destination. This data is sent to the server and stored in a database. The emotion engine also collects user emotion data again.

[0414] Step 7:

[0415] The server analyzes the collected delivery completion data and emotion data and uses them as feedback for generating the next strategy, which continuously optimizes the system to reduce food waste and improve the user experience.

[0416] Through these steps, local governments and food businesses can work together efficiently to reduce food waste, and the emotion engine can provide notifications that take users' emotions into consideration.

[0417] 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.

[0418] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0419] 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.

[0420] [Second embodiment]

[0421] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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. 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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."

[0433] The purpose of this system is to efficiently distribute food through cooperation between local governments and food businesses, thereby reducing food waste. Specific embodiments for carrying out the present invention are described below.

[0434] Data collection

[0435] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[0436] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[0437] Data analysis

[0438] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0439] Optimal strategy generation and notification

[0440] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[0441] Server: The generated strategy is notified to local governments and food businesses through notification means, such as email or push notification.

[0442] Execution instructions

[0443] Users: Local government officials and food businesses review the notified strategy and take specific actions after approval. For example, food businesses arrange for the delivery of designated food to designated areas.

[0444] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation.

[0445] Specific examples

[0446] Example 1: Supplying food to areas with food shortages

[0447] Terminal: A local government official enters food shortage information for Area A into the system.

[0448] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[0449] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0450] User: A food supplier delivers food from region B to region A.

[0451] Example 2: Redistribution of food nearing its expiry date

[0452] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0453] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0454] Server: Notifies food businesses and relevant local governments of the proposal.

[0455] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0456] This system will strengthen cooperation between local governments and food businesses, effectively reducing food waste.

[0457] The processing flow will be explained below.

[0458] Step 1:

[0459] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date, etc.) into the system. This data is updated regularly.

[0460] Step 2:

[0461] Terminal: Local government officials input local demand data (type of food needed, quantity, deadline for request, etc.) into the system. This data is also updated regularly.

[0462] Step 3:

[0463] Server: Stores collected inventory and demand data in a database. The database is updated with the latest data.

[0464] Step 4:

[0465] Server: Passes the data stored in the database to the generative AI, which evaluates and analyzes the balance between supply and demand.

[0466] Step 5:

[0467] Server: Generative AI generates optimal food delivery strategies, such as determining which foods should be delivered to which areas, delivery priorities, schedules, etc.

[0468] Step 6:

[0469] Server: The generated food delivery strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[0470] Step 7:

[0471] Users: Local government officials and food businesses review and approve the communicated strategy.

[0472] Step 8:

[0473] User: After approval, the food supplier will arrange for the delivery of the specified food, arrange for a delivery company, and coordinate the delivery destination and delivery date.

[0474] Step 9:

[0475] Terminal: After completing a delivery, the delivery company's representative reports the status to the system, including the time of delivery completion and the recipient's confirmation of receipt.

[0476] Step 10:

[0477] Server: Receives delivery completion data and updates the database. This information is used to generate future delivery strategies.

[0478] Step 11:

[0479] Server: Based on the results of the actions taken, it provides feedback to the generative AI to improve the accuracy of the next food delivery strategy, thereby improving the accuracy and effectiveness of the entire system.

[0480] In this way, a series of steps are taken: data collection, analysis, notification, action, and feedback. By repeating this process, supply optimization and food waste reduction are achieved.

[0481] Example 1

[0482] 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."

[0483] Currently, when local governments and food suppliers work together to supply food, efficient inventory management and demand forecasting are difficult, resulting in food waste. It is also difficult to create optimal delivery plans that take food expiration dates into account, resulting in the risk of expired food being delivered. Furthermore, the delivery strategies generated are notified manually, which can hinder rapid implementation. These issues need to be resolved.

[0484] 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.

[0485] In this invention, the server includes: a means for acquiring information from local governments and food suppliers; a means for using artificial intelligence to analyze supply and demand based on the acquired information; a means for generating an optimal food delivery strategy based on the analysis results; a means for notifying the local government and food suppliers of the generated strategy via email, push notification, or other means; a means for issuing food delivery instructions based on the notified strategy; and a means for collecting data after delivery is completed and providing feedback for generating the next strategy. This enables the optimization of the balance between food supply and demand and the reduction of food waste. Furthermore, by automatically generating an optimal delivery plan that takes expiration dates into account, the risk of expiration is reduced, and the automated notification function promotes rapid implementation.

[0486] A "municipality" is a local government entity that provides administrative services in a specific area.

[0487] A "food business" is a company or individual that produces, processes, distributes, sells, etc. food.

[0488] "Means of acquiring information" refers to input devices, software, and associated interfaces for collecting data.

[0489] "Artificial intelligence" is a technology that uses data analysis and predictive models to make advanced decisions.

[0490] A "generator" is an algorithm or system that automatically creates the plans and strategies needed to achieve a specific goal.

[0491] "Means of notification" refers to a system for conveying information to the target person using email, push notifications, etc.

[0492] The "means for issuing food delivery instructions" is a function for issuing instructions for executing a specific delivery plan based on the generated strategy.

[0493] The "means for collecting data after delivery completion and providing feedback" is a function for collecting result data after delivery completion and using it to help generate the next strategy.

[0494] A "food delivery strategy" is a plan that optimizes which foods to deliver to which areas and how.

[0495] "Optimization" refers to achieving the most efficient and effective state under certain conditions.

[0496] The "best before" date is the date by which food is at its most delicious. Food is still edible after this date, but the quality may deteriorate.

[0497] "Analysis results" are information obtained as a result of processing collected data.

[0498] "Data" is a collection of numbers, text, etc. that are collected to represent specific information or phenomena.

[0499] This invention is a system in which local governments and food businesses work together to efficiently distribute food and reduce food waste.

[0500] Hardware and Software

[0501] Terminals: The terminals for this system are general devices such as PCs, tablets, and smartphones used by food service providers and local government officials. These terminals are capable of connecting to the Internet and provide a user interface for inputting and transmitting the necessary data.

[0502] Server: The server runs on a cloud platform, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP). This server performs the functions of data collection, storage, analysis, and notification.

[0503] Software: Various programming languages ​​(e.g., Python, Node.js) and database systems (e.g., MySQL, PostgreSQL) are used to control the entire system. Machine learning libraries such as TensorFlow and PyTorch are used to implement generative AI models.

[0504] Data collection

[0505] Terminal: Food supplier personnel enter inventory data (food type, quantity, expiration date, etc.) through a dedicated food inventory management app. Local government personnel log in to a browser-based demand management portal and enter demand data for each region (type of food needed, quantity, required deadline, etc.).

[0506] Server: Data entered from the terminal is sent to the server via SOAP or REST API and stored in a database.

[0507] Data analysis

[0508] Server: The server uses periodic cron jobs or trigger events to collect up-to-date inventory and demand data from the database, which is then pre-processed and converted into a format that can be fed into the generative AI model.

[0509] Generative AI model: The generative AI model evaluates the balance between supply and demand based on collected data and generates an optimal food supply strategy, including which food to deliver to which areas, delivery priorities, and schedules.

[0510] Optimal strategy generation and notification

[0511] Server: Receives the output from the generative AI model and reconstructs it into a specific delivery strategy. This strategy is then communicated to local governments and food providers via email and push notifications.

[0512] Execution instructions

[0513] Users: Local government officials and food service providers receive notifications. They review the notification content and, after approval, execute specific food delivery plans. For example, food service delivery personnel use the workflow management system to create delivery tasks, allocate resources, and adjust schedules.

[0514] Server: After delivery is completed, the delivery data is collected again and stored in the database. This data is used to generate the next strategy, contributing to improving the accuracy and efficiency of the system.

[0515] Specific examples

[0516] Example 1: Supplying food to areas with food shortages

[0517] Terminal: A local government official enters food shortage information for Area A into the system.

[0518] Server: The generative AI model analyzes demand in region A and generates proposals to procure excess inventory in region B based on food supplier supply data.

[0519] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0520] User: A food supplier delivers food from region B to region A.

[0521] Example 2: Redistribution of food nearing its expiry date

[0522] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0523] Server: A generative AI model analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0524] Server: Notifies food businesses and relevant local governments of the proposal.

[0525] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0526] Prompt Sentence Examples

[0527] "Please enter food shortage information for Area A."

[0528] "Please enter information about inventory that is close to expiry into the system."

[0529] "Generate optimal food delivery strategies"

[0530] In this way, local governments and food businesses can work together to create an effective system for minimizing food waste.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] Terminal: A food supplier uses a food inventory management app to input inventory data (food type, quantity, expiration date, etc.). This data is compiled in JSON format.

[0534] Input: Inventory data such as food type, quantity, expiration date, etc.

[0535] Output: Inventory data in JSON format

[0536] Specific operation: Open the app from your device, enter inventory information into the input form, and click the "Submit" button. The submitted data is sent to the server via the API endpoint.

[0537] Step 2:

[0538] Terminal: A local government official logs into the demand management portal and enters demand data for each region (type of food needed, quantity, deadline for request, etc.). This data is also compiled in JSON format.

[0539] Input: Demand data by region

[0540] Output: Demand data in JSON format

[0541] Specific operations: Open a browser, access the demand management portal, and log in. Enter demand information and click the "Submit" button. The submitted data is sent to the server via the REST API.

[0542] Step 3:

[0543] Server: Receives the inventory and demand data and stores it in a database, for example, using a database such as MySQL or PostgreSQL.

[0544] Input: Inventory and demand data in JSON format

[0545] Output: Inventory and demand data stored in a database

[0546] What happens: The server parses the JSON data received by the API, maps it to the appropriate fields, and executes a SQL query to store it in the database.

[0547] Step 4:

[0548] Server: Collects and pre-processes the stored data using a cron job or trigger events. Pre-processing includes cleaning and shaping the data, for example using the Pandas or NumPy libraries.

[0549] Input: Inventory and demand data stored in a database

[0550] Output: Preprocessed data for generative AI models

[0551] What it does: A scheduled job extracts the latest data from the database, which is then cleaned and formatted by a Python script.

[0552] Step 5:

[0553] Server: The preprocessed data is input into a generative AI model to generate an optimal food supply strategy that evaluates the balance between supply and demand. For example, using the TensorFlow or PyTorch libraries, the AI ​​model then performs inference processing and outputs the optimal distribution plan.

[0554] Input: Preprocessed data for generative AI models

[0555] Output: Optimal food supply strategy

[0556] Specific operation: The preprocessed data is input into the AI ​​model and the inference results are obtained. These results are returned to the server in JSON format.

[0557] Step 6:

[0558] Server: Receives the results returned by the generative AI model and reconstructs them as a specific delivery strategy. The generated strategy is notified to local governments and food businesses via email or push notifications. This notification is sent using services such as Amazon SES and Firebase Cloud Messaging.

[0559] Input: Optimal food supply strategy

[0560] Output: Notifications sent to local governments and food businesses

[0561] Specific Actions: Analyze the results from the generative AI model and send information to relevant personnel via email or notification services, including specific actions to take.

[0562] Step 7:

[0563] Users: Local government officials and food service providers receive notifications, review the details, and, after providing approval as necessary, execute specific food delivery plans. Food service delivery personnel create delivery tasks in the workflow management system and allocate and schedule the necessary resources.

[0564] Input: Informed Food Supply Strategy

[0565] Output: Executed delivery tasks

[0566] Specific actions: Open the notification email or push notification and follow the instructions to carry out the delivery plan. The delivery person then uses the workflow tool to create specific tasks and allocate resources.

[0567] Step 8:

[0568] Server: After the delivery is completed, the delivery completion data is collected again and stored in the database. This data is used to generate the next strategy.

[0569] Input: Delivery completion data

[0570] Output: Delivery completion data stored in the database

[0571] How it works: The delivery person enters a completion report into the system, and the server collects and stores the report in a database. The stored data is used for subsequent analysis and training of the AI ​​model.

[0572] In this way, the system strengthens cooperation between local governments and food businesses and effectively reduces food waste.

[0573] (Application example 1)

[0574] 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."

[0575] In recent years, the problem of food waste has become more serious, and there is a need for food businesses and local governments to work together to distribute food efficiently. However, many processes, such as food inventory management, regional demand surveys, formulating optimal supply strategies, and arranging deliveries, rely on manual labor, making it difficult to operate efficiently. This makes food waste more likely to occur, and has become a social problem.

[0576] 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.

[0577] In this invention, the server includes means for collecting data from local governments and food suppliers, means for analyzing supply and demand based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for issuing food delivery instructions based on the notified strategy, means for collecting data after delivery is completed and providing feedback, means for food suppliers and local government officials to input and check data via mobile devices, and means for the generative model to generate an optimal supply strategy in real time. This makes it possible to efficiently operate the entire process from data collection to delivery instructions and effectively reduce food waste.

[0578] "Data collection means" refers to devices and systems for collecting inventory data, demand data, etc. from food businesses and local government officials.

[0579] "Analysis means" refers to a device or system that analyzes the collected data to evaluate the balance between supply and demand and generate an appropriate supply strategy based on the collected data.

[0580] The "generation means" is a device or system that generates an optimal food delivery strategy based on the analyzed data.

[0581] "Notification means" refers to a device or system for communicating the generated food delivery strategy to local governments and food businesses.

[0582] The "instruction means" is a device or system for issuing specific food delivery instructions based on the notified strategy.

[0583] A "feedback means" is a device or system that collects data after delivery is completed and provides information that is useful for generating the next strategy.

[0584] A "mobile terminal" is an information terminal device such as a smartphone or tablet that can be operated while on the move.

[0585] A "real-time generative model" is an algorithm or system that instantly analyzes collected data and generates an optimal supply strategy based on the results.

[0586] "Push notifications" are a technology that sends information directly to users' mobile devices in real time.

[0587] This invention is a system that enables local governments and food businesses to work together to efficiently distribute food and reduce food waste. The system aims to minimize food waste by automating the processes of data collection, analysis, generation of optimal strategies, notification, execution instructions, and feedback.

[0588] Specifically, the following hardware and software configuration is used.

[0589] Hardware

[0590] Server: Responsible for data aggregation, analysis, and running generative AI models. Specific examples include EC2 instances from AWS (Amazon Web Services) and Compute Engine from Google Cloud Platform.

[0591] Mobile devices: Used by food businesses and local government officials to input data and check notifications. Specifically, this applies to smartphones and tablets.

[0592] software

[0593] Mobile application: An application for entering data, receiving notifications, checking delivery instructions, etc. It runs on Android and iOS platforms.

[0594] Generative AI model: Analyzes collected data, evaluates the balance between supply and demand, and generates an optimal food delivery strategy. Specifically, OpenAI's GPT-3 and other latest generative models are used.

[0595] Notification service: A system for communicating the generated strategies to food businesses and local governments. Firebase Cloud Messaging and AWS SNS are used.

[0596] Data collection and analysis

[0597] First, food suppliers and local government officials use their mobile devices to input inventory data and local demand data. This data is sent in real time to a server, which collects the data and stores it in a database. A generative AI model analyzes the stored data and evaluates the balance between supply and demand.

[0598] Optimal strategy generation and notification

[0599] The generative AI model generates an optimal food delivery strategy based on the analyzed data. This strategy includes which food to deliver to which area, priorities, schedules, etc. The generated strategy is pushed to food businesses and local government officials via a notification service.

[0600] Instructions and feedback

[0601] Food vendors and local government officials use their mobile devices to check the notified strategies and take specific actions after approval. For example, food vendors arrange for the delivery of designated food to designated areas. Data after delivery is completed is sent back to the server and used as feedback to help generate the next strategy.

[0602] Prompt Sentence Examples

[0603] Examples of prompts that an AI model might use to create a generative strategy include:

[0604] "Suggest a redistribution of food items nearing their expiration date. Data: {'food_item': 'bread', 'expiry_date': '2023-10-31', 'quantity': 50}. Demand region: {'region_A': 30, 'region_B': 20}"

[0605] In this way, local governments and food businesses can work together to develop and implement optimal food delivery strategies in real time, creating a system that can effectively reduce food waste.

[0606] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0607] Step 1:

[0608] Users use their mobile devices to input food inventory data and local demand data. Specifically, food vendors input information such as inventory levels and expiration dates, while local government officials input demand data such as the type, quantity, and location of food needed. This input data is then sent from the device to a server.

[0609] Input: Inventory data (food type, quantity, expiration date), demand data (food required, quantity, location)

[0610] Output: Collected data stored on the server

[0611] Step 2:

[0612] The server stores the submitted inventory and demand data in a database. This data is then analyzed by the generative AI model. Specifically, the server passes the collected data set to the generative AI model to evaluate the balance between supply and demand.

[0613] Input: Collected data (stock and demand data stored in a database)

[0614] Output: Analysis results (demand and supply balance evaluation)

[0615] Step 3:

[0616] The generative AI model generates an optimal food delivery strategy based on the analysis results, including which food items should be delivered to which areas and when, as well as delivery priorities and schedules. The server receives this generated strategy and converts it into a format for notification.

[0617] Input: Analysis results (demand and supply balance evaluation)

[0618] Output: Optimal delivery strategy (food delivery strategy)

[0619] Step 4:

[0620] The server then sends the generated food delivery strategy to food businesses and local government officials as push notifications, using notification services such as Firebase Cloud Messaging and AWS SNS to deliver real-time notifications to mobile devices.

[0621] Input: Optimal delivery strategy (food delivery strategy)

[0622] Output: Notification (push notification to mobile device)

[0623] Step 5:

[0624] The user checks the notification on their mobile device and approves the strategy. The food business then makes specific arrangements to deliver the food to the designated area based on the approved strategy. Specifically, the business issues instructions to delivery companies and arranges the necessary logistics work.

[0625] Input: Notification (food delivery strategy information)

[0626] Output: Delivery arrangements (instructions to delivery companies, logistics arrangements)

[0627] Step 6:

[0628] After the food delivery is completed, the delivery company sends the delivery completion data to the server, which collects the data and stores it in a database. The collected delivery completion data is used as feedback for generating the next optimal strategy.

[0629] Input: Delivery completion data (reported data from delivery company)

[0630] Output: Feedback data (used for next strategy generation)

[0631] In this way, through a series of processing steps, local governments and food businesses work together to create a system that efficiently distributes food and reduces food waste.

[0632] 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.

[0633] The purpose of this system is to efficiently distribute food through collaboration between local governments and food businesses, thereby reducing food waste. Furthermore, by adding an emotion engine, it is possible to provide more appropriate strategies that take into account the emotions of users. Specific embodiments for implementing the present invention are described below.

[0634] Data collection

[0635] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[0636] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[0637] Data analysis

[0638] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0639] Optimal strategy generation and notification

[0640] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[0641] Server: The generated strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[0642] Emotion recognition by emotion engine

[0643] Terminal: The emotion engine recognizes the user's emotions based on the user's input data (for example, comments and behavioral patterns when entering demand data or inventory data).

[0644] Server: The emotion engine analyzes the user's emotional data and customizes the notification content based on this. For example, a user with high stress levels will receive a simple and intuitive notification.

[0645] Execution instructions

[0646] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, food businesses can arrange for the delivery of designated food to designated areas.

[0647] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[0648] Specific examples

[0649] Example 1: Supplying food to areas with food shortages

[0650] Terminal: A local government official enters food shortage information for Area A into the system.

[0651] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[0652] Server: The emotion engine analyzes the emotional data of local government officials and customizes the notification content to ensure that the proposal is appropriately accepted.

[0653] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0654] User: A food supplier delivers food from region B to region A.

[0655] Example 2: Redistribution of food nearing its expiry date

[0656] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0657] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0658] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes the notification content to ensure that the proposal is appropriately accepted.

[0659] Server: Notifies food businesses and relevant local governments of the proposal.

[0660] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0661] This system will strengthen cooperation between local governments and food businesses, and effectively reduce food waste. Furthermore, the emotion engine function will take the user's emotions into consideration when issuing notifications and instructions, improving the system's acceptability and effectiveness.

[0662] The processing flow will be explained below.

[0663] Step 1:

[0664] Terminal: A food vendor enters food inventory data (food type, quantity, expiration date, etc.) into the system. This data is periodically sent from the terminal to the system.

[0665] Step 2:

[0666] Terminal: Local government officials enter local demand data (type of food needed, quantity, deadline, etc.) into the system. This data is also periodically sent from the terminal to the system.

[0667] Step 3:

[0668] Server: Stores the input inventory and demand data in a database. The database is updated with the latest information in real time.

[0669] Step 4:

[0670] Server: Passes inventory and demand data stored in the database to the generative AI, which analyzes this data and evaluates the balance between supply and demand.

[0671] Step 5:

[0672] Server: Generative AI generates optimal food delivery strategies, including details of which foods should be delivered to which areas and when.

[0673] Step 6:

[0674] Server: Notifies the local government and food businesses of the generated food delivery strategy through notification means. This notification is sent via email or push notification.

[0675] Step 7:

[0676] Device: The emotion engine analyzes the user's input data and behavioral patterns to recognize the user's emotions. For example, if the input data indicates fatigue, it will recognize that as an emotion.

[0677] Step 8:

[0678] Server: The emotion engine analyzes the emotional data and customizes the notification content and interface. For example, a user who is feeling stressed will receive a simple and intuitive notification.

[0679] Step 9:

[0680] Users: Local government officials and food businesses review the notification strategy and customized notification content and approve it as needed.

[0681] Step 10:

[0682] User: After approval, the food supplier will arrange for the specified food to be delivered to the specified area. Specifically, the supplier will arrange for a delivery company and coordinate the delivery destination and delivery date and time.

[0683] Step 11:

[0684] Terminal: After completing delivery, the delivery company's staff reports the status to the system, including the time of delivery completion and confirmation of the recipient.

[0685] Step 12:

[0686] Server: Receives delivery completion data and updates the database. This ensures that the information required for the next strategy generation is up to date.

[0687] Step 13:

[0688] Server: The emotion engine analyzes the actual delivery results and user feedback to reflect in the next strategy generation. For example, it improves the notification method based on emotion data.

[0689] This process strengthens cooperation between local governments and food businesses, leading to efficient food distribution and reduced food waste. The emotion engine also enables flexible responses that take user emotions into account, improving the acceptability and effectiveness of the entire system.

[0690] Example 2

[0691] 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."

[0692] Food waste is a very serious problem in modern times, and there is a need to build an efficient supply chain. It is particularly difficult to accurately understand the management of food that is approaching its expiration date and fluctuations in food demand in each region, thereby minimizing waste. Furthermore, rapid and accurate information sharing is essential for smooth collaboration among the multiple parties involved (municipalities, food businesses, etc.). Furthermore, it is necessary to ensure that efforts to reduce food waste do not become a stress or burden for users.

[0693] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for periodically collecting data from local governments and food suppliers, means for analyzing supply and demand using a generative AI model based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for analyzing user emotion data using an emotion engine and customizing the notification content, means for issuing food delivery instructions based on the notified strategy, and means for collecting data and emotion data after delivery is completed and providing feedback. This enables an efficient food supply system that takes emotions into consideration.

[0694] A "local government" is a government body that manages a particular area and provides public services.

[0695] A "food business" is a business or individual that produces, processes, distributes, or sells food.

[0696] "Means for periodic data collection" refers to a system that automatically obtains necessary information from local governments and food businesses at specific time intervals.

[0697] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and predicts and evaluates supply and demand.

[0698] "Demand and supply analysis tools" are methods for calculating food needs and supplies in a specific area or period based on collected data.

[0699] The "means for generating an optimal food delivery strategy" is a method for creating a plan for efficiently distributing food based on the analysis results.

[0700] "Means of notification" refers to the mechanisms used to communicate generated strategies and information to stakeholders, including email and push notifications.

[0701] The "emotion engine" is a system that analyzes emotions from user input data and behavioral patterns and responds appropriately.

[0702] "Means for customizing notification content" refers to a method for providing information to users in the most optimal form based on analyzed emotional data.

[0703] The "means for issuing food delivery instructions" refers to the procedure for initiating and managing specific delivery operations based on the generated strategy.

[0704] "Means for collecting data and emotional data after delivery is completed and providing feedback" is a mechanism for collecting the situation after delivery is completed and the user's reactions, and using this information to generate the next strategy.

[0705] This invention relates to a system that enables local governments and food businesses to efficiently distribute food and reduce food waste. The system utilizes a generative AI model and has the ability to customize notification content based on user sentiment. Specific embodiments are described below.

[0706] 1. Data Collection

[0707] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date) into the terminal through a dedicated application. Similarly, local government officials enter demand data for each region (food type, quantity, required deadline) into the terminal. This data is converted into JSON format and sent to the server.

[0708] 2. Data Analysis

[0709] Server: The server receives the data sent from the device and stores it in a database. A generative AI model (e.g., GPT-3) is used to analyze the received inventory and demand data. The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0710] 3. Generating and notifying optimal strategies

[0711] Server: The generative AI model generates an optimal food delivery strategy based on the analysis results. This strategy includes which food to deliver to which area, delivery priorities, and schedules. The generated strategy is notified to local governments and food businesses via email, push notifications, etc.

[0712] 4. Emotion Recognition by Emotion Engine

[0713] Terminal: An emotion engine is used to analyze user comments and behavioral patterns. For example, emotional data of users is collected from comments made during data entry and operation logs.

[0714] Server: Analyzes the collected emotional data and customizes the notification content. For example, it sends a simple and intuitive notification to a user who is highly stressed, and generates appropriate notification content according to the user's state.

[0715] 5. Execution Instructions

[0716] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[0717] Server: Collects data after delivery is completed and provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[0718] Specific examples

[0719] Example 1: Supplying food to areas with food shortages

[0720] Terminal: A local government official enters food shortage information for Area A into the system.

[0721] Server: Generative AI analyzes demand data from region A and generates a proposal to procure from surplus inventory in region B based on supply data from food suppliers.

[0722] Server: The emotion engine analyzes the emotion data of local government officials and customizes notifications to ensure that the proposals are appropriately accepted.

[0723] Server: Notifies the local government and food vendors of the proposal and issues delivery instructions.

[0724] User: A food supplier delivers food from region B to region A.

[0725] Example 2: Redistribution of food nearing its expiry date

[0726] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0727] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0728] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes notifications to ensure that the proposals are appropriately accepted.

[0729] Server: Notifies food businesses and relevant local governments of the proposal.

[0730] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0731] Prompt Sentence Examples

[0732] To suggest an appropriate delivery strategy for food-insecure areas:

[0733] Area A is short of food. Area B has excess stock. Please suggest how to supply it.

[0734] If you are proposing a redistribution of food that is nearing its expiry date:

[0735] I have some food that is nearing its expiration date. Please suggest areas or facilities that can distribute it quickly.

[0736] This system enables local governments and food businesses to work together efficiently and reduce food waste. The emotion engine provides notifications that take into account the user's emotions, improving the system's acceptability and effectiveness.

[0737] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0738] Step 1: Data collection

[0739] Terminal: Food vendors use a dedicated application to input food inventory data (food type, quantity, expiration date) into the terminal. Similarly, local government officials input demand data for each region (type of food needed, quantity, required deadline) into the terminal. The input data is converted into JSON format and sent to the server.

[0740] Input: Inventory data (food type, quantity, expiration date), Demand data (food type, quantity, required deadline)

[0741] Output: JSON format data

[0742] Specific operations: The person in charge opens a dedicated application on a tablet or PC, enters the necessary data, and clicks the send button.

[0743] Step 2: Receiving and storing data

[0744] Server: The server receives the JSON format data sent from the device and stores it in a MySQL database.

[0745] Input: JSON format data sent from the terminal

[0746] Output: Inventory and demand data stored in a database

[0747] What happens: A server-side receiving script receives the JSON data and inserts it into a database using an SQL query.

[0748] Step 3: Data analysis

[0749] Server: The server retrieves inventory and demand data from the database and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0750] Input: Inventory and demand data retrieved from a database

[0751] Output: Optimal food supply strategy based on the balance between supply and demand

[0752] Specific operation: The server retrieves data using an SQL query, sends the data to the generative AI model's API, and receives the analysis results.

[0753] Step 4: Generate and communicate optimal strategies

[0754] Server: Generates an optimal food delivery strategy based on the analysis results generated by the generative AI model. Prepares notification content based on the generated strategy and notifies local governments and food businesses via email and push notifications.

[0755] Input: Analysis results of the generative AI model

[0756] Output: Notification content sent to local governments and food businesses

[0757] What it does: The server receives the results from the generative AI model, inserts them into a prefixed notification template, and sends it using an SMTP server or push notification service.

[0758] Step 5: Emotion Recognition and Notification Customization

[0759] Terminal: When a user enters comments or behavioral patterns during data entry, the emotion engine recognizes them and generates emotion data.

[0760] Input: User comments and behavior patterns

[0761] Output: Emotion data

[0762] Specific operation: The device's sensors and log acquisition system analyze the user's actions and collect emotion-related data.

[0763] Server: Analyzes the emotion data generated by the emotion engine and customizes the notification content. For example, it generates a simple and intuitive notification for users with high stress levels.

[0764] Input: Emotion data

[0765] Output: Customized notification content

[0766] Specific operation: The server generates notification content based on the analysis results of the emotion engine and sends it to the user in an appropriate format.

[0767] Step 6: Execution instructions

[0768] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[0769] Input: Customized notification content, strategic information

[0770] Output: Execution instructions after approval

[0771] Specific actions: The person in charge checks the notification and clicks the approval button. The food supplier prepares the delivery and issues delivery instructions.

[0772] Step 7: Gather feedback

[0773] Server: After the delivery is completed, the server collects the delivery completion data and the user's emotion data again and provides feedback for the next strategy generation.

[0774] Input: Delivery completion data, emotion data

[0775] Output: Feedback data for next strategy generation

[0776] Specific operation: After delivery is completed, the server automatically collects the data, stores it in a database, and reuses it as input data for the generative AI model.

[0777] Through these steps, the system can efficiently and effectively manage food supply and demand, thereby reducing food waste. Furthermore, by using an emotion engine, notifications and instructions that take into account the user's emotions can be provided, increasing the system's acceptability and effectiveness.

[0778] (Application example 2)

[0779] 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."

[0780] Food waste not only causes economic losses but also has a serious impact on the environment, necessitating the development of efficient food distribution systems. However, existing systems struggle to optimize the balance between supply and demand, and lack the ability to respond to individual user needs based on user emotions. In particular, in the food delivery field, there is a lack of effective customization of notifications based on emotion recognition for delivery routes and urgent food distribution.

[0781] 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 a means for collecting data from local governments and food businesses, a means for analyzing supply and demand based on the collected data, a means for generating an optimal food delivery strategy based on the analysis results, a means for notifying the local government and food businesses of the generated strategy, an emotion engine means for recognizing the user's emotions and customizing the notification content, a means for issuing food delivery instructions based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback. This not only enables efficient balancing of supply and demand, but also enables flexible responses tailored to the user's emotions.

[0782] A "local government" is a public institution that is responsible for local administration and provides administrative services related to the lives of local residents.

[0783] "Food businesses" are companies and organizations that produce, process, and sell food, and play an important role in food distribution.

[0784] "Data collection means" refers to a method or device for acquiring and organizing information, and in this invention, it is used to collect inventory and demand information provided by local governments and food suppliers.

[0785] "Means for supply and demand analysis" refers to methods and mechanisms that use collected data to assess and analyze where and how much food is needed and where and how much is in stock.

[0786] The "means for generating an optimal food delivery strategy" is a method or device for determining, based on the analysis results, which food items should be delivered to which areas as a priority.

[0787] "Means for notifying the generated strategy" refers to methods or devices for informing relevant parties (local governments and food businesses) of the generated delivery strategy, and includes email and push notifications.

[0788] An "emotion engine" is a system or device that analyzes the user's input data, recognizes their emotional state at the time, and presents the optimal response.

[0789] The "means for issuing instructions for food delivery" refers to a method or device for issuing instructions to relevant parties to actually deliver food based on an optimal delivery strategy.

[0790] The "means for providing feedback" refers to a method or device that collects data when delivery is completed and provides complementary information to be used in generating the next strategy.

[0791] The present invention provides a system for efficiently distributing food and reducing food waste through collaboration between local governments and food businesses. The system includes a data collection means, a data analysis means, a means for generating an optimal food delivery strategy, an emotion engine means, a notification means, a means for issuing food delivery instructions, and a means for providing feedback.

[0792] First, food vendors use terminals to input food inventory data (e.g., food type, quantity, expiration date, etc.) and local governments input demand data for each region (e.g., food type, quantity, required deadline, etc.) into the system. The server then collects the data and stores it in a database. This data is periodically collected by a data collection module using Python.

[0793] Next, using a generative AI model, the server analyzes the collected inventory and demand data to assess the balance between supply and demand. Based on the analysis results, it generates an optimal food delivery strategy, which includes which food items to deliver to which areas, delivery priorities, and schedules.

[0794] The server notifies the local government and food suppliers of the generated strategy via notification means (email, push notification, etc.). At this time, the emotion engine recognizes the user's emotions and customizes the notification content based on, for example, the user's input data and behavioral patterns. Specifically, if the user is feeling stressed, a simple and intuitive notification is sent, and if not, a detailed notification is sent.

[0795] For example, by inputting a prompt such as, "Compare the data from region A with next month's demand, have the generative AI model generate suggestions on how to efficiently distribute food that is nearing its expiration date, and use the emotion engine to customize the notification content," into the generative AI model, analysis and notification customization are performed.

[0796] Local government officials and food vendors then review the strategy and take specific action after approval. For example, a food vendor arranges for the delivery of a specified food item to a specified area. After the delivery is completed, the server collects data about the delivery again and uses it as feedback for generating the next strategy. This feedback also includes emotional data.

[0797] This will strengthen cooperation between local governments and food businesses, effectively reducing food waste, and improve the system's acceptability and effectiveness by providing appropriate notifications and instructions that take into account the user's emotions through the emotion engine.

[0798] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0799] Step 1:

[0800] The terminal receives data input from food businesses and local governments. Specifically, food businesses input food inventory data (food type, quantity, expiration date, etc.), and local governments input demand data for each region (type of food needed, quantity, required deadline, etc.). The input data is sent to a server and stored in a database. The input data is then processed by a data collection module and converted into a unified format.

[0801] Step 2:

[0802] The server retrieves inventory and demand data stored in the database. Based on this data, it uses a generative AI model to analyze the data. Specifically, it evaluates the balance between supply and demand and analyzes which foods should be prioritized for distribution to which areas. The generative AI model used here uses algorithms that forecast demand and optimize supply based on the input data.

[0803] Step 3:

[0804] The server generates an optimal food delivery strategy based on the analysis results of the generative AI model. This strategy includes information such as which food to deliver to which area, delivery priorities, and schedules. The generated strategy includes specific instructions such as food distribution routes and allocation amounts. This strategy is then saved back into the database.

[0805] Step 4:

[0806] The server notifies the local government and food suppliers of the generated delivery strategy via a notification mechanism. Here, the emotion engine recognizes the user's emotions and customizes the notification content. It analyzes the user's input data and behavioral patterns to determine whether the user is under stress and sends notifications according to their state. For example, it sends a simple notification to users who are highly stressed, and a detailed notification to users who are not.

[0807] Step 5:

[0808] Users (municipal government officials and food businesses) review and approve the notified strategy. After approval, they take specific actions. Specifically, food businesses arrange for the delivery of designated food to designated areas and begin actual delivery work on site.

[0809] Step 6:

[0810] The server collects information after delivery is complete. The data after delivery includes information on delivery time, delivery volume, and delivery destination. This data is sent to the server and stored in a database. The emotion engine also collects user emotion data again.

[0811] Step 7:

[0812] The server analyzes the collected delivery completion data and emotion data and uses them as feedback for generating the next strategy, which continuously optimizes the system to reduce food waste and improve the user experience.

[0813] Through these steps, local governments and food businesses can work together efficiently to reduce food waste, and the emotion engine can provide notifications that take users' emotions into consideration.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] [Third embodiment]

[0818] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0819] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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).

[0824] 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. 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] The purpose of this system is to efficiently distribute food through cooperation between local governments and food businesses, thereby reducing food waste. Specific embodiments for carrying out the present invention are described below.

[0831] Data collection

[0832] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[0833] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[0834] Data analysis

[0835] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[0836] Optimal strategy generation and notification

[0837] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[0838] Server: The generated strategy is notified to local governments and food businesses through notification means, such as email or push notification.

[0839] Execution instructions

[0840] Users: Local government officials and food businesses review the notified strategy and take specific actions after approval. For example, food businesses arrange for the delivery of designated food to designated areas.

[0841] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation.

[0842] Specific examples

[0843] Example 1: Supplying food to areas with food shortages

[0844] Terminal: A local government official enters food shortage information for Area A into the system.

[0845] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[0846] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0847] User: A food supplier delivers food from region B to region A.

[0848] Example 2: Redistribution of food nearing its expiry date

[0849] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0850] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0851] Server: Notifies food businesses and relevant local governments of the proposal.

[0852] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0853] This system will strengthen cooperation between local governments and food businesses, effectively reducing food waste.

[0854] The processing flow will be explained below.

[0855] Step 1:

[0856] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date, etc.) into the system. This data is updated regularly.

[0857] Step 2:

[0858] Terminal: Local government officials input local demand data (type of food needed, quantity, deadline for request, etc.) into the system. This data is also updated regularly.

[0859] Step 3:

[0860] Server: Stores collected inventory and demand data in a database. The database is updated with the latest data.

[0861] Step 4:

[0862] Server: Passes the data stored in the database to the generative AI, which evaluates and analyzes the balance between supply and demand.

[0863] Step 5:

[0864] Server: Generative AI generates optimal food delivery strategies, such as determining which foods should be delivered to which areas, delivery priorities, schedules, etc.

[0865] Step 6:

[0866] Server: The generated food delivery strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[0867] Step 7:

[0868] Users: Local government officials and food businesses review and approve the communicated strategy.

[0869] Step 8:

[0870] User: After approval, the food supplier will arrange for the delivery of the specified food, arrange for a delivery company, and coordinate the delivery destination and delivery date.

[0871] Step 9:

[0872] Terminal: After completing a delivery, the delivery company's representative reports the status to the system, including the time of delivery completion and the recipient's confirmation of receipt.

[0873] Step 10:

[0874] Server: Receives delivery completion data and updates the database. This information is used to generate future delivery strategies.

[0875] Step 11:

[0876] Server: Based on the results of the actions taken, it provides feedback to the generative AI to improve the accuracy of the next food delivery strategy, thereby improving the accuracy and effectiveness of the entire system.

[0877] In this way, a series of steps are taken: data collection, analysis, notification, action, and feedback. By repeating this process, supply optimization and food waste reduction are achieved.

[0878] Example 1

[0879] 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."

[0880] Currently, when local governments and food suppliers work together to supply food, efficient inventory management and demand forecasting are difficult, resulting in food waste. It is also difficult to create optimal delivery plans that take food expiration dates into account, resulting in the risk of expired food being delivered. Furthermore, the delivery strategies generated are notified manually, which can hinder rapid implementation. These issues need to be resolved.

[0881] 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.

[0882] In this invention, the server includes: a means for acquiring information from local governments and food suppliers; a means for using artificial intelligence to analyze supply and demand based on the acquired information; a means for generating an optimal food delivery strategy based on the analysis results; a means for notifying the local government and food suppliers of the generated strategy via email, push notification, or other means; a means for issuing food delivery instructions based on the notified strategy; and a means for collecting data after delivery is completed and providing feedback for generating the next strategy. This enables the optimization of the balance between food supply and demand and the reduction of food waste. Furthermore, by automatically generating an optimal delivery plan that takes expiration dates into account, the risk of expiration is reduced, and the automated notification function promotes rapid implementation.

[0883] A "municipality" is a local government entity that provides administrative services in a specific area.

[0884] A "food business" is a company or individual that produces, processes, distributes, sells, etc. food.

[0885] "Means of acquiring information" refers to input devices, software, and associated interfaces for collecting data.

[0886] "Artificial intelligence" is a technology that uses data analysis and predictive models to make advanced decisions.

[0887] A "generator" is an algorithm or system that automatically creates the plans and strategies needed to achieve a specific goal.

[0888] "Means of notification" refers to a system for conveying information to the target person using email, push notifications, etc.

[0889] The "means for issuing food delivery instructions" is a function for issuing instructions for executing a specific delivery plan based on the generated strategy.

[0890] The "means for collecting data after delivery completion and providing feedback" is a function for collecting result data after delivery completion and using it to help generate the next strategy.

[0891] A "food delivery strategy" is a plan that optimizes which foods to deliver to which areas and how.

[0892] "Optimization" refers to achieving the most efficient and effective state under certain conditions.

[0893] The "best before" date is the date by which food is at its most delicious. Food is still edible after this date, but the quality may deteriorate.

[0894] "Analysis results" are information obtained as a result of processing collected data.

[0895] "Data" is a collection of numbers, text, etc. that are collected to represent specific information or phenomena.

[0896] This invention is a system in which local governments and food businesses work together to efficiently distribute food and reduce food waste.

[0897] Hardware and Software

[0898] Terminals: The terminals for this system are general devices such as PCs, tablets, and smartphones used by food service providers and local government officials. These terminals are capable of connecting to the Internet and provide a user interface for inputting and transmitting the necessary data.

[0899] Server: The server runs on a cloud platform, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP). This server performs the functions of data collection, storage, analysis, and notification.

[0900] Software: Various programming languages ​​(e.g., Python, Node.js) and database systems (e.g., MySQL, PostgreSQL) are used to control the entire system. Machine learning libraries such as TensorFlow and PyTorch are used to implement generative AI models.

[0901] Data collection

[0902] Terminal: Food supplier personnel enter inventory data (food type, quantity, expiration date, etc.) through a dedicated food inventory management app. Local government personnel log in to a browser-based demand management portal and enter demand data for each region (type of food needed, quantity, required deadline, etc.).

[0903] Server: Data entered from the terminal is sent to the server via SOAP or REST API and stored in a database.

[0904] Data analysis

[0905] Server: The server uses periodic cron jobs or trigger events to collect up-to-date inventory and demand data from the database, which is then pre-processed and converted into a format that can be fed into the generative AI model.

[0906] Generative AI model: The generative AI model evaluates the balance between supply and demand based on collected data and generates an optimal food supply strategy, including which food to deliver to which areas, delivery priorities, and schedules.

[0907] Optimal strategy generation and notification

[0908] Server: Receives the output from the generative AI model and reconstructs it into a specific delivery strategy. This strategy is then communicated to local governments and food providers via email and push notifications.

[0909] Execution instructions

[0910] Users: Local government officials and food service providers receive notifications. They review the notification content and, after approval, execute specific food delivery plans. For example, food service delivery personnel use the workflow management system to create delivery tasks, allocate resources, and adjust schedules.

[0911] Server: After delivery is completed, the delivery data is collected again and stored in the database. This data is used to generate the next strategy, contributing to improving the accuracy and efficiency of the system.

[0912] Specific examples

[0913] Example 1: Supplying food to areas with food shortages

[0914] Terminal: A local government official enters food shortage information for Area A into the system.

[0915] Server: The generative AI model analyzes demand in region A and generates proposals to procure excess inventory in region B based on food supplier supply data.

[0916] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[0917] User: A food supplier delivers food from region B to region A.

[0918] Example 2: Redistribution of food nearing its expiry date

[0919] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[0920] Server: A generative AI model analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[0921] Server: Notifies food businesses and relevant local governments of the proposal.

[0922] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[0923] Prompt Sentence Examples

[0924] "Please enter food shortage information for Area A."

[0925] "Please enter information about inventory that is close to expiry into the system."

[0926] "Generate optimal food delivery strategies"

[0927] In this way, local governments and food businesses can work together to create an effective system for minimizing food waste.

[0928] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0929] Step 1:

[0930] Terminal: A food supplier uses a food inventory management app to input inventory data (food type, quantity, expiration date, etc.). This data is compiled in JSON format.

[0931] Input: Inventory data such as food type, quantity, expiration date, etc.

[0932] Output: Inventory data in JSON format

[0933] Specific operation: Open the app from your device, enter inventory information into the input form, and click the "Submit" button. The submitted data is sent to the server via the API endpoint.

[0934] Step 2:

[0935] Terminal: A local government official logs into the demand management portal and enters demand data for each region (type of food needed, quantity, deadline for request, etc.). This data is also compiled in JSON format.

[0936] Input: Demand data by region

[0937] Output: Demand data in JSON format

[0938] Specific operations: Open a browser, access the demand management portal, and log in. Enter demand information and click the "Submit" button. The submitted data is sent to the server via the REST API.

[0939] Step 3:

[0940] Server: Receives the inventory and demand data and stores it in a database, for example, using a database such as MySQL or PostgreSQL.

[0941] Input: Inventory and demand data in JSON format

[0942] Output: Inventory and demand data stored in a database

[0943] What happens: The server parses the JSON data received by the API, maps it to the appropriate fields, and executes a SQL query to store it in the database.

[0944] Step 4:

[0945] Server: Collects and pre-processes the stored data using a cron job or trigger events. Pre-processing includes cleaning and shaping the data, for example using the Pandas or NumPy libraries.

[0946] Input: Inventory and demand data stored in a database

[0947] Output: Preprocessed data for generative AI models

[0948] What it does: A scheduled job extracts the latest data from the database, which is then cleaned and formatted by a Python script.

[0949] Step 5:

[0950] Server: The preprocessed data is input into a generative AI model to generate an optimal food supply strategy that evaluates the balance between supply and demand. For example, using the TensorFlow or PyTorch libraries, the AI ​​model then performs inference processing and outputs the optimal distribution plan.

[0951] Input: Preprocessed data for generative AI models

[0952] Output: Optimal food supply strategy

[0953] Specific operation: The preprocessed data is input into the AI ​​model and the inference results are obtained. These results are returned to the server in JSON format.

[0954] Step 6:

[0955] Server: Receives the results returned by the generative AI model and reconstructs them as a specific delivery strategy. The generated strategy is notified to local governments and food businesses via email or push notifications. This notification is sent using services such as Amazon SES and Firebase Cloud Messaging.

[0956] Input: Optimal food supply strategy

[0957] Output: Notifications sent to local governments and food businesses

[0958] Specific Actions: Analyze the results from the generative AI model and send information to relevant personnel via email or notification services, including specific actions to take.

[0959] Step 7:

[0960] Users: Local government officials and food service providers receive notifications, review the details, and, after providing approval as necessary, execute specific food delivery plans. Food service delivery personnel create delivery tasks in the workflow management system and allocate and schedule the necessary resources.

[0961] Input: Informed Food Supply Strategy

[0962] Output: Executed delivery tasks

[0963] Specific actions: Open the notification email or push notification and follow the instructions to carry out the delivery plan. The delivery person then uses the workflow tool to create specific tasks and allocate resources.

[0964] Step 8:

[0965] Server: After the delivery is completed, the delivery completion data is collected again and stored in the database. This data is used to generate the next strategy.

[0966] Input: Delivery completion data

[0967] Output: Delivery completion data stored in the database

[0968] How it works: The delivery person enters a completion report into the system, and the server collects and stores the report in a database. The stored data is used for subsequent analysis and training of the AI ​​model.

[0969] In this way, the system strengthens cooperation between local governments and food businesses and effectively reduces food waste.

[0970] (Application example 1)

[0971] 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."

[0972] In recent years, the problem of food waste has become more serious, and there is a need for food businesses and local governments to work together to distribute food efficiently. However, many processes, such as food inventory management, regional demand surveys, formulating optimal supply strategies, and arranging deliveries, rely on manual labor, making it difficult to operate efficiently. This makes food waste more likely to occur, and has become a social problem.

[0973] 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.

[0974] In this invention, the server includes means for collecting data from local governments and food suppliers, means for analyzing supply and demand based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for issuing food delivery instructions based on the notified strategy, means for collecting data after delivery is completed and providing feedback, means for food suppliers and local government officials to input and check data via mobile devices, and means for the generative model to generate an optimal supply strategy in real time. This makes it possible to efficiently operate the entire process from data collection to delivery instructions and effectively reduce food waste.

[0975] "Data collection means" refers to devices and systems for collecting inventory data, demand data, etc. from food businesses and local government officials.

[0976] "Analysis means" refers to a device or system that analyzes the collected data to evaluate the balance between supply and demand and generate an appropriate supply strategy based on the collected data.

[0977] The "generation means" is a device or system that generates an optimal food delivery strategy based on the analyzed data.

[0978] "Notification means" refers to a device or system for communicating the generated food delivery strategy to local governments and food businesses.

[0979] The "instruction means" is a device or system for issuing specific food delivery instructions based on the notified strategy.

[0980] A "feedback means" is a device or system that collects data after delivery is completed and provides information that is useful for generating the next strategy.

[0981] A "mobile terminal" is an information terminal device such as a smartphone or tablet that can be operated while on the move.

[0982] A "real-time generative model" is an algorithm or system that instantly analyzes collected data and generates an optimal supply strategy based on the results.

[0983] "Push notifications" are a technology that sends information directly to users' mobile devices in real time.

[0984] This invention is a system that enables local governments and food businesses to work together to efficiently distribute food and reduce food waste. The system aims to minimize food waste by automating the processes of data collection, analysis, generation of optimal strategies, notification, execution instructions, and feedback.

[0985] Specifically, the following hardware and software configuration is used.

[0986] Hardware

[0987] Server: Responsible for data aggregation, analysis, and running generative AI models. Specific examples include EC2 instances from AWS (Amazon Web Services) and Compute Engine from Google Cloud Platform.

[0988] Mobile devices: Used by food businesses and local government officials to input data and check notifications. Specifically, this applies to smartphones and tablets.

[0989] software

[0990] Mobile application: An application for entering data, receiving notifications, checking delivery instructions, etc. It runs on Android and iOS platforms.

[0991] Generative AI model: Analyzes collected data, evaluates the balance between supply and demand, and generates an optimal food delivery strategy. Specifically, OpenAI's GPT-3 and other latest generative models are used.

[0992] Notification service: A system for communicating the generated strategies to food businesses and local governments. Firebase Cloud Messaging and AWS SNS are used.

[0993] Data collection and analysis

[0994] First, food suppliers and local government officials use their mobile devices to input inventory data and local demand data. This data is sent in real time to a server, which collects the data and stores it in a database. A generative AI model analyzes the stored data and evaluates the balance between supply and demand.

[0995] Optimal strategy generation and notification

[0996] The generative AI model generates an optimal food delivery strategy based on the analyzed data. This strategy includes which food to deliver to which area, priorities, schedules, etc. The generated strategy is pushed to food businesses and local government officials via a notification service.

[0997] Instructions and feedback

[0998] Food vendors and local government officials use their mobile devices to check the notified strategies and take specific actions after approval. For example, food vendors arrange for the delivery of designated food to designated areas. Data after delivery is completed is sent back to the server and used as feedback to help generate the next strategy.

[0999] Prompt Sentence Examples

[1000] Examples of prompts that an AI model might use to create a generative strategy include:

[1001] "Suggest a redistribution of food items nearing their expiration date. Data: {'food_item': 'bread', 'expiry_date': '2023-10-31', 'quantity': 50}. Demand region: {'region_A': 30, 'region_B': 20}"

[1002] In this way, local governments and food businesses can work together to develop and implement optimal food delivery strategies in real time, creating a system that can effectively reduce food waste.

[1003] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1004] Step 1:

[1005] Users use their mobile devices to input food inventory data and local demand data. Specifically, food vendors input information such as inventory levels and expiration dates, while local government officials input demand data such as the type, quantity, and location of food needed. This input data is then sent from the device to a server.

[1006] Input: Inventory data (food type, quantity, expiration date), demand data (food required, quantity, location)

[1007] Output: Collected data stored on the server

[1008] Step 2:

[1009] The server stores the submitted inventory and demand data in a database. This data is then analyzed by the generative AI model. Specifically, the server passes the collected data set to the generative AI model to evaluate the balance between supply and demand.

[1010] Input: Collected data (stock and demand data stored in a database)

[1011] Output: Analysis results (demand and supply balance evaluation)

[1012] Step 3:

[1013] The generative AI model generates an optimal food delivery strategy based on the analysis results, including which food items should be delivered to which areas and when, as well as delivery priorities and schedules. The server receives this generated strategy and converts it into a format for notification.

[1014] Input: Analysis results (demand and supply balance evaluation)

[1015] Output: Optimal delivery strategy (food delivery strategy)

[1016] Step 4:

[1017] The server then sends the generated food delivery strategy to food businesses and local government officials as push notifications, using notification services such as Firebase Cloud Messaging and AWS SNS to deliver real-time notifications to mobile devices.

[1018] Input: Optimal delivery strategy (food delivery strategy)

[1019] Output: Notification (push notification to mobile device)

[1020] Step 5:

[1021] The user checks the notification on their mobile device and approves the strategy. The food business then makes specific arrangements to deliver the food to the designated area based on the approved strategy. Specifically, the business issues instructions to delivery companies and arranges the necessary logistics work.

[1022] Input: Notification (food delivery strategy information)

[1023] Output: Delivery arrangements (instructions to delivery companies, logistics arrangements)

[1024] Step 6:

[1025] After the food delivery is completed, the delivery company sends the delivery completion data to the server, which collects the data and stores it in a database. The collected delivery completion data is used as feedback for generating the next optimal strategy.

[1026] Input: Delivery completion data (reported data from delivery company)

[1027] Output: Feedback data (used for next strategy generation)

[1028] In this way, through a series of processing steps, local governments and food businesses work together to create a system that efficiently distributes food and reduces food waste.

[1029] 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.

[1030] The purpose of this system is to efficiently distribute food through collaboration between local governments and food businesses, thereby reducing food waste. Furthermore, by adding an emotion engine, it is possible to provide more appropriate strategies that take into account the emotions of users. Specific embodiments for implementing the present invention are described below.

[1031] Data collection

[1032] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[1033] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[1034] Data analysis

[1035] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1036] Optimal strategy generation and notification

[1037] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[1038] Server: The generated strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[1039] Emotion recognition by emotion engine

[1040] Terminal: The emotion engine recognizes the user's emotions based on the user's input data (for example, comments and behavioral patterns when entering demand data or inventory data).

[1041] Server: The emotion engine analyzes the user's emotional data and customizes the notification content based on this. For example, a user with high stress levels will receive a simple and intuitive notification.

[1042] Execution instructions

[1043] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, food businesses can arrange for the delivery of designated food to designated areas.

[1044] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[1045] Specific examples

[1046] Example 1: Supplying food to areas with food shortages

[1047] Terminal: A local government official enters food shortage information for Area A into the system.

[1048] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[1049] Server: The emotion engine analyzes the emotional data of local government officials and customizes the notification content to ensure that the proposal is appropriately accepted.

[1050] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[1051] User: A food supplier delivers food from region B to region A.

[1052] Example 2: Redistribution of food nearing its expiry date

[1053] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1054] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1055] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes the notification content to ensure that the proposal is appropriately accepted.

[1056] Server: Notifies food businesses and relevant local governments of the proposal.

[1057] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1058] This system will strengthen cooperation between local governments and food businesses, and effectively reduce food waste. Furthermore, the emotion engine function will take the user's emotions into consideration when issuing notifications and instructions, improving the system's acceptability and effectiveness.

[1059] The processing flow will be explained below.

[1060] Step 1:

[1061] Terminal: A food vendor enters food inventory data (food type, quantity, expiration date, etc.) into the system. This data is periodically sent from the terminal to the system.

[1062] Step 2:

[1063] Terminal: Local government officials enter local demand data (type of food needed, quantity, deadline, etc.) into the system. This data is also periodically sent from the terminal to the system.

[1064] Step 3:

[1065] Server: Stores the input inventory and demand data in a database. The database is updated with the latest information in real time.

[1066] Step 4:

[1067] Server: Passes inventory and demand data stored in the database to the generative AI, which analyzes this data and evaluates the balance between supply and demand.

[1068] Step 5:

[1069] Server: Generative AI generates optimal food delivery strategies, including details of which foods should be delivered to which areas and when.

[1070] Step 6:

[1071] Server: Notifies the local government and food businesses of the generated food delivery strategy through notification means. This notification is sent via email or push notification.

[1072] Step 7:

[1073] Device: The emotion engine analyzes the user's input data and behavioral patterns to recognize the user's emotions. For example, if the input data indicates fatigue, it will recognize that as an emotion.

[1074] Step 8:

[1075] Server: The emotion engine analyzes the emotional data and customizes the notification content and interface. For example, a user who is feeling stressed will receive a simple and intuitive notification.

[1076] Step 9:

[1077] Users: Local government officials and food businesses review the notification strategy and customized notification content and approve it as needed.

[1078] Step 10:

[1079] User: After approval, the food supplier will arrange for the specified food to be delivered to the specified area. Specifically, the supplier will arrange for a delivery company and coordinate the delivery destination and delivery date and time.

[1080] Step 11:

[1081] Terminal: After completing delivery, the delivery company's staff reports the status to the system, including the time of delivery completion and confirmation of the recipient.

[1082] Step 12:

[1083] Server: Receives delivery completion data and updates the database. This ensures that the information required for the next strategy generation is up to date.

[1084] Step 13:

[1085] Server: The emotion engine analyzes the actual delivery results and user feedback to reflect in the next strategy generation. For example, it improves the notification method based on emotion data.

[1086] This process strengthens cooperation between local governments and food businesses, leading to efficient food distribution and reduced food waste. The emotion engine also enables flexible responses that take user emotions into account, improving the acceptability and effectiveness of the entire system.

[1087] Example 2

[1088] 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."

[1089] Food waste is a very serious problem in modern times, and there is a need to build an efficient supply chain. It is particularly difficult to accurately understand the management of food that is approaching its expiration date and fluctuations in food demand in each region, thereby minimizing waste. Furthermore, rapid and accurate information sharing is essential for smooth collaboration among the multiple parties involved (municipalities, food businesses, etc.). Furthermore, it is necessary to ensure that efforts to reduce food waste do not become a stress or burden for users.

[1090] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for periodically collecting data from local governments and food suppliers, means for analyzing supply and demand using a generative AI model based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for analyzing user emotion data using an emotion engine and customizing the notification content, means for issuing food delivery instructions based on the notified strategy, and means for collecting data and emotion data after delivery is completed and providing feedback. This enables an efficient food supply system that takes emotions into consideration.

[1091] A "local government" is a government body that manages a particular area and provides public services.

[1092] A "food business" is a business or individual that produces, processes, distributes, or sells food.

[1093] "Means for periodic data collection" refers to a system that automatically obtains necessary information from local governments and food businesses at specific time intervals.

[1094] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and predicts and evaluates supply and demand.

[1095] "Demand and supply analysis tools" are methods for calculating food needs and supplies in a specific area or period based on collected data.

[1096] The "means for generating an optimal food delivery strategy" is a method for creating a plan for efficiently distributing food based on the analysis results.

[1097] "Means of notification" refers to the mechanisms used to communicate generated strategies and information to stakeholders, including email and push notifications.

[1098] The "emotion engine" is a system that analyzes emotions from user input data and behavioral patterns and responds appropriately.

[1099] "Means for customizing notification content" refers to a method for providing information to users in the most optimal form based on analyzed emotional data.

[1100] The "means for issuing food delivery instructions" refers to the procedure for initiating and managing specific delivery operations based on the generated strategy.

[1101] "Means for collecting data and emotional data after delivery is completed and providing feedback" is a mechanism for collecting the situation after delivery is completed and the user's reactions, and using this information to generate the next strategy.

[1102] This invention relates to a system that enables local governments and food businesses to efficiently distribute food and reduce food waste. The system utilizes a generative AI model and has the ability to customize notification content based on user sentiment. Specific embodiments are described below.

[1103] 1. Data Collection

[1104] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date) into the terminal through a dedicated application. Similarly, local government officials enter demand data for each region (food type, quantity, required deadline) into the terminal. This data is converted into JSON format and sent to the server.

[1105] 2. Data Analysis

[1106] Server: The server receives the data sent from the device and stores it in a database. A generative AI model (e.g., GPT-3) is used to analyze the received inventory and demand data. The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1107] 3. Generating and notifying optimal strategies

[1108] Server: The generative AI model generates an optimal food delivery strategy based on the analysis results. This strategy includes which food to deliver to which area, delivery priorities, and schedules. The generated strategy is notified to local governments and food businesses via email, push notifications, etc.

[1109] 4. Emotion Recognition by Emotion Engine

[1110] Terminal: An emotion engine is used to analyze user comments and behavioral patterns. For example, emotional data of users is collected from comments made during data entry and operation logs.

[1111] Server: Analyzes the collected emotional data and customizes the notification content. For example, it sends a simple and intuitive notification to a user who is highly stressed, and generates appropriate notification content according to the user's state.

[1112] 5. Execution Instructions

[1113] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[1114] Server: Collects data after delivery is completed and provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[1115] Specific examples

[1116] Example 1: Supplying food to areas with food shortages

[1117] Terminal: A local government official enters food shortage information for Area A into the system.

[1118] Server: Generative AI analyzes demand data from region A and generates a proposal to procure from surplus inventory in region B based on supply data from food suppliers.

[1119] Server: The emotion engine analyzes the emotion data of local government officials and customizes notifications to ensure that the proposals are appropriately accepted.

[1120] Server: Notifies the local government and food vendors of the proposal and issues delivery instructions.

[1121] User: A food supplier delivers food from region B to region A.

[1122] Example 2: Redistribution of food nearing its expiry date

[1123] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1124] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1125] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes notifications to ensure that the proposals are appropriately accepted.

[1126] Server: Notifies food businesses and relevant local governments of the proposal.

[1127] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1128] Prompt Sentence Examples

[1129] To suggest an appropriate delivery strategy for food-insecure areas:

[1130] Area A is short of food. Area B has excess stock. Please suggest how to supply it.

[1131] If you are proposing a redistribution of food that is nearing its expiry date:

[1132] I have some food that is nearing its expiration date. Please suggest areas or facilities that can distribute it quickly.

[1133] This system enables local governments and food businesses to work together efficiently and reduce food waste. The emotion engine provides notifications that take into account the user's emotions, improving the system's acceptability and effectiveness.

[1134] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1135] Step 1: Data collection

[1136] Terminal: Food vendors use a dedicated application to input food inventory data (food type, quantity, expiration date) into the terminal. Similarly, local government officials input demand data for each region (type of food needed, quantity, required deadline) into the terminal. The input data is converted into JSON format and sent to the server.

[1137] Input: Inventory data (food type, quantity, expiration date), Demand data (food type, quantity, required deadline)

[1138] Output: JSON format data

[1139] Specific operations: The person in charge opens a dedicated application on a tablet or PC, enters the necessary data, and clicks the send button.

[1140] Step 2: Receiving and storing data

[1141] Server: The server receives the JSON format data sent from the device and stores it in a MySQL database.

[1142] Input: JSON format data sent from the terminal

[1143] Output: Inventory and demand data stored in a database

[1144] What happens: A server-side receiving script receives the JSON data and inserts it into a database using an SQL query.

[1145] Step 3: Data analysis

[1146] Server: The server retrieves inventory and demand data from the database and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1147] Input: Inventory and demand data retrieved from a database

[1148] Output: Optimal food supply strategy based on the balance between supply and demand

[1149] Specific operation: The server retrieves data using an SQL query, sends the data to the generative AI model's API, and receives the analysis results.

[1150] Step 4: Generate and communicate optimal strategies

[1151] Server: Generates an optimal food delivery strategy based on the analysis results generated by the generative AI model. Prepares notification content based on the generated strategy and notifies local governments and food businesses via email and push notifications.

[1152] Input: Analysis results of the generative AI model

[1153] Output: Notification content sent to local governments and food businesses

[1154] What it does: The server receives the results from the generative AI model, inserts them into a prefixed notification template, and sends it using an SMTP server or push notification service.

[1155] Step 5: Emotion Recognition and Notification Customization

[1156] Terminal: When a user enters comments or behavioral patterns during data entry, the emotion engine recognizes them and generates emotion data.

[1157] Input: User comments and behavior patterns

[1158] Output: Emotion data

[1159] Specific operation: The device's sensors and log acquisition system analyze the user's actions and collect emotion-related data.

[1160] Server: Analyzes the emotion data generated by the emotion engine and customizes the notification content. For example, it generates a simple and intuitive notification for users with high stress levels.

[1161] Input: Emotion data

[1162] Output: Customized notification content

[1163] Specific operation: The server generates notification content based on the analysis results of the emotion engine and sends it to the user in an appropriate format.

[1164] Step 6: Execution instructions

[1165] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[1166] Input: Customized notification content, strategic information

[1167] Output: Execution instructions after approval

[1168] Specific actions: The person in charge checks the notification and clicks the approval button. The food supplier prepares the delivery and issues delivery instructions.

[1169] Step 7: Gather feedback

[1170] Server: After the delivery is completed, the server collects the delivery completion data and the user's emotion data again and provides feedback for the next strategy generation.

[1171] Input: Delivery completion data, emotion data

[1172] Output: Feedback data for next strategy generation

[1173] Specific operation: After delivery is completed, the server automatically collects the data, stores it in a database, and reuses it as input data for the generative AI model.

[1174] Through these steps, the system can efficiently and effectively manage food supply and demand, thereby reducing food waste. Furthermore, by using an emotion engine, notifications and instructions that take into account the user's emotions can be provided, increasing the system's acceptability and effectiveness.

[1175] (Application example 2)

[1176] 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."

[1177] Food waste not only causes economic losses but also has a serious impact on the environment, necessitating the development of efficient food distribution systems. However, existing systems struggle to optimize the balance between supply and demand, and lack the ability to respond to individual user needs based on user emotions. In particular, in the food delivery field, there is a lack of effective customization of notifications based on emotion recognition for delivery routes and urgent food distribution.

[1178] 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 a means for collecting data from local governments and food businesses, a means for analyzing supply and demand based on the collected data, a means for generating an optimal food delivery strategy based on the analysis results, a means for notifying the local government and food businesses of the generated strategy, an emotion engine means for recognizing the user's emotions and customizing the notification content, a means for issuing food delivery instructions based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback. This not only enables efficient balancing of supply and demand, but also enables flexible responses tailored to the user's emotions.

[1179] A "local government" is a public institution that is responsible for local administration and provides administrative services related to the lives of local residents.

[1180] "Food businesses" are companies and organizations that produce, process, and sell food, and play an important role in food distribution.

[1181] "Data collection means" refers to a method or device for acquiring and organizing information, and in this invention, it is used to collect inventory and demand information provided by local governments and food suppliers.

[1182] "Means for supply and demand analysis" refers to methods and mechanisms that use collected data to assess and analyze where and how much food is needed and where and how much is in stock.

[1183] The "means for generating an optimal food delivery strategy" is a method or device for determining, based on the analysis results, which food items should be delivered to which areas as a priority.

[1184] "Means for notifying the generated strategy" refers to methods or devices for informing relevant parties (local governments and food businesses) of the generated delivery strategy, and includes email and push notifications.

[1185] An "emotion engine" is a system or device that analyzes the user's input data, recognizes their emotional state at the time, and presents the optimal response.

[1186] The "means for issuing instructions for food delivery" refers to a method or device for issuing instructions to relevant parties to actually deliver food based on an optimal delivery strategy.

[1187] The "means for providing feedback" refers to a method or device that collects data when delivery is completed and provides complementary information to be used in generating the next strategy.

[1188] The present invention provides a system for efficiently distributing food and reducing food waste through collaboration between local governments and food businesses. The system includes a data collection means, a data analysis means, a means for generating an optimal food delivery strategy, an emotion engine means, a notification means, a means for issuing food delivery instructions, and a means for providing feedback.

[1189] First, food vendors use terminals to input food inventory data (e.g., food type, quantity, expiration date, etc.) and local governments input demand data for each region (e.g., food type, quantity, required deadline, etc.) into the system. The server then collects the data and stores it in a database. This data is periodically collected by a data collection module using Python.

[1190] Next, using a generative AI model, the server analyzes the collected inventory and demand data to assess the balance between supply and demand. Based on the analysis results, it generates an optimal food delivery strategy, which includes which food items to deliver to which areas, delivery priorities, and schedules.

[1191] The server notifies the local government and food suppliers of the generated strategy via notification means (email, push notification, etc.). At this time, the emotion engine recognizes the user's emotions and customizes the notification content based on, for example, the user's input data and behavioral patterns. Specifically, if the user is feeling stressed, a simple and intuitive notification is sent, and if not, a detailed notification is sent.

[1192] For example, by inputting a prompt such as, "Compare the data from region A with next month's demand, have the generative AI model generate suggestions on how to efficiently distribute food that is nearing its expiration date, and use the emotion engine to customize the notification content," into the generative AI model, analysis and notification customization are performed.

[1193] Local government officials and food vendors then review the strategy and take specific action after approval. For example, a food vendor arranges for the delivery of a specified food item to a specified area. After the delivery is completed, the server collects data about the delivery again and uses it as feedback for generating the next strategy. This feedback also includes emotional data.

[1194] This will strengthen cooperation between local governments and food businesses, effectively reducing food waste, and improve the system's acceptability and effectiveness by providing appropriate notifications and instructions that take into account the user's emotions through the emotion engine.

[1195] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1196] Step 1:

[1197] The terminal receives data input from food businesses and local governments. Specifically, food businesses input food inventory data (food type, quantity, expiration date, etc.), and local governments input demand data for each region (type of food needed, quantity, required deadline, etc.). The input data is sent to a server and stored in a database. The input data is then processed by a data collection module and converted into a unified format.

[1198] Step 2:

[1199] The server retrieves inventory and demand data stored in the database. Based on this data, it uses a generative AI model to analyze the data. Specifically, it evaluates the balance between supply and demand and analyzes which foods should be prioritized for distribution to which areas. The generative AI model used here uses algorithms that forecast demand and optimize supply based on the input data.

[1200] Step 3:

[1201] The server generates an optimal food delivery strategy based on the analysis results of the generative AI model. This strategy includes information such as which food to deliver to which area, delivery priorities, and schedules. The generated strategy includes specific instructions such as food distribution routes and allocation amounts. This strategy is then saved back into the database.

[1202] Step 4:

[1203] The server notifies the local government and food suppliers of the generated delivery strategy via a notification mechanism. Here, the emotion engine recognizes the user's emotions and customizes the notification content. It analyzes the user's input data and behavioral patterns to determine whether the user is under stress and sends notifications according to their state. For example, it sends a simple notification to users who are highly stressed, and a detailed notification to users who are not.

[1204] Step 5:

[1205] Users (municipal government officials and food businesses) review and approve the notified strategy. After approval, they take specific actions. Specifically, food businesses arrange for the delivery of designated food to designated areas and begin actual delivery work on site.

[1206] Step 6:

[1207] The server collects information after delivery is complete. The data after delivery includes information on delivery time, delivery volume, and delivery destination. This data is sent to the server and stored in a database. The emotion engine also collects user emotion data again.

[1208] Step 7:

[1209] The server analyzes the collected delivery completion data and emotion data and uses them as feedback for generating the next strategy, which continuously optimizes the system to reduce food waste and improve the user experience.

[1210] Through these steps, local governments and food businesses can work together efficiently to reduce food waste, and the emotion engine can provide notifications that take users' emotions into consideration.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] [Fourth embodiment]

[1215] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1216] 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.

[1217] 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).

[1218] 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.

[1219] 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.

[1220] 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).

[1221] 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. 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.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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."

[1228] The purpose of this system is to efficiently distribute food through cooperation between local governments and food businesses, thereby reducing food waste. Specific embodiments for carrying out the present invention are described below.

[1229] Data collection

[1230] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[1231] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[1232] Data analysis

[1233] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1234] Optimal strategy generation and notification

[1235] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[1236] Server: The generated strategy is notified to local governments and food businesses through notification means, such as email or push notification.

[1237] Execution instructions

[1238] Users: Local government officials and food businesses review the notified strategy and take specific actions after approval. For example, food businesses arrange for the delivery of designated food to designated areas.

[1239] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation.

[1240] Specific examples

[1241] Example 1: Supplying food to areas with food shortages

[1242] Terminal: A local government official enters food shortage information for Area A into the system.

[1243] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[1244] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[1245] User: A food supplier delivers food from region B to region A.

[1246] Example 2: Redistribution of food nearing its expiry date

[1247] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1248] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1249] Server: Notifies food businesses and relevant local governments of the proposal.

[1250] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1251] This system will strengthen cooperation between local governments and food businesses, effectively reducing food waste.

[1252] The processing flow will be explained below.

[1253] Step 1:

[1254] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date, etc.) into the system. This data is updated regularly.

[1255] Step 2:

[1256] Terminal: Local government officials input local demand data (type of food needed, quantity, deadline for request, etc.) into the system. This data is also updated regularly.

[1257] Step 3:

[1258] Server: Stores collected inventory and demand data in a database. The database is updated with the latest data.

[1259] Step 4:

[1260] Server: Passes the data stored in the database to the generative AI, which evaluates and analyzes the balance between supply and demand.

[1261] Step 5:

[1262] Server: Generative AI generates optimal food delivery strategies, such as determining which foods should be delivered to which areas, delivery priorities, schedules, etc.

[1263] Step 6:

[1264] Server: The generated food delivery strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[1265] Step 7:

[1266] Users: Local government officials and food businesses review and approve the communicated strategy.

[1267] Step 8:

[1268] User: After approval, the food supplier will arrange for the delivery of the specified food, arrange for a delivery company, and coordinate the delivery destination and delivery date.

[1269] Step 9:

[1270] Terminal: After completing a delivery, the delivery company's representative reports the status to the system, including the time of delivery completion and the recipient's confirmation of receipt.

[1271] Step 10:

[1272] Server: Receives delivery completion data and updates the database. This information is used to generate future delivery strategies.

[1273] Step 11:

[1274] Server: Based on the results of the actions taken, it provides feedback to the generative AI to improve the accuracy of the next food delivery strategy, thereby improving the accuracy and effectiveness of the entire system.

[1275] In this way, a series of steps are taken: data collection, analysis, notification, action, and feedback. By repeating this process, supply optimization and food waste reduction are achieved.

[1276] Example 1

[1277] 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 robot 414 will be referred to as a "terminal."

[1278] Currently, when local governments and food suppliers work together to supply food, efficient inventory management and demand forecasting are difficult, resulting in food waste. It is also difficult to create optimal delivery plans that take food expiration dates into account, resulting in the risk of expired food being delivered. Furthermore, the delivery strategies generated are notified manually, which can hinder rapid implementation. These issues need to be resolved.

[1279] 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.

[1280] In this invention, the server includes: a means for acquiring information from local governments and food suppliers; a means for using artificial intelligence to analyze supply and demand based on the acquired information; a means for generating an optimal food delivery strategy based on the analysis results; a means for notifying the local government and food suppliers of the generated strategy via email, push notification, or other means; a means for issuing food delivery instructions based on the notified strategy; and a means for collecting data after delivery is completed and providing feedback for generating the next strategy. This enables the optimization of the balance between food supply and demand and the reduction of food waste. Furthermore, by automatically generating an optimal delivery plan that takes expiration dates into account, the risk of expiration is reduced, and the automated notification function promotes rapid implementation.

[1281] A "municipality" is a local government entity that provides administrative services in a specific area.

[1282] A "food business" is a company or individual that produces, processes, distributes, sells, etc. food.

[1283] "Means of acquiring information" refers to input devices, software, and associated interfaces for collecting data.

[1284] "Artificial intelligence" is a technology that uses data analysis and predictive models to make advanced decisions.

[1285] A "generator" is an algorithm or system that automatically creates the plans and strategies needed to achieve a specific goal.

[1286] "Means of notification" refers to a system for conveying information to the target person using email, push notifications, etc.

[1287] The "means for issuing food delivery instructions" is a function for issuing instructions for executing a specific delivery plan based on the generated strategy.

[1288] The "means for collecting data after delivery completion and providing feedback" is a function for collecting result data after delivery completion and using it to help generate the next strategy.

[1289] A "food delivery strategy" is a plan that optimizes which foods to deliver to which areas and how.

[1290] "Optimization" refers to achieving the most efficient and effective state under certain conditions.

[1291] The "best before" date is the date by which food is at its most delicious. Food is still edible after this date, but the quality may deteriorate.

[1292] "Analysis results" are information obtained as a result of processing collected data.

[1293] "Data" is a collection of numbers, text, etc. that are collected to represent specific information or phenomena.

[1294] This invention is a system in which local governments and food businesses work together to efficiently distribute food and reduce food waste.

[1295] Hardware and Software

[1296] Terminals: The terminals for this system are general devices such as PCs, tablets, and smartphones used by food service providers and local government officials. These terminals are capable of connecting to the Internet and provide a user interface for inputting and transmitting the necessary data.

[1297] Server: The server runs on a cloud platform, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP). This server performs the functions of data collection, storage, analysis, and notification.

[1298] Software: Various programming languages ​​(e.g., Python, Node.js) and database systems (e.g., MySQL, PostgreSQL) are used to control the entire system. Machine learning libraries such as TensorFlow and PyTorch are used to implement generative AI models.

[1299] Data collection

[1300] Terminal: Food supplier personnel enter inventory data (food type, quantity, expiration date, etc.) through a dedicated food inventory management app. Local government personnel log in to a browser-based demand management portal and enter demand data for each region (type of food needed, quantity, required deadline, etc.).

[1301] Server: Data entered from the terminal is sent to the server via SOAP or REST API and stored in a database.

[1302] Data analysis

[1303] Server: The server uses periodic cron jobs or trigger events to collect up-to-date inventory and demand data from the database, which is then pre-processed and converted into a format that can be fed into the generative AI model.

[1304] Generative AI model: The generative AI model evaluates the balance between supply and demand based on collected data and generates an optimal food supply strategy, including which food to deliver to which areas, delivery priorities, and schedules.

[1305] Optimal strategy generation and notification

[1306] Server: Receives the output from the generative AI model and reconstructs it into a specific delivery strategy. This strategy is then communicated to local governments and food providers via email and push notifications.

[1307] Execution instructions

[1308] Users: Local government officials and food service providers receive notifications. They review the notification content and, after approval, execute specific food delivery plans. For example, food service delivery personnel use the workflow management system to create delivery tasks, allocate resources, and adjust schedules.

[1309] Server: After delivery is completed, the delivery data is collected again and stored in the database. This data is used to generate the next strategy, contributing to improving the accuracy and efficiency of the system.

[1310] Specific examples

[1311] Example 1: Supplying food to areas with food shortages

[1312] Terminal: A local government official enters food shortage information for Area A into the system.

[1313] Server: The generative AI model analyzes demand in region A and generates proposals to procure excess inventory in region B based on food supplier supply data.

[1314] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[1315] User: A food supplier delivers food from region B to region A.

[1316] Example 2: Redistribution of food nearing its expiry date

[1317] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1318] Server: A generative AI model analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1319] Server: Notifies food businesses and relevant local governments of the proposal.

[1320] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1321] Prompt Sentence Examples

[1322] "Please enter food shortage information for Area A."

[1323] "Please enter information about inventory that is close to expiry into the system."

[1324] "Generate optimal food delivery strategies"

[1325] In this way, local governments and food businesses can work together to create an effective system for minimizing food waste.

[1326] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1327] Step 1:

[1328] Terminal: A food supplier uses a food inventory management app to input inventory data (food type, quantity, expiration date, etc.). This data is compiled in JSON format.

[1329] Input: Inventory data such as food type, quantity, expiration date, etc.

[1330] Output: Inventory data in JSON format

[1331] Specific operation: Open the app from your device, enter inventory information into the input form, and click the "Submit" button. The submitted data is sent to the server via the API endpoint.

[1332] Step 2:

[1333] Terminal: A local government official logs into the demand management portal and enters demand data for each region (type of food needed, quantity, deadline for request, etc.). This data is also compiled in JSON format.

[1334] Input: Demand data by region

[1335] Output: Demand data in JSON format

[1336] Specific operations: Open a browser, access the demand management portal, and log in. Enter demand information and click the "Submit" button. The submitted data is sent to the server via the REST API.

[1337] Step 3:

[1338] Server: Receives the inventory and demand data and stores it in a database, for example, using a database such as MySQL or PostgreSQL.

[1339] Input: Inventory and demand data in JSON format

[1340] Output: Inventory and demand data stored in a database

[1341] What happens: The server parses the JSON data received by the API, maps it to the appropriate fields, and executes a SQL query to store it in the database.

[1342] Step 4:

[1343] Server: Collects and pre-processes the stored data using a cron job or trigger events. Pre-processing includes cleaning and shaping the data, for example using the Pandas or NumPy libraries.

[1344] Input: Inventory and demand data stored in a database

[1345] Output: Preprocessed data for generative AI models

[1346] What it does: A scheduled job extracts the latest data from the database, which is then cleaned and formatted by a Python script.

[1347] Step 5:

[1348] Server: The preprocessed data is input into a generative AI model to generate an optimal food supply strategy that evaluates the balance between supply and demand. For example, using the TensorFlow or PyTorch libraries, the AI ​​model then performs inference processing and outputs the optimal distribution plan.

[1349] Input: Preprocessed data for generative AI models

[1350] Output: Optimal food supply strategy

[1351] Specific operation: The preprocessed data is input into the AI ​​model and the inference results are obtained. These results are returned to the server in JSON format.

[1352] Step 6:

[1353] Server: Receives the results returned by the generative AI model and reconstructs them as a specific delivery strategy. The generated strategy is notified to local governments and food businesses via email or push notifications. This notification is sent using services such as Amazon SES and Firebase Cloud Messaging.

[1354] Input: Optimal food supply strategy

[1355] Output: Notifications sent to local governments and food businesses

[1356] Specific Actions: Analyze the results from the generative AI model and send information to relevant personnel via email or notification services, including specific actions to take.

[1357] Step 7:

[1358] Users: Local government officials and food service providers receive notifications, review the details, and, after providing approval as necessary, execute specific food delivery plans. Food service delivery personnel create delivery tasks in the workflow management system and allocate and schedule the necessary resources.

[1359] Input: Informed Food Supply Strategy

[1360] Output: Executed delivery tasks

[1361] Specific actions: Open the notification email or push notification and follow the instructions to carry out the delivery plan. The delivery person then uses the workflow tool to create specific tasks and allocate resources.

[1362] Step 8:

[1363] Server: After the delivery is completed, the delivery completion data is collected again and stored in the database. This data is used to generate the next strategy.

[1364] Input: Delivery completion data

[1365] Output: Delivery completion data stored in the database

[1366] How it works: The delivery person enters a completion report into the system, and the server collects and stores the report in a database. The stored data is used for subsequent analysis and training of the AI ​​model.

[1367] In this way, the system strengthens cooperation between local governments and food businesses and effectively reduces food waste.

[1368] (Application example 1)

[1369] 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 robot 414 will be referred to as a "terminal."

[1370] In recent years, the problem of food waste has become more serious, and there is a need for food businesses and local governments to work together to distribute food efficiently. However, many processes, such as food inventory management, regional demand surveys, formulating optimal supply strategies, and arranging deliveries, rely on manual labor, making it difficult to operate efficiently. This makes food waste more likely to occur, and has become a social problem.

[1371] 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.

[1372] In this invention, the server includes means for collecting data from local governments and food suppliers, means for analyzing supply and demand based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for issuing food delivery instructions based on the notified strategy, means for collecting data after delivery is completed and providing feedback, means for food suppliers and local government officials to input and check data via mobile devices, and means for the generative model to generate an optimal supply strategy in real time. This makes it possible to efficiently operate the entire process from data collection to delivery instructions and effectively reduce food waste.

[1373] "Data collection means" refers to devices and systems for collecting inventory data, demand data, etc. from food businesses and local government officials.

[1374] "Analysis means" refers to a device or system that analyzes the collected data to evaluate the balance between supply and demand and generate an appropriate supply strategy based on the collected data.

[1375] The "generation means" is a device or system that generates an optimal food delivery strategy based on the analyzed data.

[1376] "Notification means" refers to a device or system for communicating the generated food delivery strategy to local governments and food businesses.

[1377] The "instruction means" is a device or system for issuing specific food delivery instructions based on the notified strategy.

[1378] A "feedback means" is a device or system that collects data after delivery is completed and provides information that is useful for generating the next strategy.

[1379] A "mobile terminal" is an information terminal device such as a smartphone or tablet that can be operated while on the move.

[1380] A "real-time generative model" is an algorithm or system that instantly analyzes collected data and generates an optimal supply strategy based on the results.

[1381] "Push notifications" are a technology that sends information directly to users' mobile devices in real time.

[1382] This invention is a system that enables local governments and food businesses to work together to efficiently distribute food and reduce food waste. The system aims to minimize food waste by automating the processes of data collection, analysis, generation of optimal strategies, notification, execution instructions, and feedback.

[1383] Specifically, the following hardware and software configuration is used.

[1384] Hardware

[1385] Server: Responsible for data aggregation, analysis, and running generative AI models. Specific examples include EC2 instances from AWS (Amazon Web Services) and Compute Engine from Google Cloud Platform.

[1386] Mobile devices: Used by food businesses and local government officials to input data and check notifications. Specifically, this applies to smartphones and tablets.

[1387] software

[1388] Mobile application: An application for entering data, receiving notifications, checking delivery instructions, etc. It runs on Android and iOS platforms.

[1389] Generative AI model: Analyzes collected data, evaluates the balance between supply and demand, and generates an optimal food delivery strategy. Specifically, OpenAI's GPT-3 and other latest generative models are used.

[1390] Notification service: A system for communicating the generated strategies to food businesses and local governments. Firebase Cloud Messaging and AWS SNS are used.

[1391] Data collection and analysis

[1392] First, food suppliers and local government officials use their mobile devices to input inventory data and local demand data. This data is sent in real time to a server, which collects the data and stores it in a database. A generative AI model analyzes the stored data and evaluates the balance between supply and demand.

[1393] Optimal strategy generation and notification

[1394] The generative AI model generates an optimal food delivery strategy based on the analyzed data. This strategy includes which food to deliver to which area, priorities, schedules, etc. The generated strategy is pushed to food businesses and local government officials via a notification service.

[1395] Instructions and feedback

[1396] Food vendors and local government officials use their mobile devices to check the notified strategies and take specific actions after approval. For example, food vendors arrange for the delivery of designated food to designated areas. Data after delivery is completed is sent back to the server and used as feedback to help generate the next strategy.

[1397] Prompt Sentence Examples

[1398] Examples of prompts that an AI model might use to create a generative strategy include:

[1399] "Suggest a redistribution of food items nearing their expiration date. Data: {'food_item': 'bread', 'expiry_date': '2023-10-31', 'quantity': 50}. Demand region: {'region_A': 30, 'region_B': 20}"

[1400] In this way, local governments and food businesses can work together to develop and implement optimal food delivery strategies in real time, creating a system that can effectively reduce food waste.

[1401] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1402] Step 1:

[1403] Users use their mobile devices to input food inventory data and local demand data. Specifically, food vendors input information such as inventory levels and expiration dates, while local government officials input demand data such as the type, quantity, and location of food needed. This input data is then sent from the device to a server.

[1404] Input: Inventory data (food type, quantity, expiration date), demand data (food required, quantity, location)

[1405] Output: Collected data stored on the server

[1406] Step 2:

[1407] The server stores the submitted inventory and demand data in a database. This data is then analyzed by the generative AI model. Specifically, the server passes the collected data set to the generative AI model to evaluate the balance between supply and demand.

[1408] Input: Collected data (stock and demand data stored in a database)

[1409] Output: Analysis results (demand and supply balance evaluation)

[1410] Step 3:

[1411] The generative AI model generates an optimal food delivery strategy based on the analysis results, including which food items should be delivered to which areas and when, as well as delivery priorities and schedules. The server receives this generated strategy and converts it into a format for notification.

[1412] Input: Analysis results (demand and supply balance evaluation)

[1413] Output: Optimal delivery strategy (food delivery strategy)

[1414] Step 4:

[1415] The server then sends the generated food delivery strategy to food businesses and local government officials as push notifications, using notification services such as Firebase Cloud Messaging and AWS SNS to deliver real-time notifications to mobile devices.

[1416] Input: Optimal delivery strategy (food delivery strategy)

[1417] Output: Notification (push notification to mobile device)

[1418] Step 5:

[1419] The user checks the notification on their mobile device and approves the strategy. The food business then makes specific arrangements to deliver the food to the designated area based on the approved strategy. Specifically, the business issues instructions to delivery companies and arranges the necessary logistics work.

[1420] Input: Notification (food delivery strategy information)

[1421] Output: Delivery arrangements (instructions to delivery companies, logistics arrangements)

[1422] Step 6:

[1423] After the food delivery is completed, the delivery company sends the delivery completion data to the server, which collects the data and stores it in a database. The collected delivery completion data is used as feedback for generating the next optimal strategy.

[1424] Input: Delivery completion data (reported data from delivery company)

[1425] Output: Feedback data (used for next strategy generation)

[1426] In this way, through a series of processing steps, local governments and food businesses work together to create a system that efficiently distributes food and reduces food waste.

[1427] 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.

[1428] The purpose of this system is to efficiently distribute food through collaboration between local governments and food businesses, thereby reducing food waste. Furthermore, by adding an emotion engine, it is possible to provide more appropriate strategies that take into account the emotions of users. Specific embodiments for implementing the present invention are described below.

[1429] Data collection

[1430] Terminal: Food vendors' staff enter food inventory data (food type, quantity, expiration date, etc.) into the system. Similarly, local government staff enter demand data for each region (food type, quantity, required deadline, etc.) into the system via terminal.

[1431] Server: The entered data is sent to the server and stored in a database. This data collection is done periodically.

[1432] Data analysis

[1433] Server: Collected inventory and demand data is analyzed by a generative AI, which evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1434] Optimal strategy generation and notification

[1435] Server: The generative AI generates an optimal food delivery strategy based on the analysis results. This strategy includes which foods to deliver to which areas, delivery priorities, and schedules.

[1436] Server: The generated strategy is notified to local governments and food businesses via notification means (email, push notification, etc.).

[1437] Emotion recognition by emotion engine

[1438] Terminal: The emotion engine recognizes the user's emotions based on the user's input data (for example, comments and behavioral patterns when entering demand data or inventory data).

[1439] Server: The emotion engine analyzes the user's emotional data and customizes the notification content based on this. For example, a user with high stress levels will receive a simple and intuitive notification.

[1440] Execution instructions

[1441] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, food businesses can arrange for the delivery of designated food to designated areas.

[1442] Server: It also has a mechanism for collecting data after delivery is completed, which provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[1443] Specific examples

[1444] Example 1: Supplying food to areas with food shortages

[1445] Terminal: A local government official enters food shortage information for Area A into the system.

[1446] Server: Generative AI analyzes demand in region A and generates proposals for procuring food that has excess inventory in region B based on supply data from food suppliers.

[1447] Server: The emotion engine analyzes the emotional data of local government officials and customizes the notification content to ensure that the proposal is appropriately accepted.

[1448] Server: Notifies local governments and food businesses of the proposal and issues delivery instructions.

[1449] User: A food supplier delivers food from region B to region A.

[1450] Example 2: Redistribution of food nearing its expiry date

[1451] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1452] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1453] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes the notification content to ensure that the proposal is appropriately accepted.

[1454] Server: Notifies food businesses and relevant local governments of the proposal.

[1455] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1456] This system will strengthen cooperation between local governments and food businesses, and effectively reduce food waste. Furthermore, the emotion engine function will take the user's emotions into consideration when issuing notifications and instructions, improving the system's acceptability and effectiveness.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] Terminal: A food vendor enters food inventory data (food type, quantity, expiration date, etc.) into the system. This data is periodically sent from the terminal to the system.

[1460] Step 2:

[1461] Terminal: Local government officials enter local demand data (type of food needed, quantity, deadline, etc.) into the system. This data is also periodically sent from the terminal to the system.

[1462] Step 3:

[1463] Server: Stores the input inventory and demand data in a database. The database is updated with the latest information in real time.

[1464] Step 4:

[1465] Server: Passes inventory and demand data stored in the database to the generative AI, which analyzes this data and evaluates the balance between supply and demand.

[1466] Step 5:

[1467] Server: Generative AI generates optimal food delivery strategies, including details of which foods should be delivered to which areas and when.

[1468] Step 6:

[1469] Server: Notifies the local government and food businesses of the generated food delivery strategy through notification means. This notification is sent via email or push notification.

[1470] Step 7:

[1471] Device: The emotion engine analyzes the user's input data and behavioral patterns to recognize the user's emotions. For example, if the input data indicates fatigue, it will recognize that as an emotion.

[1472] Step 8:

[1473] Server: The emotion engine analyzes the emotional data and customizes the notification content and interface. For example, a user who is feeling stressed will receive a simple and intuitive notification.

[1474] Step 9:

[1475] Users: Local government officials and food businesses review the notification strategy and customized notification content and approve it as needed.

[1476] Step 10:

[1477] User: After approval, the food supplier will arrange for the specified food to be delivered to the specified area. Specifically, the supplier will arrange for a delivery company and coordinate the delivery destination and delivery date and time.

[1478] Step 11:

[1479] Terminal: After completing delivery, the delivery company's staff reports the status to the system, including the time of delivery completion and confirmation of the recipient.

[1480] Step 12:

[1481] Server: Receives delivery completion data and updates the database. This ensures that the information required for the next strategy generation is up to date.

[1482] Step 13:

[1483] Server: The emotion engine analyzes the actual delivery results and user feedback to reflect in the next strategy generation. For example, it improves the notification method based on emotion data.

[1484] This process strengthens cooperation between local governments and food businesses, leading to efficient food distribution and reduced food waste. The emotion engine also enables flexible responses that take user emotions into account, improving the acceptability and effectiveness of the entire system.

[1485] Example 2

[1486] 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 robot 414 will be referred to as a "terminal."

[1487] Food waste is a very serious problem in modern times, and there is a need to build an efficient supply chain. It is particularly difficult to accurately understand the management of food that is approaching its expiration date and fluctuations in food demand in each region, thereby minimizing waste. Furthermore, rapid and accurate information sharing is essential for smooth collaboration among the multiple parties involved (municipalities, food businesses, etc.). Furthermore, it is necessary to ensure that efforts to reduce food waste do not become a stress or burden for users.

[1488] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for periodically collecting data from local governments and food suppliers, means for analyzing supply and demand using a generative AI model based on the collected data, means for generating an optimal food delivery strategy based on the analysis results, means for notifying the local government and food suppliers of the generated strategy, means for analyzing user emotion data using an emotion engine and customizing the notification content, means for issuing food delivery instructions based on the notified strategy, and means for collecting data and emotion data after delivery is completed and providing feedback. This enables an efficient food supply system that takes emotions into consideration.

[1489] A "local government" is a government body that manages a particular area and provides public services.

[1490] A "food business" is a business or individual that produces, processes, distributes, or sells food.

[1491] "Means for periodic data collection" refers to a system that automatically obtains necessary information from local governments and food businesses at specific time intervals.

[1492] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and predicts and evaluates supply and demand.

[1493] "Demand and supply analysis tools" are methods for calculating food needs and supplies in a specific area or period based on collected data.

[1494] The "means for generating an optimal food delivery strategy" is a method for creating a plan for efficiently distributing food based on the analysis results.

[1495] "Means of notification" refers to the mechanisms used to communicate generated strategies and information to stakeholders, including email and push notifications.

[1496] The "emotion engine" is a system that analyzes emotions from user input data and behavioral patterns and responds appropriately.

[1497] "Means for customizing notification content" refers to a method for providing information to users in the most optimal form based on analyzed emotional data.

[1498] The "means for issuing food delivery instructions" refers to the procedure for initiating and managing specific delivery operations based on the generated strategy.

[1499] "Means for collecting data and emotional data after delivery is completed and providing feedback" is a mechanism for collecting the situation after delivery is completed and the user's reactions, and using this information to generate the next strategy.

[1500] This invention relates to a system that enables local governments and food businesses to efficiently distribute food and reduce food waste. The system utilizes a generative AI model and has the ability to customize notification content based on user sentiment. Specific embodiments are described below.

[1501] 1. Data Collection

[1502] Terminal: Food vendors enter food inventory data (food type, quantity, expiration date) into the terminal through a dedicated application. Similarly, local government officials enter demand data for each region (food type, quantity, required deadline) into the terminal. This data is converted into JSON format and sent to the server.

[1503] 2. Data Analysis

[1504] Server: The server receives the data sent from the device and stores it in a database. A generative AI model (e.g., GPT-3) is used to analyze the received inventory and demand data. The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1505] 3. Generating and notifying optimal strategies

[1506] Server: The generative AI model generates an optimal food delivery strategy based on the analysis results. This strategy includes which food to deliver to which area, delivery priorities, and schedules. The generated strategy is notified to local governments and food businesses via email, push notifications, etc.

[1507] 4. Emotion Recognition by Emotion Engine

[1508] Terminal: An emotion engine is used to analyze user comments and behavioral patterns. For example, emotional data of users is collected from comments made during data entry and operation logs.

[1509] Server: Analyzes the collected emotional data and customizes the notification content. For example, it sends a simple and intuitive notification to a user who is highly stressed, and generates appropriate notification content according to the user's state.

[1510] 5. Execution Instructions

[1511] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[1512] Server: Collects data after delivery is completed and provides feedback for the next strategy generation. Emotional data is also included in the feedback.

[1513] Specific examples

[1514] Example 1: Supplying food to areas with food shortages

[1515] Terminal: A local government official enters food shortage information for Area A into the system.

[1516] Server: Generative AI analyzes demand data from region A and generates a proposal to procure from surplus inventory in region B based on supply data from food suppliers.

[1517] Server: The emotion engine analyzes the emotion data of local government officials and customizes notifications to ensure that the proposals are appropriately accepted.

[1518] Server: Notifies the local government and food vendors of the proposal and issues delivery instructions.

[1519] User: A food supplier delivers food from region B to region A.

[1520] Example 2: Redistribution of food nearing its expiry date

[1521] Terminal: Food vendors enter information about inventory that is nearing its expiration date into the system.

[1522] Server: A generative AI analyzes this information and suggests areas and facilities that can quickly distribute food that is nearing its expiration date.

[1523] Server: The emotion engine analyzes the emotion data of food vendor personnel and customizes notifications to ensure that the proposals are appropriately accepted.

[1524] Server: Notifies food businesses and relevant local governments of the proposal.

[1525] Users: Food businesses deliver food based on the proposal, and local governments distribute it to consumers and facilities in need.

[1526] Prompt Sentence Examples

[1527] To suggest an appropriate delivery strategy for food-insecure areas:

[1528] Area A is short of food. Area B has excess stock. Please suggest how to supply it.

[1529] If you are proposing a redistribution of food that is nearing its expiry date:

[1530] I have some food that is nearing its expiration date. Please suggest areas or facilities that can distribute it quickly.

[1531] This system enables local governments and food businesses to work together efficiently and reduce food waste. The emotion engine provides notifications that take into account the user's emotions, improving the system's acceptability and effectiveness.

[1532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1533] Step 1: Data collection

[1534] Terminal: Food vendors use a dedicated application to input food inventory data (food type, quantity, expiration date) into the terminal. Similarly, local government officials input demand data for each region (type of food needed, quantity, required deadline) into the terminal. The input data is converted into JSON format and sent to the server.

[1535] Input: Inventory data (food type, quantity, expiration date), Demand data (food type, quantity, required deadline)

[1536] Output: JSON format data

[1537] Specific operations: The person in charge opens a dedicated application on a tablet or PC, enters the necessary data, and clicks the send button.

[1538] Step 2: Receiving and storing data

[1539] Server: The server receives the JSON format data sent from the device and stores it in a MySQL database.

[1540] Input: JSON format data sent from the terminal

[1541] Output: Inventory and demand data stored in a database

[1542] What happens: A server-side receiving script receives the JSON data and inserts it into a database using an SQL query.

[1543] Step 3: Data analysis

[1544] Server: The server retrieves inventory and demand data from the database and analyzes the data using a generative AI model (e.g., GPT-3). The generative AI model evaluates the balance between supply and demand and generates an optimal food supply strategy.

[1545] Input: Inventory and demand data retrieved from a database

[1546] Output: Optimal food supply strategy based on the balance between supply and demand

[1547] Specific operation: The server retrieves data using an SQL query, sends the data to the generative AI model's API, and receives the analysis results.

[1548] Step 4: Generate and communicate optimal strategies

[1549] Server: Generates an optimal food delivery strategy based on the analysis results generated by the generative AI model. Prepares notification content based on the generated strategy and notifies local governments and food businesses via email and push notifications.

[1550] Input: Analysis results of the generative AI model

[1551] Output: Notification content sent to local governments and food businesses

[1552] What it does: The server receives the results from the generative AI model, inserts them into a prefixed notification template, and sends it using an SMTP server or push notification service.

[1553] Step 5: Emotion Recognition and Notification Customization

[1554] Terminal: When a user enters comments or behavioral patterns during data entry, the emotion engine recognizes them and generates emotion data.

[1555] Input: User comments and behavior patterns

[1556] Output: Emotion data

[1557] Specific operation: The device's sensors and log acquisition system analyze the user's actions and collect emotion-related data.

[1558] Server: Analyzes the emotion data generated by the emotion engine and customizes the notification content. For example, it generates a simple and intuitive notification for users with high stress levels.

[1559] Input: Emotion data

[1560] Output: Customized notification content

[1561] Specific operation: The server generates notification content based on the analysis results of the emotion engine and sends it to the user in an appropriate format.

[1562] Step 6: Execution instructions

[1563] Users: Local government officials and food businesses review the notified strategy and customized notification content, and take specific actions after approval. For example, a food business can arrange for the delivery of designated food to a designated area.

[1564] Input: Customized notification content, strategic information

[1565] Output: Execution instructions after approval

[1566] Specific actions: The person in charge checks the notification and clicks the approval button. The food supplier prepares the delivery and issues delivery instructions.

[1567] Step 7: Gather feedback

[1568] Server: After the delivery is completed, the server collects the delivery completion data and the user's emotion data again and provides feedback for the next strategy generation.

[1569] Input: Delivery completion data, emotion data

[1570] Output: Feedback data for next strategy generation

[1571] Specific operation: After delivery is completed, the server automatically collects the data, stores it in a database, and reuses it as input data for the generative AI model.

[1572] Through these steps, the system can efficiently and effectively manage food supply and demand, thereby reducing food waste. Furthermore, by using an emotion engine, notifications and instructions that take into account the user's emotions can be provided, increasing the system's acceptability and effectiveness.

[1573] (Application example 2)

[1574] 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 robot 414 will be referred to as a "terminal."

[1575] Food waste not only causes economic losses but also has a serious impact on the environment, necessitating the development of efficient food distribution systems. However, existing systems struggle to optimize the balance between supply and demand, and lack the ability to respond to individual user needs based on user emotions. In particular, in the food delivery field, there is a lack of effective customization of notifications based on emotion recognition for delivery routes and urgent food distribution.

[1576] 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 a means for collecting data from local governments and food businesses, a means for analyzing supply and demand based on the collected data, a means for generating an optimal food delivery strategy based on the analysis results, a means for notifying the local government and food businesses of the generated strategy, an emotion engine means for recognizing the user's emotions and customizing the notification content, a means for issuing food delivery instructions based on the notified strategy, and a means for collecting data after delivery is completed and providing feedback. This not only enables efficient balancing of supply and demand, but also enables flexible responses tailored to the user's emotions.

[1577] A "local government" is a public institution that is responsible for local administration and provides administrative services related to the lives of local residents.

[1578] "Food businesses" are companies and organizations that produce, process, and sell food, and play an important role in food distribution.

[1579] "Data collection means" refers to a method or device for acquiring and organizing information, and in this invention, it is used to collect inventory and demand information provided by local governments and food suppliers.

[1580] "Means for supply and demand analysis" refers to methods and mechanisms that use collected data to assess and analyze where and how much food is needed and where and how much is in stock.

[1581] The "means for generating an optimal food delivery strategy" is a method or device for determining, based on the analysis results, which food items should be delivered to which areas as a priority.

[1582] "Means for notifying the generated strategy" refers to methods or devices for informing relevant parties (local governments and food businesses) of the generated delivery strategy, and includes email and push notifications.

[1583] An "emotion engine" is a system or device that analyzes the user's input data, recognizes their emotional state at the time, and presents the optimal response.

[1584] The "means for issuing instructions for food delivery" refers to a method or device for issuing instructions to relevant parties to actually deliver food based on an optimal delivery strategy.

[1585] The "means for providing feedback" refers to a method or device that collects data when delivery is completed and provides complementary information to be used in generating the next strategy.

[1586] The present invention provides a system for efficiently distributing food and reducing food waste through collaboration between local governments and food businesses. The system includes a data collection means, a data analysis means, a means for generating an optimal food delivery strategy, an emotion engine means, a notification means, a means for issuing food delivery instructions, and a means for providing feedback.

[1587] First, food vendors use terminals to input food inventory data (e.g., food type, quantity, expiration date, etc.) and local governments input demand data for each region (e.g., food type, quantity, required deadline, etc.) into the system. The server then collects the data and stores it in a database. This data is periodically collected by a data collection module using Python.

[1588] Next, using a generative AI model, the server analyzes the collected inventory and demand data to assess the balance between supply and demand. Based on the analysis results, it generates an optimal food delivery strategy, which includes which food items to deliver to which areas, delivery priorities, and schedules.

[1589] The server notifies the local government and food suppliers of the generated strategy via notification means (email, push notification, etc.). At this time, the emotion engine recognizes the user's emotions and customizes the notification content based on, for example, the user's input data and behavioral patterns. Specifically, if the user is feeling stressed, a simple and intuitive notification is sent, and if not, a detailed notification is sent.

[1590] For example, by inputting a prompt such as, "Compare the data from region A with next month's demand, have the generative AI model generate suggestions on how to efficiently distribute food that is nearing its expiration date, and use the emotion engine to customize the notification content," into the generative AI model, analysis and notification customization are performed.

[1591] Local government officials and food vendors then review the strategy and take specific action after approval. For example, a food vendor arranges for the delivery of a specified food item to a specified area. After the delivery is completed, the server collects data about the delivery again and uses it as feedback for generating the next strategy. This feedback also includes emotional data.

[1592] This will strengthen cooperation between local governments and food businesses, effectively reducing food waste, and improve the system's acceptability and effectiveness by providing appropriate notifications and instructions that take into account the user's emotions through the emotion engine.

[1593] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1594] Step 1:

[1595] The terminal receives data input from food businesses and local governments. Specifically, food businesses input food inventory data (food type, quantity, expiration date, etc.), and local governments input demand data for each region (type of food needed, quantity, required deadline, etc.). The input data is sent to a server and stored in a database. The input data is then processed by a data collection module and converted into a unified format.

[1596] Step 2:

[1597] The server retrieves inventory and demand data stored in the database. Based on this data, it uses a generative AI model to analyze the data. Specifically, it evaluates the balance between supply and demand and analyzes which foods should be prioritized for distribution to which areas. The generative AI model used here uses algorithms that forecast demand and optimize supply based on the input data.

[1598] Step 3:

[1599] The server generates an optimal food delivery strategy based on the analysis results of the generative AI model. This strategy includes information such as which food to deliver to which area, delivery priorities, and schedules. The generated strategy includes specific instructions such as food distribution routes and allocation amounts. This strategy is then saved back into the database.

[1600] Step 4:

[1601] The server notifies the local government and food suppliers of the generated delivery strategy via a notification mechanism. Here, the emotion engine recognizes the user's emotions and customizes the notification content. It analyzes the user's input data and behavioral patterns to determine whether the user is under stress and sends notifications according to their state. For example, it sends a simple notification to users who are highly stressed, and a detailed notification to users who are not.

[1602] Step 5:

[1603] Users (municipal government officials and food businesses) review and approve the notified strategy. After approval, they take specific actions. Specifically, food businesses arrange for the delivery of designated food to designated areas and begin actual delivery work on site.

[1604] Step 6:

[1605] The server collects information after delivery is complete. The data after delivery includes information on delivery time, delivery volume, and delivery destination. This data is sent to the server and stored in a database. The emotion engine also collects user emotion data again.

[1606] Step 7:

[1607] The server analyzes the collected delivery completion data and emotion data and uses them as feedback for generating the next strategy, which continuously optimizes the system to reduce food waste and improve the user experience.

[1608] Through these steps, local governments and food businesses can work together efficiently to reduce food waste, and the emotion engine can provide notifications that take users' emotions into consideration.

[1609] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1610] 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.

[1611] 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 robot 414.

[1612] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1613] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1614] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1615] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1616] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1617] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1618] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1619] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1620] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1621] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1622] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1623] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1624] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1625] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1626] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1627] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1628] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1629] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1630] The following is further disclosed regarding the above embodiment.

[1631] (Claim 1)

[1632] A means of collecting data from local governments and food businesses;

[1633] A means of conducting supply and demand analysis based on the collected data;

[1634] a means for generating an optimal food delivery strategy based on the analysis results;

[1635] a means of informing local governments and food businesses of the strategies generated;

[1636] a means for directing food delivery based on the notified strategy;

[1637] a means of collecting data and providing feedback after delivery is completed;

[1638] A system including:

[1639] (Claim 2)

[1640] 10. The system of claim 1, wherein the generated strategy is optimized taking into account the shelf life of the food.

[1641] (Claim 3)

[1642] 2. The system of claim 1, wherein food delivery instructions are automatically transmitted to a delivery company.

[1643] "Example 1"

[1644] (Claim 1)

[1645] A means of obtaining information from local governments and food businesses;

[1646] A means using artificial intelligence to analyze supply and demand based on the acquired information;

[1647] a means for generating an optimal food delivery strategy based on the analysis results;

[1648] A means of notifying local governments and food businesses of the generated strategy by means such as email or push notification;

[1649] a means for directing food delivery based on the notified strategy;

[1650] A means of collecting data after delivery is completed and providing feedback for the next strategy generation;

[1651] A system including:

[1652] (Claim 2)

[1653] 10. The system of claim 1, wherein the generated strategy is optimized taking into account the shelf life of the food.

[1654] (Claim 3)

[1655] 2. The system of claim 1, wherein food delivery instructions are automatically transmitted to a logistics company.

[1656] "Application Example 1"

[1657] (Claim 1)

[1658] A means of collecting data from local governments and food businesses;

[1659] A means of conducting supply and demand analysis based on the collected data;

[1660] a means for generating an optimal food delivery strategy based on the analysis results;

[1661] a means of informing local governments and food businesses of the strategies generated;

[1662] a means for directing food delivery based on the notified strategy;

[1663] a means of collecting data and providing feedback after delivery is completed;

[1664] A means for food businesses and local government officials to enter and review data via mobile devices;

[1665] a means by which the generative model generates optimal supply strategies in real time;

[1666] A system including:

[1667] (Claim 2)

[1668] The system of claim 1, wherein the generated strategy is optimized taking into account the expiration date of food products, and food vendors can input data on food products that are approaching their expiration date via mobile terminals.

[1669] (Claim 3)

[1670] 2. The system of claim 1, wherein food delivery instructions are automatically transmitted to the delivery company and notification is provided via push notification on the mobile device.

[1671] "Example 2: Combining Emotion Engines"

[1672] (Claim 1)

[1673] A means of collecting data from local governments and food businesses on a regular basis;

[1674] A means of analyzing supply and demand using generative AI models based on the collected data; and

[1675] a means for generating an optimal food delivery strategy based on the analysis results;

[1676] a means of informing local governments and food businesses of the strategies generated;

[1677] A means for customizing notification content by analyzing user emotion data using an emotion engine;

[1678] a means for directing food delivery based on the notified strategy;

[1679] a means of collecting data and sentiment data after delivery is completed and providing feedback;

[1680] A system including:

[1681] (Claim 2)

[1682] 10. The system of claim 1, wherein the generated strategy is optimized taking into account the shelf life of the food.

[1683] (Claim 3)

[1684] 2. The system of claim 1, wherein food delivery instructions are automatically transmitted to a delivery company.

[1685] "Application example 2 when combining emotion engines"

[1686] (Claim 1)

[1687] A means of collecting data from local governments and food businesses;

[1688] A means of conducting supply and demand analysis based on the collected data;

[1689] a means for generating an optimal food delivery strategy based on the analysis results;

[1690] a means of informing local governments and food businesses of the strategies generated;

[1691] an emotion engine means for recognizing a user's emotion and customizing notification content;

[1692] a means for directing food delivery based on the notified strategy;

[1693] a means of collecting data and providing feedback after delivery is completed;

[1694] A system including:

[1695] (Claim 2)

[1696] 10. The system of claim 1, wherein the generated strategy is optimized taking into account the shelf life of the food.

[1697] (Claim 3)

[1698] 2. The system of claim 1, wherein the emotion engine customizes notification content based on the user's emotion data. [Explanation of symbols]

[1699] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data from local governments and food businesses; A means of conducting supply and demand analysis based on the collected data; a means for generating an optimal food delivery strategy based on the analysis results; a means of informing local governments and food businesses of the generated strategies; a means for directing food delivery based on the notified strategy; a means of collecting data and providing feedback after delivery is completed; A system including:

2. The system of claim 1 , wherein the generated strategy is optimized taking into account the shelf life of the food product.

3. 10. The system of claim 1, wherein food delivery instructions are automatically transmitted to a delivery company.

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