System

Abstract

A system is provided.SOLUTION: A system, comprising: a demand forecasting unit, a real-time traffic information acquisition unit, an optimal distribution route calculation unit, an environmentally-friendly transportation suggestion unit, a renewable energy vehicle allocation unit, a carbon footprint calculation unit, and a carbon offset reporting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The modern logistics industry must respond to the rapid advancement of onlineization and eco-consciousness, requiring efficient delivery systems with a low environmental impact. However, conventional logistics methods do not adequately address efficient inventory management, real-time demand forecasting, or optimal route calculations that take traffic information into account. As a result, problems such as excess inventory, out-of-stock items, delivery delays, and increased environmental impact due to traffic congestion have arisen. Furthermore, it is difficult to propose eco-friendly transportation methods or implement carbon offsets, and efforts to protect the environment are insufficient. A system that can solve these issues is needed. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. This system constantly monitors inventory levels and predicts demand to prevent excess inventory and out-of-stock. It also calculates optimal delivery routes using real-time traffic information to achieve efficient deliveries. Furthermore, it recommends the use of vehicles that utilize renewable energy and proposes eco-friendly transportation methods to minimize the burden on the environment. After delivery is completed, the carbon footprint is calculated and reported to a carbon offset program, clarifying the contribution to environmental protection. This enables efficient and eco-friendly logistics and solves several challenges facing the logistics industry.

[0006] A "demand forecasting means" is a method or device for analyzing past data and current market trends to forecast future demand.

[0007] The "real-time traffic information acquisition means" refers to a method or device for acquiring current traffic conditions in real time.

[0008] The "optimal delivery route calculation means" is a method or device for calculating the optimal delivery route taking into account traffic information and other factors.

[0009] An "eco-friendly transportation proposal means" is a method or device for proposing a transportation method that minimizes the environmental impact.

[0010] A "renewable energy vehicle allocation means" is a method or device for using vehicles that utilize renewable energy for deliveries.

[0011] A "carbon footprint calculation tool" is a method or device for calculating the carbon dioxide emissions associated with a delivery.

[0012] A "carbon offset reporting instrument" is a method or device for reporting efforts to offset carbon dioxide emissions.

[0013] "Means for monitoring inventory levels in real time" refers to a method or device for instantly monitoring current inventory status.

[0014] A "demand forecasting means for preventing excess inventory and shortages" is a method or device for managing inventory so as to prevent excess or shortages by forecasting demand.

[0015] A "means for reporting the status of a renewable energy vehicle" is a method or device for reporting the status of a vehicle that utilizes renewable energy.

[0016] A "means for enabling customers to select eco-friendly delivery options" is a method or device for providing customers with the ability to select environmentally friendly delivery options.

[0017] "Means for receiving and storing customer-generated order data" refers to a method or device for receiving and recording data on orders placed by customers. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Specific embodiments for implementing this invention will be described.

[0040] Program processing overview

[0041] 1. User places an order

[0042] Users can access the online store, select products, and select eco-friendly delivery options when placing an order. This order data is sent to the server and recorded.

[0043] 2. The server receives the order

[0044] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0045] 3. The server checks the stock level

[0046] The server retrieves real-time inventory data from the inventory sensors and ensures that the ordered items are in sufficient stock, and if necessary, issues an alert if there is insufficient stock.

[0047] 4. The server obtains traffic information

[0048] The server receives real-time traffic information via an external API, which is used to calculate the optimal delivery route.

[0049] 5. The server calculates the optimal delivery route

[0050] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0051] 6. The server assigns a delivery vehicle

[0052] The server checks available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected.

[0053] 7. The device checks the optimal route and executes the delivery

[0054] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0055] 8. The server calculates and reports the carbon footprint

[0056] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[0057] Specific examples

[0058] Next, a specific example of a delivery scenario will be described.

[0059] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[0060] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0061] 3. The server obtains real-time traffic information via an external API.

[0062] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0063] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0064] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0065] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0066] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[0070] Step 2:

[0071] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0072] Step 3:

[0073] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[0074] Step 4:

[0075] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[0076] Step 5:

[0077] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0078] Step 6:

[0079] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[0080] Step 7:

[0081] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0082] Step 8:

[0083] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[0084] Step 9:

[0085] Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[0086] This is the specific process flow of the smart delivery management system, which will realize efficient logistics and environmental protection.

[0087] Example 1

[0088] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0089] The logistics industry is in high demand for eco-friendly and efficient deliveries, but traditional systems face challenges in planning optimal delivery routes based on real-time information and effectively utilizing renewable energy vehicles. In addition, inventory management and carbon footprint reporting are often done manually, resulting in low accuracy and efficiency.

[0090] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0091] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, and a carbon footprint calculation means based on delivery distance and vehicle type. This enables eco-friendly and efficient delivery planning, and makes it possible to automate inventory management and carbon footprint reporting while reducing the burden on the environment.

[0092] A "demand forecasting means" is a means for predicting future demand for a product by analyzing past sales data and market trends.

[0093] "Means for obtaining real-time traffic information" refers to a means for obtaining the latest traffic conditions using external information services or APIs.

[0094] The "optimal delivery route calculation means" is a means for calculating the most efficient delivery route based on traffic information and inventory status.

[0095] The "eco-friendly transportation proposal method" is a method for proposing environmentally friendly transportation methods and calculating renewable energy vehicles and efficient delivery routes with priority.

[0096] The "renewable energy vehicle allocation means" is a means for checking available renewable energy vehicles and allocating them to deliveries on a priority basis.

[0097] "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions during delivery.

[0098] "Carbon Offset Reporting Instrument" means the instrument used to report a calculated carbon footprint to a carbon offset program.

[0099] The "inventory data acquisition means" is a means for acquiring inventory information in real time from an inventory sensor or the like.

[0100] The "means for calculating a delivery route based on traffic information" is a means for calculating an optimal delivery route based on the acquired traffic information.

[0101] A "means for allocating delivery vehicles" is a means for identifying available delivery vehicles and allocating them to a particular delivery task.

[0102] The "means for calculating carbon footprint based on delivery distance and vehicle type" is a means for calculating an accurate carbon footprint based on delivery distance and the type of vehicle used.

[0103] "Means for allowing users to select eco-friendly delivery options" refers to means for allowing users to select eco-friendly delivery options at online stores.

[0104] A "demand forecasting method using a generative AI model" is a method for forecasting future demand for a product using a generative AI model.

[0105] "Means for generating prompt sentences" refers to means for creating input prompt sentences for the generative AI model.

[0106] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, a carbon footprint calculation means based on delivery distance and vehicle type, a demand forecasting means using a generative AI model, and a prompt sentence generation means.

[0107] System Program

[0108] A user orders products from an online store. When placing an order, the user can select an eco-friendly delivery option. The order data is sent to the server and recorded.

[0109] The server receives the order data sent by the user and stores it in a database. It then obtains real-time inventory data from the inventory sensor to check whether the ordered item is in sufficient stock. If there is a shortage of stock, it issues a warning.

[0110] Get real-time traffic information via external APIs (e.g., Google Maps API or HERE API). Calculate the optimal delivery route based on the traffic data. Prioritize eco-friendly transportation methods and use renewable energy vehicles when calculating routes.

[0111] The server checks the list of available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected. The delivery staff's terminal (device) receives the optimal route information sent from the server and displays it to the delivery staff.

[0112] The delivery staff carries out the delivery based on the route information displayed on the terminal. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to the carbon offset program.

[0113] Specific hardware and software

[0114] The hardware used includes inventory sensors, renewable energy vehicles, and terminal devices for transportation staff, while the software used includes Google Maps API, HERE API, a real-time inventory management system, and carbon footprint calculation algorithms.

[0115] Specific examples

[0116] A specific delivery scenario is shown below.

[0117] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[0118] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0119] 3. The server obtains real-time traffic information via an external API.

[0120] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0121] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0122] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0123] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0124] Prompt Sentence Examples

[0125] Below are some examples of prompts to input to the generative AI model.

[0126] ---

[0127] "Please explain the key functions of your smart delivery management system step by step. Include detailed steps for order processing, inventory check, traffic information acquisition, optimal delivery route calculation, eco-friendly transportation, and carbon footprint calculation."

[0128] ---

[0129] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] User places an order

[0133] A user visits an online store, selects a product, and adds it to their cart. They then proceed to the order screen and select eco-friendly shipping as a shipping option. The selected information is sent as input to the server, which receives this information and stores the order data (product ID, quantity, eco-friendly shipping flag, etc.).

[0134] Step 2:

[0135] The server receives the order

[0136] The server receives order data from the user and records it in the database. At this time, it also saves the order ID, order date and time, and user information (such as delivery address). Based on the order data received as input, it creates a new record in the database as output.

[0137] Step 3:

[0138] The server checks the stock level

[0139] The server gets real-time inventory data from the inventory sensor. The inventory sensor periodically updates the stock count for each item. It receives the inventory data as input and checks whether there is enough stock of the ordered item. It outputs the inventory status and generates a warning message if there is a shortage.

[0140] Step 4:

[0141] The server obtains traffic information

[0142] The server calls external APIs (e.g., Google Maps API or HERE API) to obtain real-time traffic information. It sends API requests as input and receives traffic conditions (traffic volume, delay information, etc.) as output. Based on this data, it organizes the information necessary for delivery planning.

[0143] Step 5:

[0144] The server calculates the optimal delivery route

[0145] The server combines the acquired traffic information and inventory data to calculate the optimal delivery route. The route calculation applies an algorithm that prioritizes eco-friendly transportation methods. Traffic information and inventory data are used as input, and optimal delivery route information (route path, distance, time, etc.) is generated as output.

[0146] Step 6:

[0147] The server assigns delivery vehicles

[0148] The server checks the list of available delivery vehicles, giving preference to renewable energy vehicles if an eco-friendly delivery option is selected. It uses the available vehicle data as input and assigns the best vehicle information for the specified route as output.

[0149] Step 7:

[0150] The device checks the optimal route and executes the delivery

[0151] The terminal (the delivery staff's device) receives the optimal route information sent from the server. It acquires the optimal route information as input and displays it to the delivery staff through the navigation app. The delivery staff follows these instructions to carry out the delivery and records the progress along the way on the terminal.

[0152] Step 8:

[0153] The server calculates and reports the carbon footprint

[0154] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the vehicle type used. It takes delivery performance data (distance, vehicle type, etc.) as input and produces the calculated carbon footprint as output, which can be reported to a carbon offset program.

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Conventional delivery systems do not incorporate measures to select eco-friendly delivery methods, which results in a high environmental impact and the inability to provide optimal delivery routes that take traffic information into account. Additionally, the process of calculating and reporting carbon footprints is done manually, which is inefficient.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes a demand forecasting means, a real-time traffic information obtaining means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for calculating and reporting a carbon footprint, a means for calculating a carbon footprint based on a delivery distance and a vehicle type used, a means for a user to place an order and transmit and record order data, a means for monitoring inventory levels in real time, a demand forecasting means for preventing overstocking and stockouts, a means for reporting the status of renewable energy vehicles, a means for obtaining real-time traffic information and calculating an optimal delivery route, a means for offering eco-friendly delivery options and allocating renewable energy vehicles, a means for allowing customers to select eco-friendly delivery options, a means for receiving and storing order data generated by customers, and a means for displaying optimal route information on a device of a delivery staff member and causing the delivery to be executed, thereby realizing an efficient and eco-friendly delivery system.

[0160] 1. "Demand forecasting means" refers to a means of analyzing customer order trends and forecasting future demand.

[0161] 2. "Means for obtaining real-time traffic information" means a means for obtaining current traffic conditions in real time.

[0162] 3. "Optimal delivery route calculation means" is a means for calculating the optimal delivery route based on traffic information and inventory data.

[0163] 4. "Eco-friendly transport proposals" are proposals for delivery methods that minimize the impact on the environment.

[0164] 5. "Renewable energy vehicle allocation measure" means a measure that gives priority to the allocation of vehicles that use renewable energy for deliveries.

[0165] 6. "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions from shipping.

[0166] 7. "Carbon Offset Reporting Instrument" means a reporting instrument to a carbon offset program based on a calculated carbon footprint.

[0167] 8. "Means for monitoring inventory levels in real time" means means for monitoring inventory status in real time.

[0168] 9. "Demand forecasting measures to prevent excess inventory and shortages" are measures to prevent excess inventory and shortages by accurately forecasting demand.

[0169] 10. "Means for reporting the status of renewable energy vehicles" means means for reporting the operating status and charging state of renewable energy vehicles.

[0170] 11. "Means for customers to select eco-friendly delivery options" means means for customers to select delivery options that are environmentally friendly.

[0171] 12. "Means for receiving and storing customer-generated order data" means means for receiving order information entered by a customer and storing it in a database.

[0172] 13. "Means for displaying optimal route information on a delivery staff member's device and executing delivery" means means for displaying an optimal route on a delivery staff member's device and executing delivery according to that route.

[0173] This invention is a smart food delivery management system that combines AI and IoT technology. The specific configuration and operation of the system are described in detail below.

[0174] The server receives and stores order data when a customer places an order. The order data includes the customer's selected products and the option to select eco-friendly delivery options. This order data is then compared with inventory data, which is updated in real time, making it easy to check stock levels.

[0175] The server then retrieves real-time traffic information via external APIs such as the Google Maps API. This information is used to calculate the optimal delivery route. The server calculates the optimal delivery route based on traffic information and inventory data, and prioritizes eco-friendly delivery methods.

[0176] Once the delivery route is determined, the server prioritizes the allocation of vehicles that use renewable energy. To do this, the server monitors the operating status and charging status of renewable energy vehicles in real time and selects the most appropriate vehicle.

[0177] The optimal route information sent from the server is displayed on the delivery staff's terminal, allowing them to deliver efficiently by following the optimal route. Once the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to the carbon offset program.

[0178] The server also calculates and reports the environmental benefit gained from using renewable energy vehicles, which is used to demonstrate how much the delivery company is contributing to environmental protection.

[0179] Specific examples

[0180] 1. A user orders food using a delivery app and selects an eco-friendly delivery option.

[0181] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0182] 3. The server obtains real-time traffic information via an external API.

[0183] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0184] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0185] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0186] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0187] Prompt Sentence Examples

[0188] A user ordered a pizza and salad and selected an eco-friendly delivery option. The server checked the inventory and confirmed there was enough stock. The server retrieved real-time traffic information, calculated the optimal delivery route, and assigned a renewable energy vehicle. The delivery route was then displayed on the delivery staff's tablet, and the delivery was made. After the delivery was completed, the server calculated the carbon footprint and reported it to the carbon offset program.

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] A user orders food using a delivery app and selects an eco-friendly delivery option.

[0192] Input: User order data (product information, eco-friendly options)

[0193] Output: Order data sent to the server

[0194] What it does: A user selects the food they want within a delivery app and chooses an eco-friendly delivery option, which generates order data and sends it to the server.

[0195] Step 2:

[0196] A server receives the order and accesses inventory sensors to determine the stock levels of the ordered items.

[0197] Input: Order data received by the server

[0198] Output: Inventory check results

[0199] Specific operation: The server records the order data in a database and obtains real-time inventory information from the inventory sensor, thereby checking whether the ordered item is in stock.

[0200] Step 3:

[0201] The server obtains real-time traffic information via an external API.

[0202] Input: External API request

[0203] Output: Real-time traffic information

[0204] What happens: The server sends a request to an external API (e.g., Google Maps API) to get real-time traffic information, which is used in the next step.

[0205] Step 4:

[0206] The server calculates the optimal delivery route based on inventory data and traffic information.

[0207] Input: inventory data, traffic information

[0208] Output: Optimal delivery route

[0209] How it works: The server runs an algorithm that calculates the optimal delivery route based on inventory data and traffic information, allowing delivery staff to follow the shortest and most efficient route.

[0210] Step 5:

[0211] The server checks available vehicles and allocates renewable energy vehicles.

[0212] Input: Vehicle status data

[0213] Output: Renewable energy vehicle allocation information

[0214] Specific operation: The server prioritizes renewable energy vehicles among available delivery vehicles, thereby achieving environmentally friendly delivery.

[0215] Step 6:

[0216] The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0217] Input: Optimal delivery route information

[0218] Output: Notification to transportation staff terminal

[0219] Specific operation: The optimal delivery route information is displayed on the delivery staff's terminal. The delivery staff starts delivery according to this information.

[0220] Step 7:

[0221] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0222] Input: Delivery distance data, vehicle type data

[0223] Output: Carbon footprint calculation results, report sending

[0224] What it does: The server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[0225] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0226] This invention is a smart delivery management system that combines AI and IoT technologies, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, it incorporates an emotion engine that recognizes user emotions and analyzes the user's emotion data to suggest optimal delivery options, which is an important element for improving the user experience.

[0227] Program processing overview

[0228] 1. User places an order

[0229] Users access an online store, select products, and enter their orders. When placing an order, they can choose eco-friendly delivery options. This order data is sent to a server and recorded. In addition, the user's emotional data is also collected. This emotional data is obtained using facial recognition technology and voice analysis.

[0230] 2. The server receives the order

[0231] The server receives the order data and emotion data sent by the user and stores them in a database. At this time, it prepares to acquire data from inventory sensors to check inventory levels in real time. The emotion engine personalizes the service based on the user's emotional state.

[0232] 3. The server checks the stock level

[0233] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[0234] 4. The server obtains traffic information

[0235] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[0236] 5. The server calculates the optimal delivery route

[0237] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0238] 6. The server assigns a delivery vehicle

[0239] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[0240] 7. Personalized service with emotion engine

[0241] The emotion engine analyzes the user's emotional data and personalizes the service. For example, if it detects dissatisfaction or impatience, it can offer extra support or special offers. It can also suggest optimal delivery options based on the user's emotional data.

[0242] 8. The device checks the optimal route and executes the delivery

[0243] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0244] 9. The server calculates and reports the carbon footprint

[0245] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to a carbon offset program.

[0246] Specific examples

[0247] Next, a specific example of a delivery scenario will be described.

[0248] 1. A user places an order from an online store, selects an eco-friendly delivery option, and emotional data is collected.

[0249] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0250] 3. The server obtains real-time traffic information via an external API.

[0251] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0252] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0253] 6. The emotion engine analyzes emotional data and personalizes services.

[0254] 7. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0255] 8. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0256] 9. Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[0257] The above is the specific processing flow of the smart delivery management system. This system will realize efficient logistics and environmental protection. Furthermore, the introduction of an emotion engine will enable the provision of optimal services according to the user's emotional state.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[0261] Step 2:

[0262] The server receives the order data sent by the user and stores it in a database. At this time, the emotion engine analyzes the emotion data of the user when placing the order and records the results in the database.

[0263] Step 3:

[0264] The server analyzes real-time inventory data obtained from the inventory sensors to check whether the ordered items are in sufficient stock and, if necessary, alerts the administrator if there is a shortage of stock.

[0265] Step 4:

[0266] The server obtains real-time traffic information via an external API and calculates the optimal delivery route based on that information, adjusting the route depending on the current traffic conditions.

[0267] Step 5:

[0268] The server checks the availability of renewable energy vehicles to suggest eco-friendly transportation options, and if an eco-friendly option is selected, prioritizes the allocation of renewable energy vehicles.

[0269] Step 6:

[0270] The emotion engine analyzes the user's emotional data and evaluates whether the user is satisfied or dissatisfied. This information is used to make personalized service recommendations. If dissatisfaction is detected, the user will be offered special services or discounts.

[0271] Step 7:

[0272] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0273] Step 8:

[0274] After a delivery is completed, the server calculates the carbon footprint based on the distance traveled and the type of vehicle used, and the results are reported to a carbon offset program.

[0275] Step 9:

[0276] The emotion engine will reassess the user's emotions once delivery is complete and collect feedback about the delivery, which will be used to improve the service in the future.

[0277] Step 10:

[0278] Users can check their order and delivery status online, receive eco-friendly delivery reports if they choose eco-friendly delivery options, and receive rewards based on their positive emotional feedback.

[0279] This is the specific process flow of the smart delivery management system. This system will not only improve logistics efficiency and protect the environment, but also provide optimal services that respond to user emotions.

[0280] Example 2

[0281] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0282] Today's delivery systems require efficient, eco-friendly delivery that responds quickly to consumer needs. However, integrating many elements, such as inventory management, real-time traffic information, optimal route calculation, and the use of renewable energy vehicles, is not easy. Furthermore, there is a lack of means to personalize services based on customer sentiment, and improving the user experience is also a challenge. Therefore, there is a need for a smart delivery management system that can solve these issues in an integrated manner.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0284] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for collecting and analyzing customer emotion data, and a means for personalizing services based on the emotion data. This enables the integration of complex data processing, efficient and environmentally friendly delivery management, and the provision of services that respond to customer emotions.

[0285] A "demand forecasting means" is a system that predicts future demand fluctuations and supports the development of appropriate inventory management and delivery plans.

[0286] "Real-time traffic information acquisition means" is a system that instantly collects current traffic conditions and uses them to optimize delivery routes.

[0287] The "optimal delivery route calculation means" is a system that uses an algorithm to calculate the optimal delivery route based on acquired traffic information and inventory data.

[0288] "Eco-friendly transportation proposal method" is a system for proposing environmentally friendly transportation methods.

[0289] The "renewable energy vehicle allocation means" is a system that prioritizes the allocation of vehicles that use renewable energy as vehicles to be used for delivery.

[0290] A "carbon footprint calculation tool" is a system that calculates the amount of carbon dioxide emitted during the delivery process.

[0291] A "carbon offset reporting method" is a system that reports calculated carbon footprints to a carbon offset program and contributes to environmental protection.

[0292] The "means for collecting and analyzing customer emotional data" is a system for collecting and analyzing customer emotional information.

[0293] "Means for personalizing services based on emotional data" refers to a system that provides optimal services to each individual customer based on their emotional data.

[0294] This invention provides a novel and innovative multi-functional system for a smart delivery management system that integrates AI and IoT technologies. The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, the system includes an emotion engine that collects and analyzes user emotion data and a means for personalizing services based on the emotion data, thereby improving the user experience.

[0295] First, a user accesses an online store through a web browser or mobile app, selects a product, and places an order. During the ordering process, the user's facial expressions and voice are collected through a camera and microphone, and emotion data is generated and sent to the server. This order data and emotion data are then stored on the server.

[0296] The server uses a demand forecasting means to predict future demand and supports appropriate inventory management and delivery planning. It uses a real-time traffic information acquisition means to collect traffic information from external APIs such as Google Maps API, and calculates the optimal delivery route using the Dijkstra algorithm or A algorithm in an optimal delivery route calculation means.

[0297] The server further proposes an environmentally friendly transportation method using an eco-friendly transportation suggestion means, preferentially allocates a renewable energy vehicle suitable for delivery using a renewable energy vehicle allocation means, calculates the amount of carbon dioxide emitted in the delivery process using a carbon footprint calculation means, and reports the result to a carbon offset program using a carbon offset reporting means.

[0298] The emotion engine collects and analyzes emotional data, analyzes the user's facial expressions and tone of voice, and offers rewards and additional support if negative emotions are detected. It also personalizes services based on emotional data, optimizing delivery options and response methods.

[0299] For example, when a user orders a product from an online store and selects an eco-friendly delivery option, a camera or microphone is used to collect the user's emotional data. The server receives the order data and emotional data and stores them in a MySQL or PostgreSQL database. The server then obtains real-time traffic information and calculates the optimal delivery route. After allocating a renewable energy vehicle, the service is personalized based on the emotional data. If the emotion engine detects negative emotions in this process, rewards or additional support are provided. After delivery is completed, the carbon footprint is calculated and reported to a carbon offset program.

[0300] An example prompt might be, "Describe a scenario in which a user orders a product from an online store and selects an eco-friendly delivery option. Also, describe in natural language how emotional data is collected and analyzed to personalize the service, and how the optimal delivery route is calculated and executed."

[0301] This system integrates complex data processing, enabling efficient and environmentally friendly delivery management, while also providing optimal services in response to user emotions. The detailed configuration and operating procedures of this invention are specifically described based on the claims that combine these elements.

[0302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0303] Step 1:

[0304] A user visits an online store, selects a product, adds it to their cart, and selects the "eco-friendly delivery option" at checkout. During this process, the user's facial expressions and voice are captured using a camera and microphone.

[0305] Input: Product selection, delivery option selection, emotion data collection

[0306] Output: Order data, emotion data

[0307] Specific behavior:

[0308] 1. A user adds an item to their cart on an online store's webpage.

[0309] 2. Select "Eco-Friendly Shipping Options" at checkout.

[0310] 3. Turn on the camera and microphone to capture facial expressions and voice in real time.

[0311] 4. The collected emotion data and order data are sent to the server.

[0312] Step 2:

[0313] The server receives the order data and emotion data sent by the user and stores it in a database. It also prepares to access the inventory sensor to check the inventory level.

[0314] Input: Order data, emotion data

[0315] Output: Order data and emotion data stored in the database, ready for inventory check

[0316] Specific behavior:

[0317] 1. The server receives the order data and emotion data.

[0318] 2. The received data is saved in a database (e.g., MySQL, PostgreSQL).

[0319] 3. Prepare to acquire inventory data from the inventory sensor.

[0320] Step 3:

[0321] The server receives real-time inventory data from the inventory sensor, checks whether the ordered item is in sufficient stock, and issues an alert if the item is out of stock.

[0322] Input: Order data

[0323] Output: Inventory check result (sufficient / shortage), alert

[0324] Specific behavior:

[0325] 1. The server retrieves inventory data from the RFID system.

[0326] 2. Check stock levels of ordered items.

[0327] 3. If there is a shortage of stock, an alert will be displayed on the management dashboard and an email will be sent.

[0328] Step 4:

[0329] The server obtains real-time traffic information from an external API (e.g., Google Maps API) and uses this information to calculate the optimal delivery route.

[0330] Input: Request

[0331] Output: Real-time traffic information

[0332] Specific behavior:

[0333] 1. The server sends a request to the Google Maps API.

[0334] 2. Get real-time traffic information from API.

[0335] 3. The acquired traffic information is stored in a database.

[0336] Step 5:

[0337] The server calculates the optimal delivery route based on real-time traffic and inventory data, prioritizing eco-friendly routes using Dijkstra and A algorithms.

[0338] Input: Traffic information, inventory data

[0339] Output: Optimal delivery route

[0340] Specific behavior:

[0341] 1. The server calculates the optimal delivery route based on inventory data and traffic information.

[0342] 2. Use Dijkstra algorithm or A algorithm to select an efficient route.

[0343] 3. Save the calculation results in the database.

[0344] Step 6:

[0345] The server checks available delivery vehicles and, if an eco-friendly option is selected, allocates renewable energy vehicles with priority. The allocation information is sent to the terminal.

[0346] Input: Optimal delivery route, renewable energy vehicle status

[0347] Output: Allocation information

[0348] Specific behavior:

[0349] 1. The server checks the status of the delivery vehicle.

[0350] 2. Prioritize eco-friendly options and allocate renewable energy vehicles.

[0351] 3. Send allocation information to the terminal (transportation staff device).

[0352] Step 7:

[0353] The emotion engine analyzes collected emotional data to personalize the service, offering perks or additional support if frustration or impatience is detected.

[0354] Input: Emotion data

[0355] Output: Personalized settings, special offers and additional support

[0356] Specific behavior:

[0357] 1. The emotion engine analyzes the emotion data.

[0358] 2. Generate rewards or additional support when negative sentiment is detected.

[0359] 3. Share your personalization settings with the server.

[0360] Step 8:

[0361] The terminal (the delivery staff's device) receives the optimal delivery route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0362] Input: Optimal delivery route information

[0363] Output: Route guidance to transportation staff

[0364] Specific behavior:

[0365] 1. The terminal receives optimal delivery route information from the server.

[0366] 2. Display the received route information to the transportation staff.

[0367] 3. Transport staff executes deliveries based on information.

[0368] Step 9:

[0369] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type used and reports the results to a carbon offset program.

[0370] Input: Delivery distance, Vehicle type

[0371] Output: Carbon footprint calculation results, carbon offset report

[0372] Specific behavior:

[0373] 1. The server calculates the carbon footprint based on delivery distance and vehicle type.

[0374] 2. Report the calculation results to the carbon offset program.

[0375] 3. Record the report in the database.

[0376] (Application example 2)

[0377] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0378] Environmental protection and efficient delivery are important issues in modern logistics and delivery services. However, existing delivery systems do not adequately prioritize the use of renewable energy vehicles or propose eco-friendly transportation methods. Furthermore, the lack of personalized service based on customer sentiment makes it difficult to improve customer satisfaction. Therefore, the present invention aims to achieve both environmental protection and customer satisfaction by calculating optimal delivery routes using real-time traffic information and sentiment data, using renewable energy vehicles, and reporting carbon footprints.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0380] In this invention, the server includes a demand forecasting means, a real-time traffic information acquiring means, an optimal delivery route calculating means, an eco-friendly transportation suggesting means, a renewable energy vehicle allocation means, a carbon footprint calculating means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data, thereby enabling efficient and eco-friendly delivery and improved customer satisfaction.

[0381] The "demand forecasting means" is a means for forecasting user demand.

[0382] The "real-time traffic information acquisition means" is a means for acquiring traffic information in real time.

[0383] The "optimal delivery route calculation means" is a means for calculating an optimal delivery route based on the acquired data.

[0384] "Eco-friendly transportation proposal means" is a means for proposing environmentally friendly transportation methods.

[0385] "Renewable energy vehicle allocation means" is a means for allocating renewable energy vehicles to deliveries.

[0386] "Carbon Footprint Calculation Tool" means a tool for calculating the carbon footprint of a delivery.

[0387] "Carbon Offset Reporting Instrument" means an instrument for reporting on carbon offsets.

[0388] The "means for acquiring and analyzing user emotion data" refers to a means for acquiring user emotion data and analyzing it.

[0389] The "means for providing a personalized service based on emotional data" is a means for providing a personalized service based on analyzed emotional data.

[0390] "Means for monitoring inventory levels in real time" refers to means for monitoring the inventory status in real time.

[0391] The "demand forecasting means for preventing excess inventory and shortages" is a means for forecasting demand to prevent excess inventory and shortages.

[0392] A "means for reporting the status of a renewable energy vehicle" is a means for reporting the status of a renewable energy vehicle.

[0393] "Means for calculating and reporting carbon footprint after delivery completion" refers to means for calculating carbon footprint after delivery completion and reporting the results.

[0394] The "means for providing a customer with a benefit based on emotional data" is a means for providing a benefit based on emotional data of a customer.

[0395] "Means for customers to select eco-friendly delivery options" means means for customers to select environmentally friendly delivery options.

[0396] The "means for receiving and storing order data generated by a customer" refers to a means for receiving and storing order data generated by a customer.

[0397] The "means for proposing delivery options based on emotional data" is a means for proposing the optimal delivery option based on emotional data.

[0398] The "means for providing special support using an emotion engine" is a means for providing special support to a customer using an emotion engine.

[0399] The present invention is a system for achieving both environmental protection and customer satisfaction in logistics and delivery services. Detailed embodiments for realizing this system will be described below.

[0400] 1. Overall system configuration

[0401] The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data.

[0402] 1.1 Hardware and software used

[0403] Hardware:

[0404] Smartphones (e.g. iPhone, Android devices)

[0405] Renewable energy vehicles (e.g. electric vehicles)

[0406] Inventory sensors (e.g. RFID tags)

[0407] software:

[0408] Server (e.g. AWS)

[0409] External APIs (e.g. Google Maps API)

[0410] Emotion engine (e.g. Microsoft Azure Emotion API)

[0411] Delivery device app (e.g. iOS app, Android app)

[0412] 2. Specific processing flow

[0413] The server can predict user demand using a demand forecasting means and monitor inventory levels in real time. If inventory is low, the server will issue an alert. It also uses a real-time traffic information acquisition means to obtain traffic information from external APIs (such as Google Maps API) and calculate the optimal delivery route.

[0414] The renewable energy vehicle allocation means allows vehicles that use renewable energy to be preferentially allocated using the eco-friendly transportation suggestion means. After delivery is completed, the server calculates the carbon footprint using the carbon footprint calculation means and reports the result using the carbon offset reporting means.

[0415] Furthermore, it can acquire user emotion data, analyze it using an emotion engine (such as Microsoft Azure Emotion API), and provide personalized services based on the emotion data. For example, if a user expresses dissatisfaction during delivery, it can offer special support or coupons.

[0416] 3. Specific Examples

[0417] Below is a specific scenario from ordering to delivery.

[0418] Users use the application to select eco-friendly delivery options, and emotion data is automatically collected using facial recognition technology. The server receives order and emotion data, obtains and confirms inventory data from inventory sensors, obtains real-time traffic information, calculates the optimal delivery route, and allocates renewable energy vehicles. Meanwhile, the emotion engine analyzes the user's emotions and provides special support. After the delivery is completed, the server calculates the carbon footprint and reports it to the user.

[0419] Prompt Sentence Examples

[0420] Explain how your food delivery app uses emotional data collected through facial recognition technology to calculate optimal delivery routes and prioritize renewable energy vehicles when a user selects an eco-friendly option during the ordering process. Provide examples of how your app calculates and reports the carbon footprint after the delivery is completed.

[0421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0422] Step 1:

[0423] The user places an order in the app.

[0424] Input: User order data and emotion data

[0425] How it works: The user opens a food delivery app on their smartphone or head-mounted display and enters their order. They can select eco-friendly delivery options when placing their order. The system also simultaneously collects the user's emotional data using facial recognition and voice analysis technologies.

[0426] Output: The order data and emotion data are sent to the server.

[0427] Step 2:

[0428] A server receives and stores the order and emotion data.

[0429] Input: Order data and emotion data from users

[0430] Specific operation: The server stores the received order data and emotion data in a database. At the same time, it prepares to obtain real-time inventory data from the inventory sensor.

[0431] Output: Stored order and sentiment data, inventory data ready to be retrieved.

[0432] Step 3:

[0433] The server checks the inventory data.

[0434] Input: Real-time inventory data from inventory sensors

[0435] Specific operation: The server analyzes real-time inventory data obtained from the inventory sensor, checks whether there is sufficient stock of the ordered item, and issues an alert if there is a shortage of stock.

[0436] Output: Stock check results, alerts if necessary.

[0437] Step 4:

[0438] The server obtains real-time traffic information.

[0439] Input: Traffic information request from external API

[0440] Specific operation: The server uses an external API such as the Google Maps API to obtain real-time traffic information.

[0441] Output: Real-time traffic information data.

[0442] Step 5:

[0443] The server calculates the optimal delivery route.

[0444] Input: Traffic and inventory data

[0445] How it works: The server calculates the optimal delivery route based on real-time traffic and inventory information, prioritizing eco-friendly shipping methods.

[0446] Output: Optimal delivery route information.

[0447] Step 6:

[0448] The server allocates renewable energy vehicles.

[0449] Input: Optimal delivery route information

[0450] Specific operation: After calculating the optimal route, the server checks and allocates available renewable energy vehicles.

[0451] Output: Allocated delivery vehicle information.

[0452] Step 7:

[0453] The server personalizes the service based on the emotion data.

[0454] Input: Emotion data

[0455] Specific operation: The server uses the emotion engine to analyze the user's emotion data and provide special support or benefits as needed.

[0456] Output: Personalized service content.

[0457] Step 8:

[0458] The terminal receives the optimum route information and executes the delivery.

[0459] Input: Optimal delivery route information

[0460] Specific operation: The delivery staff's terminal receives and displays the optimal delivery route information from the server. The delivery staff then carries out delivery according to the optimal route.

[0461] Output: Delivery status.

[0462] Step 9:

[0463] The server calculates and reports the carbon footprint.

[0464] Input: Delivery distance and vehicle type

[0465] What it does: After a delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[0466] Output: Carbon footprint report.

[0467] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0468] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0469] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0470] [Second embodiment]

[0471] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0472] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0473] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0474] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0475] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0476] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0478] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0479] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0480] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0481] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0482] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0483] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Specific embodiments for implementing this invention will be described.

[0484] Program processing overview

[0485] 1. User places an order

[0486] Users can access the online store, select products, and select eco-friendly delivery options when placing an order. This order data is sent to the server and recorded.

[0487] 2. The server receives the order

[0488] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0489] 3. The server checks the stock level

[0490] The server retrieves real-time inventory data from the inventory sensors and ensures that the ordered items are in sufficient stock, and if necessary, issues an alert if there is insufficient stock.

[0491] 4. The server obtains traffic information

[0492] The server receives real-time traffic information via an external API, which is used to calculate the optimal delivery route.

[0493] 5. The server calculates the optimal delivery route

[0494] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0495] 6. The server assigns a delivery vehicle

[0496] The server checks available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected.

[0497] 7. The device checks the optimal route and executes the delivery

[0498] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0499] 8. The server calculates and reports the carbon footprint

[0500] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[0501] Specific examples

[0502] Next, a specific example of a delivery scenario will be described.

[0503] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[0504] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0505] 3. The server obtains real-time traffic information via an external API.

[0506] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0507] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0508] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0509] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0510] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[0514] Step 2:

[0515] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0516] Step 3:

[0517] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[0518] Step 4:

[0519] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[0520] Step 5:

[0521] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0522] Step 6:

[0523] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[0524] Step 7:

[0525] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0526] Step 8:

[0527] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[0528] Step 9:

[0529] Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[0530] This is the specific process flow of the smart delivery management system, which will realize efficient logistics and environmental protection.

[0531] Example 1

[0532] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0533] The logistics industry is in high demand for eco-friendly and efficient deliveries, but traditional systems face challenges in planning optimal delivery routes based on real-time information and effectively utilizing renewable energy vehicles. In addition, inventory management and carbon footprint reporting are often done manually, resulting in low accuracy and efficiency.

[0534] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0535] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, and a carbon footprint calculation means based on delivery distance and vehicle type. This enables eco-friendly and efficient delivery planning, and makes it possible to automate inventory management and carbon footprint reporting while reducing the burden on the environment.

[0536] A "demand forecasting means" is a means for predicting future demand for a product by analyzing past sales data and market trends.

[0537] "Means for obtaining real-time traffic information" refers to a means for obtaining the latest traffic conditions using external information services or APIs.

[0538] The "optimal delivery route calculation means" is a means for calculating the most efficient delivery route based on traffic information and inventory status.

[0539] The "eco-friendly transportation proposal method" is a method for proposing environmentally friendly transportation methods and calculating renewable energy vehicles and efficient delivery routes with priority.

[0540] The "renewable energy vehicle allocation means" is a means for checking available renewable energy vehicles and allocating them to deliveries on a priority basis.

[0541] "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions during delivery.

[0542] "Carbon Offset Reporting Instrument" means the instrument used to report a calculated carbon footprint to a carbon offset program.

[0543] The "inventory data acquisition means" is a means for acquiring inventory information in real time from an inventory sensor or the like.

[0544] The "means for calculating a delivery route based on traffic information" is a means for calculating an optimal delivery route based on the acquired traffic information.

[0545] A "means for allocating delivery vehicles" is a means for identifying available delivery vehicles and allocating them to a particular delivery task.

[0546] The "means for calculating carbon footprint based on delivery distance and vehicle type" is a means for calculating an accurate carbon footprint based on delivery distance and the type of vehicle used.

[0547] "Means for allowing users to select eco-friendly delivery options" refers to means for allowing users to select eco-friendly delivery options at online stores.

[0548] A "demand forecasting method using a generative AI model" is a method for forecasting future demand for a product using a generative AI model.

[0549] "Means for generating prompt sentences" refers to means for creating input prompt sentences for the generative AI model.

[0550] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, a carbon footprint calculation means based on delivery distance and vehicle type, a demand forecasting means using a generative AI model, and a prompt sentence generation means.

[0551] System Program

[0552] A user orders products from an online store. When placing an order, the user can select an eco-friendly delivery option. The order data is sent to the server and recorded.

[0553] The server receives the order data sent by the user and stores it in a database. It then obtains real-time inventory data from the inventory sensor to check whether the ordered item is in sufficient stock. If there is a shortage of stock, it issues a warning.

[0554] Get real-time traffic information via external APIs (e.g., Google Maps API or HERE API). Calculate the optimal delivery route based on the traffic data. Prioritize eco-friendly transportation methods and use renewable energy vehicles when calculating routes.

[0555] The server checks the list of available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected. The delivery staff's terminal (device) receives the optimal route information sent from the server and displays it to the delivery staff.

[0556] The delivery staff carries out the delivery based on the route information displayed on the terminal. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to the carbon offset program.

[0557] Specific hardware and software

[0558] The hardware used includes inventory sensors, renewable energy vehicles, and terminal devices for transportation staff, while the software used includes Google Maps API, HERE API, a real-time inventory management system, and carbon footprint calculation algorithms.

[0559] Specific examples

[0560] A specific delivery scenario is shown below.

[0561] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[0562] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0563] 3. The server obtains real-time traffic information via an external API.

[0564] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0565] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0566] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0567] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0568] Prompt Sentence Examples

[0569] Below are some examples of prompts to input to the generative AI model.

[0570] ---

[0571] "Please explain the key functions of your smart delivery management system step by step. Include detailed steps for order processing, inventory check, traffic information acquisition, optimal delivery route calculation, eco-friendly transportation, and carbon footprint calculation."

[0572] ---

[0573] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[0574] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0575] Step 1:

[0576] User places an order

[0577] A user visits an online store, selects a product, and adds it to their cart. They then proceed to the order screen and select eco-friendly shipping as a shipping option. The selected information is sent as input to the server, which receives this information and stores the order data (product ID, quantity, eco-friendly shipping flag, etc.).

[0578] Step 2:

[0579] The server receives the order

[0580] The server receives order data from the user and records it in the database. At this time, it also saves the order ID, order date and time, and user information (such as delivery address). Based on the order data received as input, it creates a new record in the database as output.

[0581] Step 3:

[0582] The server checks the stock level

[0583] The server gets real-time inventory data from the inventory sensor. The inventory sensor periodically updates the stock count for each item. It receives the inventory data as input and checks whether there is enough stock of the ordered item. It outputs the inventory status and generates a warning message if there is a shortage.

[0584] Step 4:

[0585] The server obtains traffic information

[0586] The server calls external APIs (e.g., Google Maps API or HERE API) to obtain real-time traffic information. It sends API requests as input and receives traffic conditions (traffic volume, delay information, etc.) as output. Based on this data, it organizes the information necessary for delivery planning.

[0587] Step 5:

[0588] The server calculates the optimal delivery route

[0589] The server combines the acquired traffic information and inventory data to calculate the optimal delivery route. The route calculation applies an algorithm that prioritizes eco-friendly transportation methods. Traffic information and inventory data are used as input, and optimal delivery route information (route path, distance, time, etc.) is generated as output.

[0590] Step 6:

[0591] The server assigns delivery vehicles

[0592] The server checks the list of available delivery vehicles, giving preference to renewable energy vehicles if an eco-friendly delivery option is selected. It uses the available vehicle data as input and assigns the best vehicle information for the specified route as output.

[0593] Step 7:

[0594] The device checks the optimal route and executes the delivery

[0595] The terminal (the delivery staff's device) receives the optimal route information sent from the server. It acquires the optimal route information as input and displays it to the delivery staff through the navigation app. The delivery staff follows these instructions to carry out the delivery and records the progress along the way on the terminal.

[0596] Step 8:

[0597] The server calculates and reports the carbon footprint

[0598] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the vehicle type used. It takes delivery performance data (distance, vehicle type, etc.) as input and produces the calculated carbon footprint as output, which can be reported to a carbon offset program.

[0599] (Application example 1)

[0600] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0601] Conventional delivery systems do not incorporate measures to select eco-friendly delivery methods, which results in a high environmental impact and the inability to provide optimal delivery routes that take traffic information into account. Additionally, the process of calculating and reporting carbon footprints is done manually, which is inefficient.

[0602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0603] In this invention, the server includes a demand forecasting means, a real-time traffic information obtaining means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for calculating and reporting a carbon footprint, a means for calculating a carbon footprint based on a delivery distance and a vehicle type used, a means for a user to place an order and transmit and record order data, a means for monitoring inventory levels in real time, a demand forecasting means for preventing overstocking and stockouts, a means for reporting the status of renewable energy vehicles, a means for obtaining real-time traffic information and calculating an optimal delivery route, a means for offering eco-friendly delivery options and allocating renewable energy vehicles, a means for allowing customers to select eco-friendly delivery options, a means for receiving and storing order data generated by customers, and a means for displaying optimal route information on a device of a delivery staff member and causing the delivery to be executed, thereby realizing an efficient and eco-friendly delivery system.

[0604] 1. "Demand forecasting means" refers to a means of analyzing customer order trends and forecasting future demand.

[0605] 2. "Means for obtaining real-time traffic information" means a means for obtaining current traffic conditions in real time.

[0606] 3. "Optimal delivery route calculation means" is a means for calculating the optimal delivery route based on traffic information and inventory data.

[0607] 4. "Eco-friendly transport proposals" are proposals for delivery methods that minimize the impact on the environment.

[0608] 5. "Renewable energy vehicle allocation measure" means a measure that gives priority to the allocation of vehicles that use renewable energy for deliveries.

[0609] 6. "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions from shipping.

[0610] 7. "Carbon Offset Reporting Instrument" means a reporting instrument to a carbon offset program based on a calculated carbon footprint.

[0611] 8. "Means for monitoring inventory levels in real time" means means for monitoring inventory status in real time.

[0612] 9. "Demand forecasting measures to prevent excess inventory and shortages" are measures to prevent excess inventory and shortages by accurately forecasting demand.

[0613] 10. "Means for reporting the status of renewable energy vehicles" means means for reporting the operating status and charging state of renewable energy vehicles.

[0614] 11. "Means for customers to select eco-friendly delivery options" means means for customers to select delivery options that are environmentally friendly.

[0615] 12. "Means for receiving and storing customer-generated order data" means means for receiving order information entered by a customer and storing it in a database.

[0616] 13. "Means for displaying optimal route information on a delivery staff member's device and executing delivery" means means for displaying an optimal route on a delivery staff member's device and executing delivery according to that route.

[0617] This invention is a smart food delivery management system that combines AI and IoT technology. The specific configuration and operation of the system are described in detail below.

[0618] The server receives and stores order data when a customer places an order. The order data includes the customer's selected products and the option to select eco-friendly delivery options. This order data is then compared with inventory data, which is updated in real time, making it easy to check stock levels.

[0619] The server then retrieves real-time traffic information via external APIs such as the Google Maps API. This information is used to calculate the optimal delivery route. The server calculates the optimal delivery route based on traffic information and inventory data, and prioritizes eco-friendly delivery methods.

[0620] Once the delivery route is determined, the server prioritizes the allocation of vehicles that use renewable energy. To do this, the server monitors the operating status and charging status of renewable energy vehicles in real time and selects the most appropriate vehicle.

[0621] The optimal route information sent from the server is displayed on the delivery staff's terminal, allowing them to deliver efficiently by following the optimal route. Once the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to the carbon offset program.

[0622] The server also calculates and reports the environmental benefit gained from using renewable energy vehicles, which is used to demonstrate how much the delivery company is contributing to environmental protection.

[0623] Specific examples

[0624] 1. A user orders food using a delivery app and selects an eco-friendly delivery option.

[0625] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0626] 3. The server obtains real-time traffic information via an external API.

[0627] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0628] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0629] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0630] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0631] Prompt Sentence Examples

[0632] A user ordered a pizza and salad and selected an eco-friendly delivery option. The server checked the inventory and confirmed there was enough stock. The server retrieved real-time traffic information, calculated the optimal delivery route, and assigned a renewable energy vehicle. The delivery route was then displayed on the delivery staff's tablet, and the delivery was made. After the delivery was completed, the server calculated the carbon footprint and reported it to the carbon offset program.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] A user orders food using a delivery app and selects an eco-friendly delivery option.

[0636] Input: User order data (product information, eco-friendly options)

[0637] Output: Order data sent to the server

[0638] What it does: A user selects the food they want within a delivery app and chooses an eco-friendly delivery option, which generates order data and sends it to the server.

[0639] Step 2:

[0640] A server receives the order and accesses inventory sensors to determine the stock levels of the ordered items.

[0641] Input: Order data received by the server

[0642] Output: Inventory check results

[0643] Specific operation: The server records the order data in a database and obtains real-time inventory information from the inventory sensor, thereby checking whether the ordered item is in stock.

[0644] Step 3:

[0645] The server obtains real-time traffic information via an external API.

[0646] Input: External API request

[0647] Output: Real-time traffic information

[0648] What happens: The server sends a request to an external API (e.g., Google Maps API) to get real-time traffic information, which is used in the next step.

[0649] Step 4:

[0650] The server calculates the optimal delivery route based on inventory data and traffic information.

[0651] Input: inventory data, traffic information

[0652] Output: Optimal delivery route

[0653] How it works: The server runs an algorithm that calculates the optimal delivery route based on inventory data and traffic information, allowing delivery staff to follow the shortest and most efficient route.

[0654] Step 5:

[0655] The server checks available vehicles and allocates renewable energy vehicles.

[0656] Input: Vehicle status data

[0657] Output: Renewable energy vehicle allocation information

[0658] Specific operation: The server prioritizes renewable energy vehicles among available delivery vehicles, thereby achieving environmentally friendly delivery.

[0659] Step 6:

[0660] The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0661] Input: Optimal delivery route information

[0662] Output: Notification to transportation staff terminal

[0663] Specific operation: The optimal delivery route information is displayed on the delivery staff's terminal. The delivery staff starts delivery according to this information.

[0664] Step 7:

[0665] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0666] Input: Delivery distance data, vehicle type data

[0667] Output: Carbon footprint calculation results, report sending

[0668] What it does: The server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[0669] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0670] This invention is a smart delivery management system that combines AI and IoT technologies, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, it incorporates an emotion engine that recognizes user emotions and analyzes the user's emotion data to suggest optimal delivery options, which is an important element for improving the user experience.

[0671] Program processing overview

[0672] 1. User places an order

[0673] Users access an online store, select products, and enter their orders. When placing an order, they can choose eco-friendly delivery options. This order data is sent to a server and recorded. In addition, the user's emotional data is also collected. This emotional data is obtained using facial recognition technology and voice analysis.

[0674] 2. The server receives the order

[0675] The server receives the order data and emotion data sent by the user and stores them in a database. At this time, it prepares to acquire data from inventory sensors to check inventory levels in real time. The emotion engine personalizes the service based on the user's emotional state.

[0676] 3. The server checks the stock level

[0677] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[0678] 4. The server obtains traffic information

[0679] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[0680] 5. The server calculates the optimal delivery route

[0681] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0682] 6. The server assigns a delivery vehicle

[0683] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[0684] 7. Personalized service with emotion engine

[0685] The emotion engine analyzes the user's emotional data and personalizes the service. For example, if it detects dissatisfaction or impatience, it can offer extra support or special offers. It can also suggest optimal delivery options based on the user's emotional data.

[0686] 8. The device checks the optimal route and executes the delivery

[0687] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0688] 9. The server calculates and reports the carbon footprint

[0689] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to a carbon offset program.

[0690] Specific examples

[0691] Next, a specific example of a delivery scenario will be described.

[0692] 1. A user places an order from an online store, selects an eco-friendly delivery option, and emotional data is collected.

[0693] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0694] 3. The server obtains real-time traffic information via an external API.

[0695] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0696] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0697] 6. The emotion engine analyzes emotional data and personalizes services.

[0698] 7. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0699] 8. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0700] 9. Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[0701] The above is the specific processing flow of the smart delivery management system. This system will realize efficient logistics and environmental protection. Furthermore, the introduction of an emotion engine will enable the provision of optimal services according to the user's emotional state.

[0702] The processing flow will be explained below.

[0703] Step 1:

[0704] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[0705] Step 2:

[0706] The server receives the order data sent by the user and stores it in a database. At this time, the emotion engine analyzes the emotion data of the user when placing the order and records the results in the database.

[0707] Step 3:

[0708] The server analyzes real-time inventory data obtained from the inventory sensors to check whether the ordered items are in sufficient stock and, if necessary, alerts the administrator if there is a shortage of stock.

[0709] Step 4:

[0710] The server obtains real-time traffic information via an external API and calculates the optimal delivery route based on that information, adjusting the route depending on the current traffic conditions.

[0711] Step 5:

[0712] The server checks the availability of renewable energy vehicles to suggest eco-friendly transportation options, and if an eco-friendly option is selected, prioritizes the allocation of renewable energy vehicles.

[0713] Step 6:

[0714] The emotion engine analyzes the user's emotional data and evaluates whether the user is satisfied or dissatisfied. This information is used to make personalized service recommendations. If dissatisfaction is detected, the user will be offered special services or discounts.

[0715] Step 7:

[0716] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0717] Step 8:

[0718] After a delivery is completed, the server calculates the carbon footprint based on the distance traveled and the type of vehicle used, and the results are reported to a carbon offset program.

[0719] Step 9:

[0720] The emotion engine will reassess the user's emotions once delivery is complete and collect feedback about the delivery, which will be used to improve the service in the future.

[0721] Step 10:

[0722] Users can check their order and delivery status online, receive eco-friendly delivery reports if they choose eco-friendly delivery options, and receive rewards based on their positive emotional feedback.

[0723] This is the specific process flow of the smart delivery management system. This system will not only improve logistics efficiency and protect the environment, but also provide optimal services that respond to user emotions.

[0724] Example 2

[0725] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0726] Today's delivery systems require efficient, eco-friendly delivery that responds quickly to consumer needs. However, integrating many elements, such as inventory management, real-time traffic information, optimal route calculation, and the use of renewable energy vehicles, is not easy. Furthermore, there is a lack of means to personalize services based on customer sentiment, and improving the user experience is also a challenge. Therefore, there is a need for a smart delivery management system that can solve these issues in an integrated manner.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0728] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for collecting and analyzing customer emotion data, and a means for personalizing services based on the emotion data. This enables the integration of complex data processing, efficient and environmentally friendly delivery management, and the provision of services that respond to customer emotions.

[0729] A "demand forecasting means" is a system that predicts future demand fluctuations and supports the development of appropriate inventory management and delivery plans.

[0730] "Real-time traffic information acquisition means" is a system that instantly collects current traffic conditions and uses them to optimize delivery routes.

[0731] The "optimal delivery route calculation means" is a system that uses an algorithm to calculate the optimal delivery route based on acquired traffic information and inventory data.

[0732] "Eco-friendly transportation proposal method" is a system for proposing environmentally friendly transportation methods.

[0733] The "renewable energy vehicle allocation means" is a system that prioritizes the allocation of vehicles that use renewable energy as vehicles to be used for delivery.

[0734] A "carbon footprint calculation tool" is a system that calculates the amount of carbon dioxide emitted during the delivery process.

[0735] A "carbon offset reporting method" is a system that reports calculated carbon footprints to a carbon offset program and contributes to environmental protection.

[0736] The "means for collecting and analyzing customer emotional data" is a system for collecting and analyzing customer emotional information.

[0737] "Means for personalizing services based on emotional data" refers to a system that provides optimal services to each individual customer based on their emotional data.

[0738] This invention provides a novel and innovative multi-functional system for a smart delivery management system that integrates AI and IoT technologies. The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, the system includes an emotion engine that collects and analyzes user emotion data and a means for personalizing services based on the emotion data, thereby improving the user experience.

[0739] First, a user accesses an online store through a web browser or mobile app, selects a product, and places an order. During the ordering process, the user's facial expressions and voice are collected through a camera and microphone, and emotion data is generated and sent to the server. This order data and emotion data are then stored on the server.

[0740] The server uses a demand forecasting means to predict future demand and supports appropriate inventory management and delivery planning. It uses a real-time traffic information acquisition means to collect traffic information from external APIs such as Google Maps API, and calculates the optimal delivery route using the Dijkstra algorithm or A algorithm in an optimal delivery route calculation means.

[0741] The server further proposes an environmentally friendly transportation method using an eco-friendly transportation suggestion means, preferentially allocates a renewable energy vehicle suitable for delivery using a renewable energy vehicle allocation means, calculates the amount of carbon dioxide emitted in the delivery process using a carbon footprint calculation means, and reports the result to a carbon offset program using a carbon offset reporting means.

[0742] The emotion engine collects and analyzes emotional data, analyzes the user's facial expressions and tone of voice, and offers rewards and additional support if negative emotions are detected. It also personalizes services based on emotional data, optimizing delivery options and response methods.

[0743] For example, when a user orders a product from an online store and selects an eco-friendly delivery option, a camera or microphone is used to collect the user's emotional data. The server receives the order data and emotional data and stores them in a MySQL or PostgreSQL database. The server then obtains real-time traffic information and calculates the optimal delivery route. After allocating a renewable energy vehicle, the service is personalized based on the emotional data. If the emotion engine detects negative emotions in this process, rewards or additional support are provided. After delivery is completed, the carbon footprint is calculated and reported to a carbon offset program.

[0744] An example prompt might be, "Describe a scenario in which a user orders a product from an online store and selects an eco-friendly delivery option. Also, describe in natural language how emotional data is collected and analyzed to personalize the service, and how the optimal delivery route is calculated and executed."

[0745] This system integrates complex data processing, enabling efficient and environmentally friendly delivery management, while also providing optimal services in response to user emotions. The detailed configuration and operating procedures of this invention are specifically described based on the claims that combine these elements.

[0746] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0747] Step 1:

[0748] A user visits an online store, selects a product, adds it to their cart, and selects the "eco-friendly delivery option" at checkout. During this process, the user's facial expressions and voice are captured using a camera and microphone.

[0749] Input: Product selection, delivery option selection, emotion data collection

[0750] Output: Order data, emotion data

[0751] Specific behavior:

[0752] 1. A user adds an item to their cart on an online store's webpage.

[0753] 2. Select "Eco-Friendly Shipping Options" at checkout.

[0754] 3. Turn on the camera and microphone to capture facial expressions and voice in real time.

[0755] 4. The collected emotion data and order data are sent to the server.

[0756] Step 2:

[0757] The server receives the order data and emotion data sent by the user and stores it in a database. It also prepares to access the inventory sensor to check the inventory level.

[0758] Input: Order data, emotion data

[0759] Output: Order data and emotion data stored in the database, ready for inventory check

[0760] Specific behavior:

[0761] 1. The server receives the order data and emotion data.

[0762] 2. The received data is saved in a database (e.g., MySQL, PostgreSQL).

[0763] 3. Prepare to acquire inventory data from the inventory sensor.

[0764] Step 3:

[0765] The server receives real-time inventory data from the inventory sensor, checks whether the ordered item is in sufficient stock, and issues an alert if the item is out of stock.

[0766] Input: Order data

[0767] Output: Inventory check result (sufficient / shortage), alert

[0768] Specific behavior:

[0769] 1. The server retrieves inventory data from the RFID system.

[0770] 2. Check stock levels of ordered items.

[0771] 3. If there is a shortage of stock, an alert will be displayed on the management dashboard and an email will be sent.

[0772] Step 4:

[0773] The server obtains real-time traffic information from an external API (e.g., Google Maps API) and uses this information to calculate the optimal delivery route.

[0774] Input: Request

[0775] Output: Real-time traffic information

[0776] Specific behavior:

[0777] 1. The server sends a request to the Google Maps API.

[0778] 2. Get real-time traffic information from API.

[0779] 3. The acquired traffic information is stored in a database.

[0780] Step 5:

[0781] The server calculates the optimal delivery route based on real-time traffic and inventory data, prioritizing eco-friendly routes using Dijkstra and A algorithms.

[0782] Input: Traffic information, inventory data

[0783] Output: Optimal delivery route

[0784] Specific behavior:

[0785] 1. The server calculates the optimal delivery route based on inventory data and traffic information.

[0786] 2. Use Dijkstra algorithm or A algorithm to select an efficient route.

[0787] 3. Save the calculation results in the database.

[0788] Step 6:

[0789] The server checks available delivery vehicles and, if an eco-friendly option is selected, allocates renewable energy vehicles with priority. The allocation information is sent to the terminal.

[0790] Input: Optimal delivery route, renewable energy vehicle status

[0791] Output: Allocation information

[0792] Specific behavior:

[0793] 1. The server checks the status of the delivery vehicle.

[0794] 2. Prioritize eco-friendly options and allocate renewable energy vehicles.

[0795] 3. Send allocation information to the terminal (transportation staff device).

[0796] Step 7:

[0797] The emotion engine analyzes collected emotional data to personalize the service, offering perks or additional support if frustration or impatience is detected.

[0798] Input: Emotion data

[0799] Output: Personalized settings, special offers and additional support

[0800] Specific behavior:

[0801] 1. The emotion engine analyzes the emotion data.

[0802] 2. Generate rewards or additional support when negative sentiment is detected.

[0803] 3. Share your personalization settings with the server.

[0804] Step 8:

[0805] The terminal (the delivery staff's device) receives the optimal delivery route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0806] Input: Optimal delivery route information

[0807] Output: Route guidance to transportation staff

[0808] Specific behavior:

[0809] 1. The terminal receives optimal delivery route information from the server.

[0810] 2. Display the received route information to the transportation staff.

[0811] 3. Transport staff executes deliveries based on information.

[0812] Step 9:

[0813] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type used and reports the results to a carbon offset program.

[0814] Input: Delivery distance, Vehicle type

[0815] Output: Carbon footprint calculation results, carbon offset report

[0816] Specific behavior:

[0817] 1. The server calculates the carbon footprint based on delivery distance and vehicle type.

[0818] 2. Report the calculation results to the carbon offset program.

[0819] 3. Record the report in the database.

[0820] (Application example 2)

[0821] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0822] Environmental protection and efficient delivery are important issues in modern logistics and delivery services. However, existing delivery systems do not adequately prioritize the use of renewable energy vehicles or propose eco-friendly transportation methods. Furthermore, the lack of personalized service based on customer sentiment makes it difficult to improve customer satisfaction. Therefore, the present invention aims to achieve both environmental protection and customer satisfaction by calculating optimal delivery routes using real-time traffic information and sentiment data, using renewable energy vehicles, and reporting carbon footprints.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0824] In this invention, the server includes a demand forecasting means, a real-time traffic information acquiring means, an optimal delivery route calculating means, an eco-friendly transportation suggesting means, a renewable energy vehicle allocation means, a carbon footprint calculating means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data, thereby enabling efficient and eco-friendly delivery and improved customer satisfaction.

[0825] The "demand forecasting means" is a means for forecasting user demand.

[0826] The "real-time traffic information acquisition means" is a means for acquiring traffic information in real time.

[0827] The "optimal delivery route calculation means" is a means for calculating an optimal delivery route based on the acquired data.

[0828] "Eco-friendly transportation proposal means" is a means for proposing environmentally friendly transportation methods.

[0829] "Renewable energy vehicle allocation means" is a means for allocating renewable energy vehicles to deliveries.

[0830] "Carbon Footprint Calculation Tool" means a tool for calculating the carbon footprint of a delivery.

[0831] "Carbon Offset Reporting Instrument" means an instrument for reporting on carbon offsets.

[0832] The "means for acquiring and analyzing user emotion data" refers to a means for acquiring user emotion data and analyzing it.

[0833] The "means for providing a personalized service based on emotional data" is a means for providing a personalized service based on analyzed emotional data.

[0834] "Means for monitoring inventory levels in real time" refers to means for monitoring the inventory status in real time.

[0835] The "demand forecasting means for preventing excess inventory and shortages" is a means for forecasting demand to prevent excess inventory and shortages.

[0836] A "means for reporting the status of a renewable energy vehicle" is a means for reporting the status of a renewable energy vehicle.

[0837] "Means for calculating and reporting carbon footprint after delivery completion" refers to means for calculating carbon footprint after delivery completion and reporting the results.

[0838] The "means for providing a customer with a benefit based on emotional data" is a means for providing a benefit based on emotional data of a customer.

[0839] "Means for customers to select eco-friendly delivery options" means means for customers to select environmentally friendly delivery options.

[0840] The "means for receiving and storing order data generated by a customer" refers to a means for receiving and storing order data generated by a customer.

[0841] The "means for proposing delivery options based on emotional data" is a means for proposing the optimal delivery option based on emotional data.

[0842] The "means for providing special support using an emotion engine" is a means for providing special support to a customer using an emotion engine.

[0843] The present invention is a system for achieving both environmental protection and customer satisfaction in logistics and delivery services. Detailed embodiments for realizing this system will be described below.

[0844] 1. Overall system configuration

[0845] The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data.

[0846] 1.1 Hardware and software used

[0847] Hardware:

[0848] Smartphones (e.g. iPhone, Android devices)

[0849] Renewable energy vehicles (e.g. electric vehicles)

[0850] Inventory sensors (e.g. RFID tags)

[0851] software:

[0852] Server (e.g. AWS)

[0853] External APIs (e.g. Google Maps API)

[0854] Emotion engine (e.g. Microsoft Azure Emotion API)

[0855] Delivery device app (e.g. iOS app, Android app)

[0856] 2. Specific processing flow

[0857] The server can predict user demand using a demand forecasting means and monitor inventory levels in real time. If inventory is low, the server will issue an alert. It also uses a real-time traffic information acquisition means to obtain traffic information from external APIs (such as Google Maps API) and calculate the optimal delivery route.

[0858] The renewable energy vehicle allocation means allows vehicles that use renewable energy to be preferentially allocated using the eco-friendly transportation suggestion means. After delivery is completed, the server calculates the carbon footprint using the carbon footprint calculation means and reports the result using the carbon offset reporting means.

[0859] Furthermore, it can acquire user emotion data, analyze it using an emotion engine (such as Microsoft Azure Emotion API), and provide personalized services based on the emotion data. For example, if a user expresses dissatisfaction during delivery, it can offer special support or coupons.

[0860] 3. Specific Examples

[0861] Below is a specific scenario from ordering to delivery.

[0862] Users use the application to select eco-friendly delivery options, and emotion data is automatically collected using facial recognition technology. The server receives order and emotion data, obtains and confirms inventory data from inventory sensors, obtains real-time traffic information, calculates the optimal delivery route, and allocates renewable energy vehicles. Meanwhile, the emotion engine analyzes the user's emotions and provides special support. After the delivery is completed, the server calculates the carbon footprint and reports it to the user.

[0863] Prompt Sentence Examples

[0864] Explain how your food delivery app uses emotional data collected through facial recognition technology to calculate optimal delivery routes and prioritize renewable energy vehicles when a user selects an eco-friendly option during the ordering process. Provide examples of how your app calculates and reports the carbon footprint after the delivery is completed.

[0865] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0866] Step 1:

[0867] The user places an order in the app.

[0868] Input: User order data and emotion data

[0869] How it works: The user opens a food delivery app on their smartphone or head-mounted display and enters their order. They can select eco-friendly delivery options when placing their order. The system also simultaneously collects the user's emotional data using facial recognition and voice analysis technologies.

[0870] Output: The order data and emotion data are sent to the server.

[0871] Step 2:

[0872] A server receives and stores the order and emotion data.

[0873] Input: Order data and emotion data from users

[0874] Specific operation: The server stores the received order data and emotion data in a database. At the same time, it prepares to obtain real-time inventory data from the inventory sensor.

[0875] Output: Stored order and sentiment data, inventory data ready to be retrieved.

[0876] Step 3:

[0877] The server checks the inventory data.

[0878] Input: Real-time inventory data from inventory sensors

[0879] Specific operation: The server analyzes real-time inventory data obtained from the inventory sensor, checks whether there is sufficient stock of the ordered item, and issues an alert if there is a shortage of stock.

[0880] Output: Stock check results, alerts if necessary.

[0881] Step 4:

[0882] The server obtains real-time traffic information.

[0883] Input: Traffic information request from external API

[0884] Specific operation: The server uses an external API such as the Google Maps API to obtain real-time traffic information.

[0885] Output: Real-time traffic information data.

[0886] Step 5:

[0887] The server calculates the optimal delivery route.

[0888] Input: Traffic and inventory data

[0889] How it works: The server calculates the optimal delivery route based on real-time traffic and inventory information, prioritizing eco-friendly shipping methods.

[0890] Output: Optimal delivery route information.

[0891] Step 6:

[0892] The server allocates renewable energy vehicles.

[0893] Input: Optimal delivery route information

[0894] Specific operation: After calculating the optimal route, the server checks and allocates available renewable energy vehicles.

[0895] Output: Allocated delivery vehicle information.

[0896] Step 7:

[0897] The server personalizes the service based on the emotion data.

[0898] Input: Emotion data

[0899] Specific operation: The server uses the emotion engine to analyze the user's emotion data and provide special support or benefits as needed.

[0900] Output: Personalized service content.

[0901] Step 8:

[0902] The terminal receives the optimum route information and executes the delivery.

[0903] Input: Optimal delivery route information

[0904] Specific operation: The delivery staff's terminal receives and displays the optimal delivery route information from the server. The delivery staff then carries out delivery according to the optimal route.

[0905] Output: Delivery status.

[0906] Step 9:

[0907] The server calculates and reports the carbon footprint.

[0908] Input: Delivery distance and vehicle type

[0909] What it does: After a delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[0910] Output: Carbon footprint report.

[0911] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0912] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0913] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0914] [Third embodiment]

[0915] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0916] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0917] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0918] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0919] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0920] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0921] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0922] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0923] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0924] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0925] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0926] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0927] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Specific embodiments for implementing this invention will be described.

[0928] Program processing overview

[0929] 1. User places an order

[0930] Users can access the online store, select products, and select eco-friendly delivery options when placing an order. This order data is sent to the server and recorded.

[0931] 2. The server receives the order

[0932] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0933] 3. The server checks the stock level

[0934] The server retrieves real-time inventory data from the inventory sensors and ensures that the ordered items are in sufficient stock, and if necessary, issues an alert if there is insufficient stock.

[0935] 4. The server obtains traffic information

[0936] The server receives real-time traffic information via an external API, which is used to calculate the optimal delivery route.

[0937] 5. The server calculates the optimal delivery route

[0938] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0939] 6. The server assigns a delivery vehicle

[0940] The server checks available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected.

[0941] 7. The device checks the optimal route and executes the delivery

[0942] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0943] 8. The server calculates and reports the carbon footprint

[0944] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[0945] Specific examples

[0946] Next, a specific example of a delivery scenario will be described.

[0947] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[0948] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[0949] 3. The server obtains real-time traffic information via an external API.

[0950] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[0951] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[0952] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[0953] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[0954] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[0955] The processing flow will be explained below.

[0956] Step 1:

[0957] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[0958] Step 2:

[0959] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[0960] Step 3:

[0961] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[0962] Step 4:

[0963] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[0964] Step 5:

[0965] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[0966] Step 6:

[0967] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[0968] Step 7:

[0969] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[0970] Step 8:

[0971] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[0972] Step 9:

[0973] Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[0974] This is the specific process flow of the smart delivery management system, which will realize efficient logistics and environmental protection.

[0975] Example 1

[0976] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0977] The logistics industry is in high demand for eco-friendly and efficient deliveries, but traditional systems face challenges in planning optimal delivery routes based on real-time information and effectively utilizing renewable energy vehicles. In addition, inventory management and carbon footprint reporting are often done manually, resulting in low accuracy and efficiency.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0979] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, and a carbon footprint calculation means based on delivery distance and vehicle type. This enables eco-friendly and efficient delivery planning, and makes it possible to automate inventory management and carbon footprint reporting while reducing the burden on the environment.

[0980] A "demand forecasting means" is a means for predicting future demand for a product by analyzing past sales data and market trends.

[0981] "Means for obtaining real-time traffic information" refers to a means for obtaining the latest traffic conditions using external information services or APIs.

[0982] The "optimal delivery route calculation means" is a means for calculating the most efficient delivery route based on traffic information and inventory status.

[0983] The "eco-friendly transportation proposal method" is a method for proposing environmentally friendly transportation methods and calculating renewable energy vehicles and efficient delivery routes with priority.

[0984] The "renewable energy vehicle allocation means" is a means for checking available renewable energy vehicles and allocating them to deliveries on a priority basis.

[0985] "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions during delivery.

[0986] "Carbon Offset Reporting Instrument" means the instrument used to report a calculated carbon footprint to a carbon offset program.

[0987] The "inventory data acquisition means" is a means for acquiring inventory information in real time from an inventory sensor or the like.

[0988] The "means for calculating a delivery route based on traffic information" is a means for calculating an optimal delivery route based on the acquired traffic information.

[0989] A "means for allocating delivery vehicles" is a means for identifying available delivery vehicles and allocating them to a particular delivery task.

[0990] The "means for calculating carbon footprint based on delivery distance and vehicle type" is a means for calculating an accurate carbon footprint based on delivery distance and the type of vehicle used.

[0991] "Means for allowing users to select eco-friendly delivery options" refers to means for allowing users to select eco-friendly delivery options at online stores.

[0992] A "demand forecasting method using a generative AI model" is a method for forecasting future demand for a product using a generative AI model.

[0993] "Means for generating prompt sentences" refers to means for creating input prompt sentences for the generative AI model.

[0994] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, a carbon footprint calculation means based on delivery distance and vehicle type, a demand forecasting means using a generative AI model, and a prompt sentence generation means.

[0995] System Program

[0996] A user orders products from an online store. When placing an order, the user can select an eco-friendly delivery option. The order data is sent to the server and recorded.

[0997] The server receives the order data sent by the user and stores it in a database. It then obtains real-time inventory data from the inventory sensor to check whether the ordered item is in sufficient stock. If there is a shortage of stock, it issues a warning.

[0998] Get real-time traffic information via external APIs (e.g., Google Maps API or HERE API). Calculate the optimal delivery route based on the traffic data. Prioritize eco-friendly transportation methods and use renewable energy vehicles when calculating routes.

[0999] The server checks the list of available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected. The delivery staff's terminal (device) receives the optimal route information sent from the server and displays it to the delivery staff.

[1000] The delivery staff carries out the delivery based on the route information displayed on the terminal. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to the carbon offset program.

[1001] Specific hardware and software

[1002] The hardware used includes inventory sensors, renewable energy vehicles, and terminal devices for transportation staff, while the software used includes Google Maps API, HERE API, a real-time inventory management system, and carbon footprint calculation algorithms.

[1003] Specific examples

[1004] A specific delivery scenario is shown below.

[1005] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[1006] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1007] 3. The server obtains real-time traffic information via an external API.

[1008] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1009] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1010] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1011] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1012] Prompt Sentence Examples

[1013] Below are some examples of prompts to input to the generative AI model.

[1014] ---

[1015] "Please explain the key functions of your smart delivery management system step by step. Include detailed steps for order processing, inventory check, traffic information acquisition, optimal delivery route calculation, eco-friendly transportation, and carbon footprint calculation."

[1016] ---

[1017] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[1018] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1019] Step 1:

[1020] User places an order

[1021] A user visits an online store, selects a product, and adds it to their cart. They then proceed to the order screen and select eco-friendly shipping as a shipping option. The selected information is sent as input to the server, which receives this information and stores the order data (product ID, quantity, eco-friendly shipping flag, etc.).

[1022] Step 2:

[1023] The server receives the order

[1024] The server receives order data from the user and records it in the database. At this time, it also saves the order ID, order date and time, and user information (such as delivery address). Based on the order data received as input, it creates a new record in the database as output.

[1025] Step 3:

[1026] The server checks the stock level

[1027] The server gets real-time inventory data from the inventory sensor. The inventory sensor periodically updates the stock count for each item. It receives the inventory data as input and checks whether there is enough stock of the ordered item. It outputs the inventory status and generates a warning message if there is a shortage.

[1028] Step 4:

[1029] The server obtains traffic information

[1030] The server calls external APIs (e.g., Google Maps API or HERE API) to obtain real-time traffic information. It sends API requests as input and receives traffic conditions (traffic volume, delay information, etc.) as output. Based on this data, it organizes the information necessary for delivery planning.

[1031] Step 5:

[1032] The server calculates the optimal delivery route

[1033] The server combines the acquired traffic information and inventory data to calculate the optimal delivery route. The route calculation applies an algorithm that prioritizes eco-friendly transportation methods. Traffic information and inventory data are used as input, and optimal delivery route information (route path, distance, time, etc.) is generated as output.

[1034] Step 6:

[1035] The server assigns delivery vehicles

[1036] The server checks the list of available delivery vehicles, giving preference to renewable energy vehicles if an eco-friendly delivery option is selected. It uses the available vehicle data as input and assigns the best vehicle information for the specified route as output.

[1037] Step 7:

[1038] The device checks the optimal route and executes the delivery

[1039] The terminal (the delivery staff's device) receives the optimal route information sent from the server. It acquires the optimal route information as input and displays it to the delivery staff through the navigation app. The delivery staff follows these instructions to carry out the delivery and records the progress along the way on the terminal.

[1040] Step 8:

[1041] The server calculates and reports the carbon footprint

[1042] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the vehicle type used. It takes delivery performance data (distance, vehicle type, etc.) as input and produces the calculated carbon footprint as output, which can be reported to a carbon offset program.

[1043] (Application example 1)

[1044] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1045] Conventional delivery systems do not incorporate measures to select eco-friendly delivery methods, which results in a high environmental impact and the inability to provide optimal delivery routes that take traffic information into account. Additionally, the process of calculating and reporting carbon footprints is done manually, which is inefficient.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1047] In this invention, the server includes a demand forecasting means, a real-time traffic information obtaining means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for calculating and reporting a carbon footprint, a means for calculating a carbon footprint based on a delivery distance and a vehicle type used, a means for a user to place an order and transmit and record order data, a means for monitoring inventory levels in real time, a demand forecasting means for preventing overstocking and stockouts, a means for reporting the status of renewable energy vehicles, a means for obtaining real-time traffic information and calculating an optimal delivery route, a means for offering eco-friendly delivery options and allocating renewable energy vehicles, a means for allowing customers to select eco-friendly delivery options, a means for receiving and storing order data generated by customers, and a means for displaying optimal route information on a device of a delivery staff member and causing the delivery to be executed, thereby realizing an efficient and eco-friendly delivery system.

[1048] 1. "Demand forecasting means" refers to a means of analyzing customer order trends and forecasting future demand.

[1049] 2. "Means for obtaining real-time traffic information" means a means for obtaining current traffic conditions in real time.

[1050] 3. "Optimal delivery route calculation means" is a means for calculating the optimal delivery route based on traffic information and inventory data.

[1051] 4. "Eco-friendly transport proposals" are proposals for delivery methods that minimize the impact on the environment.

[1052] 5. "Renewable energy vehicle allocation measure" means a measure that gives priority to the allocation of vehicles that use renewable energy for deliveries.

[1053] 6. "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions from shipping.

[1054] 7. "Carbon Offset Reporting Instrument" means a reporting instrument to a carbon offset program based on a calculated carbon footprint.

[1055] 8. "Means for monitoring inventory levels in real time" means means for monitoring inventory status in real time.

[1056] 9. "Demand forecasting measures to prevent excess inventory and shortages" are measures to prevent excess inventory and shortages by accurately forecasting demand.

[1057] 10. "Means for reporting the status of renewable energy vehicles" means means for reporting the operating status and charging state of renewable energy vehicles.

[1058] 11. "Means for customers to select eco-friendly delivery options" means means for customers to select delivery options that are environmentally friendly.

[1059] 12. "Means for receiving and storing customer-generated order data" means means for receiving order information entered by a customer and storing it in a database.

[1060] 13. "Means for displaying optimal route information on a delivery staff member's device and executing delivery" means means for displaying an optimal route on a delivery staff member's device and executing delivery according to that route.

[1061] This invention is a smart food delivery management system that combines AI and IoT technology. The specific configuration and operation of the system are described in detail below.

[1062] The server receives and stores order data when a customer places an order. The order data includes the customer's selected products and the option to select eco-friendly delivery options. This order data is then compared with inventory data, which is updated in real time, making it easy to check stock levels.

[1063] The server then retrieves real-time traffic information via external APIs such as the Google Maps API. This information is used to calculate the optimal delivery route. The server calculates the optimal delivery route based on traffic information and inventory data, and prioritizes eco-friendly delivery methods.

[1064] Once the delivery route is determined, the server prioritizes the allocation of vehicles that use renewable energy. To do this, the server monitors the operating status and charging status of renewable energy vehicles in real time and selects the most appropriate vehicle.

[1065] The optimal route information sent from the server is displayed on the delivery staff's terminal, allowing them to deliver efficiently by following the optimal route. Once the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to the carbon offset program.

[1066] The server also calculates and reports the environmental benefit gained from using renewable energy vehicles, which is used to demonstrate how much the delivery company is contributing to environmental protection.

[1067] Specific examples

[1068] 1. A user orders food using a delivery app and selects an eco-friendly delivery option.

[1069] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1070] 3. The server obtains real-time traffic information via an external API.

[1071] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1072] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1073] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1074] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1075] Prompt Sentence Examples

[1076] A user ordered a pizza and salad and selected an eco-friendly delivery option. The server checked the inventory and confirmed there was enough stock. The server retrieved real-time traffic information, calculated the optimal delivery route, and assigned a renewable energy vehicle. The delivery route was then displayed on the delivery staff's tablet, and the delivery was made. After the delivery was completed, the server calculated the carbon footprint and reported it to the carbon offset program.

[1077] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1078] Step 1:

[1079] A user orders food using a delivery app and selects an eco-friendly delivery option.

[1080] Input: User order data (product information, eco-friendly options)

[1081] Output: Order data sent to the server

[1082] What it does: A user selects the food they want within a delivery app and chooses an eco-friendly delivery option, which generates order data and sends it to the server.

[1083] Step 2:

[1084] A server receives the order and accesses inventory sensors to determine the stock levels of the ordered items.

[1085] Input: Order data received by the server

[1086] Output: Inventory check results

[1087] Specific operation: The server records the order data in a database and obtains real-time inventory information from the inventory sensor, thereby checking whether the ordered item is in stock.

[1088] Step 3:

[1089] The server obtains real-time traffic information via an external API.

[1090] Input: External API request

[1091] Output: Real-time traffic information

[1092] What happens: The server sends a request to an external API (e.g., Google Maps API) to get real-time traffic information, which is used in the next step.

[1093] Step 4:

[1094] The server calculates the optimal delivery route based on inventory data and traffic information.

[1095] Input: inventory data, traffic information

[1096] Output: Optimal delivery route

[1097] How it works: The server runs an algorithm that calculates the optimal delivery route based on inventory data and traffic information, allowing delivery staff to follow the shortest and most efficient route.

[1098] Step 5:

[1099] The server checks available vehicles and allocates renewable energy vehicles.

[1100] Input: Vehicle status data

[1101] Output: Renewable energy vehicle allocation information

[1102] Specific operation: The server prioritizes renewable energy vehicles among available delivery vehicles, thereby achieving environmentally friendly delivery.

[1103] Step 6:

[1104] The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1105] Input: Optimal delivery route information

[1106] Output: Notification to transportation staff terminal

[1107] Specific operation: The optimal delivery route information is displayed on the delivery staff's terminal. The delivery staff starts delivery according to this information.

[1108] Step 7:

[1109] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1110] Input: Delivery distance data, vehicle type data

[1111] Output: Carbon footprint calculation results, report sending

[1112] What it does: The server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[1113] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1114] This invention is a smart delivery management system that combines AI and IoT technologies, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, it incorporates an emotion engine that recognizes user emotions and analyzes the user's emotion data to suggest optimal delivery options, which is an important element for improving the user experience.

[1115] Program processing overview

[1116] 1. User places an order

[1117] Users access an online store, select products, and enter their orders. When placing an order, they can choose eco-friendly delivery options. This order data is sent to a server and recorded. In addition, the user's emotional data is also collected. This emotional data is obtained using facial recognition technology and voice analysis.

[1118] 2. The server receives the order

[1119] The server receives the order data and emotion data sent by the user and stores them in a database. At this time, it prepares to acquire data from inventory sensors to check inventory levels in real time. The emotion engine personalizes the service based on the user's emotional state.

[1120] 3. The server checks the stock level

[1121] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[1122] 4. The server obtains traffic information

[1123] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[1124] 5. The server calculates the optimal delivery route

[1125] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[1126] 6. The server assigns a delivery vehicle

[1127] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[1128] 7. Personalized service with emotion engine

[1129] The emotion engine analyzes the user's emotional data and personalizes the service. For example, if it detects dissatisfaction or impatience, it can offer extra support or special offers. It can also suggest optimal delivery options based on the user's emotional data.

[1130] 8. The device checks the optimal route and executes the delivery

[1131] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1132] 9. The server calculates and reports the carbon footprint

[1133] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to a carbon offset program.

[1134] Specific examples

[1135] Next, a specific example of a delivery scenario will be described.

[1136] 1. A user places an order from an online store, selects an eco-friendly delivery option, and emotional data is collected.

[1137] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1138] 3. The server obtains real-time traffic information via an external API.

[1139] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1140] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1141] 6. The emotion engine analyzes emotional data and personalizes services.

[1142] 7. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1143] 8. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1144] 9. Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[1145] The above is the specific processing flow of the smart delivery management system. This system will realize efficient logistics and environmental protection. Furthermore, the introduction of an emotion engine will enable the provision of optimal services according to the user's emotional state.

[1146] The processing flow will be explained below.

[1147] Step 1:

[1148] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[1149] Step 2:

[1150] The server receives the order data sent by the user and stores it in a database. At this time, the emotion engine analyzes the emotion data of the user when placing the order and records the results in the database.

[1151] Step 3:

[1152] The server analyzes real-time inventory data obtained from the inventory sensors to check whether the ordered items are in sufficient stock and, if necessary, alerts the administrator if there is a shortage of stock.

[1153] Step 4:

[1154] The server obtains real-time traffic information via an external API and calculates the optimal delivery route based on that information, adjusting the route depending on the current traffic conditions.

[1155] Step 5:

[1156] The server checks the availability of renewable energy vehicles to suggest eco-friendly transportation options, and if an eco-friendly option is selected, prioritizes the allocation of renewable energy vehicles.

[1157] Step 6:

[1158] The emotion engine analyzes the user's emotional data and evaluates whether the user is satisfied or dissatisfied. This information is used to make personalized service recommendations. If dissatisfaction is detected, the user will be offered special services or discounts.

[1159] Step 7:

[1160] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1161] Step 8:

[1162] After a delivery is completed, the server calculates the carbon footprint based on the distance traveled and the type of vehicle used, and the results are reported to a carbon offset program.

[1163] Step 9:

[1164] The emotion engine will reassess the user's emotions once delivery is complete and collect feedback about the delivery, which will be used to improve the service in the future.

[1165] Step 10:

[1166] Users can check their order and delivery status online, receive eco-friendly delivery reports if they choose eco-friendly delivery options, and receive rewards based on their positive emotional feedback.

[1167] This is the specific process flow of the smart delivery management system. This system will not only improve logistics efficiency and protect the environment, but also provide optimal services that respond to user emotions.

[1168] Example 2

[1169] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1170] Today's delivery systems require efficient, eco-friendly delivery that responds quickly to consumer needs. However, integrating many elements, such as inventory management, real-time traffic information, optimal route calculation, and the use of renewable energy vehicles, is not easy. Furthermore, there is a lack of means to personalize services based on customer sentiment, and improving the user experience is also a challenge. Therefore, there is a need for a smart delivery management system that can solve these issues in an integrated manner.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1172] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for collecting and analyzing customer emotion data, and a means for personalizing services based on the emotion data. This enables the integration of complex data processing, efficient and environmentally friendly delivery management, and the provision of services that respond to customer emotions.

[1173] A "demand forecasting means" is a system that predicts future demand fluctuations and supports the development of appropriate inventory management and delivery plans.

[1174] "Real-time traffic information acquisition means" is a system that instantly collects current traffic conditions and uses them to optimize delivery routes.

[1175] The "optimal delivery route calculation means" is a system that uses an algorithm to calculate the optimal delivery route based on acquired traffic information and inventory data.

[1176] "Eco-friendly transportation proposal method" is a system for proposing environmentally friendly transportation methods.

[1177] The "renewable energy vehicle allocation means" is a system that prioritizes the allocation of vehicles that use renewable energy as vehicles to be used for delivery.

[1178] A "carbon footprint calculation tool" is a system that calculates the amount of carbon dioxide emitted during the delivery process.

[1179] A "carbon offset reporting method" is a system that reports calculated carbon footprints to a carbon offset program and contributes to environmental protection.

[1180] The "means for collecting and analyzing customer emotional data" is a system for collecting and analyzing customer emotional information.

[1181] "Means for personalizing services based on emotional data" refers to a system that provides optimal services to each individual customer based on their emotional data.

[1182] This invention provides a novel and innovative multi-functional system for a smart delivery management system that integrates AI and IoT technologies. The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, the system includes an emotion engine that collects and analyzes user emotion data and a means for personalizing services based on the emotion data, thereby improving the user experience.

[1183] First, a user accesses an online store through a web browser or mobile app, selects a product, and places an order. During the ordering process, the user's facial expressions and voice are collected through a camera and microphone, and emotion data is generated and sent to the server. This order data and emotion data are then stored on the server.

[1184] The server uses a demand forecasting means to predict future demand and supports appropriate inventory management and delivery planning. It uses a real-time traffic information acquisition means to collect traffic information from external APIs such as Google Maps API, and calculates the optimal delivery route using the Dijkstra algorithm or A algorithm in an optimal delivery route calculation means.

[1185] The server further proposes an environmentally friendly transportation method using an eco-friendly transportation suggestion means, preferentially allocates a renewable energy vehicle suitable for delivery using a renewable energy vehicle allocation means, calculates the amount of carbon dioxide emitted in the delivery process using a carbon footprint calculation means, and reports the result to a carbon offset program using a carbon offset reporting means.

[1186] The emotion engine collects and analyzes emotional data, analyzes the user's facial expressions and tone of voice, and offers rewards and additional support if negative emotions are detected. It also personalizes services based on emotional data, optimizing delivery options and response methods.

[1187] For example, when a user orders a product from an online store and selects an eco-friendly delivery option, a camera or microphone is used to collect the user's emotional data. The server receives the order data and emotional data and stores them in a MySQL or PostgreSQL database. The server then obtains real-time traffic information and calculates the optimal delivery route. After allocating a renewable energy vehicle, the service is personalized based on the emotional data. If the emotion engine detects negative emotions in this process, rewards or additional support are provided. After delivery is completed, the carbon footprint is calculated and reported to a carbon offset program.

[1188] An example prompt might be, "Describe a scenario in which a user orders a product from an online store and selects an eco-friendly delivery option. Also, describe in natural language how emotional data is collected and analyzed to personalize the service, and how the optimal delivery route is calculated and executed."

[1189] This system integrates complex data processing, enabling efficient and environmentally friendly delivery management, while also providing optimal services in response to user emotions. The detailed configuration and operating procedures of this invention are specifically described based on the claims that combine these elements.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] A user visits an online store, selects a product, adds it to their cart, and selects the "eco-friendly delivery option" at checkout. During this process, the user's facial expressions and voice are captured using a camera and microphone.

[1193] Input: Product selection, delivery option selection, emotion data collection

[1194] Output: Order data, emotion data

[1195] Specific behavior:

[1196] 1. A user adds an item to their cart on an online store's webpage.

[1197] 2. Select "Eco-Friendly Shipping Options" at checkout.

[1198] 3. Turn on the camera and microphone to capture facial expressions and voice in real time.

[1199] 4. The collected emotion data and order data are sent to the server.

[1200] Step 2:

[1201] The server receives the order data and emotion data sent by the user and stores it in a database. It also prepares to access the inventory sensor to check the inventory level.

[1202] Input: Order data, emotion data

[1203] Output: Order data and emotion data stored in the database, ready for inventory check

[1204] Specific behavior:

[1205] 1. The server receives the order data and emotion data.

[1206] 2. The received data is saved in a database (e.g., MySQL, PostgreSQL).

[1207] 3. Prepare to acquire inventory data from the inventory sensor.

[1208] Step 3:

[1209] The server receives real-time inventory data from the inventory sensor, checks whether the ordered item is in sufficient stock, and issues an alert if the item is out of stock.

[1210] Input: Order data

[1211] Output: Inventory check result (sufficient / shortage), alert

[1212] Specific behavior:

[1213] 1. The server retrieves inventory data from the RFID system.

[1214] 2. Check stock levels of ordered items.

[1215] 3. If there is a shortage of stock, an alert will be displayed on the management dashboard and an email will be sent.

[1216] Step 4:

[1217] The server obtains real-time traffic information from an external API (e.g., Google Maps API) and uses this information to calculate the optimal delivery route.

[1218] Input: Request

[1219] Output: Real-time traffic information

[1220] Specific behavior:

[1221] 1. The server sends a request to the Google Maps API.

[1222] 2. Get real-time traffic information from API.

[1223] 3. The acquired traffic information is stored in a database.

[1224] Step 5:

[1225] The server calculates the optimal delivery route based on real-time traffic and inventory data, prioritizing eco-friendly routes using Dijkstra and A algorithms.

[1226] Input: Traffic information, inventory data

[1227] Output: Optimal delivery route

[1228] Specific behavior:

[1229] 1. The server calculates the optimal delivery route based on inventory data and traffic information.

[1230] 2. Use Dijkstra algorithm or A algorithm to select an efficient route.

[1231] 3. Save the calculation results in the database.

[1232] Step 6:

[1233] The server checks available delivery vehicles and, if an eco-friendly option is selected, allocates renewable energy vehicles with priority. The allocation information is sent to the terminal.

[1234] Input: Optimal delivery route, renewable energy vehicle status

[1235] Output: Allocation information

[1236] Specific behavior:

[1237] 1. The server checks the status of the delivery vehicle.

[1238] 2. Prioritize eco-friendly options and allocate renewable energy vehicles.

[1239] 3. Send allocation information to the terminal (transportation staff device).

[1240] Step 7:

[1241] The emotion engine analyzes collected emotional data to personalize the service, offering perks or additional support if frustration or impatience is detected.

[1242] Input: Emotion data

[1243] Output: Personalized settings, special offers and additional support

[1244] Specific behavior:

[1245] 1. The emotion engine analyzes the emotion data.

[1246] 2. Generate rewards or additional support when negative sentiment is detected.

[1247] 3. Share your personalization settings with the server.

[1248] Step 8:

[1249] The terminal (the delivery staff's device) receives the optimal delivery route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1250] Input: Optimal delivery route information

[1251] Output: Route guidance to transportation staff

[1252] Specific behavior:

[1253] 1. The terminal receives optimal delivery route information from the server.

[1254] 2. Display the received route information to the transportation staff.

[1255] 3. Transport staff executes deliveries based on information.

[1256] Step 9:

[1257] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type used and reports the results to a carbon offset program.

[1258] Input: Delivery distance, Vehicle type

[1259] Output: Carbon footprint calculation results, carbon offset report

[1260] Specific behavior:

[1261] 1. The server calculates the carbon footprint based on delivery distance and vehicle type.

[1262] 2. Report the calculation results to the carbon offset program.

[1263] 3. Record the report in the database.

[1264] (Application example 2)

[1265] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1266] Environmental protection and efficient delivery are important issues in modern logistics and delivery services. However, existing delivery systems do not adequately prioritize the use of renewable energy vehicles or propose eco-friendly transportation methods. Furthermore, the lack of personalized service based on customer sentiment makes it difficult to improve customer satisfaction. Therefore, the present invention aims to achieve both environmental protection and customer satisfaction by calculating optimal delivery routes using real-time traffic information and sentiment data, using renewable energy vehicles, and reporting carbon footprints.

[1267] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1268] In this invention, the server includes a demand forecasting means, a real-time traffic information acquiring means, an optimal delivery route calculating means, an eco-friendly transportation suggesting means, a renewable energy vehicle allocation means, a carbon footprint calculating means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data, thereby enabling efficient and eco-friendly delivery and improved customer satisfaction.

[1269] The "demand forecasting means" is a means for forecasting user demand.

[1270] The "real-time traffic information acquisition means" is a means for acquiring traffic information in real time.

[1271] The "optimal delivery route calculation means" is a means for calculating an optimal delivery route based on the acquired data.

[1272] "Eco-friendly transportation proposal means" is a means for proposing environmentally friendly transportation methods.

[1273] "Renewable energy vehicle allocation means" is a means for allocating renewable energy vehicles to deliveries.

[1274] "Carbon Footprint Calculation Tool" means a tool for calculating the carbon footprint of a delivery.

[1275] "Carbon Offset Reporting Instrument" means an instrument for reporting on carbon offsets.

[1276] The "means for acquiring and analyzing user emotion data" refers to a means for acquiring user emotion data and analyzing it.

[1277] The "means for providing a personalized service based on emotional data" is a means for providing a personalized service based on analyzed emotional data.

[1278] "Means for monitoring inventory levels in real time" refers to means for monitoring the inventory status in real time.

[1279] The "demand forecasting means for preventing excess inventory and shortages" is a means for forecasting demand to prevent excess inventory and shortages.

[1280] A "means for reporting the status of a renewable energy vehicle" is a means for reporting the status of a renewable energy vehicle.

[1281] "Means for calculating and reporting carbon footprint after delivery completion" refers to means for calculating carbon footprint after delivery completion and reporting the results.

[1282] The "means for providing a customer with a benefit based on emotional data" is a means for providing a benefit based on emotional data of a customer.

[1283] "Means for customers to select eco-friendly delivery options" means means for customers to select environmentally friendly delivery options.

[1284] The "means for receiving and storing order data generated by a customer" refers to a means for receiving and storing order data generated by a customer.

[1285] The "means for proposing delivery options based on emotional data" is a means for proposing the optimal delivery option based on emotional data.

[1286] The "means for providing special support using an emotion engine" is a means for providing special support to a customer using an emotion engine.

[1287] The present invention is a system for achieving both environmental protection and customer satisfaction in logistics and delivery services. Detailed embodiments for realizing this system will be described below.

[1288] 1. Overall system configuration

[1289] The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data.

[1290] 1.1 Hardware and software used

[1291] Hardware:

[1292] Smartphones (e.g. iPhone, Android devices)

[1293] Renewable energy vehicles (e.g. electric vehicles)

[1294] Inventory sensors (e.g. RFID tags)

[1295] software:

[1296] Server (e.g. AWS)

[1297] External APIs (e.g. Google Maps API)

[1298] Emotion engine (e.g. Microsoft Azure Emotion API)

[1299] Delivery device app (e.g. iOS app, Android app)

[1300] 2. Specific processing flow

[1301] The server can predict user demand using a demand forecasting means and monitor inventory levels in real time. If inventory is low, the server will issue an alert. It also uses a real-time traffic information acquisition means to obtain traffic information from external APIs (such as Google Maps API) and calculate the optimal delivery route.

[1302] The renewable energy vehicle allocation means allows vehicles that use renewable energy to be preferentially allocated using the eco-friendly transportation suggestion means. After delivery is completed, the server calculates the carbon footprint using the carbon footprint calculation means and reports the result using the carbon offset reporting means.

[1303] Furthermore, it can acquire user emotion data, analyze it using an emotion engine (such as Microsoft Azure Emotion API), and provide personalized services based on the emotion data. For example, if a user expresses dissatisfaction during delivery, it can offer special support or coupons.

[1304] 3. Specific Examples

[1305] Below is a specific scenario from ordering to delivery.

[1306] Users use the application to select eco-friendly delivery options, and emotion data is automatically collected using facial recognition technology. The server receives order and emotion data, obtains and confirms inventory data from inventory sensors, obtains real-time traffic information, calculates the optimal delivery route, and allocates renewable energy vehicles. Meanwhile, the emotion engine analyzes the user's emotions and provides special support. After the delivery is completed, the server calculates the carbon footprint and reports it to the user.

[1307] Prompt Sentence Examples

[1308] Explain how your food delivery app uses emotional data collected through facial recognition technology to calculate optimal delivery routes and prioritize renewable energy vehicles when a user selects an eco-friendly option during the ordering process. Provide examples of how your app calculates and reports the carbon footprint after the delivery is completed.

[1309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1310] Step 1:

[1311] The user places an order in the app.

[1312] Input: User order data and emotion data

[1313] How it works: The user opens a food delivery app on their smartphone or head-mounted display and enters their order. They can select eco-friendly delivery options when placing their order. The system also simultaneously collects the user's emotional data using facial recognition and voice analysis technologies.

[1314] Output: The order data and emotion data are sent to the server.

[1315] Step 2:

[1316] A server receives and stores the order and emotion data.

[1317] Input: Order data and emotion data from users

[1318] Specific operation: The server stores the received order data and emotion data in a database. At the same time, it prepares to obtain real-time inventory data from the inventory sensor.

[1319] Output: Stored order and sentiment data, inventory data ready to be retrieved.

[1320] Step 3:

[1321] The server checks the inventory data.

[1322] Input: Real-time inventory data from inventory sensors

[1323] Specific operation: The server analyzes real-time inventory data obtained from the inventory sensor, checks whether there is sufficient stock of the ordered item, and issues an alert if there is a shortage of stock.

[1324] Output: Stock check results, alerts if necessary.

[1325] Step 4:

[1326] The server obtains real-time traffic information.

[1327] Input: Traffic information request from external API

[1328] Specific operation: The server uses an external API such as the Google Maps API to obtain real-time traffic information.

[1329] Output: Real-time traffic information data.

[1330] Step 5:

[1331] The server calculates the optimal delivery route.

[1332] Input: Traffic and inventory data

[1333] How it works: The server calculates the optimal delivery route based on real-time traffic and inventory information, prioritizing eco-friendly shipping methods.

[1334] Output: Optimal delivery route information.

[1335] Step 6:

[1336] The server allocates renewable energy vehicles.

[1337] Input: Optimal delivery route information

[1338] Specific operation: After calculating the optimal route, the server checks and allocates available renewable energy vehicles.

[1339] Output: Allocated delivery vehicle information.

[1340] Step 7:

[1341] The server personalizes the service based on the emotion data.

[1342] Input: Emotion data

[1343] Specific operation: The server uses the emotion engine to analyze the user's emotion data and provide special support or benefits as needed.

[1344] Output: Personalized service content.

[1345] Step 8:

[1346] The terminal receives the optimum route information and executes the delivery.

[1347] Input: Optimal delivery route information

[1348] Specific operation: The delivery staff's terminal receives and displays the optimal delivery route information from the server. The delivery staff then carries out delivery according to the optimal route.

[1349] Output: Delivery status.

[1350] Step 9:

[1351] The server calculates and reports the carbon footprint.

[1352] Input: Delivery distance and vehicle type

[1353] What it does: After a delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[1354] Output: Carbon footprint report.

[1355] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1356] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1357] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1358] [Fourth embodiment]

[1359] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1360] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1361] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1362] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1363] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1364] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1365] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1366] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1367] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1368] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1369] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1370] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1371] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1372] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Specific embodiments for implementing this invention will be described.

[1373] Program processing overview

[1374] 1. User places an order

[1375] Users can access the online store, select products, and select eco-friendly delivery options when placing an order. This order data is sent to the server and recorded.

[1376] 2. The server receives the order

[1377] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[1378] 3. The server checks the stock level

[1379] The server retrieves real-time inventory data from the inventory sensors and ensures that the ordered items are in sufficient stock, and if necessary, issues an alert if there is insufficient stock.

[1380] 4. The server obtains traffic information

[1381] The server receives real-time traffic information via an external API, which is used to calculate the optimal delivery route.

[1382] 5. The server calculates the optimal delivery route

[1383] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[1384] 6. The server assigns a delivery vehicle

[1385] The server checks available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected.

[1386] 7. The device checks the optimal route and executes the delivery

[1387] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1388] 8. The server calculates and reports the carbon footprint

[1389] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[1390] Specific examples

[1391] Next, a specific example of a delivery scenario will be described.

[1392] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[1393] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1394] 3. The server obtains real-time traffic information via an external API.

[1395] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1396] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1397] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1398] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1399] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[1400] The processing flow will be explained below.

[1401] Step 1:

[1402] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[1403] Step 2:

[1404] The server receives the order data sent by the user and stores it in a database, while also preparing to acquire data from inventory sensors to check inventory levels in real time.

[1405] Step 3:

[1406] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[1407] Step 4:

[1408] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[1409] Step 5:

[1410] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[1411] Step 6:

[1412] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[1413] Step 7:

[1414] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1415] Step 8:

[1416] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[1417] Step 9:

[1418] Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[1419] This is the specific process flow of the smart delivery management system, which will realize efficient logistics and environmental protection.

[1420] Example 1

[1421] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1422] The logistics industry is in high demand for eco-friendly and efficient deliveries, but traditional systems face challenges in planning optimal delivery routes based on real-time information and effectively utilizing renewable energy vehicles. In addition, inventory management and carbon footprint reporting are often done manually, resulting in low accuracy and efficiency.

[1423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1424] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, and a carbon footprint calculation means based on delivery distance and vehicle type. This enables eco-friendly and efficient delivery planning, and makes it possible to automate inventory management and carbon footprint reporting while reducing the burden on the environment.

[1425] A "demand forecasting means" is a means for predicting future demand for a product by analyzing past sales data and market trends.

[1426] "Means for obtaining real-time traffic information" refers to a means for obtaining the latest traffic conditions using external information services or APIs.

[1427] The "optimal delivery route calculation means" is a means for calculating the most efficient delivery route based on traffic information and inventory status.

[1428] The "eco-friendly transportation proposal method" is a method for proposing environmentally friendly transportation methods and calculating renewable energy vehicles and efficient delivery routes with priority.

[1429] The "renewable energy vehicle allocation means" is a means for checking available renewable energy vehicles and allocating them to deliveries on a priority basis.

[1430] "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions during delivery.

[1431] "Carbon Offset Reporting Instrument" means the instrument used to report a calculated carbon footprint to a carbon offset program.

[1432] The "inventory data acquisition means" is a means for acquiring inventory information in real time from an inventory sensor or the like.

[1433] The "means for calculating a delivery route based on traffic information" is a means for calculating an optimal delivery route based on the acquired traffic information.

[1434] A "means for allocating delivery vehicles" is a means for identifying available delivery vehicles and allocating them to a particular delivery task.

[1435] The "means for calculating carbon footprint based on delivery distance and vehicle type" is a means for calculating an accurate carbon footprint based on delivery distance and the type of vehicle used.

[1436] "Means for allowing users to select eco-friendly delivery options" refers to means for allowing users to select eco-friendly delivery options at online stores.

[1437] A "demand forecasting method using a generative AI model" is a method for forecasting future demand for a product using a generative AI model.

[1438] "Means for generating prompt sentences" refers to means for creating input prompt sentences for the generative AI model.

[1439] This invention is a smart delivery management system that combines AI and IoT technology, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, an inventory data acquisition means, a delivery route calculation means based on traffic information, a delivery vehicle allocation means, a carbon footprint calculation means based on delivery distance and vehicle type, a demand forecasting means using a generative AI model, and a prompt sentence generation means.

[1440] System Program

[1441] A user orders products from an online store. When placing an order, the user can select an eco-friendly delivery option. The order data is sent to the server and recorded.

[1442] The server receives the order data sent by the user and stores it in a database. It then obtains real-time inventory data from the inventory sensor to check whether the ordered item is in sufficient stock. If there is a shortage of stock, it issues a warning.

[1443] Get real-time traffic information via external APIs (e.g., Google Maps API or HERE API). Calculate the optimal delivery route based on the traffic data. Prioritize eco-friendly transportation methods and use renewable energy vehicles when calculating routes.

[1444] The server checks the list of available delivery vehicles and prioritizes renewable energy vehicles if an eco-friendly transportation option is selected. The delivery staff's terminal (device) receives the optimal route information sent from the server and displays it to the delivery staff.

[1445] The delivery staff carries out the delivery based on the route information displayed on the terminal. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to the carbon offset program.

[1446] Specific hardware and software

[1447] The hardware used includes inventory sensors, renewable energy vehicles, and terminal devices for transportation staff, while the software used includes Google Maps API, HERE API, a real-time inventory management system, and carbon footprint calculation algorithms.

[1448] Specific examples

[1449] A specific delivery scenario is shown below.

[1450] 1. A user orders a product from an online store and selects an eco-friendly delivery option.

[1451] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1452] 3. The server obtains real-time traffic information via an external API.

[1453] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1454] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1455] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1456] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1457] Prompt Sentence Examples

[1458] Below are some examples of prompts to input to the generative AI model.

[1459] ---

[1460] "Please explain the key functions of your smart delivery management system step by step. Include detailed steps for order processing, inventory check, traffic information acquisition, optimal delivery route calculation, eco-friendly transportation, and carbon footprint calculation."

[1461] ---

[1462] The system provides users with an efficient and eco-friendly delivery experience, while enabling logistics companies to improve inventory management efficiency and optimize transportation, contributing to environmental protection.

[1463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1464] Step 1:

[1465] User places an order

[1466] A user visits an online store, selects a product, and adds it to their cart. They then proceed to the order screen and select eco-friendly shipping as a shipping option. The selected information is sent as input to the server, which receives this information and stores the order data (product ID, quantity, eco-friendly shipping flag, etc.).

[1467] Step 2:

[1468] The server receives the order

[1469] The server receives order data from the user and records it in the database. At this time, it also saves the order ID, order date and time, and user information (such as delivery address). Based on the order data received as input, it creates a new record in the database as output.

[1470] Step 3:

[1471] The server checks the stock level

[1472] The server gets real-time inventory data from the inventory sensor. The inventory sensor periodically updates the stock count for each item. It receives the inventory data as input and checks whether there is enough stock of the ordered item. It outputs the inventory status and generates a warning message if there is a shortage.

[1473] Step 4:

[1474] The server obtains traffic information

[1475] The server calls external APIs (e.g., Google Maps API or HERE API) to obtain real-time traffic information. It sends API requests as input and receives traffic conditions (traffic volume, delay information, etc.) as output. Based on this data, it organizes the information necessary for delivery planning.

[1476] Step 5:

[1477] The server calculates the optimal delivery route

[1478] The server combines the acquired traffic information and inventory data to calculate the optimal delivery route. The route calculation applies an algorithm that prioritizes eco-friendly transportation methods. Traffic information and inventory data are used as input, and optimal delivery route information (route path, distance, time, etc.) is generated as output.

[1479] Step 6:

[1480] The server assigns delivery vehicles

[1481] The server checks the list of available delivery vehicles, giving preference to renewable energy vehicles if an eco-friendly delivery option is selected. It uses the available vehicle data as input and assigns the best vehicle information for the specified route as output.

[1482] Step 7:

[1483] The device checks the optimal route and executes the delivery

[1484] The terminal (the delivery staff's device) receives the optimal route information sent from the server. It acquires the optimal route information as input and displays it to the delivery staff through the navigation app. The delivery staff follows these instructions to carry out the delivery and records the progress along the way on the terminal.

[1485] Step 8:

[1486] The server calculates and reports the carbon footprint

[1487] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the vehicle type used. It takes delivery performance data (distance, vehicle type, etc.) as input and produces the calculated carbon footprint as output, which can be reported to a carbon offset program.

[1488] (Application example 1)

[1489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1490] Conventional delivery systems do not incorporate measures to select eco-friendly delivery methods, which results in a high environmental impact and the inability to provide optimal delivery routes that take traffic information into account. Additionally, the process of calculating and reporting carbon footprints is done manually, which is inefficient.

[1491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1492] In this invention, the server includes a demand forecasting means, a real-time traffic information obtaining means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for calculating and reporting a carbon footprint, a means for calculating a carbon footprint based on a delivery distance and a vehicle type used, a means for a user to place an order and transmit and record order data, a means for monitoring inventory levels in real time, a demand forecasting means for preventing overstocking and stockouts, a means for reporting the status of renewable energy vehicles, a means for obtaining real-time traffic information and calculating an optimal delivery route, a means for offering eco-friendly delivery options and allocating renewable energy vehicles, a means for allowing customers to select eco-friendly delivery options, a means for receiving and storing order data generated by customers, and a means for displaying optimal route information on a device of a delivery staff member and causing the delivery to be executed, thereby realizing an efficient and eco-friendly delivery system.

[1493] 1. "Demand forecasting means" refers to a means of analyzing customer order trends and forecasting future demand.

[1494] 2. "Means for obtaining real-time traffic information" means a means for obtaining current traffic conditions in real time.

[1495] 3. "Optimal delivery route calculation means" is a means for calculating the optimal delivery route based on traffic information and inventory data.

[1496] 4. "Eco-friendly transport proposals" are proposals for delivery methods that minimize the impact on the environment.

[1497] 5. "Renewable energy vehicle allocation measure" means a measure that gives priority to the allocation of vehicles that use renewable energy for deliveries.

[1498] 6. "Carbon Footprint Calculation Tool" means a tool for calculating carbon dioxide emissions from shipping.

[1499] 7. "Carbon Offset Reporting Instrument" means a reporting instrument to a carbon offset program based on a calculated carbon footprint.

[1500] 8. "Means for monitoring inventory levels in real time" means means for monitoring inventory status in real time.

[1501] 9. "Demand forecasting measures to prevent excess inventory and shortages" are measures to prevent excess inventory and shortages by accurately forecasting demand.

[1502] 10. "Means for reporting the status of renewable energy vehicles" means means for reporting the operating status and charging state of renewable energy vehicles.

[1503] 11. "Means for customers to select eco-friendly delivery options" means means for customers to select delivery options that are environmentally friendly.

[1504] 12. "Means for receiving and storing customer-generated order data" means means for receiving order information entered by a customer and storing it in a database.

[1505] 13. "Means for displaying optimal route information on a delivery staff member's device and executing delivery" means means for displaying an optimal route on a delivery staff member's device and executing delivery according to that route.

[1506] This invention is a smart food delivery management system that combines AI and IoT technology. The specific configuration and operation of the system are described in detail below.

[1507] The server receives and stores order data when a customer places an order. The order data includes the customer's selected products and the option to select eco-friendly delivery options. This order data is then compared with inventory data, which is updated in real time, making it easy to check stock levels.

[1508] The server then retrieves real-time traffic information via external APIs such as the Google Maps API. This information is used to calculate the optimal delivery route. The server calculates the optimal delivery route based on traffic information and inventory data, and prioritizes eco-friendly delivery methods.

[1509] Once the delivery route is determined, the server prioritizes the allocation of vehicles that use renewable energy. To do this, the server monitors the operating status and charging status of renewable energy vehicles in real time and selects the most appropriate vehicle.

[1510] The optimal route information sent from the server is displayed on the delivery staff's terminal, allowing them to deliver efficiently by following the optimal route. Once the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to the carbon offset program.

[1511] The server also calculates and reports the environmental benefit gained from using renewable energy vehicles, which is used to demonstrate how much the delivery company is contributing to environmental protection.

[1512] Specific examples

[1513] 1. A user orders food using a delivery app and selects an eco-friendly delivery option.

[1514] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1515] 3. The server obtains real-time traffic information via an external API.

[1516] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1517] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1518] 6. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1519] 7. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1520] Prompt Sentence Examples

[1521] A user ordered a pizza and salad and selected an eco-friendly delivery option. The server checked the inventory and confirmed there was enough stock. The server retrieved real-time traffic information, calculated the optimal delivery route, and assigned a renewable energy vehicle. The delivery route was then displayed on the delivery staff's tablet, and the delivery was made. After the delivery was completed, the server calculated the carbon footprint and reported it to the carbon offset program.

[1522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1523] Step 1:

[1524] A user orders food using a delivery app and selects an eco-friendly delivery option.

[1525] Input: User order data (product information, eco-friendly options)

[1526] Output: Order data sent to the server

[1527] What it does: A user selects the food they want within a delivery app and chooses an eco-friendly delivery option, which generates order data and sends it to the server.

[1528] Step 2:

[1529] A server receives the order and accesses inventory sensors to determine the stock levels of the ordered items.

[1530] Input: Order data received by the server

[1531] Output: Inventory check results

[1532] Specific operation: The server records the order data in a database and obtains real-time inventory information from the inventory sensor, thereby checking whether the ordered item is in stock.

[1533] Step 3:

[1534] The server obtains real-time traffic information via an external API.

[1535] Input: External API request

[1536] Output: Real-time traffic information

[1537] What happens: The server sends a request to an external API (e.g., Google Maps API) to get real-time traffic information, which is used in the next step.

[1538] Step 4:

[1539] The server calculates the optimal delivery route based on inventory data and traffic information.

[1540] Input: inventory data, traffic information

[1541] Output: Optimal delivery route

[1542] How it works: The server runs an algorithm that calculates the optimal delivery route based on inventory data and traffic information, allowing delivery staff to follow the shortest and most efficient route.

[1543] Step 5:

[1544] The server checks available vehicles and allocates renewable energy vehicles.

[1545] Input: Vehicle status data

[1546] Output: Renewable energy vehicle allocation information

[1547] Specific operation: The server prioritizes renewable energy vehicles among available delivery vehicles, thereby achieving environmentally friendly delivery.

[1548] Step 6:

[1549] The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1550] Input: Optimal delivery route information

[1551] Output: Notification to transportation staff terminal

[1552] Specific operation: The optimal delivery route information is displayed on the delivery staff's terminal. The delivery staff starts delivery according to this information.

[1553] Step 7:

[1554] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1555] Input: Delivery distance data, vehicle type data

[1556] Output: Carbon footprint calculation results, report sending

[1557] What it does: The server calculates the carbon footprint based on the delivery distance and the type of vehicle used, and reports the results to a carbon offset program.

[1558] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1559] This invention is a smart delivery management system that combines AI and IoT technologies, and includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, it incorporates an emotion engine that recognizes user emotions and analyzes the user's emotion data to suggest optimal delivery options, which is an important element for improving the user experience.

[1560] Program processing overview

[1561] 1. User places an order

[1562] Users access an online store, select products, and enter their orders. When placing an order, they can choose eco-friendly delivery options. This order data is sent to a server and recorded. In addition, the user's emotional data is also collected. This emotional data is obtained using facial recognition technology and voice analysis.

[1563] 2. The server receives the order

[1564] The server receives the order data and emotion data sent by the user and stores them in a database. At this time, it prepares to acquire data from inventory sensors to check inventory levels in real time. The emotion engine personalizes the service based on the user's emotional state.

[1565] 3. The server checks the stock level

[1566] The server analyzes real-time inventory data obtained from the inventory sensor to check whether the ordered item is in sufficient stock and issues an alert if the item is out of stock.

[1567] 4. The server obtains traffic information

[1568] The server obtains real-time traffic information via an external API and uses this information to calculate the optimal delivery route.

[1569] 5. The server calculates the optimal delivery route

[1570] The server calculates the optimal delivery route based on real-time traffic information and current inventory data, prioritizing eco-friendly transportation methods and using renewable energy vehicles.

[1571] 6. The server assigns a delivery vehicle

[1572] After calculating the optimal delivery route, the server checks the available delivery vehicles. If an eco-friendly delivery option is selected, renewable energy vehicles are given priority.

[1573] 7. Personalized service with emotion engine

[1574] The emotion engine analyzes the user's emotional data and personalizes the service. For example, if it detects dissatisfaction or impatience, it can offer extra support or special offers. It can also suggest optimal delivery options based on the user's emotional data.

[1575] 8. The device checks the optimal route and executes the delivery

[1576] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1577] 9. The server calculates and reports the carbon footprint

[1578] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports the results to a carbon offset program.

[1579] Specific examples

[1580] Next, a specific example of a delivery scenario will be described.

[1581] 1. A user places an order from an online store, selects an eco-friendly delivery option, and emotional data is collected.

[1582] 2. The server receives the order and accesses the inventory sensor to check the stock level of the ordered item.

[1583] 3. The server obtains real-time traffic information via an external API.

[1584] 4. The server calculates the optimal delivery route based on inventory data and traffic information.

[1585] 5. The server checks available vehicles and allocates a renewable energy vehicle.

[1586] 6. The emotion engine analyzes emotional data and personalizes services.

[1587] 7. The terminal receives the optimal delivery route information and displays it to the transportation staff.

[1588] 8. After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type and reports it to the carbon offset program.

[1589] 9. Users can check their order and delivery status online and, if eco-friendly delivery is selected, receive a report on their environmental contribution.

[1590] The above is the specific processing flow of the smart delivery management system. This system will realize efficient logistics and environmental protection. Furthermore, the introduction of an emotion engine will enable the provision of optimal services according to the user's emotional state.

[1591] The processing flow will be explained below.

[1592] Step 1:

[1593] A user accesses an online store, selects a product, and enters an order. When placing an order, the user selects an eco-friendly delivery option. This order data is sent to the server.

[1594] Step 2:

[1595] The server receives the order data sent by the user and stores it in a database. At this time, the emotion engine analyzes the emotion data of the user when placing the order and records the results in the database.

[1596] Step 3:

[1597] The server analyzes real-time inventory data obtained from the inventory sensors to check whether the ordered items are in sufficient stock and, if necessary, alerts the administrator if there is a shortage of stock.

[1598] Step 4:

[1599] The server obtains real-time traffic information via an external API and calculates the optimal delivery route based on that information, adjusting the route depending on the current traffic conditions.

[1600] Step 5:

[1601] The server checks the availability of renewable energy vehicles to suggest eco-friendly transportation options, and if an eco-friendly option is selected, prioritizes the allocation of renewable energy vehicles.

[1602] Step 6:

[1603] The emotion engine analyzes the user's emotional data and evaluates whether the user is satisfied or dissatisfied. This information is used to make personalized service recommendations. If dissatisfaction is detected, the user will be offered special services or discounts.

[1604] Step 7:

[1605] The terminal (the delivery staff's device) receives the optimal route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1606] Step 8:

[1607] After a delivery is completed, the server calculates the carbon footprint based on the distance traveled and the type of vehicle used, and the results are reported to a carbon offset program.

[1608] Step 9:

[1609] The emotion engine will reassess the user's emotions once delivery is complete and collect feedback about the delivery, which will be used to improve the service in the future.

[1610] Step 10:

[1611] Users can check their order and delivery status online, receive eco-friendly delivery reports if they choose eco-friendly delivery options, and receive rewards based on their positive emotional feedback.

[1612] This is the specific process flow of the smart delivery management system. This system will not only improve logistics efficiency and protect the environment, but also provide optimal services that respond to user emotions.

[1613] Example 2

[1614] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1615] Today's delivery systems require efficient, eco-friendly delivery that responds quickly to consumer needs. However, integrating many elements, such as inventory management, real-time traffic information, optimal route calculation, and the use of renewable energy vehicles, is not easy. Furthermore, there is a lack of means to personalize services based on customer sentiment, and improving the user experience is also a challenge. Therefore, there is a need for a smart delivery management system that can solve these issues in an integrated manner.

[1616] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1617] In this invention, the server includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for collecting and analyzing customer emotion data, and a means for personalizing services based on the emotion data. This enables the integration of complex data processing, efficient and environmentally friendly delivery management, and the provision of services that respond to customer emotions.

[1618] A "demand forecasting means" is a system that predicts future demand fluctuations and supports the development of appropriate inventory management and delivery plans.

[1619] "Real-time traffic information acquisition means" is a system that instantly collects current traffic conditions and uses them to optimize delivery routes.

[1620] The "optimal delivery route calculation means" is a system that uses an algorithm to calculate the optimal delivery route based on acquired traffic information and inventory data.

[1621] "Eco-friendly transportation proposal method" is a system for proposing environmentally friendly transportation methods.

[1622] The "renewable energy vehicle allocation means" is a system that prioritizes the allocation of vehicles that use renewable energy as vehicles to be used for delivery.

[1623] A "carbon footprint calculation tool" is a system that calculates the amount of carbon dioxide emitted during the delivery process.

[1624] A "carbon offset reporting method" is a system that reports calculated carbon footprints to a carbon offset program and contributes to environmental protection.

[1625] The "means for collecting and analyzing customer emotional data" is a system for collecting and analyzing customer emotional information.

[1626] "Means for personalizing services based on emotional data" refers to a system that provides optimal services to each individual customer based on their emotional data.

[1627] This invention provides a novel and innovative multi-functional system for a smart delivery management system that integrates AI and IoT technologies. The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation proposal means, a renewable energy vehicle allocation means, a carbon footprint calculation means, and a carbon offset reporting means. Furthermore, the system includes an emotion engine that collects and analyzes user emotion data and a means for personalizing services based on the emotion data, thereby improving the user experience.

[1628] First, a user accesses an online store through a web browser or mobile app, selects a product, and places an order. During the ordering process, the user's facial expressions and voice are collected through a camera and microphone, and emotion data is generated and sent to the server. This order data and emotion data are then stored on the server.

[1629] The server uses a demand forecasting means to predict future demand and supports appropriate inventory management and delivery planning. It uses a real-time traffic information acquisition means to collect traffic information from external APIs such as Google Maps API, and calculates the optimal delivery route using the Dijkstra algorithm or A algorithm in an optimal delivery route calculation means.

[1630] The server further proposes an environmentally friendly transportation method using an eco-friendly transportation suggestion means, preferentially allocates a renewable energy vehicle suitable for delivery using a renewable energy vehicle allocation means, calculates the amount of carbon dioxide emitted in the delivery process using a carbon footprint calculation means, and reports the result to a carbon offset program using a carbon offset reporting means.

[1631] The emotion engine collects and analyzes emotional data, analyzes the user's facial expressions and tone of voice, and offers rewards and additional support if negative emotions are detected. It also personalizes services based on emotional data, optimizing delivery options and response methods.

[1632] For example, when a user orders a product from an online store and selects an eco-friendly delivery option, a camera or microphone is used to collect the user's emotional data. The server receives the order data and emotional data and stores them in a MySQL or PostgreSQL database. The server then obtains real-time traffic information and calculates the optimal delivery route. After allocating a renewable energy vehicle, the service is personalized based on the emotional data. If the emotion engine detects negative emotions in this process, rewards or additional support are provided. After delivery is completed, the carbon footprint is calculated and reported to a carbon offset program.

[1633] An example prompt might be, "Describe a scenario in which a user orders a product from an online store and selects an eco-friendly delivery option. Also, describe in natural language how emotional data is collected and analyzed to personalize the service, and how the optimal delivery route is calculated and executed."

[1634] This system integrates complex data processing, enabling efficient and environmentally friendly delivery management, while also providing optimal services in response to user emotions. The detailed configuration and operating procedures of this invention are specifically described based on the claims that combine these elements.

[1635] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1636] Step 1:

[1637] A user visits an online store, selects a product, adds it to their cart, and selects the "eco-friendly delivery option" at checkout. During this process, the user's facial expressions and voice are captured using a camera and microphone.

[1638] Input: Product selection, delivery option selection, emotion data collection

[1639] Output: Order data, emotion data

[1640] Specific behavior:

[1641] 1. A user adds an item to their cart on an online store's webpage.

[1642] 2. Select "Eco-Friendly Shipping Options" at checkout.

[1643] 3. Turn on the camera and microphone to capture facial expressions and voice in real time.

[1644] 4. The collected emotion data and order data are sent to the server.

[1645] Step 2:

[1646] The server receives the order data and emotion data sent by the user and stores it in a database. It also prepares to access the inventory sensor to check the inventory level.

[1647] Input: Order data, emotion data

[1648] Output: Order data and emotion data stored in the database, ready for inventory check

[1649] Specific behavior:

[1650] 1. The server receives the order data and emotion data.

[1651] 2. The received data is saved in a database (e.g., MySQL, PostgreSQL).

[1652] 3. Prepare to acquire inventory data from the inventory sensor.

[1653] Step 3:

[1654] The server receives real-time inventory data from the inventory sensor, checks whether the ordered item is in sufficient stock, and issues an alert if the item is out of stock.

[1655] Input: Order data

[1656] Output: Inventory check result (sufficient / shortage), alert

[1657] Specific behavior:

[1658] 1. The server retrieves inventory data from the RFID system.

[1659] 2. Check stock levels of ordered items.

[1660] 3. If there is a shortage of stock, an alert will be displayed on the management dashboard and an email will be sent.

[1661] Step 4:

[1662] The server obtains real-time traffic information from an external API (e.g., Google Maps API) and uses this information to calculate the optimal delivery route.

[1663] Input: Request

[1664] Output: Real-time traffic information

[1665] Specific behavior:

[1666] 1. The server sends a request to the Google Maps API.

[1667] 2. Get real-time traffic information from API.

[1668] 3. The acquired traffic information is stored in a database.

[1669] Step 5:

[1670] The server calculates the optimal delivery route based on real-time traffic and inventory data, prioritizing eco-friendly routes using Dijkstra and A algorithms.

[1671] Input: Traffic information, inventory data

[1672] Output: Optimal delivery route

[1673] Specific behavior:

[1674] 1. The server calculates the optimal delivery route based on inventory data and traffic information.

[1675] 2. Use Dijkstra algorithm or A algorithm to select an efficient route.

[1676] 3. Save the calculation results in the database.

[1677] Step 6:

[1678] The server checks available delivery vehicles and, if an eco-friendly option is selected, allocates renewable energy vehicles with priority. The allocation information is sent to the terminal.

[1679] Input: Optimal delivery route, renewable energy vehicle status

[1680] Output: Allocation information

[1681] Specific behavior:

[1682] 1. The server checks the status of the delivery vehicle.

[1683] 2. Prioritize eco-friendly options and allocate renewable energy vehicles.

[1684] 3. Send allocation information to the terminal (transportation staff device).

[1685] Step 7:

[1686] The emotion engine analyzes collected emotional data to personalize the service, offering perks or additional support if frustration or impatience is detected.

[1687] Input: Emotion data

[1688] Output: Personalized settings, special offers and additional support

[1689] Specific behavior:

[1690] 1. The emotion engine analyzes the emotion data.

[1691] 2. Generate rewards or additional support when negative sentiment is detected.

[1692] 3. Share your personalization settings with the server.

[1693] Step 8:

[1694] The terminal (the delivery staff's device) receives the optimal delivery route information sent from the server and displays it to the delivery staff, who then carry out the delivery based on this information.

[1695] Input: Optimal delivery route information

[1696] Output: Route guidance to transportation staff

[1697] Specific behavior:

[1698] 1. The terminal receives optimal delivery route information from the server.

[1699] 2. Display the received route information to the transportation staff.

[1700] 3. Transport staff executes deliveries based on information.

[1701] Step 9:

[1702] After the delivery is completed, the server calculates the carbon footprint based on the delivery distance and vehicle type used and reports the results to a carbon offset program.

[1703] Input: Delivery distance, Vehicle type

[1704] Output: Carbon footprint calculation results, carbon offset report

[1705] Specific behavior:

[1706] 1. The server calculates the carbon footprint based on delivery distance and vehicle type.

[1707] 2. Report the calculation results to the carbon offset program.

[1708] 3. Record the report in the database.

[1709] (Application example 2)

[1710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1711] Environmental protection and efficient delivery are important issues in modern logistics and delivery services. However, existing delivery systems do not adequately prioritize the use of renewable energy vehicles or propose eco-friendly transportation methods. Furthermore, the lack of personalized service based on customer sentiment makes it difficult to improve customer satisfaction. Therefore, the present invention aims to achieve both environmental protection and customer satisfaction by calculating optimal delivery routes using real-time traffic information and sentiment data, using renewable energy vehicles, and reporting carbon footprints.

[1712] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1713] In this invention, the server includes a demand forecasting means, a real-time traffic information acquiring means, an optimal delivery route calculating means, an eco-friendly transportation suggesting means, a renewable energy vehicle allocation means, a carbon footprint calculating means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data, thereby enabling efficient and eco-friendly delivery and improved customer satisfaction.

[1714] The "demand forecasting means" is a means for forecasting user demand.

[1715] The "real-time traffic information acquisition means" is a means for acquiring traffic information in real time.

[1716] The "optimal delivery route calculation means" is a means for calculating an optimal delivery route based on the acquired data.

[1717] "Eco-friendly transportation proposal means" is a means for proposing environmentally friendly transportation methods.

[1718] "Renewable energy vehicle allocation means" is a means for allocating renewable energy vehicles to deliveries.

[1719] "Carbon Footprint Calculation Tool" means a tool for calculating the carbon footprint of a delivery.

[1720] "Carbon Offset Reporting Instrument" means an instrument for reporting on carbon offsets.

[1721] The "means for acquiring and analyzing user emotion data" refers to a means for acquiring user emotion data and analyzing it.

[1722] The "means for providing a personalized service based on emotional data" is a means for providing a personalized service based on analyzed emotional data.

[1723] "Means for monitoring inventory levels in real time" refers to means for monitoring the inventory status in real time.

[1724] The "demand forecasting means for preventing excess inventory and shortages" is a means for forecasting demand to prevent excess inventory and shortages.

[1725] A "means for reporting the status of a renewable energy vehicle" is a means for reporting the status of a renewable energy vehicle.

[1726] "Means for calculating and reporting carbon footprint after delivery completion" refers to means for calculating carbon footprint after delivery completion and reporting the results.

[1727] The "means for providing a customer with a benefit based on emotional data" is a means for providing a benefit based on emotional data of a customer.

[1728] "Means for customers to select eco-friendly delivery options" means means for customers to select environmentally friendly delivery options.

[1729] The "means for receiving and storing order data generated by a customer" refers to a means for receiving and storing order data generated by a customer.

[1730] The "means for proposing delivery options based on emotional data" is a means for proposing the optimal delivery option based on emotional data.

[1731] The "means for providing special support using an emotion engine" is a means for providing special support to a customer using an emotion engine.

[1732] The present invention is a system for achieving both environmental protection and customer satisfaction in logistics and delivery services. Detailed embodiments for realizing this system will be described below.

[1733] 1. Overall system configuration

[1734] The system includes a demand forecasting means, a real-time traffic information acquisition means, an optimal delivery route calculation means, an eco-friendly transportation suggestion means, a renewable energy vehicle allocation means, a carbon footprint calculation means, a carbon offset reporting means, a means for acquiring and analyzing user emotion data, and a means for providing personalized services based on the emotion data.

[1735] 1.1 Hardware and software used

[1736] Hardware:

[1737] Smartphones (e.g. iPhone, Android devices)

[1738] Renewable energy vehicles (e.g. electric vehicles)

[1739] Inventory sensors (e.g. RFID tags)

[1740] software:

[1741] Server (e.g. AWS)

[1742] External APIs (e.g. Google Maps API)

[1743] Emotion engine (e.g. Microsoft Azure Emotion API)

[1744] Delivery device app (e.g. iOS app, Android app)

[1745] 2. Specific processing flow

[1746] The server can predict user demand using a demand forecasting means and monitor inventory levels in real time. If inventory is low, the server will issue an alert. It also uses a real-time traffic information acquisition means to obtain traffic information from external APIs (such as Google Maps API) and calculate the optimal delivery route.

[1747] The renewable energy vehicle allocation means allows vehicles that use renewable energy to be preferentially allocated using the eco-friendly transportation suggestion means. After delivery is completed, the server calculates the carbon footprint using the carbon footprint calculation means and reports the result using the carbon offset reporting means.

[1748] Furthermore, it can acquire user emotion data, analyze it using an emotion engine (such as Microsoft Azure Emotion API), and provide personalized services based on the emotion data. For example, if a user expresses dissatisfaction during delivery, it can offer special support or coupons.

[1749] 3. Specific Examples

[1750] Below is a specific scenario from ordering to delivery.

[1751] Users use the application to select eco-friendly delivery options, and emotion data is automatically collected using facial recognition technology. The server receives order and emotion data, obtains and confirms inventory data from inventory sensors, obtains real-time traffic information, calculates the optimal delivery route, and allocates renewable energy vehicles. Meanwhile, the emotion engine analyzes the user's emotions and provides special support. After the delivery is completed, the server calculates the carbon footprint and reports it to the user.

[1752] Prompt Sentence Examples

[1753] Explain how your food delivery app uses emotional data collected through facial recognition technology to calculate optimal delivery routes and prioritize renewable energy vehicles when a user selects an eco-friendly option during the ordering process. Provide examples of how your app calculates and reports the carbon footprint after the delivery is completed.

[1754] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1755] Step 1:

[1756] The user places an order in the app.

[1757] Input: User order data and emotion data

[1758] How it works: The user opens a food delivery app on their smartphone or head-mounted display and enters their order. They can select eco-friendly delivery options when placing their order. The system also simultaneously collects the user's emotional data using facial recognition and voice analysis technologies.

[1759] Output: The order data and emotion data are sent to the server.

[1760] Step 2:

[1761] A server receives and stores the order and emotion data.

[1762] Input: Order data and emotion data from users

[1763] Specific operation: The server stores the received order data and emotion data in a database. At the same time, it prepares to obtain real-time inventory data from the inventory sensor.

[1764] Output: Stored order and sentiment data, inventory data ready to be retrieved.

[1765] Step 3:

[1766] The server checks the inventory data.

[1767] Input: Real-time inventory data from inventory sensors

[1768] Specific operation: The server analyzes real-time inventory data obtained from the inventory sensor, checks whether there is sufficient stock of the ordered item, and issues an alert if there is a shortage of stock.

[1769] Output: Stock check results, alerts if necessary.

[1770] Step 4:

[1771] The server obtains real-time traffic information.

[1772] Input: Traffic information request from external API

[1773] Specific operation: The server uses an external API such as the Google Maps API to obtain real-time traffic information.

[1774] Output: Real-time traffic information data.

[1775] Step 5:

[1776] The server calculates the optimal delivery route.

[1777] Input: Traffic and inventory data

[1778] How it works: The server calculates the optimal delivery route based on real-time traffic and inventory information, prioritizing eco-friendly shipping methods.

[1779] Output: Optimal delivery route information.

[1780] Step 6:

[1781] The server allocates renewable energy vehicles.

[1782] Input: Optimal delivery route information

[1783] Specific operation: After calculating the optimal route, the server checks and allocates available renewable energy vehicles.

[1784] Output: Allocated delivery vehicle information.

[1785] Step 7:

[1786] The server personalizes the service based on the emotion data.

[1787] Input: Emotion data

[1788] Specific operation: The server uses the emotion engine to analyze the user's emotion data and provide special support or benefits as needed.

[1789] Output: Personalized service content.

[1790] Step 8:

[1791] The terminal receives the optimum route information and executes the delivery.

[1792] Input: Optimal delivery route information

[1793] Specific operation: The delivery staff's terminal receives and displays the optimal delivery route information from the server. The delivery staff then carries out delivery according to the optimal route.

[1794] Output: Delivery status.

[1795] Step 9:

[1796] The server calculates and reports the carbon footprint.

[1797] Input: Delivery distance and vehicle type

[1798] What it does: After a delivery is completed, the server calculates the carbon footprint based on the delivery distance and the type of vehicle used and reports it to a carbon offset program.

[1799] Output: Carbon footprint report.

[1800] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1801] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1802] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1803] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1804] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1805] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1806] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1807] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1808] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1809] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1810] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1811] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1812] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1813] 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.

[1814] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1815] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1816] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1817] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1818] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1819] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1820] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1821] The following is further disclosed regarding the above embodiment.

[1822] (Claim 1)

[1823] A demand forecasting means;

[1824] A means for obtaining real-time traffic information;

[1825] An optimal delivery route calculation means;

[1826] Eco-friendly transportation proposals,

[1827] renewable energy vehicle allocation means;

[1828] a carbon footprint calculation means;

[1829] a carbon offset reporting means;

[1830] A system including:

[1831] (Claim 2)

[1832] a means of monitoring inventory levels in real time;

[1833] Demand forecasting measures to prevent overstocking and stockouts;

[1834] a means for reporting the status of the renewable energy vehicle;

[1835] The system of claim 1 further comprising:

[1836] (Claim 3)

[1837] A means for customers to choose eco-friendly delivery options;

[1838] means for receiving and storing customer-generated order data;

[1839] The system of claim 1 further comprising:

[1840] "Example 1"

[1841] (Claim 1)

[1842] A demand forecasting means;

[1843] A means for obtaining real-time traffic information;

[1844] An optimal delivery route calculation means;

[1845] Eco-friendly transportation proposals,

[1846] renewable energy vehicle allocation means;

[1847] a carbon footprint calculation means;

[1848] a carbon offset reporting means;

[1849] Inventory data acquisition means;

[1850] A means for calculating delivery routes based on traffic information;

[1851] a means for allocating delivery vehicles;

[1852] a means for calculating a carbon footprint based on delivery distance and vehicle type;

[1853] A system including:

[1854] (Claim 2)

[1855] a means of monitoring inventory levels in real time;

[1856] Demand forecasting measures to prevent overstocking and stockouts;

[1857] a means for reporting the status of the renewable energy vehicle;

[1858] A means for users to choose eco-friendly delivery options;

[1859] means for receiving and storing user-generated order data;

[1860] The system of claim 1 further comprising:

[1861] (Claim 3)

[1862] a means for preferentially allocating renewable energy vehicles based on a delivery option selected by a user;

[1863] A means to obtain real-time inventory data and check stock levels;

[1864] Demand forecasting methods using generative AI models,

[1865] a means for generating a prompt sentence;

[1866] The system of claim 1 further comprising:

[1867] "Application Example 1"

[1868] (Claim 1)

[1869] A demand forecasting means;

[1870] A means for obtaining real-time traffic information;

[1871] An optimal delivery route calculation means;

[1872] Eco-friendly transportation proposals,

[1873] renewable energy vehicle allocation means;

[1874] a carbon footprint calculation means;

[1875] a carbon offset reporting means;

[1876] A means to calculate and report carbon footprints;

[1877] a means for calculating a carbon footprint based on delivery distance and vehicle type used;

[1878] means by which users can place orders and transmit and record order data;

[1879] A system including:

[1880] (Claim 2)

[1881] a means of monitoring inventory levels in real time;

[1882] Demand forecasting measures to prevent overstocking and stockouts;

[1883] a means for reporting the status of the renewable energy vehicle;

[1884] A means of obtaining real-time traffic information and calculating the optimal delivery route;

[1885] providing eco-friendly delivery options and allocating renewable energy vehicles;

[1886] The system of claim 1 further comprising:

[1887] (Claim 3)

[1888] A means for customers to choose eco-friendly delivery options;

[1889] means for receiving and storing customer-generated order data;

[1890] A means for displaying optimal route information on a device of a delivery staff member and allowing the delivery staff member to execute the delivery;

[1891] The system of claim 1 further comprising:

[1892] "Example 2: Combining Emotion Engines"

[1893] (Claim 1)

[1894] A demand forecasting means;

[1895] A means for obtaining real-time traffic information;

[1896] An optimal delivery route calculation means;

[1897] Eco-friendly transportation proposals,

[1898] renewable energy vehicle allocation means;

[1899] a carbon footprint calculation means;

[1900] a carbon offset reporting means;

[1901] A means of collecting and analyzing customer sentiment data;

[1902] a means for personalizing services based on emotion data;

[1903] A system including:

[1904] (Claim 2)

[1905] a means of monitoring inventory levels in real time;

[1906] Demand forecasting measures to prevent overstocking and stockouts;

[1907] a means for reporting the status of the renewable energy vehicle;

[1908] The system of claim 1 further comprising:

[1909] (Claim 3)

[1910] A means for customers to choose eco-friendly delivery options;

[1911] means for receiving and storing customer-generated order data and sentiment data;

[1912] The system of claim 1 further comprising:

[1913] "Application example 2 when combining emotion engines"

[1914] (Claim 1)

[1915] A demand forecasting means;

[1916] A means for obtaining real-time traffic information;

[1917] An optimal delivery route calculation means;

[1918] Eco-friendly transportation proposals,

[1919] renewable energy vehicle allocation means;

[1920] a carbon footprint calculation means;

[1921] a carbon offset reporting means;

[1922] means for acquiring and analyzing user emotion data;

[1923] A means for providing personalized services based on emotion data;

[1924] A system including:

[1925] (Claim 2)

[1926] a means of monitoring inventory levels in real time;

[1927] Demand forecasting measures to prevent overstocking and stockouts;

[1928] a means for reporting the status of the renewable energy vehicle;

[1929] A means to calculate and report the carbon footprint after delivery is completed;

[1930] a means for providing a reward to a user based on the emotion data;

[1931] The system of claim 1 further comprising:

[1932] (Claim 3)

[1933] A means for customers to choose eco-friendly delivery options;

[1934] means for receiving and storing customer-generated order data;

[1935] A means of suggesting delivery options based on emotion data;

[1936] A means of providing special support through an emotion engine;

[1937] The system of claim 1 further comprising: [Explanation of symbols]

[1938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a demand forecasting means; A means for obtaining real-time traffic information; An optimal delivery route calculation means; Eco-friendly transportation proposals, renewable energy vehicle allocation means; a carbon footprint calculation means; a carbon offset reporting means; A system including:

2. a means of monitoring inventory levels in real time; Demand forecasting measures to prevent overstocking and stockouts; a means for reporting the status of the renewable energy vehicle; The system of claim 1 further comprising:

3. A means for customers to choose eco-friendly delivery options; means for receiving and storing customer-generated order data; The system of claim 1 further comprising:

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