System

Abstract

A system is provided.SOLUTION: Means for storing package information received from a plurality of delivery companies in a database, means for analyzing a delivery destination address in cooperation with a geographic information system and specifying a delivery area, means for generating an efficient delivery route using an artificial intelligence algorithm, means for delivering the generated delivery route to a delivery terminal, and means for monitoring a delivery progress in real time, this system is provided with a means for transmitting an instruction as necessary, a means for recalculating a redelivery route on the basis of past data and at-home information when absent delivery is generated, a means for collecting delivery progress data, and for evaluating a workload, a means for selecting a route with less fuel consumption, and a means for evaluating an environmental load, and for reflecting it on a delivery plan.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 logistics industry faces serious challenges in 2024, including labor shortages and increased wasted time and labor due to redelivery. Efficient delivery is particularly difficult when multiple delivery companies each make individual deliveries. Furthermore, the frequent need for redelivery due to absence from the recipient poses problems of fuel consumption, CO2 emissions, and increased working hours. The present invention aims to solve these problems and achieve efficiency improvements and reduced environmental impact throughout the logistics industry. [Means for solving the problem]

[0005] The present invention is a system that includes a means for storing package information received from multiple delivery companies in a database and a means for analyzing delivery addresses and identifying delivery areas in cooperation with a geographic information system. It also includes a means for generating efficient delivery routes using an artificial intelligence algorithm, a means for distributing the generated delivery routes to delivery terminals, a means for monitoring delivery progress in real time and sending instructions as needed, and a means for recalculating redelivery routes based on past data and at-home information in the event of a missed delivery. The system also includes a means for collecting delivery progress data and evaluating workload, a means for selecting routes with minimal fuel consumption, and a means for evaluating environmental impact and reflecting this in delivery plans. This configuration improves delivery efficiency and reduces redelivery, fuel consumption, and environmental impact.

[0006] A "delivery company" refers to a company or organization that specializes in delivering packages and products to locations specified by customers.

[0007] "Parcel information" refers to detailed data such as the size, weight, delivery address, and sender information of the parcel to be delivered.

[0008] A "database" refers to an electronic information management system that efficiently organizes and stores large amounts of information and allows for quick retrieval when needed.

[0009] A "geographic information system" is a system for collecting, managing, and analyzing geographic information, and refers to a system that can handle map data and spatial data.

[0010] "Artificial intelligence algorithms" refer to numerical procedures and computational methods designed to enable computers to mimic human intelligent behavior.

[0011] "Delivery route" refers to a route planned for efficient delivery of packages.

[0012] "Delivery terminal" refers to a mobile device or terminal used by a delivery person to display delivery information and route information.

[0013] "Delivery progress" refers to information indicating the current status and progress of a package during its delivery process.

[0014] "Delivery to an unattended address" refers to a situation where the recipient is not present at the delivery address, and the package is not received and is either taken back or requires redelivery.

[0015] "Redelivery route" refers to the optimal redelivery route newly calculated when a delivery is missed.

[0016] "Progress data" refers to data that indicates the current status of delivery, and refers to information that indicates the degree of progress of delivery and the degree to which the delivery is being completed.

[0017] "Fuel consumption" refers to the amount of fuel consumed by a delivery vehicle when operating.

[0018] "Environmental load" is a general term for the impact that human activities have on the natural environment, and specifically refers to the burden on the environment such as CO2 emissions. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] ---

[0041] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[0042] 1. Collection and integration of package information

[0043] server

[0044] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[0045] 2. Delivery route generation

[0046] server

[0047] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[0048] Terminal

[0049] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[0050] 3. Delivery monitoring and redelivery support

[0051] server

[0052] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[0053] Terminal

[0054] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[0055] User

[0056] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0057] 4. Optimizing working hours and fuel consumption

[0058] server

[0059] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0060] Terminal

[0061] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[0062] Specific examples

[0063] Example 1: When the user is absent

[0064] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[0065] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[0066] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[0067] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[0068] Example 2: Optimizing delivery efficiency

[0069] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[0070] 2. Uses AI algorithms to calculate the most efficient delivery route.

[0071] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[0072] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[0073] The present invention can improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, thereby contributing to the sustainable growth of the logistics industry.

[0074] ---

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] server

[0078] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[0079] Step 2:

[0080] server

[0081] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[0082] Step 3:

[0083] server

[0084] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[0085] Step 4:

[0086] server

[0087] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[0088] Step 5:

[0089] server

[0090] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[0091] Step 6:

[0092] Terminal

[0093] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[0094] Step 7:

[0095] Terminal

[0096] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[0097] Step 8:

[0098] server

[0099] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[0100] Step 9:

[0101] server

[0102] If a delivery is not made at home, the server recalculates the redelivery route based on past data and information on whether the recipient is at home. The server then determines the optimal redelivery date and route and notifies the delivery person.

[0103] Step 10:

[0104] Terminal

[0105] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[0106] Step 11:

[0107] User

[0108] Users can check the delivery progress through a web portal or app, select redelivery options if necessary, and provide instructions to the server if the user is not at home.

[0109] Step 12:

[0110] server

[0111] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency based on past delivery history and current workload.

[0112] Step 13:

[0113] server

[0114] Selects a route with less fuel consumption. The server optimizes fuel consumption and time based on geographical information and real-time traffic information.

[0115] Step 14:

[0116] server

[0117] The server evaluates the environmental impact and reflects it in delivery plans. It collects environmental indicators such as CO2 emissions and uses this information to formulate sustainable delivery strategies.

[0118] Step 15:

[0119] Terminal

[0120] The delivery person's terminal follows instructions from the server and delivers the package along the optimal route. By making deliveries more efficient, fuel and labor costs can be saved.

[0121] These are the specific steps of the program, which will improve the efficiency of the logistics industry and reduce the burden on the environment.

[0122] Example 1

[0123] 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."

[0124] In today's logistics and delivery industries, there is a need to efficiently manage package information received from multiple delivery companies, minimize redelivery and fuel consumption, and maximize delivery efficiency. In particular, there is a need for systems that can perform complex tasks such as monitoring delivery progress in real time, issuing redelivery instructions when the recipient is absent, and evaluating environmental impact.

[0125] 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.

[0126] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information in the event of a missed delivery, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for issuing redelivery instructions based on redelivery options previously specified by the user, and means for recalculating optimal delivery routes in real time using traffic information, thereby enabling reduction in redelivery and fuel consumption, improved delivery efficiency, and reduced environmental impact.

[0127] A "delivery company" is an organization or company whose business is receiving and delivering packages.

[0128] "Package information" refers to detailed data about the package being delivered (e.g., package contents, weight, size, delivery address, etc.).

[0129] A "database" refers to a structured and managed collection of data, and is a system for storing, searching, and updating cargo information.

[0130] "Geographic Information System (GIS)" means a system for collecting, analyzing, and displaying geographic information used to locate shipping addresses.

[0131] "Delivery Address" means the location where a package is to be delivered.

[0132] "Delivery Area" means the geographic area within which a particular delivery activity occurs.

[0133] An "artificial intelligence algorithm" is a method or procedure that allows a computer to perform processing by imitating part of human intelligence.

[0134] A "delivery route" is the optimal route planned to deliver packages efficiently.

[0135] A "delivery terminal" refers to an electronic device such as a smartphone or tablet used by a delivery person.

[0136] "Delivery progress" refers to information indicating the stage of delivery.

[0137] "Real time" refers to processing that is performed immediately without delay, meaning that processing is performed in synchronization with real time.

[0138] "Instructions" refer to specific instructions or orders for actions sent to delivery personnel from a higher-level system or administrator.

[0139] A "redelivery route" is a route planned to redeliver packages that could not be delivered due to reasons such as absence of the recipient.

[0140] "Workload" refers to the amount of work and burden that a delivery person must perform.

[0141] "Fuel consumption" refers to the energy, particularly fuel, consumed in delivery operations.

[0142] "Environmental load" refers to the impact that economic and human activities have on the natural environment.

[0143] "Redelivery options" refer to options for how to redeliver a package if the user is not at home.

[0144] "Traffic information" refers to data on road congestion and traffic flow.

[0145] MODE FOR CARRYING OUT THE INVENTION

[0146] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[0147] 1. Collection and integration of package information

[0148] server

[0149] The server receives package information from the delivery company via API. Specifically, it uses the API provided by the delivery company in JSON or XML format. The received package information is converted into a unified format (for example, CSV format) by an internal data conversion module. The package information converted into the unified format is then stored in a database.

[0150] Specific examples:

[0151] Package information is received from delivery company A via API, and the JSON format data is converted to CSV format and stored in a database.

[0152] 2. Delivery route generation

[0153] server

[0154] The server retrieves package information from the database and analyzes each delivery address. The delivery address is converted into location information in conjunction with a geographic information system (GIS). For example, the GIS uses Google Maps API or OpenStreetMap. The delivery area is identified based on the location information of the analyzed delivery address.

[0155] Next, an efficient delivery route is generated within the identified delivery area using an artificial intelligence algorithm. Specifically, the shortest route is calculated using the Dijkstra algorithm or the A algorithm. The generated delivery route is sent from the server to the delivery person's device.

[0156] Terminal

[0157] The delivery person's device receives the route information and displays the optimal delivery route on the app. The device, such as a smartphone or tablet, visualizes the route using the Google Maps API.

[0158] Specific examples:

[0159] The server obtains information about the delivery area and generates the optimal delivery route using the Dijkstra algorithm. The generated route is sent to the delivery person's smartphone and displayed in the Google Maps app.

[0160] 3. Delivery monitoring and redelivery support

[0161] server

[0162] The server receives real-time delivery progress information from the delivery person's terminal. When the delivery person has delivered the package or was unable to deliver due to absence, the server reports this information to the server. The server updates the database based on this information.

[0163] If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home. The recalculated route is then sent back to the delivery person's device.

[0164] Terminal

[0165] The delivery person's terminal successively transmits delivery progress information to the server, and redelivers or changes the route as necessary. The terminal immediately receives redelivery instructions and new route information, allowing for efficient redelivery.

[0166] User

[0167] Users can notify the server of their desired redelivery options in advance through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the options specified by the user. For example, if the user requests delivery to a parcel box, that information is sent to the server and the delivery person is notified.

[0168] Specific examples:

[0169] The user sends information about their desired delivery to the delivery box to the server through the app, and the server calculates a new redelivery route based on that information and sends it to the delivery person's device.

[0170] 4. Optimizing working hours and fuel consumption

[0171] server

[0172] The server collects the workload of each delivery person from a database and evaluates it. Based on the evaluation results, it allocates packages to each delivery person with maximum efficiency. It also uses real-time traffic information to select routes with the least fuel consumption. It also evaluates the environmental impact (e.g., CO2 emissions) and creates delivery plans that take this into account.

[0173] Specific technologies include the use of Google Maps traffic information API and environmental assessment software.

[0174] Terminal

[0175] The delivery person's device then receives optimized route information from the server and delivers efficiently, for example, by choosing a route that avoids traffic jams or by providing instructions for the shortest possible delivery distance.

[0176] Specific examples:

[0177] The server receives real-time traffic information and calculates a route with the lowest CO2 emissions based on that information, which is then displayed on the delivery person's device.

[0178] Example prompts using generative AI models

[0179] Example prompt sentence:

[0180] "Generate efficient delivery routes based on past delivery history and real-time traffic information."

[0181] By inputting this prompt into a generative AI model, the complex route generation process can be automated to produce an optimized delivery route.

[0182] The above is a specific description of the embodiment of the present invention.

[0183] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0184] Step 1:

[0185] server

[0186] Parcel information is received from the delivery company using an API. The input at this time is the parcel data in JSON format provided by the delivery company. The server converts the received parcel information into CSV format using an internal data conversion module and stores it in the database. The output is the parcel information converted into a unified format.

[0187] Specific operation: Receive JSON formatted package information from delivery company A via API, convert it to CSV format, and insert it into the database.

[0188] Step 2:

[0189] server

[0190] The server retrieves package information from the database and analyzes the delivery address. The input is the delivery address data in CSV format, and the output is latitude and longitude information using a geographic information system (GIS). Next, the GIS is used to obtain the location information of the delivery address and identify the delivery area.

[0191] Specific operation: The server reads the delivery address in CSV format, obtains the latitude and longitude information using the Google Maps API, and maps the delivery area on a map.

[0192] Step 3:

[0193] server

[0194] The server generates efficient delivery routes within the specified delivery area using an artificial intelligence algorithm. The input is latitude and longitude information within the delivery area, and the output is the optimal delivery route. The server calculates the shortest route using the Dijkstra algorithm or the A algorithm, and distributes the generated delivery route to the delivery person's terminal.

[0195] Specific operation: The server calculates the optimal delivery route based on latitude and longitude information within the delivery area, and sends the determined route information to the delivery person's smartphone.

[0196] Step 4:

[0197] Terminal

[0198] The delivery person's device receives the route information and displays it on the app. The input is the optimal route information sent from the server, and the output is the delivery route displayed on a map. The device visualizes the route using the Google Maps API and provides the delivery person with the shortest route.

[0199] Specific operation: The delivery person's device displays the route information received from the server using the Google Maps API and begins delivery.

[0200] Step 5:

[0201] server

[0202] The server receives real-time delivery progress information from the delivery person's terminal. The input is the delivery progress data sent from the terminal, and the output is progress update information. The server sends new instructions to the delivery person as needed.

[0203] Specific operation: The delivery person's device sends current location information and delivery status to the server every minute, and the server updates the progress status based on this data.

[0204] Step 6:

[0205] server

[0206] When a delivery is missed, the server recalculates the redelivery route based on past data and at-home information. The input is delivery progress data and past at-home information, and the output is a new redelivery route. The server then applies the optimization algorithm to generate a new route and distributes it to the delivery person's device.

[0207] Specific operation: The server receives information about the delivery when the delivery person is not at home, references past data on when the delivery person is at home, and sends a recalculated route to the delivery person's terminal.

[0208] Step 7:

[0209] User

[0210] The user uses a web portal or app to notify the server in advance of redelivery options (e.g., use of a delivery box) in case of absence. The input is the redelivery options specified by the user, and the output is the notification information sent to the server. Based on the user's specifications, the server sends appropriate instructions to the delivery person.

[0211] Specific operation: The user uses the app to specify the use of a delivery box and sends that information to the server. The server then calculates a redelivery route based on that information and sends instructions to the delivery person.

[0212] Step 8:

[0213] server

[0214] The server evaluates the workload of each delivery person and selects a route that consumes less fuel. The input is delivery progress data and traffic information, and the output is optimized route information. The server also evaluates the environmental impact (CO2 emissions) and reflects this in the delivery plan.

[0215] Specific operation: The server recalculates the most fuel-efficient route based on delivery progress data and real-time traffic information, and sends instructions to the delivery person.

[0216] This will reduce redelivery and fuel consumption, improve delivery efficiency, and reduce the burden on the environment.

[0217] (Application example 1)

[0218] 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."

[0219] In the logistics industry, parcel deliveries are routinely carried out by multiple delivery companies, but the challenge is to centrally manage this parcel information and efficiently generate delivery routes. It is also necessary to effectively monitor delivery progress and handle redelivery requests. Furthermore, reducing fuel consumption and reducing environmental impact are also important issues. The present invention aims to solve these challenges and provide a system for effectively managing operations at logistics centers.

[0220] 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.

[0221] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an AI algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting a route with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for delivery personnel to receive instructions for the optimized route via a smart device, means for users to set redelivery options via a web portal or app, and means for optimizing delivery plans using a generative AI model. This enables delivery efficiency, reduced fuel consumption, fewer redeliveries, and shorter working hours.

[0222] "Parcel information" refers to data such as parcel identification information, delivery destination information, and delivery status information collected from multiple delivery companies.

[0223] A "database" is a system for systematically storing, managing, and searching information.

[0224] A "geographic information system" is a system for collecting, managing, and analyzing geographic data.

[0225] "Delivery address analysis" refers to the technology used to understand the delivery address information and determine the appropriate delivery area and route.

[0226] "Specifying a delivery area" means clarifying the geographical area to which the delivery destination of the package belongs.

[0227] An "artificial intelligence algorithm" is a calculation procedure that enables a computer to learn on its own and make appropriate judgments and predictions.

[0228] "Generating an efficient delivery route" means calculating a route that will complete delivery while saving the most time and money, based on the input delivery destination information.

[0229] A "delivery terminal" is an electronic device carried by a delivery person to receive delivery progress and instructions.

[0230] "Delivery progress" refers to the current progress of the package in the delivery process.

[0231] "Real-time monitoring" means instantly checking the progress and status of delivery.

[0232] "Sending instructions" means that the server sends necessary information and instructions to the delivery person via an electronic device.

[0233] "Delivery to absentee" refers to a situation where the resident at the delivery address is absent and unable to receive the package.

[0234] "Past data" refers to information such as delivery history and customer information that has been recorded to date.

[0235] "At-home information" is information about whether the resident of the delivery destination is at home.

[0236] "Recalculating the redelivery route" means recalculating a new, more efficient delivery route when a delivery is missed.

[0237] "Delivery progress data" is data that indicates the current status and progress of delivery.

[0238] "Evaluating workload" means measuring the workload of each delivery person and appropriately evaluating their efforts.

[0239] A "low fuel consumption route" is a delivery route that is planned to minimize the amount of fuel used by the delivery vehicle.

[0240] "Evaluating the environmental impact" means measuring the impact that delivery activities have on the environment and formulating plans based on this.

[0241] A "smart device" is a portable electronic device that can connect to the Internet and use a variety of applications.

[0242] "Receiving optimized route instructions" means that the delivery person receives information about a pre-calculated and optimized delivery route.

[0243] A "web portal" is a website that provides users with a variety of information and services via the Internet.

[0244] "Redelivery options" are settings and instructions regarding redelivery that customers can select in the event of a missed delivery.

[0245] A "generative AI model" is an artificial intelligence computational model used for delivery planning and route optimization.

[0246] "Optimizing" means arranging and adjusting a system or computation most effectively to achieve a specific goal.

[0247] System Overview

[0248] This invention relates to a system that realizes efficient delivery of parcels at a logistics center. This system manages parcel information received from multiple delivery companies in an integrated manner and generates efficient delivery routes using a geographic information system and an artificial intelligence algorithm. It also monitors delivery progress in real time, responding to requests for redelivery and reducing environmental impact.

[0249] Program generation and natural language explanation

[0250] Collection and integration of package information

[0251] The server collects package information from multiple delivery companies via API and file upload, converts it into a unified format, and stores it in a database, allowing package information from different delivery companies to be centrally managed.

[0252] Generate delivery routes

[0253] The server uses the collected package information to link with a geographic information system (GIS) and analyzes the delivery address. Based on the analyzed address information, an artificial intelligence algorithm is used to generate an efficient delivery route. The generated route is optimized by taking into account data such as past delivery history and absence information.

[0254] Delivery route distribution

[0255] The generated delivery route is sent from the server to the delivery person's device (smartphone or tablet), and the delivery person uses this device to make deliveries along the most efficient route.

[0256] Real-time monitoring of delivery progress

[0257] The server monitors the delivery progress data sent from the delivery person's terminal in real time and sends new instructions to the terminal as necessary. The delivery progress data includes the delivery status of each package and the delivery person's location information. This information is collected and analyzed by the server in real time.

[0258] Redelivery support

[0259] If a delivery is missed, the server recalculates the redelivery route based on past data and information on when the delivery person is at home, and instructs the delivery person on a new route. Users can pre-set redelivery options (e.g., use of a delivery box) through the web portal or app.

[0260] Optimizing working hours and fuel consumption

[0261] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0262] Specific examples

[0263] Example 1: When the user is absent

[0264] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[0265] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[0266] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[0267] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[0268] Example 2: Optimizing delivery efficiency

[0269] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[0270] 2. Uses AI algorithms to calculate the most efficient delivery route.

[0271] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[0272] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[0273] In this way, the efficiency of parcel delivery at the logistics center can be maximized. An example of a prompt sentence to be input to the generative AI model is as follows:

[0274] Prompt Sentence Examples

[0275] "You've gathered all the parcel information for tomorrow. There are currently 50 deliveries. To create the optimal delivery route, please use GIS and AI algorithms to analyze these addresses and generate efficient routes."

[0276] This system will enable more efficient delivery, reduce fuel consumption, reduce redelivery, and shorten working hours.

[0277] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0278] Program processing flow

[0279] Step 1: Collect and consolidate package information

[0280] The server receives package information from multiple delivery companies via API or file upload. The received package information is as follows:

[0281] Input: Package information data provided by the delivery company

[0282] Data processing: Converting package information into a unified format

[0283] Output: Package information data converted into a unified format

[0284] Specific operation: The program standardizes package information provided by each delivery company in different formats for storage in a database. For example, it reads data in CSV or JSON format and converts it into a unified data format.

[0285] Step 2: Address analysis and delivery area identification

[0286] Based on the parcel information converted into a unified format, the server works in conjunction with a geographic information system (GIS) to analyze the delivery address and identify the delivery area.

[0287] Input: Package information data in a unified format

[0288] Data processing: Address analysis using geographic information systems

[0289] Output: Delivery address and delivery area information

[0290] What it does: The program uses a GIS library to convert each delivery address into geographic coordinates and identify the delivery area to which each delivery address belongs.

[0291] Step 3: Generate efficient delivery routes

[0292] The server uses an artificial intelligence algorithm to generate an efficient delivery route based on the delivery address, past delivery history, and absence information.

[0293] Input: Delivery address, past delivery history, absence information

[0294] Data calculation: Route optimization using artificial intelligence algorithms

[0295] Output: Optimized delivery route

[0296] How it works: To create smart delivery plans, the program uses AI algorithms (e.g., genetic algorithms and deep learning models) to calculate the shortest and lowest-cost routes.

[0297] Step 4: Deliver your delivery route

[0298] The server delivers the optimized delivery route to the delivery person's device (smartphone or tablet).

[0299] Input: Optimized delivery route

[0300] Data calculation: None (data distribution)

[0301] Output: Delivery route sent to the delivery person's device

[0302] Specific operation: The optimized route information is sent to the delivery person's device and notified. If the delivery person's device is connected to the Internet, the route information is received immediately.

[0303] Step 5: Real-time monitoring of delivery progress

[0304] The server monitors the delivery progress data sent from the delivery person's terminal in real time.

[0305] Input: Delivery progress data sent from the terminal

[0306] Data calculations: analyzing and monitoring progress data

[0307] Output: Real-time updated delivery status

[0308] Specific operation: Receives delivery personnel's location information and delivery completion status in real time, monitors progress, and sends instructions from the server if an abnormality is detected.

[0309] Step 6: Recalculate the redelivery route

[0310] When a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the recipient is at home.

[0311] Input: Missed delivery information, past data, at-home information

[0312] Data calculation: Recalculation to generate optimal route

[0313] Output: Optimal route for redelivery

[0314] Specific operation: When a delivery is missed, the program refers to past data, recalculates the appropriate redelivery time and route, and sends a redelivery instruction to the delivery person's terminal.

[0315] Step 7: Optimizing fuel consumption and environmental impact

[0316] The server selects routes with the lowest fuel consumption based on real-time traffic and geographical information, evaluates the environmental impact, and reflects this in delivery plans.

[0317] Input: Real-time traffic information, geographic information

[0318] Data calculation: Simulation of fuel consumption and environmental impact

[0319] Output: Optimized eco-routes and delivery plans

[0320] Specific operation: The program selects a route that consumes less fuel by taking into account traffic congestion and road conditions, calculates the environmental impact, and instructs the delivery person on a delivery plan that reflects this.

[0321] This will enable more efficient delivery at logistics centers, reduce fuel consumption, reduce redeliveries, and shorten working hours.

[0322] 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.

[0323] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[0324] 1. Collection and integration of package information

[0325] server

[0326] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[0327] 2. Delivery route generation

[0328] server

[0329] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[0330] Terminal

[0331] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[0332] 3. Delivery monitoring and redelivery support

[0333] server

[0334] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[0335] Terminal

[0336] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[0337] User

[0338] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0339] 4. Optimizing working hours and fuel consumption

[0340] server

[0341] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0342] Terminal

[0343] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[0344] 5. Use of Emotion Engine

[0345] server

[0346] The system uses an emotion engine to collect user emotional information. It analyzes the emotional data provided by users through the web portal or app to recognize the user's emotional state. Based on this, it sets delivery priorities and reflects them in the generation of delivery routes.

[0347] Terminal

[0348] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[0349] User

[0350] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[0351] Specific examples

[0352] Example 1: Using emotional data to prioritize delivery

[0353] 1. The server receives emotion information provided by the user. For example, if the user requests an urgent delivery, the information is analyzed by the emotion engine.

[0354] 2. The emotion engine recognizes urgent delivery requests as high priority and reflects this when generating delivery routes.

[0355] 3. High-priority delivery routes are sent to the terminal, and delivery personnel follow those instructions to make deliveries.

[0356] Example 2: Redelivery instructions when the recipient is absent and consideration for the recipient's feelings

[0357] 1. The server receives the user's absence along with emotional information. For example, if the user strongly desires redelivery, that information is included.

[0358] 2. Delivery progress is monitored, and if redelivery becomes necessary, the optimal redelivery route is recalculated based on information analyzed by the emotion engine.

[0359] 3. A redelivery instruction that takes into account emotional information is sent to the terminal, and the delivery person acts based on that instruction, delivering the parcel to a delivery box or other location according to the user's wishes.

[0360] This invention not only improves delivery efficiency, reduces fuel consumption, reduces redelivery, and shortens working hours, but also makes it possible to provide services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[0361] ---

[0362] The processing flow will be explained below.

[0363] ---

[0364] Step 1:

[0365] server

[0366] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[0367] Step 2:

[0368] server

[0369] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[0370] Step 3:

[0371] server

[0372] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[0373] Step 4:

[0374] server

[0375] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[0376] Step 5:

[0377] server

[0378] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[0379] Step 6:

[0380] Terminal

[0381] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[0382] Step 7:

[0383] Terminal

[0384] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[0385] Step 8:

[0386] server

[0387] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[0388] Step 9:

[0389] server

[0390] If a delivery is not made at home, the system recalculates the redelivery route based on past data and information on whether the recipient is at home. It then calculates the optimal redelivery date and route and notifies the delivery person.

[0391] Step 10:

[0392] Terminal

[0393] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[0394] Step 11:

[0395] User

[0396] Through a web portal or app, users provide emotional information, such as expectations or dissatisfaction regarding delivery, which is then sent to the server.

[0397] Step 12:

[0398] server

[0399] Receives user emotional information and analyzes it with an emotion engine. Based on the user's emotional data, delivery priorities are re-established.

[0400] Step 13:

[0401] server

[0402] Based on the analysis results, an AI algorithm regenerates efficient delivery routes, and adjusts delivery routes to accommodate specific users' emotions.

[0403] Step 14:

[0404] server

[0405] Based on the emotion information, new instructions are sent to the delivery person, for example, instructing them to make deliveries with higher priority taking into consideration the emotion.

[0406] Step 15:

[0407] Terminal

[0408] The delivery person's terminal receives the updated instructions and performs the delivery based on the emotion information, thereby improving the user's satisfaction.

[0409] Step 16:

[0410] server

[0411] Real-time monitoring of delivery progress and user emotional responses will be used to improve the system. New data will be analyzed and reflected in future delivery plans.

[0412] These are the specific processing steps of the system. This process will improve the efficiency of the logistics industry, reduce the environmental impact, and realize a delivery service that takes user emotions into consideration.

[0413] ---

[0414] Example 2

[0415] 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."

[0416] Conventional delivery systems lack a means to effectively integrate package information from multiple delivery companies, making it difficult to generate efficient delivery routes, monitor delivery progress in real time, and optimize redelivery. Furthermore, fuel consumption and environmental impact are not sufficiently reduced. Furthermore, there is a lack of delivery priority settings that incorporate user emotional information and advance notification of redelivery options, making it difficult to improve overall customer satisfaction.

[0417] 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.

[0418] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses and identifying delivery areas in cooperation with a geographic information system, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impacts and reflecting them in delivery plans, means for collecting and analyzing emotion information from users and setting delivery priorities, means for generating delivery instructions that take emotion information into account and distributing them to terminals, and means for users to notify redelivery options in advance. This enables more efficient delivery, reduced fuel consumption, fewer redeliveries, shorter working hours, and the provision of services that take user emotions into consideration.

[0419] "Parcel information" refers to detailed data about parcels delivered by delivery companies, including the address, type of parcel, weight, size, desired delivery date and time, etc.

[0420] A "database" is a system for efficiently storing, managing, and searching information, and is a central repository for the integrated storage of cargo information, progress data, and other information.

[0421] A "geographic information system (GIS)" is a system for analyzing and managing geographic information, and is used for address analysis and determining delivery areas.

[0422] "Delivery address" refers to the address information of the destination where the package is to be delivered, and is data that is converted into geographic information such as latitude and longitude.

[0423] An "artificial intelligence algorithm" is a data processing method that uses AI technology and is a computational model for generating efficient delivery routes.

[0424] "Delivery route" refers to the optimal route a delivery person takes to deliver a package to each destination.

[0425] A "delivery terminal" is a mobile device (such as a smartphone or tablet) used by delivery personnel to receive delivery routes and instructions.

[0426] "Delivery progress" is data showing the delivery status of a package in real time, including the current delivery status and completion status.

[0427] A "redelivery route" is an optimized delivery route for re-delivering a package in the event of a missed delivery.

[0428] "Work volume" refers to the amount and burden of delivery work performed by delivery personnel, and is evaluated based on criteria such as multiple delivery destinations and the number of packages.

[0429] "Fuel consumption" refers to the amount of fuel consumed in delivery operations, and must be minimized to increase delivery efficiency.

[0430] "Environmental impact" refers to the impact on the environment, such as CO2 emissions and resource consumption associated with delivery operations.

[0431] "Emotional information" refers to emotional data such as expectations or dissatisfaction regarding delivery provided by the user.

[0432] "Delivery instructions" are specific instructions for actions sent from the server to the delivery person, and include information such as delivery route and priority.

[0433] "Redelivery option" means an option for an alternative method of receiving a package (e.g., a delivery box) that can be specified when the user is not at home.

[0434] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[0435] Collection and integration of package information

[0436] server

[0437] The server receives package information from delivery companies using an API or file upload. The received package information is converted into a unified format and stored in a database. This allows information from multiple delivery companies to be managed centrally. For example, if delivery company A sends package information in JSON format and delivery company B sends it in CSV format, the server converts it into a unified format (for example, CSV format) and stores it in the database.

[0438] Generate delivery routes

[0439] server

[0440] The server analyzes the delivery address based on the package information stored in the database and identifies the delivery area in conjunction with a geographic information system (GIS). An artificial intelligence algorithm is used to generate an efficient delivery route within the identified delivery area. For example, the AI ​​algorithm optimizes the route based on past delivery history, information on whether the recipient is at home or absent, and then generates the delivery route.

[0441] Terminal

[0442] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet receives this information and uses it as a guide to make deliveries efficiently.

[0443] Delivery monitoring and redelivery support

[0444] server

[0445] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home, maximizing delivery efficiency.

[0446] Terminal

[0447] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the information is also reported to the server.

[0448] User

[0449] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0450] Optimizing working hours and fuel consumption

[0451] server

[0452] The server evaluates the workload of each delivery person and allocates packages efficiently. It also selects routes with low fuel consumption based on real-time traffic information. It also evaluates the environmental impact (CO2 emissions, etc.) and reflects this in delivery plans.

[0453] Terminal

[0454] The delivery person's device receives the optimized route from the server and delivers efficiently, thereby reducing fuel consumption and working hours.

[0455] Use of emotion engine

[0456] server

[0457] The emotion engine is used to collect user emotional information. For example, it analyzes emotional data provided by users through the web portal or app (e.g., requests for urgent delivery or expressions of dissatisfaction) to recognize the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[0458] Terminal

[0459] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[0460] User

[0461] Users can provide emotional information through a web portal or app, such as expressing their expectations or dissatisfaction with the delivery. The provided emotional information is analyzed by the server and reflected in the delivery plan.

[0462] As a concrete example, the server receives emotion information provided by the user and analyzes it with an emotion engine to recognize urgent delivery requests as high priority and reflect this when generating delivery routes. Based on this, high-priority delivery routes are sent to the terminal, and delivery personnel make deliveries according to those instructions.

[0463] Example prompt sentence:

[0464] 1. Create a program that receives package information sent by delivery company A, converts it into a unified format, and saves it in a database.

[0465] 2. Create a program that analyzes the delivery address and generates the optimal delivery route using GIS. Also, write a script that sends the route to the delivery person's device.

[0466] 3. Generate a program that monitors delivery progress in real time and recalculates the redelivery route if a delivery is missed. Also, add logic to receive redelivery options from the user in advance and send instructions to the delivery person based on that information.

[0467] 4. Write a program that evaluates the workload of each delivery person and selects a route with the least fuel consumption based on real-time traffic information. Also write a script that notifies the delivery person of the results.

[0468] 5. Generate a program that collects and analyzes emotion information provided by the user to set delivery priorities. Also, write the logic to send delivery instructions to the delivery person's device based on the results.

[0469] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0470] Step 1: Receive your shipment information

[0471] server

[0472] The server receives package information from delivery companies via API or file upload. The input package information is often in JSON or CSV format. The server receives this and converts it into a format that can be processed internally. For example, the server analyzes the JSON format package information received from delivery company A and converts it into column-by-column data.

[0473] Input: Package information sent by the delivery company (JSON, CSV, etc.)

[0474] Output: Converted package information (internal format)

[0475] Step 2: Standardize data formats

[0476] server

[0477] The received package information is converted into a unified format and stored in a database. For example, the server converts JSON format data into CSV format, assigns a unique index to each piece of data, and stores it in the database. This makes it easy to search and manage.

[0478] Input: Converted package information (internal format)

[0479] Output: Package information stored in the database

[0480] Step 3: Parse the shipping address

[0481] server

[0482] The server analyzes the delivery address based on the package information stored in the database. It uses a geographic information system (GIS) to convert the address information into latitude and longitude. For example, if the address "1-1 Chiyoda, Chiyoda-ku, Tokyo 100-0001" is sent to the GIS API, the latitude and longitude are returned as 35.682839 and 139.759455, respectively.

[0483] Input: Package information stored in the database

[0484] Output: Latitude and longitude information

[0485] Step 4: Generate delivery routes

[0486] server

[0487] The server uses the latitude and longitude information obtained from the GIS and an AI algorithm to generate the optimal delivery route based on past delivery history, information on whether the delivery person is at home or not. This determines the most efficient delivery route. For example, the AI ​​algorithm calculates the fastest and most fuel-efficient route.

[0488] Input: Latitude and longitude information, past delivery history, presence information, absence information

[0489] Output: Optimal delivery route

[0490] Step 5: Deliver your delivery route

[0491] Server, terminal

[0492] The generated delivery route is sent to the delivery terminal. The server sends the generated route in JSON format to the delivery person's terminal. The terminal receives the route information and displays it so that the delivery person can check it.

[0493] Input: Optimal delivery route

[0494] Output: Delivery route displayed on the delivery person's terminal

[0495] Step 6: Monitoring delivery progress

[0496] server

[0497] Delivery progress is sent to the server in real time and monitored sequentially. The server receives progress data sent from the delivery person's terminal and updates the status. For example, the progress is updated from the terminal when the delivery is completed.

[0498] Input: Delivery progress data

[0499] Output: Updated delivery status

[0500] Step 7: Recalculate the redelivery route

[0501] server

[0502] If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home. This allows for efficient redelivery. For example, it calculates a new route based on the desired redelivery date and time.

[0503] Input: Missed delivery information, past home data

[0504] Output: Redelivery route

[0505] Step 8: Notification of redelivery options

[0506] User

[0507] Users can notify the web portal or app of redelivery options (e.g., using a delivery box) when they are not at home, allowing deliveries to be made according to the user's wishes.

[0508] Input: Redelivery options from user

[0509] Output: Redelivery options notified to the server

[0510] Step 9: Evaluate work output and fuel consumption

[0511] server

[0512] The server evaluates each delivery person's workload and fuel consumption and selects the optimal package allocation and route. For example, it analyzes delivery history to evaluate workload and selects the most fuel-efficient route based on traffic information.

[0513] Input: Delivery history, traffic information

[0514] Output: Optimized load allocation

[0515] Step 10: Assess the environmental impact

[0516] server

[0517] The server evaluates the environmental impact, such as CO2 emissions, and reflects this in delivery plans, thereby minimizing the impact on the environment.

[0518] Input: CO2 emissions data

[0519] Output: Evaluated environmental impact

[0520] Step 11: Collect and analyze emotional information

[0521] Server, User

[0522] The server collects and analyzes the emotional information provided by the user. The user enters emotional data (e.g., expressing a desire for urgent delivery or expressing dissatisfaction) through a web portal or app, and this data is sent to the server. The emotion engine analyzes this data and sets delivery priorities.

[0523] Input: Emotion data from the user

[0524] Output: Parsed emotion information

[0525] Step 12: Generate emotion-based delivery instructions

[0526] Server, terminal

[0527] Delivery instructions based on emotion information are generated and sent to the terminal. The server creates delivery instructions with a priority based on the analysis results of the emotion engine and sends them to the delivery person's terminal. The terminal receives these instructions and notifies the delivery person.

[0528] Input: Parsed emotion information

[0529] Output: Emotion-based delivery instructions

[0530] (Application example 2)

[0531] 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."

[0532] In today's logistics industry, the lack of centralized management of multiple delivery companies makes it difficult to generate efficient delivery routes. Furthermore, the frequency of missed deliveries and redeliveries increases, resulting in increased fuel consumption and working hours, placing a heavy burden on the environment. Furthermore, there is a lack of services that take into account users' feelings about the delivery experience, making it difficult to improve customer satisfaction.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for collecting user emotion information and analyzing it with an emotion engine, and means for setting delivery priorities based on the user emotion information and optimizing delivery routes. This makes it possible to improve delivery efficiency and optimize the delivery experience.

[0534] A "delivery business" is a company or organization that provides services to deliver packages or cargo to designated locations.

[0535] "Parcel information" is detailed data about a parcel, including the destination, sender, contents and size of the parcel, delivery deadline, and so on.

[0536] A database is a digital system that systematically organizes and stores information so that it can be retrieved efficiently.

[0537] A "geographic information system (GIS)" is an information system for collecting, analyzing, and visualizing geographic data, and is capable of analyzing a combination of map data and location information.

[0538] An "artificial intelligence algorithm" is a mathematical model or computational method that enables computers to automatically learn and make decisions.

[0539] A "delivery route" is a route for efficiently delivering packages.

[0540] "Delivery terminal" refers to a portable electronic device used by a delivery person, which is a device for receiving and displaying delivery route and progress information.

[0541] "Delivery progress" is information indicating how far the delivery work has progressed.

[0542] A "redelivery route" is a route optimized for retrying a delivery that has previously failed.

[0543] "Work volume" is an indicator that shows the amount of work and the load placed on delivery personnel.

[0544] "Fuel consumption" refers to the amount of energy used in delivery operations, and primarily refers to the amount of fuel used.

[0545] "Environmental load" refers to the overall impact of human activities on the environment, and is an indicator that particularly includes carbon dioxide emissions and air pollution.

[0546] "Emotional information" is data about a user's emotional state or feelings.

[0547] An "emotion engine" is a program or algorithm that analyzes the user's emotional state and controls the system's operation based on the results.

[0548] "Delivery priority" is a ranking used to determine which delivery is given priority among multiple delivery tasks.

[0549] "Delivery to an unattended address" refers to a delivery where the package was not delivered because the recipient was not present at the delivery address.

[0550] A "delivery box" is a dedicated locker or box for receiving packages, and is a facility for safely storing packages even when the recipient is not at home.

[0551] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system (GIS), generates efficient delivery routes, monitors delivery progress, and also combines it with an emotion engine that recognizes user emotions. This system is used to solve issues such as redelivery, reducing fuel consumption, and mitigating environmental impact.

[0552] 1. Collection and integration of package information

[0553] server

[0554] The server receives package information from delivery companies via API or file upload. The received package information is converted into a unified format and stored in a database. This allows package information from multiple delivery companies to be managed centrally.

[0555] 2. Delivery route generation

[0556] server

[0557] The server analyzes the delivery address based on the received package information and identifies the delivery area in cooperation with the GIS. It then uses an artificial intelligence (AI) algorithm to generate an efficient delivery route within the identified delivery area. For example, it can use KMeans clustering to generate the optimal delivery route.

[0558] Terminal

[0559] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[0560] 3. Delivery monitoring and redelivery support

[0561] server

[0562] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[0563] Terminal

[0564] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[0565] User

[0566] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0567] 4. Optimizing working hours and fuel consumption

[0568] server

[0569] The server evaluates the workload of each delivery person and allocates packages for maximum efficiency. It also selects routes with minimal fuel consumption based on GIS and real-time traffic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0570] Terminal

[0571] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[0572] 5. Use of Emotion Engine

[0573] server

[0574] The server uses an emotion engine to collect user emotional information, analyzes the emotional data provided by the user through the web portal or app, and recognizes the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[0575] Terminal

[0576] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[0577] User

[0578] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[0579] Specific examples

[0580] For particularly urgent orders, when a user selects "urgent" through the app, that emotional information is sent to the emotion engine, which immediately prioritizes the delivery route. For example, if a user inputs, "I want pasta delivered right now! I'm really in a hurry," that emotional information is analyzed and the delivery route is automatically optimized. This prompt allows the emotion engine to understand the user's level of urgency and re-optimize the delivery route based on that information.

[0581] This system will not only improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, but will also enable the provision of services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[0582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0583] Step 1:

[0584] Collection and integration of package information

[0585] The server receives package information from delivery companies via API or file upload. The input is package information provided by the delivery company, including the destination, sender, package contents and size, delivery deadline, etc. The received package information is converted into a unified format and stored in a database. This process allows for centralized management of package information from multiple delivery companies.

[0586] Step 2:

[0587] Parse delivery addresses and identify delivery areas

[0588] The server analyzes the delivery address based on the received package information. The input is package information stored in a database. This address data is analyzed in conjunction with a geographic information system (GIS) to identify the delivery area. The output is the identified delivery area. This process utilizes GPS coordinates and map data for efficient area recognition.

[0589] Step 3:

[0590] Generate efficient delivery routes

[0591] The server generates efficient delivery routes within the specified delivery area. The inputs are the specified delivery area, past delivery history, absence information, etc. An artificial intelligence algorithm (e.g., KMeans clustering) is used to generate the optimal delivery route. The output is the optimal delivery route for each delivery person. In this step, calculations are made to minimize fuel consumption and time.

[0592] Step 4:

[0593] Delivery route distribution

[0594] The server distributes the generated delivery route to each delivery person's device. The input is the delivery route generated in step 3, and the output is delivery route information that can be viewed on the delivery person's smartphone or tablet. This allows delivery people to deliver efficiently.

[0595] Step 5:

[0596] Real-time monitoring of delivery progress

[0597] The server monitors delivery progress in real time. The input is delivery progress data sent from the delivery person's terminal. The server collects this data sequentially and sends new instructions to the delivery person as needed. The output is new instructions according to the delivery progress. This process monitors the delivery status and dynamically adjusts the route.

[0598] Step 6:

[0599] Recalculating redelivery routes when delivery is not received

[0600] The server recalculates the redelivery route when a delivery is missed. The inputs are past data, information on whether the recipient is at home, and information on whether the recipient is absent. Based on this, the server calculates the optimal redelivery route and sends it to the delivery person's device. The output is the optimal route for redelivery.

[0601] Step 7:

[0602] Collecting and analyzing emotional information

[0603] The server uses an emotion engine to collect and analyze the user's emotional information. The input is emotional data provided by the user through a web portal or app. The analyzed emotional data is output, and delivery priorities are set based on this. For example, if the user inputs "I want pasta delivered right now! I'm really in a hurry," it will be recognized as a high level of urgency.

[0604] Step 8:

[0605] Delivery priority setting based on emotion information

[0606] The server sets delivery priorities and optimizes delivery routes based on the analyzed emotion information. The input is the emotion data obtained in step 7. The output is the updated delivery priorities and optimized delivery routes. This enables delivery that takes emotion into consideration for specific users.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] [Second embodiment]

[0611] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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."

[0623] ---

[0624] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[0625] 1. Collection and integration of package information

[0626] server

[0627] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[0628] 2. Delivery route generation

[0629] server

[0630] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[0631] Terminal

[0632] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[0633] 3. Delivery monitoring and redelivery support

[0634] server

[0635] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[0636] Terminal

[0637] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[0638] User

[0639] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0640] 4. Optimizing working hours and fuel consumption

[0641] server

[0642] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0643] Terminal

[0644] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[0645] Specific examples

[0646] Example 1: When the user is absent

[0647] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[0648] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[0649] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[0650] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[0651] Example 2: Optimizing delivery efficiency

[0652] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[0653] 2. Uses AI algorithms to calculate the most efficient delivery route.

[0654] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[0655] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[0656] The present invention can improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, thereby contributing to the sustainable growth of the logistics industry.

[0657] ---

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] server

[0661] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[0662] Step 2:

[0663] server

[0664] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[0665] Step 3:

[0666] server

[0667] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[0668] Step 4:

[0669] server

[0670] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[0671] Step 5:

[0672] server

[0673] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[0674] Step 6:

[0675] Terminal

[0676] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[0677] Step 7:

[0678] Terminal

[0679] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[0680] Step 8:

[0681] server

[0682] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[0683] Step 9:

[0684] server

[0685] If a delivery is not made at home, the server recalculates the redelivery route based on past data and information on whether the recipient is at home. The server then determines the optimal redelivery date and route and notifies the delivery person.

[0686] Step 10:

[0687] Terminal

[0688] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[0689] Step 11:

[0690] User

[0691] Users can check the delivery progress through a web portal or app, select redelivery options if necessary, and provide instructions to the server if the user is not at home.

[0692] Step 12:

[0693] server

[0694] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency based on past delivery history and current workload.

[0695] Step 13:

[0696] server

[0697] Selects a route with less fuel consumption. The server optimizes fuel consumption and time based on geographical information and real-time traffic information.

[0698] Step 14:

[0699] server

[0700] The server evaluates the environmental impact and reflects it in delivery plans. It collects environmental indicators such as CO2 emissions and uses this information to formulate sustainable delivery strategies.

[0701] Step 15:

[0702] Terminal

[0703] The delivery person's terminal follows instructions from the server and delivers the package along the optimal route. By making deliveries more efficient, fuel and labor costs can be saved.

[0704] These are the specific steps of the program, which will improve the efficiency of the logistics industry and reduce the burden on the environment.

[0705] Example 1

[0706] 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."

[0707] In today's logistics and delivery industries, there is a need to efficiently manage package information received from multiple delivery companies, minimize redelivery and fuel consumption, and maximize delivery efficiency. In particular, there is a need for systems that can perform complex tasks such as monitoring delivery progress in real time, issuing redelivery instructions when the recipient is absent, and evaluating environmental impact.

[0708] 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.

[0709] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information in the event of a missed delivery, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for issuing redelivery instructions based on redelivery options previously specified by the user, and means for recalculating optimal delivery routes in real time using traffic information, thereby enabling reduction in redelivery and fuel consumption, improved delivery efficiency, and reduced environmental impact.

[0710] A "delivery company" is an organization or company whose business is receiving and delivering packages.

[0711] "Package information" refers to detailed data about the package being delivered (e.g., package contents, weight, size, delivery address, etc.).

[0712] A "database" refers to a structured and managed collection of data, and is a system for storing, searching, and updating cargo information.

[0713] "Geographic Information System (GIS)" means a system for collecting, analyzing, and displaying geographic information used to locate shipping addresses.

[0714] "Delivery Address" means the location where a package is to be delivered.

[0715] "Delivery Area" means the geographic area within which a particular delivery activity occurs.

[0716] An "artificial intelligence algorithm" is a method or procedure that allows a computer to perform processing by imitating part of human intelligence.

[0717] A "delivery route" is the optimal route planned to deliver packages efficiently.

[0718] A "delivery terminal" refers to an electronic device such as a smartphone or tablet used by a delivery person.

[0719] "Delivery progress" refers to information indicating the stage of delivery.

[0720] "Real time" refers to processing that is performed immediately without delay, meaning that processing is performed in synchronization with real time.

[0721] "Instructions" refer to specific instructions or orders for actions sent to delivery personnel from a higher-level system or administrator.

[0722] A "redelivery route" is a route planned to redeliver packages that could not be delivered due to reasons such as absence of the recipient.

[0723] "Workload" refers to the amount of work and burden that a delivery person must perform.

[0724] "Fuel consumption" refers to the energy, particularly fuel, consumed in delivery operations.

[0725] "Environmental load" refers to the impact that economic and human activities have on the natural environment.

[0726] "Redelivery options" refer to options for how to redeliver a package if the user is not at home.

[0727] "Traffic information" refers to data on road congestion and traffic flow.

[0728] MODE FOR CARRYING OUT THE INVENTION

[0729] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[0730] 1. Collection and integration of package information

[0731] server

[0732] The server receives package information from the delivery company via API. Specifically, it uses the API provided by the delivery company in JSON or XML format. The received package information is converted into a unified format (for example, CSV format) by an internal data conversion module. The package information converted into the unified format is then stored in a database.

[0733] Specific examples:

[0734] Package information is received from delivery company A via API, and the JSON format data is converted to CSV format and stored in a database.

[0735] 2. Delivery route generation

[0736] server

[0737] The server retrieves package information from the database and analyzes each delivery address. The delivery address is converted into location information in conjunction with a geographic information system (GIS). For example, the GIS uses Google Maps API or OpenStreetMap. The delivery area is identified based on the location information of the analyzed delivery address.

[0738] Next, an efficient delivery route is generated within the identified delivery area using an artificial intelligence algorithm. Specifically, the shortest route is calculated using the Dijkstra algorithm or the A algorithm. The generated delivery route is sent from the server to the delivery person's device.

[0739] Terminal

[0740] The delivery person's device receives the route information and displays the optimal delivery route on the app. The device, such as a smartphone or tablet, visualizes the route using the Google Maps API.

[0741] Specific examples:

[0742] The server obtains information about the delivery area and generates the optimal delivery route using the Dijkstra algorithm. The generated route is sent to the delivery person's smartphone and displayed in the Google Maps app.

[0743] 3. Delivery monitoring and redelivery support

[0744] server

[0745] The server receives real-time delivery progress information from the delivery person's terminal. When the delivery person has delivered the package or was unable to deliver due to absence, the server reports this information to the server. The server updates the database based on this information.

[0746] If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home. The recalculated route is then sent back to the delivery person's device.

[0747] Terminal

[0748] The delivery person's terminal successively transmits delivery progress information to the server, and redelivers or changes the route as necessary. The terminal immediately receives redelivery instructions and new route information, allowing for efficient redelivery.

[0749] User

[0750] Users can notify the server of their desired redelivery options in advance through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the options specified by the user. For example, if the user requests delivery to a parcel box, that information is sent to the server and the delivery person is notified.

[0751] Specific examples:

[0752] The user sends information about their desired delivery to the delivery box to the server through the app, and the server calculates a new redelivery route based on that information and sends it to the delivery person's device.

[0753] 4. Optimizing working hours and fuel consumption

[0754] server

[0755] The server collects the workload of each delivery person from a database and evaluates it. Based on the evaluation results, it allocates packages to each delivery person with maximum efficiency. It also uses real-time traffic information to select routes with the least fuel consumption. It also evaluates the environmental impact (e.g., CO2 emissions) and creates delivery plans that take this into account.

[0756] Specific technologies include the use of Google Maps traffic information API and environmental assessment software.

[0757] Terminal

[0758] The delivery person's device then receives optimized route information from the server and delivers efficiently, for example, by choosing a route that avoids traffic jams or by providing instructions for the shortest possible delivery distance.

[0759] Specific examples:

[0760] The server receives real-time traffic information and calculates a route with the lowest CO2 emissions based on that information, which is then displayed on the delivery person's device.

[0761] Example prompts using generative AI models

[0762] Example prompt sentence:

[0763] "Generate efficient delivery routes based on past delivery history and real-time traffic information."

[0764] By inputting this prompt into a generative AI model, the complex route generation process can be automated to produce an optimized delivery route.

[0765] The above is a specific description of the embodiment of the present invention.

[0766] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0767] Step 1:

[0768] server

[0769] Parcel information is received from the delivery company using an API. The input at this time is the parcel data in JSON format provided by the delivery company. The server converts the received parcel information into CSV format using an internal data conversion module and stores it in the database. The output is the parcel information converted into a unified format.

[0770] Specific operation: Receive JSON formatted package information from delivery company A via API, convert it to CSV format, and insert it into the database.

[0771] Step 2:

[0772] server

[0773] The server retrieves package information from the database and analyzes the delivery address. The input is the delivery address data in CSV format, and the output is latitude and longitude information using a geographic information system (GIS). Next, the GIS is used to obtain the location information of the delivery address and identify the delivery area.

[0774] Specific operation: The server reads the delivery address in CSV format, obtains the latitude and longitude information using the Google Maps API, and maps the delivery area on a map.

[0775] Step 3:

[0776] server

[0777] The server generates efficient delivery routes within the specified delivery area using an artificial intelligence algorithm. The input is latitude and longitude information within the delivery area, and the output is the optimal delivery route. The server calculates the shortest route using the Dijkstra algorithm or the A algorithm, and distributes the generated delivery route to the delivery person's terminal.

[0778] Specific operation: The server calculates the optimal delivery route based on latitude and longitude information within the delivery area, and sends the determined route information to the delivery person's smartphone.

[0779] Step 4:

[0780] Terminal

[0781] The delivery person's device receives the route information and displays it on the app. The input is the optimal route information sent from the server, and the output is the delivery route displayed on a map. The device visualizes the route using the Google Maps API and provides the delivery person with the shortest route.

[0782] Specific operation: The delivery person's device displays the route information received from the server using the Google Maps API and begins delivery.

[0783] Step 5:

[0784] server

[0785] The server receives real-time delivery progress information from the delivery person's terminal. The input is the delivery progress data sent from the terminal, and the output is progress update information. The server sends new instructions to the delivery person as needed.

[0786] Specific operation: The delivery person's device sends current location information and delivery status to the server every minute, and the server updates the progress status based on this data.

[0787] Step 6:

[0788] server

[0789] When a delivery is missed, the server recalculates the redelivery route based on past data and at-home information. The input is delivery progress data and past at-home information, and the output is a new redelivery route. The server then applies the optimization algorithm to generate a new route and distributes it to the delivery person's device.

[0790] Specific operation: The server receives information about the delivery when the delivery person is not at home, references past data on when the delivery person is at home, and sends a recalculated route to the delivery person's terminal.

[0791] Step 7:

[0792] User

[0793] The user uses a web portal or app to notify the server in advance of redelivery options (e.g., use of a delivery box) in case of absence. The input is the redelivery options specified by the user, and the output is the notification information sent to the server. Based on the user's specifications, the server sends appropriate instructions to the delivery person.

[0794] Specific operation: The user uses the app to specify the use of a delivery box and sends that information to the server. The server then calculates a redelivery route based on that information and sends instructions to the delivery person.

[0795] Step 8:

[0796] server

[0797] The server evaluates the workload of each delivery person and selects a route that consumes less fuel. The input is delivery progress data and traffic information, and the output is optimized route information. The server also evaluates the environmental impact (CO2 emissions) and reflects this in the delivery plan.

[0798] Specific operation: The server recalculates the most fuel-efficient route based on delivery progress data and real-time traffic information, and sends instructions to the delivery person.

[0799] This will reduce redelivery and fuel consumption, improve delivery efficiency, and reduce the burden on the environment.

[0800] (Application example 1)

[0801] 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."

[0802] In the logistics industry, parcel deliveries are routinely carried out by multiple delivery companies, but the challenge is to centrally manage this parcel information and efficiently generate delivery routes. It is also necessary to effectively monitor delivery progress and handle redelivery requests. Furthermore, reducing fuel consumption and reducing environmental impact are also important issues. The present invention aims to solve these challenges and provide a system for effectively managing operations at logistics centers.

[0803] 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.

[0804] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an AI algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting a route with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for delivery personnel to receive instructions for the optimized route via a smart device, means for users to set redelivery options via a web portal or app, and means for optimizing delivery plans using a generative AI model. This enables delivery efficiency, reduced fuel consumption, fewer redeliveries, and shorter working hours.

[0805] "Parcel information" refers to data such as parcel identification information, delivery destination information, and delivery status information collected from multiple delivery companies.

[0806] A "database" is a system for systematically storing, managing, and searching information.

[0807] A "geographic information system" is a system for collecting, managing, and analyzing geographic data.

[0808] "Delivery address analysis" refers to the technology used to understand the delivery address information and determine the appropriate delivery area and route.

[0809] "Specifying a delivery area" means clarifying the geographical area to which the delivery destination of the package belongs.

[0810] An "artificial intelligence algorithm" is a calculation procedure that enables a computer to learn on its own and make appropriate judgments and predictions.

[0811] "Generating an efficient delivery route" means calculating a route that will complete delivery while saving the most time and money, based on the input delivery destination information.

[0812] A "delivery terminal" is an electronic device carried by a delivery person to receive delivery progress and instructions.

[0813] "Delivery progress" refers to the current progress of the package in the delivery process.

[0814] "Real-time monitoring" means instantly checking the progress and status of delivery.

[0815] "Sending instructions" means that the server sends necessary information and instructions to the delivery person via an electronic device.

[0816] "Delivery to absentee" refers to a situation where the resident at the delivery address is absent and unable to receive the package.

[0817] "Past data" refers to information such as delivery history and customer information that has been recorded to date.

[0818] "At-home information" is information about whether the resident of the delivery destination is at home.

[0819] "Recalculating the redelivery route" means recalculating a new, more efficient delivery route when a delivery is missed.

[0820] "Delivery progress data" is data that indicates the current status and progress of delivery.

[0821] "Evaluating workload" means measuring the workload of each delivery person and appropriately evaluating their efforts.

[0822] A "low fuel consumption route" is a delivery route that is planned to minimize the amount of fuel used by the delivery vehicle.

[0823] "Evaluating the environmental impact" means measuring the impact that delivery activities have on the environment and formulating plans based on this.

[0824] A "smart device" is a portable electronic device that can connect to the Internet and use a variety of applications.

[0825] "Receiving optimized route instructions" means that the delivery person receives information about a pre-calculated and optimized delivery route.

[0826] A "web portal" is a website that provides users with a variety of information and services via the Internet.

[0827] "Redelivery options" are settings and instructions regarding redelivery that customers can select in the event of a missed delivery.

[0828] A "generative AI model" is an artificial intelligence computational model used for delivery planning and route optimization.

[0829] "Optimizing" means arranging and adjusting a system or computation most effectively to achieve a specific goal.

[0830] System Overview

[0831] This invention relates to a system that realizes efficient delivery of parcels at a logistics center. This system manages parcel information received from multiple delivery companies in an integrated manner and generates efficient delivery routes using a geographic information system and an artificial intelligence algorithm. It also monitors delivery progress in real time, responding to requests for redelivery and reducing environmental impact.

[0832] Program generation and natural language explanation

[0833] Collection and integration of package information

[0834] The server collects package information from multiple delivery companies via API and file upload, converts it into a unified format, and stores it in a database, allowing package information from different delivery companies to be centrally managed.

[0835] Generate delivery routes

[0836] The server uses the collected package information to link with a geographic information system (GIS) and analyzes the delivery address. Based on the analyzed address information, an artificial intelligence algorithm is used to generate an efficient delivery route. The generated route is optimized by taking into account data such as past delivery history and absence information.

[0837] Delivery route distribution

[0838] The generated delivery route is sent from the server to the delivery person's device (smartphone or tablet), and the delivery person uses this device to make deliveries along the most efficient route.

[0839] Real-time monitoring of delivery progress

[0840] The server monitors the delivery progress data sent from the delivery person's terminal in real time and sends new instructions to the terminal as necessary. The delivery progress data includes the delivery status of each package and the delivery person's location information. This information is collected and analyzed by the server in real time.

[0841] Redelivery support

[0842] If a delivery is missed, the server recalculates the redelivery route based on past data and information on when the delivery person is at home, and instructs the delivery person on a new route. Users can pre-set redelivery options (e.g., use of a delivery box) through the web portal or app.

[0843] Optimizing working hours and fuel consumption

[0844] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0845] Specific examples

[0846] Example 1: When the user is absent

[0847] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[0848] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[0849] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[0850] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[0851] Example 2: Optimizing delivery efficiency

[0852] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[0853] 2. Uses AI algorithms to calculate the most efficient delivery route.

[0854] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[0855] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[0856] In this way, the efficiency of parcel delivery at the logistics center can be maximized. An example of a prompt sentence to be input to the generative AI model is as follows:

[0857] Prompt Sentence Examples

[0858] "You've gathered all the parcel information for tomorrow. There are currently 50 deliveries. To create the optimal delivery route, please use GIS and AI algorithms to analyze these addresses and generate efficient routes."

[0859] This system will enable more efficient delivery, reduce fuel consumption, reduce redelivery, and shorten working hours.

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Program processing flow

[0862] Step 1: Collect and consolidate package information

[0863] The server receives package information from multiple delivery companies via API or file upload. The received package information is as follows:

[0864] Input: Package information data provided by the delivery company

[0865] Data processing: Converting package information into a unified format

[0866] Output: Package information data converted into a unified format

[0867] Specific operation: The program standardizes package information provided by each delivery company in different formats for storage in a database. For example, it reads data in CSV or JSON format and converts it into a unified data format.

[0868] Step 2: Address analysis and delivery area identification

[0869] Based on the parcel information converted into a unified format, the server works in conjunction with a geographic information system (GIS) to analyze the delivery address and identify the delivery area.

[0870] Input: Package information data in a unified format

[0871] Data processing: Address analysis using geographic information systems

[0872] Output: Delivery address and delivery area information

[0873] What it does: The program uses a GIS library to convert each delivery address into geographic coordinates and identify the delivery area to which each delivery address belongs.

[0874] Step 3: Generate efficient delivery routes

[0875] The server uses an artificial intelligence algorithm to generate an efficient delivery route based on the delivery address, past delivery history, and absence information.

[0876] Input: Delivery address, past delivery history, absence information

[0877] Data calculation: Route optimization using artificial intelligence algorithms

[0878] Output: Optimized delivery route

[0879] How it works: To create smart delivery plans, the program uses AI algorithms (e.g., genetic algorithms and deep learning models) to calculate the shortest and lowest-cost routes.

[0880] Step 4: Deliver your delivery route

[0881] The server delivers the optimized delivery route to the delivery person's device (smartphone or tablet).

[0882] Input: Optimized delivery route

[0883] Data calculation: None (data distribution)

[0884] Output: Delivery route sent to the delivery person's device

[0885] Specific operation: The optimized route information is sent to the delivery person's device and notified. If the delivery person's device is connected to the Internet, the route information is received immediately.

[0886] Step 5: Real-time monitoring of delivery progress

[0887] The server monitors the delivery progress data sent from the delivery person's terminal in real time.

[0888] Input: Delivery progress data sent from the terminal

[0889] Data calculations: analyzing and monitoring progress data

[0890] Output: Real-time updated delivery status

[0891] Specific operation: Receives delivery personnel's location information and delivery completion status in real time, monitors progress, and sends instructions from the server if an abnormality is detected.

[0892] Step 6: Recalculate the redelivery route

[0893] When a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the recipient is at home.

[0894] Input: Missed delivery information, past data, at-home information

[0895] Data calculation: Recalculation to generate optimal route

[0896] Output: Optimal route for redelivery

[0897] Specific operation: When a delivery is missed, the program refers to past data, recalculates the appropriate redelivery time and route, and sends a redelivery instruction to the delivery person's terminal.

[0898] Step 7: Optimizing fuel consumption and environmental impact

[0899] The server selects routes with the lowest fuel consumption based on real-time traffic and geographical information, evaluates the environmental impact, and reflects this in delivery plans.

[0900] Input: Real-time traffic information, geographic information

[0901] Data calculation: Simulation of fuel consumption and environmental impact

[0902] Output: Optimized eco-routes and delivery plans

[0903] Specific operation: The program selects a route that consumes less fuel by taking into account traffic congestion and road conditions, calculates the environmental impact, and instructs the delivery person on a delivery plan that reflects this.

[0904] This will enable more efficient delivery at logistics centers, reduce fuel consumption, reduce redeliveries, and shorten working hours.

[0905] 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.

[0906] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[0907] 1. Collection and integration of package information

[0908] server

[0909] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[0910] 2. Delivery route generation

[0911] server

[0912] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[0913] Terminal

[0914] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[0915] 3. Delivery monitoring and redelivery support

[0916] server

[0917] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[0918] Terminal

[0919] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[0920] User

[0921] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[0922] 4. Optimizing working hours and fuel consumption

[0923] server

[0924] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[0925] Terminal

[0926] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[0927] 5. Use of Emotion Engine

[0928] server

[0929] The system uses an emotion engine to collect user emotional information. It analyzes the emotional data provided by users through the web portal or app to recognize the user's emotional state. Based on this, it sets delivery priorities and reflects them in the generation of delivery routes.

[0930] Terminal

[0931] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[0932] User

[0933] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[0934] Specific examples

[0935] Example 1: Using emotional data to prioritize delivery

[0936] 1. The server receives emotion information provided by the user. For example, if the user requests an urgent delivery, the information is analyzed by the emotion engine.

[0937] 2. The emotion engine recognizes urgent delivery requests as high priority and reflects this when generating delivery routes.

[0938] 3. High-priority delivery routes are sent to the terminal, and delivery personnel follow those instructions to make deliveries.

[0939] Example 2: Redelivery instructions when the recipient is absent and consideration for the recipient's feelings

[0940] 1. The server receives the user's absence along with emotional information. For example, if the user strongly desires redelivery, that information is included.

[0941] 2. Delivery progress is monitored, and if redelivery becomes necessary, the optimal redelivery route is recalculated based on information analyzed by the emotion engine.

[0942] 3. A redelivery instruction that takes into account emotional information is sent to the terminal, and the delivery person acts based on that instruction, delivering the parcel to a delivery box or other location according to the user's wishes.

[0943] This invention not only improves delivery efficiency, reduces fuel consumption, reduces redelivery, and shortens working hours, but also makes it possible to provide services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[0944] ---

[0945] The processing flow will be explained below.

[0946] ---

[0947] Step 1:

[0948] server

[0949] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[0950] Step 2:

[0951] server

[0952] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[0953] Step 3:

[0954] server

[0955] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[0956] Step 4:

[0957] server

[0958] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[0959] Step 5:

[0960] server

[0961] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[0962] Step 6:

[0963] Terminal

[0964] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[0965] Step 7:

[0966] Terminal

[0967] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[0968] Step 8:

[0969] server

[0970] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[0971] Step 9:

[0972] server

[0973] If a delivery is not made at home, the system recalculates the redelivery route based on past data and information on whether the recipient is at home. It then calculates the optimal redelivery date and route and notifies the delivery person.

[0974] Step 10:

[0975] Terminal

[0976] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[0977] Step 11:

[0978] User

[0979] Through a web portal or app, users provide emotional information, such as expectations or dissatisfaction regarding delivery, which is then sent to the server.

[0980] Step 12:

[0981] server

[0982] Receives user emotional information and analyzes it with an emotion engine. Based on the user's emotional data, delivery priorities are re-established.

[0983] Step 13:

[0984] server

[0985] Based on the analysis results, an AI algorithm regenerates efficient delivery routes, and adjusts delivery routes to accommodate specific users' emotions.

[0986] Step 14:

[0987] server

[0988] Based on the emotion information, new instructions are sent to the delivery person, for example, instructing them to make deliveries with higher priority taking into consideration the emotion.

[0989] Step 15:

[0990] Terminal

[0991] The delivery person's terminal receives the updated instructions and performs the delivery based on the emotion information, thereby improving the user's satisfaction.

[0992] Step 16:

[0993] server

[0994] Real-time monitoring of delivery progress and user emotional responses will be used to improve the system. New data will be analyzed and reflected in future delivery plans.

[0995] These are the specific processing steps of the system. This process will improve the efficiency of the logistics industry, reduce the environmental impact, and realize a delivery service that takes user emotions into consideration.

[0996] ---

[0997] Example 2

[0998] 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."

[0999] Conventional delivery systems lack a means to effectively integrate package information from multiple delivery companies, making it difficult to generate efficient delivery routes, monitor delivery progress in real time, and optimize redelivery. Furthermore, fuel consumption and environmental impact are not sufficiently reduced. Furthermore, there is a lack of delivery priority settings that incorporate user emotional information and advance notification of redelivery options, making it difficult to improve overall customer satisfaction.

[1000] 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.

[1001] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses and identifying delivery areas in cooperation with a geographic information system, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impacts and reflecting them in delivery plans, means for collecting and analyzing emotion information from users and setting delivery priorities, means for generating delivery instructions that take emotion information into account and distributing them to terminals, and means for users to notify redelivery options in advance. This enables more efficient delivery, reduced fuel consumption, fewer redeliveries, shorter working hours, and the provision of services that take user emotions into consideration.

[1002] "Parcel information" refers to detailed data about parcels delivered by delivery companies, including the address, type of parcel, weight, size, desired delivery date and time, etc.

[1003] A "database" is a system for efficiently storing, managing, and searching information, and is a central repository for the integrated storage of cargo information, progress data, and other information.

[1004] A "geographic information system (GIS)" is a system for analyzing and managing geographic information, and is used for address analysis and determining delivery areas.

[1005] "Delivery address" refers to the address information of the destination where the package is to be delivered, and is data that is converted into geographic information such as latitude and longitude.

[1006] An "artificial intelligence algorithm" is a data processing method that uses AI technology and is a computational model for generating efficient delivery routes.

[1007] "Delivery route" refers to the optimal route a delivery person takes to deliver a package to each destination.

[1008] A "delivery terminal" is a mobile device (such as a smartphone or tablet) used by delivery personnel to receive delivery routes and instructions.

[1009] "Delivery progress" is data showing the delivery status of a package in real time, including the current delivery status and completion status.

[1010] A "redelivery route" is an optimized delivery route for re-delivering a package in the event of a missed delivery.

[1011] "Work volume" refers to the amount and burden of delivery work performed by delivery personnel, and is evaluated based on criteria such as multiple delivery destinations and the number of packages.

[1012] "Fuel consumption" refers to the amount of fuel consumed in delivery operations, and must be minimized to increase delivery efficiency.

[1013] "Environmental impact" refers to the impact on the environment, such as CO2 emissions and resource consumption associated with delivery operations.

[1014] "Emotional information" refers to emotional data such as expectations or dissatisfaction regarding delivery provided by the user.

[1015] "Delivery instructions" are specific instructions for actions sent from the server to the delivery person, and include information such as delivery route and priority.

[1016] "Redelivery option" means an option for an alternative method of receiving a package (e.g., a delivery box) that can be specified when the user is not at home.

[1017] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[1018] Collection and integration of package information

[1019] server

[1020] The server receives package information from delivery companies using an API or file upload. The received package information is converted into a unified format and stored in a database. This allows information from multiple delivery companies to be managed centrally. For example, if delivery company A sends package information in JSON format and delivery company B sends it in CSV format, the server converts it into a unified format (for example, CSV format) and stores it in the database.

[1021] Generate delivery routes

[1022] server

[1023] The server analyzes the delivery address based on the package information stored in the database and identifies the delivery area in conjunction with a geographic information system (GIS). An artificial intelligence algorithm is used to generate an efficient delivery route within the identified delivery area. For example, the AI ​​algorithm optimizes the route based on past delivery history, information on whether the recipient is at home or absent, and then generates the delivery route.

[1024] Terminal

[1025] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet receives this information and uses it as a guide to make deliveries efficiently.

[1026] Delivery monitoring and redelivery support

[1027] server

[1028] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home, maximizing delivery efficiency.

[1029] Terminal

[1030] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the information is also reported to the server.

[1031] User

[1032] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1033] Optimizing working hours and fuel consumption

[1034] server

[1035] The server evaluates the workload of each delivery person and allocates packages efficiently. It also selects routes with low fuel consumption based on real-time traffic information. It also evaluates the environmental impact (CO2 emissions, etc.) and reflects this in delivery plans.

[1036] Terminal

[1037] The delivery person's device receives the optimized route from the server and delivers efficiently, thereby reducing fuel consumption and working hours.

[1038] Use of emotion engine

[1039] server

[1040] The emotion engine is used to collect user emotional information. For example, it analyzes emotional data provided by users through the web portal or app (e.g., requests for urgent delivery or expressions of dissatisfaction) to recognize the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[1041] Terminal

[1042] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[1043] User

[1044] Users can provide emotional information through a web portal or app, such as expressing their expectations or dissatisfaction with the delivery. The provided emotional information is analyzed by the server and reflected in the delivery plan.

[1045] As a concrete example, the server receives emotion information provided by the user and analyzes it with an emotion engine to recognize urgent delivery requests as high priority and reflect this when generating delivery routes. Based on this, high-priority delivery routes are sent to the terminal, and delivery personnel make deliveries according to those instructions.

[1046] Example prompt sentence:

[1047] 1. Create a program that receives package information sent by delivery company A, converts it into a unified format, and saves it in a database.

[1048] 2. Create a program that analyzes the delivery address and generates the optimal delivery route using GIS. Also, write a script that sends the route to the delivery person's device.

[1049] 3. Generate a program that monitors delivery progress in real time and recalculates the redelivery route if a delivery is missed. Also, add logic to receive redelivery options from the user in advance and send instructions to the delivery person based on that information.

[1050] 4. Write a program that evaluates the workload of each delivery person and selects a route with the least fuel consumption based on real-time traffic information. Also write a script that notifies the delivery person of the results.

[1051] 5. Generate a program that collects and analyzes emotion information provided by the user to set delivery priorities. Also, write the logic to send delivery instructions to the delivery person's device based on the results.

[1052] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1053] Step 1: Receive your shipment information

[1054] server

[1055] The server receives package information from delivery companies via API or file upload. The input package information is often in JSON or CSV format. The server receives this and converts it into a format that can be processed internally. For example, the server analyzes the JSON format package information received from delivery company A and converts it into column-by-column data.

[1056] Input: Package information sent by the delivery company (JSON, CSV, etc.)

[1057] Output: Converted package information (internal format)

[1058] Step 2: Standardize data formats

[1059] server

[1060] The received package information is converted into a unified format and stored in a database. For example, the server converts JSON format data into CSV format, assigns a unique index to each piece of data, and stores it in the database. This makes it easy to search and manage.

[1061] Input: Converted package information (internal format)

[1062] Output: Package information stored in the database

[1063] Step 3: Parse the shipping address

[1064] server

[1065] The server analyzes the delivery address based on the package information stored in the database. It uses a geographic information system (GIS) to convert the address information into latitude and longitude. For example, if the address "1-1 Chiyoda, Chiyoda-ku, Tokyo 100-0001" is sent to the GIS API, the latitude and longitude are returned as 35.682839 and 139.759455, respectively.

[1066] Input: Package information stored in the database

[1067] Output: Latitude and longitude information

[1068] Step 4: Generate delivery routes

[1069] server

[1070] The server uses the latitude and longitude information obtained from the GIS and an AI algorithm to generate the optimal delivery route based on past delivery history, information on whether the delivery person is at home or not. This determines the most efficient delivery route. For example, the AI ​​algorithm calculates the fastest and most fuel-efficient route.

[1071] Input: Latitude and longitude information, past delivery history, presence information, absence information

[1072] Output: Optimal delivery route

[1073] Step 5: Deliver your delivery route

[1074] Server, terminal

[1075] The generated delivery route is sent to the delivery terminal. The server sends the generated route in JSON format to the delivery person's terminal. The terminal receives the route information and displays it so that the delivery person can check it.

[1076] Input: Optimal delivery route

[1077] Output: Delivery route displayed on the delivery person's terminal

[1078] Step 6: Monitoring delivery progress

[1079] server

[1080] Delivery progress is sent to the server in real time and monitored sequentially. The server receives progress data sent from the delivery person's terminal and updates the status. For example, the progress is updated from the terminal when the delivery is completed.

[1081] Input: Delivery progress data

[1082] Output: Updated delivery status

[1083] Step 7: Recalculate the redelivery route

[1084] server

[1085] If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home. This allows for efficient redelivery. For example, it calculates a new route based on the desired redelivery date and time.

[1086] Input: Missed delivery information, past home data

[1087] Output: Redelivery route

[1088] Step 8: Notification of redelivery options

[1089] User

[1090] Users can notify the web portal or app of redelivery options (e.g., using a delivery box) when they are not at home, allowing deliveries to be made according to the user's wishes.

[1091] Input: Redelivery options from user

[1092] Output: Redelivery options notified to the server

[1093] Step 9: Evaluate work output and fuel consumption

[1094] server

[1095] The server evaluates each delivery person's workload and fuel consumption and selects the optimal package allocation and route. For example, it analyzes delivery history to evaluate workload and selects the most fuel-efficient route based on traffic information.

[1096] Input: Delivery history, traffic information

[1097] Output: Optimized load allocation

[1098] Step 10: Assess the environmental impact

[1099] server

[1100] The server evaluates the environmental impact, such as CO2 emissions, and reflects this in delivery plans, thereby minimizing the impact on the environment.

[1101] Input: CO2 emissions data

[1102] Output: Evaluated environmental impact

[1103] Step 11: Collect and analyze emotional information

[1104] Server, User

[1105] The server collects and analyzes the emotional information provided by the user. The user enters emotional data (e.g., expressing a desire for urgent delivery or expressing dissatisfaction) through a web portal or app, and this data is sent to the server. The emotion engine analyzes this data and sets delivery priorities.

[1106] Input: Emotion data from the user

[1107] Output: Parsed emotion information

[1108] Step 12: Generate emotion-based delivery instructions

[1109] Server, terminal

[1110] Delivery instructions based on emotion information are generated and sent to the terminal. The server creates delivery instructions with a priority based on the analysis results of the emotion engine and sends them to the delivery person's terminal. The terminal receives these instructions and notifies the delivery person.

[1111] Input: Parsed emotion information

[1112] Output: Emotion-based delivery instructions

[1113] (Application example 2)

[1114] 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."

[1115] In today's logistics industry, the lack of centralized management of multiple delivery companies makes it difficult to generate efficient delivery routes. Furthermore, the frequency of missed deliveries and redeliveries increases, resulting in increased fuel consumption and working hours, placing a heavy burden on the environment. Furthermore, there is a lack of services that take into account users' feelings about the delivery experience, making it difficult to improve customer satisfaction.

[1116] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for collecting user emotion information and analyzing it with an emotion engine, and means for setting delivery priorities based on the user emotion information and optimizing delivery routes. This makes it possible to improve delivery efficiency and optimize the delivery experience.

[1117] A "delivery business" is a company or organization that provides services to deliver packages or cargo to designated locations.

[1118] "Parcel information" is detailed data about a parcel, including the destination, sender, contents and size of the parcel, delivery deadline, and so on.

[1119] A database is a digital system that systematically organizes and stores information so that it can be retrieved efficiently.

[1120] A "geographic information system (GIS)" is an information system for collecting, analyzing, and visualizing geographic data, and is capable of analyzing a combination of map data and location information.

[1121] An "artificial intelligence algorithm" is a mathematical model or computational method that enables computers to automatically learn and make decisions.

[1122] A "delivery route" is a route for efficiently delivering packages.

[1123] "Delivery terminal" refers to a portable electronic device used by a delivery person, which is a device for receiving and displaying delivery route and progress information.

[1124] "Delivery progress" is information indicating how far the delivery work has progressed.

[1125] A "redelivery route" is a route optimized for retrying a delivery that has previously failed.

[1126] "Work volume" is an indicator that shows the amount of work and the load placed on delivery personnel.

[1127] "Fuel consumption" refers to the amount of energy used in delivery operations, and primarily refers to the amount of fuel used.

[1128] "Environmental load" refers to the overall impact of human activities on the environment, and is an indicator that particularly includes carbon dioxide emissions and air pollution.

[1129] "Emotional information" is data about a user's emotional state or feelings.

[1130] An "emotion engine" is a program or algorithm that analyzes the user's emotional state and controls the system's operation based on the results.

[1131] "Delivery priority" is a ranking used to determine which delivery is given priority among multiple delivery tasks.

[1132] "Delivery to an unattended address" refers to a delivery where the package was not delivered because the recipient was not present at the delivery address.

[1133] A "delivery box" is a dedicated locker or box for receiving packages, and is a facility for safely storing packages even when the recipient is not at home.

[1134] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system (GIS), generates efficient delivery routes, monitors delivery progress, and also combines it with an emotion engine that recognizes user emotions. This system is used to solve issues such as redelivery, reducing fuel consumption, and mitigating environmental impact.

[1135] 1. Collection and integration of package information

[1136] server

[1137] The server receives package information from delivery companies via API or file upload. The received package information is converted into a unified format and stored in a database. This allows package information from multiple delivery companies to be managed centrally.

[1138] 2. Delivery route generation

[1139] server

[1140] The server analyzes the delivery address based on the received package information and identifies the delivery area in cooperation with the GIS. It then uses an artificial intelligence (AI) algorithm to generate an efficient delivery route within the identified delivery area. For example, it can use KMeans clustering to generate the optimal delivery route.

[1141] Terminal

[1142] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[1143] 3. Delivery monitoring and redelivery support

[1144] server

[1145] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[1146] Terminal

[1147] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[1148] User

[1149] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1150] 4. Optimizing working hours and fuel consumption

[1151] server

[1152] The server evaluates the workload of each delivery person and allocates packages for maximum efficiency. It also selects routes with minimal fuel consumption based on GIS and real-time traffic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1153] Terminal

[1154] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[1155] 5. Use of Emotion Engine

[1156] server

[1157] The server uses an emotion engine to collect user emotional information, analyzes the emotional data provided by the user through the web portal or app, and recognizes the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[1158] Terminal

[1159] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[1160] User

[1161] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[1162] Specific examples

[1163] For particularly urgent orders, when a user selects "urgent" through the app, that emotional information is sent to the emotion engine, which immediately prioritizes the delivery route. For example, if a user inputs, "I want pasta delivered right now! I'm really in a hurry," that emotional information is analyzed and the delivery route is automatically optimized. This prompt allows the emotion engine to understand the user's level of urgency and re-optimize the delivery route based on that information.

[1164] This system will not only improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, but will also enable the provision of services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[1165] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1166] Step 1:

[1167] Collection and integration of package information

[1168] The server receives package information from delivery companies via API or file upload. The input is package information provided by the delivery company, including the destination, sender, package contents and size, delivery deadline, etc. The received package information is converted into a unified format and stored in a database. This process allows for centralized management of package information from multiple delivery companies.

[1169] Step 2:

[1170] Parse delivery addresses and identify delivery areas

[1171] The server analyzes the delivery address based on the received package information. The input is package information stored in a database. This address data is analyzed in conjunction with a geographic information system (GIS) to identify the delivery area. The output is the identified delivery area. This process utilizes GPS coordinates and map data for efficient area recognition.

[1172] Step 3:

[1173] Generate efficient delivery routes

[1174] The server generates efficient delivery routes within the specified delivery area. The inputs are the specified delivery area, past delivery history, absence information, etc. An artificial intelligence algorithm (e.g., KMeans clustering) is used to generate the optimal delivery route. The output is the optimal delivery route for each delivery person. In this step, calculations are made to minimize fuel consumption and time.

[1175] Step 4:

[1176] Delivery route distribution

[1177] The server distributes the generated delivery route to each delivery person's device. The input is the delivery route generated in step 3, and the output is delivery route information that can be viewed on the delivery person's smartphone or tablet. This allows delivery people to deliver efficiently.

[1178] Step 5:

[1179] Real-time monitoring of delivery progress

[1180] The server monitors delivery progress in real time. The input is delivery progress data sent from the delivery person's terminal. The server collects this data sequentially and sends new instructions to the delivery person as needed. The output is new instructions according to the delivery progress. This process monitors the delivery status and dynamically adjusts the route.

[1181] Step 6:

[1182] Recalculating redelivery routes when delivery is not received

[1183] The server recalculates the redelivery route when a delivery is missed. The inputs are past data, information on whether the recipient is at home, and information on whether the recipient is absent. Based on this, the server calculates the optimal redelivery route and sends it to the delivery person's device. The output is the optimal route for redelivery.

[1184] Step 7:

[1185] Collecting and analyzing emotional information

[1186] The server uses an emotion engine to collect and analyze the user's emotional information. The input is emotional data provided by the user through a web portal or app. The analyzed emotional data is output, and delivery priorities are set based on this. For example, if the user inputs "I want pasta delivered right now! I'm really in a hurry," it will be recognized as a high level of urgency.

[1187] Step 8:

[1188] Delivery priority setting based on emotion information

[1189] The server sets delivery priorities and optimizes delivery routes based on the analyzed emotion information. The input is the emotion data obtained in step 7. The output is the updated delivery priorities and optimized delivery routes. This enables delivery that takes emotion into consideration for specific users.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] [Third embodiment]

[1194] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1195] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1196] 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).

[1197] 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.

[1198] 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.

[1199] 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).

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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.

[1204] 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.

[1205] 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."

[1206] ---

[1207] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[1208] 1. Collection and integration of package information

[1209] server

[1210] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[1211] 2. Delivery route generation

[1212] server

[1213] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[1214] Terminal

[1215] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[1216] 3. Delivery monitoring and redelivery support

[1217] server

[1218] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[1219] Terminal

[1220] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[1221] User

[1222] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1223] 4. Optimizing working hours and fuel consumption

[1224] server

[1225] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1226] Terminal

[1227] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[1228] Specific examples

[1229] Example 1: When the user is absent

[1230] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[1231] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[1232] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[1233] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[1234] Example 2: Optimizing delivery efficiency

[1235] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[1236] 2. Uses AI algorithms to calculate the most efficient delivery route.

[1237] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[1238] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[1239] The present invention can improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, thereby contributing to the sustainable growth of the logistics industry.

[1240] ---

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] server

[1244] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[1245] Step 2:

[1246] server

[1247] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[1248] Step 3:

[1249] server

[1250] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[1251] Step 4:

[1252] server

[1253] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[1254] Step 5:

[1255] server

[1256] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[1257] Step 6:

[1258] Terminal

[1259] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[1260] Step 7:

[1261] Terminal

[1262] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[1263] Step 8:

[1264] server

[1265] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[1266] Step 9:

[1267] server

[1268] If a delivery is not made at home, the server recalculates the redelivery route based on past data and information on whether the recipient is at home. The server then determines the optimal redelivery date and route and notifies the delivery person.

[1269] Step 10:

[1270] Terminal

[1271] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[1272] Step 11:

[1273] User

[1274] Users can check the delivery progress through a web portal or app, select redelivery options if necessary, and provide instructions to the server if the user is not at home.

[1275] Step 12:

[1276] server

[1277] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency based on past delivery history and current workload.

[1278] Step 13:

[1279] server

[1280] Selects a route with less fuel consumption. The server optimizes fuel consumption and time based on geographical information and real-time traffic information.

[1281] Step 14:

[1282] server

[1283] The server evaluates the environmental impact and reflects it in delivery plans. It collects environmental indicators such as CO2 emissions and uses this information to formulate sustainable delivery strategies.

[1284] Step 15:

[1285] Terminal

[1286] The delivery person's terminal follows instructions from the server and delivers the package along the optimal route. By making deliveries more efficient, fuel and labor costs can be saved.

[1287] These are the specific steps of the program, which will improve the efficiency of the logistics industry and reduce the burden on the environment.

[1288] Example 1

[1289] 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."

[1290] In today's logistics and delivery industries, there is a need to efficiently manage package information received from multiple delivery companies, minimize redelivery and fuel consumption, and maximize delivery efficiency. In particular, there is a need for systems that can perform complex tasks such as monitoring delivery progress in real time, issuing redelivery instructions when the recipient is absent, and evaluating environmental impact.

[1291] 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.

[1292] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information in the event of a missed delivery, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for issuing redelivery instructions based on redelivery options previously specified by the user, and means for recalculating optimal delivery routes in real time using traffic information, thereby enabling reduction in redelivery and fuel consumption, improved delivery efficiency, and reduced environmental impact.

[1293] A "delivery company" is an organization or company whose business is receiving and delivering packages.

[1294] "Package information" refers to detailed data about the package being delivered (e.g., package contents, weight, size, delivery address, etc.).

[1295] A "database" refers to a structured and managed collection of data, and is a system for storing, searching, and updating cargo information.

[1296] "Geographic Information System (GIS)" means a system for collecting, analyzing, and displaying geographic information used to locate shipping addresses.

[1297] "Delivery Address" means the location where a package is to be delivered.

[1298] "Delivery Area" means the geographic area within which a particular delivery activity occurs.

[1299] An "artificial intelligence algorithm" is a method or procedure that allows a computer to perform processing by imitating part of human intelligence.

[1300] A "delivery route" is the optimal route planned to deliver packages efficiently.

[1301] A "delivery terminal" refers to an electronic device such as a smartphone or tablet used by a delivery person.

[1302] "Delivery progress" refers to information indicating the stage of delivery.

[1303] "Real time" refers to processing that is performed immediately without delay, meaning that processing is performed in synchronization with real time.

[1304] "Instructions" refer to specific instructions or orders for actions sent to delivery personnel from a higher-level system or administrator.

[1305] A "redelivery route" is a route planned to redeliver packages that could not be delivered due to reasons such as absence of the recipient.

[1306] "Workload" refers to the amount of work and burden that a delivery person must perform.

[1307] "Fuel consumption" refers to the energy, particularly fuel, consumed in delivery operations.

[1308] "Environmental load" refers to the impact that economic and human activities have on the natural environment.

[1309] "Redelivery options" refer to options for how to redeliver a package if the user is not at home.

[1310] "Traffic information" refers to data on road congestion and traffic flow.

[1311] MODE FOR CARRYING OUT THE INVENTION

[1312] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[1313] 1. Collection and integration of package information

[1314] server

[1315] The server receives package information from the delivery company via API. Specifically, it uses the API provided by the delivery company in JSON or XML format. The received package information is converted into a unified format (for example, CSV format) by an internal data conversion module. The package information converted into the unified format is then stored in a database.

[1316] Specific examples:

[1317] Package information is received from delivery company A via API, and the JSON format data is converted to CSV format and stored in a database.

[1318] 2. Delivery route generation

[1319] server

[1320] The server retrieves package information from the database and analyzes each delivery address. The delivery address is converted into location information in conjunction with a geographic information system (GIS). For example, the GIS uses Google Maps API or OpenStreetMap. The delivery area is identified based on the location information of the analyzed delivery address.

[1321] Next, an efficient delivery route is generated within the identified delivery area using an artificial intelligence algorithm. Specifically, the shortest route is calculated using the Dijkstra algorithm or the A algorithm. The generated delivery route is sent from the server to the delivery person's device.

[1322] Terminal

[1323] The delivery person's device receives the route information and displays the optimal delivery route on the app. The device, such as a smartphone or tablet, visualizes the route using the Google Maps API.

[1324] Specific examples:

[1325] The server obtains information about the delivery area and generates the optimal delivery route using the Dijkstra algorithm. The generated route is sent to the delivery person's smartphone and displayed in the Google Maps app.

[1326] 3. Delivery monitoring and redelivery support

[1327] server

[1328] The server receives real-time delivery progress information from the delivery person's terminal. When the delivery person has delivered the package or was unable to deliver due to absence, the server reports this information to the server. The server updates the database based on this information.

[1329] If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home. The recalculated route is then sent back to the delivery person's device.

[1330] Terminal

[1331] The delivery person's terminal successively transmits delivery progress information to the server, and redelivers or changes the route as necessary. The terminal immediately receives redelivery instructions and new route information, allowing for efficient redelivery.

[1332] User

[1333] Users can notify the server of their desired redelivery options in advance through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the options specified by the user. For example, if the user requests delivery to a parcel box, that information is sent to the server and the delivery person is notified.

[1334] Specific examples:

[1335] The user sends information about their desired delivery to the delivery box to the server through the app, and the server calculates a new redelivery route based on that information and sends it to the delivery person's device.

[1336] 4. Optimizing working hours and fuel consumption

[1337] server

[1338] The server collects the workload of each delivery person from a database and evaluates it. Based on the evaluation results, it allocates packages to each delivery person with maximum efficiency. It also uses real-time traffic information to select routes with the least fuel consumption. It also evaluates the environmental impact (e.g., CO2 emissions) and creates delivery plans that take this into account.

[1339] Specific technologies include the use of Google Maps traffic information API and environmental assessment software.

[1340] Terminal

[1341] The delivery person's device then receives optimized route information from the server and delivers efficiently, for example, by choosing a route that avoids traffic jams or by providing instructions for the shortest possible delivery distance.

[1342] Specific examples:

[1343] The server receives real-time traffic information and calculates a route with the lowest CO2 emissions based on that information, which is then displayed on the delivery person's device.

[1344] Example prompts using generative AI models

[1345] Example prompt sentence:

[1346] "Generate efficient delivery routes based on past delivery history and real-time traffic information."

[1347] By inputting this prompt into a generative AI model, the complex route generation process can be automated to produce an optimized delivery route.

[1348] The above is a specific description of the embodiment of the present invention.

[1349] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] server

[1352] Parcel information is received from the delivery company using an API. The input at this time is the parcel data in JSON format provided by the delivery company. The server converts the received parcel information into CSV format using an internal data conversion module and stores it in the database. The output is the parcel information converted into a unified format.

[1353] Specific operation: Receive JSON formatted package information from delivery company A via API, convert it to CSV format, and insert it into the database.

[1354] Step 2:

[1355] server

[1356] The server retrieves package information from the database and analyzes the delivery address. The input is the delivery address data in CSV format, and the output is latitude and longitude information using a geographic information system (GIS). Next, the GIS is used to obtain the location information of the delivery address and identify the delivery area.

[1357] Specific operation: The server reads the delivery address in CSV format, obtains the latitude and longitude information using the Google Maps API, and maps the delivery area on a map.

[1358] Step 3:

[1359] server

[1360] The server generates efficient delivery routes within the specified delivery area using an artificial intelligence algorithm. The input is latitude and longitude information within the delivery area, and the output is the optimal delivery route. The server calculates the shortest route using the Dijkstra algorithm or the A algorithm, and distributes the generated delivery route to the delivery person's terminal.

[1361] Specific operation: The server calculates the optimal delivery route based on latitude and longitude information within the delivery area, and sends the determined route information to the delivery person's smartphone.

[1362] Step 4:

[1363] Terminal

[1364] The delivery person's device receives the route information and displays it on the app. The input is the optimal route information sent from the server, and the output is the delivery route displayed on a map. The device visualizes the route using the Google Maps API and provides the delivery person with the shortest route.

[1365] Specific operation: The delivery person's device displays the route information received from the server using the Google Maps API and begins delivery.

[1366] Step 5:

[1367] server

[1368] The server receives real-time delivery progress information from the delivery person's terminal. The input is the delivery progress data sent from the terminal, and the output is progress update information. The server sends new instructions to the delivery person as needed.

[1369] Specific operation: The delivery person's device sends current location information and delivery status to the server every minute, and the server updates the progress status based on this data.

[1370] Step 6:

[1371] server

[1372] When a delivery is missed, the server recalculates the redelivery route based on past data and at-home information. The input is delivery progress data and past at-home information, and the output is a new redelivery route. The server then applies the optimization algorithm to generate a new route and distributes it to the delivery person's device.

[1373] Specific operation: The server receives information about the delivery when the delivery person is not at home, references past data on when the delivery person is at home, and sends a recalculated route to the delivery person's terminal.

[1374] Step 7:

[1375] User

[1376] The user uses a web portal or app to notify the server in advance of redelivery options (e.g., use of a delivery box) in case of absence. The input is the redelivery options specified by the user, and the output is the notification information sent to the server. Based on the user's specifications, the server sends appropriate instructions to the delivery person.

[1377] Specific operation: The user uses the app to specify the use of a delivery box and sends that information to the server. The server then calculates a redelivery route based on that information and sends instructions to the delivery person.

[1378] Step 8:

[1379] server

[1380] The server evaluates the workload of each delivery person and selects a route that consumes less fuel. The input is delivery progress data and traffic information, and the output is optimized route information. The server also evaluates the environmental impact (CO2 emissions) and reflects this in the delivery plan.

[1381] Specific operation: The server recalculates the most fuel-efficient route based on delivery progress data and real-time traffic information, and sends instructions to the delivery person.

[1382] This will reduce redelivery and fuel consumption, improve delivery efficiency, and reduce the burden on the environment.

[1383] (Application example 1)

[1384] 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."

[1385] In the logistics industry, parcel deliveries are routinely carried out by multiple delivery companies, but the challenge is to centrally manage this parcel information and efficiently generate delivery routes. It is also necessary to effectively monitor delivery progress and handle redelivery requests. Furthermore, reducing fuel consumption and reducing environmental impact are also important issues. The present invention aims to solve these challenges and provide a system for effectively managing operations at logistics centers.

[1386] 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.

[1387] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an AI algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting a route with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for delivery personnel to receive instructions for the optimized route via a smart device, means for users to set redelivery options via a web portal or app, and means for optimizing delivery plans using a generative AI model. This enables delivery efficiency, reduced fuel consumption, fewer redeliveries, and shorter working hours.

[1388] "Parcel information" refers to data such as parcel identification information, delivery destination information, and delivery status information collected from multiple delivery companies.

[1389] A "database" is a system for systematically storing, managing, and searching information.

[1390] A "geographic information system" is a system for collecting, managing, and analyzing geographic data.

[1391] "Delivery address analysis" refers to the technology used to understand the delivery address information and determine the appropriate delivery area and route.

[1392] "Specifying a delivery area" means clarifying the geographical area to which the delivery destination of the package belongs.

[1393] An "artificial intelligence algorithm" is a calculation procedure that enables a computer to learn on its own and make appropriate judgments and predictions.

[1394] "Generating an efficient delivery route" means calculating a route that will complete delivery while saving the most time and money, based on the input delivery destination information.

[1395] A "delivery terminal" is an electronic device carried by a delivery person to receive delivery progress and instructions.

[1396] "Delivery progress" refers to the current progress of the package in the delivery process.

[1397] "Real-time monitoring" means instantly checking the progress and status of delivery.

[1398] "Sending instructions" means that the server sends necessary information and instructions to the delivery person via an electronic device.

[1399] "Delivery to absentee" refers to a situation where the resident at the delivery address is absent and unable to receive the package.

[1400] "Past data" refers to information such as delivery history and customer information that has been recorded to date.

[1401] "At-home information" is information about whether the resident of the delivery destination is at home.

[1402] "Recalculating the redelivery route" means recalculating a new, more efficient delivery route when a delivery is missed.

[1403] "Delivery progress data" is data that indicates the current status and progress of delivery.

[1404] "Evaluating workload" means measuring the workload of each delivery person and appropriately evaluating their efforts.

[1405] A "low fuel consumption route" is a delivery route that is planned to minimize the amount of fuel used by the delivery vehicle.

[1406] "Evaluating the environmental impact" means measuring the impact that delivery activities have on the environment and formulating plans based on this.

[1407] A "smart device" is a portable electronic device that can connect to the Internet and use a variety of applications.

[1408] "Receiving optimized route instructions" means that the delivery person receives information about a pre-calculated and optimized delivery route.

[1409] A "web portal" is a website that provides users with a variety of information and services via the Internet.

[1410] "Redelivery options" are settings and instructions regarding redelivery that customers can select in the event of a missed delivery.

[1411] A "generative AI model" is an artificial intelligence computational model used for delivery planning and route optimization.

[1412] "Optimizing" means arranging and adjusting a system or computation most effectively to achieve a specific goal.

[1413] System Overview

[1414] This invention relates to a system that realizes efficient delivery of parcels at a logistics center. This system manages parcel information received from multiple delivery companies in an integrated manner and generates efficient delivery routes using a geographic information system and an artificial intelligence algorithm. It also monitors delivery progress in real time, responding to requests for redelivery and reducing environmental impact.

[1415] Program generation and natural language explanation

[1416] Collection and integration of package information

[1417] The server collects package information from multiple delivery companies via API and file upload, converts it into a unified format, and stores it in a database, allowing package information from different delivery companies to be centrally managed.

[1418] Generate delivery routes

[1419] The server uses the collected package information to link with a geographic information system (GIS) and analyzes the delivery address. Based on the analyzed address information, an artificial intelligence algorithm is used to generate an efficient delivery route. The generated route is optimized by taking into account data such as past delivery history and absence information.

[1420] Delivery route distribution

[1421] The generated delivery route is sent from the server to the delivery person's device (smartphone or tablet), and the delivery person uses this device to make deliveries along the most efficient route.

[1422] Real-time monitoring of delivery progress

[1423] The server monitors the delivery progress data sent from the delivery person's terminal in real time and sends new instructions to the terminal as necessary. The delivery progress data includes the delivery status of each package and the delivery person's location information. This information is collected and analyzed by the server in real time.

[1424] Redelivery support

[1425] If a delivery is missed, the server recalculates the redelivery route based on past data and information on when the delivery person is at home, and instructs the delivery person on a new route. Users can pre-set redelivery options (e.g., use of a delivery box) through the web portal or app.

[1426] Optimizing working hours and fuel consumption

[1427] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1428] Specific examples

[1429] Example 1: When the user is absent

[1430] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[1431] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[1432] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[1433] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[1434] Example 2: Optimizing delivery efficiency

[1435] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[1436] 2. Uses AI algorithms to calculate the most efficient delivery route.

[1437] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[1438] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[1439] In this way, the efficiency of parcel delivery at the logistics center can be maximized. An example of a prompt sentence to be input to the generative AI model is as follows:

[1440] Prompt Sentence Examples

[1441] "You've gathered all the parcel information for tomorrow. There are currently 50 deliveries. To create the optimal delivery route, please use GIS and AI algorithms to analyze these addresses and generate efficient routes."

[1442] This system will enable more efficient delivery, reduce fuel consumption, reduce redelivery, and shorten working hours.

[1443] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1444] Program processing flow

[1445] Step 1: Collect and consolidate package information

[1446] The server receives package information from multiple delivery companies via API or file upload. The received package information is as follows:

[1447] Input: Package information data provided by the delivery company

[1448] Data processing: Converting package information into a unified format

[1449] Output: Package information data converted into a unified format

[1450] Specific operation: The program standardizes package information provided by each delivery company in different formats for storage in a database. For example, it reads data in CSV or JSON format and converts it into a unified data format.

[1451] Step 2: Address analysis and delivery area identification

[1452] Based on the parcel information converted into a unified format, the server works in conjunction with a geographic information system (GIS) to analyze the delivery address and identify the delivery area.

[1453] Input: Package information data in a unified format

[1454] Data processing: Address analysis using geographic information systems

[1455] Output: Delivery address and delivery area information

[1456] What it does: The program uses a GIS library to convert each delivery address into geographic coordinates and identify the delivery area to which each delivery address belongs.

[1457] Step 3: Generate efficient delivery routes

[1458] The server uses an artificial intelligence algorithm to generate an efficient delivery route based on the delivery address, past delivery history, and absence information.

[1459] Input: Delivery address, past delivery history, absence information

[1460] Data calculation: Route optimization using artificial intelligence algorithms

[1461] Output: Optimized delivery route

[1462] How it works: To create smart delivery plans, the program uses AI algorithms (e.g., genetic algorithms and deep learning models) to calculate the shortest and lowest-cost routes.

[1463] Step 4: Deliver your delivery route

[1464] The server delivers the optimized delivery route to the delivery person's device (smartphone or tablet).

[1465] Input: Optimized delivery route

[1466] Data calculation: None (data distribution)

[1467] Output: Delivery route sent to the delivery person's device

[1468] Specific operation: The optimized route information is sent to the delivery person's device and notified. If the delivery person's device is connected to the Internet, the route information is received immediately.

[1469] Step 5: Real-time monitoring of delivery progress

[1470] The server monitors the delivery progress data sent from the delivery person's terminal in real time.

[1471] Input: Delivery progress data sent from the terminal

[1472] Data calculations: analyzing and monitoring progress data

[1473] Output: Real-time updated delivery status

[1474] Specific operation: Receives delivery personnel's location information and delivery completion status in real time, monitors progress, and sends instructions from the server if an abnormality is detected.

[1475] Step 6: Recalculate the redelivery route

[1476] When a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the recipient is at home.

[1477] Input: Missed delivery information, past data, at-home information

[1478] Data calculation: Recalculation to generate optimal route

[1479] Output: Optimal route for redelivery

[1480] Specific operation: When a delivery is missed, the program refers to past data, recalculates the appropriate redelivery time and route, and sends a redelivery instruction to the delivery person's terminal.

[1481] Step 7: Optimizing fuel consumption and environmental impact

[1482] The server selects routes with the lowest fuel consumption based on real-time traffic and geographical information, evaluates the environmental impact, and reflects this in delivery plans.

[1483] Input: Real-time traffic information, geographic information

[1484] Data calculation: Simulation of fuel consumption and environmental impact

[1485] Output: Optimized eco-routes and delivery plans

[1486] Specific operation: The program selects a route that consumes less fuel by taking into account traffic congestion and road conditions, calculates the environmental impact, and instructs the delivery person on a delivery plan that reflects this.

[1487] This will enable more efficient delivery at logistics centers, reduce fuel consumption, reduce redeliveries, and shorten working hours.

[1488] 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.

[1489] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[1490] 1. Collection and integration of package information

[1491] server

[1492] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[1493] 2. Delivery route generation

[1494] server

[1495] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[1496] Terminal

[1497] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[1498] 3. Delivery monitoring and redelivery support

[1499] server

[1500] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[1501] Terminal

[1502] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[1503] User

[1504] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1505] 4. Optimizing working hours and fuel consumption

[1506] server

[1507] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1508] Terminal

[1509] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[1510] 5. Use of Emotion Engine

[1511] server

[1512] The system uses an emotion engine to collect user emotional information. It analyzes the emotional data provided by users through the web portal or app to recognize the user's emotional state. Based on this, it sets delivery priorities and reflects them in the generation of delivery routes.

[1513] Terminal

[1514] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[1515] User

[1516] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[1517] Specific examples

[1518] Example 1: Using emotional data to prioritize delivery

[1519] 1. The server receives emotion information provided by the user. For example, if the user requests an urgent delivery, the information is analyzed by the emotion engine.

[1520] 2. The emotion engine recognizes urgent delivery requests as high priority and reflects this when generating delivery routes.

[1521] 3. High-priority delivery routes are sent to the terminal, and delivery personnel follow those instructions to make deliveries.

[1522] Example 2: Redelivery instructions when the recipient is absent and consideration for the recipient's feelings

[1523] 1. The server receives the user's absence along with emotional information. For example, if the user strongly desires redelivery, that information is included.

[1524] 2. Delivery progress is monitored, and if redelivery becomes necessary, the optimal redelivery route is recalculated based on information analyzed by the emotion engine.

[1525] 3. A redelivery instruction that takes into account emotional information is sent to the terminal, and the delivery person acts based on that instruction, delivering the parcel to a delivery box or other location according to the user's wishes.

[1526] This invention not only improves delivery efficiency, reduces fuel consumption, reduces redelivery, and shortens working hours, but also makes it possible to provide services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[1527] ---

[1528] The processing flow will be explained below.

[1529] ---

[1530] Step 1:

[1531] server

[1532] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[1533] Step 2:

[1534] server

[1535] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[1536] Step 3:

[1537] server

[1538] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[1539] Step 4:

[1540] server

[1541] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[1542] Step 5:

[1543] server

[1544] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[1545] Step 6:

[1546] Terminal

[1547] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[1548] Step 7:

[1549] Terminal

[1550] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[1551] Step 8:

[1552] server

[1553] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[1554] Step 9:

[1555] server

[1556] If a delivery is not made at home, the system recalculates the redelivery route based on past data and information on whether the recipient is at home. It then calculates the optimal redelivery date and route and notifies the delivery person.

[1557] Step 10:

[1558] Terminal

[1559] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[1560] Step 11:

[1561] User

[1562] Through a web portal or app, users provide emotional information, such as expectations or dissatisfaction regarding delivery, which is then sent to the server.

[1563] Step 12:

[1564] server

[1565] Receives user emotional information and analyzes it with an emotion engine. Based on the user's emotional data, delivery priorities are re-established.

[1566] Step 13:

[1567] server

[1568] Based on the analysis results, an AI algorithm regenerates efficient delivery routes, and adjusts delivery routes to accommodate specific users' emotions.

[1569] Step 14:

[1570] server

[1571] Based on the emotion information, new instructions are sent to the delivery person, for example, instructing them to make deliveries with higher priority taking into consideration the emotion.

[1572] Step 15:

[1573] Terminal

[1574] The delivery person's terminal receives the updated instructions and performs the delivery based on the emotion information, thereby improving the user's satisfaction.

[1575] Step 16:

[1576] server

[1577] Real-time monitoring of delivery progress and user emotional responses will be used to improve the system. New data will be analyzed and reflected in future delivery plans.

[1578] These are the specific processing steps of the system. This process will improve the efficiency of the logistics industry, reduce the environmental impact, and realize a delivery service that takes user emotions into consideration.

[1579] ---

[1580] Example 2

[1581] 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."

[1582] Conventional delivery systems lack a means to effectively integrate package information from multiple delivery companies, making it difficult to generate efficient delivery routes, monitor delivery progress in real time, and optimize redelivery. Furthermore, fuel consumption and environmental impact are not sufficiently reduced. Furthermore, there is a lack of delivery priority settings that incorporate user emotional information and advance notification of redelivery options, making it difficult to improve overall customer satisfaction.

[1583] 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.

[1584] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses and identifying delivery areas in cooperation with a geographic information system, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impacts and reflecting them in delivery plans, means for collecting and analyzing emotion information from users and setting delivery priorities, means for generating delivery instructions that take emotion information into account and distributing them to terminals, and means for users to notify redelivery options in advance. This enables more efficient delivery, reduced fuel consumption, fewer redeliveries, shorter working hours, and the provision of services that take user emotions into consideration.

[1585] "Parcel information" refers to detailed data about parcels delivered by delivery companies, including the address, type of parcel, weight, size, desired delivery date and time, etc.

[1586] A "database" is a system for efficiently storing, managing, and searching information, and is a central repository for the integrated storage of cargo information, progress data, and other information.

[1587] A "geographic information system (GIS)" is a system for analyzing and managing geographic information, and is used for address analysis and determining delivery areas.

[1588] "Delivery address" refers to the address information of the destination where the package is to be delivered, and is data that is converted into geographic information such as latitude and longitude.

[1589] An "artificial intelligence algorithm" is a data processing method that uses AI technology and is a computational model for generating efficient delivery routes.

[1590] "Delivery route" refers to the optimal route a delivery person takes to deliver a package to each destination.

[1591] A "delivery terminal" is a mobile device (such as a smartphone or tablet) used by delivery personnel to receive delivery routes and instructions.

[1592] "Delivery progress" is data showing the delivery status of a package in real time, including the current delivery status and completion status.

[1593] A "redelivery route" is an optimized delivery route for re-delivering a package in the event of a missed delivery.

[1594] "Work volume" refers to the amount and burden of delivery work performed by delivery personnel, and is evaluated based on criteria such as multiple delivery destinations and the number of packages.

[1595] "Fuel consumption" refers to the amount of fuel consumed in delivery operations, and must be minimized to increase delivery efficiency.

[1596] "Environmental impact" refers to the impact on the environment, such as CO2 emissions and resource consumption associated with delivery operations.

[1597] "Emotional information" refers to emotional data such as expectations or dissatisfaction regarding delivery provided by the user.

[1598] "Delivery instructions" are specific instructions for actions sent from the server to the delivery person, and include information such as delivery route and priority.

[1599] "Redelivery option" means an option for an alternative method of receiving a package (e.g., a delivery box) that can be specified when the user is not at home.

[1600] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress, as well as an emotion engine that recognizes user emotions, to provide a better delivery experience. This system can significantly contribute to solving issues such as reducing redelivery, fuel consumption, and environmental impact.

[1601] Collection and integration of package information

[1602] server

[1603] The server receives package information from delivery companies using an API or file upload. The received package information is converted into a unified format and stored in a database. This allows information from multiple delivery companies to be managed centrally. For example, if delivery company A sends package information in JSON format and delivery company B sends it in CSV format, the server converts it into a unified format (for example, CSV format) and stores it in the database.

[1604] Generate delivery routes

[1605] server

[1606] The server analyzes the delivery address based on the package information stored in the database and identifies the delivery area in conjunction with a geographic information system (GIS). An artificial intelligence algorithm is used to generate an efficient delivery route within the identified delivery area. For example, the AI ​​algorithm optimizes the route based on past delivery history, information on whether the recipient is at home or absent, and then generates the delivery route.

[1607] Terminal

[1608] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet receives this information and uses it as a guide to make deliveries efficiently.

[1609] Delivery monitoring and redelivery support

[1610] server

[1611] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home, maximizing delivery efficiency.

[1612] Terminal

[1613] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the information is also reported to the server.

[1614] User

[1615] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1616] Optimizing working hours and fuel consumption

[1617] server

[1618] The server evaluates the workload of each delivery person and allocates packages efficiently. It also selects routes with low fuel consumption based on real-time traffic information. It also evaluates the environmental impact (CO2 emissions, etc.) and reflects this in delivery plans.

[1619] Terminal

[1620] The delivery person's device receives the optimized route from the server and delivers efficiently, thereby reducing fuel consumption and working hours.

[1621] Use of emotion engine

[1622] server

[1623] The emotion engine is used to collect user emotional information. For example, it analyzes emotional data provided by users through the web portal or app (e.g., requests for urgent delivery or expressions of dissatisfaction) to recognize the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[1624] Terminal

[1625] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[1626] User

[1627] Users can provide emotional information through a web portal or app, such as expressing their expectations or dissatisfaction with the delivery. The provided emotional information is analyzed by the server and reflected in the delivery plan.

[1628] As a concrete example, the server receives emotion information provided by the user and analyzes it with an emotion engine to recognize urgent delivery requests as high priority and reflect this when generating delivery routes. Based on this, high-priority delivery routes are sent to the terminal, and delivery personnel make deliveries according to those instructions.

[1629] Example prompt sentence:

[1630] 1. Create a program that receives package information sent by delivery company A, converts it into a unified format, and saves it in a database.

[1631] 2. Create a program that analyzes the delivery address and generates the optimal delivery route using GIS. Also, write a script that sends the route to the delivery person's device.

[1632] 3. Generate a program that monitors delivery progress in real time and recalculates the redelivery route if a delivery is missed. Also, add logic to receive redelivery options from the user in advance and send instructions to the delivery person based on that information.

[1633] 4. Write a program that evaluates the workload of each delivery person and selects a route with the least fuel consumption based on real-time traffic information. Also write a script that notifies the delivery person of the results.

[1634] 5. Generate a program that collects and analyzes emotion information provided by the user to set delivery priorities. Also, write the logic to send delivery instructions to the delivery person's device based on the results.

[1635] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1636] Step 1: Receive your shipment information

[1637] server

[1638] The server receives package information from delivery companies via API or file upload. The input package information is often in JSON or CSV format. The server receives this and converts it into a format that can be processed internally. For example, the server analyzes the JSON format package information received from delivery company A and converts it into column-by-column data.

[1639] Input: Package information sent by the delivery company (JSON, CSV, etc.)

[1640] Output: Converted package information (internal format)

[1641] Step 2: Standardize data formats

[1642] server

[1643] The received package information is converted into a unified format and stored in a database. For example, the server converts JSON format data into CSV format, assigns a unique index to each piece of data, and stores it in the database. This makes it easy to search and manage.

[1644] Input: Converted package information (internal format)

[1645] Output: Package information stored in the database

[1646] Step 3: Parse the shipping address

[1647] server

[1648] The server analyzes the delivery address based on the package information stored in the database. It uses a geographic information system (GIS) to convert the address information into latitude and longitude. For example, if the address "1-1 Chiyoda, Chiyoda-ku, Tokyo 100-0001" is sent to the GIS API, the latitude and longitude are returned as 35.682839 and 139.759455, respectively.

[1649] Input: Package information stored in the database

[1650] Output: Latitude and longitude information

[1651] Step 4: Generate delivery routes

[1652] server

[1653] The server uses the latitude and longitude information obtained from the GIS and an AI algorithm to generate the optimal delivery route based on past delivery history, information on whether the delivery person is at home or not. This determines the most efficient delivery route. For example, the AI ​​algorithm calculates the fastest and most fuel-efficient route.

[1654] Input: Latitude and longitude information, past delivery history, presence information, absence information

[1655] Output: Optimal delivery route

[1656] Step 5: Deliver your delivery route

[1657] Server, terminal

[1658] The generated delivery route is sent to the delivery terminal. The server sends the generated route in JSON format to the delivery person's terminal. The terminal receives the route information and displays it so that the delivery person can check it.

[1659] Input: Optimal delivery route

[1660] Output: Delivery route displayed on the delivery person's terminal

[1661] Step 6: Monitoring delivery progress

[1662] server

[1663] Delivery progress is sent to the server in real time and monitored sequentially. The server receives progress data sent from the delivery person's terminal and updates the status. For example, the progress is updated from the terminal when the delivery is completed.

[1664] Input: Delivery progress data

[1665] Output: Updated delivery status

[1666] Step 7: Recalculate the redelivery route

[1667] server

[1668] If a delivery is missed, the server recalculates the redelivery route based on past data and information on who is at home. This allows for efficient redelivery. For example, it calculates a new route based on the desired redelivery date and time.

[1669] Input: Missed delivery information, past home data

[1670] Output: Redelivery route

[1671] Step 8: Notification of redelivery options

[1672] User

[1673] Users can notify the web portal or app of redelivery options (e.g., using a delivery box) when they are not at home, allowing deliveries to be made according to the user's wishes.

[1674] Input: Redelivery options from user

[1675] Output: Redelivery options notified to the server

[1676] Step 9: Evaluate work output and fuel consumption

[1677] server

[1678] The server evaluates each delivery person's workload and fuel consumption and selects the optimal package allocation and route. For example, it analyzes delivery history to evaluate workload and selects the most fuel-efficient route based on traffic information.

[1679] Input: Delivery history, traffic information

[1680] Output: Optimized load allocation

[1681] Step 10: Assess the environmental impact

[1682] server

[1683] The server evaluates the environmental impact, such as CO2 emissions, and reflects this in delivery plans, thereby minimizing the impact on the environment.

[1684] Input: CO2 emissions data

[1685] Output: Evaluated environmental impact

[1686] Step 11: Collect and analyze emotional information

[1687] Server, User

[1688] The server collects and analyzes the emotional information provided by the user. The user enters emotional data (e.g., expressing a desire for urgent delivery or expressing dissatisfaction) through a web portal or app, and this data is sent to the server. The emotion engine analyzes this data and sets delivery priorities.

[1689] Input: Emotion data from the user

[1690] Output: Parsed emotion information

[1691] Step 12: Generate emotion-based delivery instructions

[1692] Server, terminal

[1693] Delivery instructions based on emotion information are generated and sent to the terminal. The server creates delivery instructions with a priority based on the analysis results of the emotion engine and sends them to the delivery person's terminal. The terminal receives these instructions and notifies the delivery person.

[1694] Input: Parsed emotion information

[1695] Output: Emotion-based delivery instructions

[1696] (Application example 2)

[1697] 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."

[1698] In today's logistics industry, the lack of centralized management of multiple delivery companies makes it difficult to generate efficient delivery routes. Furthermore, the frequency of missed deliveries and redeliveries increases, resulting in increased fuel consumption and working hours, placing a heavy burden on the environment. Furthermore, there is a lack of services that take into account users' feelings about the delivery experience, making it difficult to improve customer satisfaction.

[1699] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for collecting user emotion information and analyzing it with an emotion engine, and means for setting delivery priorities based on the user emotion information and optimizing delivery routes. This makes it possible to improve delivery efficiency and optimize the delivery experience.

[1700] A "delivery business" is a company or organization that provides services to deliver packages or cargo to designated locations.

[1701] "Parcel information" is detailed data about a parcel, including the destination, sender, contents and size of the parcel, delivery deadline, and so on.

[1702] A database is a digital system that systematically organizes and stores information so that it can be retrieved efficiently.

[1703] A "geographic information system (GIS)" is an information system for collecting, analyzing, and visualizing geographic data, and is capable of analyzing a combination of map data and location information.

[1704] An "artificial intelligence algorithm" is a mathematical model or computational method that enables computers to automatically learn and make decisions.

[1705] A "delivery route" is a route for efficiently delivering packages.

[1706] "Delivery terminal" refers to a portable electronic device used by a delivery person, which is a device for receiving and displaying delivery route and progress information.

[1707] "Delivery progress" is information indicating how far the delivery work has progressed.

[1708] A "redelivery route" is a route optimized for retrying a delivery that has previously failed.

[1709] "Work volume" is an indicator that shows the amount of work and the load placed on delivery personnel.

[1710] "Fuel consumption" refers to the amount of energy used in delivery operations, and primarily refers to the amount of fuel used.

[1711] "Environmental load" refers to the overall impact of human activities on the environment, and is an indicator that particularly includes carbon dioxide emissions and air pollution.

[1712] "Emotional information" is data about a user's emotional state or feelings.

[1713] An "emotion engine" is a program or algorithm that analyzes the user's emotional state and controls the system's operation based on the results.

[1714] "Delivery priority" is a ranking used to determine which delivery is given priority among multiple delivery tasks.

[1715] "Delivery to an unattended address" refers to a delivery where the package was not delivered because the recipient was not present at the delivery address.

[1716] A "delivery box" is a dedicated locker or box for receiving packages, and is a facility for safely storing packages even when the recipient is not at home.

[1717] This invention combines a system that stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system (GIS), generates efficient delivery routes, monitors delivery progress, and also combines it with an emotion engine that recognizes user emotions. This system is used to solve issues such as redelivery, reducing fuel consumption, and mitigating environmental impact.

[1718] 1. Collection and integration of package information

[1719] server

[1720] The server receives package information from delivery companies via API or file upload. The received package information is converted into a unified format and stored in a database. This allows package information from multiple delivery companies to be managed centrally.

[1721] 2. Delivery route generation

[1722] server

[1723] The server analyzes the delivery address based on the received package information and identifies the delivery area in cooperation with the GIS. It then uses an artificial intelligence (AI) algorithm to generate an efficient delivery route within the identified delivery area. For example, it can use KMeans clustering to generate the optimal delivery route.

[1724] Terminal

[1725] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[1726] 3. Delivery monitoring and redelivery support

[1727] server

[1728] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[1729] Terminal

[1730] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[1731] User

[1732] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1733] 4. Optimizing working hours and fuel consumption

[1734] server

[1735] The server evaluates the workload of each delivery person and allocates packages for maximum efficiency. It also selects routes with minimal fuel consumption based on GIS and real-time traffic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1736] Terminal

[1737] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[1738] 5. Use of Emotion Engine

[1739] server

[1740] The server uses an emotion engine to collect user emotional information, analyzes the emotional data provided by the user through the web portal or app, and recognizes the user's emotional state. Based on this, delivery priorities are set and reflected in the generation of delivery routes.

[1741] Terminal

[1742] The delivery person's terminal receives delivery instructions based on emotion information from the server, which enables delivery to be made to specific users in a way that takes their emotions into consideration.

[1743] User

[1744] Users can provide emotional information through a web portal or app. For example, they can input their expectations or dissatisfaction regarding delivery. The provided emotional information is analyzed by the server and reflected in delivery planning.

[1745] Specific examples

[1746] For particularly urgent orders, when a user selects "urgent" through the app, that emotional information is sent to the emotion engine, which immediately prioritizes the delivery route. For example, if a user inputs, "I want pasta delivered right now! I'm really in a hurry," that emotional information is analyzed and the delivery route is automatically optimized. This prompt allows the emotion engine to understand the user's level of urgency and re-optimize the delivery route based on that information.

[1747] This system will not only improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, but will also enable the provision of services that take users' feelings into consideration, which is expected to improve overall customer satisfaction.

[1748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1749] Step 1:

[1750] Collection and integration of package information

[1751] The server receives package information from delivery companies via API or file upload. The input is package information provided by the delivery company, including the destination, sender, package contents and size, delivery deadline, etc. The received package information is converted into a unified format and stored in a database. This process allows for centralized management of package information from multiple delivery companies.

[1752] Step 2:

[1753] Parse delivery addresses and identify delivery areas

[1754] The server analyzes the delivery address based on the received package information. The input is package information stored in a database. This address data is analyzed in conjunction with a geographic information system (GIS) to identify the delivery area. The output is the identified delivery area. This process utilizes GPS coordinates and map data for efficient area recognition.

[1755] Step 3:

[1756] Generate efficient delivery routes

[1757] The server generates efficient delivery routes within the specified delivery area. The inputs are the specified delivery area, past delivery history, absence information, etc. An artificial intelligence algorithm (e.g., KMeans clustering) is used to generate the optimal delivery route. The output is the optimal delivery route for each delivery person. In this step, calculations are made to minimize fuel consumption and time.

[1758] Step 4:

[1759] Delivery route distribution

[1760] The server distributes the generated delivery route to each delivery person's device. The input is the delivery route generated in step 3, and the output is delivery route information that can be viewed on the delivery person's smartphone or tablet. This allows delivery people to deliver efficiently.

[1761] Step 5:

[1762] Real-time monitoring of delivery progress

[1763] The server monitors delivery progress in real time. The input is delivery progress data sent from the delivery person's terminal. The server collects this data sequentially and sends new instructions to the delivery person as needed. The output is new instructions according to the delivery progress. This process monitors the delivery status and dynamically adjusts the route.

[1764] Step 6:

[1765] Recalculating redelivery routes when delivery is not received

[1766] The server recalculates the redelivery route when a delivery is missed. The inputs are past data, information on whether the recipient is at home, and information on whether the recipient is absent. Based on this, the server calculates the optimal redelivery route and sends it to the delivery person's device. The output is the optimal route for redelivery.

[1767] Step 7:

[1768] Collecting and analyzing emotional information

[1769] The server uses an emotion engine to collect and analyze the user's emotional information. The input is emotional data provided by the user through a web portal or app. The analyzed emotional data is output, and delivery priorities are set based on this. For example, if the user inputs "I want pasta delivered right now! I'm really in a hurry," it will be recognized as a high level of urgency.

[1770] Step 8:

[1771] Delivery priority setting based on emotion information

[1772] The server sets delivery priorities and optimizes delivery routes based on the analyzed emotion information. The input is the emotion data obtained in step 7. The output is the updated delivery priorities and optimized delivery routes. This enables delivery that takes emotion into consideration for specific users.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] [Fourth embodiment]

[1777] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1778] 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.

[1779] 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).

[1780] 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.

[1781] 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.

[1782] 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).

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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."

[1790] ---

[1791] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[1792] 1. Collection and integration of package information

[1793] server

[1794] API and file upload are used to receive package information from delivery companies. The received package information is converted into a unified format and stored in a database. This allows for centralized management of package information from multiple delivery companies.

[1795] 2. Delivery route generation

[1796] server

[1797] The server analyzes the delivery address based on the received package information and identifies the delivery area in conjunction with a geographic information system (GIS). It then uses an artificial intelligence algorithm to generate an efficient delivery route within the identified delivery area. The generated route is optimized based on past delivery history and information on whether the customer is at home or absent.

[1798] Terminal

[1799] The generated delivery route is sent to the delivery person's smartphone or tablet, where it is used as a guide to efficiently deliver the goods.

[1800] 3. Delivery monitoring and redelivery support

[1801] server

[1802] Delivery progress is monitored in real time by the server. Delivery progress data sent from the delivery person's device is collected sequentially and new instructions are sent to the delivery person as needed. If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home, maximizing delivery efficiency.

[1803] Terminal

[1804] The delivery person's terminal sends delivery progress data to the server, and performs redelivery or route changes according to new instructions from the server. When a delivery is completed, the terminal also reports that information to the server.

[1805] User

[1806] Users can notify the server in advance of redelivery options (e.g., use of a delivery box) when they are not at home through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the redelivery options specified by the user.

[1807] 4. Optimizing working hours and fuel consumption

[1808] server

[1809] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency. It also selects routes with minimal fuel consumption based on real-time traffic and geographic information. It also evaluates environmental impact (such as CO2 emissions) and reflects this in delivery plans.

[1810] Terminal

[1811] The delivery person's device then follows the optimized route received from the server to deliver efficiently, thereby reducing fuel consumption and working hours.

[1812] Specific examples

[1813] Example 1: When the user is absent

[1814] 1. The server knows from past delivery history that the user is often out during the day on weekdays.

[1815] 2. The user sends a notification to the server in advance requesting delivery to the delivery box.

[1816] 3. The delivery person's device notifies the server that the user is not at home, and the server sends instructions to the device to redeliver the package to the delivery box.

[1817] 4. The delivery person delivers the package to the delivery box and reports the status to the server from the terminal.

[1818] Example 2: Optimizing delivery efficiency

[1819] 1. The server collects package information within the delivery area and analyzes the delivery address using GIS.

[1820] 2. Uses AI algorithms to calculate the most efficient delivery route.

[1821] 3. The optimized route is sent to the device, and the delivery person begins delivery according to the instructions.

[1822] 4. Delivery progress is sent to the server in real time, and new instructions are sent to the terminal as needed.

[1823] The present invention can improve delivery efficiency, reduce fuel consumption, reduce redelivery, and shorten working hours, thereby contributing to the sustainable growth of the logistics industry.

[1824] ---

[1825] The processing flow will be explained below.

[1826] Step 1:

[1827] server

[1828] Receive package information from delivery companies. The server receives detailed package data via each delivery company's API or file upload function.

[1829] Step 2:

[1830] server

[1831] The received package information is converted into a unified format and stored in a database, allowing for centralized management of information from multiple delivery companies.

[1832] Step 3:

[1833] server

[1834] The server analyzes the delivery address based on the package information. The server works with a geographic information system (GIS) to match the delivery address of each package with map data to identify the delivery area.

[1835] Step 4:

[1836] server

[1837] It uses artificial intelligence algorithms to generate efficient delivery routes, taking into account past delivery history and whether the customer is at home or not within a specified delivery area to calculate the optimal delivery route.

[1838] Step 5:

[1839] server

[1840] The generated delivery route is sent to the delivery person's device, and the delivery person's smartphone or tablet displays the route information received from the server.

[1841] Step 6:

[1842] Terminal

[1843] The delivery person's terminal will then proceed with the delivery according to the delivery route, and the delivery person will use this information to deliver the package efficiently.

[1844] Step 7:

[1845] Terminal

[1846] Delivery progress data is sent to the server in real time, and location information and progress status that occur during the delivery process are updated to the server successively.

[1847] Step 8:

[1848] server

[1849] The system monitors delivery progress and sends new instructions to the terminal as needed, dynamically adjusting routes and issuing redelivery instructions in response to missed deliveries and traffic conditions.

[1850] Step 9:

[1851] server

[1852] If a delivery is not made at home, the server recalculates the redelivery route based on past data and information on whether the recipient is at home. The server then determines the optimal redelivery date and route and notifies the delivery person.

[1853] Step 10:

[1854] Terminal

[1855] The delivery person's terminal will then execute the specified redelivery along the redelivery route, and redelivery to a location specified by the user, such as a delivery box.

[1856] Step 11:

[1857] User

[1858] Users can check the delivery progress through a web portal or app, select redelivery options if necessary, and provide instructions to the server if the user is not at home.

[1859] Step 12:

[1860] server

[1861] The server evaluates the workload of each delivery person and allocates packages with maximum efficiency based on past delivery history and current workload.

[1862] Step 13:

[1863] server

[1864] Selects a route with less fuel consumption. The server optimizes fuel consumption and time based on geographical information and real-time traffic information.

[1865] Step 14:

[1866] server

[1867] The server evaluates the environmental impact and reflects it in delivery plans. It collects environmental indicators such as CO2 emissions and uses this information to formulate sustainable delivery strategies.

[1868] Step 15:

[1869] Terminal

[1870] The delivery person's terminal follows instructions from the server and delivers the package along the optimal route. By making deliveries more efficient, fuel and labor costs can be saved.

[1871] These are the specific steps of the program, which will improve the efficiency of the logistics industry and reduce the burden on the environment.

[1872] Example 1

[1873] 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."

[1874] In today's logistics and delivery industries, there is a need to efficiently manage package information received from multiple delivery companies, minimize redelivery and fuel consumption, and maximize delivery efficiency. In particular, there is a need for systems that can perform complex tasks such as monitoring delivery progress in real time, issuing redelivery instructions when the recipient is absent, and evaluating environmental impact.

[1875] 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.

[1876] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an artificial intelligence algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information in the event of a missed delivery, means for collecting delivery progress data and evaluating workload, means for selecting routes with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for issuing redelivery instructions based on redelivery options previously specified by the user, and means for recalculating optimal delivery routes in real time using traffic information, thereby enabling reduction in redelivery and fuel consumption, improved delivery efficiency, and reduced environmental impact.

[1877] A "delivery company" is an organization or company whose business is receiving and delivering packages.

[1878] "Package information" refers to detailed data about the package being delivered (e.g., package contents, weight, size, delivery address, etc.).

[1879] A "database" refers to a structured and managed collection of data, and is a system for storing, searching, and updating cargo information.

[1880] "Geographic Information System (GIS)" means a system for collecting, analyzing, and displaying geographic information used to locate shipping addresses.

[1881] "Delivery Address" means the location where a package is to be delivered.

[1882] "Delivery Area" means the geographic area within which a particular delivery activity occurs.

[1883] An "artificial intelligence algorithm" is a method or procedure that allows a computer to perform processing by imitating part of human intelligence.

[1884] A "delivery route" is the optimal route planned to deliver packages efficiently.

[1885] A "delivery terminal" refers to an electronic device such as a smartphone or tablet used by a delivery person.

[1886] "Delivery progress" refers to information indicating the stage of delivery.

[1887] "Real time" refers to processing that is performed immediately without delay, meaning that processing is performed in synchronization with real time.

[1888] "Instructions" refer to specific instructions or orders for actions sent to delivery personnel from a higher-level system or administrator.

[1889] A "redelivery route" is a route planned to redeliver packages that could not be delivered due to reasons such as absence of the recipient.

[1890] "Workload" refers to the amount of work and burden that a delivery person must perform.

[1891] "Fuel consumption" refers to the energy, particularly fuel, consumed in delivery operations.

[1892] "Environmental load" refers to the impact that economic and human activities have on the natural environment.

[1893] "Redelivery options" refer to options for how to redeliver a package if the user is not at home.

[1894] "Traffic information" refers to data on road congestion and traffic flow.

[1895] MODE FOR CARRYING OUT THE INVENTION

[1896] This system stores package information received from multiple delivery companies in a database, analyzes delivery addresses in conjunction with a geographic information system, generates efficient delivery routes, and monitors delivery progress. This system is effective in reducing redelivery, fuel consumption, and environmental impact.

[1897] 1. Collection and integration of package information

[1898] server

[1899] The server receives package information from the delivery company via API. Specifically, it uses the API provided by the delivery company in JSON or XML format. The received package information is converted into a unified format (for example, CSV format) by an internal data conversion module. The package information converted into the unified format is then stored in a database.

[1900] Specific examples:

[1901] Package information is received from delivery company A via API, and the JSON format data is converted to CSV format and stored in a database.

[1902] 2. Delivery route generation

[1903] server

[1904] The server retrieves package information from the database and analyzes each delivery address. The delivery address is converted into location information in conjunction with a geographic information system (GIS). For example, the GIS uses Google Maps API or OpenStreetMap. The delivery area is identified based on the location information of the analyzed delivery address.

[1905] Next, an efficient delivery route is generated within the identified delivery area using an artificial intelligence algorithm. Specifically, the shortest route is calculated using the Dijkstra algorithm or the A algorithm. The generated delivery route is sent from the server to the delivery person's device.

[1906] Terminal

[1907] The delivery person's device receives the route information and displays the optimal delivery route on the app. The device, such as a smartphone or tablet, visualizes the route using the Google Maps API.

[1908] Specific examples:

[1909] The server obtains information about the delivery area and generates the optimal delivery route using the Dijkstra algorithm. The generated route is sent to the delivery person's smartphone and displayed in the Google Maps app.

[1910] 3. Delivery monitoring and redelivery support

[1911] server

[1912] The server receives real-time delivery progress information from the delivery person's terminal. When the delivery person has delivered the package or was unable to deliver due to absence, the server reports this information to the server. The server updates the database based on this information.

[1913] If a delivery is missed, the server recalculates the redelivery route based on past data and information on whether the delivery person is at home. The recalculated route is then sent back to the delivery person's device.

[1914] Terminal

[1915] The delivery person's terminal successively transmits delivery progress information to the server, and redelivers or changes the route as necessary. The terminal immediately receives redelivery instructions and new route information, allowing for efficient redelivery.

[1916] User

[1917] Users can notify the server of their desired redelivery options in advance through the web portal or app. This allows the server to send appropriate instructions to the delivery person based on the options specified by the user. For example, if the user requests delivery to a parcel box, that information is sent to the server and the delivery person is notified.

[1918] Specific examples:

[1919] The user sends information about their desired delivery to the delivery box to the server through the app, and the server calculates a new redelivery route based on that information and sends it to the delivery person's device.

[1920] 4. Optimizing working hours and fuel consumption

[1921] server

[1922] The server collects the workload of each delivery person from a database and evaluates it. Based on the evaluation results, it allocates packages to each delivery person with maximum efficiency. It also uses real-time traffic information to select routes with the least fuel consumption. It also evaluates the environmental impact (e.g., CO2 emissions) and creates delivery plans that take this into account.

[1923] Specific technologies include the use of Google Maps traffic information API and environmental assessment software.

[1924] Terminal

[1925] The delivery person's device then receives optimized route information from the server and delivers efficiently, for example, by choosing a route that avoids traffic jams or by providing instructions for the shortest possible delivery distance.

[1926] Specific examples:

[1927] The server receives real-time traffic information and calculates a route with the lowest CO2 emissions based on that information, which is then displayed on the delivery person's device.

[1928] Example prompts using generative AI models

[1929] Example prompt sentence:

[1930] "Generate efficient delivery routes based on past delivery history and real-time traffic information."

[1931] By inputting this prompt into a generative AI model, the complex route generation process can be automated to produce an optimized delivery route.

[1932] The above is a specific description of the embodiment of the present invention.

[1933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1934] Step 1:

[1935] server

[1936] Parcel information is received from the delivery company using an API. The input at this time is the parcel data in JSON format provided by the delivery company. The server converts the received parcel information into CSV format using an internal data conversion module and stores it in the database. The output is the parcel information converted into a unified format.

[1937] Specific operation: Receive JSON formatted package information from delivery company A via API, convert it to CSV format, and insert it into the database.

[1938] Step 2:

[1939] server

[1940] The server retrieves package information from the database and analyzes the delivery address. The input is the delivery address data in CSV format, and the output is latitude and longitude information using a geographic information system (GIS). Next, the GIS is used to obtain the location information of the delivery address and identify the delivery area.

[1941] Specific operation: The server reads the delivery address in CSV format, obtains the latitude and longitude information using the Google Maps API, and maps the delivery area on a map.

[1942] Step 3:

[1943] server

[1944] The server generates efficient delivery routes within the specified delivery area using an artificial intelligence algorithm. The input is latitude and longitude information within the delivery area, and the output is the optimal delivery route. The server calculates the shortest route using the Dijkstra algorithm or the A algorithm, and distributes the generated delivery route to the delivery person's terminal.

[1945] Specific operation: The server calculates the optimal delivery route based on latitude and longitude information within the delivery area, and sends the determined route information to the delivery person's smartphone.

[1946] Step 4:

[1947] Terminal

[1948] The delivery person's device receives the route information and displays it on the app. The input is the optimal route information sent from the server, and the output is the delivery route displayed on a map. The device visualizes the route using the Google Maps API and provides the delivery person with the shortest route.

[1949] Specific operation: The delivery person's device displays the route information received from the server using the Google Maps API and begins delivery.

[1950] Step 5:

[1951] server

[1952] The server receives real-time delivery progress information from the delivery person's terminal. The input is the delivery progress data sent from the terminal, and the output is progress update information. The server sends new instructions to the delivery person as needed.

[1953] Specific operation: The delivery person's device sends current location information and delivery status to the server every minute, and the server updates the progress status based on this data.

[1954] Step 6:

[1955] server

[1956] When a delivery is missed, the server recalculates the redelivery route based on past data and at-home information. The input is delivery progress data and past at-home information, and the output is a new redelivery route. The server then applies the optimization algorithm to generate a new route and distributes it to the delivery person's device.

[1957] Specific operation: The server receives information about the delivery when the delivery person is not at home, references past data on when the delivery person is at home, and sends a recalculated route to the delivery person's terminal.

[1958] Step 7:

[1959] User

[1960] The user uses a web portal or app to notify the server in advance of redelivery options (e.g., use of a delivery box) in case of absence. The input is the redelivery options specified by the user, and the output is the notification information sent to the server. Based on the user's specifications, the server sends appropriate instructions to the delivery person.

[1961] Specific operation: The user uses the app to specify the use of a delivery box and sends that information to the server. The server then calculates a redelivery route based on that information and sends instructions to the delivery person.

[1962] Step 8:

[1963] server

[1964] The server evaluates the workload of each delivery person and selects a route that consumes less fuel. The input is delivery progress data and traffic information, and the output is optimized route information. The server also evaluates the environmental impact (CO2 emissions) and reflects this in the delivery plan.

[1965] Specific operation: The server recalculates the most fuel-efficient route based on delivery progress data and real-time traffic information, and sends instructions to the delivery person.

[1966] This will reduce redelivery and fuel consumption, improve delivery efficiency, and reduce the burden on the environment.

[1967] (Application example 1)

[1968] 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."

[1969] In the logistics industry, parcel deliveries are routinely carried out by multiple delivery companies, but the challenge is to centrally manage this parcel information and efficiently generate delivery routes. It is also necessary to effectively monitor delivery progress and handle redelivery requests. Furthermore, reducing fuel consumption and reducing environmental impact are also important issues. The present invention aims to solve these challenges and provide a system for effectively managing operations at logistics centers.

[1970] 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.

[1971] In this invention, the server includes means for storing package information received from multiple delivery companies in a database, means for analyzing delivery addresses in cooperation with a geographic information system and identifying delivery areas, means for generating efficient delivery routes using an AI algorithm, means for distributing the generated delivery routes to delivery terminals, means for monitoring delivery progress in real time and sending instructions as necessary, means for recalculating redelivery routes based on past data and at-home information when a delivery is missed, means for collecting delivery progress data and evaluating workload, means for selecting a route with minimal fuel consumption, means for evaluating environmental impact and reflecting this in delivery plans, means for delivery personnel to receive instructions for the optimized route via a smart device, means for users to set redelivery options via a web portal or app, and means for optimizing delivery plans using a generative AI model. This enables delivery efficiency, reduced fuel consumption, fewer redeliveries, and shorter working hours.

[1972] "Parcel information" refers to data such as parcel identification information, delivery destination information, and delivery status information collected from multiple delivery companies.

[1973] A "database" is a system for systematically storing, managing, and searching information.

[1974] A "geographic information system" is a system for collecting, managing, and analyzing geographic data.

[1975] "Delivery address analysis" refers to the technology used to understand the delivery address information and determine the appropriate delivery area and route.

[1976] "Specifying a delivery area" means clarifying the geographical area to which the delivery destination of the package belongs.

[1977] An "artificial intelligence algorithm" is a calculation procedure that enables a computer to learn on its own and make appropriate judgments and predictions.

[1978] "Generating an efficient delivery route" means calculating a route that will complete delivery while saving the most time and money, based on the input delivery destination information.

[1979] A "delivery terminal" is an electronic device carried by a delivery person to receive delivery progress and instructions.

[1980] "Delivery progress" refers to the current progress of the package in the delivery process.

[1981] "Real-time monitoring" means instantly checking the progress and status of delivery.

[1982] "Sending instructions" means that the server sends necessary information and instructions to the delivery person via an electronic device.

[1983] "Delivery to absentee" refers to a situation where the resident at the delivery address is absent and unable to receive the package.

[1984] "Past data" refers to information such as delivery history and customer information that has been recorded to date.

[1985] "At-home information" is information about whether the resident of the delivery destination is at home.

[1986] "Recalculating the redelivery route" means recalculating a new, more efficient delivery route when a delivery is missed.

[1987] "Delivery progress data" is data that indicates the current status and progress of delivery.

[1988] "Evaluating workload" means measuring the workload of each delivery person and appropriately evaluating their efforts.

[1989] A "low fuel consumption route" is a delivery route that is planned to minimize the amount of fuel used by the delivery vehicle.

[1990] "Evaluating the environmental impact" means measuring the impact that delivery activities have on the environment and formulating plans based on this.

[1991] A "smart device" is a portable electronic device that can connect to the Internet and use a variety of applications.

[1992] "Receiving optimized route instructions" means that the delivery person receives information about a pre-calculated and optimized delivery route.

[1993] A "web portal" is a website that provides users with a variety of information and services via the Internet.

[1994] "Redelivery options" are settings and instructions regarding redelivery that customers can select in the event of a missed delivery.

[1995] A "generative AI model" is an artificial intelligence computational model used for delivery planning and route optimization.

[1996] "Optimizing" means arranging and adjusting a system or computation most effectively to achieve a specific goal.

[1997] System Overview

[1998] This invention relates to a system that realizes efficient delivery of parcels at a logistics center. This system manages parcel information received from multiple delivery companies in an integrated manner and generates efficient delivery routes using a geographic information system and an artificial intelligence algorithm. It also monitors delivery progress in real time, responding to requests for redelivery and reducing environmental impact.

[1999] Program generation and natural language explanation

[2000] Collection and integration of package information

[2001] The server collects package information from multiple delivery companies via API and file upload, converts it into a unified format, and stores it in a database, allowing package information from different delivery companies to be centrally managed.

[2002] Generate delivery routes

[2003] The server uses the collected package information to link with a geographic information system (GIS) and analyzes the delivery address. Based on the analyzed address information, an artificial intelligence algorithm is used to generate an efficient delivery route. The generated route is optimized by taking into account data such as past delivery history and absence information.

[2004] Delivery route distribution

[2005] The generated delivery route is sent from the server to the delivery person's device (smartphone or tabl...

Claims

1. A means for storing package information received from a plurality of delivery companies in a database; a means for analyzing a delivery address in conjunction with a geographic information system and identifying a delivery area; a means for generating efficient delivery routes using an artificial intelligence algorithm; means for distributing the generated delivery route to a delivery terminal; a means of monitoring delivery progress in real time and sending instructions as needed; In the event of a missed delivery, there is a way to recalculate the redelivery route based on past data and information on whether the recipient is at home. a means of collecting delivery progress data and evaluating workload; A means for selecting a route that consumes less fuel; A means of assessing environmental impact and reflecting it in delivery plans; A system including:

2. 2. The system of claim 1, wherein the means for monitoring delivery progress includes means for receiving delivery progress data from a terminal and transmitting the data to a server in real time.

3. 2. The system according to claim 1, further comprising means for issuing an instruction to deliver the package to a delivery box designated in advance by the user when a delivery is missed.

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