system

Abstract

Provide a system. A means for receiving a delivery request; A means for receiving and updating the current location and shipping status of participating carriers in real time; A means for analyzing past delivery history data and current traffic information to calculate the optimal delivery route and means; a means for calculating a total delivery cost based on the calculated delivery route and means; means for transmitting the calculated optimal delivery route and means, and the total delivery cost to a user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, while the delivery industry has been required to improve logistics efficiency, delays due to empty trucks and traffic congestion have become a problem. Furthermore, it is difficult to appropriately combine various delivery methods (land transport, rail, airplane, drone, and ship), and optimal delivery routes and methods are not being selected adequately. There is a need for a new system to improve this situation and achieve delivery efficiency and cost reduction. [Means for solving the problem]

[0005] To address the above-mentioned issues, the present invention provides a system that includes a means for receiving delivery requests, a means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, a means for calculating the optimal delivery route and method by analyzing past delivery history data and current traffic information, a means for calculating the total delivery cost based on the calculated delivery route and method, and a means for transmitting the calculated optimal delivery route, method, and total delivery cost to a user terminal. This system periodically updates the current location and delivery status of the delivery company, notifies the delivery company's terminal of new delivery requests, and provides a means for the delivery company to accept or reject the request. The system also includes a means for selecting the optimal delivery company and delivery method based on the delivery origin, delivery destination, desired date and time, and priority conditions included in the received delivery request, displaying the selected delivery method, route information, and total delivery cost on the user terminal, and allowing the user to select the proposal. This reduces wasteful empty-cargo travel and facilitates the selection of the optimal delivery route and method.

[0006] A "delivery request" is information sent by a user requesting delivery of a package, and includes information such as the sender, destination, desired date and time, and priority conditions.

[0007] "Delivery company" refers to a company or individual that delivers packages, including trucking companies, individual drivers, railroad companies, and airlines.

[0008] "Current location" refers to the real-time geographic coordinates of a particular object using a location information system such as GPS.

[0009] "Transportation status" is information indicating the current status of the delivery company or delivery means, and includes statuses such as empty, in transit, and on break.

[0010] "Past delivery history data" refers to detailed information about previous deliveries, including delivery dates, routes, times, costs, and the like.

[0011] "Traffic information" refers to information about current and forecast traffic conditions, such as road congestion, speed limits, and weather.

[0012] The "optimal delivery route" refers to the most efficient route calculated taking into account factors such as the time it takes to reach the destination and the cost.

[0013] "Delivery means" refers to the method or equipment used to deliver goods, including land transport (trucks), rail, airplanes, drones, ships, etc.

[0014] "Total delivery cost" refers to all costs incurred using a particular delivery route and means, including fuel costs, tolls, and labor costs.

[0015] "User terminal" refers to a device used by a user to access the service and input and receive information, such as a smartphone, tablet, or PC. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal.

[0038] Server-side explanation

[0039] 1. Data reception and updating

[0040] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, which keeps the current location, empty load status, and operational status (in transit, on break, etc.) of each truck and delivery vehicle up to date.

[0041] 2. Data Analysis

[0042] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route, as well as the optimal delivery method.

[0043] 3. Matching process

[0044] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "shortest time," "cheapest price"). Based on this information, the server selects the optimal delivery company and delivery method.

[0045] 4. Price Calculation

[0046] The server calculates the total delivery cost based on the selected delivery route and vehicle, taking into account fuel costs, tolls, labor costs, etc.

[0047] 5. Submit your proposal

[0048] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[0049] Explanation on the user terminal side

[0050] 1. Submit a request

[0051] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[0052] 2. Confirmation of proposal contents

[0053] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0054] 3. Final Decision

[0055] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0056] Explanation on the delivery company terminal

[0057] 1. Current location and status transmission

[0058] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0059] 2. Receiving a request notification

[0060] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[0061] 3. Accepting or Rejecting a Request

[0062] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0063] Specific examples

[0064] User Flow

[0065] When a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow" and selects the fastest option, the server calculates the optimal combination of trucks and trains. For example, a route is proposed in which truck A transports goods from Tokyo to Nagoya, and then transports goods from Nagoya to Osaka by train. The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0066] Delivery company process

[0067] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and empty status to the server. He then receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver begins delivery according to that route.

[0068] In this way, the present invention increases delivery efficiency, reduces empty trips, and provides optimal delivery means and routes, thereby reducing overall costs.

[0069] The processing flow will be explained below.

[0070] Server-side processing

[0071] Step 1:

[0072] The server receives GPS data sent from the delivery company's terminal in real time, which allows the current location of each truck or delivery vehicle to be determined.

[0073] Step 2:

[0074] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[0075] Step 3:

[0076] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[0077] Step 4:

[0078] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[0079] Step 5:

[0080] The server executes a matching algorithm to select the optimal delivery company and delivery method based on the received request information. For example, if "fastest" is selected, the server calculates the route and method that will deliver the item in the shortest time.

[0081] Step 6:

[0082] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0083] Step 7:

[0084] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0085] Processing on the user terminal side

[0086] Step 1:

[0087] The user launches the app and inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (such as "fastest," "cheapest," etc.).

[0088] Step 2:

[0089] The user sends the input request information to the server.

[0090] Step 3:

[0091] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[0092] Step 4:

[0093] The user reviews the suggestions and selects the best option, which is then sent to the server.

[0094] Processing on the delivery company terminal

[0095] Step 1:

[0096] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[0097] Step 2:

[0098] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0099] Step 3:

[0100] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0101] Step 4:

[0102] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0103] This allows all stakeholders to share information in real time and execute optimal delivery plans.

[0104] Example 1

[0105] 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."

[0106] In modern logistics systems, managing delivery requests and optimizing delivery routes are key challenges. Traditional systems struggle with real-time updates on delivery company status and effectively utilizing past delivery history and current traffic information to calculate optimal delivery routes. Accurately calculating total delivery costs and providing them in a format that users can easily view are also challenges. This makes it difficult to achieve efficient deliveries, resulting in cost and time waste.

[0107] 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.

[0108] In this invention, the server includes a means for receiving delivery requests, a means for receiving and updating the current location and delivery status of the delivery company in real time, a means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, a means for calculating the total delivery cost based on the calculated delivery route and means, and a means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal. This makes it possible to grasp the current location and operating status of the delivery company in real time and select the optimal delivery route based on past data and current traffic information. Furthermore, by accurately calculating the total delivery cost and quickly providing that information to the user, efficient and effective delivery management is realized.

[0109] A "delivery request" refers to a request by a user to deliver a specific package from a location designated by the user to a location designated by the user.

[0110] "Delivery Provider" means a company or individual that provides services to transport packages from a specified location to a destination.

[0111] "Current Location" refers to the real-time geographic coordinates of the carrier's vehicle (e.g., truck, motorcycle, etc.).

[0112] "Transportation status" refers to information indicating the current business status of the delivery company (e.g., empty, in transit, on break, etc.).

[0113] "Receiving and updating in real time" means that the current location and shipping status of the delivery company are continuously and immediately sent to the server and reflected in the database.

[0114] "Past delivery history data" refers to detailed records of past deliveries made by a delivery company (e.g., delivery route, travel time, cost, etc.).

[0115] "Current traffic information" refers to real-time traffic data such as current road conditions, traffic congestion, and construction information.

[0116] The "means" used in "data analysis" refers to software and algorithms used to perform statistical analysis and machine learning predictive models based on collected data.

[0117] "Optimal delivery route" refers to the most effective delivery route calculated based on factors such as cost, time, and efficiency.

[0118] "Delivery Method" refers to the method used to transport the goods (e.g. truck, train, motorcycle, etc.).

[0119] "Total delivery cost" refers to the total cost calculated based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc.

[0120] "User terminal" refers to a device such as a computer or smartphone used by a user to input delivery requests and confirm proposal contents.

[0121] A "machine learning model" refers to an algorithm that learns from past data and makes predictions and classifications for new data.

[0122] "Data Analysis Library" refers to software tools and frameworks for collecting, processing, analyzing, and visualizing data.

[0123] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a lightweight data exchange format for structuring data, storing it, and transferring it.

[0124] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery method, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal. Each component and its function are described in detail below.

[0125] Server-side explanation

[0126] 1. Data reception and updating

[0127] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database. Specifically, the delivery company enters their current location and transportation status (empty, in transit, on break, etc.) using a dedicated app and sends this to the server. The server stores this information in a database (e.g., MySQL (registered trademark)) and keeps it up to date.

[0128] 2. Data Analysis

[0129] The server performs data analysis based on the received current location data, past delivery history data, and traffic information. Specifically, it uses Python and data analysis libraries (e.g., Pandas and NumPy) to calculate the average time and cost required for each route and determine the optimal delivery method. Based on the results of this analysis, it uses machine learning models (e.g., scikit-learn) to optimize delivery routes.

[0130] 3. Matching process

[0131] The server receives a delivery request sent by the user. This request includes the delivery origin, delivery destination, desired date and time, and priority conditions (shortest time, cheapest price, etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[0132] 4. Price Calculation

[0133] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. Detailed cost calculations are performed using a Python calculation module.

[0134] 5. Submit your proposal

[0135] The server sends the calculation results to the user's device and displays the proposed details (delivery company, route, means, price). A dedicated mobile app (e.g., developed with React Native) is installed on the user's device, which displays the latest information in real time.

[0136] Explanation on the user terminal side

[0137] 1. Submit a request

[0138] The user inputs a delivery request using a dedicated app, setting the origin, destination, desired date and time, and priority conditions (fastest, cheapest, etc.), and then sends this information to the server.

[0139] 2. Confirmation of proposal contents

[0140] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0141] 3. Final Decision

[0142] The user selects the most suitable option from the presented options and requests delivery.

[0143] Explanation on the delivery company terminal

[0144] 1. Current location and status transmission

[0145] Delivery companies use a dedicated app to periodically send their current location and status information to a server.

[0146] 2. Receiving a request notification

[0147] The delivery company receives a new delivery request from the server and checks the request details (package details, delivery route, reward, etc.) displayed in the app.

[0148] 3. Accepting or Rejecting a Request

[0149] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0150] Specific examples

[0151] For example, if a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server analyzes the GPS data and traffic information sent by the delivery company to select the optimal delivery company and method. From the analysis results, it is determined that the optimal route is to transport the item from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka, with a total cost calculated to be 10,000 yen. The user receives this proposal and accepts it after checking it. On the delivery company side, the driver of Truck A receives this information, accepts the request, and makes the delivery according to the route information provided by the server.

[0152] Example prompts for a Generative AI model

[0153] Prompt: "Delivery from Tokyo to Osaka by 4pm tomorrow. Please suggest the fastest option."

[0154] Expected output: "The fastest route is to transport from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka. The total delivery cost is 10,000 yen."

[0155] In this way, the present invention increases delivery efficiency, reduces empty runs, and provides optimal delivery means and routes, thereby achieving overall cost reduction.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1: Receive and update data

[0158] Description: The server receives and updates GPS data and status information (empty, in transit, on break, etc.) from the delivery company's terminal in real time. The delivery company periodically inputs and sends the current location and status using a dedicated app.

[0159] Input: GPS data (latitude, longitude) sent from the delivery company terminal, status information

[0160] Data processing and calculation: The server saves the received data in a database (e.g., MySQL) in real time. It compares it with existing data to identify items that need to be updated and saves them as the latest information.

[0161] Output: The latest carrier location and status are stored in the database.

[0162] Specific operation: When the data "Latitude: 35.6895, Longitude: 139.6917, Status: Empty" is sent from the delivery company terminal, the server receives this and updates the database as "Delivery company A: Latitude 35.6895, Longitude 139.6917, Status: Empty."

[0163] Step 2: Data analysis

[0164] Description: The server performs data analysis using past delivery history data and current traffic information to calculate the average travel time and cost for each route.

[0165] Input: Past delivery history data stored in the database, real-time traffic information

[0166] Data processing and calculation: Using Python and data analysis libraries (e.g., Pandas, NumPy), data is aggregated and the average travel time, cost, and traffic patterns for each route are analyzed. The results are used to update the predictive model and derive the optimal delivery route.

[0167] Output: Average travel time and cost information for each route

[0168] Specific operation: Imports past delivery data, generates analysis results such as "average time required: 3 hours, average cost: 5,000 yen," and saves them in a database.

[0169] Step 3: Matching

[0170] Description: The server receives a delivery request from the user terminal and selects the optimal delivery company and delivery method based on this.

[0171] Input: Delivery request from user terminal (delivery origin, delivery destination, desired date and time, priority conditions)

[0172] Data processing and calculation: Based on the received request, the current location, status, historical data, and traffic information in the database are collated, and the most suitable company and means are evaluated and selected using a machine learning model (e.g., scikit-learn).

[0173] Output: Selected optimal delivery company and method

[0174] Specific operation: If a user sends a request saying, "I want delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server determines that Truck A is the best option and selects the route as "Take Truck A from Tokyo to Nagoya, then take the train from Nagoya to Osaka."

[0175] Step 4: Price Calculation

[0176] Description: The server calculates the total delivery cost based on the selected delivery route and means.

[0177] Input: Selected delivery route and delivery method

[0178] Data processing and calculation: Using a Python calculation module, fuel costs, tolls, labor costs, etc. are calculated to calculate the total delivery cost. Each cost element is tallied and the total value is calculated.

[0179] Output: Total shipping cost

[0180] Specific operation: For example, based on the selected route, the total fuel cost is calculated as 3,000 yen, tolls as 2,000 yen, and labor costs as 5,000 yen, and the total delivery cost is calculated as 10,000 yen.

[0181] Step 5: Submit your proposal

[0182] Description: The server sends the calculation results to the user's terminal and displays the proposed contents (delivery company, route, means, price) to the user.

[0183] Inputs: Total shipping cost, selected shipping carrier and method

[0184] Data processing and calculation: Proposal content is generated in JSON format and sent to the user's terminal.

[0185] Output: The proposed details (delivery company, route, means, price) are displayed on the user's terminal.

[0186] Specific operation: "Proposal: Truck A, Tokyo to Nagoya, Nagoya to train, total cost 10,000 yen" is displayed on the user's terminal and can be confirmed by the user.

[0187] Step 6: Review and finalize the request

[0188] Description: The user reviews the suggestions sent by the server and selects the best option.

[0189] Input: Proposal from the server

[0190] Data processing and calculation: The user selects one of the options presented, and the selected information is sent back to the server.

[0191] Output: The final delivery request information is sent to the server and processing begins.

[0192] Specific operation: When the user confirms and selects "Proposal: Truck A, total cost 10,000 yen," the selection information is sent to the server and registered as an official delivery request.

[0193] Step 7: Carrier Status Update and Request Acceptance

[0194] Description: Using a dedicated app, the delivery company periodically sends its current location and status information to the server and accepts the request.

[0195] Input: Current location and status information from the delivery company terminal, new delivery request

[0196] Data processing and calculation: Based on the received information, the server notifies the delivery company of a new delivery request, and the delivery company then accepts or rejects the request.

[0197] Output: Information that the carrier has accepted the request, updated current location and status information

[0198] Specific operation: The delivery company sends the data "Current location: Latitude 35.6895, Longitude 139.6917, Status: Empty" to the server and receives a new delivery request "From Tokyo to Osaka, arrive by 4:00 PM tomorrow." When the delivery company accepts the request, the server sends detailed delivery route information to the company's terminal.

[0199] (Application example 1)

[0200] 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."

[0201] With conventional delivery management systems, it takes time to process delivery requests, making it difficult to quickly identify the optimal delivery route and method. It is also difficult to keep the current location and status of the delivery person updated in real time, making it difficult to select the appropriate delivery person, resulting in reduced delivery efficiency. These issues can reduce the reliability of recommendations provided to users and increase overall delivery costs.

[0202] 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.

[0203] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal, means for selecting the optimal delivery company and transportation means based on request data from the user, and means for displaying information on the selected delivery company, transportation means, and cost information to the user. This enables rapid processing of delivery requests, selection of an appropriate delivery company in real time, and proposal of an efficient delivery route.

[0204] A "delivery request" is information in which a user requests that a specific package be delivered to a specified location.

[0205] A "delivery company" is a company or individual that responds to a user's delivery request and transports the package.

[0206] "Current location" refers to latitude and longitude information indicating the real-time location of a delivery company or its transportation vehicle.

[0207] "Transportation status" refers to information indicating the current operating status of a delivery company or its transportation means and the loading status of cargo.

[0208] "Traffic information" refers to data about the current driving environment, including road congestion, accident information, construction information, etc.

[0209] "Delivery route" refers to information indicating the optimal route from the delivery source to the delivery destination.

[0210] "Delivery vehicle" refers to the type of transportation method or vehicle used to transport a package.

[0211] "Total delivery cost" refers to all costs required to make a delivery, including fuel costs, tolls, labor costs, etc.

[0212] A "user terminal" refers to a device such as a computer or smartphone used by a user.

[0213] "Request data" refers to information entered by the user, such as the delivery origin, delivery destination, desired date and time, and priority conditions.

[0214] "Selection" refers to the process of determining the most suitable carrier and transportation method based on specified criteria.

[0215] The "display means" refers to a mechanism for visually presenting selected information to the user.

[0216] "Feedback" refers to the evaluation or opinion provided by the user after delivery is completed.

[0217] "Evaluation data" refers to data that quantifies the performance of delivery companies based on feedback.

[0218] The present invention relates to a system for receiving a delivery request from a food delivery service, selecting the most suitable delivery company and delivery means, and presenting the results to a user. Specific embodiments for carrying out the present invention are described below.

[0219] Overall system configuration

[0220] The system consists of a server, a user terminal, and a delivery company terminal.

[0221] Server Processing

[0222] 1. Data reception and updating

[0223] The server receives real-time GPS data sent from the delivery company's terminal and stores it in a database. This keeps the current location and delivery status of each delivery person up to date. The server uses Python and Flask.

[0224] 2. Data Analysis

[0225] The server analyzes the received data based on the current location data, past delivery history data, and traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Machine learning algorithms and statistical models are used for data analysis.

[0226] 3. Matching process

[0227] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions ("fastest", "cheapest", etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[0228] 4. Price Calculation

[0229] The server calculates the total delivery cost based on the selected delivery route and means, taking into account fuel costs, tolls, labor costs, etc. The calculation is performed using transaction data from the database.

[0230] 5. Submit your proposal

[0231] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[0232] User terminal processing

[0233] 1. Submit a request

[0234] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[0235] 2. Confirmation of proposal contents

[0236] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0237] 3. Final Decision

[0238] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0239] Delivery company terminal processing

[0240] 1. Current location and status transmission

[0241] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0242] 2. Receiving a request notification

[0243] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[0244] 3. Accepting or Rejecting a Request

[0245] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0246] Specific examples

[0247] When a user inputs a request such as "I want food delivered from Restaurant A (origin) to my home (destination) in the shortest time possible," and selects the fastest option, the server selects the optimal delivery person (e.g., a delivery person with a car) and presents the total delivery cost of 1,000 yen. The user confirms this on their device and accepts.

[0248] The delivery process involves a delivery person (e.g., ID: 2) sending their current location (latitude 35.6804, longitude 139.7690) and status (empty) to the server. After that, they receive a notification from the server saying, "New delivery request: From Restaurant A to home, arrive in the shortest time possible." After confirming the request, they accept it. The server then provides detailed route information, and the delivery person begins delivery according to that route.

[0249] Prompt Sentence Examples

[0250] User: Wants food delivered from Restaurant A to his / her home in the shortest time possible

[0251] Server: Selecting a delivery person. Please wait...

[0252] Server: Optimal delivery person and total cost proposal completed. Delivery person ID: 2, Cost: 1000 yen, Estimated time: 30 minutes

[0253] The above is a specific description of an embodiment of the present invention, which allows users to receive a fast and optimal delivery service and enables delivery companies to carry out their work efficiently.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1: Receiving a shipping request

[0256] The user inputs a delivery request through the app. The input includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). This is sent from the user's device to the server. Input data: delivery origin, delivery destination, desired date and time, priority conditions. Output data: delivery request data sent to the server.

[0257] Step 2: Receive and update carrier location and status

[0258] The server receives GPS data sent from delivery company terminals in real time and stores it in a database. This keeps the current location and status (empty, in transit, on break, etc.) of each delivery company up to date. Input data: Delivery company GPS data, status information. Output data: Current location and status information in the updated database.

[0259] Step 3: Data analysis

[0260] The server performs data analysis based on the received current location data, past delivery history data, and current traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Input data: current location data, delivery history data, traffic information. Output data: optimal delivery route and method information.

[0261] Step 4: Selecting a carrier and method

[0262] The server compares and analyzes the delivery request data received from the user with the delivery company information in the database to select the optimal delivery company and delivery method. Input data: delivery request data, delivery company data. Output data: selected optimal delivery company and delivery method.

[0263] Step 5: Cost calculation

[0264] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. The calculated total delivery cost is sent to the user terminal. Input data: Selected delivery route and means. Output data: Total delivery cost.

[0265] Step 6: Submit and review your proposal

[0266] The server sends the calculation results to the user's device and presents suggestions (delivery company, route, means of transport, price). The user checks this information in the app and selects the optimal option. Input data: Calculation results (delivery company, route, means of transport, price). Output data: Suggestions sent to the user's device, content displayed on the user interface.

[0267] Step 7: Final decision

[0268] The user checks the proposals from the server, selects the best option from the presented options, and requests delivery. The selection result is sent to the server, and delivery begins. Input data: User's selection result. Output data: Final decision on delivery request, selection result sent to the server.

[0269] Step 8: Notify the carrier

[0270] The server notifies the selected delivery company's terminal of the new delivery request. The delivery company checks the request details (package details, delivery route, reward, etc.) on the app and accepts or rejects it. Input data: Final decision on delivery request. Output data: Notification to delivery company terminal, display of request details.

[0271] Step 9: Accepting or Rejecting the Request

[0272] If the delivery company accepts the request, it notifies the server. If accepted, the delivery company is provided with detailed delivery route and instructions and begins delivery. Input data: Acceptance or rejection by the delivery company. Output data: Notification to the server, detailed delivery route and instructions.

[0273] Step 10: Delivery Progress and Final Report

[0274] The delivery company updates its current location and delivery status in real time and continues to send them to the server. Once delivery is complete, it sends a final report to the server. Input data: current location data, delivery status, final report. Output data: updated status, confirmation of delivery completion.

[0275] 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.

[0276] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user, and also combines it with an emotion engine that recognizes the user's emotions. The system is composed of a server, a user terminal, a delivery company terminal, and an emotion engine.

[0277] Server-side processing

[0278] 1. Data reception and updating

[0279] The server receives GPS data sent from the delivery company terminal in real time and stores it in a database, which keeps the current location, empty load status, and operation status of each truck and delivery vehicle always up to date.

[0280] 2. Data Analysis

[0281] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route and the optimal delivery method.

[0282] 3. Emotion analysis

[0283] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0284] 4. Matching process

[0285] The server receives delivery requests sent by users. The requests include the sender, destination, desired date and time, and priority conditions. Taking into account the emotion data provided by the emotion engine, the server selects the optimal delivery company and delivery method.

[0286] 5. Price Calculation

[0287] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0288] 6. Submit your proposal

[0289] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0290] Processing on the user terminal side

[0291] 1. Submit a request

[0292] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[0293] 2. Confirmation of proposal contents

[0294] The user checks the proposals sent from the server in the app and selects the best option based on the suggested delivery company, delivery route, method, price information, and sentiment data.

[0295] 3. Final Decision

[0296] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0297] Processing on the delivery company terminal

[0298] 1. Current location and status transmission

[0299] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0300] 2. Receiving a request notification

[0301] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0302] 3. Request confirmation and acceptance

[0303] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0304] 4. Receiving detailed route information

[0305] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0306] Emotion engine processing

[0307] 1. Emotional Data Analysis

[0308] The emotion engine analyzes the emotion data provided by the user to determine the user's current emotional state.

[0309] 2. Sending Emotional Data

[0310] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[0311] Specific examples

[0312] User Flow

[0313] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0314] Delivery company process

[0315] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0316] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[0317] The processing flow will be explained below.

[0318] Server-side processing

[0319] Step 1:

[0320] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, allowing the current location of each truck or delivery vehicle to be determined.

[0321] Step 2:

[0322] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[0323] Step 3:

[0324] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[0325] Step 4:

[0326] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0327] Step 5:

[0328] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[0329] Step 6:

[0330] The server runs a matching algorithm to select the optimal delivery company and delivery method based on the received request information and emotion data. For example, if "fastest" is selected and impatience is detected, the server calculates the shortest delivery route and method.

[0331] Step 7:

[0332] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0333] Step 8:

[0334] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0335] Processing on the user terminal side

[0336] Step 1:

[0337] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[0338] Step 2:

[0339] The user transmits the input request information and emotion data to the server.

[0340] Step 3:

[0341] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[0342] Step 4:

[0343] The user reviews the suggestions and selects the best option, which is then sent to the server.

[0344] Processing on the delivery company terminal

[0345] Step 1:

[0346] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[0347] Step 2:

[0348] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0349] Step 3:

[0350] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0351] Step 4:

[0352] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0353] Emotion engine processing

[0354] Step 1:

[0355] The emotion engine receives emotion data provided by the user, for example, by analyzing the user's facial expressions and voice via the smartphone's camera and microphone.

[0356] Step 2:

[0357] The emotion engine analyzes the user's emotional state in real time based on the received data, identifying emotions such as impatience, anxiety, and satisfaction.

[0358] Step 3:

[0359] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[0360] Specific examples

[0361] User Flow

[0362] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0363] Delivery company process

[0364] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0365] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[0366] Example 2

[0367] 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."

[0368] In today's logistics industry, improving delivery efficiency and reducing costs are important challenges. However, conventional systems do not provide sufficient information to select the optimal delivery method and delivery company for each delivery request, and are therefore unable to make specific recommendations that take into account the user's emotions and priorities. Furthermore, issues with the timeliness of delivery company status updates and notifications make it difficult to make real-time decisions, making it difficult to improve overall service quality.

[0369] 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.

[0370] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for receiving and analyzing user emotion data and generating an optimal proposal based on this. This allows for the provision of specific and optimal proposals in response to delivery requests that take into account the user's emotions and priorities, and further allows for real-time updates and notifications of delivery company status, thereby improving overall service quality.

[0371] The "means for receiving a delivery request" is a mechanism for receiving information such as the delivery source, delivery destination, desired date and time, and priority conditions from the user, and transmitting it to the server.

[0372] "Means for receiving and updating the current location and transportation status of delivery companies in real time" refers to a system for transmitting GPS data and operation status provided by delivery companies to a server in real time and updating it.

[0373] "Means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information" refers to algorithms and methods for calculating the optimal route and delivery method based on history data and traffic information.

[0374] The "means for calculating total delivery costs" is a system for calculating the total cost of delivery, taking into account fuel costs, tolls, labor costs, etc.

[0375] "Means for sending to user terminal" refers to an interface or protocol for sending information such as calculation results and optimal delivery routes to the user's device.

[0376] The "means for receiving and analyzing emotional data and generating optimal proposals based on this" is a mechanism for optimizing delivery proposals by acquiring and analyzing the user's emotional information.

[0377] "Means for notifying the delivery company's terminal of new delivery requests and providing a means for the delivery company to accept or reject the request" refers to a system for notifying the delivery company of new delivery requests and confirming whether they are accepted or rejected.

[0378] The "means for displaying on the user terminal and allowing the user to select the proposed content" is an interface that displays the proposed content sent from the server on the user's device and allows the user to select it.

[0379] This invention is a system that receives delivery requests, selects the optimal delivery company and delivery method, and presents them to the user. A distinctive feature is that it generates optimal proposals that take into account the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0380] System Configuration

[0381] The system consists of the following main components:

[0382] 1. Server

[0383] The server receives GPS data sent from the delivery company terminal in real time and stores it in a PostgreSQL database, which keeps the delivery company's location information, empty load status, and operation status up to date.

[0384] The server uses Python libraries such as Pandas and NumPy to analyze past delivery history data and current traffic information to calculate the optimal delivery route and means, thereby enabling the creation of efficient delivery plans.

[0385] The server also receives and analyzes user emotion data provided by an emotion engine equipped with a machine learning model (e.g., BERT or GPT-3 (registered trademark)) to identify the user's emotional state, such as impatience, anxiety, or satisfaction.

[0386] The server selects the optimal delivery company and method based on the delivery request from the user, and then calculates the total delivery cost based on the results. The calculation is performed using a price calculation algorithm implemented in JavaScript and Python.

[0387] 2. User Device

[0388] Users input delivery requests through a dedicated application. They set the delivery origin, destination, desired date and time, and preferences (e.g., "fastest," "cheapest"), and then click the submit button to send the request to the server. Emotional data is also provided at the same time. This input form is built using React Native and Flutter (registered trademark).

[0389] The app can then confirm the proposals sent from the server (optimal delivery company, delivery route, method, total cost), and the user can select the optimal option based on the proposals and request delivery.

[0390] 3. Delivery company terminal

[0391] The delivery agents use a dedicated app to periodically update their current location and status and send it to the server, which uses React Native and Kotlin to send location information in real time.

[0392] After receiving a new delivery request and checking its contents, the server either accepts or rejects it. The result is also notified to the server. If the request is accepted, the server receives detailed delivery route information and starts delivery according to the instructions.

[0393] 4. Emotion Engine

[0394] The emotion engine analyzes user-supplied text and voice data to identify the user's emotional state, using text analysis models (e.g., BERT and GPT-3).

[0395] The analysis results are sent to the server, which uses them to generate optimal delivery suggestions that take the user's emotions into account.

[0396] Specific examples

[0397] User Flow

[0398] If a user requests "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option, the emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0399] Delivery company process

[0400] The driver of Truck A periodically sends his current location (latitude 35.6895, longitude 139.6917) and the available load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0401] Example prompts

[0402] "Describe a scenario where a user enters a delivery request and selects the fastest option. Show how the emotion engine would detect the user's impatience and send the result to the server."

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Processing Steps

[0405] Server-side processing

[0406] Step 1: Receiving a shipping request

[0407] Input: Delivery request data from the user terminal (delivery origin, delivery destination, desired date and time, priority conditions).

[0408] Specific operation: The server receives an HTTP request, analyzes its contents, and extracts the necessary information.

[0409] Output: The extracted delivery request data is stored in the server.

[0410] Step 2: Real-time updates on carrier location and status

[0411] Input: Current location data and transportation status data from the delivery company terminal.

[0412] Specific operation: The server receives GPS data and status information sent from the delivery company terminal in real time and updates the database.

[0413] Output: The shipping company information in the database is updated to the latest version.

[0414] Step 3: Data analysis

[0415] Input: Past delivery history data, current traffic information, and delivery company location data.

[0416] Specific operation: The server uses Python libraries (e.g., Pandas, NumPy) to analyze this input data and calculate the optimal delivery route, taking into account traffic information and past historical data to calculate the shortest travel time and lowest cost.

[0417] Output: Optimal delivery route and means.

[0418] Step 4: Sentiment Analysis

[0419] Input: Emotion data from the user device.

[0420] How it works: The server uses machine learning models (e.g., BERT, GPT-3) to analyze the emotion data and identify the user's emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0421] Output: Parsed user emotional state data.

[0422] Step 5: Delivery Selection and Matching

[0423] Input: Delivery request data, parsed sentiment data, optimal delivery route and means.

[0424] Specific operation: The server integrates these data and selects the optimal delivery company and method taking into account the user's emotional state and priorities.

[0425] Output: Details of the selected carrier and method, along with the resulting total shipping cost.

[0426] Step 6: Submit your proposal

[0427] Input: Selected carrier and method details, total shipping cost.

[0428] Specific operation: The server sends the generated proposal content to the user terminal via an HTTP request.

[0429] Output: The proposal is displayed on the user's terminal.

[0430] Processing on the user terminal side

[0431] Step 1: Enter and submit your request

[0432] Input: User inputs the shipping origin, destination, desired date and time, priority conditions, and emotional data.

[0433] What happens: The user launches the application, enters the required information, and then clicks the submit button to send the request to the server.

[0434] Output: A delivery request is sent to the server.

[0435] Step 2: Review the proposal

[0436] Input: Proposal sent from the server (optimal delivery carrier, delivery route, means, total cost).

[0437] Specific operation: The user uses the interface in the application to review the proposals and compare the details.

[0438] Output: The displayed suggestions.

[0439] Step 3: Select options and finalize

[0440] Input: User's choice based on suggestions (selecting the best option).

[0441] Specific operation: The user selects the most suitable option and clicks the approval button to finalize the delivery request.

[0442] Output: The final decision is sent to the server and delivery is requested.

[0443] Processing on the delivery company terminal

[0444] Step 1: Regular location and status updates

[0445] Input: Current location data and empty cargo status from the delivery company's terminal.

[0446] How it works: Using a dedicated app, delivery agents periodically send their current location and status to a server, using real-time GPS and status updates.

[0447] Output: Latest location and status.

[0448] Step 2: Receive request notification

[0449] Input: A new delivery request sent by the server.

[0450] What happens: The delivery company will be notified and a new delivery request will be displayed.

[0451] Output: The displayed request.

[0452] Step 3: Review the request and accept / reject it

[0453] Input: New shipping request details.

[0454] Specific operation: The delivery company checks the request and decides whether to accept or reject it. If it accepts, it clicks the corresponding button to notify the server.

[0455] Output: The result of acceptance or rejection is sent to the server.

[0456] Step 4: Receive detailed route

[0457] Input: Detailed route information based on accepted requests.

[0458] What happens: Once the request is accepted, the server sends the app detailed delivery route and instructions.

[0459] Output: Start delivery according to the received detailed route information.

[0460] (Application example 2)

[0461] 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."

[0462] Conventional delivery systems select the optimal delivery company and delivery method without considering the user's emotional state, which can lead to low user satisfaction. Furthermore, because delivery request processing is standardized, it can sometimes fail to respond appropriately to urgent requests or requests from users with specific emotional states. To solve these problems, a system is needed that analyzes user emotions and selects the optimal delivery route and method based on emotional data.

[0463] The specification processing by the specification 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 receiving a delivery request, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for analyzing the user's emotional state and selecting the optimal delivery route and means based on that data. This makes it possible to propose optimal delivery based on the user's emotional state, thereby improving user satisfaction.

[0464] A "delivery request" is a request by a user to have an item delivered from a specific location to another location.

[0465] "Delivery Agent" means an entity or its employees that specializes in the delivery of goods.

[0466] "Current Location" means the real-time geographic location of a carrier's vehicle or staff.

[0467] "Transportation status" is information indicating the current stage of the delivery company, and includes states such as "empty," "in transit," and "on break."

[0468] "Past delivery history data" refers to historical information about previous deliveries, including delivery dates and times, routes, required times, delivery costs, and the like.

[0469] "Traffic information" refers to real-time data such as current road conditions, traffic congestion, and accident information.

[0470] An "optimal delivery route" is a route that optimizes conditions such as time and cost between a specified delivery source and a delivery destination.

[0471] "Delivery method" refers to the method or equipment used to deliver goods, including trucks, trains, drones, etc.

[0472] "Total delivery cost" means the total cost of all delivery expenses, including fuel, tolls, and labor costs.

[0473] "Emotional state" refers to the emotional state the user is currently experiencing, such as impatience, anxiety, or satisfaction.

[0474] "Emotion data" is data obtained by analyzing the user's emotional state.

[0475] A "user terminal" is an electronic device used by a user, including a smartphone, tablet, computer, etc.

[0476] "Data transmission means" refers to the technology or method for transmitting data from a user terminal to a server or another terminal.

[0477] "Means of receiving and updating in real time" refers to the ability or technology to continuously receive data and update it immediately within the system.

[0478] A "periodic updating means" is a technique or method for updating data at specific time intervals.

[0479] "Request notification means" refers to a technique or method for notifying the delivery company terminal of a delivery request.

[0480] A "delivery proposal" is a proposal that presents the user with the optimal delivery route and means, as well as the total delivery cost.

[0481] "Analysis tools" are techniques and methods for interpreting collected data and extracting meaningful information.

[0482] This invention is a system that selects the optimal delivery company and delivery means based on user emotion data. The main components are a server, a user terminal, a delivery company terminal, and an emotion engine.

[0483] System Program

[0484] 1. User terminal processing

[0485] The user terminal provides an interface for inputting delivery requests and acquires the user's emotional data. The user inputs information such as the delivery origin, delivery destination, desired date and time, and priorities. The smartphone's camera and microphone are used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine identifies the user's emotional state using OpenFace or the Google® Cloud Natural Language API and sends the data to the server.

[0486] 2. Server Processing

[0487] The server receives delivery requests and emotion data sent from the user's device. The server receives and updates the delivery company's current location and delivery status in real time and stores them in a database. It also performs data analysis based on past delivery history data and current traffic information to calculate the optimal delivery route and means. It also selects the optimal delivery means based on the emotion data provided by the emotion engine and calculates the total delivery cost based on that. Finally, it sends the calculated optimal delivery route and means, as well as the total delivery cost, to the user's device.

[0488] 3. Processing on delivery company terminal

[0489] The delivery company terminal periodically updates its current location and delivery status and sends them to the server. When a new delivery request is received, the server notifies the delivery company, who can then accept or reject the request. If the request is accepted, the delivery company is provided with detailed delivery routes and instructions, and follows those instructions to carry out the delivery.

[0490] Specific examples

[0491] The user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," and the emotion engine analyzes the emotion of impatience. The server calculates the fastest delivery method and proposes a route, for example, "Transport from Tokyo to Nagoya by Truck A, then transport from Nagoya to Osaka by train." The user checks this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it. The delivery company driver sends his current location and available load status to the server, and receives a notification of a new delivery request from the server. After checking the request, he accepts it and begins delivery based on the detailed route information.

[0492] Prompt Sentence Examples

[0493] 1. "If a user is in a rush, please suggest the fastest delivery method. What should I do specifically?"

[0494] 2. "How can I design an algorithm to select the best delivery company, taking into account the user's emotional state?"

[0495] This invention can increase user satisfaction by providing optimal delivery services that take into account the user's emotional state. The server can utilize real-time data to make highly accurate delivery suggestions that reflect the user's emotional state.

[0496] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0497] Step 1:

[0498] The user inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). The smartphone's camera and microphone are also used to capture facial expressions and voice, which are then analyzed by the emotion engine. The input data is sent from the user's device to the server. The emotion engine (e.g., OpenFace, Google Cloud Natural Language API) analyzes the user's facial and voice data to identify their emotional state. The identified emotion data is also sent to the server.

[0499] Input: Delivery origin, delivery destination, desired date and time, priority conditions, facial expression data, voice data

[0500] Output: Emotion data, delivery request data

[0501] Step 2:

[0502] The server receives delivery request data and emotion data. It also receives real-time location information and delivery status from the delivery company terminal and stores them in a database. The server frequently receives GPS data from the delivery company and updates the data.

[0503] Input: Delivery request data, emotion data, GPS data, delivery status

[0504] Output: Updated database

[0505] Step 3:

[0506] The server analyzes past delivery history data and current traffic information to calculate the optimal delivery route and method. It also takes into account emotional data to select the optimal delivery method for a specific user's emotional state. For example, if the user is in a hurry, it will prioritize the fastest route.

[0507] Input: Past delivery history data, current traffic information, emotion data

[0508] Output: Optimal delivery route and means

[0509] Step 4:

[0510] Based on the calculated optimal delivery route and means, the total delivery cost is calculated, including fuel costs, toll fees, labor costs, etc. The server collects all cost data and calculates the total cost.

[0511] Input: Optimal delivery route and mode

[0512] Output: Total shipping cost

[0513] Step 5:

[0514] The server then sends the calculated optimal delivery route and method, as well as the total delivery cost, to the user's device. The user can then review the suggestions in the app, look at the details of each option, and select the best option. Since the suggestions also reflect emotional data, the user can choose the best option for their situation.

[0515] Input: Optimal delivery route and mode, total delivery cost

[0516] Output: Proposal content to user device

[0517] Step 6:

[0518] Once the user has reviewed the suggestions and selected the final delivery option, the server updates the request and notifies the selected delivery company of the request, providing detailed route and instructions. The delivery company then carries out the delivery as instructed.

[0519] Input: Final selected shipping option

[0520] Output: Request notification to delivery company and route information

[0521] Step 7:

[0522] The delivery company terminal periodically updates its current location and delivery status and sends it to the server. When there is a new delivery request, it receives a notification from the server and can accept or reject the request. If the request is accepted, the delivery company will be provided with detailed delivery route and instructions, and will begin delivery according to that route.

[0523] Input: current carrier location, delivery status, new delivery request

[0524] Output: Updated current location and status, delivery route information

[0525] In this way, an optimal delivery service that takes into account the user's emotional state is realized through a series of processes.

[0526] 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.

[0527] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0528] 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.

[0529] [Second embodiment]

[0530] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0531] 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.

[0532] 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).

[0533] 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.

[0534] 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.

[0535] 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).

[0536] 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.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] In the smart glasses 214, 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.

[0541] 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."

[0542] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal.

[0543] Server-side explanation

[0544] 1. Data reception and updating

[0545] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, which keeps the current location, empty load status, and operational status (in transit, on break, etc.) of each truck and delivery vehicle up to date.

[0546] 2. Data Analysis

[0547] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route, as well as the optimal delivery method.

[0548] 3. Matching process

[0549] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "shortest time," "cheapest price"). Based on this information, the server selects the optimal delivery company and delivery method.

[0550] 4. Price Calculation

[0551] The server calculates the total delivery cost based on the selected delivery route and vehicle, taking into account fuel costs, tolls, labor costs, etc.

[0552] 5. Submit your proposal

[0553] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[0554] Explanation on the user terminal side

[0555] 1. Submit a request

[0556] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[0557] 2. Confirmation of proposal contents

[0558] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0559] 3. Final Decision

[0560] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0561] Explanation on the delivery company terminal

[0562] 1. Current location and status transmission

[0563] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0564] 2. Receiving a request notification

[0565] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[0566] 3. Accepting or Rejecting a Request

[0567] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0568] Specific examples

[0569] User Flow

[0570] When a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow" and selects the fastest option, the server calculates the optimal combination of trucks and trains. For example, a route is proposed in which truck A transports goods from Tokyo to Nagoya, and then transports goods from Nagoya to Osaka by train. The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0571] Delivery company process

[0572] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and empty status to the server. He then receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver begins delivery according to that route.

[0573] In this way, the present invention increases delivery efficiency, reduces empty trips, and provides optimal delivery means and routes, thereby reducing overall costs.

[0574] The processing flow will be explained below.

[0575] Server-side processing

[0576] Step 1:

[0577] The server receives GPS data sent from the delivery company's terminal in real time, which allows the current location of each truck or delivery vehicle to be determined.

[0578] Step 2:

[0579] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[0580] Step 3:

[0581] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[0582] Step 4:

[0583] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[0584] Step 5:

[0585] The server executes a matching algorithm to select the optimal delivery company and delivery method based on the received request information. For example, if "fastest" is selected, the server calculates the route and method that will deliver the item in the shortest time.

[0586] Step 6:

[0587] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0588] Step 7:

[0589] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0590] Processing on the user terminal side

[0591] Step 1:

[0592] The user launches the app and inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (such as "fastest," "cheapest," etc.).

[0593] Step 2:

[0594] The user sends the input request information to the server.

[0595] Step 3:

[0596] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[0597] Step 4:

[0598] The user reviews the suggestions and selects the best option, which is then sent to the server.

[0599] Processing on the delivery company terminal

[0600] Step 1:

[0601] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[0602] Step 2:

[0603] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0604] Step 3:

[0605] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0606] Step 4:

[0607] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0608] This allows all stakeholders to share information in real time and execute optimal delivery plans.

[0609] Example 1

[0610] 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."

[0611] In modern logistics systems, managing delivery requests and optimizing delivery routes are key challenges. Traditional systems struggle with real-time updates on delivery company status and effectively utilizing past delivery history and current traffic information to calculate optimal delivery routes. Accurately calculating total delivery costs and providing them in a format that users can easily view are also challenges. This makes it difficult to achieve efficient deliveries, resulting in cost and time waste.

[0612] 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.

[0613] In this invention, the server includes a means for receiving delivery requests, a means for receiving and updating the current location and delivery status of the delivery company in real time, a means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, a means for calculating the total delivery cost based on the calculated delivery route and means, and a means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal. This makes it possible to grasp the current location and operating status of the delivery company in real time and select the optimal delivery route based on past data and current traffic information. Furthermore, by accurately calculating the total delivery cost and quickly providing that information to the user, efficient and effective delivery management is realized.

[0614] A "delivery request" refers to a request by a user to deliver a specific package from a location designated by the user to a location designated by the user.

[0615] "Delivery Provider" means a company or individual that provides services to transport packages from a specified location to a destination.

[0616] "Current Location" refers to the real-time geographic coordinates of the carrier's vehicle (e.g., truck, motorcycle, etc.).

[0617] "Transportation status" refers to information indicating the current business status of the delivery company (e.g., empty, in transit, on break, etc.).

[0618] "Receiving and updating in real time" means that the current location and shipping status of the delivery company are continuously and immediately sent to the server and reflected in the database.

[0619] "Past delivery history data" refers to detailed records of past deliveries made by a delivery company (e.g., delivery route, travel time, cost, etc.).

[0620] "Current traffic information" refers to real-time traffic data such as current road conditions, traffic congestion, and construction information.

[0621] The "means" used in "data analysis" refers to software and algorithms used to perform statistical analysis and machine learning predictive models based on collected data.

[0622] "Optimal delivery route" refers to the most effective delivery route calculated based on factors such as cost, time, and efficiency.

[0623] "Delivery Method" refers to the method used to transport the goods (e.g. truck, train, motorcycle, etc.).

[0624] "Total delivery cost" refers to the total cost calculated based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc.

[0625] "User terminal" refers to a device such as a computer or smartphone used by a user to input delivery requests and confirm proposal contents.

[0626] A "machine learning model" refers to an algorithm that learns from past data and makes predictions and classifications for new data.

[0627] "Data Analysis Library" refers to software tools and frameworks for collecting, processing, analyzing, and visualizing data.

[0628] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format for storing and transferring structured data.

[0629] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery method, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal. Each component and its function are described in detail below.

[0630] Server-side explanation

[0631] 1. Data reception and updating

[0632] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database. Specifically, the delivery company enters their current location and transportation status (empty, in transit, on break, etc.) using a dedicated app and sends this to the server. The server stores this information in a database (e.g., MySQL) and keeps it up to date.

[0633] 2. Data Analysis

[0634] The server performs data analysis based on the received current location data, past delivery history data, and traffic information. Specifically, it uses Python and data analysis libraries (e.g., Pandas and NumPy) to calculate the average time and cost required for each route and determine the optimal delivery method. Based on the results of this analysis, it uses machine learning models (e.g., scikit-learn) to optimize delivery routes.

[0635] 3. Matching process

[0636] The server receives a delivery request sent by the user. This request includes the delivery origin, delivery destination, desired date and time, and priority conditions (shortest time, cheapest price, etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[0637] 4. Price Calculation

[0638] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. Detailed cost calculations are performed using a Python calculation module.

[0639] 5. Submit your proposal

[0640] The server sends the calculation results to the user's device and displays the proposed details (delivery company, route, means, price). A dedicated mobile app (e.g., developed with React Native) is installed on the user's device, which displays the latest information in real time.

[0641] Explanation on the user terminal side

[0642] 1. Submit a request

[0643] The user inputs a delivery request using a dedicated app, setting the origin, destination, desired date and time, and priority conditions (fastest, cheapest, etc.), and then sends this information to the server.

[0644] 2. Confirmation of proposal contents

[0645] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0646] 3. Final Decision

[0647] The user selects the most suitable option from the presented options and requests delivery.

[0648] Explanation on the delivery company terminal

[0649] 1. Current location and status transmission

[0650] Delivery companies use a dedicated app to periodically send their current location and status information to a server.

[0651] 2. Receiving a request notification

[0652] The delivery company receives a new delivery request from the server and checks the request details (package details, delivery route, reward, etc.) displayed in the app.

[0653] 3. Accepting or Rejecting a Request

[0654] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0655] Specific examples

[0656] For example, if a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server analyzes the GPS data and traffic information sent by the delivery company to select the optimal delivery company and method. From the analysis results, it is determined that the optimal route is to transport the item from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka, with a total cost calculated to be 10,000 yen. The user receives this proposal and accepts it after checking it. On the delivery company side, the driver of Truck A receives this information, accepts the request, and makes the delivery according to the route information provided by the server.

[0657] Example prompts for a Generative AI model

[0658] Prompt: "Delivery from Tokyo to Osaka by 4pm tomorrow. Please suggest the fastest option."

[0659] Expected output: "The fastest route is to transport from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka. The total delivery cost is 10,000 yen."

[0660] In this way, the present invention increases delivery efficiency, reduces empty runs, and provides optimal delivery means and routes, thereby achieving overall cost reduction.

[0661] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0662] Step 1: Receive and update data

[0663] Description: The server receives and updates GPS data and status information (empty, in transit, on break, etc.) from the delivery company's terminal in real time. The delivery company periodically inputs and sends the current location and status using a dedicated app.

[0664] Input: GPS data (latitude, longitude) sent from the delivery company terminal, status information

[0665] Data processing and calculation: The server saves the received data in a database (e.g., MySQL) in real time. It compares it with existing data to identify items that need to be updated and saves them as the latest information.

[0666] Output: The latest carrier location and status are stored in the database.

[0667] Specific operation: When the data "Latitude: 35.6895, Longitude: 139.6917, Status: Empty" is sent from the delivery company terminal, the server receives this and updates the database as "Delivery company A: Latitude 35.6895, Longitude 139.6917, Status: Empty."

[0668] Step 2: Data analysis

[0669] Description: The server performs data analysis using past delivery history data and current traffic information to calculate the average travel time and cost for each route.

[0670] Input: Past delivery history data stored in the database, real-time traffic information

[0671] Data processing and calculation: Using Python and data analysis libraries (e.g., Pandas, NumPy), data is aggregated and the average travel time, cost, and traffic patterns for each route are analyzed. The results are used to update the predictive model and derive the optimal delivery route.

[0672] Output: Average travel time and cost information for each route

[0673] Specific operation: Imports past delivery data, generates analysis results such as "average time required: 3 hours, average cost: 5,000 yen," and saves them in a database.

[0674] Step 3: Matching

[0675] Description: The server receives a delivery request from the user terminal and selects the optimal delivery company and delivery method based on this.

[0676] Input: Delivery request from user terminal (delivery origin, delivery destination, desired date and time, priority conditions)

[0677] Data processing and calculation: Based on the received request, the current location, status, historical data, and traffic information in the database are collated, and the most suitable company and means are evaluated and selected using a machine learning model (e.g., scikit-learn).

[0678] Output: Selected optimal delivery company and method

[0679] Specific operation: If a user sends a request saying, "I want delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server determines that Truck A is the best option and selects the route as "Take Truck A from Tokyo to Nagoya, then take the train from Nagoya to Osaka."

[0680] Step 4: Price Calculation

[0681] Description: The server calculates the total delivery cost based on the selected delivery route and means.

[0682] Input: Selected delivery route and delivery method

[0683] Data processing and calculation: Using a Python calculation module, fuel costs, tolls, labor costs, etc. are calculated to calculate the total delivery cost. Each cost element is tallied and the total value is calculated.

[0684] Output: Total shipping cost

[0685] Specific operation: For example, based on the selected route, the total fuel cost is calculated as 3,000 yen, tolls as 2,000 yen, and labor costs as 5,000 yen, and the total delivery cost is calculated as 10,000 yen.

[0686] Step 5: Submit your proposal

[0687] Description: The server sends the calculation results to the user's terminal and displays the proposed contents (delivery company, route, means, price) to the user.

[0688] Inputs: Total shipping cost, selected shipping carrier and method

[0689] Data processing and calculation: Proposal content is generated in JSON format and sent to the user's terminal.

[0690] Output: The proposed details (delivery company, route, means, price) are displayed on the user's terminal.

[0691] Specific operation: "Proposal: Truck A, Tokyo to Nagoya, Nagoya to train, total cost 10,000 yen" is displayed on the user's terminal and can be confirmed by the user.

[0692] Step 6: Review and finalize the request

[0693] Description: The user reviews the suggestions sent by the server and selects the best option.

[0694] Input: Proposal from the server

[0695] Data processing and calculation: The user selects one of the options presented, and the selected information is sent back to the server.

[0696] Output: The final delivery request information is sent to the server and processing begins.

[0697] Specific operation: When the user confirms and selects "Proposal: Truck A, total cost 10,000 yen," the selection information is sent to the server and registered as an official delivery request.

[0698] Step 7: Carrier Status Update and Request Acceptance

[0699] Description: Using a dedicated app, the delivery company periodically sends its current location and status information to the server and accepts the request.

[0700] Input: Current location and status information from the delivery company terminal, new delivery request

[0701] Data processing and calculation: Based on the received information, the server notifies the delivery company of a new delivery request, and the delivery company then accepts or rejects the request.

[0702] Output: Information that the carrier has accepted the request, updated current location and status information

[0703] Specific operation: The delivery company sends the data "Current location: Latitude 35.6895, Longitude 139.6917, Status: Empty" to the server and receives a new delivery request "From Tokyo to Osaka, arrive by 4:00 PM tomorrow." When the delivery company accepts the request, the server sends detailed delivery route information to the company's terminal.

[0704] (Application example 1)

[0705] 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."

[0706] With conventional delivery management systems, it takes time to process delivery requests, making it difficult to quickly identify the optimal delivery route and method. It is also difficult to keep the current location and status of the delivery person updated in real time, making it difficult to select the appropriate delivery person, resulting in reduced delivery efficiency. These issues can reduce the reliability of recommendations provided to users and increase overall delivery costs.

[0707] 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.

[0708] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal, means for selecting the optimal delivery company and transportation means based on request data from the user, and means for displaying information on the selected delivery company, transportation means, and cost information to the user. This enables rapid processing of delivery requests, selection of an appropriate delivery company in real time, and proposal of an efficient delivery route.

[0709] A "delivery request" is information in which a user requests that a specific package be delivered to a specified location.

[0710] A "delivery company" is a company or individual that responds to a user's delivery request and transports the package.

[0711] "Current location" refers to latitude and longitude information indicating the real-time location of a delivery company or its transportation vehicle.

[0712] "Transportation status" refers to information indicating the current operating status of a delivery company or its transportation means and the loading status of cargo.

[0713] "Traffic information" refers to data about the current driving environment, including road congestion, accident information, construction information, etc.

[0714] "Delivery route" refers to information indicating the optimal route from the delivery source to the delivery destination.

[0715] "Delivery vehicle" refers to the type of transportation method or vehicle used to transport a package.

[0716] "Total delivery cost" refers to all costs required to make a delivery, including fuel costs, tolls, labor costs, etc.

[0717] A "user terminal" refers to a device such as a computer or smartphone used by a user.

[0718] "Request data" refers to information entered by the user, such as the delivery origin, delivery destination, desired date and time, and priority conditions.

[0719] "Selection" refers to the process of determining the most suitable carrier and transportation method based on specified criteria.

[0720] The "display means" refers to a mechanism for visually presenting selected information to the user.

[0721] "Feedback" refers to the evaluation or opinion provided by the user after delivery is completed.

[0722] "Evaluation data" refers to data that quantifies the performance of delivery companies based on feedback.

[0723] The present invention relates to a system for receiving a delivery request from a food delivery service, selecting the most suitable delivery company and delivery means, and presenting the results to a user. Specific embodiments for carrying out the present invention are described below.

[0724] Overall system configuration

[0725] The system consists of a server, a user terminal, and a delivery company terminal.

[0726] Server Processing

[0727] 1. Data reception and updating

[0728] The server receives real-time GPS data sent from the delivery company's terminal and stores it in a database. This keeps the current location and delivery status of each delivery person up to date. The server uses Python and Flask.

[0729] 2. Data Analysis

[0730] The server analyzes the received data based on the current location data, past delivery history data, and traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Machine learning algorithms and statistical models are used for data analysis.

[0731] 3. Matching process

[0732] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions ("fastest", "cheapest", etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[0733] 4. Price Calculation

[0734] The server calculates the total delivery cost based on the selected delivery route and means, taking into account fuel costs, tolls, labor costs, etc. The calculation is performed using transaction data from the database.

[0735] 5. Submit your proposal

[0736] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[0737] User terminal processing

[0738] 1. Submit a request

[0739] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[0740] 2. Confirmation of proposal contents

[0741] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[0742] 3. Final Decision

[0743] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0744] Delivery company terminal processing

[0745] 1. Current location and status transmission

[0746] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0747] 2. Receiving a request notification

[0748] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[0749] 3. Accepting or Rejecting a Request

[0750] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[0751] Specific examples

[0752] When a user inputs a request such as "I want food delivered from Restaurant A (origin) to my home (destination) in the shortest time possible," and selects the fastest option, the server selects the optimal delivery person (e.g., a delivery person with a car) and presents the total delivery cost of 1,000 yen. The user confirms this on their device and accepts.

[0753] The delivery process involves a delivery person (e.g., ID: 2) sending their current location (latitude 35.6804, longitude 139.7690) and status (empty) to the server. After that, they receive a notification from the server saying, "New delivery request: From Restaurant A to home, arrive in the shortest time possible." After confirming the request, they accept it. The server then provides detailed route information, and the delivery person begins delivery according to that route.

[0754] Prompt Sentence Examples

[0755] User: Wants food delivered from Restaurant A to his / her home in the shortest time possible

[0756] Server: Selecting a delivery person. Please wait...

[0757] Server: Optimal delivery person and total cost proposal completed. Delivery person ID: 2, Cost: 1000 yen, Estimated time: 30 minutes

[0758] The above is a specific description of an embodiment of the present invention, which allows users to receive a fast and optimal delivery service and enables delivery companies to carry out their work efficiently.

[0759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0760] Step 1: Receiving a shipping request

[0761] The user inputs a delivery request through the app. The input includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). This is sent from the user's device to the server. Input data: delivery origin, delivery destination, desired date and time, priority conditions. Output data: delivery request data sent to the server.

[0762] Step 2: Receive and update carrier location and status

[0763] The server receives GPS data sent from delivery company terminals in real time and stores it in a database. This keeps the current location and status (empty, in transit, on break, etc.) of each delivery company up to date. Input data: Delivery company GPS data, status information. Output data: Current location and status information in the updated database.

[0764] Step 3: Data analysis

[0765] The server performs data analysis based on the received current location data, past delivery history data, and current traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Input data: current location data, delivery history data, traffic information. Output data: optimal delivery route and method information.

[0766] Step 4: Selecting a carrier and method

[0767] The server compares and analyzes the delivery request data received from the user with the delivery company information in the database to select the optimal delivery company and delivery method. Input data: delivery request data, delivery company data. Output data: selected optimal delivery company and delivery method.

[0768] Step 5: Cost calculation

[0769] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. The calculated total delivery cost is sent to the user terminal. Input data: Selected delivery route and means. Output data: Total delivery cost.

[0770] Step 6: Submit and review your proposal

[0771] The server sends the calculation results to the user's device and presents suggestions (delivery company, route, means of transport, price). The user checks this information in the app and selects the optimal option. Input data: Calculation results (delivery company, route, means of transport, price). Output data: Suggestions sent to the user's device, content displayed on the user interface.

[0772] Step 7: Final decision

[0773] The user checks the proposals from the server, selects the best option from the presented options, and requests delivery. The selection result is sent to the server, and delivery begins. Input data: User's selection result. Output data: Final decision on delivery request, selection result sent to the server.

[0774] Step 8: Notify the carrier

[0775] The server notifies the selected delivery company's terminal of the new delivery request. The delivery company checks the request details (package details, delivery route, reward, etc.) on the app and accepts or rejects it. Input data: Final decision on delivery request. Output data: Notification to delivery company terminal, display of request details.

[0776] Step 9: Accepting or Rejecting the Request

[0777] If the delivery company accepts the request, it notifies the server. If accepted, the delivery company is provided with detailed delivery route and instructions and begins delivery. Input data: Acceptance or rejection by the delivery company. Output data: Notification to the server, detailed delivery route and instructions.

[0778] Step 10: Delivery Progress and Final Report

[0779] The delivery company updates its current location and delivery status in real time and continues to send them to the server. Once delivery is complete, it sends a final report to the server. Input data: current location data, delivery status, final report. Output data: updated status, confirmation of delivery completion.

[0780] 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.

[0781] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user, and also combines it with an emotion engine that recognizes the user's emotions. The system is composed of a server, a user terminal, a delivery company terminal, and an emotion engine.

[0782] Server-side processing

[0783] 1. Data reception and updating

[0784] The server receives GPS data sent from the delivery company terminal in real time and stores it in a database, which keeps the current location, empty load status, and operation status of each truck and delivery vehicle always up to date.

[0785] 2. Data Analysis

[0786] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route and the optimal delivery method.

[0787] 3. Emotion analysis

[0788] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0789] 4. Matching process

[0790] The server receives delivery requests sent by users. The requests include the sender, destination, desired date and time, and priority conditions. Taking into account the emotion data provided by the emotion engine, the server selects the optimal delivery company and delivery method.

[0791] 5. Price Calculation

[0792] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0793] 6. Submit your proposal

[0794] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0795] Processing on the user terminal side

[0796] 1. Submit a request

[0797] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[0798] 2. Confirmation of proposal contents

[0799] The user checks the proposals sent from the server in the app and selects the best option based on the suggested delivery company, delivery route, method, price information, and sentiment data.

[0800] 3. Final Decision

[0801] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[0802] Processing on the delivery company terminal

[0803] 1. Current location and status transmission

[0804] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[0805] 2. Receiving a request notification

[0806] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0807] 3. Request confirmation and acceptance

[0808] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0809] 4. Receiving detailed route information

[0810] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0811] Emotion engine processing

[0812] 1. Emotional Data Analysis

[0813] The emotion engine analyzes the emotion data provided by the user to determine the user's current emotional state.

[0814] 2. Sending Emotional Data

[0815] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[0816] Specific examples

[0817] User Flow

[0818] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0819] Delivery company process

[0820] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0821] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[0822] The processing flow will be explained below.

[0823] Server-side processing

[0824] Step 1:

[0825] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, allowing the current location of each truck or delivery vehicle to be determined.

[0826] Step 2:

[0827] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[0828] Step 3:

[0829] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[0830] Step 4:

[0831] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0832] Step 5:

[0833] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[0834] Step 6:

[0835] The server runs a matching algorithm to select the optimal delivery company and delivery method based on the received request information and emotion data. For example, if "fastest" is selected and impatience is detected, the server calculates the shortest delivery route and method.

[0836] Step 7:

[0837] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[0838] Step 8:

[0839] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[0840] Processing on the user terminal side

[0841] Step 1:

[0842] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[0843] Step 2:

[0844] The user transmits the input request information and emotion data to the server.

[0845] Step 3:

[0846] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[0847] Step 4:

[0848] The user reviews the suggestions and selects the best option, which is then sent to the server.

[0849] Processing on the delivery company terminal

[0850] Step 1:

[0851] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[0852] Step 2:

[0853] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[0854] Step 3:

[0855] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[0856] Step 4:

[0857] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[0858] Emotion engine processing

[0859] Step 1:

[0860] The emotion engine receives emotion data provided by the user, for example, by analyzing the user's facial expressions and voice via the smartphone's camera and microphone.

[0861] Step 2:

[0862] The emotion engine analyzes the user's emotional state in real time based on the received data, identifying emotions such as impatience, anxiety, and satisfaction.

[0863] Step 3:

[0864] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[0865] Specific examples

[0866] User Flow

[0867] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0868] Delivery company process

[0869] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0870] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[0871] Example 2

[0872] 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."

[0873] In today's logistics industry, improving delivery efficiency and reducing costs are important challenges. However, conventional systems do not provide sufficient information to select the optimal delivery method and delivery company for each delivery request, and are therefore unable to make specific recommendations that take into account the user's emotions and priorities. Furthermore, issues with the timeliness of delivery company status updates and notifications make it difficult to make real-time decisions, making it difficult to improve overall service quality.

[0874] 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.

[0875] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for receiving and analyzing user emotion data and generating an optimal proposal based on this. This allows for the provision of specific and optimal proposals in response to delivery requests that take into account the user's emotions and priorities, and further allows for real-time updates and notifications of delivery company status, thereby improving overall service quality.

[0876] The "means for receiving a delivery request" is a mechanism for receiving information such as the delivery source, delivery destination, desired date and time, and priority conditions from the user, and transmitting it to the server.

[0877] "Means for receiving and updating the current location and transportation status of delivery companies in real time" refers to a system for transmitting GPS data and operation status provided by delivery companies to a server in real time and updating it.

[0878] "Means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information" refers to algorithms and methods for calculating the optimal route and delivery method based on history data and traffic information.

[0879] The "means for calculating total delivery costs" is a system for calculating the total cost of delivery, taking into account fuel costs, tolls, labor costs, etc.

[0880] "Means for sending to user terminal" refers to an interface or protocol for sending information such as calculation results and optimal delivery routes to the user's device.

[0881] The "means for receiving and analyzing emotional data and generating optimal proposals based on this" is a mechanism for optimizing delivery proposals by acquiring and analyzing the user's emotional information.

[0882] "Means for notifying the delivery company's terminal of new delivery requests and providing a means for the delivery company to accept or reject the request" refers to a system for notifying the delivery company of new delivery requests and confirming whether they are accepted or rejected.

[0883] The "means for displaying on the user terminal and allowing the user to select the proposed content" is an interface that displays the proposed content sent from the server on the user's device and allows the user to select it.

[0884] This invention is a system that receives delivery requests, selects the optimal delivery company and delivery method, and presents them to the user. A distinctive feature is that it generates optimal proposals that take into account the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[0885] System Configuration

[0886] The system consists of the following main components:

[0887] 1. Server

[0888] The server receives GPS data sent from the delivery company terminal in real time and stores it in a PostgreSQL database, which keeps the delivery company's location information, empty load status, and operation status up to date.

[0889] The server uses Python libraries such as Pandas and NumPy to analyze past delivery history data and current traffic information to calculate the optimal delivery route and means, thereby enabling the creation of efficient delivery plans.

[0890] The server also receives and analyzes user emotion data provided by an emotion engine equipped with a machine learning model (e.g., BERT or GPT-3) to identify the user's emotional state, such as impatience, anxiety, or satisfaction.

[0891] The server selects the optimal delivery company and method based on the delivery request from the user, and then calculates the total delivery cost based on the results. The calculation is performed using a price calculation algorithm implemented in JavaScript and Python.

[0892] 2. User Device

[0893] Users input delivery requests through a dedicated application. They set the origin, destination, desired date and time, and preferences (e.g., "fastest," "cheapest"), and then click the submit button to send the request to the server. Emotional data is also provided at the same time. This input form is built using React Native and Flutter.

[0894] The app can then confirm the proposals sent from the server (optimal delivery company, delivery route, method, total cost), and the user can select the optimal option based on the proposals and request delivery.

[0895] 3. Delivery company terminal

[0896] The delivery agents use a dedicated app to periodically update their current location and status and send it to the server, which uses React Native and Kotlin to send location information in real time.

[0897] After receiving a new delivery request and checking its contents, the server either accepts or rejects it. The result is also notified to the server. If the request is accepted, the server receives detailed delivery route information and starts delivery according to the instructions.

[0898] 4. Emotion Engine

[0899] The emotion engine analyzes user-supplied text and voice data to identify the user's emotional state, using text analysis models (e.g., BERT and GPT-3).

[0900] The analysis results are sent to the server, which uses them to generate optimal delivery suggestions that take the user's emotions into account.

[0901] Specific examples

[0902] User Flow

[0903] If a user requests "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option, the emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[0904] Delivery company process

[0905] The driver of Truck A periodically sends his current location (latitude 35.6895, longitude 139.6917) and the available load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[0906] Example prompts

[0907] "Describe a scenario where a user enters a delivery request and selects the fastest option. Show how the emotion engine would detect the user's impatience and send the result to the server."

[0908] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0909] Processing Steps

[0910] Server-side processing

[0911] Step 1: Receiving a shipping request

[0912] Input: Delivery request data from the user terminal (delivery origin, delivery destination, desired date and time, priority conditions).

[0913] Specific operation: The server receives an HTTP request, analyzes its contents, and extracts the necessary information.

[0914] Output: The extracted delivery request data is stored in the server.

[0915] Step 2: Real-time updates on carrier location and status

[0916] Input: Current location data and transportation status data from the delivery company terminal.

[0917] Specific operation: The server receives GPS data and status information sent from the delivery company terminal in real time and updates the database.

[0918] Output: The shipping company information in the database is updated to the latest version.

[0919] Step 3: Data analysis

[0920] Input: Past delivery history data, current traffic information, and delivery company location data.

[0921] Specific operation: The server uses Python libraries (e.g., Pandas, NumPy) to analyze this input data and calculate the optimal delivery route, taking into account traffic information and past historical data to calculate the shortest travel time and lowest cost.

[0922] Output: Optimal delivery route and means.

[0923] Step 4: Sentiment Analysis

[0924] Input: Emotion data from the user device.

[0925] How it works: The server uses machine learning models (e.g., BERT, GPT-3) to analyze the emotion data and identify the user's emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[0926] Output: Parsed user emotional state data.

[0927] Step 5: Delivery Selection and Matching

[0928] Input: Delivery request data, parsed sentiment data, optimal delivery route and means.

[0929] Specific operation: The server integrates these data and selects the optimal delivery company and method taking into account the user's emotional state and priorities.

[0930] Output: Details of the selected carrier and method, along with the resulting total shipping cost.

[0931] Step 6: Submit your proposal

[0932] Input: Selected carrier and method details, total shipping cost.

[0933] Specific operation: The server sends the generated proposal content to the user terminal via an HTTP request.

[0934] Output: The proposal is displayed on the user's terminal.

[0935] Processing on the user terminal side

[0936] Step 1: Enter and submit your request

[0937] Input: User inputs the shipping origin, destination, desired date and time, priority conditions, and emotional data.

[0938] What happens: The user launches the application, enters the required information, and then clicks the submit button to send the request to the server.

[0939] Output: A delivery request is sent to the server.

[0940] Step 2: Review the proposal

[0941] Input: Proposal sent from the server (optimal delivery carrier, delivery route, means, total cost).

[0942] Specific operation: The user uses the interface in the application to review the proposals and compare the details.

[0943] Output: The displayed suggestions.

[0944] Step 3: Select options and finalize

[0945] Input: User's choice based on suggestions (selecting the best option).

[0946] Specific operation: The user selects the most suitable option and clicks the approval button to finalize the delivery request.

[0947] Output: The final decision is sent to the server and delivery is requested.

[0948] Processing on the delivery company terminal

[0949] Step 1: Regular location and status updates

[0950] Input: Current location data and empty cargo status from the delivery company's terminal.

[0951] How it works: Using a dedicated app, delivery agents periodically send their current location and status to a server, using real-time GPS and status updates.

[0952] Output: Latest location and status.

[0953] Step 2: Receive request notification

[0954] Input: A new delivery request sent by the server.

[0955] What happens: The delivery company will be notified and a new delivery request will be displayed.

[0956] Output: The displayed request.

[0957] Step 3: Review the request and accept / reject it

[0958] Input: New shipping request details.

[0959] Specific operation: The delivery company checks the request and decides whether to accept or reject it. If it accepts, it clicks the corresponding button to notify the server.

[0960] Output: The result of acceptance or rejection is sent to the server.

[0961] Step 4: Receive detailed route

[0962] Input: Detailed route information based on accepted requests.

[0963] What happens: Once the request is accepted, the server sends the app detailed delivery route and instructions.

[0964] Output: Start delivery according to the received detailed route information.

[0965] (Application example 2)

[0966] 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."

[0967] Conventional delivery systems select the optimal delivery company and delivery method without considering the user's emotional state, which can lead to low user satisfaction. Furthermore, because delivery request processing is standardized, it can sometimes fail to respond appropriately to urgent requests or requests from users with specific emotional states. To solve these problems, a system is needed that analyzes user emotions and selects the optimal delivery route and method based on emotional data.

[0968] The specification processing by the specification 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 receiving a delivery request, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for analyzing the user's emotional state and selecting the optimal delivery route and means based on that data. This makes it possible to propose optimal delivery based on the user's emotional state, thereby improving user satisfaction.

[0969] A "delivery request" is a request by a user to have an item delivered from a specific location to another location.

[0970] "Delivery Agent" means an entity or its employees that specializes in the delivery of goods.

[0971] "Current Location" means the real-time geographic location of a carrier's vehicle or staff.

[0972] "Transportation status" is information indicating the current stage of the delivery company, and includes states such as "empty," "in transit," and "on break."

[0973] "Past delivery history data" refers to historical information about previous deliveries, including delivery dates and times, routes, required times, delivery costs, and the like.

[0974] "Traffic information" refers to real-time data such as current road conditions, traffic congestion, and accident information.

[0975] An "optimal delivery route" is a route that optimizes conditions such as time and cost between a specified delivery source and a delivery destination.

[0976] "Delivery method" refers to the method or equipment used to deliver goods, including trucks, trains, drones, etc.

[0977] "Total delivery cost" means the total cost of all delivery expenses, including fuel, tolls, and labor costs.

[0978] "Emotional state" refers to the emotional state the user is currently experiencing, such as impatience, anxiety, or satisfaction.

[0979] "Emotion data" is data obtained by analyzing the user's emotional state.

[0980] A "user terminal" is an electronic device used by a user, including a smartphone, tablet, computer, etc.

[0981] "Data transmission means" refers to the technology or method for transmitting data from a user terminal to a server or another terminal.

[0982] "Means of receiving and updating in real time" refers to the ability or technology to continuously receive data and update it immediately within the system.

[0983] A "periodic updating means" is a technique or method for updating data at specific time intervals.

[0984] "Request notification means" refers to a technique or method for notifying the delivery company terminal of a delivery request.

[0985] A "delivery proposal" is a proposal that presents the user with the optimal delivery route and means, as well as the total delivery cost.

[0986] "Analysis tools" are techniques and methods for interpreting collected data and extracting meaningful information.

[0987] This invention is a system that selects the optimal delivery company and delivery means based on user emotion data. The main components are a server, a user terminal, a delivery company terminal, and an emotion engine.

[0988] System Program

[0989] 1. User terminal processing

[0990] The user device provides an interface for inputting delivery requests and acquires the user's emotional data. The user inputs information such as the delivery origin, delivery destination, desired date and time, and priorities. The smartphone's camera and microphone are also used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine uses OpenFace and Google Cloud Natural Language API to identify the user's emotional state and transmits the data to the server.

[0991] 2. Server Processing

[0992] The server receives delivery requests and emotion data sent from the user's device. The server receives and updates the delivery company's current location and delivery status in real time and stores them in a database. It also performs data analysis based on past delivery history data and current traffic information to calculate the optimal delivery route and means. It also selects the optimal delivery means based on the emotion data provided by the emotion engine and calculates the total delivery cost based on that. Finally, it sends the calculated optimal delivery route and means, as well as the total delivery cost, to the user's device.

[0993] 3. Processing on delivery company terminal

[0994] The delivery company terminal periodically updates its current location and delivery status and sends them to the server. When a new delivery request is received, the server notifies the delivery company, who can then accept or reject the request. If the request is accepted, the delivery company is provided with detailed delivery routes and instructions, and follows those instructions to carry out the delivery.

[0995] Specific examples

[0996] The user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," and the emotion engine analyzes the emotion of impatience. The server calculates the fastest delivery method and proposes a route, for example, "Transport from Tokyo to Nagoya by Truck A, then transport from Nagoya to Osaka by train." The user checks this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it. The delivery company driver sends his current location and available load status to the server, and receives a notification of a new delivery request from the server. After checking the request, he accepts it and begins delivery based on the detailed route information.

[0997] Prompt Sentence Examples

[0998] 1. "If a user is in a rush, please suggest the fastest delivery method. What should I do specifically?"

[0999] 2. "How can I design an algorithm to select the best delivery company, taking into account the user's emotional state?"

[1000] This invention can increase user satisfaction by providing optimal delivery services that take into account the user's emotional state. The server can utilize real-time data to make highly accurate delivery suggestions that reflect the user's emotional state.

[1001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1002] Step 1:

[1003] The user inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). The smartphone's camera and microphone are also used to capture facial expressions and voice, which are then analyzed by the emotion engine. The input data is sent from the user's device to the server. The emotion engine (e.g., OpenFace, Google Cloud Natural Language API) analyzes the user's facial and voice data to identify their emotional state. The identified emotion data is also sent to the server.

[1004] Input: Delivery origin, delivery destination, desired date and time, priority conditions, facial expression data, voice data

[1005] Output: Emotion data, delivery request data

[1006] Step 2:

[1007] The server receives delivery request data and emotion data. It also receives real-time location information and delivery status from the delivery company terminal and stores them in a database. The server frequently receives GPS data from the delivery company and updates the data.

[1008] Input: Delivery request data, emotion data, GPS data, delivery status

[1009] Output: Updated database

[1010] Step 3:

[1011] The server analyzes past delivery history data and current traffic information to calculate the optimal delivery route and method. It also takes into account emotional data to select the optimal delivery method for a specific user's emotional state. For example, if the user is in a hurry, it will prioritize the fastest route.

[1012] Input: Past delivery history data, current traffic information, emotion data

[1013] Output: Optimal delivery route and means

[1014] Step 4:

[1015] Based on the calculated optimal delivery route and means, the total delivery cost is calculated, including fuel costs, toll fees, labor costs, etc. The server collects all cost data and calculates the total cost.

[1016] Input: Optimal delivery route and mode

[1017] Output: Total shipping cost

[1018] Step 5:

[1019] The server then sends the calculated optimal delivery route and method, as well as the total delivery cost, to the user's device. The user can then review the suggestions in the app, look at the details of each option, and select the best option. Since the suggestions also reflect emotional data, the user can choose the best option for their situation.

[1020] Input: Optimal delivery route and mode, total delivery cost

[1021] Output: Proposal content to user device

[1022] Step 6:

[1023] Once the user has reviewed the suggestions and selected the final delivery option, the server updates the request and notifies the selected delivery company of the request, providing detailed route and instructions. The delivery company then carries out the delivery as instructed.

[1024] Input: Final selected shipping option

[1025] Output: Request notification to delivery company and route information

[1026] Step 7:

[1027] The delivery company terminal periodically updates its current location and delivery status and sends it to the server. When there is a new delivery request, it receives a notification from the server and can accept or reject the request. If the request is accepted, the delivery company will be provided with detailed delivery route and instructions, and will begin delivery according to that route.

[1028] Input: current carrier location, delivery status, new delivery request

[1029] Output: Updated current location and status, delivery route information

[1030] In this way, an optimal delivery service that takes into account the user's emotional state is realized through a series of processes.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] [Third embodiment]

[1035] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1036] 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.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] 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).

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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."

[1047] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal.

[1048] Server-side explanation

[1049] 1. Data reception and updating

[1050] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, which keeps the current location, empty load status, and operational status (in transit, on break, etc.) of each truck and delivery vehicle up to date.

[1051] 2. Data Analysis

[1052] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route, as well as the optimal delivery method.

[1053] 3. Matching process

[1054] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "shortest time," "cheapest price"). Based on this information, the server selects the optimal delivery company and delivery method.

[1055] 4. Price Calculation

[1056] The server calculates the total delivery cost based on the selected delivery route and vehicle, taking into account fuel costs, tolls, labor costs, etc.

[1057] 5. Submit your proposal

[1058] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[1059] Explanation on the user terminal side

[1060] 1. Submit a request

[1061] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[1062] 2. Confirmation of proposal contents

[1063] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1064] 3. Final Decision

[1065] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1066] Explanation on the delivery company terminal

[1067] 1. Current location and status transmission

[1068] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1069] 2. Receiving a request notification

[1070] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[1071] 3. Accepting or Rejecting a Request

[1072] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1073] Specific examples

[1074] User Flow

[1075] When a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow" and selects the fastest option, the server calculates the optimal combination of trucks and trains. For example, a route is proposed in which truck A transports goods from Tokyo to Nagoya, and then transports goods from Nagoya to Osaka by train. The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1076] Delivery company process

[1077] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and empty status to the server. He then receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver begins delivery according to that route.

[1078] In this way, the present invention increases delivery efficiency, reduces empty trips, and provides optimal delivery means and routes, thereby reducing overall costs.

[1079] The processing flow will be explained below.

[1080] Server-side processing

[1081] Step 1:

[1082] The server receives GPS data sent from the delivery company's terminal in real time, which allows the current location of each truck or delivery vehicle to be determined.

[1083] Step 2:

[1084] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[1085] Step 3:

[1086] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[1087] Step 4:

[1088] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[1089] Step 5:

[1090] The server executes a matching algorithm to select the optimal delivery company and delivery method based on the received request information. For example, if "fastest" is selected, the server calculates the route and method that will deliver the item in the shortest time.

[1091] Step 6:

[1092] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1093] Step 7:

[1094] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1095] Processing on the user terminal side

[1096] Step 1:

[1097] The user launches the app and inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (such as "fastest," "cheapest," etc.).

[1098] Step 2:

[1099] The user sends the input request information to the server.

[1100] Step 3:

[1101] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[1102] Step 4:

[1103] The user reviews the suggestions and selects the best option, which is then sent to the server.

[1104] Processing on the delivery company terminal

[1105] Step 1:

[1106] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[1107] Step 2:

[1108] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1109] Step 3:

[1110] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1111] Step 4:

[1112] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1113] This allows all stakeholders to share information in real time and execute optimal delivery plans.

[1114] Example 1

[1115] 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."

[1116] In modern logistics systems, managing delivery requests and optimizing delivery routes are key challenges. Traditional systems struggle with real-time updates on delivery company status and effectively utilizing past delivery history and current traffic information to calculate optimal delivery routes. Accurately calculating total delivery costs and providing them in a format that users can easily view are also challenges. This makes it difficult to achieve efficient deliveries, resulting in cost and time waste.

[1117] 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.

[1118] In this invention, the server includes a means for receiving delivery requests, a means for receiving and updating the current location and delivery status of the delivery company in real time, a means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, a means for calculating the total delivery cost based on the calculated delivery route and means, and a means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal. This makes it possible to grasp the current location and operating status of the delivery company in real time and select the optimal delivery route based on past data and current traffic information. Furthermore, by accurately calculating the total delivery cost and quickly providing that information to the user, efficient and effective delivery management is realized.

[1119] A "delivery request" refers to a request by a user to deliver a specific package from a location designated by the user to a location designated by the user.

[1120] "Delivery Provider" means a company or individual that provides services to transport packages from a specified location to a destination.

[1121] "Current Location" refers to the real-time geographic coordinates of the carrier's vehicle (e.g., truck, motorcycle, etc.).

[1122] "Transportation status" refers to information indicating the current business status of the delivery company (e.g., empty, in transit, on break, etc.).

[1123] "Receiving and updating in real time" means that the current location and shipping status of the delivery company are continuously and immediately sent to the server and reflected in the database.

[1124] "Past delivery history data" refers to detailed records of past deliveries made by a delivery company (e.g., delivery route, travel time, cost, etc.).

[1125] "Current traffic information" refers to real-time traffic data such as current road conditions, traffic congestion, and construction information.

[1126] The "means" used in "data analysis" refers to software and algorithms used to perform statistical analysis and machine learning predictive models based on collected data.

[1127] "Optimal delivery route" refers to the most effective delivery route calculated based on factors such as cost, time, and efficiency.

[1128] "Delivery Method" refers to the method used to transport the goods (e.g. truck, train, motorcycle, etc.).

[1129] "Total delivery cost" refers to the total cost calculated based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc.

[1130] "User terminal" refers to a device such as a computer or smartphone used by a user to input delivery requests and confirm proposal contents.

[1131] A "machine learning model" refers to an algorithm that learns from past data and makes predictions and classifications for new data.

[1132] "Data Analysis Library" refers to software tools and frameworks for collecting, processing, analyzing, and visualizing data.

[1133] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format for storing and transferring structured data.

[1134] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery method, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal. Each component and its function are described in detail below.

[1135] Server-side explanation

[1136] 1. Data reception and updating

[1137] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database. Specifically, the delivery company enters their current location and transportation status (empty, in transit, on break, etc.) using a dedicated app and sends this to the server. The server stores this information in a database (e.g., MySQL) and keeps it up to date.

[1138] 2. Data Analysis

[1139] The server performs data analysis based on the received current location data, past delivery history data, and traffic information. Specifically, it uses Python and data analysis libraries (e.g., Pandas and NumPy) to calculate the average time and cost required for each route and determine the optimal delivery method. Based on the results of this analysis, it uses machine learning models (e.g., scikit-learn) to optimize delivery routes.

[1140] 3. Matching process

[1141] The server receives a delivery request sent by the user. This request includes the delivery origin, delivery destination, desired date and time, and priority conditions (shortest time, cheapest price, etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[1142] 4. Price Calculation

[1143] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. Detailed cost calculations are performed using a Python calculation module.

[1144] 5. Submit your proposal

[1145] The server sends the calculation results to the user's device and displays the proposed details (delivery company, route, means, price). A dedicated mobile app (e.g., developed with React Native) is installed on the user's device, which displays the latest information in real time.

[1146] Explanation on the user terminal side

[1147] 1. Submit a request

[1148] The user inputs a delivery request using a dedicated app, setting the origin, destination, desired date and time, and priority conditions (fastest, cheapest, etc.), and then sends this information to the server.

[1149] 2. Confirmation of proposal contents

[1150] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1151] 3. Final Decision

[1152] The user selects the most suitable option from the presented options and requests delivery.

[1153] Explanation on the delivery company terminal

[1154] 1. Current location and status transmission

[1155] Delivery companies use a dedicated app to periodically send their current location and status information to a server.

[1156] 2. Receiving a request notification

[1157] The delivery company receives a new delivery request from the server and checks the request details (package details, delivery route, reward, etc.) displayed in the app.

[1158] 3. Accepting or Rejecting a Request

[1159] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1160] Specific examples

[1161] For example, if a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server analyzes the GPS data and traffic information sent by the delivery company to select the optimal delivery company and method. From the analysis results, it is determined that the optimal route is to transport the item from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka, with a total cost calculated to be 10,000 yen. The user receives this proposal and accepts it after checking it. On the delivery company side, the driver of Truck A receives this information, accepts the request, and makes the delivery according to the route information provided by the server.

[1162] Example prompts for a Generative AI model

[1163] Prompt: "Delivery from Tokyo to Osaka by 4pm tomorrow. Please suggest the fastest option."

[1164] Expected output: "The fastest route is to transport from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka. The total delivery cost is 10,000 yen."

[1165] In this way, the present invention increases delivery efficiency, reduces empty runs, and provides optimal delivery means and routes, thereby achieving overall cost reduction.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1: Receive and update data

[1168] Description: The server receives and updates GPS data and status information (empty, in transit, on break, etc.) from the delivery company's terminal in real time. The delivery company periodically inputs and sends the current location and status using a dedicated app.

[1169] Input: GPS data (latitude, longitude) sent from the delivery company terminal, status information

[1170] Data processing and calculation: The server saves the received data in a database (e.g., MySQL) in real time. It compares it with existing data to identify items that need to be updated and saves them as the latest information.

[1171] Output: The latest carrier location and status are stored in the database.

[1172] Specific operation: When the data "Latitude: 35.6895, Longitude: 139.6917, Status: Empty" is sent from the delivery company terminal, the server receives this and updates the database as "Delivery company A: Latitude 35.6895, Longitude 139.6917, Status: Empty."

[1173] Step 2: Data analysis

[1174] Description: The server performs data analysis using past delivery history data and current traffic information to calculate the average travel time and cost for each route.

[1175] Input: Past delivery history data stored in the database, real-time traffic information

[1176] Data processing and calculation: Using Python and data analysis libraries (e.g., Pandas, NumPy), data is aggregated and the average travel time, cost, and traffic patterns for each route are analyzed. The results are used to update the predictive model and derive the optimal delivery route.

[1177] Output: Average travel time and cost information for each route

[1178] Specific operation: Imports past delivery data, generates analysis results such as "average time required: 3 hours, average cost: 5,000 yen," and saves them in a database.

[1179] Step 3: Matching

[1180] Description: The server receives a delivery request from the user terminal and selects the optimal delivery company and delivery method based on this.

[1181] Input: Delivery request from user terminal (delivery origin, delivery destination, desired date and time, priority conditions)

[1182] Data processing and calculation: Based on the received request, the current location, status, historical data, and traffic information in the database are collated, and the most suitable company and means are evaluated and selected using a machine learning model (e.g., scikit-learn).

[1183] Output: Selected optimal delivery company and method

[1184] Specific operation: If a user sends a request saying, "I want delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server determines that Truck A is the best option and selects the route as "Take Truck A from Tokyo to Nagoya, then take the train from Nagoya to Osaka."

[1185] Step 4: Price Calculation

[1186] Description: The server calculates the total delivery cost based on the selected delivery route and means.

[1187] Input: Selected delivery route and delivery method

[1188] Data processing and calculation: Using a Python calculation module, fuel costs, tolls, labor costs, etc. are calculated to calculate the total delivery cost. Each cost element is tallied and the total value is calculated.

[1189] Output: Total shipping cost

[1190] Specific operation: For example, based on the selected route, the total fuel cost is calculated as 3,000 yen, tolls as 2,000 yen, and labor costs as 5,000 yen, and the total delivery cost is calculated as 10,000 yen.

[1191] Step 5: Submit your proposal

[1192] Description: The server sends the calculation results to the user's terminal and displays the proposed contents (delivery company, route, means, price) to the user.

[1193] Inputs: Total shipping cost, selected shipping carrier and method

[1194] Data processing and calculation: Proposal content is generated in JSON format and sent to the user's terminal.

[1195] Output: The proposed details (delivery company, route, means, price) are displayed on the user's terminal.

[1196] Specific operation: "Proposal: Truck A, Tokyo to Nagoya, Nagoya to train, total cost 10,000 yen" is displayed on the user's terminal and can be confirmed by the user.

[1197] Step 6: Review and finalize the request

[1198] Description: The user reviews the suggestions sent by the server and selects the best option.

[1199] Input: Proposal from the server

[1200] Data processing and calculation: The user selects one of the options presented, and the selected information is sent back to the server.

[1201] Output: The final delivery request information is sent to the server and processing begins.

[1202] Specific operation: When the user confirms and selects "Proposal: Truck A, total cost 10,000 yen," the selection information is sent to the server and registered as an official delivery request.

[1203] Step 7: Carrier Status Update and Request Acceptance

[1204] Description: Using a dedicated app, the delivery company periodically sends its current location and status information to the server and accepts the request.

[1205] Input: Current location and status information from the delivery company terminal, new delivery request

[1206] Data processing and calculation: Based on the received information, the server notifies the delivery company of a new delivery request, and the delivery company then accepts or rejects the request.

[1207] Output: Information that the carrier has accepted the request, updated current location and status information

[1208] Specific operation: The delivery company sends the data "Current location: Latitude 35.6895, Longitude 139.6917, Status: Empty" to the server and receives a new delivery request "From Tokyo to Osaka, arrive by 4:00 PM tomorrow." When the delivery company accepts the request, the server sends detailed delivery route information to the company's terminal.

[1209] (Application example 1)

[1210] 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."

[1211] With conventional delivery management systems, it takes time to process delivery requests, making it difficult to quickly identify the optimal delivery route and method. It is also difficult to keep the current location and status of the delivery person updated in real time, making it difficult to select the appropriate delivery person, resulting in reduced delivery efficiency. These issues can reduce the reliability of recommendations provided to users and increase overall delivery costs.

[1212] 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.

[1213] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal, means for selecting the optimal delivery company and transportation means based on request data from the user, and means for displaying information on the selected delivery company, transportation means, and cost information to the user. This enables rapid processing of delivery requests, selection of an appropriate delivery company in real time, and proposal of an efficient delivery route.

[1214] A "delivery request" is information in which a user requests that a specific package be delivered to a specified location.

[1215] A "delivery company" is a company or individual that responds to a user's delivery request and transports the package.

[1216] "Current location" refers to latitude and longitude information indicating the real-time location of a delivery company or its transportation vehicle.

[1217] "Transportation status" refers to information indicating the current operating status of a delivery company or its transportation means and the loading status of cargo.

[1218] "Traffic information" refers to data about the current driving environment, including road congestion, accident information, construction information, etc.

[1219] "Delivery route" refers to information indicating the optimal route from the delivery source to the delivery destination.

[1220] "Delivery vehicle" refers to the type of transportation method or vehicle used to transport a package.

[1221] "Total delivery cost" refers to all costs required to make a delivery, including fuel costs, tolls, labor costs, etc.

[1222] A "user terminal" refers to a device such as a computer or smartphone used by a user.

[1223] "Request data" refers to information entered by the user, such as the delivery origin, delivery destination, desired date and time, and priority conditions.

[1224] "Selection" refers to the process of determining the most suitable carrier and transportation method based on specified criteria.

[1225] The "display means" refers to a mechanism for visually presenting selected information to the user.

[1226] "Feedback" refers to the evaluation or opinion provided by the user after delivery is completed.

[1227] "Evaluation data" refers to data that quantifies the performance of delivery companies based on feedback.

[1228] The present invention relates to a system for receiving a delivery request from a food delivery service, selecting the most suitable delivery company and delivery means, and presenting the results to a user. Specific embodiments for carrying out the present invention are described below.

[1229] Overall system configuration

[1230] The system consists of a server, a user terminal, and a delivery company terminal.

[1231] Server Processing

[1232] 1. Data reception and updating

[1233] The server receives real-time GPS data sent from the delivery company's terminal and stores it in a database. This keeps the current location and delivery status of each delivery person up to date. The server uses Python and Flask.

[1234] 2. Data Analysis

[1235] The server analyzes the received data based on the current location data, past delivery history data, and traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Machine learning algorithms and statistical models are used for data analysis.

[1236] 3. Matching process

[1237] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions ("fastest", "cheapest", etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[1238] 4. Price Calculation

[1239] The server calculates the total delivery cost based on the selected delivery route and means, taking into account fuel costs, tolls, labor costs, etc. The calculation is performed using transaction data from the database.

[1240] 5. Submit your proposal

[1241] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[1242] User terminal processing

[1243] 1. Submit a request

[1244] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[1245] 2. Confirmation of proposal contents

[1246] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1247] 3. Final Decision

[1248] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1249] Delivery company terminal processing

[1250] 1. Current location and status transmission

[1251] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1252] 2. Receiving a request notification

[1253] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[1254] 3. Accepting or Rejecting a Request

[1255] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1256] Specific examples

[1257] When a user inputs a request such as "I want food delivered from Restaurant A (origin) to my home (destination) in the shortest time possible," and selects the fastest option, the server selects the optimal delivery person (e.g., a delivery person with a car) and presents the total delivery cost of 1,000 yen. The user confirms this on their device and accepts.

[1258] The delivery process involves a delivery person (e.g., ID: 2) sending their current location (latitude 35.6804, longitude 139.7690) and status (empty) to the server. After that, they receive a notification from the server saying, "New delivery request: From Restaurant A to home, arrive in the shortest time possible." After confirming the request, they accept it. The server then provides detailed route information, and the delivery person begins delivery according to that route.

[1259] Prompt Sentence Examples

[1260] User: Wants food delivered from Restaurant A to his / her home in the shortest time possible

[1261] Server: Selecting a delivery person. Please wait...

[1262] Server: Optimal delivery person and total cost proposal completed. Delivery person ID: 2, Cost: 1000 yen, Estimated time: 30 minutes

[1263] The above is a specific description of an embodiment of the present invention, which allows users to receive a fast and optimal delivery service and enables delivery companies to carry out their work efficiently.

[1264] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1265] Step 1: Receiving a shipping request

[1266] The user inputs a delivery request through the app. The input includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). This is sent from the user's device to the server. Input data: delivery origin, delivery destination, desired date and time, priority conditions. Output data: delivery request data sent to the server.

[1267] Step 2: Receive and update carrier location and status

[1268] The server receives GPS data sent from delivery company terminals in real time and stores it in a database. This keeps the current location and status (empty, in transit, on break, etc.) of each delivery company up to date. Input data: Delivery company GPS data, status information. Output data: Current location and status information in the updated database.

[1269] Step 3: Data analysis

[1270] The server performs data analysis based on the received current location data, past delivery history data, and current traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Input data: current location data, delivery history data, traffic information. Output data: optimal delivery route and method information.

[1271] Step 4: Selecting a carrier and method

[1272] The server compares and analyzes the delivery request data received from the user with the delivery company information in the database to select the optimal delivery company and delivery method. Input data: delivery request data, delivery company data. Output data: selected optimal delivery company and delivery method.

[1273] Step 5: Cost calculation

[1274] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. The calculated total delivery cost is sent to the user terminal. Input data: Selected delivery route and means. Output data: Total delivery cost.

[1275] Step 6: Submit and review your proposal

[1276] The server sends the calculation results to the user's device and presents suggestions (delivery company, route, means of transport, price). The user checks this information in the app and selects the optimal option. Input data: Calculation results (delivery company, route, means of transport, price). Output data: Suggestions sent to the user's device, content displayed on the user interface.

[1277] Step 7: Final decision

[1278] The user checks the proposals from the server, selects the best option from the presented options, and requests delivery. The selection result is sent to the server, and delivery begins. Input data: User's selection result. Output data: Final decision on delivery request, selection result sent to the server.

[1279] Step 8: Notify the carrier

[1280] The server notifies the selected delivery company's terminal of the new delivery request. The delivery company checks the request details (package details, delivery route, reward, etc.) on the app and accepts or rejects it. Input data: Final decision on delivery request. Output data: Notification to delivery company terminal, display of request details.

[1281] Step 9: Accepting or Rejecting the Request

[1282] If the delivery company accepts the request, it notifies the server. If accepted, the delivery company is provided with detailed delivery route and instructions and begins delivery. Input data: Acceptance or rejection by the delivery company. Output data: Notification to the server, detailed delivery route and instructions.

[1283] Step 10: Delivery Progress and Final Report

[1284] The delivery company updates its current location and delivery status in real time and continues to send them to the server. Once delivery is complete, it sends a final report to the server. Input data: current location data, delivery status, final report. Output data: updated status, confirmation of delivery completion.

[1285] 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.

[1286] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user, and also combines it with an emotion engine that recognizes the user's emotions. The system is composed of a server, a user terminal, a delivery company terminal, and an emotion engine.

[1287] Server-side processing

[1288] 1. Data reception and updating

[1289] The server receives GPS data sent from the delivery company terminal in real time and stores it in a database, which keeps the current location, empty load status, and operation status of each truck and delivery vehicle always up to date.

[1290] 2. Data Analysis

[1291] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route and the optimal delivery method.

[1292] 3. Emotion analysis

[1293] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[1294] 4. Matching process

[1295] The server receives delivery requests sent by users. The requests include the sender, destination, desired date and time, and priority conditions. Taking into account the emotion data provided by the emotion engine, the server selects the optimal delivery company and delivery method.

[1296] 5. Price Calculation

[1297] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1298] 6. Submit your proposal

[1299] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1300] Processing on the user terminal side

[1301] 1. Submit a request

[1302] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[1303] 2. Confirmation of proposal contents

[1304] The user checks the proposals sent from the server in the app and selects the best option based on the suggested delivery company, delivery route, method, price information, and sentiment data.

[1305] 3. Final Decision

[1306] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1307] Processing on the delivery company terminal

[1308] 1. Current location and status transmission

[1309] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1310] 2. Receiving a request notification

[1311] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1312] 3. Request confirmation and acceptance

[1313] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1314] 4. Receiving detailed route information

[1315] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1316] Emotion engine processing

[1317] 1. Emotional Data Analysis

[1318] The emotion engine analyzes the emotion data provided by the user to determine the user's current emotional state.

[1319] 2. Sending Emotional Data

[1320] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[1321] Specific examples

[1322] User Flow

[1323] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1324] Delivery company process

[1325] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[1326] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[1327] The processing flow will be explained below.

[1328] Server-side processing

[1329] Step 1:

[1330] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, allowing the current location of each truck or delivery vehicle to be determined.

[1331] Step 2:

[1332] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[1333] Step 3:

[1334] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[1335] Step 4:

[1336] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[1337] Step 5:

[1338] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[1339] Step 6:

[1340] The server runs a matching algorithm to select the optimal delivery company and delivery method based on the received request information and emotion data. For example, if "fastest" is selected and impatience is detected, the server calculates the shortest delivery route and method.

[1341] Step 7:

[1342] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1343] Step 8:

[1344] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1345] Processing on the user terminal side

[1346] Step 1:

[1347] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[1348] Step 2:

[1349] The user transmits the input request information and emotion data to the server.

[1350] Step 3:

[1351] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[1352] Step 4:

[1353] The user reviews the suggestions and selects the best option, which is then sent to the server.

[1354] Processing on the delivery company terminal

[1355] Step 1:

[1356] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[1357] Step 2:

[1358] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1359] Step 3:

[1360] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1361] Step 4:

[1362] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1363] Emotion engine processing

[1364] Step 1:

[1365] The emotion engine receives emotion data provided by the user, for example, by analyzing the user's facial expressions and voice via the smartphone's camera and microphone.

[1366] Step 2:

[1367] The emotion engine analyzes the user's emotional state in real time based on the received data, identifying emotions such as impatience, anxiety, and satisfaction.

[1368] Step 3:

[1369] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[1370] Specific examples

[1371] User Flow

[1372] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1373] Delivery company process

[1374] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[1375] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[1376] Example 2

[1377] 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."

[1378] In today's logistics industry, improving delivery efficiency and reducing costs are important challenges. However, conventional systems do not provide sufficient information to select the optimal delivery method and delivery company for each delivery request, and are therefore unable to make specific recommendations that take into account the user's emotions and priorities. Furthermore, issues with the timeliness of delivery company status updates and notifications make it difficult to make real-time decisions, making it difficult to improve overall service quality.

[1379] 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.

[1380] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for receiving and analyzing user emotion data and generating an optimal proposal based on this. This allows for the provision of specific and optimal proposals in response to delivery requests that take into account the user's emotions and priorities, and further allows for real-time updates and notifications of delivery company status, thereby improving overall service quality.

[1381] The "means for receiving a delivery request" is a mechanism for receiving information such as the delivery source, delivery destination, desired date and time, and priority conditions from the user, and transmitting it to the server.

[1382] "Means for receiving and updating the current location and transportation status of delivery companies in real time" refers to a system for transmitting GPS data and operation status provided by delivery companies to a server in real time and updating it.

[1383] "Means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information" refers to algorithms and methods for calculating the optimal route and delivery method based on history data and traffic information.

[1384] The "means for calculating total delivery costs" is a system for calculating the total cost of delivery, taking into account fuel costs, tolls, labor costs, etc.

[1385] "Means for sending to user terminal" refers to an interface or protocol for sending information such as calculation results and optimal delivery routes to the user's device.

[1386] The "means for receiving and analyzing emotional data and generating optimal proposals based on this" is a mechanism for optimizing delivery proposals by acquiring and analyzing the user's emotional information.

[1387] "Means for notifying the delivery company's terminal of new delivery requests and providing a means for the delivery company to accept or reject the request" refers to a system for notifying the delivery company of new delivery requests and confirming whether they are accepted or rejected.

[1388] The "means for displaying on the user terminal and allowing the user to select the proposed content" is an interface that displays the proposed content sent from the server on the user's device and allows the user to select it.

[1389] This invention is a system that receives delivery requests, selects the optimal delivery company and delivery method, and presents them to the user. A distinctive feature is that it generates optimal proposals that take into account the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1390] System Configuration

[1391] The system consists of the following main components:

[1392] 1. Server

[1393] The server receives GPS data sent from the delivery company terminal in real time and stores it in a PostgreSQL database, which keeps the delivery company's location information, empty load status, and operation status up to date.

[1394] The server uses Python libraries such as Pandas and NumPy to analyze past delivery history data and current traffic information to calculate the optimal delivery route and means, thereby enabling the creation of efficient delivery plans.

[1395] The server also receives and analyzes user emotion data provided by an emotion engine equipped with a machine learning model (e.g., BERT or GPT-3) to identify the user's emotional state, such as impatience, anxiety, or satisfaction.

[1396] The server selects the optimal delivery company and method based on the delivery request from the user, and then calculates the total delivery cost based on the results. The calculation is performed using a price calculation algorithm implemented in JavaScript and Python.

[1397] 2. User Device

[1398] Users input delivery requests through a dedicated application. They set the origin, destination, desired date and time, and preferences (e.g., "fastest," "cheapest"), and then click the submit button to send the request to the server. Emotional data is also provided at the same time. This input form is built using React Native and Flutter.

[1399] The app can then confirm the proposals sent from the server (optimal delivery company, delivery route, method, total cost), and the user can select the optimal option based on the proposals and request delivery.

[1400] 3. Delivery company terminal

[1401] The delivery agents use a dedicated app to periodically update their current location and status and send it to the server, which uses React Native and Kotlin to send location information in real time.

[1402] After receiving a new delivery request and checking its contents, the server either accepts or rejects it. The result is also notified to the server. If the request is accepted, the server receives detailed delivery route information and starts delivery according to the instructions.

[1403] 4. Emotion Engine

[1404] The emotion engine analyzes user-supplied text and voice data to identify the user's emotional state, using text analysis models (e.g., BERT and GPT-3).

[1405] The analysis results are sent to the server, which uses them to generate optimal delivery suggestions that take the user's emotions into account.

[1406] Specific examples

[1407] User Flow

[1408] If a user requests "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option, the emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1409] Delivery company process

[1410] The driver of Truck A periodically sends his current location (latitude 35.6895, longitude 139.6917) and the available load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[1411] Example prompts

[1412] "Describe a scenario where a user enters a delivery request and selects the fastest option. Show how the emotion engine would detect the user's impatience and send the result to the server."

[1413] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1414] Processing Steps

[1415] Server-side processing

[1416] Step 1: Receiving a shipping request

[1417] Input: Delivery request data from the user terminal (delivery origin, delivery destination, desired date and time, priority conditions).

[1418] Specific operation: The server receives an HTTP request, analyzes its contents, and extracts the necessary information.

[1419] Output: The extracted delivery request data is stored in the server.

[1420] Step 2: Real-time updates on carrier location and status

[1421] Input: Current location data and transportation status data from the delivery company terminal.

[1422] Specific operation: The server receives GPS data and status information sent from the delivery company terminal in real time and updates the database.

[1423] Output: The shipping company information in the database is updated to the latest version.

[1424] Step 3: Data analysis

[1425] Input: Past delivery history data, current traffic information, and delivery company location data.

[1426] Specific operation: The server uses Python libraries (e.g., Pandas, NumPy) to analyze this input data and calculate the optimal delivery route, taking into account traffic information and past historical data to calculate the shortest travel time and lowest cost.

[1427] Output: Optimal delivery route and means.

[1428] Step 4: Sentiment Analysis

[1429] Input: Emotion data from the user device.

[1430] How it works: The server uses machine learning models (e.g., BERT, GPT-3) to analyze the emotion data and identify the user's emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[1431] Output: Parsed user emotional state data.

[1432] Step 5: Delivery Selection and Matching

[1433] Input: Delivery request data, parsed sentiment data, optimal delivery route and means.

[1434] Specific operation: The server integrates these data and selects the optimal delivery company and method taking into account the user's emotional state and priorities.

[1435] Output: Details of the selected carrier and method, along with the resulting total shipping cost.

[1436] Step 6: Submit your proposal

[1437] Input: Selected carrier and method details, total shipping cost.

[1438] Specific operation: The server sends the generated proposal content to the user terminal via an HTTP request.

[1439] Output: The proposal is displayed on the user's terminal.

[1440] Processing on the user terminal side

[1441] Step 1: Enter and submit your request

[1442] Input: User inputs the shipping origin, destination, desired date and time, priority conditions, and emotional data.

[1443] What happens: The user launches the application, enters the required information, and then clicks the submit button to send the request to the server.

[1444] Output: A delivery request is sent to the server.

[1445] Step 2: Review the proposal

[1446] Input: Proposal sent from the server (optimal delivery carrier, delivery route, means, total cost).

[1447] Specific operation: The user uses the interface in the application to review the proposals and compare the details.

[1448] Output: The displayed suggestions.

[1449] Step 3: Select options and finalize

[1450] Input: User's choice based on suggestions (selecting the best option).

[1451] Specific operation: The user selects the most suitable option and clicks the approval button to finalize the delivery request.

[1452] Output: The final decision is sent to the server and delivery is requested.

[1453] Processing on the delivery company terminal

[1454] Step 1: Regular location and status updates

[1455] Input: Current location data and empty cargo status from the delivery company's terminal.

[1456] How it works: Using a dedicated app, delivery agents periodically send their current location and status to a server, using real-time GPS and status updates.

[1457] Output: Latest location and status.

[1458] Step 2: Receive request notification

[1459] Input: A new delivery request sent by the server.

[1460] What happens: The delivery company will be notified and a new delivery request will be displayed.

[1461] Output: The displayed request.

[1462] Step 3: Review the request and accept / reject it

[1463] Input: New shipping request details.

[1464] Specific operation: The delivery company checks the request and decides whether to accept or reject it. If it accepts, it clicks the corresponding button to notify the server.

[1465] Output: The result of acceptance or rejection is sent to the server.

[1466] Step 4: Receive detailed route

[1467] Input: Detailed route information based on accepted requests.

[1468] What happens: Once the request is accepted, the server sends the app detailed delivery route and instructions.

[1469] Output: Start delivery according to the received detailed route information.

[1470] (Application example 2)

[1471] 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."

[1472] Conventional delivery systems select the optimal delivery company and delivery method without considering the user's emotional state, which can lead to low user satisfaction. Furthermore, because delivery request processing is standardized, it can sometimes fail to respond appropriately to urgent requests or requests from users with specific emotional states. To solve these problems, a system is needed that analyzes user emotions and selects the optimal delivery route and method based on emotional data.

[1473] The specification processing by the specification 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 receiving a delivery request, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for analyzing the user's emotional state and selecting the optimal delivery route and means based on that data. This makes it possible to propose optimal delivery based on the user's emotional state, thereby improving user satisfaction.

[1474] A "delivery request" is a request by a user to have an item delivered from a specific location to another location.

[1475] "Delivery Agent" means an entity or its employees that specializes in the delivery of goods.

[1476] "Current Location" means the real-time geographic location of a carrier's vehicle or staff.

[1477] "Transportation status" is information indicating the current stage of the delivery company, and includes states such as "empty," "in transit," and "on break."

[1478] "Past delivery history data" refers to historical information about previous deliveries, including delivery dates and times, routes, required times, delivery costs, and the like.

[1479] "Traffic information" refers to real-time data such as current road conditions, traffic congestion, and accident information.

[1480] An "optimal delivery route" is a route that optimizes conditions such as time and cost between a specified delivery source and a delivery destination.

[1481] "Delivery method" refers to the method or equipment used to deliver goods, including trucks, trains, drones, etc.

[1482] "Total delivery cost" means the total cost of all delivery expenses, including fuel, tolls, and labor costs.

[1483] "Emotional state" refers to the emotional state the user is currently experiencing, such as impatience, anxiety, or satisfaction.

[1484] "Emotion data" is data obtained by analyzing the user's emotional state.

[1485] A "user terminal" is an electronic device used by a user, including a smartphone, tablet, computer, etc.

[1486] "Data transmission means" refers to the technology or method for transmitting data from a user terminal to a server or another terminal.

[1487] "Means of receiving and updating in real time" refers to the ability or technology to continuously receive data and update it immediately within the system.

[1488] A "periodic updating means" is a technique or method for updating data at specific time intervals.

[1489] "Request notification means" refers to a technique or method for notifying the delivery company terminal of a delivery request.

[1490] A "delivery proposal" is a proposal that presents the user with the optimal delivery route and means, as well as the total delivery cost.

[1491] "Analysis tools" are techniques and methods for interpreting collected data and extracting meaningful information.

[1492] This invention is a system that selects the optimal delivery company and delivery means based on user emotion data. The main components are a server, a user terminal, a delivery company terminal, and an emotion engine.

[1493] System Program

[1494] 1. User terminal processing

[1495] The user device provides an interface for inputting delivery requests and acquires the user's emotional data. The user inputs information such as the delivery origin, delivery destination, desired date and time, and priorities. The smartphone's camera and microphone are also used to capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine uses OpenFace and Google Cloud Natural Language API to identify the user's emotional state and transmits the data to the server.

[1496] 2. Server Processing

[1497] The server receives delivery requests and emotion data sent from the user's device. The server receives and updates the delivery company's current location and delivery status in real time and stores them in a database. It also performs data analysis based on past delivery history data and current traffic information to calculate the optimal delivery route and means. It also selects the optimal delivery means based on the emotion data provided by the emotion engine and calculates the total delivery cost based on that. Finally, it sends the calculated optimal delivery route and means, as well as the total delivery cost, to the user's device.

[1498] 3. Processing on delivery company terminal

[1499] The delivery company terminal periodically updates its current location and delivery status and sends them to the server. When a new delivery request is received, the server notifies the delivery company, who can then accept or reject the request. If the request is accepted, the delivery company is provided with detailed delivery routes and instructions, and follows those instructions to carry out the delivery.

[1500] Specific examples

[1501] The user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," and the emotion engine analyzes the emotion of impatience. The server calculates the fastest delivery method and proposes a route, for example, "Transport from Tokyo to Nagoya by Truck A, then transport from Nagoya to Osaka by train." The user checks this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it. The delivery company driver sends his current location and available load status to the server, and receives a notification of a new delivery request from the server. After checking the request, he accepts it and begins delivery based on the detailed route information.

[1502] Prompt Sentence Examples

[1503] 1. "If a user is in a rush, please suggest the fastest delivery method. What should I do specifically?"

[1504] 2. "How can I design an algorithm to select the best delivery company, taking into account the user's emotional state?"

[1505] This invention can increase user satisfaction by providing optimal delivery services that take into account the user's emotional state. The server can utilize real-time data to make highly accurate delivery suggestions that reflect the user's emotional state.

[1506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1507] Step 1:

[1508] The user inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). The smartphone's camera and microphone are also used to capture facial expressions and voice, which are then analyzed by the emotion engine. The input data is sent from the user's device to the server. The emotion engine (e.g., OpenFace, Google Cloud Natural Language API) analyzes the user's facial and voice data to identify their emotional state. The identified emotion data is also sent to the server.

[1509] Input: Delivery origin, delivery destination, desired date and time, priority conditions, facial expression data, voice data

[1510] Output: Emotion data, delivery request data

[1511] Step 2:

[1512] The server receives delivery request data and emotion data. It also receives real-time location information and delivery status from the delivery company terminal and stores them in a database. The server frequently receives GPS data from the delivery company and updates the data.

[1513] Input: Delivery request data, emotion data, GPS data, delivery status

[1514] Output: Updated database

[1515] Step 3:

[1516] The server analyzes past delivery history data and current traffic information to calculate the optimal delivery route and method. It also takes into account emotional data to select the optimal delivery method for a specific user's emotional state. For example, if the user is in a hurry, it will prioritize the fastest route.

[1517] Input: Past delivery history data, current traffic information, emotion data

[1518] Output: Optimal delivery route and means

[1519] Step 4:

[1520] Based on the calculated optimal delivery route and means, the total delivery cost is calculated, including fuel costs, toll fees, labor costs, etc. The server collects all cost data and calculates the total cost.

[1521] Input: Optimal delivery route and mode

[1522] Output: Total shipping cost

[1523] Step 5:

[1524] The server then sends the calculated optimal delivery route and method, as well as the total delivery cost, to the user's device. The user can then review the suggestions in the app, look at the details of each option, and select the best option. Since the suggestions also reflect emotional data, the user can choose the best option for their situation.

[1525] Input: Optimal delivery route and mode, total delivery cost

[1526] Output: Proposal content to user device

[1527] Step 6:

[1528] Once the user has reviewed the suggestions and selected the final delivery option, the server updates the request and notifies the selected delivery company of the request, providing detailed route and instructions. The delivery company then carries out the delivery as instructed.

[1529] Input: Final selected shipping option

[1530] Output: Request notification to delivery company and route information

[1531] Step 7:

[1532] The delivery company terminal periodically updates its current location and delivery status and sends it to the server. When there is a new delivery request, it receives a notification from the server and can accept or reject the request. If the request is accepted, the delivery company will be provided with detailed delivery route and instructions, and will begin delivery according to that route.

[1533] Input: current carrier location, delivery status, new delivery request

[1534] Output: Updated current location and status, delivery route information

[1535] In this way, an optimal delivery service that takes into account the user's emotional state is realized through a series of processes.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] [Fourth embodiment]

[1540] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1541] 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.

[1542] 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).

[1543] 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.

[1544] 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.

[1545] 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).

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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."

[1553] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal.

[1554] Server-side explanation

[1555] 1. Data reception and updating

[1556] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, which keeps the current location, empty load status, and operational status (in transit, on break, etc.) of each truck and delivery vehicle up to date.

[1557] 2. Data Analysis

[1558] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route, as well as the optimal delivery method.

[1559] 3. Matching process

[1560] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "shortest time," "cheapest price"). Based on this information, the server selects the optimal delivery company and delivery method.

[1561] 4. Price Calculation

[1562] The server calculates the total delivery cost based on the selected delivery route and vehicle, taking into account fuel costs, tolls, labor costs, etc.

[1563] 5. Submit your proposal

[1564] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[1565] Explanation on the user terminal side

[1566] 1. Submit a request

[1567] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[1568] 2. Confirmation of proposal contents

[1569] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1570] 3. Final Decision

[1571] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1572] Explanation on the delivery company terminal

[1573] 1. Current location and status transmission

[1574] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1575] 2. Receiving a request notification

[1576] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[1577] 3. Accepting or Rejecting a Request

[1578] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1579] Specific examples

[1580] User Flow

[1581] When a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow" and selects the fastest option, the server calculates the optimal combination of trucks and trains. For example, a route is proposed in which truck A transports goods from Tokyo to Nagoya, and then transports goods from Nagoya to Osaka by train. The user reviews this proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1582] Delivery company process

[1583] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and empty status to the server. He then receives a notification from the server saying "New delivery request: Tokyo to Osaka, arrive by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver begins delivery according to that route.

[1584] In this way, the present invention increases delivery efficiency, reduces empty trips, and provides optimal delivery means and routes, thereby reducing overall costs.

[1585] The processing flow will be explained below.

[1586] Server-side processing

[1587] Step 1:

[1588] The server receives GPS data sent from the delivery company's terminal in real time, which allows the current location of each truck or delivery vehicle to be determined.

[1589] Step 2:

[1590] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[1591] Step 3:

[1592] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[1593] Step 4:

[1594] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[1595] Step 5:

[1596] The server executes a matching algorithm to select the optimal delivery company and delivery method based on the received request information. For example, if "fastest" is selected, the server calculates the route and method that will deliver the item in the shortest time.

[1597] Step 6:

[1598] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1599] Step 7:

[1600] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1601] Processing on the user terminal side

[1602] Step 1:

[1603] The user launches the app and inputs a delivery request, specifying the delivery origin, delivery destination, desired date and time, and priority conditions (such as "fastest," "cheapest," etc.).

[1604] Step 2:

[1605] The user sends the input request information to the server.

[1606] Step 3:

[1607] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[1608] Step 4:

[1609] The user reviews the suggestions and selects the best option, which is then sent to the server.

[1610] Processing on the delivery company terminal

[1611] Step 1:

[1612] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[1613] Step 2:

[1614] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1615] Step 3:

[1616] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1617] Step 4:

[1618] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1619] This allows all stakeholders to share information in real time and execute optimal delivery plans.

[1620] Example 1

[1621] 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."

[1622] In modern logistics systems, managing delivery requests and optimizing delivery routes are key challenges. Traditional systems struggle with real-time updates on delivery company status and effectively utilizing past delivery history and current traffic information to calculate optimal delivery routes. Accurately calculating total delivery costs and providing them in a format that users can easily view are also challenges. This makes it difficult to achieve efficient deliveries, resulting in cost and time waste.

[1623] 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.

[1624] In this invention, the server includes a means for receiving delivery requests, a means for receiving and updating the current location and delivery status of the delivery company in real time, a means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, a means for calculating the total delivery cost based on the calculated delivery route and means, and a means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal. This makes it possible to grasp the current location and operating status of the delivery company in real time and select the optimal delivery route based on past data and current traffic information. Furthermore, by accurately calculating the total delivery cost and quickly providing that information to the user, efficient and effective delivery management is realized.

[1625] A "delivery request" refers to a request by a user to deliver a specific package from a location designated by the user to a location designated by the user.

[1626] "Delivery Provider" means a company or individual that provides services to transport packages from a specified location to a destination.

[1627] "Current Location" refers to the real-time geographic coordinates of the carrier's vehicle (e.g., truck, motorcycle, etc.).

[1628] "Transportation status" refers to information indicating the current business status of the delivery company (e.g., empty, in transit, on break, etc.).

[1629] "Receiving and updating in real time" means that the current location and shipping status of the delivery company are continuously and immediately sent to the server and reflected in the database.

[1630] "Past delivery history data" refers to detailed records of past deliveries made by a delivery company (e.g., delivery route, travel time, cost, etc.).

[1631] "Current traffic information" refers to real-time traffic data such as current road conditions, traffic congestion, and construction information.

[1632] The "means" used in "data analysis" refers to software and algorithms used to perform statistical analysis and machine learning predictive models based on collected data.

[1633] "Optimal delivery route" refers to the most effective delivery route calculated based on factors such as cost, time, and efficiency.

[1634] "Delivery Method" refers to the method used to transport the goods (e.g. truck, train, motorcycle, etc.).

[1635] "Total delivery cost" refers to the total cost calculated based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc.

[1636] "User terminal" refers to a device such as a computer or smartphone used by a user to input delivery requests and confirm proposal contents.

[1637] A "machine learning model" refers to an algorithm that learns from past data and makes predictions and classifications for new data.

[1638] "Data Analysis Library" refers to software tools and frameworks for collecting, processing, analyzing, and visualizing data.

[1639] "JSON format" stands for JavaScript Object Notation and refers to a lightweight data exchange format for storing and transferring structured data.

[1640] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery method, and presents them to the user. The system is composed of a server, a user terminal, and a delivery company terminal. Each component and its function are described in detail below.

[1641] Server-side explanation

[1642] 1. Data reception and updating

[1643] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database. Specifically, the delivery company enters their current location and transportation status (empty, in transit, on break, etc.) using a dedicated app and sends this to the server. The server stores this information in a database (e.g., MySQL) and keeps it up to date.

[1644] 2. Data Analysis

[1645] The server performs data analysis based on the received current location data, past delivery history data, and traffic information. Specifically, it uses Python and data analysis libraries (e.g., Pandas and NumPy) to calculate the average time and cost required for each route and determine the optimal delivery method. Based on the results of this analysis, it uses machine learning models (e.g., scikit-learn) to optimize delivery routes.

[1646] 3. Matching process

[1647] The server receives a delivery request sent by the user. This request includes the delivery origin, delivery destination, desired date and time, and priority conditions (shortest time, cheapest price, etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[1648] 4. Price Calculation

[1649] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. Detailed cost calculations are performed using a Python calculation module.

[1650] 5. Submit your proposal

[1651] The server sends the calculation results to the user's device and displays the proposed details (delivery company, route, means, price). A dedicated mobile app (e.g., developed with React Native) is installed on the user's device, which displays the latest information in real time.

[1652] Explanation on the user terminal side

[1653] 1. Submit a request

[1654] The user inputs a delivery request using a dedicated app, setting the origin, destination, desired date and time, and priority conditions (fastest, cheapest, etc.), and then sends this information to the server.

[1655] 2. Confirmation of proposal contents

[1656] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1657] 3. Final Decision

[1658] The user selects the most suitable option from the presented options and requests delivery.

[1659] Explanation on the delivery company terminal

[1660] 1. Current location and status transmission

[1661] Delivery companies use a dedicated app to periodically send their current location and status information to a server.

[1662] 2. Receiving a request notification

[1663] The delivery company receives a new delivery request from the server and checks the request details (package details, delivery route, reward, etc.) displayed in the app.

[1664] 3. Accepting or Rejecting a Request

[1665] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1666] Specific examples

[1667] For example, if a user inputs a request such as "Delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server analyzes the GPS data and traffic information sent by the delivery company to select the optimal delivery company and method. From the analysis results, it is determined that the optimal route is to transport the item from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka, with a total cost calculated to be 10,000 yen. The user receives this proposal and accepts it after checking it. On the delivery company side, the driver of Truck A receives this information, accepts the request, and makes the delivery according to the route information provided by the server.

[1668] Example prompts for a Generative AI model

[1669] Prompt: "Delivery from Tokyo to Osaka by 4pm tomorrow. Please suggest the fastest option."

[1670] Expected output: "The fastest route is to transport from Tokyo to Nagoya by Truck A, and then by train from Nagoya to Osaka. The total delivery cost is 10,000 yen."

[1671] In this way, the present invention increases delivery efficiency, reduces empty runs, and provides optimal delivery means and routes, thereby achieving overall cost reduction.

[1672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1673] Step 1: Receive and update data

[1674] Description: The server receives and updates GPS data and status information (empty, in transit, on break, etc.) from the delivery company's terminal in real time. The delivery company periodically inputs and sends the current location and status using a dedicated app.

[1675] Input: GPS data (latitude, longitude) sent from the delivery company terminal, status information

[1676] Data processing and calculation: The server saves the received data in a database (e.g., MySQL) in real time. It compares it with existing data to identify items that need to be updated and saves them as the latest information.

[1677] Output: The latest carrier location and status are stored in the database.

[1678] Specific operation: When the data "Latitude: 35.6895, Longitude: 139.6917, Status: Empty" is sent from the delivery company terminal, the server receives this and updates the database as "Delivery company A: Latitude 35.6895, Longitude 139.6917, Status: Empty."

[1679] Step 2: Data analysis

[1680] Description: The server performs data analysis using past delivery history data and current traffic information to calculate the average travel time and cost for each route.

[1681] Input: Past delivery history data stored in the database, real-time traffic information

[1682] Data processing and calculation: Using Python and data analysis libraries (e.g., Pandas, NumPy), data is aggregated and the average travel time, cost, and traffic patterns for each route are analyzed. The results are used to update the predictive model and derive the optimal delivery route.

[1683] Output: Average travel time and cost information for each route

[1684] Specific operation: Imports past delivery data, generates analysis results such as "average time required: 3 hours, average cost: 5,000 yen," and saves them in a database.

[1685] Step 3: Matching

[1686] Description: The server receives a delivery request from the user terminal and selects the optimal delivery company and delivery method based on this.

[1687] Input: Delivery request from user terminal (delivery origin, delivery destination, desired date and time, priority conditions)

[1688] Data processing and calculation: Based on the received request, the current location, status, historical data, and traffic information in the database are collated, and the most suitable company and means are evaluated and selected using a machine learning model (e.g., scikit-learn).

[1689] Output: Selected optimal delivery company and method

[1690] Specific operation: If a user sends a request saying, "I want delivery from Tokyo to Osaka by 4:00 PM tomorrow," the server determines that Truck A is the best option and selects the route as "Take Truck A from Tokyo to Nagoya, then take the train from Nagoya to Osaka."

[1691] Step 4: Price Calculation

[1692] Description: The server calculates the total delivery cost based on the selected delivery route and means.

[1693] Input: Selected delivery route and delivery method

[1694] Data processing and calculation: Using a Python calculation module, fuel costs, tolls, labor costs, etc. are calculated to calculate the total delivery cost. Each cost element is tallied and the total value is calculated.

[1695] Output: Total shipping cost

[1696] Specific operation: For example, based on the selected route, the total fuel cost is calculated as 3,000 yen, tolls as 2,000 yen, and labor costs as 5,000 yen, and the total delivery cost is calculated as 10,000 yen.

[1697] Step 5: Submit your proposal

[1698] Description: The server sends the calculation results to the user's terminal and displays the proposed contents (delivery company, route, means, price) to the user.

[1699] Inputs: Total shipping cost, selected shipping carrier and method

[1700] Data processing and calculation: Proposal content is generated in JSON format and sent to the user's terminal.

[1701] Output: The proposed details (delivery company, route, means, price) are displayed on the user's terminal.

[1702] Specific operation: "Proposal: Truck A, Tokyo to Nagoya, Nagoya to train, total cost 10,000 yen" is displayed on the user's terminal and can be confirmed by the user.

[1703] Step 6: Review and finalize the request

[1704] Description: The user reviews the suggestions sent by the server and selects the best option.

[1705] Input: Proposal from the server

[1706] Data processing and calculation: The user selects one of the options presented, and the selected information is sent back to the server.

[1707] Output: The final delivery request information is sent to the server and processing begins.

[1708] Specific operation: When the user confirms and selects "Proposal: Truck A, total cost 10,000 yen," the selection information is sent to the server and registered as an official delivery request.

[1709] Step 7: Carrier Status Update and Request Acceptance

[1710] Description: Using a dedicated app, the delivery company periodically sends its current location and status information to the server and accepts the request.

[1711] Input: Current location and status information from the delivery company terminal, new delivery request

[1712] Data processing and calculation: Based on the received information, the server notifies the delivery company of a new delivery request, and the delivery company then accepts or rejects the request.

[1713] Output: Information that the carrier has accepted the request, updated current location and status information

[1714] Specific operation: The delivery company sends the data "Current location: Latitude 35.6895, Longitude 139.6917, Status: Empty" to the server and receives a new delivery request "From Tokyo to Osaka, arrive by 4:00 PM tomorrow." When the delivery company accepts the request, the server sends detailed delivery route information to the company's terminal.

[1715] (Application example 1)

[1716] 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."

[1717] With conventional delivery management systems, it takes time to process delivery requests, making it difficult to quickly identify the optimal delivery route and method. It is also difficult to keep the current location and status of the delivery person updated in real time, making it difficult to select the appropriate delivery person, resulting in reduced delivery efficiency. These issues can reduce the reliability of recommendations provided to users and increase overall delivery costs.

[1718] 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.

[1719] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to a user terminal, means for selecting the optimal delivery company and transportation means based on request data from the user, and means for displaying information on the selected delivery company, transportation means, and cost information to the user. This enables rapid processing of delivery requests, selection of an appropriate delivery company in real time, and proposal of an efficient delivery route.

[1720] A "delivery request" is information in which a user requests that a specific package be delivered to a specified location.

[1721] A "delivery company" is a company or individual that responds to a user's delivery request and transports the package.

[1722] "Current location" refers to latitude and longitude information indicating the real-time location of a delivery company or its transportation vehicle.

[1723] "Transportation status" refers to information indicating the current operating status of a delivery company or its transportation means and the loading status of cargo.

[1724] "Traffic information" refers to data about the current driving environment, including road congestion, accident information, construction information, etc.

[1725] "Delivery route" refers to information indicating the optimal route from the delivery source to the delivery destination.

[1726] "Delivery vehicle" refers to the type of transportation method or vehicle used to transport a package.

[1727] "Total delivery cost" refers to all costs required to make a delivery, including fuel costs, tolls, labor costs, etc.

[1728] A "user terminal" refers to a device such as a computer or smartphone used by a user.

[1729] "Request data" refers to information entered by the user, such as the delivery origin, delivery destination, desired date and time, and priority conditions.

[1730] "Selection" refers to the process of determining the most suitable carrier and transportation method based on specified criteria.

[1731] The "display means" refers to a mechanism for visually presenting selected information to the user.

[1732] "Feedback" refers to the evaluation or opinion provided by the user after delivery is completed.

[1733] "Evaluation data" refers to data that quantifies the performance of delivery companies based on feedback.

[1734] The present invention relates to a system for receiving a delivery request from a food delivery service, selecting the most suitable delivery company and delivery means, and presenting the results to a user. Specific embodiments for carrying out the present invention are described below.

[1735] Overall system configuration

[1736] The system consists of a server, a user terminal, and a delivery company terminal.

[1737] Server Processing

[1738] 1. Data reception and updating

[1739] The server receives real-time GPS data sent from the delivery company's terminal and stores it in a database. This keeps the current location and delivery status of each delivery person up to date. The server uses Python and Flask.

[1740] 2. Data Analysis

[1741] The server analyzes the received data based on the current location data, past delivery history data, and traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Machine learning algorithms and statistical models are used for data analysis.

[1742] 3. Matching process

[1743] The server receives delivery requests sent by users. The request includes the delivery origin, delivery destination, desired date and time, and priority conditions ("fastest", "cheapest", etc.). Based on this information, the server selects the optimal delivery company and delivery method.

[1744] 4. Price Calculation

[1745] The server calculates the total delivery cost based on the selected delivery route and means, taking into account fuel costs, tolls, labor costs, etc. The calculation is performed using transaction data from the database.

[1746] 5. Submit your proposal

[1747] The server sends the calculation results to the user's terminal and displays the proposed details (delivery company, route, means, price).

[1748] User terminal processing

[1749] 1. Submit a request

[1750] The user uses the app to input a delivery request, specifying the origin, destination, desired date and time, and priority conditions (such as "fastest", "cheapest", etc.), and then sends this information to the server.

[1751] 2. Confirmation of proposal contents

[1752] The user checks the proposals sent from the server in the app and selects the best option based on the proposed delivery company, delivery route, method, and price information.

[1753] 3. Final Decision

[1754] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1755] Delivery company terminal processing

[1756] 1. Current location and status transmission

[1757] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1758] 2. Receiving a request notification

[1759] The delivery company receives a new delivery request from the server and displays it in the app. The delivery company confirms the proposed request details (package details, delivery route, reward, etc.).

[1760] 3. Accepting or Rejecting a Request

[1761] If the delivery company accepts the request, it notifies the server. If accepted, the server provides detailed delivery routes and procedures, and the delivery company carries out the delivery accordingly.

[1762] Specific examples

[1763] When a user inputs a request such as "I want food delivered from Restaurant A (origin) to my home (destination) in the shortest time possible," and selects the fastest option, the server selects the optimal delivery person (e.g., a delivery person with a car) and presents the total delivery cost of 1,000 yen. The user confirms this on their device and accepts.

[1764] The delivery process involves a delivery person (e.g., ID: 2) sending their current location (latitude 35.6804, longitude 139.7690) and status (empty) to the server. After that, they receive a notification from the server saying, "New delivery request: From Restaurant A to home, arrive in the shortest time possible." After confirming the request, they accept it. The server then provides detailed route information, and the delivery person begins delivery according to that route.

[1765] Prompt Sentence Examples

[1766] User: Wants food delivered from Restaurant A to his / her home in the shortest time possible

[1767] Server: Selecting a delivery person. Please wait...

[1768] Server: Optimal delivery person and total cost proposal completed. Delivery person ID: 2, Cost: 1000 yen, Estimated time: 30 minutes

[1769] The above is a specific description of an embodiment of the present invention, which allows users to receive a fast and optimal delivery service and enables delivery companies to carry out their work efficiently.

[1770] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1771] Step 1: Receiving a shipping request

[1772] The user inputs a delivery request through the app. The input includes the delivery origin, delivery destination, desired date and time, and priority conditions (e.g., "fastest," "cheapest," etc.). This is sent from the user's device to the server. Input data: delivery origin, delivery destination, desired date and time, priority conditions. Output data: delivery request data sent to the server.

[1773] Step 2: Receive and update carrier location and status

[1774] The server receives GPS data sent from delivery company terminals in real time and stores it in a database. This keeps the current location and status (empty, in transit, on break, etc.) of each delivery company up to date. Input data: Delivery company GPS data, status information. Output data: Current location and status information in the updated database.

[1775] Step 3: Data analysis

[1776] The server performs data analysis based on the received current location data, past delivery history data, and current traffic information. This allows it to calculate the average time and cost required for each route, as well as the optimal delivery method. Input data: current location data, delivery history data, traffic information. Output data: optimal delivery route and method information.

[1777] Step 4: Selecting a carrier and method

[1778] The server compares and analyzes the delivery request data received from the user with the delivery company information in the database to select the optimal delivery company and delivery method. Input data: delivery request data, delivery company data. Output data: selected optimal delivery company and delivery method.

[1779] Step 5: Cost calculation

[1780] The server calculates the total delivery cost based on the selected delivery route and means, including fuel costs, tolls, labor costs, etc. The calculated total delivery cost is sent to the user terminal. Input data: Selected delivery route and means. Output data: Total delivery cost.

[1781] Step 6: Submit and review your proposal

[1782] The server sends the calculation results to the user's device and presents suggestions (delivery company, route, means of transport, price). The user checks this information in the app and selects the optimal option. Input data: Calculation results (delivery company, route, means of transport, price). Output data: Suggestions sent to the user's device, content displayed on the user interface.

[1783] Step 7: Final decision

[1784] The user checks the proposals from the server, selects the best option from the presented options, and requests delivery. The selection result is sent to the server, and delivery begins. Input data: User's selection result. Output data: Final decision on delivery request, selection result sent to the server.

[1785] Step 8: Notify the carrier

[1786] The server notifies the selected delivery company's terminal of the new delivery request. The delivery company checks the request details (package details, delivery route, reward, etc.) on the app and accepts or rejects it. Input data: Final decision on delivery request. Output data: Notification to delivery company terminal, display of request details.

[1787] Step 9: Accepting or Rejecting the Request

[1788] If the delivery company accepts the request, it notifies the server. If accepted, the delivery company is provided with detailed delivery route and instructions and begins delivery. Input data: Acceptance or rejection by the delivery company. Output data: Notification to the server, detailed delivery route and instructions.

[1789] Step 10: Delivery Progress and Final Report

[1790] The delivery company updates its current location and delivery status in real time and continues to send them to the server. Once delivery is complete, it sends a final report to the server. Input data: current location data, delivery status, final report. Output data: updated status, confirmation of delivery completion.

[1791] 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.

[1792] This invention is a system that receives a delivery request, selects the optimal delivery company and delivery means, and presents them to the user, and also combines it with an emotion engine that recognizes the user's emotions. The system is composed of a server, a user terminal, a delivery company terminal, and an emotion engine.

[1793] Server-side processing

[1794] 1. Data reception and updating

[1795] The server receives GPS data sent from the delivery company terminal in real time and stores it in a database, which keeps the current location, empty load status, and operation status of each truck and delivery vehicle always up to date.

[1796] 2. Data Analysis

[1797] The server analyzes the received current location data, past delivery history data, and traffic information to calculate the average time and cost required for each route and the optimal delivery method.

[1798] 3. Emotion analysis

[1799] The server receives the user's emotional data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[1800] 4. Matching process

[1801] The server receives delivery requests sent by users. The requests include the sender, destination, desired date and time, and priority conditions. Taking into account the emotion data provided by the emotion engine, the server selects the optimal delivery company and delivery method.

[1802] 5. Price Calculation

[1803] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1804] 6. Submit your proposal

[1805] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1806] Processing on the user terminal side

[1807] 1. Submit a request

[1808] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[1809] 2. Confirmation of proposal contents

[1810] The user checks the proposals sent from the server in the app and selects the best option based on the suggested delivery company, delivery route, method, price information, and sentiment data.

[1811] 3. Final Decision

[1812] The user checks the proposals from the server, selects the most suitable option from the presented options, and requests delivery.

[1813] Processing on the delivery company terminal

[1814] 1. Current location and status transmission

[1815] Using the app, delivery agents periodically update their current location and status (e.g., empty, in transit, on break) and send it to the server.

[1816] 2. Receiving a request notification

[1817] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1818] 3. Request confirmation and acceptance

[1819] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1820] 4. Receiving detailed route information

[1821] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1822] Emotion engine processing

[1823] 1. Emotional Data Analysis

[1824] The emotion engine analyzes the emotion data provided by the user to determine the user's current emotional state.

[1825] 2. Sending Emotional Data

[1826] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[1827] Specific examples

[1828] User Flow

[1829] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1830] Delivery company process

[1831] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[1832] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[1833] The processing flow will be explained below.

[1834] Server-side processing

[1835] Step 1:

[1836] The server receives GPS data sent from the delivery company's terminal in real time and stores it in a database, allowing the current location of each truck or delivery vehicle to be determined.

[1837] Step 2:

[1838] The server uses the received GPS data to update the database with the empty load status and operation status of each delivery company. For example, it records that Truck A is traveling empty.

[1839] Step 3:

[1840] The server retrieves past delivery history data and current traffic information from the database and analyzes them to calculate the average travel time and cost for each route.

[1841] Step 4:

[1842] The server receives the user's emotion data provided by the emotion engine, and analyzes the user's current emotional state (e.g., impatience, anxiety, satisfaction, etc.).

[1843] Step 5:

[1844] The server receives a delivery request sent by a user, which includes the sender, destination, desired date and time, and priority conditions.

[1845] Step 6:

[1846] The server runs a matching algorithm to select the optimal delivery company and delivery method based on the received request information and emotion data. For example, if "fastest" is selected and impatience is detected, the server calculates the shortest delivery route and method.

[1847] Step 7:

[1848] The server calculates the total delivery cost based on the selected delivery route and vehicle, including fuel costs, tolls, labor costs, etc.

[1849] Step 8:

[1850] The server transmits the calculation results (optimal route, means, total cost) to the user terminal.

[1851] Processing on the user terminal side

[1852] Step 1:

[1853] The user launches the app and inputs a delivery request. They specify the delivery origin, destination, desired date and time, and priority criteria (such as "fastest," "cheapest," etc.). They also provide emotion data.

[1854] Step 2:

[1855] The user transmits the input request information and emotion data to the server.

[1856] Step 3:

[1857] The user checks the proposal sent from the server in the app. For example, the information displayed is "Truck A + train route, total cost 10,000 yen, estimated arrival time 3:30 PM."

[1858] Step 4:

[1859] The user reviews the suggestions and selects the best option, which is then sent to the server.

[1860] Processing on the delivery company terminal

[1861] Step 1:

[1862] Using the app, the delivery person periodically updates their current location and status (empty, in transit, at rest, etc.) For example, they might enter their current location as latitude 35.6895, longitude 139.6917, and their status as empty.

[1863] Step 2:

[1864] The delivery company receives a new delivery request from the server. For example, a notification will be displayed saying "New delivery request: Tokyo to Osaka, arriving by 4pm tomorrow."

[1865] Step 3:

[1866] The delivery company checks the request and decides whether to accept or reject it. If it accepts, it notifies the server.

[1867] Step 4:

[1868] Once the delivery company accepts the request, the server provides detailed delivery routes and instructions, and the delivery company then carries out the delivery according to those routes.

[1869] Emotion engine processing

[1870] Step 1:

[1871] The emotion engine receives emotion data provided by the user, for example, by analyzing the user's facial expressions and voice via the smartphone's camera and microphone.

[1872] Step 2:

[1873] The emotion engine analyzes the user's emotional state in real time based on the received data, identifying emotions such as impatience, anxiety, and satisfaction.

[1874] Step 3:

[1875] The emotion engine sends the analysis results to the server, which can then make optimal delivery suggestions taking into account the user's emotional state.

[1876] Specific examples

[1877] User Flow

[1878] The user inputs a request for "Delivery from Tokyo to Osaka by 4 PM tomorrow" and selects the fastest option. The emotion engine detects the user's impatience and sends the result to the server. The server calculates the optimal combination of trucks and trains (e.g., truck A from Tokyo to Nagoya, and then trains from Nagoya to Osaka). The user reviews the proposal, sees that the total delivery cost is 10,000 yen, and finally accepts it.

[1879] Delivery company process

[1880] The driver of Truck A sends his current location (latitude 35.6895, longitude 139.6917) and the empty load status to the server. After that, he receives a notification from the server saying "New delivery request: Tokyo to Osaka, arriving by 4:00 PM tomorrow." After checking the request, he accepts it. The server provides detailed route information, and the driver starts delivery according to that route.

[1881] In this way, by combining the emotion engine, the present invention can provide a more satisfying delivery service by taking into account the user's emotional state. By reflecting the user's emotional data, the accuracy of the proposal content is improved, and the overall service quality is improved.

[1882] Example 2

[1883] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1884] In today's logistics industry, improving delivery efficiency and reducing costs are important challenges. However, conventional systems do not provide sufficient information to select the optimal delivery method and delivery company for each delivery request, and are therefore unable to make specific recommendations that take into account the user's emotions and priorities. Furthermore, issues with the timeliness of delivery company status updates and notifications make it difficult to make real-time decisions, making it difficult to improve overall service quality.

[1885] 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.

[1886] In this invention, the server includes means for receiving delivery requests, means for receiving and updating the current locations and delivery statuses of affiliated delivery companies in real time, means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information, means for calculating the total delivery cost based on the calculated delivery route and means, means for transmitting the calculated optimal delivery route and means and the total delivery cost to the user terminal, and means for receiving and analyzing user emotion data and generating an optimal proposal based on this. This allows for the provision of specific and optimal proposals in response to delivery requests that take into account the user's emotions and priorities, and further allows for real-time updates and notifications of delivery company status, thereby improving overall service quality.

[1887] The "means for receiving a delivery request" is a mechanism for receiving information such as the delivery source, delivery destination, desired date and time, and priority conditions from the user, and transmitting it to the server.

[1888] "Means for receiving and updating the current location and transportation status of delivery companies in real time" refers to a system for transmitting GPS data and operation status provided by delivery companies to a server in real time and updating it.

[1889] "Means for calculating the optimal delivery route and means by analyzing past delivery history data and current traffic information" refers to algorithms and methods for calculating the optimal route and delivery method based on history data and traffic information.

[1890] The "means for calculating total delivery costs" is a system for calculating the total cost of delivery, taking into account fuel costs, tolls, labor costs, etc.

[1891] "Means for sending to user terminal" refers to an interface or protocol for sending information such as calculation results and optimal delivery routes to the user's device.

[1892] The "means for receiving and analyzing emotional data and generating optimal proposals based on this" is a mechanism for optimizing delivery proposals by acquiring and analyzing the user's emotional information.

[1893] "Means for notifying the delivery company's terminal of new delivery requests and providing a means for the delivery company to accept or reject the request" refers to a system for notifying the delivery company of new delivery requests and confirming whether they are accepted or rejected.

[1894] The "means for displaying on the user terminal and allowing the user to select the proposed content" is an interface that displays the proposed content sent from the server on the user's device and allows the user to select it.

[1895] This invention is a system that receives delivery requests, selects the optimal delivery company and delivery method, and presents them to the user. A distinctive feature is that it generates optimal proposals that take into account the user's emotional state by combining it with an emotion engine that recognizes the user's emotions.

[1896] System Configuration

[1897] The system consists of the following mai...

Claims

1. means for receiving a delivery request; A means for receiving and updating the current location and shipping status of participating carriers in real time; A means for analyzing past delivery history data and current traffic information to calculate the optimal delivery route and means; a means for calculating a total delivery cost based on the calculated delivery route and means; means for transmitting the calculated optimal delivery route and means, and the total delivery cost to a user terminal; A system including:

2. a means of providing regular updates on the carrier's current location and shipment status; a means for notifying a carrier terminal of a new delivery request and providing a means for the carrier to accept or reject the request; The system of claim 1 , comprising:

3. A means for selecting the optimal delivery company and delivery method based on the delivery origin, delivery destination, desired date and time, and priority conditions included in the received delivery request; a means for displaying the selected delivery means, route information, and total delivery cost on a user terminal, and allowing the user to select a proposal; The system of claim 1 , comprising:

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