system

Abstract

To provide a system.SOLUTION: The system includes: means for determining, by AI, the delivery route of the day in question when the probability of re-delivery is reduced by using traffic information data, record data of re-delivery or absence of a resident, delivery package information of the day, electric meter information, and feature data of a route in the view of a local delivery; means for linking the route determined by the AI with a vehicle navigation system; means for collecting emotion data including voice, texts, facial expressions, and body actions through the interaction with the user; and means for analyzing the collected emotion data and re-optimizing the delivery route on the basis of the result of analysis.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional delivery route determination method, there are problems that the possibility of redelivery cannot be sufficiently considered, and differences due to the experience of drivers are likely to occur.

Means for Solving the Problems

[0005] In the present invention, traffic information data, absentee / redelivery performance data of residents, delivery cargo information on the day, electric meter information, and route feature data from the perspective of area drivers are utilized, and an AI determines the delivery route of the day with a reduced possibility of daily redelivery. Furthermore, by linking the route determined by AI with a car navigation system, it becomes possible to maintain the delivery level regardless of whether the driver is a veteran or a novice.

Brief Description of the Drawings

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16]It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when combined with an emotion engine. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Example 2 of Form Example 2 when combined with an emotion engine. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Example 3 of Form Example 3 when combined with an emotion engine. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0012] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc., and the communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of the present invention is a delivery route determination system. This system collects traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the time residents are absent and the history of redeliveries in order to reduce the likelihood of redeliveries.

[0029] "Example of form 2"

[0030] Furthermore, the route determined by the AI ​​is displayed in conjunction with the car navigation system. This allows drivers to deliver according to the optimal route determined by the AI. Specifically, the route determined by the AI ​​is displayed on the car navigation screen, and the driver delivers according to that route. This makes it possible to maintain a consistent delivery level regardless of whether the driver is a veteran or a rookie.

[0031] "Example of form 3"

[0032] As a concrete example, a driver responsible for deliveries in a certain area inputs information about the day's deliveries and data on residents' absences / redelivery records into the system. Based on this information, along with traffic data, electricity meter information, and route characteristic data from the driver's perspective, the AI ​​determines the optimal delivery route. This route is displayed on the car's navigation system, and the driver follows this route for deliveries. This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The delivery route determination system collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0036] Step 2: Based on the collected data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the residents' absence times and past redelivery data in order to minimize the possibility of redelivery.

[0037] "Example of form 2"

[0038] Step 1: The route determined by the AI ​​will be displayed in conjunction with the car navigation system.

[0039] Step 2: Drivers follow the instructions on the car's navigation system to make deliveries. This ensures that delivery levels are maintained regardless of whether the driver is experienced or new.

[0040] "Example of form 3"

[0041] Step 1: A driver responsible for deliveries in a specific area enters the day's delivery information and data on residents' absences / redelivery attempts into the system.

[0042] Step 2: Based on this information, traffic data, electricity meter information, and route feature data from the perspective of area drivers, the system's AI determines the optimal delivery route.

[0043] Step 3: This route will be displayed on the car's navigation system, and the driver will follow this route for deliveries. This will reduce the likelihood of redeliveries and help maintain a consistent delivery level.

[0044] (Example 1)

[0045] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0046] Conventional delivery route determination systems had a high probability of redelivery, making efficient delivery difficult. Furthermore, they failed to determine the optimal route considering traffic information and residents' availability, resulting in reduced delivery efficiency. Additionally, disparities in delivery quality between veteran and novice drivers were a significant challenge.

[0047] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0048] In this invention, the server includes means for collecting traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; means for preprocessing the collected data; means for training a machine learning model based on the preprocessed data; means for receiving delivery requests from users; means for calculating the optimal delivery route using the trained machine learning model; means for transmitting the calculated optimal route to a terminal; and means for displaying the route information received by the terminal to the user. This reduces the likelihood of redeliveries and enables the determination of efficient delivery routes. It also helps maintain a consistent delivery level between veteran and novice drivers.

[0049] "Traffic information data" refers to data that shows real-time traffic conditions and road congestion.

[0050] "Resident absence / redelivery history data" refers to data showing the time residents were absent during past deliveries and the history of redeliveries.

[0051] "Same-day delivery package information" refers to data that shows detailed information about packages to be delivered on that day.

[0052] "Electricity meter information" refers to data on electricity usage used to estimate whether residents are at home.

[0053] "Route characteristic data from the perspective of area drivers" refers to data on route characteristics based on feedback from drivers.

[0054] "Means of collection" refers to the methods and devices used to acquire the necessary data.

[0055] "Preprocessing means" refers to methods and devices for converting collected data into a format suitable for analysis and model training.

[0056] "Means for training machine learning models" refers to methods and devices for training machine learning algorithms using collected and pre-processed data.

[0057] "Means of receiving delivery requests" refers to methods or devices for receiving delivery requests from users.

[0058] "Means for calculating the optimal delivery route" refers to methods or devices that use trained machine learning models to calculate the most efficient delivery route.

[0059] "Means of transmission to terminal" refers to methods or devices for transmitting the calculated delivery route to the user's terminal.

[0060] "Means for displaying route information received by a terminal to the user" refers to methods or devices for visually presenting delivery route information received by a terminal to the user.

[0061] This invention relates to a delivery route determination system that determines the optimal delivery route by utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0062] Program generation

[0063] The programs for this system are generated using Python. Key libraries include requests for data collection, pandas for data processing, and scikit-learn and TENSORFLOW® for building machine learning models.

[0064] Program Processing Description

[0065] The server collects the following data:

[0066] Traffic information data: Real-time traffic information is obtained using a standard map API.

[0067] Resident absence / redelivery record data: Past delivery record data is obtained from the delivery company's database.

[0068] Delivery information for the day: Retrieve the delivery list for the day from the delivery management system.

[0069] Electricity meter information: Use the smart meter API to obtain electricity usage data to estimate whether residents are at home.

[0070] Route characteristic data from the perspective of area drivers: Collect feedback from drivers and save route characteristics in a database.

[0071] After collecting this data, the server preprocesses it using pandas. Specifically, it performs tasks such as imputing missing values ​​and normalizing the data.

[0072] Next, the server uses scikit-learn and TensorFlow to build a machine learning model. This model optimizes delivery routes by considering residents' absence times and past redelivery data to reduce the likelihood of redeliveries.

[0073] After the model has finished training, the server calculates the optimal delivery route each time a new delivery request comes in and sends it to the terminal.

[0074] Specific examples and prompt statements

[0075] As a concrete example, consider the following scenario:

[0076] The user enters a new delivery request into the system. For example, the user enters "I will make 10 deliveries in Tokyo on October 10, 2023." Based on this information, the server calculates the optimal delivery route and sends it to the terminal.

[0077] Examples of prompts to input into a generative AI model:

[0078] "We plan to make 10 deliveries in Tokyo on October 10, 2023. Please calculate the optimal delivery route, taking into account traffic information data, data on residents' absence / redelivery history, information on packages delivered on that day, electricity meter information, and route characteristic data from the perspective of area drivers."

[0079] By inputting this prompt into the generating AI model, the AI ​​calculates the optimal delivery route and provides it to the user.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0083] Inputs: Traffic information API, delivery company database, delivery management system, smart meter API, driver feedback

[0084] Output: Various collected data

[0085] Specific operation: The server calls a traffic information API to obtain real-time traffic information and retrieves past delivery performance data from the delivery company's database. It also retrieves the day's delivery list from the delivery management system and obtains electricity usage data using the smart meter API. Furthermore, it collects driver feedback and stores it in the database.

[0086] Step 2:

[0087] The server preprocesses the collected data.

[0088] Input: Various collected data

[0089] Output: Preprocessed data

[0090] Specific operation: The server uses the pandas library to impute missing values ​​in the data and normalize data at different scales. It also combines data obtained from different sources into a single dataframe.

[0091] Step 3:

[0092] The server trains a machine learning model based on the pre-processed data.

[0093] Input: Preprocessed data

[0094] Output: Trained machine learning model

[0095] Specific operation: The server builds machine learning models using scikit-learn or TensorFlow. It selects features and the optimal algorithm (e.g., random forest, neural network) to train the model.

[0096] Step 4:

[0097] The user enters a new delivery request into the system.

[0098] Input: Delivery request (Example: "Make 10 deliveries in Tokyo on October 10, 2023")

[0099] Output: Delivery request data

[0100] Specific operation: The user enters a delivery request into the system interface. The server receives this request and saves it to the database.

[0101] Step 5:

[0102] The server uses a pre-trained machine learning model to calculate the optimal delivery route.

[0103] Input: Delivery request data, trained machine learning model

[0104] Output: Optimal delivery route

[0105] Specific operation: The server converts the delivery request data into a format that can be input into the model, and uses the trained machine learning model to predict the optimal delivery route.

[0106] Step 6:

[0107] The server sends the calculated optimal route to the terminal.

[0108] Input: Optimal delivery route

[0109] Output: Route information sent to the terminal

[0110] Specific operation: The server calls an API to send the calculated optimal route to the terminal, and then sends the route information to the terminal.

[0111] Step 7:

[0112] The device displays the received route information to the user.

[0113] Input: Route information sent to the terminal

[0114] Output: Route information displayed to the user

[0115] Specific operation: The terminal provides an interface for visually displaying the received route information to the user.

[0116] (Application Example 1)

[0117] Next, we will describe Application Example 1 of Form 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."

[0118] Traditional delivery systems failed to fully utilize traffic information and data on residents' absences, resulting in frequent redeliveries. Furthermore, insufficient optimization of delivery routes made predicting delivery times difficult. This led to decreased delivery efficiency and reduced customer satisfaction.

[0119] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0120] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day using AI that minimizes the possibility of daily redeliveries; a means of linking the route determined by the AI ​​with a car navigation system; a means of optimizing the delivery route using an application installed on a smartphone; and a means of predicting delivery times by reflecting real-time traffic information. This makes it possible to reduce the frequency of redeliveries and improve delivery efficiency.

[0121] "Traffic information data" refers to information about traffic that is collected in real time, such as road congestion, traffic accidents, and construction information.

[0122] "Resident absence / redelivery history data" refers to data showing the time periods when a particular resident was absent in the past and the history of redelivery requests.

[0123] "Delivery information for the day" refers to detailed information about packages scheduled for delivery on that day, including data such as package type, quantity, and delivery address.

[0124] "Electricity meter information" is data that shows residents' electricity usage and is used to estimate whether or not residents are at home.

[0125] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics and points to note about routes that drivers responsible for a specific area have learned through experience.

[0126] "Using AI to determine the optimal delivery route" refers to a method of using artificial intelligence to decide on the best delivery route.

[0127] "Methods for linking with car navigation systems" refers to methods for linking the determined delivery route with the in-vehicle navigation system.

[0128] "Applications installed on smartphones" are software that runs on smartphones and is used to optimize delivery routes and predict delivery times.

[0129] "A method for predicting delivery times by reflecting real-time traffic information" refers to a method for predicting the time it will take to deliver a package, taking into account the current traffic conditions.

[0130] The following system configuration will be described as an embodiment for carrying out this invention.

[0131] System Configuration

[0132] This system consists of a server, a smartphone, a car navigation system, and various data sources. The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, AI determines the optimal delivery route.

[0133] Hardware and software to be used

[0134] Hardware: Servers, smartphones, car navigation systems

[0135] Software: Python, Pandas (data processing), Scikit-learn (machine learning), Geopy (geographic information processing)

[0136] Data processing and data calculation

[0137] The server processes the data in the following steps:

[0138] 1. Data Import: Import traffic information, resident absence / redelivery record data, daily delivery package information, electricity meter information, and route characteristic data from the perspective of area drivers from a CSV file.

[0139] 2. Data preprocessing: Combine data and handle missing values.

[0140] 3. Setting features and targets: Set delivery time as the target variable and other data as features.

[0141] 4. Model Training: Train the model using RandomForestRegressor.

[0142] 5. Optimizing delivery routes: Determine the optimal delivery route using geodesic distance.

[0143] 6. Delivery Route Prediction: Predict delivery times based on the optimal delivery route.

[0144] Specific example

[0145] For example, if there are multiple delivery destinations within Tokyo, the server will determine the optimal route and predict delivery times by considering traffic information and data on residents' absences. This can reduce the frequency of redeliveries and improve delivery efficiency.

[0146] Example of a prompt

[0147] "Create a Python program that determines the optimal route and predicts delivery time for multiple delivery destinations within Tokyo, taking into account traffic information and resident absence data."

[0148] The above describes the embodiments for carrying out this invention. This system makes it possible to reduce the frequency of redeliveries and improve delivery efficiency.

[0149] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0150] Step 1:

[0151] The server reads traffic information data, resident absence / redelivery data, daily delivery package information, electricity meter information, and route characteristic data from the perspective of area drivers from CSV files. This provides all the data necessary for optimizing delivery routes. The input is various data files, and the output is an integrated dataset.

[0152] Step 2:

[0153] The server preprocesses the loaded data. Specifically, it combines the data and handles missing values. For example, it combines traffic information data and resident absence data using address as the key, and imputes missing values ​​with the mean or median. The input is a combined dataset, and the output is a preprocessed dataset.

[0154] Step 3:

[0155] The server sets features and target variables from a preprocessed dataset. Features are factors that affect delivery time (e.g., traffic information, time residents are away, electricity meter information, etc.), and the target variable is the delivery time. The input is the preprocessed dataset, and the output is a set of features and target variables.

[0156] Step 4:

[0157] The server trains a machine learning model (RandomForestRegressor) using features and target variables. It then fits the model to the training data to generate a model for predicting delivery times. The input is a set of features and target variables, and the output is the trained machine learning model.

[0158] Step 5:

[0159] The server determines the optimal delivery route using geodesic distance. Specifically, it optimizes the route by selecting the delivery destination closest to the current location, and then selecting the next delivery destination closest to that location. The input is a list of delivery destinations, and the output is the optimized delivery route.

[0160] Step 6:

[0161] The server uses a trained machine learning model to predict delivery times based on optimized delivery routes. The input is the optimized delivery route, and the output is the predicted delivery time for each delivery destination.

[0162] Step 7:

[0163] The server sends optimized delivery routes and estimated delivery times to an application installed on the driver's smartphone. This allows the driver to check the optimal delivery route and estimated delivery time in real time. The input is the optimized delivery route and estimated delivery time, and the output is the transmission of data to the smartphone application.

[0164] Step 8:

[0165] The user (driver) uses a smartphone application to deliver according to an optimized delivery route. The application reflects real-time traffic information and recalculates the route as needed. The input is data transmitted from the server, and the output is the actual delivery work.

[0166] (Example 2)

[0167] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0168] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, it was challenging to maintain a consistent level of delivery quality regardless of whether the driver was experienced or new. Additionally, they were unable to provide optimal routes that took real-time traffic and weather information into account. This resulted in decreased delivery efficiency and lower customer satisfaction.

[0169] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0170] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route using artificial intelligence that minimizes the possibility of redelivery; a means to link the route determined by the artificial intelligence with a navigation system; a means to receive a list of delivery addresses and obtain location information for each address using a geographic information system; a means to obtain weather information and collect traffic condition data; a means to preprocess the acquired data and input it into a generated AI model to calculate the optimal delivery route; and a means to transmit and display the generated optimal route to a navigation system. This makes it possible to reduce the possibility of redelivery and provide an efficient delivery route. Furthermore, it is possible to maintain a consistent delivery level regardless of whether the driver is a veteran or a rookie. In addition, it is possible to provide an optimal route that takes into account real-time traffic conditions and weather information, thereby improving delivery efficiency and customer satisfaction.

[0171] "Traffic information data" refers to information about traffic flow, such as road congestion, accident information, and road closure information.

[0172] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent during past deliveries and the number of times redeliveries were made.

[0173] "Same-day delivery information" refers to detailed information about packages to be delivered on that day, including the quantity, size, weight, and delivery address.

[0174] "Electricity meter information" refers to data showing the electricity usage of the residence or building to which delivery is made, and is used to estimate whether residents are at home.

[0175] "Route characteristic data from the perspective of area drivers" refers to information that shows the characteristics and points to note about routes based on the experience and knowledge of drivers in a specific area.

[0176] "Artificial intelligence" refers to algorithms and models that use machine learning and data analysis to calculate the optimal delivery route.

[0177] A "navigation system" refers to a device installed in a vehicle that provides map information and route guidance functions.

[0178] A "Geographic Information System" refers to a system for collecting, managing, and analyzing geographical data.

[0179] A "generative AI model" refers to an algorithm or model built using a machine learning framework to generate the optimal delivery route.

[0180] "Preprocessing" refers to the process of converting collected data into a format necessary for inputting it into the generating AI model.

[0181] This invention relates to a system for efficiently calculating and displaying routes in delivery operations. Specific embodiments of this system are described below.

[0182] System Overview

[0183] This system consists of three main elements: a server, a terminal, and a user. The server collects and preprocesses data and calculates routes using a generative AI model. The terminal functions as a car navigation system, displaying the optimal route sent from the server. The user enters a list of delivery addresses and makes deliveries according to the system's instructions.

[0184] Hardware and software to be used

[0185] Servers: Use servers with high-performance computing capabilities. Specifically, cloud services such as Google Cloud Platform and Amazon Web Services can be used.

[0186] Terminal: Use a standard navigation device (e.g., Garmin or TomTom) as the car navigation system.

[0187] Software: Data collection is done using APIs such as Google Maps API, OpenWeatherMap API, and Waze API. Machine learning frameworks such as TensorFlow and PyTorch are used to build generative AI models.

[0188] Data collection and preprocessing

[0189] The server receives a list of delivery addresses entered by the user. For example, the user enters "1-1 Marunouchi, Chiyoda-ku, Tokyo". The server uses the Google Maps API to obtain the latitude and longitude of each address. Furthermore, it uses the OpenWeatherMap API to obtain current weather information and the Waze API to collect traffic data. This data is preprocessed for input into a generative AI model. Specifically, the address data is converted to latitude and longitude, and the weather information and traffic data are converted to numerical data.

[0190] Root calculation using a generative AI model

[0191] The preprocessed data is input into a generative AI model. This generative AI model, built using TensorFlow or PyTorch, calculates the optimal delivery route. For example, it takes multiple delivery destination data as a single batch and generates an optimal route such as destination A → destination B → destination C.

[0192] Displaying routes and executing deliveries

[0193] The generated optimal route is sent from the server to the terminal. The terminal analyzes the received route information and displays it on the car navigation system screen. The driver makes deliveries according to the route displayed on the car navigation system. The car navigation system updates the route information in real time and notifies the driver.

[0194] Examples of specific cases and prompt statements

[0195] Specific example:

[0196] The user enters a list of delivery addresses.

[0197] The server uses the Google Maps API to obtain the latitude and longitude of each address and the OpenWeatherMap API to obtain current weather information.

[0198] The server preprocesses this data and inputs it into the generating AI model.

[0199] The AI ​​model calculates the optimal delivery route and transmits that route information to the car navigation system.

[0200] The driver makes deliveries following the route displayed on the car's navigation system.

[0201] Example of a prompt:

[0202] Please enter a list of delivery addresses. The system will calculate the optimal delivery route and display it on your car's navigation system.

[0203] example:

[0204] 1. 1-1 Marunouchi, Chiyoda-ku, Tokyo

[0205] 2. 1-1 Jinnan, Shibuya-ku, Tokyo

[0206] 3. 2-8 Nishi-Shinjuku, Shinjuku-ku, Tokyo

[0207] In this way, the system provides efficient delivery routes, reduces the possibility of redelivery, and improves delivery efficiency and customer satisfaction.

[0208] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0209] Step 1:

[0210] The user enters a list of delivery addresses.

[0211] Input: List of delivery addresses (Example: 1-1 Marunouchi, Chiyoda-ku, Tokyo; 1-1 Jinnan, Shibuya-ku, Tokyo; 2-8 Nishi-Shinjuku, Shinjuku-ku, Tokyo)

[0212] Output: A list of delivery addresses is sent to the server.

[0213] Specific action: The user enters the delivery address in text format into the system's input form and clicks the "Submit" button.

[0214] Step 2:

[0215] The server uses the Google Maps API to obtain the latitude and longitude of each address.

[0216] Input: List of delivery addresses

[0217] Output: Latitude and longitude information for each address (e.g., 35.681236, 139.767125)

[0218] Specific operation: The server sends a request to the Google Maps API for each address and receives latitude and longitude information as a response.

[0219] Step 3:

[0220] The server uses the OpenWeatherMap API to retrieve current weather information.

[0221] Input: Latitude and longitude information for each address

[0222] Output: Weather information for each address (e.g., sunny, temperature 25 degrees Celsius, wind speed 5 m / s)

[0223] Specific operation: The server sends a request to the OpenWeatherMap API based on latitude and longitude information and receives weather information as a response.

[0224] Step 4:

[0225] The server uses the Waze API to collect traffic data.

[0226] Input: Latitude and longitude information for each address

[0227] Output: Traffic data for each address (e.g., congestion, road closures)

[0228] Specific operation: The server sends a request to the Waze API based on latitude and longitude information and receives traffic condition data as a response.

[0229] Step 5:

[0230] The server preprocesses the data it has acquired.

[0231] Input: Latitude and longitude information, weather information, traffic data

[0232] Output: Preprocessed dataset (Example: { "latitude": 35.681236, "longitude": 139.767125, "weather": 1, "temperature": 25, "wind_speed": 5, "traffic": "normal"})

[0233] Specific operation: The server converts address data into latitude and longitude, and converts weather information and traffic data into numerical data, and combines them into a single dataset.

[0234] Step 6:

[0235] The server inputs the pre-processed dataset into the AI ​​model.

[0236] Input: Preprocessed dataset

[0237] Output: Optimal delivery route (Example: Delivery destination A → Delivery destination B → Delivery destination C)

[0238] Specific operation: The server inputs a dataset into the generated AI model, and the model calculates the optimal delivery route.

[0239] Step 7:

[0240] The server sends the generated optimal route to the navigation system.

[0241] Input: Optimal delivery route

[0242] Output: Route information sent to the navigation system

[0243] Specific operation: The server sends route information to the navigation system's API using an HTTP POST request.

[0244] Step 8:

[0245] The device analyzes the route information it receives and displays it on the screen.

[0246] Input: Route information

[0247] Output: The route displayed on the screen (e.g., the route shown as a blue line on the map)

[0248] Specific operation: The device analyzes the received route information, displays the route on the map, and provides voice guidance for the next turn.

[0249] Step 9:

[0250] The driver makes deliveries following the route displayed on the car's navigation system.

[0251] Input: Route displayed on the car navigation system

[0252] Output: Arrival at delivery destination

[0253] Specific actions: The driver drives the car according to the instructions on the car navigation system and makes deliveries according to the designated route.

[0254] Step 10:

[0255] The device updates route information in real time and notifies the driver.

[0256] Input: Real-time traffic data

[0257] Output: Updated route information

[0258] Specific operation: The terminal periodically acquires traffic data, recalculates the route as needed, and notifies the driver.

[0259] (Application Example 2)

[0260] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0261] Traditional delivery systems struggled to determine optimal delivery routes considering traffic conditions and residents' absences, leading to a high likelihood of redeliveries. Furthermore, maintaining consistent delivery quality was difficult, regardless of whether the driver was experienced or new. Additionally, the lack of real-time updates on delivery progress and post-delivery feedback created a need for improved delivery efficiency.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0263] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day using AI that minimizes the possibility of daily redeliveries; a means of linking the route determined by the AI ​​with a car navigation system; a means of displaying the optimal delivery route determined by the AI ​​in real time using an application installed on a smartphone; and a means of updating delivery progress in real time and providing feedback after delivery is completed. This makes it possible to reduce the possibility of redeliveries, maintain a consistent delivery level regardless of whether the driver is veteran or new, and improve delivery efficiency.

[0264] "Traffic information data" refers to data that includes information related to traffic, such as road congestion, traffic accidents, and construction information.

[0265] "Resident absence / redelivery history data" refers to data that includes information about the time periods when residents were absent and the history of redeliveries.

[0266] "Delivery information for the day" refers to data that includes information about the type and quantity of packages to be delivered on that day, as well as the delivery address.

[0267] "Electricity meter information" is data that shows residents' electricity usage and is used to estimate whether residents are at home.

[0268] "Route characteristic data from the perspective of area drivers" refers to data that includes information about route characteristics based on the experience and knowledge of drivers in a specific area.

[0269] "Using AI to determine the route" refers to a method of using artificial intelligence to calculate and determine the optimal delivery route.

[0270] "Methods for linking with car navigation systems" refer to methods that display the determined delivery route on the car navigation system, allowing the driver to drive according to that route.

[0271] An "application installed on a smartphone" is software that runs on a smartphone and displays delivery routes and manages delivery progress.

[0272] "A means of updating delivery progress in real time" refers to a method of recording and updating the progress of deliveries in real time.

[0273] "Means of providing feedback after delivery completion" refers to methods of providing evaluations and suggestions for improvement to drivers and systems after a delivery has been completed.

[0274] The system for implementing this invention utilizes traffic information data, resident absence / redelivery performance data, same-day delivery cargo information, electricity meter information, and route feature data from the perspective of an area driver to determine, by means of AI, the same-day delivery route that reduces the likelihood of daily redelivery, and means for linking the route determined by AI with a car navigation system, and means for using an application installed on a smartphone to display, in real time, the optimal delivery route determined by AI, and means for updating the delivery progress in real time and providing feedback after delivery completion.

[0275] Explanation of Program Processing

[0276] Hardware to be Used

[0277] Server

[0278] Smartphone

[0279] Software to be Used

[0280] [[ID=​​​​​​​​​​​​​​​​​​​3. The smartphone displays the received optimal route in its car navigation app using the Google Maps API.

[0286] 4. The smartphone updates delivery progress in real time and provides feedback after delivery is complete.

[0287] Specific example

[0288] For example, if the starting point is Tokyo Station (latitude 35.6895, longitude 139.6917) and the destination is Tokyo Tower (latitude 35.6586, longitude 139.7454), the server inputs this location information into an AI model to calculate the optimal delivery route. The calculated route information is then sent to a smartphone, which uses the Google Maps API to display the route in its car navigation app.

[0289] Example of a prompt

[0290] Please enter your departure and destination points. The AI ​​will calculate the optimal route and display it on your car's navigation system.

[0291] Example: Departure point: Tokyo Station (35.6895, 139.6917), Destination: Tokyo Tower (35.6586, 139.7454)

[0292] In this way, the embodiment for carrying out the invention can reduce the possibility of redelivery, maintain a consistent delivery level regardless of whether the driver is experienced or new, and improve delivery efficiency.

[0293] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0294] Step 1:

[0295] The server collects traffic information data, records of residents' absence / redelivery, delivery package information for the day, electricity meter information, and route feature data from the perspective of area drivers. These data are obtained from various sensors and databases. The input data includes traffic information, residents' absence time, redelivery history, details of delivery packages, power usage status, and drivers' experience information. These data are integrated and preprocessed for input into the AI model.

[0296] Step 2:

[0297] Based on the preprocessed data, the server uses TensorFlow to load the AI model and calculate the optimal delivery route. The input data includes preprocessed traffic information, residents' absence time, redelivery history, details of delivery packages, power usage status, and drivers' experience information. The AI model analyzes these data and outputs the optimal delivery route.

[0298] Step 3:

[0299] The server sends the calculated optimal route to the smartphone. The input data includes the optimal route information output by the AI model. The server sends this information to the smartphone, and the smartphone receives it.

[0300] Step 4:

[0301] The smartphone uses the Google Maps API to display the received optimal route on the car navigation app. The input data includes the optimal route information received from the server. The smartphone calls the Google Maps API and displays the optimal route on the car navigation app.

[0302] Step 5:

[0303] The user (driver) begins driving according to the optimal route displayed in the car navigation app. The input data includes the optimal route information displayed in the car navigation app. The user drives based on this information.

[0304] Step 6:

[0305] The smartphone updates delivery progress in real time. Input data includes the user's current location and delivery status. Based on this information, the smartphone updates delivery progress in real time and sends it to the server.

[0306] Step 7:

[0307] After delivery is complete, the smartphone provides feedback. Input data includes delivery completion information and user feedback. Based on this information, the smartphone generates and provides post-delivery feedback to the user.

[0308] (Example 3)

[0309] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0310] Traditional delivery systems failed to adequately utilize data on residents' absences and redelivery history, resulting in a high frequency of redeliveries. Furthermore, the lack of consideration for traffic information and route characteristics from the perspective of local drivers made it difficult to determine efficient delivery routes. Additionally, disparities in delivery quality between veteran and novice drivers were a significant challenge.

[0311] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0312] This invention includes a server that uses traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of redelivery, using artificial intelligence; a means to link the route determined by artificial intelligence with an in-vehicle navigation system; a means for the user to input delivery information into a terminal and for the terminal to transmit the input data to the server; a means for the server to receive the data and perform preprocessing; a means for the server to acquire traffic information and integrate electricity meter information and route characteristic data; a means for the server to input data into a machine learning model and calculate the optimal delivery route; a means for the server to transmit the calculation results to a terminal and for the terminal to display the optimal delivery route on the in-vehicle navigation system; and a means for the user to perform deliveries according to the instructions of the in-vehicle navigation system. This makes it possible to reduce the possibility of redelivery and achieve efficient deliveries.

[0313] "Traffic information data" refers to data that shows real-time traffic conditions, including congestion information and road closure information.

[0314] "Resident absence / redelivery history data" refers to data that shows the history of residents being absent and information related to redeliveries, such as past delivery dates and times and the number of redeliveries.

[0315] "Delivery information for the day" refers to information about the package to be delivered, including the package ID, delivery address, and package type.

[0316] "Electricity meter information" is data that indicates whether residents are at home, and it is used to determine whether residents are at home based on their electricity usage.

[0317] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics that drivers consider when selecting a delivery route, such as the width of roads within the delivery area, the number of traffic lights, and the availability of parking spaces.

[0318] "Artificial intelligence" is a technology that uses machine learning models to analyze data and calculate the optimal delivery route.

[0319] An "in-vehicle navigation system" is a navigation device installed in a vehicle that displays the optimal delivery route and guides the driver through voice guidance and map display.

[0320] A "terminal" is a device used by the user to input delivery information, and includes smartphones, tablets, and other similar devices.

[0321] A "server" is a computer system that receives data sent from terminals and performs preprocessing, data integration, and calculations using machine learning models.

[0322] A "machine learning model" is an algorithm used to analyze data and calculate the optimal delivery route, and is implemented using libraries such as Scikit-learn.

[0323] This invention is a system aimed at improving the efficiency of delivery operations. It utilizes traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the delivery route for the day using artificial intelligence, minimizing the likelihood of redeliveries.

[0324] System Configuration

[0325] This system consists of the following main components:

[0326] 1. Terminal: A device used by the user to enter delivery information, including smartphones and tablets.

[0327] 2. Server: A computer system that receives data sent from terminals and performs preprocessing, data integration, and calculations using machine learning models.

[0328] 3. In-vehicle navigation system: A device that displays the optimal delivery route and guides the driver through voice guidance and map display.

[0329] Hardware and software to be used

[0330] Hardware: Smartphones, tablets, servers, in-car navigation systems

[0331] Software: Python, Pandas, Scikit-learn, Google Maps API

[0332] Program processing

[0333] The user (driver) enters the day's delivery information (package ID, delivery address, package type) and resident absence / redelivery history data (past delivery dates and times, number of redeliveries) into the terminal. The terminal converts the entered data into JSON format and sends it to the server using the HTTPS protocol.

[0334] The server converts the received JSON data into a DataFrame using Pandas and performs preprocessing such as imputing missing values ​​and normalizing the data. Next, the server uses the Google Maps API to retrieve real-time traffic information, including congestion and road closure information.

[0335] The server integrates electricity meter information (data indicating residents' occupancy status) and route feature data from the perspective of area drivers (road width, number of traffic lights, availability of parking spaces) to gather all the data necessary for optimizing delivery routes. The integrated data is then input into a machine learning model using Scikit-learn to calculate the optimal delivery route. The model proposes a route that minimizes the likelihood of redelivery.

[0336] The server converts the calculated optimal delivery route into JSON format and sends it to the terminal using the HTTPS protocol. The terminal displays the received optimal delivery route on the vehicle's navigation system. The vehicle's navigation system guides the driver through voice guidance and map display.

[0337] The user (driver) makes deliveries according to the instructions of the in-vehicle navigation system. This reduces the possibility of redeliveries and enables efficient deliveries.

[0338] Examples of specific cases and prompt statements

[0339] As a specific example, the driver enters the following information:

[0340] Delivery information: Package ID 12345, Delivery address 1-2-3 Shibuya-ku, Tokyo, Package type Books

[0341] Absence / Redelivery Record Data: Past delivery date and time 2023-10-01 14:00, Number of redeliveries 2

[0342] Traffic Information Data: Congestion Information - Congestion is occurring on major roads in Shibuya Ward.

[0343] Electric meter information: Home

[0344] Route characteristics data: Narrow road width, many traffic lights, no parking spaces.

[0345] Example of a prompt:

[0346] Delivery information:

[0347] Package ID: 12345

[0348] Delivery address: 1-2-3 Shibuya-ku, Tokyo

[0349] Type of item: Books

[0350] Absence / redelivery data:

[0351] Past delivery date and time: 2023-10-01 14:00

[0352] Number of redelivery attempts: 2

[0353] Traffic information data:

[0354] Traffic Information: Traffic congestion is occurring on major roads in Shibuya Ward.

[0355] Electric meter information:

[0356] Status at home: At home

[0357] Route feature data:

[0358] Road width: Narrow

[0359] Number of traffic lights: Many

[0360] Parking space: None

[0361] When this prompt is input into the generating AI model, the AI ​​calculates the optimal delivery route and displays it on the in-vehicle navigation system. By following this route, the driver can reduce the likelihood of redeliveries and achieve efficient delivery. The flow of the specific processing in Example 3 is explained using Figure 15.

[0362] Step 1:

[0363] The user enters the delivery information into the terminal.

[0364] Users use their smartphones or tablets to enter delivery package information (e.g., Package ID 12345, Delivery address 1-2-3 Shibuya-ku, Tokyo, Package type: Books) and missed / redelivery history data (e.g., Past delivery date and time 2023-10-01 14:00, Number of redeliveries 2) into a dedicated app. The entered data is converted to JSON format within the device.

[0365] Input: Delivery package information, missed delivery / redelivery record data

[0366] Output: Data in JSON format

[0367] Step 2:

[0368] The terminal sends the entered data to the server.

[0369] The device sends data converted to JSON format to the server using the HTTPS protocol. The transmitted data reaches the server via a secure communication channel.

[0370] Input: Data in JSON format

[0371] Output: Data sent to the server

[0372] Step 3:

[0373] The server receives the data and performs preprocessing.

[0374] The server converts the received JSON data into a DataFrame using Pandas. Next, it performs missing value imputation (e.g., imputing 0 if the number of redeliveries is not entered) and data normalization (e.g., converting address data to a standard format).

[0375] Input: Data sent to the server

[0376] Output: Preprocessed data frame

[0377] Step 4:

[0378] The server uses the Google Maps API to retrieve traffic information.

[0379] The server calls the Google Maps API to retrieve real-time traffic information for a specified area (e.g., Shibuya Ward, Tokyo). The retrieved data includes congestion information and road closure information for major roads.

[0380] Input: Specified area information

[0381] Output: Real-time traffic information

[0382] Step 5:

[0383] The server integrates electricity meter information and route characteristic data.

[0384] The server integrates electricity meter information (e.g., whether residents are at home) and route characteristic data from the perspective of area drivers (e.g., narrow roads, many traffic lights, lack of parking spaces) to gather all the data necessary to optimize delivery routes.

[0385] Input: Electric meter information, route characteristic data

[0386] Output: Integrated data

[0387] Step 6:

[0388] The server inputs data into a machine learning model and calculates the optimal delivery route.

[0389] The server inputs the integrated data into a machine learning model using Scikit-learn to calculate the optimal delivery route. The model proposes a route that minimizes the likelihood of redelivery.

[0390] Input: Integrated data

[0391] Output: Optimal delivery route

[0392] Step 7:

[0393] The server sends the calculation results to the terminal.

[0394] The server converts the calculated optimal delivery route into JSON format and sends it to the terminal using the HTTPS protocol.

[0395] Input: Optimal delivery route

[0396] Output: Data sent to the terminal

[0397] Step 8:

[0398] The terminal displays the optimal delivery route on the vehicle's navigation system.

[0399] The terminal displays the optimal delivery route received on the vehicle's navigation system. The vehicle's navigation system guides the driver through voice guidance and map display.

[0400] Input: Data sent to the terminal

[0401] Output: Route displayed on the in-car navigation system

[0402] Step 9:

[0403] The user makes the delivery following the instructions of the in-car navigation system.

[0404] The user (driver) makes deliveries according to the instructions of the in-car navigation system. For example, if the car navigation system instructs the driver to "turn right at the next intersection," the driver will follow the instructions and turn right. This reduces the possibility of redeliveries and enables efficient deliveries.

[0405] Input: Instructions from the car's navigation system

[0406] Output: Achieving efficient delivery

[0407] (Application Example 3)

[0408] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0409] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, in food delivery, the inability to provide optimal routes considering traffic information and past delivery data meant that delivery times could not be shortened or efficiency improved. This resulted in delivery delays and redeliveries, leading to decreased customer satisfaction.

[0410] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0411] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to input food delivery information and determine the optimal delivery route based on traffic information data, past delivery performance data, and area characteristic data; and a means to display the optimal route on a smartphone's car navigation app. This reduces the possibility of redeliveries and enables shorter delivery times and increased efficiency.

[0412] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[0413] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent in the past and the number of times redelivery was attempted.

[0414] "Delivery information for the day" refers to information about packages to be delivered on that day, such as the contents of the package, the delivery address, and the estimated delivery time.

[0415] "Electricity meter information" is data that shows the electricity usage of residents and is used to estimate whether or not residents are at home.

[0416] "Route characteristic data from the perspective of area drivers" refers to information about the characteristics of roads and traffic conditions within the delivery area, as well as route characteristics based on the driver's experience.

[0417] "AI" is an abbreviation for artificial intelligence, a technology that calculates the optimal delivery route based on data.

[0418] A "car navigation system" is a navigation system installed in a vehicle that provides map information and route guidance.

[0419] "Food delivery" is a service that delivers food and beverages to a location specified by the customer.

[0420] A "smartphone car navigation app" is a navigation application that runs on a smartphone and is software that provides map information and route guidance.

[0421] An "optimal delivery route" is a delivery route calculated based on traffic information and past delivery performance data in order to shorten delivery times and improve efficiency.

[0422] A system for carrying out this invention includes a server, a smartphone, a car navigation application, and an AI model. Specific embodiments of this system are described below.

[0423] System Configuration

[0424] 1. Server:

[0425] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, an AI model is used to calculate the optimal delivery route.

[0426] The server is programmed using Python and has interfaces for retrieving traffic information APIs and past delivery performance data.

[0427] 2. Smartphone:

[0428] Smartphones are devices used by food delivery drivers and have applications installed for entering delivery information.

[0429] The smartphone application receives the optimal delivery route from the server and displays it on the car navigation application.

[0430] 3. Car navigation application:

[0431] The car navigation application runs on a smartphone and displays the optimal delivery route received from the server.

[0432] Drivers use this car navigation application to make deliveries following the optimal route.

[0433] Data processing

[0434] The server processes the data in the following steps:

[0435] 1. Data collection:

[0436] The server retrieves the latest traffic information data from the traffic information API.

[0437] The server reads past delivery data from a local file.

[0438] The server receives the delivery package information for the day entered by the food delivery driver.

[0439] 2. Calculation of the optimal route using an AI model:

[0440] The server uses a random forest regression model to calculate the optimal delivery route based on the collected data.

[0441] The model uses traffic information, past delivery performance data, and area characteristic data as features to shorten delivery times and improve efficiency.

[0442] 3. Delivery of the optimal route:

[0443] The server sends the calculated optimal route to the smartphone application.

[0444] The smartphone application displays the received optimal route on the car navigation application.

[0445] Specific example

[0446] For example, suppose the driver entered the following delivery information.

[0447] Order 1: Address 1, Delivery time 12:00

[0448] Order 2: Address 2, Delivery time 12:30

[0449] The server obtains the latest traffic information from a traffic information API and calculates the optimal delivery route based on past delivery data. For example, if past data indicates that "address 1 experiences less traffic congestion in the morning," the server will suggest a route that "delivers to address 1 first, and then proceeds to address 2."

[0450] Example of a prompt

[0451] Please calculate the optimal delivery route based on the following delivery information.

[0452] Order 1: Address 1, Delivery time 12:00

[0453] Order 2: Address 2, Delivery time 12:30

[0454] Obtain the latest traffic information from the traffic information API and propose the optimal route based on past delivery performance data.

[0455] In this way, by combining servers, smartphones, car navigation applications, and AI models, it becomes possible to reduce the likelihood of redelivery and shorten and streamline delivery times.

[0456] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0457] Step 1:

[0458] The user (food delivery driver) uses a smartphone application to enter the day's delivery information (order details, delivery address, estimated delivery time, etc.).

[0459] Input: Delivery information (order details, delivery address, estimated delivery time)

[0460] Output: Delivery information sent to the server

[0461] Step 2:

[0462] The server retrieves the latest traffic information data from the traffic information API.

[0463] Input: Request to the traffic information API

[0464] Output: Traffic information data (road congestion, traffic accidents, construction information, etc.)

[0465] Step 3:

[0466] The server reads past delivery data from a local file.

[0467] Input: Local file (past delivery data)

[0468] Output: Past delivery data (resident absence times, redelivery history, etc.)

[0469] Step 4:

[0470] The server uses a random forest regression model to calculate the optimal delivery route based on the collected data (delivery information, traffic information data, and past delivery performance data).

[0471] Input: Delivery information, traffic information data, past delivery performance data

[0472] Output: Optimal delivery route (delivery order, estimated delivery time, etc.)

[0473] Specific operation: The server extracts features (traffic information, past delivery records, area characteristics) and inputs them into a random forest regression model to predict the optimal delivery route.

[0474] Step 5:

[0475] The server sends the calculated optimal route to the smartphone application.

[0476] Input: Optimal delivery route

[0477] Output: Optimal route sent to the smartphone application

[0478] Step 6:

[0479] The smartphone application displays the received optimal route on the car navigation application.

[0480] Input: Optimal delivery route

[0481] Output: Optimal route displayed in the car navigation application

[0482] Specific operation: The smartphone application analyzes the optimal route received from the server and converts it into a format for display on the car navigation application.

[0483] Step 7:

[0484] The user (food delivery driver) makes deliveries following the optimal route displayed on the car navigation application.

[0485] Input: Optimal route displayed in the car navigation application

[0486] Output: Delivery completed

[0487] Specific actions: The driver will follow the instructions of the car navigation application to reach the delivery destination and complete the delivery efficiently.

[0488] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0489] "Example of form 1"

[0490] One embodiment of the present invention provides a delivery route determination system incorporating an emotion engine. This system recognizes the user's emotions and optimizes the delivery route accordingly. Specifically, it analyzes information such as voice, text, facial expressions, and body movements obtained during interaction with the user, and determines the delivery route based on the results. For example, if a user shows anger or dissatisfaction, the system can improve user satisfaction by prioritizing deliveries to that user.

[0491] "Example of form 2"

[0492] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine adjusts the delivery time and delivery method. Specifically, the delivery time is sped up or delayed according to the user's emotions. In addition, if the user shows joy or excitement, the system can also devise a delivery method, such as delivering a gift as a surprise. This makes it possible to provide a service that responds to the user's emotions and further improves user satisfaction. "Example of Embodiment 3"

[0493] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine and an AI work together. Specifically, the emotion engine recognizes the user's emotions and provides that information to the AI. The AI ​​then optimizes the delivery route based on that information. For example, if the user is expressing happiness, the AI ​​prioritizes deliveries to that user. Conversely, if the user is expressing anger, the AI ​​delays deliveries to that user and prioritizes deliveries to other users. This makes it possible to optimize delivery routes in response to the user's emotions.

[0494] The following describes the processing flow for each example of the form.

[0495] "Example of form 1"

[0496] Step 1: The emotion engine collects information such as voice, text, facial expressions, and body movements obtained during interactions with the user.

[0497] Step 2: The emotion engine analyzes the collected information and recognizes the user's emotions.

[0498] Step 3: Based on the recognized emotions, the AI ​​optimizes the delivery route.

[0499] "Example of form 2"

[0500] Step 1: The emotion engine collects information obtained through interaction with the user and recognizes the user's emotions.

[0501] Step 2: Based on the recognized emotions, the emotion engine adjusts the delivery time and delivery method.

[0502] Step 3: Based on the adjusted delivery time and method, the AI ​​optimizes the delivery route.

[0503] "Example of form 3"

[0504] Step 1: The emotion engine collects information obtained through interaction with the user and recognizes the user's emotions.

[0505] Step 2: Provide the emotional information recognized by the emotion engine to the AI.

[0506] Step 3: Based on the provided emotional information, the AI ​​optimizes the delivery route.

[0507] (Example 1)

[0508] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0509] Conventional delivery route determination systems can optimize delivery routes by considering traffic information and resident absence data, but they do not take user emotions into account. Therefore, there is a need to re-optimize delivery routes to improve user satisfaction. In addition, there is a need for means to maintain delivery quality regardless of experience level.

[0510] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0511] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using artificial intelligence; a means of linking the route determined by artificial intelligence with a vehicle navigation system; a means of collecting emotional data such as voice, text, facial expressions, and body movements through interaction with the user; and a means of analyzing the collected emotional data and re-optimizing the delivery route based on the results. This makes it possible to optimize the delivery route while considering the user's emotions, thereby improving user satisfaction. Furthermore, it is possible to maintain a consistent delivery level regardless of experience level.

[0512] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[0513] "Resident absence / redelivery record data" refers to information based on past delivery history, such as the time periods when residents were absent and the number of times redelivery was attempted.

[0514] "Delivery information for the day" refers to detailed information such as the type, quantity, and delivery address of packages to be delivered on that day.

[0515] "Electricity meter information" refers to data that shows residents' electricity consumption, and is obtained from smart meters and other sources.

[0516] "Route characteristic data from the perspective of area drivers" refers to feedback information based on the driver's experience, such as road conditions within the delivery area and the characteristics of delivery destinations.

[0517] "Artificial intelligence" is a computer program that uses machine learning and data analysis to calculate the optimal delivery route.

[0518] A "vehicle navigation system" is an electronic device installed in a vehicle that provides route guidance to a destination.

[0519] "User interaction" refers to communication with users during delivery through voice, text, facial expressions, body language, etc.

[0520] "Emotional data" refers to information about a user's emotions obtained from their voice, text, facial expressions, body movements, etc.

[0521] "Emotional analysis" is the process of analyzing collected emotional data to identify the user's emotional state.

[0522] "Re-optimizing delivery routes" is a process of recalculating existing delivery routes based on the results of sentiment analysis and changing them to the optimal route.

[0523] This invention is a system that uses artificial intelligence (AI) to determine the delivery route for the day, minimizing the likelihood of daily redeliveries, by utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. It also includes a function to re-optimize the delivery route by taking user emotions into consideration.

[0524] Hardware and software to be used

[0525] Hardware: Servers, terminals, sensors (electricity meters, cameras, etc.)

[0526] Software: AI models (TensorFlow, PyTorch, etc.), sentiment analysis engine, database management system (MySQL®, PostgreSQL, etc.)

[0527] Data collection and processing

[0528] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter data, and route characteristic data from the perspective of area drivers. This data is obtained from sources such as the Google Maps API, delivery management systems, and smart meters. The collected data is stored in databases such as MySQL and PostgreSQL.

[0529] The server uses an AI model based on stored data to calculate the optimal delivery route. Specifically, it uses TensorFlow or PyTorch to build a route optimization model, taking into account residents' absence times and past redelivery data to minimize the likelihood of redelivery.

[0530] The device collects emotional data through interaction with the user during delivery. Specifically, it uses a voice assistant and camera to acquire information such as voice, text, facial expressions, and body movements.

[0531] The server analyzes the collected emotional data using an emotion analysis engine. Specifically, it uses OpenAI's (registered trademark) emotion analysis model to recognize the user's emotions and identify feelings such as anger and dissatisfaction.

[0532] The server reoptimizes delivery routes based on the results of sentiment analysis. Specifically, if a user expresses anger or dissatisfaction, the route is recalculated to prioritize deliveries to that user.

[0533] Specific example

[0534] For example, when deciding on a delivery route for a given day, the following steps are taken:

[0535] 1. The server collects traffic information data (e.g., Google Maps API), data on residents' absence / redelivery history (e.g., past delivery history), information on packages delivered on the day (e.g., data from the delivery management system), electricity meter information (e.g., smart meter data), and route characteristic data from the perspective of area drivers (e.g., driver feedback).

[0536] 2. The server stores this data in a database and uses an AI model (e.g., a route optimization model using TensorFlow) to calculate the optimal delivery route.

[0537] 3. The device collects user emotion data through interactions with the user during delivery (e.g., voice assistant or facial recognition via camera).

[0538] 4. The server uses an emotion analysis engine (e.g., OpenAI's emotion analysis model) to analyze the collected emotion data and, if a user is showing anger or dissatisfaction, recalculates the delivery route to prioritize deliveries to that user.

[0539] Example of a prompt

[0540] "Create an AI model that determines the optimal delivery route based on traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter data, and route characteristic data from the perspective of area drivers. Additionally, add a function that recognizes user emotions and optimizes the delivery route accordingly."

[0541] In this way, we will explain a specific implementation of the system while clearly defining the roles of the server, terminal, and user.

[0542] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0543] Step 1:

[0544] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0545] Inputs: Traffic information from Google Maps API, data on residents' absence / redelivery history from the delivery management system, information on packages delivered on the day, electricity meter information from smart meters, and driver feedback.

[0546] Data processing: Integrate information obtained from various data sources and store it in a database.

[0547] Output: Integrated dataset.

[0548] Specific operation: The server sends API requests to obtain traffic information, retrieves data on residents' absences / redeliveries from the delivery management system, and obtains electricity meter information from smart meters. This data is then integrated and stored in a database.

[0549] Step 2:

[0550] The server uses an AI model based on the stored data to calculate the optimal delivery route.

[0551] Input: Integrated dataset.

[0552] Data Calculation: Use TensorFlow or PyTorch to build route optimization models, taking into account residents' absence times and past redelivery data to reduce the likelihood of redelivery.

[0553] Output: Optimal delivery route.

[0554] Specific operation: The server inputs the integrated dataset into an AI model and calculates the optimal delivery route. The calculation results are sent to the vehicle's navigation system.

[0555] Step 3:

[0556] The device collects emotional data through interactions with the user during delivery.

[0557] Input: User interaction (voice, text, facial expressions, body movements).

[0558] Data processing: Speech recognition, text analysis, facial recognition, and motion analysis are performed to extract emotional data.

[0559] Output: Extracted sentiment data.

[0560] Specific operation: The device uses a voice assistant and camera to collect the user's voice, text, facial expressions, and body movements, and analyzes this data to extract emotional data.

[0561] Step 4:

[0562] The server analyzes the collected emotional data using an emotion analysis engine.

[0563] Input: Extracted sentiment data.

[0564] Data processing: Use OpenAI's emotion analysis model to recognize user emotions and identify feelings such as anger and frustration.

[0565] Output: Results of the sentiment analysis.

[0566] Specific operation: The server inputs emotional data into the emotion analysis engine to identify the user's emotional state. The analysis results are used to re-optimize the delivery route.

[0567] Step 5:

[0568] The server re-optimizes the delivery route based on the results of the sentiment analysis.

[0569] Input: Based on the results of sentiment analysis, the optimal delivery route.

[0570] Data processing: Recalculate existing delivery routes, taking into account the results of sentiment analysis.

[0571] Output: Re-optimized delivery route.

[0572] Specific operation: Based on the sentiment analysis results, the server recalculates the route to prioritize deliveries to users who have expressed anger or dissatisfaction. The re-optimized route is then sent to the vehicle's navigation system.

[0573] (Application Example 1)

[0574] Next, we will describe Application Example 1 of Form 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."

[0575] Traditional delivery route planning systems could consider traffic information and data on residents' absences and redelivery attempts, but they did not optimize delivery routes in consideration of user emotions. Therefore, it was difficult to improve user satisfaction, and there was a particular challenge in responding appropriately to users who expressed dissatisfaction or anger. Furthermore, there was a need to maintain delivery levels without relying on the experience of individual delivery drivers.

[0576] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0577] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to recognize the user's emotions and optimize the delivery route accordingly; and a means to analyze information such as voice, text, facial expressions, and body movements. This makes it possible to optimize the delivery route while considering the user's emotions, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a consistent delivery level without relying on the experience of the delivery driver.

[0578] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[0579] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent during past deliveries and the number of times redeliveries were made.

[0580] "Today's delivery information" refers to detailed information such as the type, quantity, and delivery address of packages scheduled to be delivered on that day.

[0581] "Electric meter information" refers to power consumption data from electric meters used to estimate whether residents are at home.

[0582] "Route characteristic data from the perspective of area drivers" refers to route characteristic information based on the driver's experience, such as road conditions within the delivery area and the characteristics of delivery destinations.

[0583] "AI" is an abbreviation for artificial intelligence, and it refers to algorithms and systems that analyze data and determine the optimal delivery route.

[0584] A "car navigation system" is a navigation system installed in a vehicle that provides route guidance to a destination.

[0585] "User emotions" refer to the emotional state exhibited by users of the delivery service, and are analyzed from sources such as voice, text, facial expressions, and body movements.

[0586] "Information such as voice, text, facial expressions, and body movements" refers to a variety of data formats used to recognize a user's emotions.

[0587] The system for implementing this invention collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers, and the AI ​​determines the optimal delivery route. Furthermore, it includes a function to recognize the user's emotions and optimize the delivery route according to those emotions.

[0588] System Configuration

[0589] hardware

[0590] Server: Collects, analyzes, and stores data.

[0591] Smartphone: Used by delivery drivers and linked to the car's navigation system.

[0592] Smart meter: An electric meter used to estimate whether a user is at home.

[0593] Camera: Used to recognize the user's facial expressions.

[0594] Microphone: Used to collect the user's voice.

[0595] software

[0596] Google Maps API: Collects real-time traffic information.

[0597] Firebase: Manages user absence / redelivery history data and delivery order information for the day.

[0598] Smart Meter API: Collects electricity meter information.

[0599] Google Cloud Speech-to-Text: Converts speech data into text.

[0600] Google Cloud Natural Language: Performs sentiment analysis on text data.

[0601] OpenCV: Used to analyze facial expressions.

[0602] TensorFlow: Used to analyze body movements.

[0603] Data collection and analysis

[0604] The server uses the Google Maps API to collect real-time traffic information. It also uses Firebase to manage user absence / redelivery history data and daily delivery order information. The smart meter API is used to collect electricity meter information to estimate whether users are at home.

[0605] Embedding an emotion engine

[0606] The server uses Google Cloud Speech-to-Text to convert speech data to text and Google Cloud Natural Language to perform sentiment analysis on the text data. Furthermore, it uses OpenCV to analyze facial expressions and TensorFlow to analyze body movements.

[0607] Optimizing delivery routes

[0608] The server uses AI to determine the optimal delivery route based on collected data. It also takes user emotions into consideration, prioritizing deliveries for users who express dissatisfaction or anger to improve customer satisfaction.

[0609] Specific example

[0610] For example, if a user orders food delivery and has a history of frequent redeliveries, the server will optimize the delivery route by considering the user's availability at home. Also, if a user expresses anger or dissatisfaction, prioritizing their delivery will improve their satisfaction.

[0611] Example of a prompt

[0612] "Design a system where, when a user orders food delivery, AI collects traffic information, past missed / redelivery data, current delivery order information, electricity meter data, and route characteristic data from the perspective of the area driver, and then determines the optimal delivery route. Additionally, incorporate an emotion engine to recognize the user's emotions and optimize the delivery route accordingly."

[0613] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0614] Step 1:

[0615] The server uses the Google Maps API to collect real-time traffic information. It takes current traffic conditions and road congestion information as input and generates traffic data necessary for optimizing delivery routes as output. Specifically, it sends API requests and parses the response data.

[0616] Step 2:

[0617] The server uses Firebase to manage user absence / redelivery history data and daily delivery order information. It takes past delivery history and daily order information as input and generates user data necessary for optimizing delivery routes as output. Specifically, it executes database queries and extracts the required data.

[0618] Step 3:

[0619] The server collects electricity meter information using a smart meter API. It takes user electricity consumption data as input and generates data to estimate the user's presence at home as output. Specifically, it sends API requests and parses the response data.

[0620] Step 4:

[0621] The server uses Google Cloud Speech-to-Text to convert speech data to text. It takes user speech data as input and generates text data as output. Specifically, it uploads the speech file to the cloud service and receives the text conversion result.

[0622] Step 5:

[0623] The server uses Google Cloud Natural Language to perform sentiment analysis on text data. It takes converted text data as input and generates sentiment analysis results as output. Specifically, it sends text data to the cloud service and receives a sentiment score.

[0624] Step 6:

[0625] The server uses OpenCV to analyze facial expressions. It takes user facial image data as input and generates facial expression analysis results as output. Specifically, it applies image processing algorithms to extract facial feature points.

[0626] Step 7:

[0627] The server uses TensorFlow to analyze body movements. It takes user movement data as input and generates movement analysis results as output. Specifically, it applies machine learning models to extract movement characteristics.

[0628] Step 8:

[0629] The server uses AI to determine the optimal delivery route based on all the collected data. It takes traffic information data, user data, electricity meter information, emotion analysis results, facial expression analysis results, and motion analysis results as input, and generates the optimal delivery route as output. Specifically, it applies an AI algorithm to calculate the optimal route.

[0630] Step 9:

[0631] The server integrates with the car navigation system to determine the optimal delivery route. It takes optimal route data as input and sends route information to the car navigation system as output. Specifically, it sends data to the car navigation system and starts route guidance.

[0632] Step 10:

[0633] The user delivers packages according to an optimized delivery route via the delivery driver's smartphone. The user receives route guidance from the car's navigation system as input and reports the completion of the delivery as output. Specifically, the user uses the smartphone's navigation function to arrive at the delivery destination.

[0634] (Example 2)

[0635] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0636] Traditional delivery systems struggled to determine delivery routes considering traffic information and residents' absences, leading to a high likelihood of redeliveries. Furthermore, their reliance on the delivery driver's experience made it difficult for new drivers to deliver efficiently. Additionally, the inability to provide services tailored to user needs meant that improving user satisfaction remained a challenge.

[0637] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0638] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route that minimizes the possibility of redelivery using a generating AI model; a means of linking the route determined by the generating AI model with a car navigation system; a means of analyzing user emotion data and adjusting delivery time and method; and a means of delivering a surprise gift according to the user's emotions. This reduces the possibility of redelivery, enables efficient delivery regardless of whether the driver is veteran or rookie, and allows for the provision of services that respond to the user's emotions.

[0639] "Traffic information data" refers to information that influences the determination of delivery routes, such as traffic conditions, road congestion levels, and accident information.

[0640] "Resident absence / redelivery history data" refers to information about the time periods when residents were absent during past deliveries and the history of redeliveries.

[0641] "Delivery information for the day" refers to detailed information about packages scheduled for delivery on that day, such as the number and size of packages, and the delivery address.

[0642] "Electricity meter information" refers to electricity consumption data used to estimate whether the resident at the delivery address is at home.

[0643] "Route characteristic data from the perspective of area drivers" refers to route characteristics and points to note based on the experience and knowledge of drivers in a specific area.

[0644] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to calculate the optimal delivery route.

[0645] A "car navigation system" refers to a navigation system installed in a vehicle that provides map information and route guidance.

[0646] "User emotional data" refers to data that indicates a user's emotional state, and includes information obtained from smartphone sensors and wearable devices.

[0647] An "emotion engine" refers to a system that analyzes user emotional data and adjusts delivery times and methods based on the results.

[0648] "Methods for delivering gifts as a surprise" refers to methods and processes for delivering special gifts in addition to regular deliveries, depending on the user's emotions.

[0649] Modes for carrying out the invention

[0650] This invention is a system that uses traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the delivery route for the day using a generating AI model that minimizes the possibility of daily redeliveries. Furthermore, it has a function that links the route determined by the generating AI model with a car navigation system, analyzes user sentiment data, and adjusts the delivery time and delivery method.

[0651] Server Role

[0652] The server first receives a delivery request from the user. This request includes the delivery address, desired delivery time, and any special instructions. Next, the server uses a generative AI model to calculate the optimal delivery route. In this process, it utilizes map information services such as the Google Maps API to take traffic conditions and distance into consideration. The calculated route is then sent from the server to the car navigation system. Furthermore, the server receives the user's sentiment data and analyzes it using an sentiment engine. Based on the analysis results, it adjusts the delivery time and method, and adds a surprise gift if necessary.

[0653] The role of the terminal (car navigation system)

[0654] The terminal (car navigation system) receives optimal route information transmitted from the server. Based on the received route information, the car navigation system displays the route on its screen. The driver then makes deliveries following this route. The car navigation system can also update location information in real time and recalculate the route as needed.

[0655] User roles

[0656] Users submit delivery requests to the system. These requests can include the delivery address, preferred delivery time, and any special instructions. User emotion data is also sent to the system. The emotion engine analyzes this data and adjusts the delivery time and method based on the user's emotions. For example, if a user expresses joy or excitement, the system may deliver a gift as a surprise.

[0657] Specific example

[0658] For example, if a user submits a request to deliver to "1-1-1 Shibuya, Shibuya-ku, Tokyo," the server uses the Google Maps API to calculate the optimal route. The calculated route is sent from the server to the car's navigation system and displayed on the navigation screen. The driver then makes the delivery according to this route.

[0659] Furthermore, if a user requests delivery to "1-1-1 Shibuya, Shibuya-ku, Tokyo" and also indicates "joy" as emotional data, the emotion engine analyzes this data and speeds up the delivery time. It may also add a small gift as a surprise.

[0660] Example of a prompt

[0661] "If a user requests delivery to 1-1-1 Shibuya, Shibuya-ku, Tokyo, and their emotional data indicates happiness, please explain how to calculate the optimal delivery route, expedite the delivery time, and add a surprise gift."

[0662] In this way, the system provides the optimal delivery route and method based on user requests and sentiment data.

[0663] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0664] Program processing flow

[0665] Step 1: Receiving a delivery request

[0666] The server receives delivery requests from users. These requests include the delivery address, preferred delivery time, and any special instructions.

[0667] Input: Delivery request from the user (address, preferred delivery time, special instructions)

[0668] Output: Delivery request data

[0669] Specific operation: The server receives an HTTP request and extracts the necessary information from the request body.

[0670] Step 2: Calculating the optimal route

[0671] The server uses a generated AI model to calculate the optimal delivery route. In doing so, it takes into account traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0672] Inputs: Delivery request data, traffic information data, resident absence / redelivery record data, delivery package information for the day, electricity meter information, route characteristic data from the perspective of the area driver.

[0673] Output: Optimal route information

[0674] Specific operation: The server sends a request to the Google Maps API, analyzes the returned data, and determines the optimal route.

[0675] Step 3: Send route information

[0676] The server sends the calculated optimal route information to the car navigation system.

[0677] Input: Optimal route information

[0678] Output: Send route information to car navigation system

[0679] Specific operation: The server sends route information to the car navigation system using WebSocket or HTTP POST requests.

[0680] Step 4: Display the route

[0681] The terminal (car navigation system) receives optimal route information sent from the server and displays the route on the car navigation screen.

[0682] Input: Route information from the server

[0683] Output: Route displayed on the car navigation screen

[0684] Specific operation: The device uses a screen rendering API to display the route on the map.

[0685] Step 5: Updating real-time location information

[0686] The device (car navigation system) updates location information in real time and recalculates the route as needed.

[0687] Input: GPS data

[0688] Output: Updated route information

[0689] Specific operation: The device acquires GPS data and sends it to the server. It also requests a new route as needed.

[0690] Step 6: Receiving and analyzing emotional data

[0691] The server receives user emotion data and analyzes it using an emotion engine.

[0692] Input: User sentiment data

[0693] Output: Analysis results (user's emotional state)

[0694] Specific operation: The server calls an emotion analysis API to analyze the user's emotion data.

[0695] Step 7: Adjusting the delivery method

[0696] The server adjusts delivery times and methods based on the analysis results. For example, if the user expresses delight, it might expedite delivery or add a surprise gift.

[0697] Input: Analysis results (user's emotional state)

[0698] Output: Adjusted delivery schedule and delivery method

[0699] Specific actions: The server updates the delivery schedule and instructs the addition of gifts as needed.

[0700] Step 8: Delivery Confirmation

[0701] After delivery is complete, the user reviews the delivery contents and provides feedback.

[0702] Input: Delivery completion notification

[0703] Output: User feedback

[0704] Specific actions: The user receives a delivery completion notification via a smartphone app and confirms the delivery details. They also fill out and submit a feedback form.

[0705] (Application Example 2)

[0706] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0707] Traditional delivery systems can determine delivery routes by considering traffic information and resident absence data, but they cannot adjust delivery times or methods to suit user preferences, thus failing to adequately improve user satisfaction. Furthermore, they are dependent on the experience of delivery drivers, making it difficult to maintain consistent delivery levels. Additionally, they are unable to provide value-added services such as surprise gift deliveries tailored to user needs.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0709] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to detect user emotion data and adjust the delivery time and method; and a means to deliver a surprise gift according to the user's emotions. This enables flexible adjustment of delivery time and method according to the user's emotions, thereby improving user satisfaction. Furthermore, it enables maintaining a high level of delivery regardless of the delivery driver's experience and provides value-added services to users.

[0710] "Traffic information data" refers to information about traffic that affects delivery routes, such as road congestion, traffic accidents, and construction information.

[0711] "Resident absence / redelivery history data" refers to information about the time periods when residents were absent during past deliveries and the history of redeliveries.

[0712] "Delivery information for the day" refers to information about packages scheduled to be delivered on that day, including the number and size of packages, and the delivery address.

[0713] "Electricity meter information" refers to data about residents' electricity usage and is used to estimate whether or not residents are at home.

[0714] "Route characteristic data from the perspective of area drivers" refers to information about route characteristics and points to note based on the experience of drivers in a specific area.

[0715] "Using AI to determine the optimal delivery route" refers to a method that uses artificial intelligence to decide the best delivery route.

[0716] "Methods for linking with car navigation systems" refers to methods that display the determined delivery route on the in-vehicle navigation system, allowing the driver to follow that route while driving.

[0717] "User emotion data" refers to data used to detect a user's emotional state, including emotions such as joy, excitement, and sadness.

[0718] "Means of adjusting delivery time and delivery method" refers to methods of speeding up or delaying delivery times, or changing delivery methods, based on user sentiment data.

[0719] "Methods for delivering surprise gifts" refers to methods of delivering additional gifts in response to the user's emotions.

[0720] The system for implementing this invention includes means for using AI to determine a delivery route for the day that minimizes the possibility of daily redeliveries, utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; means for linking the route determined by the AI ​​with a car navigation system; means for detecting user emotion data and adjusting delivery time and delivery method; and means for delivering a surprise gift according to the user's emotions.

[0721] System Configuration

[0722] hardware

[0723] Server: Performs data processing and runs AI models.

[0724] Smartphone: Used by the driver and linked to the car navigation system.

[0725] Emotion detection devices: Devices used to collect user emotion data (e.g., smartphone cameras or wearable devices).

[0726] software

[0727] AI Routing API: An API for determining the optimal delivery route.

[0728] Emotion detection library: A library for detecting a user's emotions.

[0729] Car navigation application: An application that displays routes determined by AI.

[0730] Data processing and data calculation

[0731] The server collects traffic information data, data on residents' absence / redelivery history, information on packages delivered that day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, it uses an AI routing API to determine the optimal delivery route. The determined route is sent to the driver's smartphone navigation application and displayed to them.

[0732] User emotion data is collected through emotion detection devices and analyzed using emotion detection libraries. Based on the analysis results, the server adjusts the delivery time and method, and delivers surprise gifts as needed.

[0733] Specific example

[0734] For example, if user A orders food delivery, the server determines the optimal delivery route based on traffic data and resident absence data, and displays it on the driver's smartphone. If user A's mood is detected as "happy," the server instructs the delivery of an additional surprise gift.

[0735] Example of a prompt

[0736] "Develop a food delivery application that adjusts delivery times and methods based on the user's emotions. It will use AI to determine the optimal delivery route and an emotion engine to adjust deliveries according to the user's feelings. Specifically, if the user expresses joy or excitement, it will deliver a surprise gift."

[0737] In this way, it becomes possible to flexibly adjust delivery times and methods according to the user's feelings, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a high level of delivery regardless of the delivery driver's experience, and to provide value-added services to users.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0739] Step 1:

[0740] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. This data is obtained from various sensors and databases. Input data includes traffic information, resident absence data, package delivery information, electricity meter information, and route characteristic data. Based on this data, the server preprocesses the data and converts it into a format for input into the AI ​​model.

[0741] Step 2:

[0742] The server sends the pre-processed data to the AI ​​routing API to determine the optimal delivery route. The AI ​​routing API calculates the best route based on the input data and returns the result to the server. The output data includes the optimal delivery route information. The server receives this route information and proceeds to the next step.

[0743] Step 3:

[0744] The server sends the determined optimal delivery route information to the driver's smartphone. The smartphone's navigation application receives this route information and displays it to the driver. The input data includes the optimal delivery route information, and the output data includes the route information displayed on the smartphone's navigation screen. The driver begins driving according to this route.

[0745] Step 4:

[0746] The user collects emotional data using an emotion detection device (e.g., a smartphone camera or wearable device). The emotion detection device analyzes the user's facial expressions, voice, etc., and generates emotional data. The input data includes the user's facial expressions and voice data, and the output data is the generated emotional data.

[0747] Step 5:

[0748] The server sends the user's emotion data to an emotion detection library, which analyzes the user's emotional state. The emotion detection library determines the user's emotion based on the input data and returns the result to the server. The output data includes the user's emotional state (e.g., happy, neutral, sad). The server receives this emotional state and proceeds to the next step.

[0749] Step 6:

[0750] The server adjusts delivery time and method based on the user's emotional state. For example, if the user is determined to be "happy," the server may expedite delivery or add a surprise gift. Input data includes the user's emotional state, and output data includes the adjusted delivery time and method.

[0751] Step 7:

[0752] The server sends the adjusted delivery time and method to the driver's smartphone. The smartphone's navigation application receives this information and displays it to the driver. The input data includes the adjusted delivery time and method, and the output data includes the information displayed on the smartphone's navigation screen. The driver makes deliveries according to this information.

[0753] By following these steps, it becomes possible to flexibly adjust delivery times and methods in accordance with the user's feelings, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a high level of delivery regardless of the delivery driver's experience, and to provide value-added services to users.

[0754] (Example 3)

[0755] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0756] Traditional delivery systems suffered from frequent redeliveries and difficulty in determining efficient delivery routes. Furthermore, maintaining consistent delivery levels was challenging due to reliance on driver experience and skills. Additionally, the inability to effectively utilize real-time traffic information and resident absence data often led to decreased delivery efficiency. To address these challenges, a system utilizing more advanced data analysis and AI technology is necessary.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0758] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route that minimizes the possibility of redelivery using a generating AI model; a means for linking the route determined by the generating AI model with a navigation system; a means for the user to input delivery information; and a means for the server to collect and integrate additional data. This makes it possible to determine efficient delivery routes while minimizing the possibility of redelivery and maintaining delivery levels.

[0759] "Traffic information data" refers to data that includes real-time traffic conditions, road congestion levels, accident information, and more.

[0760] "Resident absence / redelivery history data" refers to data that includes historical information such as the number of times residents were absent during past deliveries and the number of times redeliveries were made.

[0761] "Delivery information for the day" refers to data that includes information such as the type and quantity of packages scheduled to be delivered on that day, and the delivery address.

[0762] "Electricity meter information" is data that shows each resident's electricity usage and is used to estimate whether or not a resident is at home.

[0763] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics and points to note about delivery routes in a specific area, based on past delivery history and the driver's experience.

[0764] A "generative AI model" refers to an algorithm or program that uses machine learning and artificial intelligence technologies to calculate the optimal delivery route based on input data.

[0765] A "navigation system" is a system that provides route guidance to the driver, such as a car navigation device installed in the vehicle or a smartphone app.

[0766] "Means for users to input delivery information" refers to interfaces or applications that drivers use on smartphones or tablets to input information about delivered packages and data on residents' absences / redelivery records.

[0767] "Means for the server to collect and integrate additional data" refers to the function by which the server collects traffic information, electricity meter information, and other data from external data sources, integrates them, and processes them for input into the AI ​​model.

[0768] This invention is a system designed to maximize the efficiency of delivery drivers and reduce the likelihood of redeliveries. This system utilizes traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers, and uses a generated AI model to determine the optimal delivery route.

[0769] The server first receives delivery package information for the day entered by the user (driver) and data on residents' absences / redelivery records. Next, the server collects traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers. This data is obtained from map service APIs (e.g., Google Maps API), smart meter databases of power companies, and databases of past route history.

[0770] The server integrates this data and feeds it into a generative AI model. This AI model is built using machine learning frameworks such as TensorFlow and PyTorch. Based on historical data and real-time information, the AI ​​model calculates the optimal route that minimizes the likelihood of redelivery.

[0771] The calculated optimal route is transmitted to the navigation system and displayed on the driver's terminal. The driver then makes deliveries following the route displayed on the navigation system. This reduces the likelihood of redeliveries and helps maintain delivery levels.

[0772] As a concrete example, a driver responsible for deliveries in a certain area enters the delivery package information for the day, such as "Package A, Package B, Package C," into the system. They also enter data on residents' absence / redelivery history, such as "Resident X has been absent 3 times in the past, Resident Y has had a redelivery once in the past." Based on this information, along with traffic information data obtained from a map service API, electricity meter information obtained from the power company, and past route history data, the system's generating AI model determines the optimal delivery route.

[0773] Examples of prompts to input into a generative AI model include the following:

[0774] "Delivery package information: Package A, Package B, Package C. Resident absence / redelivery history data: Resident X has been absent 3 times in the past, Resident Y has had 1 redelivery in the past. Based on traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers, please calculate the optimal delivery route."

[0775] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine and a generative AI model work in cooperation. Specifically, the emotion engine recognizes the user's emotions and provides this information to the generative AI model. The generative AI model optimizes the delivery route based on this information. For example, if the user is expressing happiness, the generative AI model prioritizes delivery to that user. Conversely, if the user is expressing anger, the generative AI model delays delivery to that user and prioritizes delivery to other users. This makes it possible to optimize the delivery route in response to the user's emotions.

[0776] In this way, the present invention provides a system that utilizes advanced data analysis and AI technology to maximize delivery efficiency and reduce the possibility of redelivery. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[0777] Step 1:

[0778] The user (driver) uses a smartphone or tablet to input information about the day's deliveries and data on residents' absences / redeliveries into a dedicated application. For example, they might enter "Package A, Package B, Package C" into the app's input form and select information such as "Resident X has been absent 3 times in the past, Resident Y has had a redelivery once in the past." The entered data is then sent to the server.

[0779] Step 2:

[0780] The server receives delivery package information and resident absence / redelivery data sent by users. Next, the server collects traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers. Specifically, the server calls a map service API to obtain real-time traffic information and accesses the power company's smart meter database to obtain electricity meter information. It also collects route characteristic data from the perspective of area drivers from a database of past route history. This data is integrated within the server.

[0781] Step 3:

[0782] The server preprocesses the integrated data and converts it into a format for input into the generative AI model. For example, it converts the data into JSON format and inputs it into the generative AI model. Specifically, it generates JSON data like the following:

[0783] json

[0784] {

[0785] "deliveries": ["package A", "package B", "package C"],

[0786] "absence_data": {"Resident X": 3, "Resident Y": 1},

[0787] "traffic_data": {...},

[0788] "meter_data": {...},

[0789] "route_features": {...}

[0790] }

[0791] This data is input into the AI ​​model for generating data.

[0792] Step 4:

[0793] The server inputs integrated data into a generative AI model to calculate the optimal delivery route. Based on historical data and real-time information, the generative AI model calculates the route that minimizes the likelihood of redelivery. For example, it might generate a route like "Route 1: Package A -> Package B -> Package C". The calculation results are stored on the server.

[0794] Step 5:

[0795] The server sends the calculated optimal delivery route to the navigation system. Specifically, it uses the navigation system's API to send route information, which is then displayed on the driver's terminal. For example, information such as "Route 1: Package A -> Package B -> Package C" is displayed on the navigation system.

[0796] Step 6:

[0797] The user (driver) delivers packages according to the optimal delivery route displayed on the navigation system. For example, following the navigation system's instructions, they deliver packages in the order of "deliver package A first, then package B, and finally package C." This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[0798] (Application Example 3)

[0799] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0800] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, delivery routes were not optimized to consider user emotions, making it challenging to improve user satisfaction. In particular, in food delivery, delivery delays and redeliveries significantly impact customer satisfaction, so these issues need to be addressed.

[0801] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0802] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means for an emotion engine to recognize the user's emotions and provide that information to the AI; a means to optimize the delivery route according to the user's emotions; and a means to display the optimized delivery route on a smartphone navigation app. This makes it possible to minimize the possibility of redeliveries and optimize the delivery route according to the user's emotions.

[0803] "Traffic information data" refers to information about traffic that affects delivery routes, such as road congestion, traffic accidents, and construction information.

[0804] "Resident absence / redelivery record data" refers to records of times when residents were absent during past deliveries and records of redeliveries.

[0805] "Delivery information for the day" refers to detailed information about packages to be delivered on that day, including the delivery address, delivery time, and type of package.

[0806] "Electricity meter information" refers to data showing the usage status of electricity meters at each delivery location, and is used to estimate whether residents are at home.

[0807] "Route characteristic data from the perspective of area drivers" refers to information about route characteristics, points to note, and efficient delivery methods held by drivers who are familiar with the delivery area.

[0808] "AI" is an abbreviation for artificial intelligence, and it refers to algorithms and systems that calculate the optimal delivery route based on data.

[0809] A "car navigation system" is a navigation system installed in a vehicle that guides the driver along the optimal route.

[0810] An "emotion engine" is a technology that recognizes a user's emotions and adjusts the system's operation based on that information.

[0811] A "smartphone navigation app" is a navigation application that runs on a smartphone and is software that displays the optimal route for the user.

[0812] The following system configuration and processing procedure will be described as embodiments for carrying out this invention.

[0813] System Configuration

[0814] This system consists of the following hardware and software:

[0815] hardware

[0816] server

[0817] smartphone

[0818] Car navigation system installed in the vehicle

[0819] software

[0820] AI libraries (e.g., RouteOptimizer)

[0821] Emotion Engine (e.g., EmotionEngine)

[0822] Smartphone navigation app

[0823] Processing procedure

[0824] 1. Enter delivery information

[0825] Using a terminal (smartphone), the delivery driver enters the delivery package information for the day. This includes the delivery address, delivery time slot, and package type.

[0826] 2. Obtaining traffic information

[0827] The server retrieves the latest traffic data from a traffic information API. This data includes information such as road congestion, traffic accidents, and construction work.

[0828] 3. Obtaining electricity meter information

[0829] The server retrieves electricity meter information for each delivery address. Based on this information, it estimates whether residents are at home.

[0830] 4. Optimizing delivery routes

[0831] The server uses an AI library (RouteOptimizer) to calculate the optimal delivery route based on delivery information, traffic information, electricity meter information, data on residents' absence / redelivery history, and route characteristic data from the perspective of area drivers.

[0832] 5. Utilizing the Emotion Engine

[0833] The server uses an EmotionEngine to recognize the user's emotions. The EmotionEngine provides the user's emotional information to the AI, which then uses this information to optimize delivery routes.

[0834] 6. Displaying the optimized route

[0835] The optimized delivery route is displayed in the smartphone's navigation app. The driver follows this route to make deliveries.

[0836] Specific example

[0837] For example, suppose one of the delivery destinations is "Address 1," and past data shows that the resident is often absent in the morning. Based on this information, the AI ​​will incorporate "Address 1" into the afternoon delivery route. Also, if the emotion engine recognizes that the resident of "Address 2" is happy, the AI ​​will prioritize incorporating "Address 2" into the delivery route.

[0838] Example of a prompt

[0839] "Please generate the optimal delivery route, taking into account the feelings of the residents at the delivery destination. Based on delivery information, traffic information, and electricity meter information, adjust the route according to the user's feelings."

[0840] The above describes the embodiments for carrying out this invention. This system reduces the likelihood of redelivery and enables the optimization of delivery routes according to the user's emotions.

[0841] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0842] Step 1:

[0843] Using a terminal (smartphone), delivery drivers enter information about the day's deliveries. This information includes the delivery address, delivery time slot, and package type. This information is sent to a server and stored in a database.

[0844] Step 2:

[0845] The server retrieves the latest traffic data from a traffic information API. This data includes road congestion, traffic accidents, and construction information. The server analyzes this data and extracts factors that affect delivery routes.

[0846] Step 3:

[0847] The server retrieves electricity meter information for each delivery address. Using an electricity meter information API, it obtains electricity usage data for each address and estimates whether residents are at home. This information is also stored in the database.

[0848] Step 4:

[0849] The server retrieves data on residents' absences and redelivery history from a database. Based on past delivery data, it analyzes each resident's absence times and redelivery frequency.

[0850] Step 5:

[0851] The server acquires route characteristic data from the perspective of area drivers. This data includes information about route features, points to note, and efficient delivery methods from drivers who are familiar with the delivery area.

[0852] Step 6:

[0853] The server uses an AI library (RouteOptimizer) to calculate the optimal delivery route based on delivery information, traffic information, electricity meter information, data on residents' absence / redelivery history, and route characteristic data from the perspective of area drivers. The AI ​​receives this data as input and outputs the optimal route to minimize the possibility of redelivery.

[0854] Step 7:

[0855] The server uses an EmotionEngine to recognize the user's emotions. The EmotionEngine provides the AI ​​with the user's emotional information, which is then used to optimize delivery routes. For example, if a user is happy, delivery to that user is prioritized.

[0856] Step 8:

[0857] The server sends an optimized delivery route to the smartphone's navigation app. The driver uses the smartphone's navigation app to deliver according to the optimized route. The navigation app provides real-time route guidance and supports the driver.

[0858] The above outlines the specific processing steps of this system. This reduces the likelihood of redelivery and enables the optimization of delivery routes based on the user's preferences.

[0859] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0860] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0861] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0862] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0863] [Second Embodiment]

[0864] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0865] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0866] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0867] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0868] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0869] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0870] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0871] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0872] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0873] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0874] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0875] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0876] "Example of form 1"

[0877] One embodiment of the present invention is a delivery route determination system. This system collects traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the time residents are absent and the history of redeliveries in order to reduce the likelihood of redeliveries.

[0878] "Example of form 2"

[0879] Furthermore, the route determined by the AI ​​is displayed in conjunction with the car navigation system. This allows drivers to deliver according to the optimal route determined by the AI. Specifically, the route determined by the AI ​​is displayed on the car navigation screen, and the driver delivers according to that route. This makes it possible to maintain a consistent delivery level regardless of whether the driver is a veteran or a rookie.

[0880] "Example of form 3"

[0881] As a concrete example, a driver responsible for deliveries in a certain area inputs information about the day's deliveries and data on residents' absences / redelivery records into the system. Based on this information, along with traffic data, electricity meter information, and route characteristic data from the driver's perspective, the AI ​​determines the optimal delivery route. This route is displayed on the car's navigation system, and the driver follows this route for deliveries. This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[0882] The following describes the processing flow for each example of the form.

[0883] "Example of form 1"

[0884] Step 1: The delivery route determination system collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0885] Step 2: Based on the collected data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the residents' absence times and past redelivery data in order to minimize the possibility of redelivery.

[0886] "Example of form 2"

[0887] Step 1: The route determined by the AI ​​will be displayed in conjunction with the car navigation system.

[0888] Step 2: Drivers follow the instructions on the car's navigation system to make deliveries. This ensures that delivery levels are maintained regardless of whether the driver is experienced or new.

[0889] "Example of form 3"

[0890] Step 1: A driver responsible for deliveries in a specific area enters the day's delivery information and data on residents' absences / redelivery attempts into the system.

[0891] Step 2: Based on this information, traffic data, electricity meter information, and route feature data from the perspective of area drivers, the system's AI determines the optimal delivery route.

[0892] Step 3: This route will be displayed on the car's navigation system, and the driver will follow this route for deliveries. This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[0893] (Example 1)

[0894] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0895] Conventional delivery route determination systems had a high probability of redelivery, making efficient delivery difficult. Furthermore, they failed to determine the optimal route considering traffic information and residents' availability, resulting in reduced delivery efficiency. Additionally, disparities in delivery quality between veteran and novice drivers were a significant challenge.

[0896] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0897] In this invention, the server includes means for collecting traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; means for preprocessing the collected data; means for training a machine learning model based on the preprocessed data; means for receiving delivery requests from users; means for calculating the optimal delivery route using the trained machine learning model; means for transmitting the calculated optimal route to a terminal; and means for displaying the route information received by the terminal to the user. This reduces the likelihood of redeliveries and enables the determination of efficient delivery routes. It also helps maintain a consistent delivery level between veteran and novice drivers.

[0898] "Traffic information data" refers to data that shows real-time traffic conditions and road congestion.

[0899] "Resident absence / redelivery history data" refers to data showing the time residents were absent during past deliveries and the history of redeliveries.

[0900] "Delivery information for the day" refers to data that shows detailed information about packages to be delivered on that day.

[0901] "Electricity meter information" refers to data on electricity usage used to estimate whether residents are at home.

[0902] "Route characteristic data from the perspective of area drivers" refers to data on route characteristics based on feedback from drivers.

[0903] "Means of collection" refers to the methods and devices used to acquire the necessary data.

[0904] "Preprocessing means" refers to methods and devices for converting collected data into a format suitable for analysis and model training.

[0905] "Means for training machine learning models" refers to methods and devices for training machine learning algorithms using collected and pre-processed data.

[0906] "Means of receiving delivery requests" refers to methods or devices for receiving delivery requests from users.

[0907] "Means for calculating the optimal delivery route" refers to methods or devices that use trained machine learning models to calculate the most efficient delivery route.

[0908] "Means of transmission to terminal" refers to methods or devices for transmitting the calculated delivery route to the user's terminal.

[0909] "Means for displaying route information received by a terminal to the user" refers to methods or devices for visually presenting delivery route information received by a terminal to the user.

[0910] This invention relates to a delivery route determination system that uses traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the optimal delivery route.

[0911] Program generation

[0912] The programs for this system are generated using Python. Key libraries include requests for data collection, pandas for data processing, and scikit-learn and TensorFlow for building machine learning models.

[0913] Program Processing Description

[0914] The server collects the following data:

[0915] Traffic information data: Real-time traffic information is obtained using a standard map API.

[0916] Resident absence / redelivery record data: Past delivery record data is obtained from the delivery company's database.

[0917] Delivery information for the day: Retrieve the delivery list for the day from the delivery management system.

[0918] Electricity meter information: Use the smart meter API to obtain electricity usage data to estimate whether residents are at home.

[0919] Route characteristic data from the perspective of area drivers: Collect feedback from drivers and save route characteristics in a database.

[0920] After collecting this data, the server preprocesses it using pandas. Specifically, it performs tasks such as imputing missing values ​​and normalizing the data.

[0921] Next, the server uses scikit-learn and TensorFlow to build a machine learning model. This model optimizes delivery routes by considering residents' absence times and past redelivery data to reduce the likelihood of redeliveries.

[0922] After the model has finished training, the server calculates the optimal delivery route each time a new delivery request comes in and sends it to the terminal.

[0923] Specific examples and prompt statements

[0924] As a concrete example, consider the following scenario:

[0925] The user enters a new delivery request into the system. For example, the user enters "I will make 10 deliveries in Tokyo on October 10, 2023." Based on this information, the server calculates the optimal delivery route and sends it to the terminal.

[0926] Examples of prompts to input into a generative AI model:

[0927] "We plan to make 10 deliveries in Tokyo on October 10, 2023. Please calculate the optimal delivery route, taking into account traffic information data, data on residents' absence / redelivery history, information on packages delivered on that day, electricity meter information, and route characteristic data from the perspective of area drivers."

[0928] By inputting this prompt into the generating AI model, the AI ​​calculates the optimal delivery route and provides it to the user.

[0929] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0930] Step 1:

[0931] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[0932] Inputs: Traffic information API, delivery company database, delivery management system, smart meter API, driver feedback

[0933] Output: Various collected data

[0934] Specific operation: The server calls a traffic information API to obtain real-time traffic information and retrieves past delivery performance data from the delivery company's database. It also retrieves the day's delivery list from the delivery management system and obtains electricity usage data using the smart meter API. Furthermore, it collects driver feedback and stores it in the database.

[0935] Step 2:

[0936] The server preprocesses the collected data.

[0937] Input: Various collected data

[0938] Output: Preprocessed data

[0939] Specific operation: The server uses the pandas library to impute missing values ​​in the data and normalize data at different scales. It also combines data obtained from different sources into a single dataframe.

[0940] Step 3:

[0941] The server trains a machine learning model based on the pre-processed data.

[0942] Input: Preprocessed data

[0943] Output: Trained machine learning model

[0944] Specific operation: The server builds machine learning models using scikit-learn or TensorFlow. It selects features and the optimal algorithm (e.g., random forest, neural network) to train the model.

[0945] Step 4:

[0946] The user enters a new delivery request into the system.

[0947] Input: Delivery request (Example: "Make 10 deliveries in Tokyo on October 10, 2023")

[0948] Output: Delivery request data

[0949] Specific operation: The user enters a delivery request into the system interface. The server receives this request and saves it to the database.

[0950] Step 5:

[0951] The server uses a pre-trained machine learning model to calculate the optimal delivery route.

[0952] Input: Delivery request data, trained machine learning model

[0953] Output: Optimal delivery route

[0954] Specific operation: The server converts the delivery request data into a format that can be input into the model, and uses the trained machine learning model to predict the optimal delivery route.

[0955] Step 6:

[0956] The server sends the calculated optimal route to the terminal.

[0957] Input: Optimal delivery route

[0958] Output: Route information sent to the terminal

[0959] Specific operation: The server calls an API to send the calculated optimal route to the terminal, and then sends the route information to the terminal.

[0960] Step 7:

[0961] The device displays the received route information to the user.

[0962] Input: Route information sent to the terminal

[0963] Output: Route information displayed to the user

[0964] Specific operation: The terminal provides an interface for visually displaying the received route information to the user.

[0965] (Application Example 1)

[0966] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0967] Traditional delivery systems failed to fully utilize traffic information and data on residents' absences, resulting in frequent redeliveries. Furthermore, insufficient optimization of delivery routes made predicting delivery times difficult. This led to decreased delivery efficiency and reduced customer satisfaction.

[0968] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0969] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day using AI that minimizes the possibility of daily redeliveries; a means of linking the route determined by the AI ​​with a car navigation system; a means of optimizing the delivery route using an application installed on a smartphone; and a means of predicting delivery times by reflecting real-time traffic information. This makes it possible to reduce the frequency of redeliveries and improve delivery efficiency.

[0970] "Traffic information data" refers to information about traffic that is collected in real time, such as road congestion, traffic accidents, and construction information.

[0971] "Resident absence / redelivery history data" refers to data showing the time periods when a particular resident was absent in the past and the history of redelivery requests.

[0972] "Delivery information for the day" refers to detailed information about packages scheduled for delivery on that day, including data such as package type, quantity, and delivery address.

[0973] "Electricity meter information" is data that shows residents' electricity usage and is used to estimate whether or not residents are at home.

[0974] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics and points to note about routes that drivers responsible for a specific area have learned through experience.

[0975] "Using AI to determine the optimal delivery route" refers to a method of using artificial intelligence to decide on the best delivery route.

[0976] "Methods for linking with car navigation systems" refers to methods for linking the determined delivery route with the in-vehicle navigation system.

[0977] "Applications installed on smartphones" are software that runs on smartphones and is used to optimize delivery routes and predict delivery times.

[0978] "A method for predicting delivery times by reflecting real-time traffic information" refers to a method for predicting the time it will take to deliver a package, taking into account the current traffic conditions.

[0979] The following system configuration will be described as an embodiment for carrying out this invention.

[0980] System Configuration

[0981] This system consists of a server, a smartphone, a car navigation system, and various data sources. The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, AI determines the optimal delivery route.

[0982] Hardware and software to be used

[0983] Hardware: Servers, smartphones, car navigation systems

[0984] Software: Python, Pandas (data processing), Scikit-learn (machine learning), Geopy (geographic information processing)

[0985] Data processing and data calculation

[0986] The server processes the data in the following steps:

[0987] 1. Data Import: Import traffic information, resident absence / redelivery record data, daily delivery package information, electricity meter information, and route characteristic data from the perspective of area drivers from a CSV file.

[0988] 2. Data preprocessing: Combine data and handle missing values.

[0989] 3. Setting features and targets: Set delivery time as the target variable and other data as features.

[0990] 4. Model Training: Train the model using RandomForestRegressor.

[0991] 5. Optimizing delivery routes: Determine the optimal delivery route using geodesic distance.

[0992] 6. Delivery Route Prediction: Predict delivery times based on the optimal delivery route.

[0993] Specific example

[0994] For example, if there are multiple delivery destinations within Tokyo, the server will determine the optimal route and predict delivery times by considering traffic information and data on residents' absences. This can reduce the frequency of redeliveries and improve delivery efficiency.

[0995] Example of a prompt

[0996] "Create a Python program that determines the optimal route and predicts delivery time for multiple delivery destinations within Tokyo, taking into account traffic information and resident absence data."

[0997] The above describes the embodiments for carrying out this invention. This system makes it possible to reduce the frequency of redeliveries and improve delivery efficiency.

[0998] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0999] Step 1:

[1000] The server reads traffic information data, resident absence / redelivery data, daily delivery package information, electricity meter information, and route characteristic data from the perspective of area drivers from CSV files. This provides all the data necessary for optimizing delivery routes. The input is various data files, and the output is an integrated dataset.

[1001] Step 2:

[1002] The server preprocesses the loaded data. Specifically, it combines the data and handles missing values. For example, it combines traffic information data and resident absence data using address as the key, and imputes missing values ​​with the mean or median. The input is a combined dataset, and the output is a preprocessed dataset.

[1003] Step 3:

[1004] The server sets features and target variables from a preprocessed dataset. Features are factors that affect delivery time (e.g., traffic information, time residents are away, electricity meter information, etc.), and the target variable is the delivery time. The input is the preprocessed dataset, and the output is a set of features and target variables.

[1005] Step 4:

[1006] The server trains a machine learning model (RandomForestRegressor) using features and target variables. It then fits the model to the training data to generate a model for predicting delivery times. The input is a set of features and target variables, and the output is the trained machine learning model.

[1007] Step 5:

[1008] The server determines the optimal delivery route using geodesic distance. Specifically, it optimizes the route by selecting the delivery destination closest to the current location, and then selecting the next delivery destination closest to that location. The input is a list of delivery destinations, and the output is the optimized delivery route.

[1009] Step 6:

[1010] The server uses a trained machine learning model to predict delivery times based on optimized delivery routes. The input is the optimized delivery route, and the output is the predicted delivery time for each delivery destination.

[1011] Step 7:

[1012] The server sends optimized delivery routes and estimated delivery times to an application installed on the driver's smartphone. This allows the driver to check the optimal delivery route and estimated delivery time in real time. The input is the optimized delivery route and estimated delivery time, and the output is the transmission of data to the smartphone application.

[1013] Step 8:

[1014] The user (driver) uses a smartphone application to perform deliveries according to an optimized delivery route. The application reflects real-time traffic information and recalculates the route as needed. The input is data transmitted from the server, and the output is the actual delivery work.

[1015] (Example 2)

[1016] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1017] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, it was challenging to maintain a consistent level of delivery quality regardless of whether the driver was experienced or new. Additionally, they were unable to provide optimal routes that took real-time traffic and weather information into account. This resulted in decreased delivery efficiency and lower customer satisfaction.

[1018] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1019] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route using artificial intelligence that minimizes the possibility of redelivery; a means to link the route determined by the artificial intelligence with a navigation system; a means to receive a list of delivery addresses and obtain location information for each address using a geographic information system; a means to obtain weather information and collect traffic condition data; a means to preprocess the acquired data and input it into a generated AI model to calculate the optimal delivery route; and a means to transmit and display the generated optimal route to a navigation system. This makes it possible to reduce the possibility of redelivery and provide an efficient delivery route. Furthermore, it is possible to maintain a consistent delivery level regardless of whether the driver is a veteran or a rookie. In addition, it is possible to provide an optimal route that takes into account real-time traffic conditions and weather information, thereby improving delivery efficiency and customer satisfaction.

[1020] "Traffic information data" refers to information about traffic flow, such as road congestion, accident information, and road closure information.

[1021] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent during past deliveries and the number of times redeliveries were made.

[1022] "Same-day delivery information" refers to detailed information about packages to be delivered on that day, including the quantity, size, weight, and delivery address.

[1023] "Electricity meter information" refers to data showing the electricity usage of the residence or building to which delivery is made, and is used to estimate whether residents are at home.

[1024] "Route characteristic data from the perspective of area drivers" refers to information that shows the characteristics and points to note about routes based on the experience and knowledge of drivers in a specific area.

[1025] "Artificial intelligence" refers to algorithms and models that use machine learning and data analysis to calculate the optimal delivery route.

[1026] A "navigation system" refers to a device installed in a vehicle that provides map information and route guidance functions.

[1027] A "Geographic Information System" refers to a system for collecting, managing, and analyzing geographical data.

[1028] A "generative AI model" refers to an algorithm or model built using a machine learning framework to generate the optimal delivery route.

[1029] "Preprocessing" refers to the process of converting collected data into a format necessary for inputting it into the generating AI model.

[1030] This invention relates to a system for efficiently calculating and displaying routes in delivery operations. Specific embodiments of this system are described below.

[1031] System Overview

[1032] This system consists of three main elements: a server, a terminal, and a user. The server collects and preprocesses data and calculates routes using a generative AI model. The terminal functions as a car navigation system, displaying the optimal route sent from the server. The user enters a list of delivery addresses and makes deliveries according to the system's instructions.

[1033] Hardware and software to be used

[1034] Servers: Use servers with high-performance computing capabilities. Specifically, cloud services such as Google Cloud Platform and Amazon Web Services can be used.

[1035] Terminal: Use a standard navigation device (e.g., Garmin or TomTom) as the car navigation system.

[1036] Software: Data collection uses APIs such as Google Maps API, OpenWeatherMap API, and Waze API. Machine learning frameworks such as TensorFlow and PyTorch are used to build generative AI models.

[1037] Data collection and preprocessing

[1038] The server receives a list of delivery addresses entered by the user. For example, the user enters "1-1 Marunouchi, Chiyoda-ku, Tokyo". The server uses the Google Maps API to obtain the latitude and longitude of each address. Furthermore, it uses the OpenWeatherMap API to obtain current weather information and the Waze API to collect traffic data. This data is preprocessed for input into a generative AI model. Specifically, the address data is converted to latitude and longitude, and the weather information and traffic data are converted to numerical data.

[1039] Root calculation using a generative AI model

[1040] The preprocessed data is input into a generative AI model. This generative AI model, built using TensorFlow or PyTorch, calculates the optimal delivery route. For example, it takes multiple delivery destination data as a single batch and generates an optimal route such as destination A → destination B → destination C.

[1041] Displaying routes and executing deliveries

[1042] The generated optimal route is sent from the server to the terminal. The terminal analyzes the received route information and displays it on the car navigation system screen. The driver makes deliveries according to the route displayed on the car navigation system. The car navigation system updates the route information in real time and notifies the driver.

[1043] Examples of specific cases and prompt statements

[1044] Specific example:

[1045] The user enters a list of delivery addresses.

[1046] The server uses the Google Maps API to obtain the latitude and longitude of each address and the OpenWeatherMap API to obtain current weather information.

[1047] The server preprocesses this data and inputs it into the generating AI model.

[1048] The AI ​​model calculates the optimal delivery route and transmits that route information to the car navigation system.

[1049] The driver makes deliveries following the route displayed on the car's navigation system.

[1050] Example of a prompt:

[1051] Please enter a list of delivery addresses. The system will calculate the optimal delivery route and display it on your car's navigation system.

[1052] example:

[1053] 1. 1-1 Marunouchi, Chiyoda-ku, Tokyo

[1054] 2. 1-1 Jinnan, Shibuya-ku, Tokyo

[1055] 3. 2-8 Nishi-Shinjuku, Shinjuku-ku, Tokyo

[1056] In this way, the system provides efficient delivery routes, reduces the possibility of redelivery, and improves delivery efficiency and customer satisfaction.

[1057] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1058] Step 1:

[1059] The user enters a list of delivery addresses.

[1060] Input: List of delivery addresses (Example: 1-1 Marunouchi, Chiyoda-ku, Tokyo; 1-1 Jinnan, Shibuya-ku, Tokyo; 2-8 Nishi-Shinjuku, Shinjuku-ku, Tokyo)

[1061] Output: A list of delivery addresses is sent to the server.

[1062] Specific action: The user enters the delivery address in text format into the system's input form and clicks the "Submit" button.

[1063] Step 2:

[1064] The server uses the Google Maps API to obtain the latitude and longitude of each address.

[1065] Input: List of delivery addresses

[1066] Output: Latitude and longitude information for each address (e.g., 35.681236, 139.767125)

[1067] Specific operation: The server sends a request to the Google Maps API for each address and receives latitude and longitude information as a response.

[1068] Step 3:

[1069] The server uses the OpenWeatherMap API to retrieve current weather information.

[1070] Input: Latitude and longitude information for each address

[1071] Output: Weather information for each address (e.g., sunny, temperature 25 degrees Celsius, wind speed 5 m / s)

[1072] Specific operation: The server sends a request to the OpenWeatherMap API based on latitude and longitude information and receives weather information as a response.

[1073] Step 4:

[1074] The server uses the Waze API to collect traffic data.

[1075] Input: Latitude and longitude information for each address

[1076] Output: Traffic data for each address (e.g., congestion, road closures)

[1077] Specific operation: The server sends a request to the Waze API based on latitude and longitude information and receives traffic condition data as a response.

[1078] Step 5:

[1079] The server preprocesses the data it has acquired.

[1080] Input: Latitude and longitude information, weather information, traffic data

[1081] Output: Preprocessed dataset (Example: { "latitude": 35.681236, "longitude": 139.767125, "weather": 1, "temperature": 25, "wind_speed": 5, "traffic": "normal"})

[1082] Specific operation: The server converts address data into latitude and longitude, and converts weather information and traffic data into numerical data, and combines them into a single dataset.

[1083] Step 6:

[1084] The server inputs the pre-processed dataset into the AI ​​model.

[1085] Input: Preprocessed dataset

[1086] Output: Optimal delivery route (Example: Delivery destination A → Delivery destination B → Delivery destination C)

[1087] Specific operation: The server inputs a dataset into the generated AI model, and the model calculates the optimal delivery route.

[1088] Step 7:

[1089] The server sends the generated optimal route to the navigation system.

[1090] Input: Optimal delivery route

[1091] Output: Route information sent to the navigation system

[1092] Specific operation: The server sends route information to the navigation system's API using an HTTP POST request.

[1093] Step 8:

[1094] The device analyzes the route information it receives and displays it on the screen.

[1095] Input: Route information

[1096] Output: The route displayed on the screen (e.g., the route shown as a blue line on the map)

[1097] Specific operation: The device analyzes the received route information, displays the route on a map, and provides voice guidance for the next turn.

[1098] Step 9:

[1099] The driver makes deliveries following the route displayed on the car's navigation system.

[1100] Input: Route displayed on the car navigation system

[1101] Output: Arrival at delivery destination

[1102] Specific actions: The driver drives the car according to the instructions on the car navigation system and makes deliveries according to the designated route.

[1103] Step 10:

[1104] The device updates route information in real time and notifies the driver.

[1105] Input: Real-time traffic data

[1106] Output: Updated route information

[1107] Specific operation: The terminal periodically acquires traffic data, recalculates the route as needed, and notifies the driver.

[1108] (Application Example 2)

[1109] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1110] Traditional delivery systems struggled to determine optimal delivery routes considering traffic conditions and residents' absences, leading to a high likelihood of redeliveries. Furthermore, maintaining consistent delivery quality was difficult, regardless of whether the driver was experienced or new. Additionally, the lack of real-time updates on delivery progress and post-delivery feedback created a need for improved delivery efficiency.

[1111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1112] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day using AI that minimizes the possibility of daily redeliveries; a means of linking the route determined by the AI ​​with a car navigation system; a means of displaying the optimal delivery route determined by the AI ​​in real time using an application installed on a smartphone; and a means of updating delivery progress in real time and providing feedback after delivery is completed. This makes it possible to reduce the possibility of redeliveries, maintain a consistent delivery level regardless of whether the driver is veteran or new, and improve delivery efficiency.

[1113] "Traffic information data" refers to data that includes information related to traffic, such as road congestion, traffic accidents, and construction information.

[1114] "Resident absence / redelivery history data" refers to data that includes information about the time periods when residents were absent and the history of redeliveries.

[1115] "Delivery information for the day" refers to data that includes information about the type and quantity of packages to be delivered on that day, as well as the delivery address.

[1116] "Electricity meter information" is data that shows residents' electricity usage and is used to estimate whether residents are at home.

[1117] "Route characteristic data from the perspective of area drivers" refers to data that includes information about route characteristics based on the experience and knowledge of drivers in a specific area.

[1118] "Using AI to determine the route" refers to a method of using artificial intelligence to calculate and determine the optimal delivery route.

[1119] "Methods for linking with car navigation systems" refer to methods that display the determined delivery route on the car navigation system, allowing the driver to drive according to that route.

[1120] An "application installed on a smartphone" is software that runs on a smartphone and displays delivery routes and manages delivery progress.

[1121] "A means of updating delivery progress in real time" refers to a method of recording and updating the progress of deliveries in real time.

[1122] "Means of providing feedback after delivery completion" refers to methods of providing evaluations and suggestions for improvement to drivers and systems after a delivery has been completed.

[1123] The system for implementing this invention includes a means for using AI to determine a delivery route for the day that minimizes the possibility of daily redeliveries, utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; a means for linking the route determined by the AI ​​with a car navigation system; a means for displaying the optimal delivery route determined by the AI ​​in real time using an application installed on a smartphone; and a means for updating delivery progress in real time and providing feedback after delivery is completed.

[1124] Program Processing Description

[1125] Hardware to use

[1126] server

[1127] smartphone

[1128] Software to use

[1129] TensorFlow: Used for loading and predicting AI models.

[1130] Google Maps API: Used to display routes.

[1131] Data processing and calculation

[1132] 1. The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, an AI model is used to calculate the optimal delivery route. TensorFlow is used for the AI ​​model.

[1133] 2. The server sends the calculated optimal route to the smartphone.

[1134] 3. The smartphone displays the received optimal route in its car navigation app using the Google Maps API.

[1135] 4. The smartphone updates delivery progress in real time and provides feedback after delivery is complete.

[1136] Specific example

[1137] For example, if the starting point is Tokyo Station (latitude 35.6895, longitude 139.6917) and the destination is Tokyo Tower (latitude 35.6586, longitude 139.7454), the server inputs this location information into an AI model to calculate the optimal delivery route. The calculated route information is then sent to a smartphone, which uses the Google Maps API to display the route in its car navigation app.

[1138] Example of a prompt

[1139] Please enter your departure and destination points. The AI ​​will calculate the optimal route and display it on your car's navigation system.

[1140] Example: Departure point: Tokyo Station (35.6895, 139.6917), Destination: Tokyo Tower (35.6586, 139.7454)

[1141] In this way, the embodiment for carrying out the invention can reduce the possibility of redelivery, maintain a consistent delivery level regardless of whether the driver is experienced or new, and improve delivery efficiency.

[1142] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1143] Step 1:

[1144] The server collects traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. This data is obtained from various sensors and databases. Input data includes traffic information, residents' absence times, redelivery history, details of delivered packages, electricity usage, and driver experience information. This data is integrated and preprocessed for input into the AI ​​model.

[1145] Step 2:

[1146] The server loads an AI model using TensorFlow based on pre-processed data and calculates the optimal delivery route. Input data includes pre-processed traffic information, resident absence times, redelivery history, delivery package details, power usage, and driver experience. The AI ​​model analyzes this data and outputs the optimal delivery route.

[1147] Step 3:

[1148] The server sends the calculated optimal route to the smartphone. The input data includes the optimal route information output by the AI ​​model. The server sends this information to the smartphone, which then receives it.

[1149] Step 4:

[1150] The smartphone displays the received optimal route in its navigation app using the Google Maps API. The input data includes the optimal route information received from the server. The smartphone calls the Google Maps API and displays the optimal route in its navigation app.

[1151] Step 5:

[1152] The user (driver) begins driving according to the optimal route displayed in the car navigation app. The input data includes the optimal route information displayed in the car navigation app. The user drives based on this information.

[1153] Step 6:

[1154] The smartphone updates delivery progress in real time. Input data includes the user's current location and delivery status. Based on this information, the smartphone updates delivery progress in real time and sends it to the server.

[1155] Step 7:

[1156] After delivery is complete, the smartphone provides feedback. Input data includes delivery completion information and user feedback. Based on this information, the smartphone generates and provides post-delivery feedback to the user.

[1157] (Example 3)

[1158] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1159] Traditional delivery systems failed to adequately utilize data on residents' absences and redelivery history, resulting in a high frequency of redeliveries. Furthermore, the lack of consideration for traffic information and route characteristics from the perspective of local drivers made it difficult to determine efficient delivery routes. Additionally, disparities in delivery quality between veteran and novice drivers were a significant challenge.

[1160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1161] This invention includes a server that uses traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of redelivery, using artificial intelligence; a means to link the route determined by artificial intelligence with an in-vehicle navigation system; a means for the user to input delivery information into a terminal and for the terminal to transmit the input data to the server; a means for the server to receive the data and perform preprocessing; a means for the server to acquire traffic information and integrate electricity meter information and route characteristic data; a means for the server to input data into a machine learning model and calculate the optimal delivery route; a means for the server to transmit the calculation results to a terminal and for the terminal to display the optimal delivery route on the in-vehicle navigation system; and a means for the user to perform deliveries according to the instructions of the in-vehicle navigation system. This makes it possible to reduce the possibility of redelivery and achieve efficient deliveries.

[1162] "Traffic information data" refers to data that shows real-time traffic conditions, including congestion information and road closure information.

[1163] "Resident absence / redelivery history data" refers to data that shows the history of residents being absent and information related to redeliveries, such as past delivery dates and times and the number of redeliveries.

[1164] "Delivery information for the day" refers to information about the package to be delivered, including the package ID, delivery address, and package type.

[1165] "Electricity meter information" is data that indicates whether residents are at home, and it is used to determine whether residents are at home based on their electricity usage.

[1166] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics that drivers consider when selecting a delivery route, such as the width of roads within the delivery area, the number of traffic lights, and the availability of parking spaces.

[1167] "Artificial intelligence" is a technology that uses machine learning models to analyze data and calculate the optimal delivery route.

[1168] An "in-vehicle navigation system" is a navigation device installed in a vehicle that displays the optimal delivery route and guides the driver through voice guidance and map display.

[1169] A "terminal" is a device used by the user to enter delivery information, and includes smartphones, tablets, and other similar devices.

[1170] A "server" is a computer system that receives data sent from terminals and performs preprocessing, data integration, and calculations using machine learning models.

[1171] A "machine learning model" is an algorithm used to analyze data and calculate the optimal delivery route, and is implemented using libraries such as Scikit-learn.

[1172] This invention is a system aimed at improving the efficiency of delivery operations. It utilizes traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the delivery route for the day using artificial intelligence, minimizing the likelihood of redeliveries.

[1173] System Configuration

[1174] This system consists of the following main components:

[1175] 1. Terminal: A device used by the user to enter delivery information, including smartphones and tablets.

[1176] 2. Server: A computer system that receives data sent from terminals and performs preprocessing, data integration, and calculations using machine learning models.

[1177] 3. In-vehicle navigation system: A device that displays the optimal delivery route and guides the driver through voice guidance and map display.

[1178] Hardware and software to be used

[1179] Hardware: Smartphones, tablets, servers, in-car navigation systems

[1180] Software: Python, Pandas, Scikit-learn, Google Maps API

[1181] Program processing

[1182] The user (driver) enters the day's delivery information (package ID, delivery address, package type) and resident absence / redelivery history data (past delivery dates and times, number of redeliveries) into the terminal. The terminal converts the entered data into JSON format and sends it to the server using the HTTPS protocol.

[1183] The server converts the received JSON data into a DataFrame using Pandas and performs preprocessing such as imputing missing values ​​and normalizing the data. Next, the server uses the Google Maps API to retrieve real-time traffic information, including congestion and road closure information.

[1184] The server integrates electricity meter information (data indicating residents' occupancy status) and route feature data from the perspective of area drivers (road width, number of traffic lights, availability of parking spaces) to gather all the data necessary for optimizing delivery routes. The integrated data is then input into a machine learning model using Scikit-learn to calculate the optimal delivery route. The model proposes a route that minimizes the likelihood of redelivery.

[1185] The server converts the calculated optimal delivery route into JSON format and sends it to the terminal using the HTTPS protocol. The terminal displays the received optimal delivery route on the vehicle's navigation system. The vehicle's navigation system guides the driver through voice guidance and map display.

[1186] The user (driver) makes deliveries according to the instructions of the in-vehicle navigation system. This reduces the possibility of redeliveries and enables efficient deliveries.

[1187] Examples of specific cases and prompt statements

[1188] As a specific example, the driver enters the following information:

[1189] Delivery information: Package ID 12345, Delivery address 1-2-3 Shibuya-ku, Tokyo, Package type Books

[1190] Absence / Redelivery Record Data: Past delivery date and time 2023-10-01 14:00, Number of redeliveries 2

[1191] Traffic Information Data: Congestion Information - Congestion is occurring on major roads in Shibuya Ward.

[1192] Electric meter information: Home

[1193] Route characteristics data: Narrow road width, many traffic lights, no parking spaces.

[1194] Example of a prompt:

[1195] Delivery information:

[1196] Package ID: 12345

[1197] Delivery address: 1-2-3 Shibuya-ku, Tokyo

[1198] Type of item: Books

[1199] Absence / redelivery data:

[1200] Past delivery date and time: 2023-10-01 14:00

[1201] Number of redelivery attempts: 2

[1202] Traffic information data:

[1203] Traffic Information: Traffic congestion is occurring on major roads in Shibuya Ward.

[1204] Electric meter information:

[1205] Status at home: At home

[1206] Route feature data:

[1207] Road width: Narrow

[1208] Number of traffic lights: Many

[1209] Parking space: None

[1210] When this prompt is input into the generating AI model, the AI ​​calculates the optimal delivery route and displays it on the in-vehicle navigation system. By following this route, the driver can reduce the likelihood of redeliveries and achieve efficient delivery. The flow of the specific processing in Example 3 is explained using Figure 15.

[1211] Step 1:

[1212] The user enters the delivery information into the terminal.

[1213] Users use their smartphones or tablets to enter delivery package information (e.g., Package ID 12345, Delivery address 1-2-3 Shibuya-ku, Tokyo, Package type: Books) and missed / redelivery history data (e.g., Past delivery date and time 2023-10-01 14:00, Number of redeliveries 2) into a dedicated app. The entered data is converted to JSON format within the device.

[1214] Input: Delivery package information, missed delivery / redelivery record data

[1215] Output: Data in JSON format

[1216] Step 2:

[1217] The terminal sends the entered data to the server.

[1218] The device sends data converted to JSON format to the server using the HTTPS protocol. The transmitted data reaches the server via a secure communication channel.

[1219] Input: Data in JSON format

[1220] Output: Data sent to the server

[1221] Step 3:

[1222] The server receives the data and performs preprocessing.

[1223] The server converts the received JSON data into a DataFrame using Pandas. Next, it performs missing value imputation (e.g., imputing 0 if the number of redeliveries is not entered) and data normalization (e.g., converting address data to a standard format).

[1224] Input: Data sent to the server

[1225] Output: Preprocessed data frame

[1226] Step 4:

[1227] The server uses the Google Maps API to retrieve traffic information.

[1228] The server calls the Google Maps API to retrieve real-time traffic information for a specified area (e.g., Shibuya Ward, Tokyo). The retrieved data includes congestion information and road closure information for major roads.

[1229] Input: Specified area information

[1230] Output: Real-time traffic information

[1231] Step 5:

[1232] The server integrates electricity meter information and route characteristic data.

[1233] The server integrates electricity meter information (e.g., whether residents are at home) and route characteristic data from the perspective of area drivers (e.g., narrow roads, many traffic lights, lack of parking spaces) to gather all the data necessary to optimize delivery routes.

[1234] Input: Electric meter information, route characteristic data

[1235] Output: Integrated data

[1236] Step 6:

[1237] The server inputs data into a machine learning model and calculates the optimal delivery route.

[1238] The server inputs the integrated data into a machine learning model using Scikit-learn to calculate the optimal delivery route. The model proposes a route that minimizes the likelihood of redelivery.

[1239] Input: Integrated data

[1240] Output: Optimal delivery route

[1241] Step 7:

[1242] The server sends the calculation results to the terminal.

[1243] The server converts the calculated optimal delivery route into JSON format and sends it to the terminal using the HTTPS protocol.

[1244] Input: Optimal delivery route

[1245] Output: Data sent to the terminal

[1246] Step 8:

[1247] The terminal displays the optimal delivery route on the vehicle's navigation system.

[1248] The terminal displays the optimal delivery route received on the vehicle's navigation system. The vehicle's navigation system guides the driver through voice guidance and map display.

[1249] Input: Data sent to the terminal

[1250] Output: Route displayed on the in-car navigation system

[1251] Step 9:

[1252] The user makes the delivery following the instructions of the in-car navigation system.

[1253] The user (driver) makes deliveries according to the instructions of the in-car navigation system. For example, if the car navigation system instructs the driver to "turn right at the next intersection," the driver will follow the instructions and turn right. This reduces the possibility of redeliveries and enables efficient deliveries.

[1254] Input: Instructions from the car's navigation system

[1255] Output: Achieving efficient delivery

[1256] (Application Example 3)

[1257] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1258] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, in food delivery, the inability to provide optimal routes considering traffic information and past delivery data meant that delivery times could not be shortened or efficiency improved. This resulted in delivery delays and redeliveries, leading to decreased customer satisfaction.

[1259] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1260] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to input food delivery information and determine the optimal delivery route based on traffic information data, past delivery performance data, and area characteristic data; and a means to display the optimal route on a smartphone's car navigation app. This reduces the possibility of redeliveries and enables shorter delivery times and increased efficiency.

[1261] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[1262] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent in the past and the number of times redelivery was attempted.

[1263] "Delivery information for the day" refers to information about packages to be delivered on that day, such as the contents of the package, the delivery address, and the estimated delivery time.

[1264] "Electricity meter information" is data that shows the electricity usage of residents and is used to estimate whether or not residents are at home.

[1265] "Route characteristic data from the perspective of area drivers" refers to information about the characteristics of roads and traffic conditions within the delivery area, as well as route characteristics based on the driver's experience.

[1266] "AI" is an abbreviation for artificial intelligence, a technology that calculates the optimal delivery route based on data.

[1267] A "car navigation system" is a navigation system installed in a vehicle that provides map information and route guidance.

[1268] "Food delivery" is a service that delivers food and beverages to a location specified by the customer.

[1269] A "smartphone car navigation app" is a navigation application that runs on a smartphone and is software that provides map information and route guidance.

[1270] An "optimal delivery route" is a delivery route calculated based on traffic information and past delivery performance data in order to shorten delivery times and improve efficiency.

[1271] A system for carrying out this invention includes a server, a smartphone, a car navigation application, and an AI model. Specific embodiments of this system are described below.

[1272] System Configuration

[1273] 1. Server:

[1274] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, an AI model is used to calculate the optimal delivery route.

[1275] The server is programmed using Python and has interfaces for retrieving traffic information APIs and past delivery performance data.

[1276] 2. Smartphone:

[1277] Smartphones are devices used by food delivery drivers and have applications installed for entering delivery information.

[1278] The smartphone application receives the optimal delivery route from the server and displays it on the car navigation application.

[1279] 3. Car navigation application:

[1280] The car navigation application runs on a smartphone and displays the optimal delivery route received from the server.

[1281] Drivers use this car navigation application to make deliveries following the optimal route.

[1282] Data processing

[1283] The server processes the data in the following steps:

[1284] 1. Data collection:

[1285] The server retrieves the latest traffic information data from the traffic information API.

[1286] The server reads past delivery data from a local file.

[1287] The server receives the delivery package information for the day entered by the food delivery driver.

[1288] 2. Calculation of the optimal route using an AI model:

[1289] The server uses a random forest regression model to calculate the optimal delivery route based on the collected data.

[1290] The model uses traffic information, past delivery performance data, and area characteristic data as features to shorten delivery times and improve efficiency.

[1291] 3. Delivery of the optimal route:

[1292] The server sends the calculated optimal route to the smartphone application.

[1293] The smartphone application displays the received optimal route on the car navigation application.

[1294] Specific example

[1295] For example, suppose the driver entered the following delivery information.

[1296] Order 1: Address 1, Delivery time 12:00

[1297] Order 2: Address 2, Delivery time 12:30

[1298] The server obtains the latest traffic information from a traffic information API and calculates the optimal delivery route based on past delivery data. For example, if past data indicates that "address 1 experiences less traffic congestion in the morning," the server will suggest a route that "delivers to address 1 first, and then proceeds to address 2."

[1299] Example of a prompt

[1300] Please calculate the optimal delivery route based on the following delivery information.

[1301] Order 1: Address 1, Delivery time 12:00

[1302] Order 2: Address 2, Delivery time 12:30

[1303] Obtain the latest traffic information from the traffic information API and propose the optimal route based on past delivery performance data.

[1304] In this way, by combining servers, smartphones, car navigation applications, and AI models, it becomes possible to reduce the likelihood of redelivery and shorten and streamline delivery times.

[1305] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1306] Step 1:

[1307] The user (food delivery driver) uses a smartphone application to enter the day's delivery information (order details, delivery address, estimated delivery time, etc.).

[1308] Input: Delivery information (order details, delivery address, estimated delivery time)

[1309] Output: Delivery information sent to the server

[1310] Step 2:

[1311] The server retrieves the latest traffic information data from the traffic information API.

[1312] Input: Request to the traffic information API

[1313] Output: Traffic information data (road congestion, traffic accidents, construction information, etc.)

[1314] Step 3:

[1315] The server reads past delivery data from a local file.

[1316] Input: Local file (past delivery data)

[1317] Output: Past delivery data (resident absence times, redelivery history, etc.)

[1318] Step 4:

[1319] The server uses a random forest regression model to calculate the optimal delivery route based on the collected data (delivery information, traffic information data, and past delivery performance data).

[1320] Input: Delivery information, traffic information data, past delivery performance data

[1321] Output: Optimal delivery route (delivery order, estimated delivery time, etc.)

[1322] Specific operation: The server extracts features (traffic information, past delivery records, area characteristics) and inputs them into a random forest regression model to predict the optimal delivery route.

[1323] Step 5:

[1324] The server sends the calculated optimal route to the smartphone application.

[1325] Input: Optimal delivery route

[1326] Output: Optimal route sent to the smartphone application

[1327] Step 6:

[1328] The smartphone application displays the received optimal route on the car navigation application.

[1329] Input: Optimal delivery route

[1330] Output: Optimal route displayed in the car navigation application

[1331] Specific operation: The smartphone application analyzes the optimal route received from the server and converts it into a format for display on the car navigation application.

[1332] Step 7:

[1333] The user (food delivery driver) makes deliveries following the optimal route displayed on the car navigation application.

[1334] Input: Optimal route displayed in the car navigation application

[1335] Output: Delivery completed

[1336] Specific actions: The driver will follow the instructions of the car navigation application to reach the delivery destination and complete the delivery efficiently.

[1337] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1338] "Example of form 1"

[1339] One embodiment of the present invention provides a delivery route determination system incorporating an emotion engine. This system recognizes the user's emotions and optimizes the delivery route accordingly. Specifically, it analyzes information such as voice, text, facial expressions, and body movements obtained during interaction with the user, and determines the delivery route based on the results. For example, if a user shows anger or dissatisfaction, the system can improve user satisfaction by prioritizing deliveries to that user.

[1340] "Example of form 2"

[1341] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine adjusts the delivery time and delivery method. Specifically, the delivery time is sped up or delayed according to the user's emotions. In addition, if the user shows joy or excitement, the system can also devise a delivery method, such as delivering a gift as a surprise. This makes it possible to provide a service that responds to the user's emotions and further improves user satisfaction. "Example of Embodiment 3"

[1342] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine and an AI work together. Specifically, the emotion engine recognizes the user's emotions and provides that information to the AI. The AI ​​then optimizes the delivery route based on that information. For example, if the user is expressing happiness, the AI ​​prioritizes deliveries to that user. Conversely, if the user is expressing anger, the AI ​​delays deliveries to that user and prioritizes deliveries to other users. This makes it possible to optimize delivery routes in response to the user's emotions.

[1343] The following describes the processing flow for each example of the form.

[1344] "Example of form 1"

[1345] Step 1: The emotion engine collects information such as voice, text, facial expressions, and body movements obtained during interactions with the user.

[1346] Step 2: The emotion engine analyzes the collected information and recognizes the user's emotions.

[1347] Step 3: Based on the recognized emotions, the AI ​​optimizes the delivery route.

[1348] "Example of form 2"

[1349] Step 1: The emotion engine collects information obtained through interaction with the user and recognizes the user's emotions.

[1350] Step 2: Based on the recognized emotions, the emotion engine adjusts the delivery time and delivery method.

[1351] Step 3: Based on the adjusted delivery time and method, the AI ​​optimizes the delivery route.

[1352] "Example of form 3"

[1353] Step 1: The emotion engine collects information obtained through interaction with the user and recognizes the user's emotions.

[1354] Step 2: Provide the emotional information recognized by the emotion engine to the AI.

[1355] Step 3: Based on the provided emotional information, the AI ​​optimizes the delivery route.

[1356] (Example 1)

[1357] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1358] Conventional delivery route determination systems can optimize delivery routes by considering traffic information and resident absence data, but they do not take user emotions into account. Therefore, there is a need to re-optimize delivery routes to improve user satisfaction. In addition, there is a need for means to maintain delivery quality regardless of experience level.

[1359] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1360] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using artificial intelligence; a means of linking the route determined by artificial intelligence with a vehicle navigation system; a means of collecting emotional data such as voice, text, facial expressions, and body movements through interaction with the user; and a means of analyzing the collected emotional data and re-optimizing the delivery route based on the results. This makes it possible to optimize the delivery route while considering the user's emotions, thereby improving user satisfaction. Furthermore, it is possible to maintain a consistent delivery level regardless of experience level.

[1361] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[1362] "Resident absence / redelivery record data" refers to information based on past delivery history, such as the time periods when residents were absent and the number of times redelivery was attempted.

[1363] "Delivery information for the day" refers to detailed information such as the type, quantity, and delivery address of packages to be delivered on that day.

[1364] "Electricity meter information" refers to data that shows residents' electricity consumption, and is obtained from smart meters and other sources.

[1365] "Route characteristic data from the perspective of area drivers" refers to feedback information based on the driver's experience, such as road conditions within the delivery area and the characteristics of delivery destinations.

[1366] "Artificial intelligence" is a computer program that uses machine learning and data analysis to calculate the optimal delivery route.

[1367] A "vehicle navigation system" is an electronic device installed in a vehicle that provides route guidance to a destination.

[1368] "User interaction" refers to communication with users during delivery through voice, text, facial expressions, body language, etc.

[1369] "Emotional data" refers to information about a user's emotions obtained from their voice, text, facial expressions, body movements, etc.

[1370] "Emotional analysis" is the process of analyzing collected emotional data to identify the user's emotional state.

[1371] "Re-optimizing delivery routes" is a process of recalculating existing delivery routes based on the results of sentiment analysis and changing them to the optimal route.

[1372] This invention is a system that uses artificial intelligence (AI) to determine the delivery route for the day, minimizing the likelihood of daily redeliveries, by utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. It also includes a function to re-optimize the delivery route by taking user emotions into consideration.

[1373] Hardware and software to be used

[1374] Hardware: Servers, terminals, sensors (electricity meters, cameras, etc.)

[1375] Software: AI models (TensorFlow, PyTorch, etc.), sentiment analysis engines, database management systems (MySQL, PostgreSQL, etc.)

[1376] Data collection and processing

[1377] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter data, and route characteristic data from the perspective of area drivers. This data is obtained from sources such as the Google Maps API, delivery management systems, and smart meters. The collected data is stored in databases such as MySQL and PostgreSQL.

[1378] The server uses an AI model based on stored data to calculate the optimal delivery route. Specifically, it uses TensorFlow or PyTorch to build a route optimization model, taking into account residents' absence times and past redelivery data to minimize the likelihood of redelivery.

[1379] The device collects emotional data through interaction with the user during delivery. Specifically, it uses a voice assistant and camera to acquire information such as voice, text, facial expressions, and body movements.

[1380] The server analyzes the collected emotional data using an emotion analysis engine. Specifically, it uses OpenAI's emotion analysis model to recognize the user's emotions and identify feelings such as anger and dissatisfaction.

[1381] The server reoptimizes delivery routes based on the results of sentiment analysis. Specifically, if a user expresses anger or dissatisfaction, the route is recalculated to prioritize deliveries to that user.

[1382] Specific example

[1383] For example, when deciding on a delivery route for a given day, the following steps are taken:

[1384] 1. The server collects traffic information data (e.g., Google Maps API), data on residents' absence / redelivery history (e.g., past delivery history), information on packages delivered on the day (e.g., data from the delivery management system), electricity meter information (e.g., smart meter data), and route characteristic data from the perspective of area drivers (e.g., driver feedback).

[1385] 2. The server stores this data in a database and uses an AI model (e.g., a route optimization model using TensorFlow) to calculate the optimal delivery route.

[1386] 3. The device collects user emotion data through interactions with the user during delivery (e.g., voice assistant or facial recognition via camera).

[1387] 4. The server uses an emotion analysis engine (e.g., OpenAI's emotion analysis model) to analyze the collected emotion data and, if a user is showing anger or dissatisfaction, recalculates the delivery route to prioritize deliveries to that user.

[1388] Example of a prompt

[1389] "Create an AI model that determines the optimal delivery route based on traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter data, and route characteristic data from the perspective of area drivers. Additionally, add a function that recognizes user emotions and optimizes the delivery route accordingly."

[1390] In this way, we will explain a specific implementation of the system while clearly defining the roles of the server, terminal, and user.

[1391] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1392] Step 1:

[1393] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[1394] Inputs: Traffic information from Google Maps API, data on residents' absence / redelivery history from the delivery management system, information on packages delivered on the day, electricity meter information from smart meters, and driver feedback.

[1395] Data processing: Integrate information obtained from various data sources and store it in a database.

[1396] Output: Integrated dataset.

[1397] Specific operation: The server sends API requests to obtain traffic information, retrieves data on residents' absences / redeliveries from the delivery management system, and obtains electricity meter information from smart meters. This data is then integrated and stored in a database.

[1398] Step 2:

[1399] The server uses an AI model based on the stored data to calculate the optimal delivery route.

[1400] Input: Integrated dataset.

[1401] Data Calculation: Use TensorFlow or PyTorch to build route optimization models, taking into account residents' absence times and past redelivery data to reduce the likelihood of redelivery.

[1402] Output: Optimal delivery route.

[1403] Specific operation: The server inputs the integrated dataset into an AI model and calculates the optimal delivery route. The calculation results are sent to the vehicle's navigation system.

[1404] Step 3:

[1405] The device collects emotional data through interactions with the user during delivery.

[1406] Input: User interaction (voice, text, facial expressions, body movements).

[1407] Data processing: Speech recognition, text analysis, facial recognition, and motion analysis are performed to extract emotional data.

[1408] Output: Extracted sentiment data.

[1409] Specific operation: The device uses a voice assistant and camera to collect the user's voice, text, facial expressions, and body movements, and analyzes this data to extract emotional data.

[1410] Step 4:

[1411] The server analyzes the collected emotional data using an emotion analysis engine.

[1412] Input: Extracted sentiment data.

[1413] Data processing: Use OpenAI's emotion analysis model to recognize user emotions and identify feelings such as anger and frustration.

[1414] Output: Results of the sentiment analysis.

[1415] Specific operation: The server inputs emotional data into the emotion analysis engine to identify the user's emotional state. The analysis results are used to re-optimize the delivery route.

[1416] Step 5:

[1417] The server re-optimizes the delivery route based on the results of the sentiment analysis.

[1418] Input: Based on the results of sentiment analysis, the optimal delivery route.

[1419] Data processing: Recalculate existing delivery routes, taking into account the results of sentiment analysis.

[1420] Output: Re-optimized delivery route.

[1421] Specific operation: Based on the sentiment analysis results, the server recalculates the route to prioritize deliveries to users who have expressed anger or dissatisfaction. The re-optimized route is then sent to the vehicle's navigation system.

[1422] (Application Example 1)

[1423] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1424] Traditional delivery route planning systems could consider traffic information and data on residents' absences and redelivery attempts, but they did not optimize delivery routes in consideration of user emotions. Therefore, it was difficult to improve user satisfaction, and there was a particular challenge in responding appropriately to users who expressed dissatisfaction or anger. Furthermore, there was a need to maintain delivery levels without relying on the experience of individual delivery drivers.

[1425] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1426] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to recognize the user's emotions and optimize the delivery route accordingly; and a means to analyze information such as voice, text, facial expressions, and body movements. This makes it possible to optimize the delivery route while considering the user's emotions, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a consistent delivery level without relying on the experience of the delivery driver.

[1427] "Traffic information data" refers to real-time information about traffic, such as road congestion, traffic accidents, and construction information.

[1428] "Resident absence / redelivery history data" refers to historical information such as the time periods when residents were absent during past deliveries and the number of times redeliveries were made.

[1429] "Today's delivery information" refers to detailed information such as the type, quantity, and delivery address of packages scheduled to be delivered on that day.

[1430] "Electric meter information" refers to power consumption data from electric meters used to estimate whether residents are at home.

[1431] "Route characteristic data from the perspective of area drivers" refers to route characteristic information based on the driver's experience, such as road conditions within the delivery area and the characteristics of delivery destinations.

[1432] "AI" is an abbreviation for artificial intelligence, and it refers to algorithms and systems that analyze data and determine the optimal delivery route.

[1433] A "car navigation system" is a navigation system installed in a vehicle that provides route guidance to a destination.

[1434] "User emotions" refer to the emotional state exhibited by users of the delivery service, and are analyzed from sources such as voice, text, facial expressions, and body movements.

[1435] "Information such as voice, text, facial expressions, and body movements" refers to a variety of data formats used to recognize a user's emotions.

[1436] The system for implementing this invention collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers, and the AI ​​determines the optimal delivery route. Furthermore, it includes a function to recognize the user's emotions and optimize the delivery route according to those emotions.

[1437] System Configuration

[1438] hardware

[1439] Server: Collects, analyzes, and stores data.

[1440] Smartphone: Used by delivery drivers and linked to the car's navigation system.

[1441] Smart meter: An electric meter used to estimate whether a user is at home.

[1442] Camera: Used to recognize the user's facial expressions.

[1443] Microphone: Used to collect the user's voice.

[1444] software

[1445] Google Maps API: Collects real-time traffic information.

[1446] Firebase: Manages user absence / redelivery history data and delivery order information for the day.

[1447] Smart Meter API: Collects electricity meter information.

[1448] Google Cloud Speech-to-Text: Converts speech data into text.

[1449] Google Cloud Natural Language: Performs sentiment analysis on text data.

[1450] OpenCV: Used to analyze facial expressions.

[1451] TensorFlow: Used to analyze body movements.

[1452] Data collection and analysis

[1453] The server uses the Google Maps API to collect real-time traffic information. It also uses Firebase to manage user absence / redelivery history data and daily delivery order information. The smart meter API is used to collect electricity meter information to estimate whether users are at home.

[1454] Embedding an emotion engine

[1455] The server uses Google Cloud Speech-to-Text to convert speech data to text and Google Cloud Natural Language to perform sentiment analysis on the text data. Furthermore, it uses OpenCV to analyze facial expressions and TensorFlow to analyze body movements.

[1456] Optimizing delivery routes

[1457] The server uses AI to determine the optimal delivery route based on collected data. It also takes user emotions into consideration, prioritizing deliveries for users who express dissatisfaction or anger to improve customer satisfaction.

[1458] Specific example

[1459] For example, if a user orders food delivery and has a history of frequent redeliveries, the server will optimize the delivery route by considering the user's availability at home. Also, if a user expresses anger or dissatisfaction, prioritizing their delivery will improve their satisfaction.

[1460] Example of a prompt

[1461] "Design a system where, when a user orders food delivery, AI collects traffic information, past missed / redelivery data, current delivery order information, electricity meter data, and route characteristic data from the perspective of the area driver, and then determines the optimal delivery route. Additionally, incorporate an emotion engine to recognize the user's emotions and optimize the delivery route accordingly."

[1462] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1463] Step 1:

[1464] The server uses the Google Maps API to collect real-time traffic information. It takes current traffic conditions and road congestion information as input and generates traffic data necessary for optimizing delivery routes as output. Specifically, it sends API requests and parses the response data.

[1465] Step 2:

[1466] The server uses Firebase to manage user absence / redelivery history data and daily delivery order information. It takes past delivery history and daily order information as input and generates user data necessary for optimizing delivery routes as output. Specifically, it executes database queries and extracts the required data.

[1467] Step 3:

[1468] The server collects electricity meter information using a smart meter API. It takes user electricity consumption data as input and generates data to estimate the user's presence at home as output. Specifically, it sends API requests and parses the response data.

[1469] Step 4:

[1470] The server uses Google Cloud Speech-to-Text to convert speech data to text. It takes user speech data as input and generates text data as output. Specifically, it uploads the speech file to the cloud service and receives the text conversion result.

[1471] Step 5:

[1472] The server uses Google Cloud Natural Language to perform sentiment analysis on text data. It takes converted text data as input and generates sentiment analysis results as output. Specifically, it sends text data to the cloud service and receives a sentiment score.

[1473] Step 6:

[1474] The server uses OpenCV to analyze facial expressions. It takes user facial image data as input and generates facial expression analysis results as output. Specifically, it applies image processing algorithms to extract facial feature points.

[1475] Step 7:

[1476] The server uses TensorFlow to analyze body movements. It takes user movement data as input and generates movement analysis results as output. Specifically, it applies machine learning models to extract movement characteristics.

[1477] Step 8:

[1478] The server uses AI to determine the optimal delivery route based on all the collected data. It takes traffic information data, user data, electricity meter information, emotion analysis results, facial expression analysis results, and motion analysis results as input, and generates the optimal delivery route as output. Specifically, it applies an AI algorithm to calculate the optimal route.

[1479] Step 9:

[1480] The server integrates with the car navigation system to determine the optimal delivery route. It takes optimal route data as input and sends route information to the car navigation system as output. Specifically, it sends data to the car navigation system and starts route guidance.

[1481] Step 10:

[1482] The user delivers packages according to an optimized delivery route via the delivery driver's smartphone. The user receives route guidance from the car's navigation system as input and reports the completion of the delivery as output. Specifically, the user uses the smartphone's navigation function to arrive at the delivery destination.

[1483] (Example 2)

[1484] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1485] Traditional delivery systems struggled to determine delivery routes considering traffic information and residents' absences, leading to a high likelihood of redeliveries. Furthermore, their reliance on the delivery driver's experience made it difficult for new drivers to deliver efficiently. Additionally, the inability to provide services tailored to user needs meant that improving user satisfaction remained a challenge.

[1486] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1487] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route that minimizes the possibility of redelivery using a generating AI model; a means of linking the route determined by the generating AI model with a car navigation system; a means of analyzing user emotion data and adjusting delivery time and method; and a means of delivering a surprise gift according to the user's emotions. This reduces the possibility of redelivery, enables efficient delivery regardless of whether the driver is veteran or rookie, and allows for the provision of services that respond to the user's emotions.

[1488] "Traffic information data" refers to information that influences the determination of delivery routes, such as traffic conditions, road congestion levels, and accident information.

[1489] "Resident absence / redelivery history data" refers to information about the time periods when residents were absent during past deliveries and the history of redeliveries.

[1490] "Information on packages to be delivered on the day" refers to detailed information about packages scheduled for delivery on that day, such as the number and size of packages, and the delivery address.

[1491] "Electricity meter information" refers to electricity consumption data used to estimate whether the resident at the delivery address is at home.

[1492] "Route characteristic data from the perspective of area drivers" refers to route characteristics and points to note based on the experience and knowledge of drivers in a specific area.

[1493] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to calculate the optimal delivery route.

[1494] A "car navigation system" refers to a navigation system installed in a vehicle that provides map information and route guidance.

[1495] "User emotional data" refers to data that indicates a user's emotional state, and includes information obtained from smartphone sensors and wearable devices.

[1496] An "emotion engine" refers to a system that analyzes user emotional data and adjusts delivery times and methods based on the results.

[1497] "Methods for delivering gifts as a surprise" refers to methods and processes for delivering special gifts in addition to regular deliveries, depending on the user's emotions.

[1498] Modes for carrying out the invention

[1499] This invention is a system that uses traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the delivery route for the day using a generating AI model that minimizes the possibility of daily redeliveries. Furthermore, it has a function that links the route determined by the generating AI model with a car navigation system, analyzes user sentiment data, and adjusts the delivery time and delivery method.

[1500] Server Role

[1501] The server first receives a delivery request from the user. This request includes the delivery address, desired delivery time, and any special instructions. Next, the server uses a generative AI model to calculate the optimal delivery route. In this process, it utilizes map information services such as the Google Maps API to take traffic conditions and distance into consideration. The calculated route is then sent from the server to the car navigation system. Furthermore, the server receives the user's sentiment data and analyzes it using an sentiment engine. Based on the analysis results, it adjusts the delivery time and method, and adds a surprise gift if necessary.

[1502] The role of the terminal (car navigation system)

[1503] The terminal (car navigation system) receives optimal route information transmitted from the server. Based on the received route information, the car navigation system displays the route on its screen. The driver then makes deliveries following this route. The car navigation system can also update location information in real time and recalculate the route as needed.

[1504] User roles

[1505] Users submit delivery requests to the system. These requests can include the delivery address, preferred delivery time, and any special instructions. User emotion data is also sent to the system. The emotion engine analyzes this data and adjusts the delivery time and method based on the user's emotions. For example, if a user expresses joy or excitement, the system may deliver a gift as a surprise.

[1506] Specific example

[1507] For example, if a user submits a request to deliver to "1-1-1 Shibuya, Shibuya-ku, Tokyo," the server uses the Google Maps API to calculate the optimal route. The calculated route is sent from the server to the car's navigation system and displayed on the navigation screen. The driver then makes the delivery according to this route.

[1508] Furthermore, if a user requests delivery to "1-1-1 Shibuya, Shibuya-ku, Tokyo" and also indicates "joy" as emotional data, the emotion engine analyzes this data and speeds up the delivery time. It may also add a small gift as a surprise.

[1509] Example of a prompt

[1510] "If a user requests delivery to 1-1-1 Shibuya, Shibuya-ku, Tokyo, and their emotional data indicates happiness, please explain how to calculate the optimal delivery route, expedite the delivery time, and add a surprise gift."

[1511] In this way, the system provides the optimal delivery route and method based on user requests and sentiment data.

[1512] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1513] Program processing flow

[1514] Step 1: Receiving a delivery request

[1515] The server receives delivery requests from users. These requests include the delivery address, preferred delivery time, and any special instructions.

[1516] Input: Delivery request from the user (address, preferred delivery time, special instructions)

[1517] Output: Delivery request data

[1518] Specific operation: The server receives an HTTP request and extracts the necessary information from the request body.

[1519] Step 2: Calculating the optimal route

[1520] The server uses a generated AI model to calculate the optimal delivery route. In doing so, it takes into account traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[1521] Inputs: Delivery request data, traffic information data, resident absence / redelivery record data, delivery package information for the day, electricity meter information, route characteristic data from the perspective of the area driver.

[1522] Output: Optimal route information

[1523] Specific operation: The server sends a request to the Google Maps API, analyzes the returned data, and determines the optimal route.

[1524] Step 3: Send route information

[1525] The server sends the calculated optimal route information to the car navigation system.

[1526] Input: Optimal route information

[1527] Output: Send route information to car navigation system

[1528] Specific operation: The server sends route information to the car navigation system using WebSocket or HTTP POST requests.

[1529] Step 4: Display the route

[1530] The terminal (car navigation system) receives optimal route information sent from the server and displays the route on the car navigation screen.

[1531] Input: Route information from the server

[1532] Output: Route displayed on the car navigation screen

[1533] Specific operation: The device uses a screen rendering API to display the route on the map.

[1534] Step 5: Updating real-time location information

[1535] The device (car navigation system) updates location information in real time and recalculates the route as needed.

[1536] Input: GPS data

[1537] Output: Updated route information

[1538] Specific operation: The device acquires GPS data and sends it to the server. It also requests a new route as needed.

[1539] Step 6: Receiving and analyzing emotional data

[1540] The server receives user emotion data and analyzes it using an emotion engine.

[1541] Input: User sentiment data

[1542] Output: Analysis results (user's emotional state)

[1543] Specific operation: The server calls an emotion analysis API to analyze the user's emotion data.

[1544] Step 7: Adjusting the delivery method

[1545] The server adjusts delivery times and methods based on the analysis results. For example, if the user expresses delight, it might expedite delivery or add a surprise gift.

[1546] Input: Analysis results (user's emotional state)

[1547] Output: Adjusted delivery schedule and delivery method

[1548] Specific actions: The server updates the delivery schedule and instructs the addition of gifts as needed.

[1549] Step 8: Delivery Confirmation

[1550] After delivery is complete, the user reviews the delivery contents and provides feedback.

[1551] Input: Delivery completion notification

[1552] Output: User feedback

[1553] Specific actions: The user receives a delivery completion notification via a smartphone app and confirms the delivery details. They also fill out and submit a feedback form.

[1554] (Application Example 2)

[1555] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1556] Traditional delivery systems can determine delivery routes by considering traffic information and resident absence data, but they cannot adjust delivery times or methods to suit user preferences, thus failing to adequately improve user satisfaction. Furthermore, they are dependent on the experience of delivery drivers, making it difficult to maintain consistent delivery levels. Additionally, they are unable to provide value-added services such as surprise gift deliveries tailored to user needs.

[1557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1558] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means to detect user emotion data and adjust the delivery time and method; and a means to deliver a surprise gift according to the user's emotions. This enables flexible adjustment of delivery time and method according to the user's emotions, thereby improving user satisfaction. Furthermore, it enables maintaining a high level of delivery regardless of the delivery driver's experience and provides value-added services to users.

[1559] "Traffic information data" refers to information about traffic that affects delivery routes, such as road congestion, traffic accidents, and construction information.

[1560] "Resident absence / redelivery history data" refers to information about the time periods when residents were absent during past deliveries and the history of redeliveries.

[1561] "Delivery information for the day" refers to information about packages scheduled to be delivered on that day, including the number and size of packages, and the delivery address.

[1562] "Electricity meter information" refers to data about residents' electricity usage and is used to estimate whether or not residents are at home.

[1563] "Route characteristic data from the perspective of area drivers" refers to information about route characteristics and points to note based on the experience of drivers in a specific area.

[1564] "Using AI to determine the optimal delivery route" refers to a method that uses artificial intelligence to decide the best delivery route.

[1565] "Methods for linking with car navigation systems" refers to methods that display the determined delivery route on the in-vehicle navigation system, allowing the driver to follow that route.

[1566] "User emotion data" refers to data used to detect a user's emotional state, including emotions such as joy, excitement, and sadness.

[1567] "Means of adjusting delivery time and delivery method" refers to methods of speeding up or delaying delivery times, or changing delivery methods, based on user sentiment data.

[1568] "Methods for delivering surprise gifts" refers to methods of delivering additional gifts in response to the user's emotions.

[1569] The system for implementing this invention includes means for using AI to determine a delivery route for the day that minimizes the possibility of daily redeliveries, utilizing traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; means for linking the route determined by the AI ​​with a car navigation system; means for detecting user emotion data and adjusting delivery time and delivery method; and means for delivering a surprise gift according to the user's emotions.

[1570] System Configuration

[1571] hardware

[1572] Server: Performs data processing and runs AI models.

[1573] Smartphone: Used by the driver and linked to the car navigation system.

[1574] Emotion detection devices: Devices used to collect user emotion data (e.g., smartphone cameras or wearable devices).

[1575] software

[1576] AI Routing API: An API for determining the optimal delivery route.

[1577] Emotion detection library: A library for detecting a user's emotions.

[1578] Car navigation application: An application that displays routes determined by AI.

[1579] Data processing and data calculation

[1580] The server collects traffic information data, data on residents' absence / redelivery history, information on packages delivered that day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, it uses an AI routing API to determine the optimal delivery route. The determined route is sent to the driver's smartphone navigation application and displayed to them.

[1581] User emotion data is collected through emotion detection devices and analyzed using emotion detection libraries. Based on the analysis results, the server adjusts the delivery time and method, and delivers surprise gifts as needed.

[1582] Specific example

[1583] For example, if user A orders food delivery, the server determines the optimal delivery route based on traffic data and resident absence data, and displays it on the driver's smartphone. If user A's mood is detected as "happy," the server instructs the delivery of an additional surprise gift.

[1584] Example of a prompt

[1585] "Develop a food delivery application that adjusts delivery times and methods based on the user's emotions. It will use AI to determine the optimal delivery route and an emotion engine to adjust deliveries according to the user's feelings. Specifically, if the user expresses joy or excitement, it will deliver a surprise gift."

[1586] In this way, it becomes possible to flexibly adjust delivery times and methods according to the user's feelings, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a high level of delivery regardless of the delivery driver's experience, and to provide value-added services to users.

[1587] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1588] Step 1:

[1589] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. This data is obtained from various sensors and databases. Input data includes traffic information, resident absence data, package delivery information, electricity meter information, and route characteristic data. Based on this data, the server preprocesses the data and converts it into a format for input into the AI ​​model.

[1590] Step 2:

[1591] The server sends the pre-processed data to the AI ​​routing API to determine the optimal delivery route. The AI ​​routing API calculates the best route based on the input data and returns the result to the server. The output data includes the optimal delivery route information. The server receives this route information and proceeds to the next step.

[1592] Step 3:

[1593] The server sends the determined optimal delivery route information to the driver's smartphone. The smartphone's navigation application receives this route information and displays it to the driver. The input data includes the optimal delivery route information, and the output data includes the route information displayed on the smartphone's navigation screen. The driver begins driving according to this route.

[1594] Step 4:

[1595] The user collects emotional data using an emotion detection device (e.g., a smartphone camera or wearable device). The emotion detection device analyzes the user's facial expressions, voice, etc., and generates emotional data. The input data includes the user's facial expressions and voice data, and the output data is the generated emotional data.

[1596] Step 5:

[1597] The server sends the user's emotion data to an emotion detection library, which analyzes the user's emotional state. The emotion detection library determines the user's emotion based on the input data and returns the result to the server. The output data includes the user's emotional state (e.g., happy, neutral, sad). The server receives this emotional state and proceeds to the next step.

[1598] Step 6:

[1599] The server adjusts delivery time and method based on the user's emotional state. For example, if the user is determined to be "happy," the server may expedite delivery or add a surprise gift. Input data includes the user's emotional state, and output data includes the adjusted delivery time and method.

[1600] Step 7:

[1601] The server sends the adjusted delivery time and method to the driver's smartphone. The smartphone's navigation application receives this information and displays it to the driver. The input data includes the adjusted delivery time and method, and the output data includes the information displayed on the smartphone's navigation screen. The driver makes deliveries according to this information.

[1602] By following these steps, it becomes possible to flexibly adjust delivery times and methods in accordance with the user's feelings, thereby improving user satisfaction. Furthermore, it becomes possible to maintain a high level of delivery regardless of the delivery driver's experience, and to provide value-added services to users.

[1603] (Example 3)

[1604] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1605] Traditional delivery systems suffered from frequent redeliveries and difficulty in determining efficient delivery routes. Furthermore, maintaining consistent delivery levels was challenging due to reliance on driver experience and skills. Additionally, the inability to effectively utilize real-time traffic information and resident absence data often led to decreased delivery efficiency. To address these challenges, a system utilizing more advanced data analysis and AI technology is necessary.

[1606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1607] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a daily delivery route that minimizes the possibility of redelivery using a generating AI model; a means for linking the route determined by the generating AI model with a navigation system; a means for the user to input delivery information; and a means for the server to collect and integrate additional data. This makes it possible to determine efficient delivery routes while minimizing the possibility of redelivery and maintaining delivery levels.

[1608] "Traffic information data" refers to data that includes real-time traffic conditions, road congestion levels, accident information, and more.

[1609] "Resident absence / redelivery history data" refers to data that includes historical information such as the number of times residents were absent during past deliveries and the number of times redeliveries were made.

[1610] "Delivery information for the day" refers to data that includes information such as the type and quantity of packages scheduled to be delivered on that day, and the delivery address.

[1611] "Electricity meter information" is data that shows each resident's electricity usage and is used to estimate whether or not a resident is at home.

[1612] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics and points to note about delivery routes in a specific area, based on past delivery history and the driver's experience.

[1613] A "generative AI model" refers to an algorithm or program that uses machine learning and artificial intelligence technologies to calculate the optimal delivery route based on input data.

[1614] A "navigation system" is a system that provides route guidance to the driver, such as a car navigation device installed in the vehicle or a smartphone app.

[1615] "Means for users to input delivery information" refers to interfaces or applications that drivers use on smartphones or tablets to input information about delivered packages and data on residents' absences / redelivery records.

[1616] "Means for the server to collect and integrate additional data" refers to the function by which the server collects traffic information, electricity meter information, and other data from external data sources, integrates them, and processes them for input into the AI ​​model.

[1617] This invention is a system designed to maximize the efficiency of delivery drivers and reduce the likelihood of redeliveries. This system utilizes traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers, and uses a generated AI model to determine the optimal delivery route.

[1618] The server first receives delivery package information for the day entered by the user (driver) and data on residents' absences / redelivery records. Next, the server collects traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers. This data is obtained from map service APIs (e.g., Google Maps API), smart meter databases of power companies, and databases of past route history.

[1619] The server integrates this data and feeds it into a generative AI model. This AI model is built using machine learning frameworks such as TensorFlow and PyTorch. Based on historical data and real-time information, the AI ​​model calculates the optimal route that minimizes the likelihood of redelivery.

[1620] The calculated optimal route is transmitted to the navigation system and displayed on the driver's terminal. The driver then makes deliveries following the route displayed on the navigation system. This reduces the likelihood of redeliveries and helps maintain delivery levels.

[1621] As a concrete example, a driver responsible for deliveries in a certain area enters the delivery package information for the day, such as "Package A, Package B, Package C," into the system. They also enter data on residents' absence / redelivery history, such as "Resident X has been absent 3 times in the past, Resident Y has had a redelivery once in the past." Based on this information, along with traffic information data obtained from a map service API, electricity meter information obtained from the power company, and past route history data, the system's generating AI model determines the optimal delivery route.

[1622] Examples of prompts to input into a generative AI model include the following:

[1623] "Delivery package information: Package A, Package B, Package C. Resident absence / redelivery history data: Resident X has been absent 3 times in the past, Resident Y has had 1 redelivery in the past. Based on traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers, please calculate the optimal delivery route."

[1624] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine and a generative AI model work in cooperation. Specifically, the emotion engine recognizes the user's emotions and provides this information to the generative AI model. The generative AI model optimizes the delivery route based on this information. For example, if the user is expressing happiness, the generative AI model prioritizes delivery to that user. Conversely, if the user is expressing anger, the generative AI model delays delivery to that user and prioritizes delivery to other users. This makes it possible to optimize the delivery route in response to the user's emotions.

[1625] In this way, the present invention provides a system that utilizes advanced data analysis and AI technology to maximize delivery efficiency and reduce the possibility of redelivery. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[1626] Step 1:

[1627] The user (driver) uses a smartphone or tablet to input information about the day's deliveries and data on residents' absences / redeliveries into a dedicated application. For example, they might enter "Package A, Package B, Package C" into the app's input form and select information such as "Resident X has been absent 3 times in the past, Resident Y has had a redelivery once in the past." The entered data is then sent to the server.

[1628] Step 2:

[1629] The server receives delivery package information and resident absence / redelivery data sent by users. Next, the server collects traffic information data, electricity meter information, and route characteristic data from the perspective of area drivers. Specifically, the server calls a map service API to obtain real-time traffic information and accesses the power company's smart meter database to obtain electricity meter information. It also collects route characteristic data from the perspective of area drivers from a database of past route history. This data is integrated within the server.

[1630] Step 3:

[1631] The server preprocesses the integrated data and converts it into a format for input into the generative AI model. For example, it converts the data into JSON format and inputs it into the generative AI model. Specifically, it generates JSON data like the following:

[1632] json

[1633] {

[1634] "deliveries": ["package A", "package B", "package C"],

[1635] "absence_data": {"Resident X": 3, "Resident Y": 1},

[1636] "traffic_data": {...},

[1637] "meter_data": {...},

[1638] "route_features": {...}

[1639] }

[1640] This data is input into the AI ​​model for generating data.

[1641] Step 4:

[1642] The server inputs integrated data into a generative AI model to calculate the optimal delivery route. Based on historical data and real-time information, the generative AI model calculates the route that minimizes the likelihood of redelivery. For example, it might generate a route like "Route 1: Package A -> Package B -> Package C". The calculation results are stored on the server.

[1643] Step 5:

[1644] The server sends the calculated optimal delivery route to the navigation system. Specifically, it uses the navigation system's API to send route information, which is then displayed on the driver's terminal. For example, information such as "Route 1: Package A -> Package B -> Package C" is displayed on the navigation system.

[1645] Step 6:

[1646] The user (driver) delivers packages according to the optimal delivery route displayed on the navigation system. For example, following the navigation system's instructions, they deliver packages in the order of "deliver package A first, then package B, and finally package C." This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[1647] (Application Example 3)

[1648] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1649] Traditional delivery systems had a high probability of redelivery and made it difficult to determine efficient delivery routes. Furthermore, delivery routes were not optimized to consider user emotions, making it challenging to improve user satisfaction. In particular, in food delivery, delivery delays and redeliveries significantly impact customer satisfaction, so these issues need to be addressed.

[1650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1651] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day that minimizes the possibility of daily redeliveries using AI; a means to link the route determined by the AI ​​with a car navigation system; a means for an emotion engine to recognize the user's emotions and provide that information to the AI; a means to optimize the delivery route according to the user's emotions; and a means to display the optimized delivery route on a smartphone navigation app. This makes it possible to minimize the possibility of redeliveries and optimize the delivery route according to the user's emotions.

[1652] "Traffic information data" refers to information about traffic that affects delivery routes, such as road congestion, traffic accidents, and construction information.

[1653] "Resident absence / redelivery record data" refers to records of times when residents were absent during past deliveries and records of redeliveries.

[1654] "Delivery information for the day" refers to detailed information about packages to be delivered on that day, including the delivery address, delivery time, and type of package.

[1655] "Electricity meter information" refers to data showing the usage status of electricity meters at each delivery location, and is used to estimate whether residents are at home.

[1656] "Route characteristic data from the perspective of area drivers" refers to information about route characteristics, points to note, and efficient delivery methods held by drivers who are familiar with the delivery area.

[1657] "AI" is an abbreviation for artificial intelligence, and it refers to algorithms and systems that calculate the optimal delivery route based on data.

[1658] A "car navigation system" is a navigation system installed in a vehicle that guides the driver along the optimal route.

[1659] An "emotion engine" is a technology that recognizes a user's emotions and adjusts the system's operation based on that information.

[1660] A "smartphone navigation app" is a navigation application that runs on a smartphone and is software that displays the optimal route for the user.

[1661] The following system configuration and processing procedure will be described as embodiments for carrying out this invention.

[1662] System Configuration

[1663] This system consists of the following hardware and software:

[1664] hardware

[1665] server

[1666] smartphone

[1667] Car navigation system installed in the vehicle

[1668] software

[1669] AI libraries (e.g., RouteOptimizer)

[1670] Emotion Engine (e.g., EmotionEngine)

[1671] Smartphone navigation app

[1672] Processing procedure

[1673] 1. Enter delivery information

[1674] Using a terminal (smartphone), the delivery driver enters the delivery package information for the day. This includes the delivery address, delivery time slot, and package type.

[1675] 2. Obtaining traffic information

[1676] The server retrieves the latest traffic data from a traffic information API. This data includes information such as road congestion, traffic accidents, and construction work.

[1677] 3. Obtaining electricity meter information

[1678] The server retrieves electricity meter information for each delivery address. Based on this information, it estimates whether residents are at home.

[1679] 4. Optimizing delivery routes

[1680] The server uses an AI library (RouteOptimizer) to calculate the optimal delivery route based on delivery information, traffic information, electricity meter information, data on residents' absence / redelivery history, and route characteristic data from the perspective of area drivers.

[1681] 5. Utilizing the Emotion Engine

[1682] The server uses an EmotionEngine to recognize the user's emotions. The EmotionEngine provides the user's emotional information to the AI, which then uses this information to optimize delivery routes.

[1683] 6. Displaying the optimized route

[1684] The optimized delivery route is displayed in the smartphone's navigation app. The driver follows this route to make deliveries.

[1685] Specific example

[1686] For example, suppose one of the delivery destinations is "Address 1," and past data shows that the resident is often absent in the morning. Based on this information, the AI ​​will incorporate "Address 1" into the afternoon delivery route. Also, if the emotion engine recognizes that the resident of "Address 2" is happy, the AI ​​will prioritize incorporating "Address 2" into the delivery route.

[1687] Example of a prompt

[1688] "Please generate the optimal delivery route, taking into account the feelings of the residents at the delivery destination. Based on delivery information, traffic information, and electricity meter information, adjust the route according to the user's feelings."

[1689] The above describes the embodiments for carrying out this invention. This system reduces the likelihood of redelivery and enables the optimization of delivery routes according to the user's emotions.

[1690] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1691] Step 1:

[1692] Using a terminal (smartphone), delivery drivers enter information about the day's deliveries. This information includes the delivery address, delivery time slot, and package type. This information is sent to a server and stored in a database.

[1693] Step 2:

[1694] The server retrieves the latest traffic data from a traffic information API. This data includes road congestion, traffic accidents, and construction information. The server analyzes this data and extracts factors that affect delivery routes.

[1695] Step 3:

[1696] The server retrieves electricity meter information for each delivery address. Using an electricity meter information API, it obtains electricity usage data for each address and estimates whether residents are at home. This information is also stored in the database.

[1697] Step 4:

[1698] The server retrieves data on residents' absences and redelivery history from a database. Based on past delivery data, it analyzes each resident's absence times and redelivery frequency.

[1699] Step 5:

[1700] The server acquires route characteristic data from the perspective of area drivers. This data includes information about route features, points to note, and efficient delivery methods from drivers who are familiar with the delivery area.

[1701] Step 6:

[1702] The server uses an AI library (RouteOptimizer) to calculate the optimal delivery route based on delivery information, traffic information, electricity meter information, data on residents' absence / redelivery history, and route characteristic data from the perspective of area drivers. The AI ​​receives this data as input and outputs the optimal route to minimize the possibility of redelivery.

[1703] Step 7:

[1704] The server uses an EmotionEngine to recognize the user's emotions. The EmotionEngine provides the AI ​​with the user's emotional information, which is then used to optimize delivery routes. For example, if a user is happy, delivery to that user is prioritized.

[1705] Step 8:

[1706] The server sends an optimized delivery route to the smartphone's navigation app. The driver uses the smartphone's navigation app to deliver according to the optimized route. The navigation app provides real-time route guidance and supports the driver.

[1707] The above outlines the specific processing steps of this system. This reduces the likelihood of redelivery and enables the optimization of delivery routes based on the user's preferences.

[1708] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1709] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1710] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1711] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1712] [Third Embodiment]

[1713] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1714] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1715] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1716] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1717] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1718] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1719] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1720] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1721] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1722] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1723] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1724] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1725] "Example of form 1"

[1726] One embodiment of the present invention is a delivery route determination system. This system collects traffic information data, data on residents' absence / redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the time residents are absent and the history of redeliveries in order to reduce the likelihood of redeliveries.

[1727] "Example of form 2"

[1728] Furthermore, the route determined by the AI ​​is displayed in conjunction with the car navigation system. This allows drivers to deliver according to the optimal route determined by the AI. Specifically, the route determined by the AI ​​is displayed on the car navigation screen, and the driver delivers according to that route. This makes it possible to maintain a consistent delivery level regardless of whether the driver is a veteran or a rookie.

[1729] "Example of form 3"

[1730] As a concrete example, a driver responsible for deliveries in a certain area inputs information about the day's deliveries and data on residents' absences / redelivery records into the system. Based on this information, along with traffic data, electricity meter information, and route characteristic data from the driver's perspective, the AI ​​determines the optimal delivery route. This route is displayed on the car's navigation system, and the driver follows this route for deliveries. This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[1731] The following describes the processing flow for each example of the form.

[1732] "Example of form 1"

[1733] Step 1: The delivery route determination system collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[1734] Step 2: Based on the collected data, the AI ​​determines the optimal delivery route. Specifically, the AI ​​determines the delivery route by considering the residents' absence times and past redelivery data in order to minimize the possibility of redelivery.

[1735] "Example of form 2"

[1736] Step 1: The route determined by the AI ​​will be displayed in conjunction with the car navigation system.

[1737] Step 2: Drivers follow the instructions on the car's navigation system to make deliveries. This ensures that delivery levels are maintained regardless of whether the driver is experienced or new.

[1738] "Example of form 3"

[1739] Step 1: A driver responsible for deliveries in a specific area enters the day's delivery information and data on residents' absences / redelivery attempts into the system.

[1740] Step 2: Based on this information, traffic data, electricity meter information, and route feature data from the perspective of area drivers, the system's AI determines the optimal delivery route.

[1741] Step 3: This route will be displayed on the car's navigation system, and the driver will follow this route for deliveries. This reduces the likelihood of redeliveries and helps maintain a consistent delivery level.

[1742] (Example 1)

[1743] Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1744] Conventional delivery route determination systems had a high probability of redelivery, making efficient delivery difficult. Furthermore, they failed to determine the optimal route considering traffic information and residents' availability, resulting in reduced delivery efficiency. Additionally, disparities in delivery quality between veteran and novice drivers were a significant challenge.

[1745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1746] In this invention, the server includes means for collecting traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers; means for preprocessing the collected data; means for training a machine learning model based on the preprocessed data; means for receiving delivery requests from users; means for calculating the optimal delivery route using the trained machine learning model; means for transmitting the calculated optimal route to a terminal; and means for displaying the route information received by the terminal to the user. This reduces the likelihood of redeliveries and enables the determination of efficient delivery routes. It also helps maintain a consistent delivery level between veteran and novice drivers.

[1747] "Traffic information data" refers to data that shows real-time traffic conditions and road congestion.

[1748] "Resident absence / redelivery history data" refers to data showing the time residents were absent during past deliveries and the history of redeliveries.

[1749] "Delivery information for the day" refers to data that shows detailed information about packages to be delivered on that day.

[1750] "Electricity meter information" refers to data on electricity usage used to estimate whether residents are at home.

[1751] "Route characteristic data from the perspective of area drivers" refers to data on route characteristics based on feedback from drivers.

[1752] "Means of collection" refers to the methods and devices used to acquire the necessary data.

[1753] "Preprocessing means" refers to methods and devices for converting collected data into a format suitable for analysis and model training.

[1754] "Means for training machine learning models" refers to methods and devices for training machine learning algorithms using collected and pre-processed data.

[1755] "Means of receiving delivery requests" refers to methods or devices for receiving delivery requests from users.

[1756] "Means for calculating the optimal delivery route" refers to methods or devices that use trained machine learning models to calculate the most efficient delivery route.

[1757] "Means of transmission to terminal" refers to methods or devices for transmitting the calculated delivery route to the user's terminal.

[1758] "Means for displaying route information received by a terminal to the user" refers to methods or devices for visually presenting delivery route information received by a terminal to the user.

[1759] This invention relates to a delivery route determination system that uses traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine the optimal delivery route.

[1760] Program generation

[1761] The programs for this system are generated using Python. Key libraries include requests for data collection, pandas for data processing, and scikit-learn and TensorFlow for building machine learning models.

[1762] Program Processing Description

[1763] The server collects the following data:

[1764] Traffic information data: Real-time traffic information is obtained using a standard map API.

[1765] Resident absence / redelivery record data: Past delivery record data is obtained from the delivery company's database.

[1766] Delivery information for the day: Retrieve the delivery list for the day from the delivery management system.

[1767] Electricity meter information: Use the smart meter API to obtain electricity usage data to estimate whether residents are at home.

[1768] Route characteristic data from the perspective of area drivers: Collect feedback from drivers and save route characteristics in a database.

[1769] After collecting this data, the server preprocesses it using pandas. Specifically, it performs tasks such as imputing missing values ​​and normalizing the data.

[1770] Next, the server uses scikit-learn and TensorFlow to build a machine learning model. This model optimizes delivery routes by considering residents' absence times and past redelivery data to reduce the likelihood of redeliveries.

[1771] After the model has finished training, the server calculates the optimal delivery route each time a new delivery request comes in and sends it to the terminal.

[1772] Specific examples and prompt statements

[1773] As a concrete example, consider the following scenario:

[1774] The user enters a new delivery request into the system. For example, the user enters "I will make 10 deliveries in Tokyo on October 10, 2023." Based on this information, the server calculates the optimal delivery route and sends it to the terminal.

[1775] Examples of prompts to input into a generative AI model:

[1776] "We plan to make 10 deliveries in Tokyo on October 10, 2023. Please calculate the optimal delivery route, taking into account traffic information data, data on residents' absence / redelivery history, information on packages delivered on that day, electricity meter information, and route characteristic data from the perspective of area drivers."

[1777] By inputting this prompt into the generating AI model, the AI ​​calculates the optimal delivery route and provides it to the user.

[1778] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1779] Step 1:

[1780] The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers.

[1781] Inputs: Traffic information API, delivery company database, delivery management system, smart meter API, driver feedback

[1782] Output: Various collected data

[1783] Specific operation: The server calls a traffic information API to obtain real-time traffic information and retrieves past delivery performance data from the delivery company's database. It also retrieves the day's delivery list from the delivery management system and obtains electricity usage data using the smart meter API. Furthermore, it collects driver feedback and stores it in the database.

[1784] Step 2:

[1785] The server preprocesses the collected data.

[1786] Input: Various collected data

[1787] Output: Preprocessed data

[1788] Specific operation: The server uses the pandas library to impute missing values ​​in the data and normalize data at different scales. It also combines data obtained from different sources into a single dataframe.

[1789] Step 3:

[1790] The server trains a machine learning model based on the pre-processed data.

[1791] Input: Preprocessed data

[1792] Output: Trained machine learning model

[1793] Specific operation: The server builds machine learning models using scikit-learn or TensorFlow. It selects features and the optimal algorithm (e.g., random forest, neural network) to train the model.

[1794] Step 4:

[1795] The user enters a new delivery request into the system.

[1796] Input: Delivery request (Example: "Make 10 deliveries in Tokyo on October 10, 2023")

[1797] Output: Delivery request data

[1798] Specific operation: The user enters a delivery request into the system interface. The server receives this request and saves it to the database.

[1799] Step 5:

[1800] The server uses a pre-trained machine learning model to calculate the optimal delivery route.

[1801] Input: Delivery request data, trained machine learning model

[1802] Output: Optimal delivery route

[1803] Specific operation: The server converts the delivery request data into a format that can be input into the model, and uses the trained machine learning model to predict the optimal delivery route.

[1804] Step 6:

[1805] The server sends the calculated optimal route to the terminal.

[1806] Input: Optimal delivery route

[1807] Output: Route information sent to the terminal

[1808] Specific operation: The server calls an API to send the calculated optimal route to the terminal, and then sends the route information to the terminal.

[1809] Step 7:

[1810] The device displays the received route information to the user.

[1811] Input: Route information sent to the terminal

[1812] Output: Route information displayed to the user

[1813] Specific operation: The terminal provides an interface for visually displaying the received route information to the user.

[1814] (Application Example 1)

[1815] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1816] Traditional delivery systems failed to fully utilize traffic information and data on residents' absences, resulting in frequent redeliveries. Furthermore, insufficient optimization of delivery routes made predicting delivery times difficult. This led to decreased delivery efficiency and reduced customer satisfaction.

[1817] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1818] This invention includes a server that utilizes traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers to determine a delivery route for the day using AI that minimizes the possibility of daily redeliveries; a means of linking the route determined by the AI ​​with a car navigation system; a means of optimizing the delivery route using an application installed on a smartphone; and a means of predicting delivery times by reflecting real-time traffic information. This makes it possible to reduce the frequency of redeliveries and improve delivery efficiency.

[1819] "Traffic information data" refers to information about traffic that is collected in real time, such as road congestion, traffic accidents, and construction information.

[1820] "Resident absence / redelivery history data" refers to data showing the time periods when a particular resident was absent in the past and the history of redelivery requests.

[1821] "Delivery information for the day" refers to detailed information about packages scheduled for delivery on that day, including data such as package type, quantity, and delivery address.

[1822] "Electricity meter information" is data that shows residents' electricity usage and is used to estimate whether or not residents are at home.

[1823] "Route characteristic data from the perspective of area drivers" refers to data that shows the characteristics and points to note about routes that drivers responsible for a specific area have learned through experience.

[1824] "Using AI to determine the optimal delivery route" refers to a method of using artificial intelligence to decide on the best delivery route.

[1825] "Methods for linking with car navigation systems" refers to methods for linking the determined delivery route with the in-vehicle navigation system.

[1826] "Applications installed on smartphones" are software that runs on smartphones and is used to optimize delivery routes and predict delivery times.

[1827] "A method for predicting delivery times by reflecting real-time traffic information" refers to a method for predicting the time it will take to deliver a package, taking into account the current traffic conditions.

[1828] The following system configuration will be described as an embodiment for carrying out this invention.

[1829] System Configuration

[1830] This system consists of a server, a smartphone, a car navigation system, and various data sources. The server collects traffic information data, data on residents' absence / redelivery records, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers. Based on this data, AI determines the optimal delivery route.

[1831] Hardware and software to be used

[1832] Hardware: Servers, smartphones, car navigation systems

[1833] Software: Python, Pandas (data processing), Scikit-learn (machine learning), Geopy (geographic information processing)

[1834] Data processing and data calculation

[1835] The server processes the data in the following steps:

[1836] 1. Data Import: Import traffic information, resident absence / redelivery record data, daily delivery package information, electricity meter information, and route characteristic data from the perspective of area drivers from a CSV file. ...

Claims

[Claim 1] A means for determining a daily delivery route that minimizes the possibility of redelivery by using artificial intelligence, which is a route optimization model constructed by machine learning with delivery time as the target variable. This model collects traffic information data, data on residents' absence or redelivery history, information on packages delivered on the day, electricity meter information, and route characteristic data from the perspective of area drivers from a traffic information API, a delivery company's database, a delivery management system, a smart meter API, and driver feedback. Using the delivery address as the key, it combines and integrates the traffic information data, the data on residents' absence or redelivery history, the information on packages delivered on the day, the electricity meter information, and route characteristic data from the perspective of area drivers to generate a dataset. Based on this dataset, in order to reduce the possibility of redelivery, the estimated presence or absence of residents using the electricity meter information, the time residents are absent, the traffic information data, the data on residents' absence or redelivery history, the information on packages delivered on the day, and route characteristic data from the perspective of area drivers are set as learning features, and delivery time is used as the target variable to predict the delivery time for each delivery destination and determine the delivery order for each destination based on the prediction results. A means for linking the route determined by the aforementioned artificial intelligence with the vehicle's navigation system, A means of collecting emotional data, including voice, text, facial expressions, and body movements, through interaction with the user, A system comprising: analyzing collected emotional data using an emotional analysis engine to recognize the user's emotions; providing the recognition results to the artificial intelligence; and, if the recognition results identify the user as showing anger or dissatisfaction, recalculating and reoptimizing the delivery route using the route optimization model to prioritize delivery to the user.

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