system

Abstract

We provide the system. [Solution] A means of automatically transporting agricultural products from producers to sales outlets, A means for packaging agricultural products and attaching identification information, A means of analyzing flow data and making demand forecasts, Means to adjust the amount of goods transported to sales outlets and markets as needed, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem is that the burden related to the transportation and sale of agricultural products in farmers is increasing. In particular, the shortage of labor due to aging and the abandonment of agriculture is becoming serious, and the efficiency of the supply chain of agricultural products is required. In addition, appropriate supply according to fluctuations in demand is not carried out, and problems such as excessive inventory and unsold products also occur. As a result, a new system is needed to reduce the burden on farmers and optimize sales.

Means for Solving the Problems

[0005] This invention provides an automated transportation system using unmanned vehicles, including means for efficiently transporting agricultural products from producers to sales outlets. In addition, it incorporates a system for automatically packaging agricultural products and assigning identification information, thereby automating shipping operations. Furthermore, it includes means for analyzing flow data and using algorithms to predict fluctuations in demand, dynamically adjusting the amount of goods transported to sales outlets and markets. This means reduces the workload of farmers and optimizes the supply-demand balance.

[0006] "Agricultural products" refer to plants or parts thereof that producers cultivate and harvest through agricultural activities.

[0007] The term "producer" refers to an individual or group that engages in agricultural activities and produces agricultural products.

[0008] A "sales outlet" refers to a place or facility where agricultural products are sold to consumers.

[0009] "Means for automated transportation" refers to systems that use technologies such as unmanned vehicles to move agricultural products between specific points.

[0010] "Means for packaging and attaching identification information" refers to technologies that allow for the identification of the origin and characteristics of agricultural products by attaching information labels or codes when protecting them and organizing them for transport.

[0011] "Flow data" refers to information that shows the movement and travel trends of people in a specific area.

[0012] "Means for forecasting demand" refers to algorithms and technologies that estimate future consumer demand by analyzing sales trends, pedestrian flow data, and other factors.

[0013] "Means for adjusting transport volume" refers to technologies that optimize the supply of agricultural products based on demand forecasts and modify transport plans to prevent surpluses and shortages. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system for efficiently transporting and selling agricultural products, and specific embodiments thereof are described below.

[0036] The system primarily consists of a server, terminals, and users. The server plays a central role, collecting and analyzing data on agricultural products received from producers, sales data from distribution centers, and human traffic data. Based on these results, it forecasts demand and creates optimal transportation schedules and routes. Terminals are installed at each producer's facility and can automatically pack and label agricultural products based on instructions from the server. For example, a terminal can trigger an automated packing machine, and the generated label will include shipping information.

[0037] The user is responsible for monitoring and managing the automated transport. They operate the unmanned vehicles and execute transport tasks according to a schedule from the server. The unmanned vehicles collect agricultural products along pre-programmed routes and transport them to sales outlets. For example, when a user starts an unmanned vehicle heading to sales outlet A, the truck will visit producer B to load tomatoes and then proceed to sales outlet A.

[0038] These program processes allow the server to monitor transaction data from sales outlets in real time and update logistics plans in preparation for future demand fluctuations. This enables effective identification of best-selling agricultural products and highly accurate demand forecasting. For example, if the server detects that tomato sales at direct sales outlet B are strong, it will reflect this in the next delivery plan and increase the tomato shipment volume.

[0039] This system reduces the burden on producers and minimizes crop waste, thereby supporting more sustainable agricultural management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects data from producers regarding crop inventory and production capacity. This information is configured to be automatically sent from the terminal to the server.

[0043] Step 2:

[0044] The server analyzes historical sales data and real-time foot traffic data from sales locations and runs a demand forecasting algorithm. Based on the results of this analysis, it generates the optimal transportation schedule and route.

[0045] Step 3:

[0046] The server transmits instructions for the next shipment to each producer via their terminal. These instructions include which crops to ship and in what quantities.

[0047] Step 4:

[0048] The terminal operates an automated packaging machine to package and label agricultural products according to shipping instructions from the server. The labels contain shipping destination information and product identification information.

[0049] Step 5:

[0050] The user manages the operation of unmanned vehicles that perform automated transportation. The unmanned vehicles follow routes planned on the server, transporting goods from producers to designated sales locations.

[0051] Step 6:

[0052] The server monitors sales trends at distribution centers in real time and adjusts the next shipment plan based on sales performance. If necessary, it instructs the distribution center to restock or to transfer surplus goods to the wholesale market.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In recent years, the agricultural sector has been required to develop effective logistics systems that meet production efficiency and market needs. However, conventional systems have not sufficiently improved the accuracy of agricultural product sales forecasts and automated transportation, resulting in problems such as increased waste of agricultural products and increased burden on producers. Solving this problem is a challenge.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for an information processing device to collect and analyze data on agricultural products provided from production sites, means for the information processing device to integrate sales information and aggregated data from sales bases and execute an algorithm for forecasting demand, and means for the information processing device to generate an optimal transportation plan and route based on the demand forecast. This makes it possible to reduce the burden on producers and reduce waste of agricultural products.

[0058] An "information processing device" refers to a computer and software used to collect and analyze data on agricultural products.

[0059] "Agricultural products" refer to products cultivated and produced by farmland and producers.

[0060] A "sales base" refers to a place where agricultural products are offered to the market and purchased by consumers.

[0061] "Sales information" refers to detailed data regarding the transaction of a product, including elements such as sales quantity and price.

[0062] "Aggregated data" refers to quantitative data related to the flow of people and products, and is a set of data analyzed for demand forecasting.

[0063] "Demand forecasting" refers to the process of predicting future consumption trends based on past and present data.

[0064] An "algorithm" refers to a set of processing steps designed for a specific computation or problem-solving task.

[0065] "Transportation planning" refers to the schedule and methods for efficiently moving agricultural products from production sites to sales locations.

[0066] "Route" refers to the physical path that agricultural products take when they are moved.

[0067] An "unmanned transport device" refers to a transport device that operates automatically without a driver on board.

[0068] A "generative AI model" refers to a machine learning system that learns patterns from data and makes predictions.

[0069] This invention is a system for the efficient transportation and sale of agricultural products. The system mainly consists of a server, terminals, and users.

[0070] The server functions as an information processing unit, collecting data on agricultural products provided from production sites and storing it in a database. The server then integrates sales information and aggregated data from each sales base to perform demand forecasting. To do this, it utilizes generative AI models and algorithms based on historical and current sales data. Using this data, the server predicts demand for agricultural products and generates optimal transportation plans and routes.

[0071] The terminals are deployed at each production site and automatically package agricultural products and add identification information based on instructions from the server. In this process, by linking the automated packaging machine with a label printer, it is possible to quickly display and print the information necessary for shipping.

[0072] Users operate unmanned transport vehicles and monitor and manage the transport of agricultural products according to the transport schedule and plan provided by the server. Users use terminals to check the vehicle's current location and operational status in real time and intervene as needed to ensure smooth transport.

[0073] For example, if a user prompts the server with a request such as, "Please suggest how to optimize the next tomato shipment schedule," the server will use a generative AI model to appropriately adjust the shipment plan, ensuring that tomatoes are delivered in the optimal quantity to the sales locations with the highest demand. This system reduces the burden on producers and minimizes waste of agricultural products, leading to more sustainable agricultural management.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server retrieves agricultural product data from production sites. Specifically, it receives data provided by producers and registers it in a database. Inputs include the type, quantity, and quality of agricultural products, which are then stored in the database. The output is a structured data set that is used for subsequent processing.

[0077] Step 2:

[0078] The server integrates sales information and aggregated data from sales locations to perform demand forecasting. This process uses sales information (sales volume, revenue data) and pedestrian flow data (consumer movement data) as inputs. A generative AI model is used to analyze this data and forecast demand. The output is the demand forecast result, which is used to develop an optimal transportation plan.

[0079] Step 3:

[0080] The server generates optimal transportation plans and routes based on demand forecasts. The inputs are demand forecast results and geographical information. The server uses an algorithm to calculate the most efficient delivery routes and schedules. The output is transportation schedule and route information for automated transport devices.

[0081] Step 4:

[0082] The terminal receives instructions from the server to automatically package agricultural products and generate identification information. It receives product data (number of items shipped, label information) from the server as input and drives the automatic packaging machine. It controls the label printer to affix labels with shipping information to the products. The output is packaged agricultural products ready for shipment.

[0083] Step 5:

[0084] The user operates an automated transport system, monitoring and managing the transportation of agricultural products according to the transport schedule. Inputs include transport plans and real-time location information provided by a server. Specific actions include checking the transport status on a terminal and adjusting the route as needed. The output is the safe delivery of agricultural products to sales locations.

[0085] Step 6:

[0086] The server monitors real-time transaction information and updates the logistics plan. It uses transaction data from sales locations (sales, inventory changes) as input. The server analyzes this data and updates the logistics plan if demand fluctuations are anticipated. The output is the updated logistics plan, which will be reflected in the next delivery.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] There is a need to effectively adjust transportation plans in response to fluctuations in agricultural demand, reducing the burden on producers and sales outlets while minimizing inventory surpluses and shortages. However, conventional systems struggle with real-time demand forecasting and transportation schedule adjustments, resulting in cumbersome and inefficient operations.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] This invention includes a server that displays the inventory status, transportation plans, and route information of agricultural products in real time via a communication device, and a means for supporting the administrator in efficiently controlling the system; a function for analyzing past trade information and trend data to forecast demand; and a function for adjusting the quantity shipped to sales bases. This enables more efficient inventory management and transportation scheduling of agricultural products.

[0092] "Agricultural products" are plant-based food products produced in a specific region and transported for sale and consumption.

[0093] "Production area" refers to the region or facility where agricultural products are cultivated and harvested.

[0094] A "sales base" refers to a place where agricultural products are distributed and sold so that they reach consumers.

[0095] A "mechanism for automatic movement" refers to a system that transports agricultural products from one location to another without requiring human intervention.

[0096] "Identification information" refers to data that includes details such as the type of agricultural product, its origin, and its destination, enabling tracking throughout the distribution process.

[0097] "Past trade information" refers to sales and transportation performance data, which is used to create demand forecasts and logistics plans.

[0098] "Trend data" refers to information that shows the state of public activity, such as people's movement and purchasing behavior.

[0099] "Functions for demand forecasting" refer to technologies that use collected data to predict future demand for agricultural products.

[0100] "A function to adjust shipment quantities" refers to technology that optimizes the amount of agricultural products delivered according to market demand.

[0101] "Communication equipment" is a general term for hardware and software used to send and receive information, and is used for relaying data.

[0102] "Real time" refers to a period of time when information is processed and used, which is imperceptible to human senses. It is also known as real-time.

[0103] To implement this invention, a server plays a central role. The server collects and analyzes agricultural data obtained from production areas, transaction data from sales outlets, and market trend data. Based on the collected data, demand forecasting is performed, and the results serve as a basis for optimizing transportation schedules and routes. The server utilizes a cloud computing platform and database software (e.g., AWS®, PostgreSQL) for data analysis.

[0104] The terminals are deployed at the production site. They receive instructions from a server and perform automated packaging of agricultural products and generate identification labels. An automated packaging machine is connected to the terminal, and packaging is carried out according to the instructions. Identification information is printed by a label printer and affixed to the agricultural products.

[0105] Users, acting as logistics managers, utilize smartphones and tablets to check real-time inventory status, shipping schedules, and route information provided by the server. Based on this information, they make manual adjustments as needed. Specific applications (e.g., React Native apps) are installed on the smartphones, enabling intuitive operation.

[0106] For example, if tomato supply surges at a particular sales location, the server immediately updates the demand forecast and incorporates it into the next delivery plan. The user can then use their smartphone to check this information and formulate a prompt for the generative AI model, such as "Should we increase the amount of tomatoes shipped to direct sales outlet A?"

[0107] An example of a prompt would be, "Given the increased demand for tomatoes at direct sales outlet A, how should we adjust the next shipment volume?" Based on this prompt, the AI ​​model is expected to suggest appropriate shipment volumes and transportation routes.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server receives agricultural data collected at production sites, transaction data from sales outlets, and data on people's behavior. This data is stored in a cloud database. The server then uses data mining algorithms to extract recognition patterns from this data. As an output, it generates a rich dataset necessary for demand forecasting.

[0111] Step 2:

[0112] The server utilizes the data obtained in Step 1 to perform demand forecasting using a predictive model. The input data includes historical sales information and data on people's movements. By using machine learning algorithms (e.g., regression analysis, time series analysis), it performs short-term and medium-to-long-term demand projection and outputs the optimal transportation schedule and route.

[0113] Step 3:

[0114] The terminal receives the transport schedule and route information instructed by the server. Based on this data, the automated packaging machine starts packing the produce. A label printer prints identification information and attaches it to each individual produce unit. This prepares each produce unit for transport.

[0115] Step 4:

[0116] Users receive real-time information from the server using their smartphones and check the displayed inventory status and shipping schedule. The smartphone application visually displays the information, allowing users to easily respond to changes in demand. If necessary, users can manually adjust shipping quantities. Feedback is provided to the user as output, and they decide on their next action based on that feedback.

[0117] Step 5:

[0118] When a user recognizes an increase in demand at a specific sales location, they use a generative AI model to create a prompt. For example, "Demand for tomatoes at direct sales location A has surged. How should we adjust shipment quantities?" This prompt becomes input to the AI ​​model, which then recommends the optimal shipping strategy. As a result, users can take quick and effective countermeasures.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system aimed at the efficient distribution of agricultural products, and it improves the accuracy of demand forecasting by incorporating an emotion engine. This system is realized by integrating the emotion engine around a server, terminal, and user.

[0121] The server is responsible for collecting and analyzing agricultural product inventory and sales data. This data includes real-time foot traffic information, historical consumption trends, and consumer sentiment data. The sentiment engine analyzes consumer emotional states at specific times and events, generating demand forecasts that take into account emotional fluctuations in purchasing intent.

[0122] The terminal is installed at the producer's facility and operates an automated packaging machine and applies the necessary labels to the produce based on shipping instructions sent from the server. These labels also include sales forecast information based on sentiment engine analysis. For example, the terminal can reflect seasonal promotional information on the labels.

[0123] The user takes on the role of managing the unmanned vehicles that perform automated transportation and the logistics plan. The unmanned vehicles transport agricultural products according to a plan adjusted by an emotion engine. For example, if the server detects high consumer expectations for a particular season, the user will increase logistics for that period.

[0124] This system enables supply and demand adjustments based on consumer sentiment, allowing producers to ensure appropriate shipments while consumers smoothly receive products that match their needs. This maximizes sales at retail outlets while minimizing agricultural waste.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects inventory information and shipping capacity data for agricultural products from producers. This data is automatically transmitted from the terminals to the server.

[0128] Step 2:

[0129] The server runs a demand forecasting model based on historical sales data from the sales location and real-time pedestrian flow data. During this process, it uses an emotion engine to analyze consumer sentiment data and incorporates it into the demand forecast.

[0130] Step 3:

[0131] The terminal receives shipping instructions from the server and activates the automated packaging machine to begin packaging the agricultural products. A labeling system prints sales promotion information on the labels, taking consumer sentiment into consideration.

[0132] Step 4:

[0133] Users activate unmanned vehicles based on a transport schedule generated by the server. The unmanned vehicles then proceed to transport goods to areas with high demand, based on analysis results from an emotion engine.

[0134] Step 5:

[0135] The server continuously monitors transaction data from sales outlets and changes in consumer sentiment to identify new consumption trends. Based on this, it adjusts future shipping plans and volumes.

[0136] Step 6:

[0137] Users receive update information from the server, coordinate the entire logistics system, and reconfigure the routes of unmanned vehicles to ensure efficient delivery of agricultural products.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] The current agricultural product distribution system suffers from low-accuracy demand forecasting and difficulty in flexibly adjusting supply and demand based on consumer sentiment and events. This results in problems such as excess inventory and stockouts, leading to lost sales opportunities and wasted agricultural products. To address these challenges, highly accurate demand forecasting utilizing consumer sentiment data and efficient distribution planning based on that forecasting are needed.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes a device for automatically transporting agricultural products from production sites to sales sites, a device for packaging agricultural products and printing identification information on them, a device for analyzing past sales history and movement information and performing demand forecasting incorporating consumer sentiment data, and a device for adjusting the transportation plan according to consumers' expectations for seasonal events. This enables supply and demand adjustment that takes into account changes in consumer sentiment, thereby maximizing sales opportunities and reducing agricultural product waste.

[0143] "Agricultural products" refers to plant-based products cultivated or harvested at production sites, and includes goods distributed for sale.

[0144] A "production base" refers to a place where agricultural products are cultivated or harvested, and includes farms, greenhouses, and other similar facilities.

[0145] A "sales outlet" refers to a place where agricultural products are provided to the final consumer, and includes supermarkets and markets.

[0146] "Automated transportation equipment" refers to machines or mechanisms that transport agricultural products from production sites to sales sites without human intervention, and includes unmanned transport aircraft and drones.

[0147] "Identification information" refers to data indicating the type of agricultural product, shipping date, sales promotion information, etc., and includes information printed on labels and tags.

[0148] "Consumer sentiment data" refers to information that shows emotional trends that influence consumers' purchasing intentions and preferences, and includes data obtained from social media and survey results.

[0149] "Demand forecasting" refers to the analysis of predicting the consumption of agricultural products over a specific period, and is performed using historical data and sentiment data.

[0150] "Transportation planning" refers to a plan for efficiently distributing agricultural products, including adjusting distribution routes and transport volumes.

[0151] This invention is a system for improving the efficiency of agricultural product distribution, in which a server, terminals, and users each play their respective roles. The server plays a central role in collecting and analyzing agricultural product inventory and sales data. Specifically, the server uses a "data stream management tool" for data stream management and a "relational database" for data storage. The server also uses natural language processing technology to analyze sentiment data in order to evaluate consumer sentiment. This analysis uses data collected from social media to extract consumer sentiment related to specific times or events.

[0152] The terminal is installed at the production site and, upon receiving instructions for shipping agricultural products, operates an automated packaging machine using a "control device." This control device automates the packaging process, and a label printer prints the necessary identification information on the agricultural products. This information also includes promotional information related to consumer sentiment based on demand forecasts.

[0153] Users manage logistics plans and execute efficient deliveries using unmanned transport vehicles. They utilize geographic information systems to determine optimal delivery routes, supporting seamless logistics operations. This enables flexible responses to fluctuating demand and allows for the delivery of goods to consumers.

[0154] For example, if sentiment analysis predicts that demand for a particular crop will increase with the arrival of spring, the server will create a forecast based on that data. The terminal will print this information as a label, and the user will adjust the delivery schedule. This entire process ensures that crops are delivered to consumers at the appropriate time, avoiding excess inventory and stockouts.

[0155] As an example of a prompt, the AI ​​model is used in the form of, "Please propose a data analysis method for constructing a delivery plan based on demand trends for agricultural products in the next season." In this way, the present invention realizes market responses that take into account the emotional state of consumers and enables efficient distribution of agricultural products.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server collects agricultural product inventory data, sales history, and real-time pedestrian flow information. It receives inventory information from production sites, sales information from markets, and pedestrian flow data from sensor devices as input. Using this data, it organizes it with a data stream management tool and stores it in a relational database. Specifically, it periodically retrieves data from each data source via APIs and writes it to the database.

[0159] Step 2:

[0160] The server analyzes consumer sentiment data based on collected data. It obtains consumer posts collected from social media as input. Using natural language processing techniques, it analyzes these posts and extracts the consumer's emotional state. This analysis generates sentiment data, such as consumers' interests and feelings towards events. Specifically, it implements a text analysis algorithm to scan consumer posts.

[0161] Step 3:

[0162] The server uses sentiment data to forecast demand. Inputs include past sales history, current inventory data, and consumer sentiment data. A machine learning model is applied to create demand forecasts that reflect consumer sentiment and past trends. This forecast is generated and output as basic data for distribution planning. Specific operations include data analysis and execution of forecasting algorithms using Python.

[0163] Step 4:

[0164] The terminal receives shipping instructions based on demand forecasts, packs agricultural products, and prints labels. It receives shipping instruction data and forecast-based label information from a server as input. The control unit operates the automated packaging machine and prints labels, including identification information, using a label printer. Specifically, the control unit manages the packaging process and transmits label information to the printer.

[0165] Step 5:

[0166] The user adjusts the transportation plan based on logistics data from the server. It receives generated demand forecast data and label information as input, and uses a geographic information system to calculate the optimal delivery route. It then schedules the unmanned transport aircraft and puts the delivery plan into action. Specifically, it utilizes a web dashboard to determine routes in real time and send commands to the transport aircraft.

[0167] Step 6:

[0168] The user delivers agricultural products to consumers using an unmanned transport vehicle (SDV) based on a planned route. The inputs include an optimized delivery route and time schedule. The SUV uses IoT sensors and GPS to ensure accurate delivery, bringing the agricultural products to consumers. Specifically, the SUV executes an autonomous driving algorithm, navigating safely to designated delivery points.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] In today's distribution industry, accurately predicting and appropriately responding to fluctuations in demand based on consumer sentiment presents a significant challenge. Furthermore, inefficient inventory management at logistics centers contributes to considerable time and effort. This creates a situation where oversupply leads to excess inventory, while shortages result in lost sales opportunities.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes a device for automatically transporting agricultural products from suppliers to sales locations, a device for analyzing consumer sentiment data, and a device for displaying inventory information on workers' visual devices. This makes it possible to analyze consumers' emotional states in real time and make accurate demand forecasts, enabling efficient inventory management at logistics centers.

[0174] "Agricultural products" refer to goods and materials related to agriculture, including agricultural products and their processed products.

[0175] "Supplier" refers to an individual or legal entity that produces or supplies agricultural products.

[0176] A "sales outlet" is a physical or digital location for selling agricultural products to consumers.

[0177] "Automatic transport devices" are machines or systems that have the function of moving agricultural products to designated locations without human intervention.

[0178] "Identification information" refers to data and information attached to agricultural products that indicates the type and characteristics of the product.

[0179] "Consumer sentiment data" refers to information that quantifies or classifies the emotional state of consumers at a specific time or event.

[0180] "Visual devices" are devices that allow people to receive information visually, and include smart glasses and displays.

[0181] "Inventory information" refers to data on the quantity and types of agricultural products at logistics centers.

[0182] A "computational method" refers to a technique or algorithm used for data analysis and prediction.

[0183] This invention is a system for optimizing the distribution of agricultural products, and in particular, improves efficiency by incorporating sentiment data into demand forecasting. Embodiments of this system are described below.

[0184] The server comprehensively collects and analyzes agricultural product inventory information, consumer sentiment data, and historical sales and foot traffic data. Sentiment data is obtained from consumers' emotional states, particularly during specific periods or events that influence consumer behavior. This sentiment data is collected in real time, for example, using an API, and sent to a sentiment analysis engine. The analysis engine then generates demand forecasts based on all the collected data.

[0185] Regarding terminals, smart glasses will be provided to workers in the logistics center to display inventory information. These smart glasses will receive real-time data transmitted from a server and provide instructions to optimize picking and packing procedures. This will allow for prioritizing the handling of products with high demand based on collected sentiment data.

[0186] The user's role is to automatically transport agricultural products using unmanned transport vehicles (AGVs). Here, demand forecasts based on sentiment data analysis received from a server are used to optimize transportation plans. For example, the process from picking to delivery is adjusted to meet rising demand, ensuring fast and efficient distribution.

[0187] For example, if high demand for a particular fruit is predicted for Christmas, the smart glasses will instruct workers to prioritize picking that fruit. Users can also develop rapid fruit delivery plans to smoothly respond to fluctuations in demand.

[0188] An example of a prompt for a generative AI model is: "Use the emotion engine to analyze when demand for a particular product will increase based on current consumer sentiment and predicted demand, and devise a way to display this on the smart glasses' display."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server retrieves inventory information for agricultural products from suppliers and sales outlets into a database and accesses a sentiment analysis engine. Inputs include inventory information, sentiment data, and historical sales data. Based on this data, the server performs data integration and preprocessing to create a unified dataset. The output is a refined dataset.

[0192] Step 2:

[0193] The server analyzes consumer sentiment data in real time through an emotion analysis engine and predicts demand fluctuations caused by specific seasons or events. In this process, the input is the dataset created in the previous step, and the output is the demand forecast result from the demand forecasting model.

[0194] Step 3:

[0195] The terminal delivers demand forecast results sent from the server to the smart glasses. The input is the demand forecast results from the server, and the terminal generates visual data based on this. The output appears as picking instructions displayed on the smart glasses. Specific actions include prioritizing high-demand items and providing visual feedback to the worker.

[0196] Step 4:

[0197] The user creates a transportation plan for operating an automated guided vehicle (AGV). Inputs include demand forecast data from a server and inventory information from each location. Based on this information, the user uses an algorithm to create an optimal transportation schedule. The output is a plan for efficient delivery routes and timings. Specific actions include determining the departure times and routes of the transport vehicles.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention relates to a system for efficiently transporting and selling agricultural products, and specific embodiments thereof are described below.

[0215] The system primarily consists of a server, terminals, and users. The server plays a central role, collecting and analyzing data on agricultural products received from producers, sales data from distribution centers, and human traffic data. Based on these results, it forecasts demand and creates optimal transportation schedules and routes. Terminals are installed at each producer's facility and can automatically pack and label agricultural products based on instructions from the server. For example, a terminal can trigger an automated packing machine, and the generated label will include shipping information.

[0216] The user is responsible for monitoring and managing the automated transport. They operate the unmanned vehicles and execute transport tasks according to a schedule from the server. The unmanned vehicles collect agricultural products along pre-programmed routes and transport them to sales outlets. For example, when a user starts an unmanned vehicle heading to sales outlet A, the truck will visit producer B to load tomatoes and then proceed to sales outlet A.

[0217] These program processes allow the server to monitor transaction data from sales outlets in real time and update logistics plans in preparation for future demand fluctuations. This enables effective identification of best-selling agricultural products and highly accurate demand forecasting. For example, if the server detects that tomato sales at direct sales outlet B are strong, it will reflect this in the next delivery plan and increase the tomato shipment volume.

[0218] This system reduces the burden on producers and minimizes crop waste, thereby supporting more sustainable agricultural management.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server collects data from producers regarding crop inventory and production capacity. This information is configured to be automatically sent from the terminal to the server.

[0222] Step 2:

[0223] The server analyzes historical sales data and real-time foot traffic data from sales locations and runs a demand forecasting algorithm. Based on the results of this analysis, it generates the optimal transportation schedule and route.

[0224] Step 3:

[0225] The server transmits instructions for the next shipment to each producer via their terminal. These instructions include which crops to ship and in what quantities.

[0226] Step 4:

[0227] The terminal operates an automated packaging machine to package and label agricultural products according to shipping instructions from the server. The labels contain shipping destination information and product identification information.

[0228] Step 5:

[0229] The user manages the operation of unmanned vehicles that perform automated transportation. The unmanned vehicles follow routes planned on the server, transporting goods from producers to designated sales locations.

[0230] Step 6:

[0231] The server monitors sales trends at distribution centers in real time and adjusts the next shipment plan based on sales performance. If necessary, it instructs the distribution center to restock or to transfer surplus goods to the wholesale market.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] In recent years, the agricultural sector has been required to develop effective logistics systems that meet production efficiency and market needs. However, conventional systems have not sufficiently improved the accuracy of agricultural product sales forecasts and automated transportation, resulting in problems such as increased waste of agricultural products and increased burden on producers. Solving this problem is a challenge.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for an information processing device to collect and analyze data on agricultural products provided from production sites, means for the information processing device to integrate sales information and aggregated data from sales bases and execute an algorithm for forecasting demand, and means for the information processing device to generate an optimal transportation plan and route based on the demand forecast. This makes it possible to reduce the burden on producers and reduce waste of agricultural products.

[0237] An "information processing device" refers to a computer and software used to collect and analyze data on agricultural products.

[0238] "Agricultural products" refer to products cultivated and produced by farmland and producers.

[0239] A "sales base" refers to a place where agricultural products are offered to the market and purchased by consumers.

[0240] "Sales information" refers to detailed data regarding the transaction of a product, including elements such as sales quantity and price.

[0241] "Aggregated data" refers to quantitative data related to the flow of people and products, and is a set of data analyzed for demand forecasting.

[0242] "Demand forecasting" refers to the process of predicting future consumption trends based on past and present data.

[0243] An "algorithm" refers to a set of processing steps designed for a specific computation or problem-solving task.

[0244] "Transportation planning" refers to the schedule and methods for efficiently moving agricultural products from production sites to sales locations.

[0245] "Route" refers to the physical path that agricultural products take when they are moved.

[0246] An "unmanned transport device" refers to a transport device that operates automatically without a driver on board.

[0247] A "generative AI model" refers to a machine learning system that learns patterns from data and makes predictions.

[0248] This invention is a system for the efficient transportation and sale of agricultural products. The system mainly consists of a server, terminals, and users.

[0249] The server functions as an information processing unit, collecting data on agricultural products provided from production sites and storing it in a database. The server then integrates sales information and aggregated data from each sales base to perform demand forecasting. To do this, it utilizes generative AI models and algorithms based on historical and current sales data. Using this data, the server predicts demand for agricultural products and generates optimal transportation plans and routes.

[0250] The terminals are deployed at each production site and automatically package agricultural products and add identification information based on instructions from the server. In this process, by linking the automated packaging machine with a label printer, it is possible to quickly display and print the information necessary for shipping.

[0251] Users operate unmanned transport vehicles and monitor and manage the transport of agricultural products according to the transport schedule and plan provided by the server. Users use terminals to check the vehicle's current location and operational status in real time and intervene as needed to ensure smooth transport.

[0252] For example, if a user prompts the server with a request such as, "Please suggest how to optimize the next tomato shipment schedule," the server will use a generative AI model to appropriately adjust the shipment plan, ensuring that tomatoes are delivered in the optimal quantity to the sales locations with the highest demand. This system reduces the burden on producers and minimizes waste of agricultural products, leading to more sustainable agricultural management.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server retrieves agricultural product data from production sites. Specifically, it receives data provided by producers and registers it in a database. Inputs include the type, quantity, and quality of agricultural products, which are then stored in the database. The output is a structured data set that is used for subsequent processing.

[0256] Step 2:

[0257] The server integrates sales information and aggregated data from sales locations to perform demand forecasting. This process uses sales information (sales volume, revenue data) and pedestrian flow data (consumer movement data) as inputs. A generative AI model is used to analyze this data and forecast demand. The output is the demand forecast result, which is used to develop an optimal transportation plan.

[0258] Step 3:

[0259] The server generates optimal transportation plans and routes based on demand forecasts. The inputs are demand forecast results and geographical information. The server uses an algorithm to calculate the most efficient delivery routes and schedules. The output is transportation schedule and route information for automated transport devices.

[0260] Step 4:

[0261] The terminal receives instructions from the server to automatically package agricultural products and generate identification information. It receives product data (number of items shipped, label information) from the server as input and drives the automatic packaging machine. It controls the label printer to affix labels with shipping information to the products. The output is packaged agricultural products ready for shipment.

[0262] Step 5:

[0263] The user operates an automated transport system, monitoring and managing the transportation of agricultural products according to the transport schedule. Inputs include transport plans and real-time location information provided by a server. Specific actions include checking the transport status on a terminal and adjusting the route as needed. The output is the safe delivery of agricultural products to sales locations.

[0264] Step 6:

[0265] The server monitors real-time transaction information and updates the logistics plan. It uses transaction data from sales locations (sales, inventory changes) as input. The server analyzes this data and updates the logistics plan if demand fluctuations are anticipated. The output is the updated logistics plan, which will be reflected in the next delivery.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] There is a need to effectively adjust transportation plans in response to fluctuations in agricultural demand, reducing the burden on producers and sales outlets while minimizing inventory surpluses and shortages. However, conventional systems struggle with real-time demand forecasting and transportation schedule adjustments, resulting in cumbersome and inefficient operations.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] This invention includes a server that displays the inventory status, transportation plans, and route information of agricultural products in real time via a communication device, and a means for supporting the administrator in efficiently controlling the system; a function for analyzing past trade information and trend data to forecast demand; and a function for adjusting the quantity shipped to sales bases. This enables more efficient inventory management and transportation scheduling of agricultural products.

[0271] "Agricultural products" are plant-based food products produced in a specific region and transported for sale and consumption.

[0272] "Production area" refers to the region or facility where agricultural products are cultivated and harvested.

[0273] A "sales base" refers to a place where agricultural products are distributed and sold so that they reach consumers.

[0274] A "mechanism for automatic movement" refers to a system that transports agricultural products from one location to another without requiring human intervention.

[0275] "Identification information" refers to data that includes details such as the type of agricultural product, its origin, and its destination, enabling tracking throughout the distribution process.

[0276] "Past trade information" refers to sales and transportation performance data, which is used to create demand forecasts and logistics plans.

[0277] "Trend data" refers to information that shows the state of public activity, such as people's movement and purchasing behavior.

[0278] "Functions for demand forecasting" refer to technologies that use collected data to predict future demand for agricultural products.

[0279] "A function to adjust shipment quantities" refers to technology that optimizes the amount of agricultural products delivered according to market demand.

[0280] "Communication equipment" is a general term for hardware and software used to send and receive information, and is used for relaying data.

[0281] "Real time" refers to a period of time when information is processed and used, which is imperceptible to human senses. It is also known as real-time.

[0282] To implement this invention, first, the server plays a central role. The server collects crop data obtained from crop production areas, transaction data from sales bases, and data on market trends, and analyzes this data. Based on the collected data, demand forecasting is performed, and the results serve as a basis for optimizing the transportation schedule and route. The server utilizes a cloud computing platform and database software (e.g., AWS, PostgreSQL) for data analysis.

[0283] The terminal is placed at the production site. It receives instructions from the server and performs automatic packaging of crops and generation of identification labels. An automatic packaging machine is connected to the terminal, and packaging is carried out based on the instructions. The identification information is printed by a label printer and affixed to the crops.

[0284] The user, as a logistics manager, utilizes a smartphone or tablet device to check the real-time inventory status, transportation schedule, and route information provided by the server. Based on this information, manual adjustments can be made if necessary. A specific application (e.g., React Native app) is installed on the smartphone, enabling intuitive operation.

[0285] As a specific example, when the supply of tomatoes suddenly increases at a specific sales base, the server immediately updates the demand forecast and reflects it in the next delivery plan. The user can use a smartphone to check this information and consider a prompt to the generative AI model, such as "Should the shipment volume of tomatoes to Direct Sales Store A be increased?"

[0286] Examples of prompt texts include "With the increasing demand for tomatoes at Direct Sales Store A, how should the shipment volume be adjusted for the next delivery?" Based on this prompt, the AI model is expected to propose an appropriate shipment volume and transportation route.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server receives agricultural data collected at production sites, transaction data from sales outlets, and data on people's behavior. This data is stored in a cloud database. The server then uses data mining algorithms to extract recognition patterns from this data. As an output, it generates a rich dataset necessary for demand forecasting.

[0290] Step 2:

[0291] The server utilizes the data obtained in Step 1 to perform demand forecasting using a predictive model. The input data includes historical sales information and data on people's movements. By using machine learning algorithms (e.g., regression analysis, time series analysis), it performs short-term and medium-to-long-term demand projection and outputs the optimal transportation schedule and route.

[0292] Step 3:

[0293] The terminal receives the transport schedule and route information instructed by the server. Based on this data, the automated packaging machine starts packing the produce. A label printer prints identification information and attaches it to each individual produce unit. This prepares each produce unit for transport.

[0294] Step 4:

[0295] Users receive real-time information from the server using their smartphones and check the displayed inventory status and shipping schedule. The smartphone application visually displays the information, allowing users to easily respond to changes in demand. If necessary, users can manually adjust shipping quantities. Feedback is provided to the user as output, and they decide on their next action based on that feedback.

[0296] Step 5:

[0297] When a user recognizes an increase in demand at a specific sales location, they use a generative AI model to create a prompt. For example, "Demand for tomatoes at direct sales location A has surged. How should we adjust shipment quantities?" This prompt becomes input to the AI ​​model, which then recommends the optimal shipping strategy. As a result, users can take quick and effective countermeasures.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention is a system aimed at the efficient distribution of agricultural products, and it improves the accuracy of demand forecasting by incorporating an emotion engine. This system is realized by integrating the emotion engine around a server, terminal, and user.

[0300] The server is responsible for collecting and analyzing agricultural product inventory and sales data. This data includes real-time foot traffic information, historical consumption trends, and consumer sentiment data. The sentiment engine analyzes consumer emotional states at specific times and events, generating demand forecasts that take into account emotional fluctuations in purchasing intent.

[0301] The terminal is installed at the producer's facility and operates an automated packaging machine and applies the necessary labels to the produce based on shipping instructions sent from the server. These labels also include sales forecast information based on sentiment engine analysis. For example, the terminal can reflect seasonal promotional information on the labels.

[0302] The user plays a role in managing the unmanned vehicles that execute automatic transportation and the logistics plan. The unmanned vehicles transport agricultural products according to a plan adjusted by the emotion engine. For example, when the server detects a high expectation of consumers for a certain season, the user tries to enhance the logistics for that period.

[0303] With this system, it becomes possible to adjust supply and demand based on consumers' emotions. Producers can ensure appropriate shipments, and consumers can smoothly receive products that match their emotions. This realizes maximizing sales at the sales outlets while minimizing waste of agricultural products.

[0304] The following explains the processing flow.

[0305] Step 1:

[0306] The server collects inventory information of agricultural products and available shipment quantity data from producers. These data are automatically sent from the terminal to the server.

[0307] Step 2:

[0308] The server executes a demand prediction model based on past sales data of the sales outlets and footfall data obtained in real time. At this time, the emotion engine is used to analyze consumers' emotion data and reflect it in the demand prediction.

[0309] Step 3:

[0310] The terminal receives a shipment instruction from the server and starts packing the agricultural products by operating the automatic packing machine. Sales promotion information considering consumers' emotions is printed on the label by the labeling system.

[0311] Step 4:

[0312] Users activate unmanned vehicles based on a transport schedule generated by the server. The unmanned vehicles then proceed to transport goods to areas with high demand, based on analysis results from an emotion engine.

[0313] Step 5:

[0314] The server continuously monitors transaction data from sales outlets and changes in consumer sentiment to identify new consumption trends. Based on this, it adjusts future shipping plans and volumes.

[0315] Step 6:

[0316] Users receive update information from the server, coordinate the entire logistics system, and reconfigure the routes of unmanned vehicles to ensure efficient delivery of agricultural products.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] The current agricultural product distribution system suffers from low-accuracy demand forecasting and difficulty in flexibly adjusting supply and demand based on consumer sentiment and events. This results in problems such as excess inventory and stockouts, leading to lost sales opportunities and wasted agricultural products. To address these challenges, highly accurate demand forecasting utilizing consumer sentiment data and efficient distribution planning based on that forecasting are needed.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes a device for automatically transporting agricultural products from production sites to sales sites, a device for packaging agricultural products and printing identification information on them, a device for analyzing past sales history and movement information and performing demand forecasting incorporating consumer sentiment data, and a device for adjusting the transportation plan according to consumers' expectations for seasonal events. This enables supply and demand adjustment that takes into account changes in consumer sentiment, thereby maximizing sales opportunities and reducing agricultural product waste.

[0322] "Agricultural products" refers to plant-based products cultivated or harvested at production sites, and includes goods distributed for sale.

[0323] A "production base" refers to a place where agricultural products are cultivated or harvested, and includes farms, greenhouses, and other similar facilities.

[0324] A "sales outlet" refers to a place where agricultural products are provided to the final consumer, and includes supermarkets and markets.

[0325] "Automated transportation equipment" refers to machines or mechanisms that transport agricultural products from production sites to sales sites without human intervention, and includes unmanned transport aircraft and drones.

[0326] "Identification information" refers to data indicating the type of agricultural product, shipping date, sales promotion information, etc., and includes information printed on labels and tags.

[0327] "Consumer sentiment data" refers to information that shows emotional trends that influence consumers' purchasing intentions and preferences, and includes data obtained from social media and survey results.

[0328] "Demand forecasting" refers to the analysis of predicting the consumption of agricultural products over a specific period, and is performed using historical data and sentiment data.

[0329] "Transportation planning" refers to a plan for efficiently distributing agricultural products, including adjusting distribution routes and transport volumes.

[0330] This invention is a system for improving the efficiency of agricultural product distribution, in which a server, terminals, and users each play their respective roles. The server plays a central role in collecting and analyzing agricultural product inventory and sales data. Specifically, the server uses a "data stream management tool" for data stream management and a "relational database" for data storage. The server also uses natural language processing technology to analyze sentiment data in order to evaluate consumer sentiment. This analysis uses data collected from social media to extract consumer sentiment related to specific times or events.

[0331] The terminal is installed at the production site and, upon receiving instructions for shipping agricultural products, operates an automated packaging machine using a "control device." This control device automates the packaging process, and a label printer prints the necessary identification information on the agricultural products. This information also includes promotional information related to consumer sentiment based on demand forecasts.

[0332] Users manage logistics plans and execute efficient deliveries using unmanned transport vehicles. They utilize geographic information systems to determine optimal delivery routes, supporting seamless logistics operations. This enables flexible responses to fluctuating demand and allows for the delivery of goods to consumers.

[0333] For example, if sentiment analysis predicts that demand for a particular crop will increase with the arrival of spring, the server will create a forecast based on that data. The terminal will print this information as a label, and the user will adjust the delivery schedule. This entire process ensures that crops are delivered to consumers at the appropriate time, avoiding excess inventory and stockouts.

[0334] As an example of a prompt, the AI ​​model is used in the form of, "Please propose a data analysis method for constructing a delivery plan based on demand trends for agricultural products in the next season." In this way, the present invention realizes market responses that take into account the emotional state of consumers and enables efficient distribution of agricultural products.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The server collects agricultural product inventory data, sales history, and real-time pedestrian flow information. It receives inventory information from production sites, sales information from markets, and pedestrian flow data from sensor devices as input. Using this data, it organizes it with a data stream management tool and stores it in a relational database. Specifically, it periodically retrieves data from each data source via APIs and writes it to the database.

[0338] Step 2:

[0339] The server analyzes consumer sentiment data based on collected data. It obtains consumer posts collected from social media as input. Using natural language processing techniques, it analyzes these posts and extracts the consumer's emotional state. This analysis generates sentiment data, such as consumers' interests and feelings towards events. Specifically, it implements a text analysis algorithm to scan consumer posts.

[0340] Step 3:

[0341] The server uses sentiment data to forecast demand. Inputs include past sales history, current inventory data, and consumer sentiment data. A machine learning model is applied to create demand forecasts that reflect consumer sentiment and past trends. This forecast is generated and output as basic data for distribution planning. Specific operations include data analysis and execution of forecasting algorithms using Python.

[0342] Step 4:

[0343] The terminal receives shipping instructions based on demand forecasts, packs agricultural products, and prints labels. It receives shipping instruction data and forecast-based label information from a server as input. The control unit operates the automated packaging machine and prints labels, including identification information, using a label printer. Specifically, the control unit manages the packaging process and transmits label information to the printer.

[0344] Step 5:

[0345] The user adjusts the transportation plan based on logistics data from the server. It receives generated demand forecast data and label information as input, and uses a geographic information system to calculate the optimal delivery route. It then schedules the unmanned transport aircraft and puts the delivery plan into action. Specifically, it utilizes a web dashboard to determine routes in real time and send commands to the transport aircraft.

[0346] Step 6:

[0347] The user delivers agricultural products to consumers using an unmanned transport vehicle (SDV) based on a planned route. The inputs include an optimized delivery route and time schedule. The SUV uses IoT sensors and GPS to ensure accurate delivery, bringing the agricultural products to consumers. Specifically, the SUV executes an autonomous driving algorithm, navigating safely to designated delivery points.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] In today's distribution industry, accurately predicting and appropriately responding to fluctuations in demand based on consumer sentiment presents a significant challenge. Furthermore, inefficient inventory management at logistics centers contributes to considerable time and effort. This creates a situation where oversupply leads to excess inventory, while shortages result in lost sales opportunities.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] In this invention, the server includes a device for automatically transporting agricultural products from suppliers to sales locations, a device for analyzing consumer sentiment data, and a device for displaying inventory information on workers' visual devices. This makes it possible to analyze consumers' emotional states in real time and make accurate demand forecasts, enabling efficient inventory management at logistics centers.

[0353] "Agricultural products" refer to goods and materials related to agriculture, including agricultural products and their processed products.

[0354] "Supplier" refers to an individual or legal entity that produces or supplies agricultural products.

[0355] A "sales outlet" is a physical or digital location for selling agricultural products to consumers.

[0356] "Automatic transport devices" are machines or systems that have the function of moving agricultural products to designated locations without human intervention.

[0357] "Identification information" refers to data and information attached to agricultural products that indicates the type and characteristics of the product.

[0358] "Consumer sentiment data" refers to information that quantifies or classifies the emotional state of consumers at a specific time or event.

[0359] "Visual devices" are devices that allow people to receive information visually, and include smart glasses and displays.

[0360] "Inventory information" refers to data on the quantity and types of agricultural products at logistics centers.

[0361] A "computational method" refers to a technique or algorithm used for data analysis and prediction.

[0362] This invention is a system for optimizing the distribution of agricultural products, and in particular, improves efficiency by incorporating sentiment data into demand forecasting. Embodiments of this system are described below.

[0363] The server comprehensively collects and analyzes agricultural product inventory information, consumer sentiment data, and historical sales and foot traffic data. Sentiment data is obtained from consumers' emotional states, particularly during specific periods or events that influence consumer behavior. This sentiment data is collected in real time, for example, using an API, and sent to a sentiment analysis engine. The analysis engine then generates demand forecasts based on all the collected data.

[0364] Regarding terminals, smart glasses will be provided to workers in the logistics center to display inventory information. These smart glasses will receive real-time data transmitted from a server and provide instructions to optimize picking and packing procedures. This will allow for prioritizing the handling of products with high demand based on collected sentiment data.

[0365] The user's role is to automatically transport agricultural products using unmanned transport vehicles (AGVs). Here, demand forecasts based on sentiment data analysis received from a server are used to optimize transportation plans. For example, the process from picking to delivery is adjusted to meet rising demand, ensuring fast and efficient distribution.

[0366] For example, if high demand for a particular fruit is predicted for Christmas, the smart glasses will instruct workers to prioritize picking that fruit. Users can also develop rapid fruit delivery plans to smoothly respond to fluctuations in demand.

[0367] An example of a prompt for a generative AI model is: "Use the emotion engine to analyze when demand for a particular product will increase based on current consumer sentiment and predicted demand, and devise a way to display this on the smart glasses' display."

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The server retrieves inventory information for agricultural products from suppliers and sales outlets into a database and accesses a sentiment analysis engine. Inputs include inventory information, sentiment data, and historical sales data. Based on this data, the server performs data integration and preprocessing to create a unified dataset. The output is a refined dataset.

[0371] Step 2:

[0372] The server analyzes consumer sentiment data in real time through an emotion analysis engine and predicts demand fluctuations caused by specific seasons or events. In this process, the input is the dataset created in the previous step, and the output is the demand forecast result from the demand forecasting model.

[0373] Step 3:

[0374] The terminal delivers demand forecast results sent from the server to the smart glasses. The input is the demand forecast results from the server, and the terminal generates visual data based on this. The output appears as picking instructions displayed on the smart glasses. Specific actions include prioritizing high-demand items and providing visual feedback to the worker.

[0375] Step 4:

[0376] The user creates a transportation plan for operating an automated guided vehicle (AGV). Inputs include demand forecast data from a server and inventory information from each location. Based on this information, the user uses an algorithm to create an optimal transportation schedule. The output is a plan for efficient delivery routes and timings. Specific actions include determining the departure times and routes of the transport vehicles.

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0388] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] This invention relates to a system for efficiently transporting and selling agricultural products, and specific embodiments thereof are described below.

[0394] The system primarily consists of a server, terminals, and users. The server plays a central role, collecting and analyzing data on agricultural products received from producers, sales data from distribution centers, and human traffic data. Based on these results, it forecasts demand and creates optimal transportation schedules and routes. Terminals are installed at each producer's facility and can automatically pack and label agricultural products based on instructions from the server. For example, a terminal can trigger an automated packing machine, and the generated label will include shipping information.

[0395] The user is responsible for monitoring and managing the automated transport. They operate the unmanned vehicles and execute transport tasks according to a schedule from the server. The unmanned vehicles collect agricultural products along pre-programmed routes and transport them to sales outlets. For example, when a user starts an unmanned vehicle heading to sales outlet A, the truck will visit producer B to load tomatoes and then proceed to sales outlet A.

[0396] These program processes allow the server to monitor transaction data from sales outlets in real time and update logistics plans in preparation for future demand fluctuations. This enables effective identification of best-selling agricultural products and highly accurate demand forecasting. For example, if the server detects that tomato sales at direct sales outlet B are strong, it will reflect this in the next delivery plan and increase the tomato shipment volume.

[0397] This system reduces the burden on producers and minimizes crop waste, thereby supporting more sustainable agricultural management.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The server collects data from producers regarding crop inventory and production capacity. This information is configured to be automatically sent from the terminal to the server.

[0401] Step 2:

[0402] The server analyzes historical sales data and real-time foot traffic data from sales locations and runs a demand forecasting algorithm. Based on the results of this analysis, it generates the optimal transportation schedule and route.

[0403] Step 3:

[0404] The server transmits instructions for the next shipment to each producer via their terminal. These instructions include which crops to ship and in what quantities.

[0405] Step 4:

[0406] The terminal operates an automated packaging machine to package and label agricultural products according to shipping instructions from the server. The labels contain shipping destination information and product identification information.

[0407] Step 5:

[0408] The user manages the operation of unmanned vehicles that perform automated transportation. The unmanned vehicles follow routes planned on the server, transporting goods from producers to designated sales locations.

[0409] Step 6:

[0410] The server monitors sales trends at distribution centers in real time and adjusts the next shipment plan based on sales performance. If necessary, it instructs the distribution center to restock or to transfer surplus goods to the wholesale market.

[0411] (Example 1)

[0412] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0413] In recent years, the agricultural sector has been required to develop effective logistics systems that meet production efficiency and market needs. However, conventional systems have not sufficiently improved the accuracy of agricultural product sales forecasts and automated transportation, resulting in problems such as increased waste of agricultural products and increased burden on producers. Solving this problem is a challenge.

[0414] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0415] In this invention, the server includes means for an information processing device to collect and analyze data on agricultural products provided from production sites, means for the information processing device to integrate sales information and aggregated data from sales bases and execute an algorithm for forecasting demand, and means for the information processing device to generate an optimal transportation plan and route based on the demand forecast. This makes it possible to reduce the burden on producers and reduce waste of agricultural products.

[0416] An "information processing device" refers to a computer and software used to collect and analyze data on agricultural products.

[0417] "Agricultural products" refer to products cultivated and produced by farmland and producers.

[0418] A "sales base" refers to a place where agricultural products are offered to the market and purchased by consumers.

[0419] "Sales information" refers to detailed data regarding the transaction of a product, including elements such as sales quantity and price.

[0420] "Aggregated data" refers to quantitative data related to the flow of people and products, and is a set of data analyzed for demand forecasting.

[0421] "Demand forecasting" refers to the process of predicting future consumption trends based on past and present data.

[0422] An "algorithm" refers to a set of processing steps designed for a specific computation or problem-solving task.

[0423] "Transportation planning" refers to the schedule and methods for efficiently moving agricultural products from production sites to sales locations.

[0424] "Route" refers to the physical path that agricultural products take when they are moved.

[0425] An "unmanned transport device" refers to a transport device that operates automatically without a driver on board.

[0426] A "generative AI model" refers to a machine learning system that learns patterns from data and makes predictions.

[0427] This invention is a system for the efficient transportation and sale of agricultural products. The system mainly consists of a server, terminals, and users.

[0428] The server functions as an information processing unit, collecting data on agricultural products provided from production sites and storing it in a database. The server then integrates sales information and aggregated data from each sales base to perform demand forecasting. To do this, it utilizes generative AI models and algorithms based on historical and current sales data. Using this data, the server predicts demand for agricultural products and generates optimal transportation plans and routes.

[0429] The terminals are deployed at each production site and automatically package agricultural products and add identification information based on instructions from the server. In this process, by linking the automated packaging machine with a label printer, it is possible to quickly display and print the information necessary for shipping.

[0430] Users operate unmanned transport vehicles and monitor and manage the transport of agricultural products according to the transport schedule and plan provided by the server. Users use terminals to check the vehicle's current location and operational status in real time and intervene as needed to ensure smooth transport.

[0431] For example, if a user prompts the server with a request such as, "Please suggest how to optimize the next tomato shipment schedule," the server will use a generative AI model to appropriately adjust the shipment plan, ensuring that tomatoes are delivered in the optimal quantity to the sales locations with the highest demand. This system reduces the burden on producers and minimizes waste of agricultural products, leading to more sustainable agricultural management.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The server retrieves agricultural product data from production sites. Specifically, it receives data provided by producers and registers it in a database. Inputs include the type, quantity, and quality of agricultural products, which are then stored in the database. The output is a structured data set that is used for subsequent processing.

[0435] Step 2:

[0436] The server integrates sales information and aggregated data from sales locations to perform demand forecasting. This process uses sales information (sales volume, revenue data) and pedestrian flow data (consumer movement data) as inputs. A generative AI model is used to analyze this data and forecast demand. The output is the demand forecast result, which is used to develop an optimal transportation plan.

[0437] Step 3:

[0438] The server generates optimal transportation plans and routes based on demand forecasts. The inputs are demand forecast results and geographical information. The server uses an algorithm to calculate the most efficient delivery routes and schedules. The output is transportation schedule and route information for automated transport devices.

[0439] Step 4:

[0440] The terminal receives instructions from the server to automatically package agricultural products and generate identification information. It receives product data (number of items shipped, label information) from the server as input and drives the automatic packaging machine. It controls the label printer to affix labels with shipping information to the products. The output is packaged agricultural products ready for shipment.

[0441] Step 5:

[0442] The user operates an automated transport system, monitoring and managing the transportation of agricultural products according to the transport schedule. Inputs include transport plans and real-time location information provided by a server. Specific actions include checking the transport status on a terminal and adjusting the route as needed. The output is the safe delivery of agricultural products to sales locations.

[0443] Step 6:

[0444] The server monitors real-time transaction information and updates the logistics plan. It uses transaction data from sales locations (sales, inventory changes) as input. The server analyzes this data and updates the logistics plan if demand fluctuations are anticipated. The output is the updated logistics plan, which will be reflected in the next delivery.

[0445] (Application Example 1)

[0446] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] There is a need to effectively adjust transportation plans in response to fluctuations in agricultural demand, reducing the burden on producers and sales outlets while minimizing inventory surpluses and shortages. However, conventional systems struggle with real-time demand forecasting and transportation schedule adjustments, resulting in cumbersome and inefficient operations.

[0448] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0449] This invention includes a server that displays the inventory status, transportation plans, and route information of agricultural products in real time via a communication device, and a means for supporting the administrator in efficiently controlling the system; a function for analyzing past trade information and trend data to forecast demand; and a function for adjusting the quantity shipped to sales bases. This enables more efficient inventory management and transportation scheduling of agricultural products.

[0450] "Agricultural products" are plant-based food products produced in a specific region and transported for sale and consumption.

[0451] "Production area" refers to the region or facility where agricultural products are cultivated and harvested.

[0452] A "sales base" refers to a place where agricultural products are distributed and sold so that they reach consumers.

[0453] A "mechanism for automatic movement" refers to a system that transports agricultural products from one location to another without requiring human intervention.

[0454] "Identification information" refers to data that includes details such as the type of agricultural product, its origin, and its destination, enabling tracking throughout the distribution process.

[0455] "Past trade information" refers to sales and transportation performance data, which is used to create demand forecasts and logistics plans.

[0456] "Trend data" refers to information that shows the state of public activity, such as people's movement and purchasing behavior.

[0457] "Functions for demand forecasting" refer to technologies that use collected data to predict future demand for agricultural products.

[0458] "A function to adjust shipment quantities" refers to technology that optimizes the amount of agricultural products delivered according to market demand.

[0459] "Communication equipment" is a general term for hardware and software used to send and receive information, and is used for relaying data.

[0460] "Real time" refers to a period of time when information is processed and used, which is imperceptible to human senses. It is also known as real-time.

[0461] To implement this invention, a server plays a central role. The server collects and analyzes agricultural data obtained from production areas, transaction data from sales outlets, and market trend data. Based on the collected data, demand forecasts are made, and the results serve as a basis for optimizing transportation schedules and routes. The server utilizes a cloud computing platform and database software (e.g., AWS, PostgreSQL) for data analysis.

[0462] The terminals are deployed at the production site. They receive instructions from a server and perform automated packaging of agricultural products and generate identification labels. An automated packaging machine is connected to the terminal, and packaging is carried out according to the instructions. Identification information is printed by a label printer and affixed to the agricultural products.

[0463] Users, acting as logistics managers, utilize smartphones and tablets to check real-time inventory status, shipping schedules, and route information provided by the server. Based on this information, they make manual adjustments as needed. Specific applications (e.g., React Native apps) are installed on the smartphones, enabling intuitive operation.

[0464] For example, if tomato supply surges at a particular sales location, the server immediately updates the demand forecast and incorporates it into the next delivery plan. The user can then use their smartphone to check this information and formulate a prompt for the generative AI model, such as "Should we increase the amount of tomatoes shipped to direct sales outlet A?"

[0465] An example of a prompt would be, "Given the increased demand for tomatoes at direct sales outlet A, how should we adjust the next shipment volume?" Based on this prompt, the AI ​​model is expected to suggest appropriate shipment volumes and transportation routes.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The server receives agricultural data collected at production sites, transaction data from sales outlets, and data on people's behavior. This data is stored in a cloud database. The server then uses data mining algorithms to extract recognition patterns from this data. As an output, it generates a rich dataset necessary for demand forecasting.

[0469] Step 2:

[0470] The server utilizes the data obtained in Step 1 to perform demand forecasting using a predictive model. The input data includes historical sales information and data on people's movements. By using machine learning algorithms (e.g., regression analysis, time series analysis), it performs short-term and medium-to-long-term demand projection and outputs the optimal transportation schedule and route.

[0471] Step 3:

[0472] The terminal receives the transport schedule and route information instructed by the server. Based on this data, the automated packaging machine starts packing the produce. A label printer prints identification information and attaches it to each individual produce unit. This prepares each produce unit for transport.

[0473] Step 4:

[0474] Users receive real-time information from the server using their smartphones and check the displayed inventory status and shipping schedule. The smartphone application visually displays the information, allowing users to easily respond to changes in demand. If necessary, users can manually adjust shipping quantities. Feedback is provided to the user as output, and they decide on their next action based on that feedback.

[0475] Step 5:

[0476] When a user recognizes an increase in demand at a specific sales location, they use a generative AI model to create a prompt. For example, "Demand for tomatoes at direct sales location A has surged. How should we adjust shipment quantities?" This prompt becomes input to the AI ​​model, which then recommends the optimal shipping strategy. As a result, users can take quick and effective countermeasures.

[0477] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0478] This invention is a system aimed at the efficient distribution of agricultural products, and it improves the accuracy of demand forecasting by incorporating an emotion engine. This system is realized by integrating the emotion engine around a server, terminal, and user.

[0479] The server is responsible for collecting and analyzing agricultural product inventory and sales data. This data includes real-time foot traffic information, historical consumption trends, and consumer sentiment data. The sentiment engine analyzes consumer emotional states at specific times and events, generating demand forecasts that take into account emotional fluctuations in purchasing intent.

[0480] The terminal is installed at the producer's facility and operates an automated packaging machine and applies the necessary labels to the produce based on shipping instructions sent from the server. These labels also include sales forecast information based on sentiment engine analysis. For example, the terminal can reflect seasonal promotional information on the labels.

[0481] The user takes on the role of managing the unmanned vehicles that perform automated transportation and the logistics plan. The unmanned vehicles transport agricultural products according to a plan adjusted by an emotion engine. For example, if the server detects high consumer expectations for a particular season, the user will increase logistics for that period.

[0482] This system enables supply and demand adjustments based on consumer sentiment, allowing producers to ensure appropriate shipments while consumers smoothly receive products that match their needs. This maximizes sales at retail outlets while minimizing agricultural waste.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The server collects inventory information and shipping capacity data for agricultural products from producers. This data is automatically transmitted from the terminals to the server.

[0486] Step 2:

[0487] The server runs a demand forecasting model based on historical sales data from the sales location and real-time pedestrian flow data. During this process, it uses an emotion engine to analyze consumer sentiment data and incorporates it into the demand forecast.

[0488] Step 3:

[0489] The terminal receives shipping instructions from the server and activates the automated packaging machine to begin packaging the agricultural products. A labeling system prints sales promotion information on the labels, taking consumer sentiment into consideration.

[0490] Step 4:

[0491] Users activate unmanned vehicles based on a transport schedule generated by the server. The unmanned vehicles then proceed to transport goods to areas with high demand, based on analysis results from an emotion engine.

[0492] Step 5:

[0493] The server continuously monitors transaction data from sales outlets and changes in consumer sentiment to identify new consumption trends. Based on this, it adjusts future shipping plans and volumes.

[0494] Step 6:

[0495] Users receive update information from the server, coordinate the entire logistics system, and reconfigure the routes of unmanned vehicles to ensure efficient delivery of agricultural products.

[0496] (Example 2)

[0497] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] The current agricultural product distribution system suffers from low-accuracy demand forecasting and difficulty in flexibly adjusting supply and demand based on consumer sentiment and events. This results in problems such as excess inventory and stockouts, leading to lost sales opportunities and wasted agricultural products. To address these challenges, highly accurate demand forecasting utilizing consumer sentiment data and efficient distribution planning based on that forecasting are needed.

[0499] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0500] In this invention, the server includes a device for automatically transporting agricultural products from production sites to sales sites, a device for packaging agricultural products and printing identification information on them, a device for analyzing past sales history and movement information and performing demand forecasting incorporating consumer sentiment data, and a device for adjusting the transportation plan according to consumers' expectations for seasonal events. This enables supply and demand adjustment that takes into account changes in consumer sentiment, thereby maximizing sales opportunities and reducing agricultural product waste.

[0501] "Agricultural products" refers to plant-based products cultivated or harvested at production sites, and includes goods distributed for sale.

[0502] A "production base" refers to a place where agricultural products are cultivated or harvested, and includes farms, greenhouses, and other similar facilities.

[0503] A "sales outlet" refers to a place where agricultural products are provided to the final consumer, and includes supermarkets and markets.

[0504] "Automated transportation equipment" refers to machines or mechanisms that transport agricultural products from production sites to sales sites without human intervention, and includes unmanned transport aircraft and drones.

[0505] "Identification information" refers to data indicating the type of agricultural product, shipping date, sales promotion information, etc., and includes information printed on labels and tags.

[0506] "Consumer sentiment data" refers to information that shows emotional trends that influence consumers' purchasing intentions and preferences, and includes data obtained from social media and survey results.

[0507] "Demand forecasting" refers to the analysis of predicting the consumption of agricultural products over a specific period, and is performed using historical data and sentiment data.

[0508] "Transportation planning" refers to a plan for efficiently distributing agricultural products, including adjusting distribution routes and transport volumes.

[0509] This invention is a system for improving the efficiency of agricultural product distribution, in which a server, terminals, and users each play their respective roles. The server plays a central role in collecting and analyzing agricultural product inventory and sales data. Specifically, the server uses a "data stream management tool" for data stream management and a "relational database" for data storage. The server also uses natural language processing technology to analyze sentiment data in order to evaluate consumer sentiment. This analysis uses data collected from social media to extract consumer sentiment related to specific times or events.

[0510] The terminal is installed at the production site and, upon receiving instructions for shipping agricultural products, operates an automated packaging machine using a "control device." This control device automates the packaging process, and a label printer prints the necessary identification information on the agricultural products. This information also includes promotional information related to consumer sentiment based on demand forecasts.

[0511] Users manage logistics plans and execute efficient deliveries using unmanned transport vehicles. They utilize geographic information systems to determine optimal delivery routes, supporting seamless logistics operations. This enables flexible responses to fluctuating demand and allows for the delivery of goods to consumers.

[0512] For example, if sentiment analysis predicts that demand for a particular crop will increase with the arrival of spring, the server will create a forecast based on that data. The terminal will print this information as a label, and the user will adjust the delivery schedule. This entire process ensures that crops are delivered to consumers at the appropriate time, avoiding excess inventory and stockouts.

[0513] As an example of a prompt, the AI ​​model is used in the form of, "Please propose a data analysis method for constructing a delivery plan based on demand trends for agricultural products in the next season." In this way, the present invention realizes market responses that take into account the emotional state of consumers and enables efficient distribution of agricultural products.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server collects agricultural product inventory data, sales history, and real-time pedestrian flow information. It receives inventory information from production sites, sales information from markets, and pedestrian flow data from sensor devices as input. Using this data, it organizes it with a data stream management tool and stores it in a relational database. Specifically, it periodically retrieves data from each data source via APIs and writes it to the database.

[0517] Step 2:

[0518] The server analyzes consumer sentiment data based on collected data. It obtains consumer posts collected from social media as input. Using natural language processing techniques, it analyzes these posts and extracts the consumer's emotional state. This analysis generates sentiment data, such as consumers' interests and feelings towards events. Specifically, it implements a text analysis algorithm to scan consumer posts.

[0519] Step 3:

[0520] The server uses sentiment data to forecast demand. Inputs include past sales history, current inventory data, and consumer sentiment data. A machine learning model is applied to create demand forecasts that reflect consumer sentiment and past trends. This forecast is generated and output as basic data for distribution planning. Specific operations include data analysis and execution of forecasting algorithms using Python.

[0521] Step 4:

[0522] The terminal receives shipping instructions based on demand forecasts, packs agricultural products, and prints labels. It receives shipping instruction data and forecast-based label information from a server as input. The control unit operates the automated packaging machine and prints labels, including identification information, using a label printer. Specifically, the control unit manages the packaging process and transmits label information to the printer.

[0523] Step 5:

[0524] The user adjusts the transportation plan based on logistics data from the server. It receives generated demand forecast data and label information as input, and uses a geographic information system to calculate the optimal delivery route. It then schedules the unmanned transport aircraft and puts the delivery plan into action. Specifically, it utilizes a web dashboard to determine routes in real time and send commands to the transport aircraft.

[0525] Step 6:

[0526] The user delivers agricultural products to consumers using an unmanned transport vehicle (SDV) based on a planned route. The inputs include an optimized delivery route and time schedule. The SUV uses IoT sensors and GPS to ensure accurate delivery, bringing the agricultural products to consumers. Specifically, the SUV executes an autonomous driving algorithm, navigating safely to designated delivery points.

[0527] (Application Example 2)

[0528] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] In today's distribution industry, accurately predicting and appropriately responding to fluctuations in demand based on consumer sentiment presents a significant challenge. Furthermore, inefficient inventory management at logistics centers contributes to considerable time and effort. This creates a situation where oversupply leads to excess inventory, while shortages result in lost sales opportunities.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0531] In this invention, the server includes a device for automatically transporting agricultural products from suppliers to sales locations, a device for analyzing consumer sentiment data, and a device for displaying inventory information on workers' visual devices. This makes it possible to analyze consumers' emotional states in real time and make accurate demand forecasts, enabling efficient inventory management at logistics centers.

[0532] "Agricultural products" refer to goods and materials related to agriculture, including agricultural products and their processed products.

[0533] "Supplier" refers to an individual or legal entity that produces or supplies agricultural products.

[0534] A "sales outlet" is a physical or digital location for selling agricultural products to consumers.

[0535] "Automatic transport devices" are machines or systems that have the function of moving agricultural products to designated locations without human intervention.

[0536] "Identification information" refers to data and information attached to agricultural products that indicates the type and characteristics of the product.

[0537] "Consumer sentiment data" refers to information that quantifies or classifies the emotional state of consumers at a specific time or event.

[0538] "Visual devices" are devices that allow people to receive information visually, and include smart glasses and displays.

[0539] "Inventory information" refers to data on the quantity and types of agricultural products at logistics centers.

[0540] A "computational method" refers to a technique or algorithm used for data analysis and prediction.

[0541] This invention is a system for optimizing the distribution of agricultural products, and in particular, improves efficiency by incorporating sentiment data into demand forecasting. Embodiments of this system are described below.

[0542] The server comprehensively collects and analyzes agricultural product inventory information, consumer sentiment data, and historical sales and foot traffic data. Sentiment data is obtained from consumers' emotional states, particularly during specific periods or events that influence consumer behavior. This sentiment data is collected in real time, for example, using an API, and sent to a sentiment analysis engine. The analysis engine then generates demand forecasts based on all the collected data.

[0543] Regarding terminals, smart glasses will be provided to workers in the logistics center to display inventory information. These smart glasses will receive real-time data transmitted from a server and provide instructions to optimize picking and packing procedures. This will allow for prioritizing the handling of products with high demand based on collected sentiment data.

[0544] The user's role is to automatically transport agricultural products using unmanned transport vehicles (AGVs). Here, demand forecasts based on sentiment data analysis received from a server are used to optimize transportation plans. For example, the process from picking to delivery is adjusted to meet rising demand, ensuring fast and efficient distribution.

[0545] For example, if high demand for a particular fruit is predicted for Christmas, the smart glasses will instruct workers to prioritize picking that fruit. Users can also develop rapid fruit delivery plans to smoothly respond to fluctuations in demand.

[0546] An example of a prompt for a generative AI model is: "Use the emotion engine to analyze when demand for a particular product will increase based on current consumer sentiment and predicted demand, and devise a way to display this on the smart glasses' display."

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The server retrieves inventory information for agricultural products from suppliers and sales outlets into a database and accesses a sentiment analysis engine. Inputs include inventory information, sentiment data, and historical sales data. Based on this data, the server performs data integration and preprocessing to create a unified dataset. The output is a refined dataset.

[0550] Step 2:

[0551] The server analyzes consumer sentiment data in real time through an emotion analysis engine and predicts demand fluctuations caused by specific seasons or events. In this process, the input is the dataset created in the previous step, and the output is the demand forecast result from the demand forecasting model.

[0552] Step 3:

[0553] The terminal delivers demand forecast results sent from the server to the smart glasses. The input is the demand forecast results from the server, and the terminal generates visual data based on this. The output appears as picking instructions displayed on the smart glasses. Specific actions include prioritizing high-demand items and providing visual feedback to the worker.

[0554] Step 4:

[0555] The user creates a transportation plan for operating an automated guided vehicle (AGV). Inputs include demand forecast data from a server and inventory information from each location. Based on this information, the user uses an algorithm to create an optimal transportation schedule. The output is a plan for efficient delivery routes and timings. Specific actions include determining the departure times and routes of the transport vehicles.

[0556] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0557] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0558] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0562] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0563] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0564] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0565] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0566] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0567] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0568] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0569] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0570] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0571] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0572] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0573] This invention relates to a system for efficiently transporting and selling agricultural products, and specific embodiments thereof are described below.

[0574] The system primarily consists of a server, terminals, and users. The server plays a central role, collecting and analyzing data on agricultural products received from producers, sales data from distribution centers, and human traffic data. Based on these results, it forecasts demand and creates optimal transportation schedules and routes. Terminals are installed at each producer's facility and can automatically pack and label agricultural products based on instructions from the server. For example, a terminal can trigger an automated packing machine, and the generated label will include shipping information.

[0575] The user is responsible for monitoring and managing the automated transport. They operate the unmanned vehicles and execute transport tasks according to a schedule from the server. The unmanned vehicles collect agricultural products along pre-programmed routes and transport them to sales outlets. For example, when a user starts an unmanned vehicle heading to sales outlet A, the truck will visit producer B to load tomatoes and then proceed to sales outlet A.

[0576] These program processes allow the server to monitor transaction data from sales outlets in real time and update logistics plans in preparation for future demand fluctuations. This enables effective identification of best-selling agricultural products and highly accurate demand forecasting. For example, if the server detects that tomato sales at direct sales outlet B are strong, it will reflect this in the next delivery plan and increase the tomato shipment volume.

[0577] This system reduces the burden on producers and minimizes crop waste, thereby supporting more sustainable agricultural management.

[0578] The following describes the processing flow.

[0579] Step 1:

[0580] The server collects data from producers regarding crop inventory and production capacity. This information is configured to be automatically sent from the terminal to the server.

[0581] Step 2:

[0582] The server analyzes historical sales data and real-time foot traffic data from sales locations and runs a demand forecasting algorithm. Based on the results of this analysis, it generates the optimal transportation schedule and route.

[0583] Step 3:

[0584] The server transmits instructions for the next shipment to each producer via their terminal. These instructions include which crops to ship and in what quantities.

[0585] Step 4:

[0586] The terminal operates an automated packaging machine to package and label agricultural products according to shipping instructions from the server. The labels contain shipping destination information and product identification information.

[0587] Step 5:

[0588] The user manages the operation of unmanned vehicles that perform automated transportation. The unmanned vehicles follow routes planned on the server, transporting goods from producers to designated sales locations.

[0589] Step 6:

[0590] The server monitors sales trends at distribution centers in real time and adjusts the next shipment plan based on sales performance. If necessary, it instructs the distribution center to restock or to transfer surplus goods to the wholesale market.

[0591] (Example 1)

[0592] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0593] In recent years, the agricultural sector has been required to develop effective logistics systems that meet production efficiency and market needs. However, conventional systems have not sufficiently improved the accuracy of agricultural product sales forecasts and automated transportation, resulting in problems such as increased waste of agricultural products and increased burden on producers. Solving this problem is a challenge.

[0594] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0595] In this invention, the server includes means for an information processing device to collect and analyze data on agricultural products provided from production sites, means for the information processing device to integrate sales information and aggregated data from sales bases and execute an algorithm for forecasting demand, and means for the information processing device to generate an optimal transportation plan and route based on the demand forecast. This makes it possible to reduce the burden on producers and reduce waste of agricultural products.

[0596] An "information processing device" refers to a computer and software used to collect and analyze data on agricultural products.

[0597] "Agricultural products" refer to products cultivated and produced by farmland and producers.

[0598] A "sales base" refers to a place where agricultural products are offered to the market and purchased by consumers.

[0599] "Sales information" refers to detailed data regarding the transaction of a product, including elements such as sales quantity and price.

[0600] "Aggregated data" refers to quantitative data related to the flow of people and products, and is a set of data analyzed for demand forecasting.

[0601] "Demand forecasting" refers to the process of predicting future consumption trends based on past and present data.

[0602] An "algorithm" refers to a set of processing steps designed for a specific computation or problem-solving task.

[0603] "Transportation planning" refers to the schedule and methods for efficiently moving agricultural products from production sites to sales locations.

[0604] "Route" refers to the physical path that agricultural products take when they are moved.

[0605] An "unmanned transport device" refers to a transport device that operates automatically without a driver on board.

[0606] A "generative AI model" refers to a machine learning system that learns patterns from data and makes predictions.

[0607] This invention is a system for the efficient transportation and sale of agricultural products. The system mainly consists of a server, terminals, and users.

[0608] The server functions as an information processing unit, collecting data on agricultural products provided from production sites and storing it in a database. The server then integrates sales information and aggregated data from each sales base to perform demand forecasting. To do this, it utilizes generative AI models and algorithms based on historical and current sales data. Using this data, the server predicts demand for agricultural products and generates optimal transportation plans and routes.

[0609] The terminals are deployed at each production site and automatically package agricultural products and add identification information based on instructions from the server. In this process, by linking the automated packaging machine with a label printer, it is possible to quickly display and print the information necessary for shipping.

[0610] Users operate unmanned transport vehicles and monitor and manage the transport of agricultural products according to the transport schedule and plan provided by the server. Users use terminals to check the vehicle's current location and operational status in real time and intervene as needed to ensure smooth transport.

[0611] For example, if a user prompts the server with a request such as, "Please suggest how to optimize the next tomato shipment schedule," the server will use a generative AI model to appropriately adjust the shipment plan, ensuring that tomatoes are delivered in the optimal quantity to the sales locations with the highest demand. This system reduces the burden on producers and minimizes waste of agricultural products, leading to more sustainable agricultural management.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The server retrieves agricultural product data from production sites. Specifically, it receives data provided by producers and registers it in a database. Inputs include the type, quantity, and quality of agricultural products, which are then stored in the database. The output is a structured data set that is used for subsequent processing.

[0615] Step 2:

[0616] The server integrates sales information and aggregated data from sales locations to perform demand forecasting. This process uses sales information (sales volume, revenue data) and pedestrian flow data (consumer movement data) as inputs. A generative AI model is used to analyze this data and forecast demand. The output is the demand forecast result, which is used to develop an optimal transportation plan.

[0617] Step 3:

[0618] The server generates optimal transportation plans and routes based on demand forecasts. The inputs are demand forecast results and geographical information. The server uses an algorithm to calculate the most efficient delivery routes and schedules. The output is transportation schedule and route information for automated transport devices.

[0619] Step 4:

[0620] The terminal receives instructions from the server to automatically package agricultural products and generate identification information. It receives product data (number of items shipped, label information) from the server as input and drives the automatic packaging machine. It controls the label printer to affix labels with shipping information to the products. The output is packaged agricultural products ready for shipment.

[0621] Step 5:

[0622] The user operates an automated transport system, monitoring and managing the transportation of agricultural products according to the transport schedule. Inputs include transport plans and real-time location information provided by a server. Specific actions include checking the transport status on a terminal and adjusting the route as needed. The output is the safe delivery of agricultural products to sales locations.

[0623] Step 6:

[0624] The server monitors real-time transaction information and updates the logistics plan. It uses transaction data from sales locations (sales, inventory changes) as input. The server analyzes this data and updates the logistics plan if demand fluctuations are anticipated. The output is the updated logistics plan, which will be reflected in the next delivery.

[0625] (Application Example 1)

[0626] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] There is a need to effectively adjust transportation plans in response to fluctuations in agricultural demand, reducing the burden on producers and sales outlets while minimizing inventory surpluses and shortages. However, conventional systems struggle with real-time demand forecasting and transportation schedule adjustments, resulting in cumbersome and inefficient operations.

[0628] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0629] This invention includes a server that displays the inventory status, transportation plans, and route information of agricultural products in real time via a communication device, and a means for supporting the administrator in efficiently controlling the system; a function for analyzing past trade information and trend data to forecast demand; and a function for adjusting the quantity shipped to sales bases. This enables more efficient inventory management and transportation scheduling of agricultural products.

[0630] "Agricultural products" are plant-based food products produced in a specific region and transported for sale and consumption.

[0631] "Production area" refers to the region or facility where agricultural products are cultivated and harvested.

[0632] A "sales base" refers to a place where agricultural products are distributed and sold so that they reach consumers.

[0633] A "mechanism for automatic movement" refers to a system that transports agricultural products from one location to another without requiring human intervention.

[0634] "Identification information" refers to data that includes details such as the type of agricultural product, its origin, and its destination, enabling tracking throughout the distribution process.

[0635] "Past trade information" refers to sales and transportation performance data, which is used to create demand forecasts and logistics plans.

[0636] "Trend data" refers to information that shows the state of public activity, such as people's movement and purchasing behavior.

[0637] "Functions for demand forecasting" refer to technologies that use collected data to predict future demand for agricultural products.

[0638] "A function to adjust shipment quantities" refers to technology that optimizes the amount of agricultural products delivered according to market demand.

[0639] "Communication equipment" is a general term for hardware and software used to send and receive information, and is used for relaying data.

[0640] "Real time" refers to a period of time when information is processed and used, which is imperceptible to human senses. It is also known as real-time.

[0641] To implement this invention, a server plays a central role. The server collects and analyzes agricultural data obtained from production areas, transaction data from sales outlets, and market trend data. Based on the collected data, demand forecasts are made, and the results serve as a basis for optimizing transportation schedules and routes. The server utilizes a cloud computing platform and database software (e.g., AWS, PostgreSQL) for data analysis.

[0642] The terminals are deployed at the production site. They receive instructions from a server and perform automated packaging of agricultural products and generate identification labels. An automated packaging machine is connected to the terminal, and packaging is carried out according to the instructions. Identification information is printed by a label printer and affixed to the agricultural products.

[0643] Users, acting as logistics managers, utilize smartphones and tablets to check real-time inventory status, shipping schedules, and route information provided by the server. Based on this information, they make manual adjustments as needed. Specific applications (e.g., React Native apps) are installed on the smartphones, enabling intuitive operation.

[0644] For example, if tomato supply surges at a particular sales location, the server immediately updates the demand forecast and incorporates it into the next delivery plan. The user can then use their smartphone to check this information and formulate a prompt for the generative AI model, such as "Should we increase the amount of tomatoes shipped to direct sales outlet A?"

[0645] An example of a prompt would be, "Given the increased demand for tomatoes at direct sales outlet A, how should we adjust the next shipment volume?" Based on this prompt, the AI ​​model is expected to suggest appropriate shipment volumes and transportation routes.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The server receives agricultural data collected at production sites, transaction data from sales outlets, and data on people's behavior. This data is stored in a cloud database. The server then uses data mining algorithms to extract recognition patterns from this data. As an output, it generates a rich dataset necessary for demand forecasting.

[0649] Step 2:

[0650] The server utilizes the data obtained in Step 1 to perform demand forecasting using a predictive model. The input data includes historical sales information and data on people's movements. By using machine learning algorithms (e.g., regression analysis, time series analysis), it performs short-term and medium-to-long-term demand projection and outputs the optimal transportation schedule and route.

[0651] Step 3:

[0652] The terminal receives the transport schedule and route information instructed by the server. Based on this data, the automated packaging machine starts packing the produce. A label printer prints identification information and attaches it to each individual produce unit. This prepares each produce unit for transport.

[0653] Step 4:

[0654] Users receive real-time information from the server using their smartphones and check the displayed inventory status and shipping schedule. The smartphone application visually displays the information, allowing users to easily respond to changes in demand. If necessary, users can manually adjust shipping quantities. Feedback is provided to the user as output, and they decide on their next action based on that feedback.

[0655] Step 5:

[0656] When a user recognizes an increase in demand at a specific sales location, they use a generative AI model to create a prompt. For example, "Demand for tomatoes at direct sales location A has surged. How should we adjust shipment quantities?" This prompt becomes input to the AI ​​model, which then recommends the optimal shipping strategy. As a result, users can take quick and effective countermeasures.

[0657] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0658] This invention is a system aimed at the efficient distribution of agricultural products, and it improves the accuracy of demand forecasting by incorporating an emotion engine. This system is realized by integrating the emotion engine around a server, terminal, and user.

[0659] The server is responsible for collecting and analyzing agricultural product inventory and sales data. This data includes real-time foot traffic information, historical consumption trends, and consumer sentiment data. The sentiment engine analyzes consumer emotional states at specific times and events, generating demand forecasts that take into account emotional fluctuations in purchasing intent.

[0660] The terminal is installed at the producer's facility and operates an automated packaging machine and applies the necessary labels to the produce based on shipping instructions sent from the server. These labels also include sales forecast information based on sentiment engine analysis. For example, the terminal can reflect seasonal promotional information on the labels.

[0661] The user takes on the role of managing the unmanned vehicles that perform automated transportation and the logistics plan. The unmanned vehicles transport agricultural products according to a plan adjusted by an emotion engine. For example, if the server detects high consumer expectations for a particular season, the user will increase logistics for that period.

[0662] This system enables supply and demand adjustments based on consumer sentiment, allowing producers to ensure appropriate shipments while consumers smoothly receive products that match their needs. This maximizes sales at retail outlets while minimizing agricultural waste.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The server collects inventory information and shipping capacity data for agricultural products from producers. This data is automatically transmitted from the terminals to the server.

[0666] Step 2:

[0667] The server runs a demand forecasting model based on historical sales data from the sales location and real-time pedestrian flow data. During this process, it uses an emotion engine to analyze consumer sentiment data and incorporates it into the demand forecast.

[0668] Step 3:

[0669] The terminal receives shipping instructions from the server and activates the automated packaging machine to begin packaging the agricultural products. A labeling system prints sales promotion information on the labels, taking consumer sentiment into consideration.

[0670] Step 4:

[0671] Users activate unmanned vehicles based on a transport schedule generated by the server. The unmanned vehicles then proceed to transport goods to areas with high demand, based on analysis results from an emotion engine.

[0672] Step 5:

[0673] The server continuously monitors transaction data from sales outlets and changes in consumer sentiment to identify new consumption trends. Based on this, it adjusts future shipping plans and volumes.

[0674] Step 6:

[0675] Users receive update information from the server, coordinate the entire logistics system, and reconfigure the routes of unmanned vehicles to ensure efficient delivery of agricultural products.

[0676] (Example 2)

[0677] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] The current agricultural product distribution system suffers from low-accuracy demand forecasting and difficulty in flexibly adjusting supply and demand based on consumer sentiment and events. This results in problems such as excess inventory and stockouts, leading to lost sales opportunities and wasted agricultural products. To address these challenges, highly accurate demand forecasting utilizing consumer sentiment data and efficient distribution planning based on that forecasting are needed.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes a device for automatically transporting agricultural products from production sites to sales sites, a device for packaging agricultural products and printing identification information on them, a device for analyzing past sales history and movement information and performing demand forecasting incorporating consumer sentiment data, and a device for adjusting the transportation plan according to consumers' expectations for seasonal events. This enables supply and demand adjustment that takes into account changes in consumer sentiment, thereby maximizing sales opportunities and reducing agricultural product waste.

[0681] "Agricultural products" refers to plant-based products cultivated or harvested at production sites, and includes goods distributed for sale.

[0682] A "production base" refers to a place where agricultural products are cultivated or harvested, and includes farms, greenhouses, and other similar facilities.

[0683] A "sales outlet" refers to a place where agricultural products are provided to the final consumer, and includes supermarkets and markets.

[0684] "Automated transportation equipment" refers to machines or mechanisms that transport agricultural products from production sites to sales sites without human intervention, and includes unmanned transport aircraft and drones.

[0685] "Identification information" refers to data indicating the type of agricultural product, shipping date, sales promotion information, etc., and includes information printed on labels and tags.

[0686] "Consumer sentiment data" refers to information that shows emotional trends that influence consumers' purchasing intentions and preferences, and includes data obtained from social media and survey results.

[0687] "Demand forecasting" refers to the analysis of predicting the consumption of agricultural products over a specific period, and is performed using historical data and sentiment data.

[0688] "Transportation planning" refers to a plan for efficiently distributing agricultural products, including adjusting distribution routes and transport volumes.

[0689] This invention is a system for improving the efficiency of agricultural product distribution, in which a server, terminals, and users each play their respective roles. The server plays a central role in collecting and analyzing agricultural product inventory and sales data. Specifically, the server uses a "data stream management tool" for data stream management and a "relational database" for data storage. The server also uses natural language processing technology to analyze sentiment data in order to evaluate consumer sentiment. This analysis uses data collected from social media to extract consumer sentiment related to specific times or events.

[0690] The terminal is installed at the production site and, upon receiving instructions for shipping agricultural products, operates an automated packaging machine using a "control device." This control device automates the packaging process, and a label printer prints the necessary identification information on the agricultural products. This information also includes promotional information related to consumer sentiment based on demand forecasts.

[0691] Users manage logistics plans and execute efficient deliveries using unmanned transport vehicles. They utilize geographic information systems to determine optimal delivery routes, supporting seamless logistics operations. This enables flexible responses to fluctuating demand and allows for the delivery of goods to consumers.

[0692] For example, if sentiment analysis predicts that demand for a particular crop will increase with the arrival of spring, the server will create a forecast based on that data. The terminal will print this information as a label, and the user will adjust the delivery schedule. This entire process ensures that crops are delivered to consumers at the appropriate time, avoiding excess inventory and stockouts.

[0693] As an example of a prompt, the AI ​​model is used in the form of, "Please propose a data analysis method for constructing a delivery plan based on demand trends for agricultural products in the next season." In this way, the present invention realizes market responses that take into account the emotional state of consumers and enables efficient distribution of agricultural products.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] The server collects agricultural product inventory data, sales history, and real-time pedestrian flow information. It receives inventory information from production sites, sales information from markets, and pedestrian flow data from sensor devices as input. Using this data, it organizes it with a data stream management tool and stores it in a relational database. Specifically, it periodically retrieves data from each data source via APIs and writes it to the database.

[0697] Step 2:

[0698] The server analyzes consumer sentiment data based on collected data. It obtains consumer posts collected from social media as input. Using natural language processing techniques, it analyzes these posts and extracts the consumer's emotional state. This analysis generates sentiment data, such as consumers' interests and feelings towards events. Specifically, it implements a text analysis algorithm to scan consumer posts.

[0699] Step 3:

[0700] The server uses sentiment data to forecast demand. Inputs include past sales history, current inventory data, and consumer sentiment data. A machine learning model is applied to create demand forecasts that reflect consumer sentiment and past trends. This forecast is generated and output as basic data for distribution planning. Specific operations include data analysis and execution of forecasting algorithms using Python.

[0701] Step 4:

[0702] The terminal receives shipping instructions based on demand forecasts, packs agricultural products, and prints labels. It receives shipping instruction data and forecast-based label information from a server as input. The control unit operates the automated packaging machine and prints labels, including identification information, using a label printer. Specifically, the control unit manages the packaging process and transmits label information to the printer.

[0703] Step 5:

[0704] The user adjusts the transportation plan based on logistics data from the server. It receives generated demand forecast data and label information as input, and uses a geographic information system to calculate the optimal delivery route. It then schedules the unmanned transport aircraft and puts the delivery plan into action. Specifically, it utilizes a web dashboard to determine routes in real time and send commands to the transport aircraft.

[0705] Step 6:

[0706] The user delivers agricultural products to consumers using an unmanned transport vehicle (SDV) based on a planned route. The inputs include an optimized delivery route and time schedule. The SUV uses IoT sensors and GPS to ensure accurate delivery, bringing the agricultural products to consumers. Specifically, the SUV executes an autonomous driving algorithm, navigating safely to designated delivery points.

[0707] (Application Example 2)

[0708] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In today's distribution industry, accurately predicting and appropriately responding to fluctuations in demand based on consumer sentiment presents a significant challenge. Furthermore, inefficient inventory management at logistics centers contributes to considerable time and effort. This creates a situation where oversupply leads to excess inventory, while shortages result in lost sales opportunities.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0711] In this invention, the server includes a device for automatically transporting agricultural products from suppliers to sales locations, a device for analyzing consumer sentiment data, and a device for displaying inventory information on workers' visual devices. This makes it possible to analyze consumers' emotional states in real time and make accurate demand forecasts, enabling efficient inventory management at logistics centers.

[0712] "Agricultural products" refer to goods and materials related to agriculture, including agricultural products and their processed products.

[0713] "Supplier" refers to an individual or legal entity that produces or supplies agricultural products.

[0714] A "sales outlet" is a physical or digital location for selling agricultural products to consumers.

[0715] "Automatic transport devices" are machines or systems that have the function of moving agricultural products to designated locations without human intervention.

[0716] "Identification information" refers to data and information attached to agricultural products that indicates the type and characteristics of the product.

[0717] "Consumer sentiment data" refers to information that quantifies or classifies the emotional state of consumers at a specific time or event.

[0718] "Visual devices" are devices that allow people to receive information visually, and include smart glasses and displays.

[0719] "Inventory information" refers to data on the quantity and types of agricultural products at logistics centers.

[0720] A "computational method" refers to a technique or algorithm used for data analysis and prediction.

[0721] This invention is a system for optimizing the distribution of agricultural products, and in particular, improves efficiency by incorporating sentiment data into demand forecasting. Embodiments of this system are described below.

[0722] The server comprehensively collects and analyzes agricultural product inventory information, consumer sentiment data, and historical sales and foot traffic data. Sentiment data is obtained from consumers' emotional states, particularly during specific periods or events that influence consumer behavior. This sentiment data is collected in real time, for example, using an API, and sent to a sentiment analysis engine. The analysis engine then generates demand forecasts based on all the collected data.

[0723] Regarding terminals, smart glasses will be provided to workers in the logistics center to display inventory information. These smart glasses will receive real-time data transmitted from a server and provide instructions to optimize picking and packing procedures. This will allow for prioritizing the handling of products with high demand based on collected sentiment data.

[0724] The user's role is to automatically transport agricultural products using unmanned transport vehicles (AGVs). Here, demand forecasts based on sentiment data analysis received from a server are used to optimize transportation plans. For example, the process from picking to delivery is adjusted to meet rising demand, ensuring fast and efficient distribution.

[0725] For example, if high demand for a particular fruit is predicted for Christmas, the smart glasses will instruct workers to prioritize picking that fruit. Users can also develop rapid fruit delivery plans to smoothly respond to fluctuations in demand.

[0726] An example of a prompt for a generative AI model is: "Use the emotion engine to analyze when demand for a particular product will increase based on current consumer sentiment and predicted demand, and devise a way to display this on the smart glasses' display."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The server retrieves inventory information for agricultural products from suppliers and sales outlets into a database and accesses a sentiment analysis engine. Inputs include inventory information, sentiment data, and historical sales data. Based on this data, the server performs data integration and preprocessing to create a unified dataset. The output is a refined dataset.

[0730] Step 2:

[0731] The server analyzes consumer sentiment data in real time through an emotion analysis engine and predicts demand fluctuations caused by specific seasons or events. In this process, the input is the dataset created in the previous step, and the output is the demand forecast result from the demand forecasting model.

[0732] Step 3:

[0733] The terminal delivers demand forecast results sent from the server to the smart glasses. The input is the demand forecast results from the server, and the terminal generates visual data based on this. The output appears as picking instructions displayed on the smart glasses. Specific actions include prioritizing high-demand items and providing visual feedback to the worker.

[0734] Step 4:

[0735] The user creates a transportation plan for operating an automated guided vehicle (AGV). Inputs include demand forecast data from a server and inventory information from each location. Based on this information, the user uses an algorithm to create an optimal transportation schedule. The output is a plan for efficient delivery routes and timings. Specific actions include determining the departure times and routes of the transport vehicles.

[0736] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0737] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0738] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0739] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0740] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0741] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0742] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0743] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0744] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0745] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0746] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0747] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0748] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0749] 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.

[0750] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0751] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0752] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0753] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0754] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0755] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0756] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0757] The following is further disclosed regarding the embodiments described above.

[0758] (Claim 1)

[0759] A means of automatically transporting agricultural products from producers to sales outlets,

[0760] A means for packaging agricultural products and attaching identification information,

[0761] A means of analyzing flow data and making demand forecasts,

[0762] Means to adjust the amount of goods transported to sales outlets and markets as needed,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, wherein the means for automatically transporting is an unmanned vehicle.

[0766] (Claim 3)

[0767] The system according to claim 1, wherein the means for performing the demand forecast uses an algorithm based on past sales data and pedestrian flow data.

[0768] "Example 1"

[0769] (Claim 1)

[0770] The information processing device includes means for collecting and analyzing data on agricultural products provided from production sites,

[0771] The information processing device includes means for integrating sales information and aggregated data from sales bases and executing an algorithm for forecasting demand,

[0772] The information processing device includes means for generating an optimal transportation plan and route based on demand forecasts,

[0773] The device includes means for automating transportation preparation and attaching identification information to the product,

[0774] A means for an administrator to monitor and control automated transportation using unmanned transport devices,

[0775] The information processing device provides a means for monitoring transaction information in real time and updating logistics plans,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, wherein the unmanned transport device transports agricultural products according to a pre-set route.

[0779] (Claim 3)

[0780] The system according to claim 1, wherein the algorithm for performing the demand forecast uses a generative AI model based on past sales information and aggregated data.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A mechanism for automatically moving agricultural products from production sites to sales locations,

[0784] Functions for packaging agricultural products and attaching identification information,

[0785] It has a function to analyze past trade information and trend data to forecast demand,

[0786] A function to adjust the quantity shipped to sales outlets,

[0787] A means to display the inventory status of agricultural products, movement plans, and route information in real time via a communication device, and to support administrators in efficiently controlling the system.

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, which uses an unmanned vehicle to move agricultural products.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the function for performing the demand forecast uses a calculation method based on past transaction information and individual movement data.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A device for automatically transporting agricultural products from production sites to sales sites,

[0796] A device for packaging agricultural products and printing identification information,

[0797] A device for analyzing past sales history and movement information, and for performing demand forecasting that incorporates consumer sentiment data,

[0798] A device for adjusting transportation plans in accordance with consumers' expectations for seasonal events,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the device for automatically transporting is an unmanned transport aircraft.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the device for performing the demand forecast uses a machine learning model and a natural language processing model.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A device for automatically transporting agricultural products from suppliers to sales outlets,

[0807] A device for packaging agricultural products and attaching identification information,

[0808] A device for analyzing flow data and making demand forecasts,

[0809] A device for analyzing consumer sentiment data,

[0810] A device for dynamically adjusting transportation plans based on emotion analysis data,

[0811] A device for displaying inventory information on a worker's visual device,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the device for automatically transporting is an unmanned transporter.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the device for performing the demand forecast uses a calculation method based on past sales information and movement data. [Explanation of Symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically transporting agricultural products from producers to sales outlets, A means for packaging agricultural products and attaching identification information, A means of analyzing flow data and making demand forecasts, Means to adjust the amount of goods transported to sales outlets and markets as needed, A system that includes this.

2. The system according to claim 1, wherein the means for automatically transporting is an unmanned vehicle.

3. The system according to claim 1, wherein the means for performing the demand forecast uses an algorithm based on past sales data and pedestrian flow data.

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