System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting traffic conditions, weather information, and item information in real-time; means for filtering and processing the collected traffic conditions, weather information, and item information; means for generating an optimal delivery route based on the filtered and processed information using a generative model; means for updating the delivery route based on the real-time information; and means for displaying the generated delivery route on a user device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional logistics systems, delivery route optimization is often based on static information, making it difficult to respond to real-time traffic conditions and weather fluctuations. This has led to problems such as reduced delivery efficiency and increased costs. These issues are particularly pronounced in Japan, where there is a high rate of traffic congestion in urban areas and deliveries to remote areas in rural areas. [Means for solving the problem]

[0005] The present invention provides a system that collects traffic, weather, and product information in real time, filters, and processes the information, and generates an optimal delivery route based on the provided information using a generative model. It also provides a system that includes a means for updating the delivery route based on the real-time information and a means for displaying the generated delivery route on a user device. This allows for adapting to real-time changes in conditions, improving delivery efficiency, and reducing costs.

[0006] "Traffic conditions" refers to all information related to traffic on roads, such as road congestion, traffic jams, accident occurrence status, and speed limit information.

[0007] "Weather information" means data about current and forecasted weather conditions, including temperature, precipitation, wind speed, humidity, and the presence or absence of storms.

[0008] "Item information" is information about the package to be delivered, and includes details such as the delivery address, package weight, size, and handling precautions.

[0009] "Real-time" refers to the acquisition, processing, and display of information very close to the current time, with almost no delay.

[0010] "Means of collection" refers to various sensors, APIs, or other data acquisition methods for obtaining traffic conditions, weather information, and product information.

[0011] "Filtering and processing means" refers to algorithms and methods used to remove unnecessary information from collected data and extract and organize necessary information.

[0012] A "generative model" refers to an algorithm or AI model that calculates optimal delivery routes based on traffic conditions, weather information, and product information. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention relates to a system that collects traffic conditions, weather information, and product information in real time, processes the information, and generates and updates optimal delivery routes in order to improve delivery efficiency in logistics. A specific description of an embodiment of the invention will be given below.

[0035] Overall program flow

[0036] Examples of data collection

[0037] The server uses a traffic information API to collect real-time traffic conditions. This API provides information on current road congestion and speed limits. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server through the API. The server then uses a weather information API to obtain current and forecast weather data for the area. For example, if freezing rain is forecast in the next few hours, that information is also retrieved.

[0038] Examples of data processing

[0039] The collected data is filtered and processed on the server. For traffic condition data, only the main congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as the main data. For goods information, the weight and size of the delivery items, as well as handling precautions, are organized.

[0040] Example of delivery route generation

[0041] The server uses a generative model based on the collected and processed data to generate an optimal delivery route. The generative model uses machine learning algorithms, for example, to calculate the shortest and most efficient route based on each data point. It also considers alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0042] Real-time update example

[0043] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the device sends the data to the server and requests a route recalculation. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0044] User interface examples

[0045] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface shows the route highlighted on a map, allowing the user to follow the instructions to efficiently complete the delivery.

[0046] The system of the present invention collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes, thereby significantly improving delivery efficiency in logistics, thereby reducing costs and shortening delivery times.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The server accesses the traffic information API to obtain current traffic condition data, such as traffic congestion information and speed limit information on major roads.

[0050] Step 2:

[0051] The server accesses the weather information API to retrieve current and forecast weather data, such as temperature, precipitation, and wind speed for a specific area.

[0052] Step 3:

[0053] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0054] Step 4:

[0055] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0056] Step 5:

[0057] The server filters the collected weather data to extract only important information that will affect delivery, such as forecasts of heavy rain or storms along delivery routes.

[0058] Step 6:

[0059] The server organizes shipment information and identifies the handling requirements for each package, optimizing delivery routes based on package size and weight.

[0060] Step 7:

[0061] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate the optimal delivery route.

[0062] Step 8:

[0063] The server then validates the generated optimal delivery route to ensure its feasibility, and if necessary, makes minor adjustments, such as avoiding traffic congestion in the Tokyo area.

[0064] Step 9:

[0065] The server sends the generated delivery route to the terminal, and the delivery person holding the terminal receives this route information.

[0066] Step 10:

[0067] The device periodically retrieves the latest data from the server, checks real-time traffic and weather conditions, and receives and updates any new information.

[0068] Step 11:

[0069] The device requests the server to recalculate the optimal route based on the latest data received, thereby adapting to changes in the situation.

[0070] Step 12:

[0071] The server recalculates the optimal delivery route based on real-time data and sends the new route information to the terminal.

[0072] Step 13:

[0073] The terminal displays the latest delivery route information to the user (delivery person), who can then efficiently deliver the goods by following the displayed route.

[0074] Step 14:

[0075] The user follows the navigation on the device to make deliveries and deliver packages efficiently. As new information is received, the device updates the route and suggests the best route.

[0076] This step allows the system to respond to real-time situation changes and continue to provide the optimal delivery route.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] In the modern logistics industry, unexpected obstacles such as traffic congestion and bad weather often have a significant negative impact on delivery efficiency. Furthermore, few systems can take into account specific item handling requirements and delivery conditions. Current systems have limited real-time information collection and route recalculation capabilities, and lack the flexibility to provide efficient delivery routes. This results in longer delivery times, increased costs, and lower customer satisfaction.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for collecting traffic condition information from external data sources, means for collecting weather information, means for collecting item information, means for filtering and processing the collected traffic condition information, means for filtering and processing the collected weather information, means for organizing the collected item information, means for calculating an optimal delivery route using a generative model based on the filtered and processed traffic condition information, weather information, and item information, means for checking whether the current delivery route is appropriate based on real-time information and recalculating the route as necessary, and means for displaying the generated delivery route on a user device and providing visual and audio guidance, thereby enabling significant improvement in delivery efficiency, cost reduction, and increased customer satisfaction.

[0082] "External data sources" are services or data providers that provide traffic and weather information via the Internet or other networks.

[0083] "Traffic condition information" refers to various information related to road traffic, such as road congestion levels, speed limits, and traffic accident occurrence status.

[0084] "Weather Information" means current and forecasted weather data, including precipitation, temperature, wind speed, weather alerts, etc.

[0085] "Item information" refers to detailed data about a shipment, including weight, size, handling requirements, temperature control needs, etc.

[0086] "Means of collection" refers to the technical means for obtaining the necessary data using various APIs and network interfaces.

[0087] "Filtering and processing measures" are technical measures for removing unnecessary information from collected data and extracting and organizing important data.

[0088] A "generative model" is a model that uses machine learning algorithms to analyze and process data and create optimal delivery routes.

[0089] A "delivery route" is a driving route selected for a delivery vehicle to reach its destination most efficiently.

[0090] A "user device" is a terminal used by a delivery person, and refers to a mobile information terminal such as a smartphone or tablet.

[0091] "Visual and audio guidance" means the manner in which driving directions and route change notifications are provided to a user through a user device, including map displays and audio prompts.

[0092] "Real-time information" refers to data that changes every moment, such as current road conditions and weather conditions.

[0093] This invention is a system for improving delivery efficiency in logistics, which collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes. This system is mainly composed of a server, terminals, and users.

[0094] Collection methods

[0095] The server collects real-time data using traffic information APIs and weather information APIs. Examples of traffic information APIs include the Google Maps API and the Here API, which are used to obtain current road congestion and speed limit information. Weather information APIs include the OpenWeather API and the Weatherbit API, which obtain current and forecast weather data. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server via the API. Next, weather data such as whether heavy rain is forecast within the next few hours is obtained.

[0096] Filtering and processing methods

[0097] The server filters and processes the collected data. For traffic condition data, only key congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as primary data. For goods information, the weight, size, and handling precautions of the delivery items are obtained from the logistics management system (WMS: Warehouse Management System), and the information includes instructions such as "Delivery A requires temperature control."

[0098] A means of calculating the optimal delivery route

[0099] The server uses the collected and processed data to calculate the optimal delivery route using a generative AI model. This generative AI model incorporates machine learning algorithms to calculate the shortest and most efficient route based on each data point. It also takes into account alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0100] A way to recalculate your route in real time

[0101] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there are any new traffic jams or changes in the weather, the device sends the data to the server to request a recalculation of the route. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0102] Guidance by user device

[0103] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface displays the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions efficiently and complete the delivery. Specific actions include a color-coded route on the map, voice guidance, and notification of route changes.

[0104] Prompt Sentence Examples

[0105] "Design a system that uses traffic and weather APIs to calculate the optimal delivery route based on current traffic conditions and weather. Use Google Maps API for traffic information and OpenWeather API for weather information. Explain how you would filter only the important data and generate the optimal route using a machine learning algorithm."

[0106] This invention will significantly improve delivery efficiency in logistics, reduce costs, and shorten delivery times. In addition, real-time information updates will always provide optimal delivery routes, contributing to improved customer satisfaction.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The server collects real-time data using traffic information APIs and weather information APIs. Specifically, it obtains current road congestion and speed limit information from Google Maps API and Here API, and current and forecast weather data from OpenWeather API and Weatherbit API. The input is real-time data from the APIs, and the output is traffic condition information and weather information.

[0110] Step 2:

[0111] The server filters and processes the collected traffic information. Specifically, it extracts only the main congestion information and removes information that does not exceed the speed limit and other noise. For example, if the congestion level exceeds a certain threshold, it retains that information as important data. The input is data from the traffic information API, and the output is filtered traffic information.

[0112] Step 3:

[0113] The server filters and processes the collected weather information. For example, it extracts data on severe weather, such as heavy rain or freezing rain, from the current weather. It keeps only the information that directly affects delivery and removes the rest. The input is data from the weather information API, and the output is the filtered weather information.

[0114] Step 4:

[0115] The server collects and organizes item information from the logistics management system (WMS). Specifically, it obtains and organizes data such as the weight, size, and handling precautions of delivery items. For example, if a specific item requires temperature control, this information is included. The input is item data from the WMS, and the output is organized item information.

[0116] Step 5:

[0117] The server uses a generative AI model to calculate the optimal delivery route based on the filtered and processed traffic, weather, and product information. Specifically, it uses a machine learning algorithm to generate the optimal delivery route that avoids traffic congestion, weather, and product handling requirements. The inputs are traffic, weather, and product information, and the output is the optimal delivery route.

[0118] Step 6:

[0119] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If new traffic congestion or weather changes are detected, the terminal sends the data to the server and requests a recalculation of the route. The server then recalculates the optimal route and sends the data to the terminal. The input is the latest data from the server, and the output is the recalculated delivery route.

[0120] Step 7:

[0121] The user (delivery person) can view the new optimized delivery route on the terminal display. The user interface is intuitive, showing the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions and efficiently carry out the delivery. The input is delivery route information from the terminal, and the output is efficient delivery work by the user.

[0122] (Application example 1)

[0123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] Improving delivery efficiency in logistics is difficult due to external factors such as traffic congestion and weather fluctuations. Furthermore, there is a need for rapid response to real-time situation changes and a user interface that delivery personnel can intuitively understand. To solve these issues, it is necessary to develop a system that can quickly and effectively process traffic, weather, and product information, and always provide the optimal delivery route.

[0125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0126] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, and means for generating an optimal delivery route using a generative model based on the filtered and processed information. This makes it possible to collect and process information in real time and always provide an optimal delivery route.

[0127] "Traffic conditions" refers to information such as road congestion, speed limits, accidents, and construction, and is real-time data on road driving conditions.

[0128] "Weather information" is data on current and forecasted weather conditions, including environmental factors such as precipitation, temperature, and wind speed.

[0129] "Item information" is detailed data such as the weight, size, and handling precautions of the item to be delivered.

[0130] "Means of collection" refers to a system for obtaining necessary information from external data sources such as traffic information APIs and weather information APIs.

[0131] "Filtering and processing means" refers to the process of narrowing down the collected raw data to the necessary information, and is a system that has the function of removing noise and shaping the data.

[0132] "Means for generating optimal delivery routes using generative models" refers to a system that uses mathematical models such as machine learning algorithms to calculate delivery routes based on collected and processed data.

[0133] "Means for updating delivery routes based on real-time information" refers to a system that periodically checks for new traffic and weather data and recalculates existing delivery routes as needed.

[0134] The "means for displaying on the user device" refers to a display or interface for visually presenting the generated delivery route to the user.

[0135] The "means for providing an intuitive map display" is map software that displays delivery routes and additional information in a format that is easily understood by the user.

[0136] "Means for notifying delivery route updates in real time" refers to a notification system that quickly notifies users when new information is updated during delivery.

[0137] To put this invention into practice, a system is required that collects traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes based on this information. A specific embodiment of this system is shown below.

[0138] Hardware and software used

[0139] Traffic Information API: Obtains information such as road congestion, speed limits, accidents, and construction.

[0140] Weather Information API: Obtain weather data such as precipitation, temperature, and wind speed.

[0141] Data filtering and processing system: Filters collected data and extracts only the information you need.

[0142] Machine learning algorithm (generative model): Calculates the optimal delivery route based on the filtered data.

[0143] User Interface (UI): Provides delivery personnel with information such as intuitive map displays.

[0144] Notification system: Real-time delivery route updates.

[0145] Data collection and processing

[0146] The server uses traffic information APIs and weather information APIs to collect real-time traffic and weather information. Item information is also obtained from a local database or JSON file. The server filters this data to extract only important information, such as traffic congestion and worsening weather.

[0147] Delivery route generation

[0148] Based on the filtered information, the server uses a generative model to generate the optimal delivery route. This generative model uses linear regression, a type of machine learning algorithm, to calculate the shortest and most efficient route based on past data. For example, it might suggest a route that avoids urban traffic jams and uses suburban highways.

[0149] Real-time updates

[0150] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the server recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0151] User Interface

[0152] The delivery driver can view the new optimized delivery route on their device display. The intuitive user interface shows the route highlighted on a map, allowing the delivery driver to follow the instructions efficiently.

[0153] Prompt Sentence Examples

[0154] An example of a prompt to input to a generative AI model is as follows:

[0155] The goal of this program is to create a smartphone application for a logistics center that collects traffic, weather, and product information and generates optimal delivery routes. Write the code based on the following criteria:

[0156] Traffic information is obtained from the traffic information API, and weather information is obtained from the weather information API.

[0157] Item information is read from a local JSON file.

[0158] Filter the data to retain only the key information.

[0159] Calculate optimal delivery routes using machine learning algorithms (linear regression).

[0160] The route is updated in real time and displayed in the user interface.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] The server collects real-time data from the traffic and weather APIs. In this step, it sends API requests to retrieve road congestion, speed limits, accident information, and current and forecast weather conditions. The input is the API endpoint, and the output is the retrieved traffic and weather data in JSON format.

[0164] Step 2:

[0165] The server filters and processes the acquired data. For traffic data, it extracts only information where congestion exceeds a certain threshold, and for weather data, it extracts only information about bad weather that will affect deliveries. The input is the JSON data collected in step 1, and the output is the filtered important information. Specifically, it removes unnecessary noise data.

[0166] Step 3:

[0167] The server uses the filtered data to run a machine learning algorithm (linear regression) to generate an optimal delivery route. In this step, past delivery data and current traffic and weather data are input to calculate the shortest and most efficient route. The input is the filtered data, and the output is the generated delivery route. The specific operations are to run the algorithm and calculate the delivery route.

[0168] Step 4:

[0169] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, it sends the data to the server and requests a route recalculation. The input is the delivery route generated in step 3 and the latest traffic and weather data, and the output is the updated delivery route.

[0170] Step 5:

[0171] The server recalculates the optimal route and sends the new route information to the device. In this process, the machine learning algorithm is run again to calculate the optimal route based on the new traffic and weather conditions. The input is the latest data and current route information, and the output is the recalculated delivery route. The specific operation involves sending data from the server to the device.

[0172] Step 6:

[0173] The user, a delivery person, can view the new optimized delivery route on the terminal display. The user interface is intuitively designed, and the route is highlighted on a map. The input is the updated delivery route sent from the server, and the output is the map display on the user interface. The specific operation is to display the route information on the terminal display.

[0174] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0175] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes that information, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions. A specific description of an embodiment of the present invention will be given below.

[0176] Overall program flow

[0177] Examples of data collection

[0178] The server accesses a traffic information API to obtain current traffic data, including traffic congestion and speed limit information for major roads. The server then accesses a weather information API to obtain current and forecast weather data. For example, if rain is predicted for a particular area within the next few hours, that information is also obtained. The server then obtains detailed information about the shipment from internal systems and external package information APIs, including the delivery address, package weight, size, and handling instructions.

[0179] Examples of data processing

[0180] The server filters the collected traffic data to remove noise and unnecessary information, extracting only important traffic congestion information. Next, the server filters the collected weather data to extract only important information that affects delivery. Finally, the server organizes the package information and identifies the handling requirements for each package, for example, classifying it into the appropriate category based on size and weight.

[0181] Example of delivery route generation

[0182] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to generate optimal delivery routes based on these data points. For example, a route might be calculated that uses suburban highways to avoid city congestion and complete the delivery before bad weather arrives.

[0183] Examples of emotion engines

[0184] The emotion engine built into the device recognizes the user's (delivery worker's) emotions in real time through facial recognition and voice analysis. For example, if the user feels stressed or tired during a delivery, the emotion engine will detect this. The device then sends this emotion data to the server and adjusts the route as needed.

[0185] Real-time update example

[0186] The device periodically obtains the latest traffic, weather, and emotion information from the server to check whether the current delivery route is appropriate. If new information is available, the server recalculates the optimal route based on that information and sends the new route information to the device.

[0187] User interface examples

[0188] The user (delivery worker) sees the new optimized delivery route on the terminal display. The intuitive user interface shows the route highlighted on a map. Furthermore, if the user feels stressed or shows signs of fatigue, rest points are automatically inserted, reducing the user's workload.

[0189] This system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the user's emotional state, improving delivery efficiency and reducing the burden on the user, thereby effectively reducing delivery costs and time.

[0190] The processing flow will be explained below.

[0191] Step 1:

[0192] The server accesses the traffic information API to obtain current traffic situation data, such as real-time congestion information and speed limit data for major roads.

[0193] Step 2:

[0194] The server accesses a weather information API to retrieve current and forecast weather data, such as if rain or strong winds are forecast for a particular area in the next few hours.

[0195] Step 3:

[0196] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0197] Step 4:

[0198] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0199] Step 5:

[0200] The server filters the collected weather data and extracts only important data that will affect delivery, such as heavy rain or storms forecasted along the delivery route.

[0201] Step 6:

[0202] The server collates the package information and identifies the handling requirements for each package, which includes sorting it into the appropriate category based on its size and weight.

[0203] Step 7:

[0204] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate optimal delivery routes, such as routes that avoid city congestion and use highways.

[0205] Step 8:

[0206] The server then validates the generated optimal delivery route to ensure feasibility, making fine adjustments as needed to accommodate traffic and weather conditions.

[0207] Step 9:

[0208] The server then sends the confirmed delivery route information to the terminal, which the delivery person with the terminal receives and confirms the route.

[0209] Step 10:

[0210] The emotion engine built into the device analyzes the facial expressions and voice of the user (delivery worker) in real time to recognize their emotional state. For example, it can detect if the user is feeling stressed during a delivery.

[0211] Step 11:

[0212] The device transmits the recognized user emotion data to the server in real time, and the server receives the data and decides whether to reflect it in the delivery route.

[0213] Step 12:

[0214] The server recalculates the delivery route as needed based on the received user emotion data. For example, if the user is feeling stressed, the server generates a route that includes rest stops to reduce stress.

[0215] Step 13:

[0216] The server sends the recalculated optimal route to the terminal, which receives this information and presents the new route to the user.

[0217] Step 14:

[0218] The terminal displays the new delivery route information to the user (delivery person), and the user interface highlights the optimal route on a map, allowing for visual viewing.

[0219] Step 15:

[0220] The user follows the displayed new route to ensure efficient delivery. When the emotion engine recognizes the user's stress or fatigue, it will suggest appropriate rest points.

[0221] Through these steps, the system responds to real-time changes in the situation and the user's emotional state, and always provides the optimal delivery route, thereby improving delivery efficiency and reducing the user's burden.

[0222] Example 2

[0223] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0224] Conventional delivery systems could calculate optimal routes by taking into account real-time traffic conditions and weather information, but they were unable to take into account the emotional state of the delivery person. As a result, delivery people could become stressed or fatigued, resulting in reduced delivery efficiency. Furthermore, it was difficult to dynamically update routes based on data collected in real time and select optimal routes in a timely manner.

[0225] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting traffic data, weather data, and item data in real time, means for filtering and processing the collected traffic data, weather data, and item data, means for generating an optimal delivery route using a generative model based on the filtered and processed data, means for collecting and processing emotion data of the delivery person, means for adjusting the delivery route based on the emotion data, means for updating the delivery route based on the real-time data, and means for displaying the generated delivery route on a display device. As a result, by generating an optimal delivery route in real time and taking into account the emotional state of the delivery person, it is possible to improve delivery efficiency and reduce the burden on the delivery person.

[0226] "Traffic data" refers to information that indicates traffic conditions and road conditions, and specifically includes congestion information and speed limit information.

[0227] "Weather data" refers to information relating to current weather conditions and future weather forecasts, and specifically includes rainfall, snowfall, wind speed, and the like.

[0228] "Item data" refers to detailed information about the item being delivered, including the delivery address, weight, size, handling precautions, etc.

[0229] "Filtering" refers to the process of extracting necessary information from collected data and removing unnecessary noise.

[0230] A "generative model" is a model built on machine learning algorithms to generate optimal results from given data.

[0231] "Delivery route" refers to the route a delivery vehicle takes to deliver a delivery item.

[0232] "Emotion data" is information that indicates the emotional state of the delivery person, and specifically includes stress, fatigue, etc.

[0233] A "display device" is a device that visually presents generated information to a user, and specifically includes a display and a monitor.

[0234] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0235] An "interface" refers to the means of connection through which a data sender and receiver exchange information.

[0236] This invention is a system that collects traffic data, weather data, and product data in real time and uses them to generate and update optimal delivery routes. Furthermore, it is characterized by recognizing and taking into account the emotional state of delivery personnel, thereby improving delivery efficiency and user comfort.

[0237] The system mainly consists of a server, a terminal, and a user device. Each element is explained in detail below.

[0238] Server Features

[0239] The server accesses traffic and weather information APIs to obtain necessary data in real time, such as traffic congestion information for major roads, speed limits, current weather data, and forecast data. It also obtains detailed information about deliveries (such as delivery address, package weight, size, and handling precautions) from internal systems and external package information APIs.

[0240] The server filters the acquired data to remove noise and unnecessary information, extracting only important information about traffic congestion and weather that may affect deliveries. It also organizes package information and categorizes it into appropriate categories based on size and weight.

[0241] The server then uses a generative AI model to generate optimal delivery routes based on the filtered data. The generative AI model contains machine learning algorithms that generate optimal results from the input data.

[0242] Device Features

[0243] The device is equipped with an emotion engine that recognizes the emotions of delivery personnel in real time by analyzing their facial expressions and voice. The recognized emotion data is sent from the device to a server and used to readjust the delivery route.

[0244] The device also periodically retrieves the latest traffic, weather, and emotion information from the server. The server recalculates the optimal delivery route based on the newly collected data and sends the results to the device, allowing it to respond to changes in the situation in real time.

[0245] User Interface

[0246] The user (delivery worker) can view the optimized new delivery route on the terminal display. The route is intuitively displayed highlighted on the map. If the user feels stressed or fatigued, the system automatically inserts rest points.

[0247] Specific examples

[0248] For example, when a delivery person is making deliveries in a specific area in the morning, the device collects traffic congestion data and rainfall forecast data and recognizes from the delivery person's facial expression that they are feeling stressed. As a result, the server calculates a new route and sends it to the device, including rest stops to reduce stress.

[0249] Prompt Sentence Examples

[0250] Here are some example prompts to input to the AI ​​model:

[0251] Current traffic conditions: Traffic congestion information

[0252] Current weather information: Rainfall forecast

[0253] Package information: shipping address, weight, size

[0254] Please generate the optimal delivery route taking into consideration transportation conditions such as avoiding traffic congestion and areas where rain is expected.

[0255] In this way, the system uses real-time data to generate optimal delivery routes and takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the workload of the delivery person.

[0256] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0257] Step 1: Collect data

[0258] The server accesses the traffic information API and weather information API to collect traffic and weather data. The input is a request sent to the API endpoint, and the output is response data in JSON or XML format. Specifically, this includes congestion information and speed limit information obtained from the traffic information API, and rainfall and snowfall forecast information obtained from the weather information API. The server also collects item data from internal systems and external parcel information APIs. Input includes delivery address, weight, size, handling precautions, etc., and this information is stored in the database as a response.

[0259] Step 2: Filtering the data

[0260] The server filters the collected traffic data, weather data, and product data. The input includes the raw data saved in step 1. Specifically, the server filters out noise data and extracts only traffic congestion information and important weather information. For example, it selects areas with serious congestion or areas where rain is expected, and ignores other data. The output is the filtered traffic data, weather data, and classified product data.

[0261] Step 3: Generate delivery routes

[0262] The server uses a generative AI model to generate an optimal delivery route based on the filtered data. Inputs include filtered traffic data, weather data, and product data. The generative model calculates the optimal route based on a prompt provided to it. An example of a prompt is:

[0263] "Generate the optimal delivery route taking into consideration current traffic conditions: traffic congestion information, current weather information: rainfall forecast, package information: delivery address, weight, size, and transportation conditions, avoiding traffic congestion and areas where rainfall is forecast."

[0264] The output is data on the optimal delivery route.

[0265] Step 4: Collect and process emotion data

[0266] The device uses an emotion engine to analyze the delivery person's facial expressions and voice and recognize their emotions in real time. Input includes facial expression and voice data acquired from a camera and microphone. The device's emotion engine analyzes this data and evaluates their stress level and fatigue state. The output is data about the delivery person's emotional state, which is immediately sent to the server.

[0267] Step 5: Real-time route adjustments

[0268] The server receives the delivery person's emotional data and recalculates the delivery route as necessary. The inputs are the latest emotional data, traffic data, and weather data. The server re-runs the generative AI model based on this data and calculates a new optimal route. The output is the adjusted delivery route data. This route data is sent to the terminal in real time.

[0269] Step 6: Route Display and User Interface

[0270] The user checks the latest optimized delivery route on the device display. The input is the new delivery route data sent from the server. The device launches a map app based on this data and displays the highlighted route. If the user feels stressed or tired during delivery, the system automatically inserts rest points and displays that information on the map. This allows the user to intuitively understand the optimal route and rest points.

[0271] The above is the specific processing steps and operational flow of the program of this system.

[0272] (Application example 2)

[0273] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Conventional delivery systems have shown some effectiveness in generating optimal delivery routes based on traffic and weather information, but they do not take into account the emotional state of the delivery person, which means they cannot provide the optimal route even when the delivery person is stressed or tired. Furthermore, the display on the user's device is simple, which means it cannot quickly respond to changes in the delivery person's emotional state. This raises concerns that delivery efficiency will decrease and the burden on the delivery person will increase.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0276] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, means for generating an optimal delivery route using a generative model based on the filtered and processed information, means for updating the delivery route based on the real-time information, means for displaying the generated delivery route on a user device, and means for adjusting the delivery route based on the user's emotional state using an emotion engine that recognizes the user's emotions, thereby improving delivery efficiency and reducing the burden on delivery personnel.

[0277] "Traffic conditions" is information that indicates the current state of road congestion and traffic flow.

[0278] "Weather information" is information that indicates current and forecast weather conditions.

[0279] "Item information" refers to information about the item to be delivered, including the address, weight, size, handling precautions, etc.

[0280] "Real-time" refers to data and information being collected and updated almost immediately.

[0281] "Gathering means" refers to hardware or software mechanisms for acquiring traffic conditions, weather information, and product information.

[0282] "Filtering" is the process of extracting necessary information from collected data and removing unnecessary information.

[0283] A "processing means" is a hardware or software mechanism for organizing and converting data into a required format.

[0284] A "generative model" is software that has an algorithm for calculating the optimal delivery route based on collected data.

[0285] The "optimal delivery route" is a route that takes into account traffic conditions, weather information, and product information to complete delivery in the shortest time and at the lowest cost.

[0286] An "updating means" is a hardware or software mechanism for recalculating existing delivery routes based on new information and keeping them up to date.

[0287] A "user device" is an electronic device such as a smartphone or tablet carried by a delivery person.

[0288] A "displaying means" is a mechanism for visually presenting information on the screen of a user device.

[0289] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0290] "Emotional state" refers to the user's psychological and physiological state, including stress, fatigue, and the like.

[0291] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes them, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions and adjusts routes based on those emotions. The following describes in detail an embodiment of the present invention.

[0292] System Program

[0293] The system first collects data from the traffic information API, weather information API, and goods information API. The traffic information API retrieves information on traffic congestion and speed limits on major roads. The weather information API retrieves current weather and forecast data, such as if rain is predicted for a specific area within the next few hours. The goods information API retrieves detailed information about the delivery, such as the delivery address, package weight, size, and handling precautions.

[0294] The collected data is filtered by the server to extract only the important information. Traffic data is given special emphasis on traffic congestion information, and weather data is focused on factors that affect delivery. Item information is also organized and appropriately categorized according to the size and weight of the package.

[0295] Generative models use this processed data to calculate optimal delivery routes, using machine learning algorithms to take into account numerous data points and generate routes that, for example, avoid traffic jams in cities and complete deliveries before bad weather arrives.

[0296] Emotion Engine

[0297] The emotion engine recognizes the delivery person's (user's) emotions in real time through facial expression recognition and voice analysis. If the user feels stressed or tired, the device detects this and sends the emotion data to the server. The server then reevaluates the emotion data along with the latest traffic, weather, and product information, and recalculates the optimal route. At this time, rest stops are inserted into the route according to the user's stress level.

[0298] User Interface

[0299] The device displays the new optimized delivery route. The intuitive user interface visually shows the route highlighted on a map. Additionally, rest stops are automatically inserted if the user feels stressed or fatigued, improving delivery efficiency and reducing user stress.

[0300] Hardware and software used

[0301] Hardware: User devices such as smartphones and tablets, and servers connected to the internet

[0302] Software: Traffic information API, weather information API, product information API, machine learning algorithm, emotion engine

[0303] Implementation example

[0304] For example, food delivery drivers can use the app to find the optimal delivery route in real time, and if stress is detected through facial expression analysis, it will automatically provide rest stops, improving delivery efficiency and reducing the driver's burden.

[0305] Prompt Sentence Examples

[0306] "Get current traffic congestion information from a traffic API and upcoming rainfall forecasts from a weather API. Then use an emotion engine to detect the driver's emotional state in real time. Based on this data, generate optimal delivery routes and suggest rest stops if the driver is feeling stressed."

[0307] As described above, this system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the burden on the delivery person.

[0308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0309] Step 1:

[0310] The server accesses the traffic information API, weather information API, and product information API to collect traffic conditions, weather information, and product information. The input data is the raw data obtained from each API, and the output data is the collected traffic conditions, weather information, and product information. The server temporarily stores this data and passes it to the next step.

[0311] Step 2:

[0312] The server filters the collected traffic data and extracts only important traffic congestion information. The input data is raw traffic data, which contains noise and unnecessary information. The server analyzes this data and selects the important parts. The output data is filtered traffic congestion information.

[0313] Step 3:

[0314] The server filters the collected weather data and extracts only the important information that affects delivery. The input data is raw weather information data, which includes various weather conditions. The server selects the data based on factors that directly affect delivery, and obtains important weather information as output.

[0315] Step 4:

[0316] The server organizes the collected item information and identifies the handling requirements for each package. The input data is raw item information data, including delivery address, package weight, size, handling precautions, etc. The server organizes this information and generates organized item information as output.

[0317] Step 5:

[0318] The server inputs the filtered and processed traffic data, weather data, and item data into a generative model to generate an optimal delivery route. The input data is the filtered traffic data, weather data, and organized item data. The server uses a machine learning algorithm based on this data to calculate the optimal delivery route. The output data is the generated optimal delivery route.

[0319] Step 6:

[0320] The emotion engine recognizes emotions in real time through facial expression recognition and voice analysis of the user. Input data is image and voice data of the user. The device analyzes this data to identify the user's emotional state and sends the recognized emotional data as output to the server.

[0321] Step 7:

[0322] The server re-evaluates the emotion data and the latest traffic, weather, and product information, and re-calculates the optimal route. The input data is the emotion data and the latest traffic, weather, and product information. The server re-evaluates based on this information and adjusts the delivery route as necessary. The output data is the adjusted delivery route.

[0323] Step 8:

[0324] The terminal displays the optimized delivery route on the user device. The input data is the optimized delivery route information sent from the server. The terminal displays this information on the display of the user device, visually showing the route. The output data is the route information displayed to the user.

[0325] Through the above processing steps, the system can respond to real-time changes in the situation, always provide the optimal delivery route, and also take into account the user's emotional state, thereby improving delivery efficiency and reducing the burden on the user.

[0326] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0327] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0328] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0329] [Second embodiment]

[0330] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0331] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0332] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0333] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0334] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0335] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0336] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0337] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0338] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0339] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0340] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0341] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0342] This invention relates to a system that collects traffic conditions, weather information, and product information in real time, processes the information, and generates and updates optimal delivery routes in order to improve delivery efficiency in logistics. A specific description of an embodiment of the invention will be given below.

[0343] Overall program flow

[0344] Examples of data collection

[0345] The server uses a traffic information API to collect real-time traffic conditions. This API provides information on current road congestion and speed limits. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server through the API. The server then uses a weather information API to obtain current and forecast weather data for the area. For example, if freezing rain is forecast in the next few hours, that information is also retrieved.

[0346] Examples of data processing

[0347] The collected data is filtered and processed on the server. For traffic condition data, only the main congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as the main data. For goods information, the weight and size of the delivery items, as well as handling precautions, are organized.

[0348] Example of delivery route generation

[0349] The server uses a generative model based on the collected and processed data to generate an optimal delivery route. The generative model uses machine learning algorithms, for example, to calculate the shortest and most efficient route based on each data point. It also considers alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0350] Real-time update example

[0351] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the device sends the data to the server and requests a route recalculation. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0352] User interface examples

[0353] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface shows the route highlighted on a map, allowing the user to follow the instructions to efficiently complete the delivery.

[0354] The system of the present invention collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes, thereby significantly improving delivery efficiency in logistics, thereby reducing costs and shortening delivery times.

[0355] The processing flow will be explained below.

[0356] Step 1:

[0357] The server accesses the traffic information API to obtain current traffic condition data, such as traffic congestion information and speed limit information on major roads.

[0358] Step 2:

[0359] The server accesses the weather information API to retrieve current and forecast weather data, such as temperature, precipitation, and wind speed for a specific area.

[0360] Step 3:

[0361] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0362] Step 4:

[0363] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0364] Step 5:

[0365] The server filters the collected weather data to extract only important information that will affect delivery, such as forecasts of heavy rain or storms along delivery routes.

[0366] Step 6:

[0367] The server organizes shipment information and identifies the handling requirements for each package, optimizing delivery routes based on package size and weight.

[0368] Step 7:

[0369] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate the optimal delivery route.

[0370] Step 8:

[0371] The server then validates the generated optimal delivery route to ensure its feasibility, and if necessary, makes minor adjustments, such as avoiding traffic congestion in the Tokyo area.

[0372] Step 9:

[0373] The server sends the generated delivery route to the terminal, and the delivery person holding the terminal receives this route information.

[0374] Step 10:

[0375] The device periodically retrieves the latest data from the server, checks real-time traffic and weather conditions, and receives and updates any new information.

[0376] Step 11:

[0377] The device requests the server to recalculate the optimal route based on the latest data received, thereby adapting to changes in the situation.

[0378] Step 12:

[0379] The server recalculates the optimal delivery route based on real-time data and sends the new route information to the terminal.

[0380] Step 13:

[0381] The terminal displays the latest delivery route information to the user (delivery person), who can then efficiently deliver the goods by following the displayed route.

[0382] Step 14:

[0383] The user follows the navigation on the device to make deliveries and deliver packages efficiently. As new information is received, the device updates the route and suggests the best route.

[0384] This step allows the system to respond to real-time situation changes and continue to provide the optimal delivery route.

[0385] Example 1

[0386] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0387] In the modern logistics industry, unexpected obstacles such as traffic congestion and bad weather often have a significant negative impact on delivery efficiency. Furthermore, few systems can take into account specific item handling requirements and delivery conditions. Current systems have limited real-time information collection and route recalculation capabilities, and lack the flexibility to provide efficient delivery routes. This results in longer delivery times, increased costs, and lower customer satisfaction.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0389] In this invention, the server includes means for collecting traffic condition information from external data sources, means for collecting weather information, means for collecting item information, means for filtering and processing the collected traffic condition information, means for filtering and processing the collected weather information, means for organizing the collected item information, means for calculating an optimal delivery route using a generative model based on the filtered and processed traffic condition information, weather information, and item information, means for checking whether the current delivery route is appropriate based on real-time information and recalculating the route as necessary, and means for displaying the generated delivery route on a user device and providing visual and audio guidance, thereby enabling significant improvement in delivery efficiency, cost reduction, and increased customer satisfaction.

[0390] "External data sources" are services or data providers that provide traffic and weather information via the Internet or other networks.

[0391] "Traffic condition information" refers to various information related to road traffic, such as road congestion levels, speed limits, and traffic accident occurrence status.

[0392] "Weather Information" means current and forecasted weather data, including precipitation, temperature, wind speed, weather alerts, etc.

[0393] "Item information" refers to detailed data about a shipment, including weight, size, handling requirements, temperature control needs, etc.

[0394] "Means of collection" refers to the technical means for obtaining the necessary data using various APIs and network interfaces.

[0395] "Filtering and processing measures" are technical measures for removing unnecessary information from collected data and extracting and organizing important data.

[0396] A "generative model" is a model that uses machine learning algorithms to analyze and process data and create optimal delivery routes.

[0397] A "delivery route" is a driving route selected for a delivery vehicle to reach its destination most efficiently.

[0398] A "user device" is a terminal used by a delivery person, and refers to a mobile information terminal such as a smartphone or tablet.

[0399] "Visual and audio guidance" means the manner in which driving directions and route change notifications are provided to a user through a user device, including map displays and audio prompts.

[0400] "Real-time information" refers to data that changes every moment, such as current road conditions and weather conditions.

[0401] This invention is a system for improving delivery efficiency in logistics, which collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes. This system is mainly composed of a server, terminals, and users.

[0402] Collection methods

[0403] The server collects real-time data using traffic information APIs and weather information APIs. Examples of traffic information APIs include the Google Maps API and the Here API, which are used to obtain current road congestion and speed limit information. Weather information APIs include the OpenWeather API and the Weatherbit API, which obtain current and forecast weather data. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server via the API. Next, weather data such as whether heavy rain is forecast within the next few hours is obtained.

[0404] Filtering and processing methods

[0405] The server filters and processes the collected data. For traffic condition data, only key congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as primary data. For goods information, the weight, size, and handling precautions of the delivery items are obtained from the logistics management system (WMS: Warehouse Management System), and the information includes instructions such as "Delivery A requires temperature control."

[0406] A means of calculating the optimal delivery route

[0407] The server uses the collected and processed data to calculate the optimal delivery route using a generative AI model. This generative AI model incorporates machine learning algorithms to calculate the shortest and most efficient route based on each data point. It also takes into account alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0408] A way to recalculate your route in real time

[0409] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there are any new traffic jams or changes in the weather, the device sends the data to the server to request a recalculation of the route. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0410] Guidance by user device

[0411] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface displays the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions efficiently and complete the delivery. Specific actions include a color-coded route on the map, voice guidance, and notification of route changes.

[0412] Prompt Sentence Examples

[0413] "Design a system that uses traffic and weather APIs to calculate the optimal delivery route based on current traffic conditions and weather. Use Google Maps API for traffic information and OpenWeather API for weather information. Explain how you would filter only the important data and generate the optimal route using a machine learning algorithm."

[0414] This invention will significantly improve delivery efficiency in logistics, reduce costs, and shorten delivery times. In addition, real-time information updates will always provide optimal delivery routes, contributing to improved customer satisfaction.

[0415] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0416] Step 1:

[0417] The server collects real-time data using traffic information APIs and weather information APIs. Specifically, it obtains current road congestion and speed limit information from Google Maps API and Here API, and current and forecast weather data from OpenWeather API and Weatherbit API. The input is real-time data from the APIs, and the output is traffic condition information and weather information.

[0418] Step 2:

[0419] The server filters and processes the collected traffic information. Specifically, it extracts only the main congestion information and removes information that does not exceed the speed limit and other noise. For example, if the congestion level exceeds a certain threshold, it retains that information as important data. The input is data from the traffic information API, and the output is filtered traffic information.

[0420] Step 3:

[0421] The server filters and processes the collected weather information. For example, it extracts data on severe weather, such as heavy rain or freezing rain, from the current weather. It keeps only the information that directly affects delivery and removes the rest. The input is data from the weather information API, and the output is the filtered weather information.

[0422] Step 4:

[0423] The server collects and organizes item information from the logistics management system (WMS). Specifically, it obtains and organizes data such as the weight, size, and handling precautions of delivery items. For example, if a specific item requires temperature control, this information is included. The input is item data from the WMS, and the output is organized item information.

[0424] Step 5:

[0425] The server uses a generative AI model to calculate the optimal delivery route based on the filtered and processed traffic, weather, and product information. Specifically, it uses a machine learning algorithm to generate the optimal delivery route that avoids traffic congestion, weather, and product handling requirements. The inputs are traffic, weather, and product information, and the output is the optimal delivery route.

[0426] Step 6:

[0427] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If new traffic congestion or weather changes are detected, the terminal sends the data to the server and requests a recalculation of the route. The server then recalculates the optimal route and sends the data to the terminal. The input is the latest data from the server, and the output is the recalculated delivery route.

[0428] Step 7:

[0429] The user (delivery person) can view the new optimized delivery route on the terminal display. The user interface is intuitive, showing the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions and efficiently carry out the delivery. The input is delivery route information from the terminal, and the output is efficient delivery work by the user.

[0430] (Application example 1)

[0431] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0432] Improving delivery efficiency in logistics is difficult due to external factors such as traffic congestion and weather fluctuations. Furthermore, there is a need for rapid response to real-time situation changes and a user interface that delivery personnel can intuitively understand. To solve these issues, it is necessary to develop a system that can quickly and effectively process traffic, weather, and product information, and always provide the optimal delivery route.

[0433] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0434] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, and means for generating an optimal delivery route using a generative model based on the filtered and processed information. This makes it possible to collect and process information in real time and always provide an optimal delivery route.

[0435] "Traffic conditions" refers to information such as road congestion, speed limits, accidents, and construction, and is real-time data on road driving conditions.

[0436] "Weather information" is data on current and forecasted weather conditions, including environmental factors such as precipitation, temperature, and wind speed.

[0437] "Item information" is detailed data such as the weight, size, and handling precautions of the item to be delivered.

[0438] "Means of collection" refers to a system for obtaining necessary information from external data sources such as traffic information APIs and weather information APIs.

[0439] "Filtering and processing means" refers to the process of narrowing down the collected raw data to the necessary information, and is a system that has the function of removing noise and shaping the data.

[0440] "Means for generating optimal delivery routes using generative models" refers to a system that uses mathematical models such as machine learning algorithms to calculate delivery routes based on collected and processed data.

[0441] "Means for updating delivery routes based on real-time information" refers to a system that periodically checks for new traffic and weather data and recalculates existing delivery routes as needed.

[0442] The "means for displaying on the user device" refers to a display or interface for visually presenting the generated delivery route to the user.

[0443] The "means for providing an intuitive map display" is map software that displays delivery routes and additional information in a format that is easily understood by the user.

[0444] "Means for notifying delivery route updates in real time" refers to a notification system that quickly notifies users when new information is updated during delivery.

[0445] To put this invention into practice, a system is required that collects traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes based on this information. A specific embodiment of this system is shown below.

[0446] Hardware and software used

[0447] Traffic Information API: Obtains information such as road congestion, speed limits, accidents, and construction.

[0448] Weather Information API: Obtain weather data such as precipitation, temperature, and wind speed.

[0449] Data filtering and processing system: Filters collected data and extracts only the information you need.

[0450] Machine learning algorithm (generative model): Calculates the optimal delivery route based on the filtered data.

[0451] User Interface (UI): Provides delivery personnel with information such as intuitive map displays.

[0452] Notification system: Real-time delivery route updates.

[0453] Data collection and processing

[0454] The server uses traffic information APIs and weather information APIs to collect real-time traffic and weather information. Item information is also obtained from a local database or JSON file. The server filters this data to extract only important information, such as traffic congestion and worsening weather.

[0455] Delivery route generation

[0456] Based on the filtered information, the server uses a generative model to generate the optimal delivery route. This generative model uses linear regression, a type of machine learning algorithm, to calculate the shortest and most efficient route based on past data. For example, it might suggest a route that avoids urban traffic jams and uses suburban highways.

[0457] Real-time updates

[0458] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the server recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0459] User Interface

[0460] The delivery driver can view the new optimized delivery route on their device display. The intuitive user interface shows the route highlighted on a map, allowing the delivery driver to follow the instructions efficiently.

[0461] Prompt Sentence Examples

[0462] An example of a prompt to input to a generative AI model is as follows:

[0463] The goal of this program is to create a smartphone application for a logistics center that collects traffic, weather, and product information and generates optimal delivery routes. Write the code based on the following criteria:

[0464] Traffic information is obtained from the traffic information API, and weather information is obtained from the weather information API.

[0465] Item information is read from a local JSON file.

[0466] Filter the data to retain only the key information.

[0467] Calculate optimal delivery routes using machine learning algorithms (linear regression).

[0468] The route is updated in real time and displayed in the user interface.

[0469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0470] Step 1:

[0471] The server collects real-time data from the traffic and weather APIs. In this step, it sends API requests to retrieve road congestion, speed limits, accident information, and current and forecast weather conditions. The input is the API endpoint, and the output is the retrieved traffic and weather data in JSON format.

[0472] Step 2:

[0473] The server filters and processes the acquired data. For traffic data, it extracts only information where congestion exceeds a certain threshold, and for weather data, it extracts only information about bad weather that will affect deliveries. The input is the JSON data collected in step 1, and the output is the filtered important information. Specifically, it removes unnecessary noise data.

[0474] Step 3:

[0475] The server uses the filtered data to run a machine learning algorithm (linear regression) to generate an optimal delivery route. In this step, past delivery data and current traffic and weather data are input to calculate the shortest and most efficient route. The input is the filtered data, and the output is the generated delivery route. The specific operations are to run the algorithm and calculate the delivery route.

[0476] Step 4:

[0477] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, it sends the data to the server and requests a route recalculation. The input is the delivery route generated in step 3 and the latest traffic and weather data, and the output is the updated delivery route.

[0478] Step 5:

[0479] The server recalculates the optimal route and sends the new route information to the device. In this process, the machine learning algorithm is run again to calculate the optimal route based on the new traffic and weather conditions. The input is the latest data and current route information, and the output is the recalculated delivery route. The specific operation involves sending data from the server to the device.

[0480] Step 6:

[0481] The user, a delivery person, can view the new optimized delivery route on the terminal display. The user interface is intuitively designed, and the route is highlighted on a map. The input is the updated delivery route sent from the server, and the output is the map display on the user interface. The specific operation is to display the route information on the terminal display.

[0482] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0483] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes that information, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions. A specific description of an embodiment of the present invention will be given below.

[0484] Overall program flow

[0485] Examples of data collection

[0486] The server accesses a traffic information API to obtain current traffic data, including traffic congestion and speed limit information for major roads. The server then accesses a weather information API to obtain current and forecast weather data. For example, if rain is predicted for a particular area within the next few hours, that information is also obtained. The server then obtains detailed information about the shipment from internal systems and external package information APIs, including the delivery address, package weight, size, and handling instructions.

[0487] Examples of data processing

[0488] The server filters the collected traffic data to remove noise and unnecessary information, extracting only important traffic congestion information. Next, the server filters the collected weather data to extract only important information that affects delivery. Finally, the server organizes the package information and identifies the handling requirements for each package, for example, classifying it into the appropriate category based on size and weight.

[0489] Example of delivery route generation

[0490] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to generate optimal delivery routes based on these data points. For example, a route might be calculated that uses suburban highways to avoid city congestion and complete the delivery before bad weather arrives.

[0491] Examples of emotion engines

[0492] The emotion engine built into the device recognizes the user's (delivery worker's) emotions in real time through facial recognition and voice analysis. For example, if the user feels stressed or tired during a delivery, the emotion engine will detect this. The device then sends this emotion data to the server and adjusts the route as needed.

[0493] Real-time update example

[0494] The device periodically obtains the latest traffic, weather, and emotion information from the server to check whether the current delivery route is appropriate. If new information is available, the server recalculates the optimal route based on that information and sends the new route information to the device.

[0495] User interface examples

[0496] The user (delivery worker) sees the new optimized delivery route on the terminal display. The intuitive user interface shows the route highlighted on a map. Furthermore, if the user feels stressed or shows signs of fatigue, rest points are automatically inserted, reducing the user's workload.

[0497] This system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the user's emotional state, improving delivery efficiency and reducing the burden on the user, thereby effectively reducing delivery costs and time.

[0498] The processing flow will be explained below.

[0499] Step 1:

[0500] The server accesses the traffic information API to obtain current traffic situation data, such as real-time congestion information and speed limit data for major roads.

[0501] Step 2:

[0502] The server accesses a weather information API to retrieve current and forecast weather data, such as if rain or strong winds are forecast for a particular area in the next few hours.

[0503] Step 3:

[0504] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0505] Step 4:

[0506] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0507] Step 5:

[0508] The server filters the collected weather data and extracts only important data that will affect delivery, such as heavy rain or storms forecasted along the delivery route.

[0509] Step 6:

[0510] The server collates the package information and identifies the handling requirements for each package, which includes sorting it into the appropriate category based on its size and weight.

[0511] Step 7:

[0512] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate optimal delivery routes, such as routes that avoid city congestion and use highways.

[0513] Step 8:

[0514] The server then validates the generated optimal delivery route to ensure feasibility, making fine adjustments as needed to accommodate traffic and weather conditions.

[0515] Step 9:

[0516] The server then sends the confirmed delivery route information to the terminal, which the delivery person with the terminal receives and confirms the route.

[0517] Step 10:

[0518] The emotion engine built into the device analyzes the facial expressions and voice of the user (delivery worker) in real time to recognize their emotional state. For example, it can detect if the user is feeling stressed during a delivery.

[0519] Step 11:

[0520] The device transmits the recognized user emotion data to the server in real time, and the server receives the data and decides whether to reflect it in the delivery route.

[0521] Step 12:

[0522] The server recalculates the delivery route as needed based on the received user emotion data. For example, if the user is feeling stressed, the server generates a route that includes rest stops to reduce stress.

[0523] Step 13:

[0524] The server sends the recalculated optimal route to the terminal, which receives this information and presents the new route to the user.

[0525] Step 14:

[0526] The terminal displays the new delivery route information to the user (delivery person), and the user interface highlights the optimal route on a map, allowing for visual viewing.

[0527] Step 15:

[0528] The user follows the displayed new route to ensure efficient delivery. When the emotion engine recognizes the user's stress or fatigue, it will suggest appropriate rest points.

[0529] Through these steps, the system responds to real-time changes in the situation and the user's emotional state, and always provides the optimal delivery route, thereby improving delivery efficiency and reducing the user's burden.

[0530] Example 2

[0531] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0532] Conventional delivery systems could calculate optimal routes by taking into account real-time traffic conditions and weather information, but they were unable to take into account the emotional state of the delivery person. As a result, delivery people could become stressed or fatigued, resulting in reduced delivery efficiency. Furthermore, it was difficult to dynamically update routes based on data collected in real time and select optimal routes in a timely manner.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting traffic data, weather data, and item data in real time, means for filtering and processing the collected traffic data, weather data, and item data, means for generating an optimal delivery route using a generative model based on the filtered and processed data, means for collecting and processing emotion data of the delivery person, means for adjusting the delivery route based on the emotion data, means for updating the delivery route based on the real-time data, and means for displaying the generated delivery route on a display device. As a result, by generating an optimal delivery route in real time and taking into account the emotional state of the delivery person, it is possible to improve delivery efficiency and reduce the burden on the delivery person.

[0534] "Traffic data" refers to information that indicates traffic conditions and road conditions, and specifically includes congestion information and speed limit information.

[0535] "Weather data" refers to information relating to current weather conditions and future weather forecasts, and specifically includes rainfall, snowfall, wind speed, and the like.

[0536] "Item data" refers to detailed information about the item being delivered, including the delivery address, weight, size, handling precautions, etc.

[0537] "Filtering" refers to the process of extracting necessary information from collected data and removing unnecessary noise.

[0538] A "generative model" is a model built on machine learning algorithms to generate optimal results from given data.

[0539] "Delivery route" refers to the route a delivery vehicle takes to deliver a delivery item.

[0540] "Emotion data" is information that indicates the emotional state of the delivery person, and specifically includes stress, fatigue, etc.

[0541] A "display device" is a device that visually presents generated information to a user, and specifically includes a display and a monitor.

[0542] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0543] An "interface" refers to the means of connection through which a data sender and receiver exchange information.

[0544] This invention is a system that collects traffic data, weather data, and product data in real time and uses them to generate and update optimal delivery routes. Furthermore, it is characterized by recognizing and taking into account the emotional state of delivery personnel, thereby improving delivery efficiency and user comfort.

[0545] The system mainly consists of a server, a terminal, and a user device. Each element is explained in detail below.

[0546] Server Features

[0547] The server accesses traffic and weather information APIs to obtain necessary data in real time, such as traffic congestion information for major roads, speed limits, current weather data, and forecast data. It also obtains detailed information about deliveries (such as delivery address, package weight, size, and handling precautions) from internal systems and external package information APIs.

[0548] The server filters the acquired data to remove noise and unnecessary information, extracting only important information about traffic congestion and weather that may affect deliveries. It also organizes package information and categorizes it into appropriate categories based on size and weight.

[0549] The server then uses a generative AI model to generate optimal delivery routes based on the filtered data. The generative AI model contains machine learning algorithms that generate optimal results from the input data.

[0550] Device Features

[0551] The device is equipped with an emotion engine that recognizes the emotions of delivery personnel in real time by analyzing their facial expressions and voice. The recognized emotion data is sent from the device to a server and used to readjust the delivery route.

[0552] The device also periodically retrieves the latest traffic, weather, and emotion information from the server. The server recalculates the optimal delivery route based on the newly collected data and sends the results to the device, allowing it to respond to changes in the situation in real time.

[0553] User Interface

[0554] The user (delivery worker) can view the optimized new delivery route on the terminal display. The route is intuitively displayed highlighted on the map. If the user feels stressed or fatigued, the system automatically inserts rest points.

[0555] Specific examples

[0556] For example, when a delivery person is making deliveries in a specific area in the morning, the device collects traffic congestion data and rainfall forecast data and recognizes from the delivery person's facial expression that they are feeling stressed. As a result, the server calculates a new route and sends it to the device, including rest stops to reduce stress.

[0557] Prompt Sentence Examples

[0558] Here are some example prompts to input to the AI ​​model:

[0559] Current traffic conditions: Traffic congestion information

[0560] Current weather information: Rainfall forecast

[0561] Package information: shipping address, weight, size

[0562] Please generate the optimal delivery route taking into consideration transportation conditions such as avoiding traffic congestion and areas where rain is expected.

[0563] In this way, the system uses real-time data to generate optimal delivery routes and takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the workload of the delivery person.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1: Collect data

[0566] The server accesses the traffic information API and weather information API to collect traffic and weather data. The input is a request sent to the API endpoint, and the output is response data in JSON or XML format. Specifically, this includes congestion information and speed limit information obtained from the traffic information API, and rainfall and snowfall forecast information obtained from the weather information API. The server also collects item data from internal systems and external parcel information APIs. Input includes delivery address, weight, size, handling precautions, etc., and this information is stored in the database as a response.

[0567] Step 2: Filtering the data

[0568] The server filters the collected traffic data, weather data, and product data. The input includes the raw data saved in step 1. Specifically, the server filters out noise data and extracts only traffic congestion information and important weather information. For example, it selects areas with serious congestion or areas where rain is expected, and ignores other data. The output is the filtered traffic data, weather data, and classified product data.

[0569] Step 3: Generate delivery routes

[0570] The server uses a generative AI model to generate an optimal delivery route based on the filtered data. Inputs include filtered traffic data, weather data, and product data. The generative model calculates the optimal route based on a prompt provided to it. An example of a prompt is:

[0571] "Generate the optimal delivery route taking into consideration current traffic conditions: traffic congestion information, current weather information: rainfall forecast, package information: delivery address, weight, size, and transportation conditions, avoiding traffic congestion and areas where rainfall is forecast."

[0572] The output is data on the optimal delivery route.

[0573] Step 4: Collect and process emotion data

[0574] The device uses an emotion engine to analyze the delivery person's facial expressions and voice and recognize their emotions in real time. Input includes facial expression and voice data acquired from a camera and microphone. The device's emotion engine analyzes this data and evaluates their stress level and fatigue state. The output is data about the delivery person's emotional state, which is immediately sent to the server.

[0575] Step 5: Real-time route adjustments

[0576] The server receives the delivery person's emotional data and recalculates the delivery route as necessary. The inputs are the latest emotional data, traffic data, and weather data. The server re-runs the generative AI model based on this data and calculates a new optimal route. The output is the adjusted delivery route data. This route data is sent to the terminal in real time.

[0577] Step 6: Route Display and User Interface

[0578] The user checks the latest optimized delivery route on the device display. The input is the new delivery route data sent from the server. The device launches a map app based on this data and displays the highlighted route. If the user feels stressed or tired during delivery, the system automatically inserts rest points and displays that information on the map. This allows the user to intuitively understand the optimal route and rest points.

[0579] The above is the specific processing steps and operational flow of the program of this system.

[0580] (Application example 2)

[0581] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0582] Conventional delivery systems have shown some effectiveness in generating optimal delivery routes based on traffic and weather information, but they do not take into account the emotional state of the delivery person, which means they cannot provide the optimal route even when the delivery person is stressed or tired. Furthermore, the display on the user's device is simple, which means it cannot quickly respond to changes in the delivery person's emotional state. This raises concerns that delivery efficiency will decrease and the burden on the delivery person will increase.

[0583] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0584] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, means for generating an optimal delivery route using a generative model based on the filtered and processed information, means for updating the delivery route based on the real-time information, means for displaying the generated delivery route on a user device, and means for adjusting the delivery route based on the user's emotional state using an emotion engine that recognizes the user's emotions, thereby improving delivery efficiency and reducing the burden on delivery personnel.

[0585] "Traffic conditions" is information that indicates the current state of road congestion and traffic flow.

[0586] "Weather information" is information that indicates current and forecast weather conditions.

[0587] "Item information" refers to information about the item to be delivered, including the address, weight, size, handling precautions, etc.

[0588] "Real-time" refers to data and information being collected and updated almost immediately.

[0589] "Gathering means" refers to hardware or software mechanisms for acquiring traffic conditions, weather information, and product information.

[0590] "Filtering" is the process of extracting necessary information from collected data and removing unnecessary information.

[0591] A "processing means" is a hardware or software mechanism for organizing and converting data into a required format.

[0592] A "generative model" is software that has an algorithm for calculating the optimal delivery route based on collected data.

[0593] The "optimal delivery route" is a route that takes into account traffic conditions, weather information, and product information to complete delivery in the shortest time and at the lowest cost.

[0594] An "updating means" is a hardware or software mechanism for recalculating existing delivery routes based on new information and keeping them up to date.

[0595] A "user device" is an electronic device such as a smartphone or tablet carried by a delivery person.

[0596] A "displaying means" is a mechanism for visually presenting information on the screen of a user device.

[0597] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0598] "Emotional state" refers to the user's psychological and physiological state, including stress, fatigue, and the like.

[0599] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes them, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions and adjusts routes based on those emotions. The following describes in detail an embodiment of the present invention.

[0600] System Program

[0601] The system first collects data from the traffic information API, weather information API, and goods information API. The traffic information API retrieves information on traffic congestion and speed limits on major roads. The weather information API retrieves current weather and forecast data, such as if rain is predicted for a specific area within the next few hours. The goods information API retrieves detailed information about the delivery, such as the delivery address, package weight, size, and handling precautions.

[0602] The collected data is filtered by the server to extract only the important information. Traffic data is given special emphasis on traffic congestion information, and weather data is focused on factors that affect delivery. Item information is also organized and appropriately categorized according to the size and weight of the package.

[0603] Generative models use this processed data to calculate optimal delivery routes, using machine learning algorithms to take into account numerous data points and generate routes that, for example, avoid traffic jams in cities and complete deliveries before bad weather arrives.

[0604] Emotion Engine

[0605] The emotion engine recognizes the delivery person's (user's) emotions in real time through facial expression recognition and voice analysis. If the user feels stressed or tired, the device detects this and sends the emotion data to the server. The server then reevaluates the emotion data along with the latest traffic, weather, and product information, and recalculates the optimal route. At this time, rest stops are inserted into the route according to the user's stress level.

[0606] User Interface

[0607] The device displays the new optimized delivery route. The intuitive user interface visually shows the route highlighted on a map. Additionally, rest stops are automatically inserted if the user feels stressed or fatigued, improving delivery efficiency and reducing user stress.

[0608] Hardware and software used

[0609] Hardware: User devices such as smartphones and tablets, and servers connected to the internet

[0610] Software: Traffic information API, weather information API, product information API, machine learning algorithm, emotion engine

[0611] Implementation example

[0612] For example, food delivery drivers can use the app to find the optimal delivery route in real time, and if stress is detected through facial expression analysis, it will automatically provide rest stops, improving delivery efficiency and reducing the driver's burden.

[0613] Prompt Sentence Examples

[0614] "Get current traffic congestion information from a traffic API and upcoming rainfall forecasts from a weather API. Then use an emotion engine to detect the driver's emotional state in real time. Based on this data, generate optimal delivery routes and suggest rest stops if the driver is feeling stressed."

[0615] As described above, this system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the burden on the delivery person.

[0616] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0617] Step 1:

[0618] The server accesses the traffic information API, weather information API, and product information API to collect traffic conditions, weather information, and product information. The input data is the raw data obtained from each API, and the output data is the collected traffic conditions, weather information, and product information. The server temporarily stores this data and passes it to the next step.

[0619] Step 2:

[0620] The server filters the collected traffic data and extracts only important traffic congestion information. The input data is raw traffic data, which contains noise and unnecessary information. The server analyzes this data and selects the important parts. The output data is filtered traffic congestion information.

[0621] Step 3:

[0622] The server filters the collected weather data and extracts only the important information that affects delivery. The input data is raw weather information data, which includes various weather conditions. The server selects the data based on factors that directly affect delivery, and obtains important weather information as output.

[0623] Step 4:

[0624] The server organizes the collected item information and identifies the handling requirements for each package. The input data is raw item information data, including delivery address, package weight, size, handling precautions, etc. The server organizes this information and generates organized item information as output.

[0625] Step 5:

[0626] The server inputs the filtered and processed traffic data, weather data, and item data into a generative model to generate an optimal delivery route. The input data is the filtered traffic data, weather data, and organized item data. The server uses a machine learning algorithm based on this data to calculate the optimal delivery route. The output data is the generated optimal delivery route.

[0627] Step 6:

[0628] The emotion engine recognizes emotions in real time through facial expression recognition and voice analysis of the user. Input data is image and voice data of the user. The device analyzes this data to identify the user's emotional state and sends the recognized emotional data as output to the server.

[0629] Step 7:

[0630] The server re-evaluates the emotion data and the latest traffic, weather, and product information, and re-calculates the optimal route. The input data is the emotion data and the latest traffic, weather, and product information. The server re-evaluates based on this information and adjusts the delivery route as necessary. The output data is the adjusted delivery route.

[0631] Step 8:

[0632] The terminal displays the optimized delivery route on the user device. The input data is the optimized delivery route information sent from the server. The terminal displays this information on the display of the user device, visually showing the route. The output data is the route information displayed to the user.

[0633] Through the above processing steps, the system can respond to real-time changes in the situation, always provide the optimal delivery route, and also take into account the user's emotional state, thereby improving delivery efficiency and reducing the burden on the user.

[0634] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0635] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0636] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0637] [Third embodiment]

[0638] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0639] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0640] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0641] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0642] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0643] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0644] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0645] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0646] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0647] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0648] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0649] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0650] This invention relates to a system that collects traffic conditions, weather information, and product information in real time, processes the information, and generates and updates optimal delivery routes in order to improve delivery efficiency in logistics. A specific description of an embodiment of the invention will be given below.

[0651] Overall program flow

[0652] Examples of data collection

[0653] The server uses a traffic information API to collect real-time traffic conditions. This API provides information on current road congestion and speed limits. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server through the API. The server then uses a weather information API to obtain current and forecast weather data for the area. For example, if freezing rain is forecast in the next few hours, that information is also retrieved.

[0654] Examples of data processing

[0655] The collected data is filtered and processed on the server. For traffic condition data, only the main congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as the main data. For goods information, the weight and size of the delivery items, as well as handling precautions, are organized.

[0656] Example of delivery route generation

[0657] The server uses a generative model based on the collected and processed data to generate an optimal delivery route. The generative model uses machine learning algorithms, for example, to calculate the shortest and most efficient route based on each data point. It also considers alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0658] Real-time update example

[0659] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the device sends the data to the server and requests a route recalculation. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0660] User interface examples

[0661] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface shows the route highlighted on a map, allowing the user to follow the instructions to efficiently complete the delivery.

[0662] The system of the present invention collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes, thereby significantly improving delivery efficiency in logistics, thereby reducing costs and shortening delivery times.

[0663] The processing flow will be explained below.

[0664] Step 1:

[0665] The server accesses the traffic information API to obtain current traffic condition data, such as traffic congestion information and speed limit information on major roads.

[0666] Step 2:

[0667] The server accesses the weather information API to retrieve current and forecast weather data, such as temperature, precipitation, and wind speed for a specific area.

[0668] Step 3:

[0669] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0670] Step 4:

[0671] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0672] Step 5:

[0673] The server filters the collected weather data to extract only important information that will affect delivery, such as forecasts of heavy rain or storms along delivery routes.

[0674] Step 6:

[0675] The server organizes shipment information and identifies the handling requirements for each package, optimizing delivery routes based on package size and weight.

[0676] Step 7:

[0677] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate the optimal delivery route.

[0678] Step 8:

[0679] The server then validates the generated optimal delivery route to ensure its feasibility, and if necessary, makes minor adjustments, such as avoiding traffic congestion in the Tokyo area.

[0680] Step 9:

[0681] The server sends the generated delivery route to the terminal, and the delivery person holding the terminal receives this route information.

[0682] Step 10:

[0683] The device periodically retrieves the latest data from the server, checks real-time traffic and weather conditions, and receives and updates any new information.

[0684] Step 11:

[0685] The device requests the server to recalculate the optimal route based on the latest data received, thereby adapting to changes in the situation.

[0686] Step 12:

[0687] The server recalculates the optimal delivery route based on real-time data and sends the new route information to the terminal.

[0688] Step 13:

[0689] The terminal displays the latest delivery route information to the user (delivery person), who can then efficiently deliver the goods by following the displayed route.

[0690] Step 14:

[0691] The user follows the navigation on the device to make deliveries and deliver packages efficiently. As new information is received, the device updates the route and suggests the best route.

[0692] This step allows the system to respond to real-time situation changes and continue to provide the optimal delivery route.

[0693] Example 1

[0694] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0695] In the modern logistics industry, unexpected obstacles such as traffic congestion and bad weather often have a significant negative impact on delivery efficiency. Furthermore, few systems can take into account specific item handling requirements and delivery conditions. Current systems have limited real-time information collection and route recalculation capabilities, and lack the flexibility to provide efficient delivery routes. This results in longer delivery times, increased costs, and lower customer satisfaction.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0697] In this invention, the server includes means for collecting traffic condition information from external data sources, means for collecting weather information, means for collecting item information, means for filtering and processing the collected traffic condition information, means for filtering and processing the collected weather information, means for organizing the collected item information, means for calculating an optimal delivery route using a generative model based on the filtered and processed traffic condition information, weather information, and item information, means for checking whether the current delivery route is appropriate based on real-time information and recalculating the route as necessary, and means for displaying the generated delivery route on a user device and providing visual and audio guidance, thereby enabling significant improvement in delivery efficiency, cost reduction, and increased customer satisfaction.

[0698] "External data sources" are services or data providers that provide traffic and weather information via the Internet or other networks.

[0699] "Traffic condition information" refers to various information related to road traffic, such as road congestion levels, speed limits, and traffic accident occurrence status.

[0700] "Weather Information" means current and forecasted weather data, including precipitation, temperature, wind speed, weather alerts, etc.

[0701] "Item information" refers to detailed data about a shipment, including weight, size, handling requirements, temperature control needs, etc.

[0702] "Means of collection" refers to the technical means for obtaining the necessary data using various APIs and network interfaces.

[0703] "Filtering and processing measures" are technical measures for removing unnecessary information from collected data and extracting and organizing important data.

[0704] A "generative model" is a model that uses machine learning algorithms to analyze and process data and create optimal delivery routes.

[0705] A "delivery route" is a driving route selected for a delivery vehicle to reach its destination most efficiently.

[0706] A "user device" is a terminal used by a delivery person, and refers to a mobile information terminal such as a smartphone or tablet.

[0707] "Visual and audio guidance" means the manner in which driving directions and route change notifications are provided to a user through a user device, including map displays and audio prompts.

[0708] "Real-time information" refers to data that changes every moment, such as current road conditions and weather conditions.

[0709] This invention is a system for improving delivery efficiency in logistics, which collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes. This system is mainly composed of a server, terminals, and users.

[0710] Collection methods

[0711] The server collects real-time data using traffic information APIs and weather information APIs. Examples of traffic information APIs include the Google Maps API and the Here API, which are used to obtain current road congestion and speed limit information. Weather information APIs include the OpenWeather API and the Weatherbit API, which obtain current and forecast weather data. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server via the API. Next, weather data such as whether heavy rain is forecast within the next few hours is obtained.

[0712] Filtering and processing methods

[0713] The server filters and processes the collected data. For traffic condition data, only key congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as primary data. For goods information, the weight, size, and handling precautions of the delivery items are obtained from the logistics management system (WMS: Warehouse Management System), and the information includes instructions such as "Delivery A requires temperature control."

[0714] A means of calculating the optimal delivery route

[0715] The server uses the collected and processed data to calculate the optimal delivery route using a generative AI model. This generative AI model incorporates machine learning algorithms to calculate the shortest and most efficient route based on each data point. It also takes into account alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0716] A way to recalculate your route in real time

[0717] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there are any new traffic jams or changes in the weather, the device sends the data to the server to request a recalculation of the route. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0718] Guidance by user device

[0719] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface displays the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions efficiently and complete the delivery. Specific actions include a color-coded route on the map, voice guidance, and notification of route changes.

[0720] Prompt Sentence Examples

[0721] "Design a system that uses traffic and weather APIs to calculate the optimal delivery route based on current traffic conditions and weather. Use Google Maps API for traffic information and OpenWeather API for weather information. Explain how you would filter only the important data and generate the optimal route using a machine learning algorithm."

[0722] This invention will significantly improve delivery efficiency in logistics, reduce costs, and shorten delivery times. In addition, real-time information updates will always provide optimal delivery routes, contributing to improved customer satisfaction.

[0723] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0724] Step 1:

[0725] The server collects real-time data using traffic information APIs and weather information APIs. Specifically, it obtains current road congestion and speed limit information from Google Maps API and Here API, and current and forecast weather data from OpenWeather API and Weatherbit API. The input is real-time data from the APIs, and the output is traffic condition information and weather information.

[0726] Step 2:

[0727] The server filters and processes the collected traffic information. Specifically, it extracts only the main congestion information and removes information that does not exceed the speed limit and other noise. For example, if the congestion level exceeds a certain threshold, it retains that information as important data. The input is data from the traffic information API, and the output is filtered traffic information.

[0728] Step 3:

[0729] The server filters and processes the collected weather information. For example, it extracts data on severe weather, such as heavy rain or freezing rain, from the current weather. It keeps only the information that directly affects delivery and removes the rest. The input is data from the weather information API, and the output is the filtered weather information.

[0730] Step 4:

[0731] The server collects and organizes item information from the logistics management system (WMS). Specifically, it obtains and organizes data such as the weight, size, and handling precautions of delivery items. For example, if a specific item requires temperature control, this information is included. The input is item data from the WMS, and the output is organized item information.

[0732] Step 5:

[0733] The server uses a generative AI model to calculate the optimal delivery route based on the filtered and processed traffic, weather, and product information. Specifically, it uses a machine learning algorithm to generate the optimal delivery route that avoids traffic congestion, weather, and product handling requirements. The inputs are traffic, weather, and product information, and the output is the optimal delivery route.

[0734] Step 6:

[0735] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If new traffic congestion or weather changes are detected, the terminal sends the data to the server and requests a recalculation of the route. The server then recalculates the optimal route and sends the data to the terminal. The input is the latest data from the server, and the output is the recalculated delivery route.

[0736] Step 7:

[0737] The user (delivery person) can view the new optimized delivery route on the terminal display. The user interface is intuitive, showing the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions and efficiently carry out the delivery. The input is delivery route information from the terminal, and the output is efficient delivery work by the user.

[0738] (Application example 1)

[0739] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0740] Improving delivery efficiency in logistics is difficult due to external factors such as traffic congestion and weather fluctuations. Furthermore, there is a need for rapid response to real-time situation changes and a user interface that delivery personnel can intuitively understand. To solve these issues, it is necessary to develop a system that can quickly and effectively process traffic, weather, and product information, and always provide the optimal delivery route.

[0741] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0742] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, and means for generating an optimal delivery route using a generative model based on the filtered and processed information. This makes it possible to collect and process information in real time and always provide an optimal delivery route.

[0743] "Traffic conditions" refers to information such as road congestion, speed limits, accidents, and construction, and is real-time data on road driving conditions.

[0744] "Weather information" is data on current and forecasted weather conditions, including environmental factors such as precipitation, temperature, and wind speed.

[0745] "Item information" is detailed data such as the weight, size, and handling precautions of the item to be delivered.

[0746] "Means of collection" refers to a system for obtaining necessary information from external data sources such as traffic information APIs and weather information APIs.

[0747] "Filtering and processing means" refers to the process of narrowing down the collected raw data to the necessary information, and is a system that has the function of removing noise and shaping the data.

[0748] "Means for generating optimal delivery routes using generative models" refers to a system that uses mathematical models such as machine learning algorithms to calculate delivery routes based on collected and processed data.

[0749] "Means for updating delivery routes based on real-time information" refers to a system that periodically checks for new traffic and weather data and recalculates existing delivery routes as needed.

[0750] The "means for displaying on the user device" refers to a display or interface for visually presenting the generated delivery route to the user.

[0751] The "means for providing an intuitive map display" is map software that displays delivery routes and additional information in a format that is easily understood by the user.

[0752] "Means for notifying delivery route updates in real time" refers to a notification system that quickly notifies users when new information is updated during delivery.

[0753] To put this invention into practice, a system is required that collects traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes based on this information. A specific embodiment of this system is shown below.

[0754] Hardware and software used

[0755] Traffic Information API: Obtains information such as road congestion, speed limits, accidents, and construction.

[0756] Weather Information API: Obtain weather data such as precipitation, temperature, and wind speed.

[0757] Data filtering and processing system: Filters collected data and extracts only the information you need.

[0758] Machine learning algorithm (generative model): Calculates the optimal delivery route based on the filtered data.

[0759] User Interface (UI): Provides delivery personnel with information such as intuitive map displays.

[0760] Notification system: Real-time delivery route updates.

[0761] Data collection and processing

[0762] The server uses traffic information APIs and weather information APIs to collect real-time traffic and weather information. Item information is also obtained from a local database or JSON file. The server filters this data to extract only important information, such as traffic congestion and worsening weather.

[0763] Delivery route generation

[0764] Based on the filtered information, the server uses a generative model to generate the optimal delivery route. This generative model uses linear regression, a type of machine learning algorithm, to calculate the shortest and most efficient route based on past data. For example, it might suggest a route that avoids urban traffic jams and uses suburban highways.

[0765] Real-time updates

[0766] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the server recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0767] User Interface

[0768] The delivery driver can view the new optimized delivery route on their device display. The intuitive user interface shows the route highlighted on a map, allowing the delivery driver to follow the instructions efficiently.

[0769] Prompt Sentence Examples

[0770] An example of a prompt to input to a generative AI model is as follows:

[0771] The goal of this program is to create a smartphone application for a logistics center that collects traffic, weather, and product information and generates optimal delivery routes. Write the code based on the following criteria:

[0772] Traffic information is obtained from the traffic information API, and weather information is obtained from the weather information API.

[0773] Item information is read from a local JSON file.

[0774] Filter the data to retain only the key information.

[0775] Calculate optimal delivery routes using machine learning algorithms (linear regression).

[0776] The route is updated in real time and displayed in the user interface.

[0777] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0778] Step 1:

[0779] The server collects real-time data from the traffic and weather APIs. In this step, it sends API requests to retrieve road congestion, speed limits, accident information, and current and forecast weather conditions. The input is the API endpoint, and the output is the retrieved traffic and weather data in JSON format.

[0780] Step 2:

[0781] The server filters and processes the acquired data. For traffic data, it extracts only information where congestion exceeds a certain threshold, and for weather data, it extracts only information about bad weather that will affect deliveries. The input is the JSON data collected in step 1, and the output is the filtered important information. Specifically, it removes unnecessary noise data.

[0782] Step 3:

[0783] The server uses the filtered data to run a machine learning algorithm (linear regression) to generate an optimal delivery route. In this step, past delivery data and current traffic and weather data are input to calculate the shortest and most efficient route. The input is the filtered data, and the output is the generated delivery route. The specific operations are to run the algorithm and calculate the delivery route.

[0784] Step 4:

[0785] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, it sends the data to the server and requests a route recalculation. The input is the delivery route generated in step 3 and the latest traffic and weather data, and the output is the updated delivery route.

[0786] Step 5:

[0787] The server recalculates the optimal route and sends the new route information to the device. In this process, the machine learning algorithm is run again to calculate the optimal route based on the new traffic and weather conditions. The input is the latest data and current route information, and the output is the recalculated delivery route. The specific operation involves sending data from the server to the device.

[0788] Step 6:

[0789] The user, a delivery person, can view the new optimized delivery route on the terminal display. The user interface is intuitively designed, and the route is highlighted on a map. The input is the updated delivery route sent from the server, and the output is the map display on the user interface. The specific operation is to display the route information on the terminal display.

[0790] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0791] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes that information, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions. A specific description of an embodiment of the present invention will be given below.

[0792] Overall program flow

[0793] Examples of data collection

[0794] The server accesses a traffic information API to obtain current traffic data, including traffic congestion and speed limit information for major roads. The server then accesses a weather information API to obtain current and forecast weather data. For example, if rain is predicted for a particular area within the next few hours, that information is also obtained. The server then obtains detailed information about the shipment from internal systems and external package information APIs, including the delivery address, package weight, size, and handling instructions.

[0795] Examples of data processing

[0796] The server filters the collected traffic data to remove noise and unnecessary information, extracting only important traffic congestion information. Next, the server filters the collected weather data to extract only important information that affects delivery. Finally, the server organizes the package information and identifies the handling requirements for each package, for example, classifying it into the appropriate category based on size and weight.

[0797] Example of delivery route generation

[0798] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to generate optimal delivery routes based on these data points. For example, a route might be calculated that uses suburban highways to avoid city congestion and complete the delivery before bad weather arrives.

[0799] Examples of emotion engines

[0800] The emotion engine built into the device recognizes the user's (delivery worker's) emotions in real time through facial recognition and voice analysis. For example, if the user feels stressed or tired during a delivery, the emotion engine will detect this. The device then sends this emotion data to the server and adjusts the route as needed.

[0801] Real-time update example

[0802] The device periodically obtains the latest traffic, weather, and emotion information from the server to check whether the current delivery route is appropriate. If new information is available, the server recalculates the optimal route based on that information and sends the new route information to the device.

[0803] User interface examples

[0804] The user (delivery worker) sees the new optimized delivery route on the terminal display. The intuitive user interface shows the route highlighted on a map. Furthermore, if the user feels stressed or shows signs of fatigue, rest points are automatically inserted, reducing the user's workload.

[0805] This system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the user's emotional state, improving delivery efficiency and reducing the burden on the user, thereby effectively reducing delivery costs and time.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] The server accesses the traffic information API to obtain current traffic situation data, such as real-time congestion information and speed limit data for major roads.

[0809] Step 2:

[0810] The server accesses a weather information API to retrieve current and forecast weather data, such as if rain or strong winds are forecast for a particular area in the next few hours.

[0811] Step 3:

[0812] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0813] Step 4:

[0814] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0815] Step 5:

[0816] The server filters the collected weather data and extracts only important data that will affect delivery, such as heavy rain or storms forecasted along the delivery route.

[0817] Step 6:

[0818] The server collates the package information and identifies the handling requirements for each package, which includes sorting it into the appropriate category based on its size and weight.

[0819] Step 7:

[0820] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate optimal delivery routes, such as routes that avoid city congestion and use highways.

[0821] Step 8:

[0822] The server then validates the generated optimal delivery route to ensure feasibility, making fine adjustments as needed to accommodate traffic and weather conditions.

[0823] Step 9:

[0824] The server then sends the confirmed delivery route information to the terminal, which the delivery person with the terminal receives and confirms the route.

[0825] Step 10:

[0826] The emotion engine built into the device analyzes the facial expressions and voice of the user (delivery worker) in real time to recognize their emotional state. For example, it can detect if the user is feeling stressed during a delivery.

[0827] Step 11:

[0828] The device transmits the recognized user emotion data to the server in real time, and the server receives the data and decides whether to reflect it in the delivery route.

[0829] Step 12:

[0830] The server recalculates the delivery route as needed based on the received user emotion data. For example, if the user is feeling stressed, the server generates a route that includes rest stops to reduce stress.

[0831] Step 13:

[0832] The server sends the recalculated optimal route to the terminal, which receives this information and presents the new route to the user.

[0833] Step 14:

[0834] The terminal displays the new delivery route information to the user (delivery person), and the user interface highlights the optimal route on a map, allowing for visual viewing.

[0835] Step 15:

[0836] The user follows the displayed new route to ensure efficient delivery. When the emotion engine recognizes the user's stress or fatigue, it will suggest appropriate rest points.

[0837] Through these steps, the system responds to real-time changes in the situation and the user's emotional state, and always provides the optimal delivery route, thereby improving delivery efficiency and reducing the user's burden.

[0838] Example 2

[0839] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0840] Conventional delivery systems could calculate optimal routes by taking into account real-time traffic conditions and weather information, but they were unable to take into account the emotional state of the delivery person. As a result, delivery people could become stressed or fatigued, resulting in reduced delivery efficiency. Furthermore, it was difficult to dynamically update routes based on data collected in real time and select optimal routes in a timely manner.

[0841] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting traffic data, weather data, and item data in real time, means for filtering and processing the collected traffic data, weather data, and item data, means for generating an optimal delivery route using a generative model based on the filtered and processed data, means for collecting and processing emotion data of the delivery person, means for adjusting the delivery route based on the emotion data, means for updating the delivery route based on the real-time data, and means for displaying the generated delivery route on a display device. As a result, by generating an optimal delivery route in real time and taking into account the emotional state of the delivery person, it is possible to improve delivery efficiency and reduce the burden on the delivery person.

[0842] "Traffic data" refers to information that indicates traffic conditions and road conditions, and specifically includes congestion information and speed limit information.

[0843] "Weather data" refers to information relating to current weather conditions and future weather forecasts, and specifically includes rainfall, snowfall, wind speed, and the like.

[0844] "Item data" refers to detailed information about the item being delivered, including the delivery address, weight, size, handling precautions, etc.

[0845] "Filtering" refers to the process of extracting necessary information from collected data and removing unnecessary noise.

[0846] A "generative model" is a model built on machine learning algorithms to generate optimal results from given data.

[0847] "Delivery route" refers to the route a delivery vehicle takes to deliver a delivery item.

[0848] "Emotion data" is information that indicates the emotional state of the delivery person, and specifically includes stress, fatigue, etc.

[0849] A "display device" is a device that visually presents generated information to a user, and specifically includes a display and a monitor.

[0850] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0851] An "interface" refers to the means of connection through which a data sender and receiver exchange information.

[0852] This invention is a system that collects traffic data, weather data, and product data in real time and uses them to generate and update optimal delivery routes. Furthermore, it is characterized by recognizing and taking into account the emotional state of delivery personnel, thereby improving delivery efficiency and user comfort.

[0853] The system mainly consists of a server, a terminal, and a user device. Each element is explained in detail below.

[0854] Server Features

[0855] The server accesses traffic and weather information APIs to obtain necessary data in real time, such as traffic congestion information for major roads, speed limits, current weather data, and forecast data. It also obtains detailed information about deliveries (such as delivery address, package weight, size, and handling precautions) from internal systems and external package information APIs.

[0856] The server filters the acquired data to remove noise and unnecessary information, extracting only important information about traffic congestion and weather that may affect deliveries. It also organizes package information and categorizes it into appropriate categories based on size and weight.

[0857] The server then uses a generative AI model to generate optimal delivery routes based on the filtered data. The generative AI model contains machine learning algorithms that generate optimal results from the input data.

[0858] Device Features

[0859] The device is equipped with an emotion engine that recognizes the emotions of delivery personnel in real time by analyzing their facial expressions and voice. The recognized emotion data is sent from the device to a server and used to readjust the delivery route.

[0860] The device also periodically retrieves the latest traffic, weather, and emotion information from the server. The server recalculates the optimal delivery route based on the newly collected data and sends the results to the device, allowing it to respond to changes in the situation in real time.

[0861] User Interface

[0862] The user (delivery worker) can view the optimized new delivery route on the terminal display. The route is intuitively displayed highlighted on the map. If the user feels stressed or fatigued, the system automatically inserts rest points.

[0863] Specific examples

[0864] For example, when a delivery person is making deliveries in a specific area in the morning, the device collects traffic congestion data and rainfall forecast data and recognizes from the delivery person's facial expression that they are feeling stressed. As a result, the server calculates a new route and sends it to the device, including rest stops to reduce stress.

[0865] Prompt Sentence Examples

[0866] Here are some example prompts to input to the AI ​​model:

[0867] Current traffic conditions: Traffic congestion information

[0868] Current weather information: Rainfall forecast

[0869] Package information: shipping address, weight, size

[0870] Please generate the optimal delivery route taking into consideration transportation conditions such as avoiding traffic congestion and areas where rain is expected.

[0871] In this way, the system uses real-time data to generate optimal delivery routes and takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the workload of the delivery person.

[0872] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0873] Step 1: Collect data

[0874] The server accesses the traffic information API and weather information API to collect traffic and weather data. The input is a request sent to the API endpoint, and the output is response data in JSON or XML format. Specifically, this includes congestion information and speed limit information obtained from the traffic information API, and rainfall and snowfall forecast information obtained from the weather information API. The server also collects item data from internal systems and external parcel information APIs. Input includes delivery address, weight, size, handling precautions, etc., and this information is stored in the database as a response.

[0875] Step 2: Filtering the data

[0876] The server filters the collected traffic data, weather data, and product data. The input includes the raw data saved in step 1. Specifically, the server filters out noise data and extracts only traffic congestion information and important weather information. For example, it selects areas with serious congestion or areas where rain is expected, and ignores other data. The output is the filtered traffic data, weather data, and classified product data.

[0877] Step 3: Generate delivery routes

[0878] The server uses a generative AI model to generate an optimal delivery route based on the filtered data. Inputs include filtered traffic data, weather data, and product data. The generative model calculates the optimal route based on a prompt provided to it. An example of a prompt is:

[0879] "Generate the optimal delivery route taking into consideration current traffic conditions: traffic congestion information, current weather information: rainfall forecast, package information: delivery address, weight, size, and transportation conditions, avoiding traffic congestion and areas where rainfall is forecast."

[0880] The output is data on the optimal delivery route.

[0881] Step 4: Collect and process emotion data

[0882] The device uses an emotion engine to analyze the delivery person's facial expressions and voice and recognize their emotions in real time. Input includes facial expression and voice data acquired from a camera and microphone. The device's emotion engine analyzes this data and evaluates their stress level and fatigue state. The output is data about the delivery person's emotional state, which is immediately sent to the server.

[0883] Step 5: Real-time route adjustments

[0884] The server receives the delivery person's emotional data and recalculates the delivery route as necessary. The inputs are the latest emotional data, traffic data, and weather data. The server re-runs the generative AI model based on this data and calculates a new optimal route. The output is the adjusted delivery route data. This route data is sent to the terminal in real time.

[0885] Step 6: Route Display and User Interface

[0886] The user checks the latest optimized delivery route on the device display. The input is the new delivery route data sent from the server. The device launches a map app based on this data and displays the highlighted route. If the user feels stressed or tired during delivery, the system automatically inserts rest points and displays that information on the map. This allows the user to intuitively understand the optimal route and rest points.

[0887] The above is the specific processing steps and operational flow of the program of this system.

[0888] (Application example 2)

[0889] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0890] Conventional delivery systems have shown some effectiveness in generating optimal delivery routes based on traffic and weather information, but they do not take into account the emotional state of the delivery person, which means they cannot provide the optimal route even when the delivery person is stressed or tired. Furthermore, the display on the user's device is simple, which means it cannot quickly respond to changes in the delivery person's emotional state. This raises concerns that delivery efficiency will decrease and the burden on the delivery person will increase.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0892] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, means for generating an optimal delivery route using a generative model based on the filtered and processed information, means for updating the delivery route based on the real-time information, means for displaying the generated delivery route on a user device, and means for adjusting the delivery route based on the user's emotional state using an emotion engine that recognizes the user's emotions, thereby improving delivery efficiency and reducing the burden on delivery personnel.

[0893] "Traffic conditions" is information that indicates the current state of road congestion and traffic flow.

[0894] "Weather information" is information that indicates current and forecast weather conditions.

[0895] "Item information" refers to information about the item to be delivered, including the address, weight, size, handling precautions, etc.

[0896] "Real-time" refers to data and information being collected and updated almost immediately.

[0897] "Gathering means" refers to hardware or software mechanisms for acquiring traffic conditions, weather information, and product information.

[0898] "Filtering" is the process of extracting necessary information from collected data and removing unnecessary information.

[0899] A "processing means" is a hardware or software mechanism for organizing and converting data into a required format.

[0900] A "generative model" is software that has an algorithm for calculating the optimal delivery route based on collected data.

[0901] The "optimal delivery route" is a route that takes into account traffic conditions, weather information, and product information to complete delivery in the shortest time and at the lowest cost.

[0902] An "updating means" is a hardware or software mechanism for recalculating existing delivery routes based on new information and keeping them up to date.

[0903] A "user device" is an electronic device such as a smartphone or tablet carried by a delivery person.

[0904] A "displaying means" is a mechanism for visually presenting information on the screen of a user device.

[0905] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[0906] "Emotional state" refers to the user's psychological and physiological state, including stress, fatigue, and the like.

[0907] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes them, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions and adjusts routes based on those emotions. The following describes in detail an embodiment of the present invention.

[0908] System Program

[0909] The system first collects data from the traffic information API, weather information API, and goods information API. The traffic information API retrieves information on traffic congestion and speed limits on major roads. The weather information API retrieves current weather and forecast data, such as if rain is predicted for a specific area within the next few hours. The goods information API retrieves detailed information about the delivery, such as the delivery address, package weight, size, and handling precautions.

[0910] The collected data is filtered by the server to extract only the important information. Traffic data is given special emphasis on traffic congestion information, and weather data is focused on factors that affect delivery. Item information is also organized and appropriately categorized according to the size and weight of the package.

[0911] Generative models use this processed data to calculate optimal delivery routes, using machine learning algorithms to take into account numerous data points and generate routes that, for example, avoid traffic jams in cities and complete deliveries before bad weather arrives.

[0912] Emotion Engine

[0913] The emotion engine recognizes the delivery person's (user's) emotions in real time through facial expression recognition and voice analysis. If the user feels stressed or tired, the device detects this and sends the emotion data to the server. The server then reevaluates the emotion data along with the latest traffic, weather, and product information, and recalculates the optimal route. At this time, rest stops are inserted into the route according to the user's stress level.

[0914] User Interface

[0915] The device displays the new optimized delivery route. The intuitive user interface visually shows the route highlighted on a map. Additionally, rest stops are automatically inserted if the user feels stressed or fatigued, improving delivery efficiency and reducing user stress.

[0916] Hardware and software used

[0917] Hardware: User devices such as smartphones and tablets, and servers connected to the internet

[0918] Software: Traffic information API, weather information API, product information API, machine learning algorithm, emotion engine

[0919] Implementation example

[0920] For example, food delivery drivers can use the app to find the optimal delivery route in real time, and if stress is detected through facial expression analysis, it will automatically provide rest stops, improving delivery efficiency and reducing the driver's burden.

[0921] Prompt Sentence Examples

[0922] "Get current traffic congestion information from a traffic API and upcoming rainfall forecasts from a weather API. Then use an emotion engine to detect the driver's emotional state in real time. Based on this data, generate optimal delivery routes and suggest rest stops if the driver is feeling stressed."

[0923] As described above, this system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the burden on the delivery person.

[0924] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0925] Step 1:

[0926] The server accesses the traffic information API, weather information API, and product information API to collect traffic conditions, weather information, and product information. The input data is the raw data obtained from each API, and the output data is the collected traffic conditions, weather information, and product information. The server temporarily stores this data and passes it to the next step.

[0927] Step 2:

[0928] The server filters the collected traffic data and extracts only important traffic congestion information. The input data is raw traffic data, which contains noise and unnecessary information. The server analyzes this data and selects the important parts. The output data is filtered traffic congestion information.

[0929] Step 3:

[0930] The server filters the collected weather data and extracts only the important information that affects delivery. The input data is raw weather information data, which includes various weather conditions. The server selects the data based on factors that directly affect delivery, and obtains important weather information as output.

[0931] Step 4:

[0932] The server organizes the collected item information and identifies the handling requirements for each package. The input data is raw item information data, including delivery address, package weight, size, handling precautions, etc. The server organizes this information and generates organized item information as output.

[0933] Step 5:

[0934] The server inputs the filtered and processed traffic data, weather data, and item data into a generative model to generate an optimal delivery route. The input data is the filtered traffic data, weather data, and organized item data. The server uses a machine learning algorithm based on this data to calculate the optimal delivery route. The output data is the generated optimal delivery route.

[0935] Step 6:

[0936] The emotion engine recognizes emotions in real time through facial expression recognition and voice analysis of the user. Input data is image and voice data of the user. The device analyzes this data to identify the user's emotional state and sends the recognized emotional data as output to the server.

[0937] Step 7:

[0938] The server re-evaluates the emotion data and the latest traffic, weather, and product information, and re-calculates the optimal route. The input data is the emotion data and the latest traffic, weather, and product information. The server re-evaluates based on this information and adjusts the delivery route as necessary. The output data is the adjusted delivery route.

[0939] Step 8:

[0940] The terminal displays the optimized delivery route on the user device. The input data is the optimized delivery route information sent from the server. The terminal displays this information on the display of the user device, visually showing the route. The output data is the route information displayed to the user.

[0941] Through the above processing steps, the system can respond to real-time changes in the situation, always provide the optimal delivery route, and also take into account the user's emotional state, thereby improving delivery efficiency and reducing the burden on the user.

[0942] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0943] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0944] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0945] [Fourth embodiment]

[0946] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0947] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0948] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0949] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0950] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0951] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0952] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0953] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0954] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0955] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0956] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0957] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0958] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0959] This invention relates to a system that collects traffic conditions, weather information, and product information in real time, processes the information, and generates and updates optimal delivery routes in order to improve delivery efficiency in logistics. A specific description of an embodiment of the invention will be given below.

[0960] Overall program flow

[0961] Examples of data collection

[0962] The server uses a traffic information API to collect real-time traffic conditions. This API provides information on current road congestion and speed limits. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server through the API. The server then uses a weather information API to obtain current and forecast weather data for the area. For example, if freezing rain is forecast in the next few hours, that information is also retrieved.

[0963] Examples of data processing

[0964] The collected data is filtered and processed on the server. For traffic condition data, only the main congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as the main data. For goods information, the weight and size of the delivery items, as well as handling precautions, are organized.

[0965] Example of delivery route generation

[0966] The server uses a generative model based on the collected and processed data to generate an optimal delivery route. The generative model uses machine learning algorithms, for example, to calculate the shortest and most efficient route based on each data point. It also considers alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[0967] Real-time update example

[0968] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the device sends the data to the server and requests a route recalculation. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[0969] User interface examples

[0970] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface shows the route highlighted on a map, allowing the user to follow the instructions to efficiently complete the delivery.

[0971] The system of the present invention collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes, thereby significantly improving delivery efficiency in logistics, thereby reducing costs and shortening delivery times.

[0972] The processing flow will be explained below.

[0973] Step 1:

[0974] The server accesses the traffic information API to obtain current traffic condition data, such as traffic congestion information and speed limit information on major roads.

[0975] Step 2:

[0976] The server accesses the weather information API to retrieve current and forecast weather data, such as temperature, precipitation, and wind speed for a specific area.

[0977] Step 3:

[0978] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[0979] Step 4:

[0980] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[0981] Step 5:

[0982] The server filters the collected weather data to extract only important information that will affect delivery, such as forecasts of heavy rain or storms along delivery routes.

[0983] Step 6:

[0984] The server organizes shipment information and identifies the handling requirements for each package, optimizing delivery routes based on package size and weight.

[0985] Step 7:

[0986] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate the optimal delivery route.

[0987] Step 8:

[0988] The server then validates the generated optimal delivery route to ensure its feasibility, and if necessary, makes minor adjustments, such as avoiding traffic congestion in the Tokyo area.

[0989] Step 9:

[0990] The server sends the generated delivery route to the terminal, and the delivery person holding the terminal receives this route information.

[0991] Step 10:

[0992] The device periodically retrieves the latest data from the server, checks real-time traffic and weather conditions, and receives and updates any new information.

[0993] Step 11:

[0994] The device requests the server to recalculate the optimal route based on the latest data received, thereby adapting to changes in the situation.

[0995] Step 12:

[0996] The server recalculates the optimal delivery route based on real-time data and sends the new route information to the terminal.

[0997] Step 13:

[0998] The terminal displays the latest delivery route information to the user (delivery person), who can then efficiently deliver the goods by following the displayed route.

[0999] Step 14:

[1000] The user follows the navigation on the device to make deliveries and deliver packages efficiently. As new information is received, the device updates the route and suggests the best route.

[1001] This step allows the system to respond to real-time situation changes and continue to provide the optimal delivery route.

[1002] Example 1

[1003] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1004] In the modern logistics industry, unexpected obstacles such as traffic congestion and bad weather often have a significant negative impact on delivery efficiency. Furthermore, few systems can take into account specific item handling requirements and delivery conditions. Current systems have limited real-time information collection and route recalculation capabilities, and lack the flexibility to provide efficient delivery routes. This results in longer delivery times, increased costs, and lower customer satisfaction.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1006] In this invention, the server includes means for collecting traffic condition information from external data sources, means for collecting weather information, means for collecting item information, means for filtering and processing the collected traffic condition information, means for filtering and processing the collected weather information, means for organizing the collected item information, means for calculating an optimal delivery route using a generative model based on the filtered and processed traffic condition information, weather information, and item information, means for checking whether the current delivery route is appropriate based on real-time information and recalculating the route as necessary, and means for displaying the generated delivery route on a user device and providing visual and audio guidance, thereby enabling significant improvement in delivery efficiency, cost reduction, and increased customer satisfaction.

[1007] "External data sources" are services or data providers that provide traffic and weather information via the Internet or other networks.

[1008] "Traffic condition information" refers to various information related to road traffic, such as road congestion levels, speed limits, and traffic accident occurrence status.

[1009] "Weather Information" means current and forecasted weather data, including precipitation, temperature, wind speed, weather alerts, etc.

[1010] "Item information" refers to detailed data about a shipment, including weight, size, handling requirements, temperature control needs, etc.

[1011] "Means of collection" refers to the technical means for obtaining the necessary data using various APIs and network interfaces.

[1012] "Filtering and processing measures" are technical measures for removing unnecessary information from collected data and extracting and organizing important data.

[1013] A "generative model" is a model that uses machine learning algorithms to analyze and process data and create optimal delivery routes.

[1014] A "delivery route" is a driving route selected for a delivery vehicle to reach its destination most efficiently.

[1015] A "user device" is a terminal used by a delivery person, and refers to a mobile information terminal such as a smartphone or tablet.

[1016] "Visual and audio guidance" means the manner in which driving directions and route change notifications are provided to a user through a user device, including map displays and audio prompts.

[1017] "Real-time information" refers to data that changes every moment, such as current road conditions and weather conditions.

[1018] This invention is a system for improving delivery efficiency in logistics, which collects and processes traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes. This system is mainly composed of a server, terminals, and users.

[1019] Collection methods

[1020] The server collects real-time data using traffic information APIs and weather information APIs. Examples of traffic information APIs include the Google Maps API and the Here API, which are used to obtain current road congestion and speed limit information. Weather information APIs include the OpenWeather API and the Weatherbit API, which obtain current and forecast weather data. For example, if there is traffic congestion on a major highway in a city, that information is sent to the server via the API. Next, weather data such as whether heavy rain is forecast within the next few hours is obtained.

[1021] Filtering and processing methods

[1022] The server filters and processes the collected data. For traffic condition data, only key congestion information is extracted and unnecessary noise is removed. Similarly, for weather information, information on bad weather that directly affects deliveries is treated as primary data. For goods information, the weight, size, and handling precautions of the delivery items are obtained from the logistics management system (WMS: Warehouse Management System), and the information includes instructions such as "Delivery A requires temperature control."

[1023] A means of calculating the optimal delivery route

[1024] The server uses the collected and processed data to calculate the optimal delivery route using a generative AI model. This generative AI model incorporates machine learning algorithms to calculate the shortest and most efficient route based on each data point. It also takes into account alternative routes to avoid traffic congestion and bad weather. For example, it may suggest a route that avoids urban congestion and uses suburban highways.

[1025] A way to recalculate your route in real time

[1026] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there are any new traffic jams or changes in the weather, the device sends the data to the server to request a recalculation of the route. The server then recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[1027] Guidance by user device

[1028] The user (delivery worker) can view the new optimized delivery route on the device display. The intuitive user interface displays the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions efficiently and complete the delivery. Specific actions include a color-coded route on the map, voice guidance, and notification of route changes.

[1029] Prompt Sentence Examples

[1030] "Design a system that uses traffic and weather APIs to calculate the optimal delivery route based on current traffic conditions and weather. Use Google Maps API for traffic information and OpenWeather API for weather information. Explain how you would filter only the important data and generate the optimal route using a machine learning algorithm."

[1031] This invention will significantly improve delivery efficiency in logistics, reduce costs, and shorten delivery times. In addition, real-time information updates will always provide optimal delivery routes, contributing to improved customer satisfaction.

[1032] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1033] Step 1:

[1034] The server collects real-time data using traffic information APIs and weather information APIs. Specifically, it obtains current road congestion and speed limit information from Google Maps API and Here API, and current and forecast weather data from OpenWeather API and Weatherbit API. The input is real-time data from the APIs, and the output is traffic condition information and weather information.

[1035] Step 2:

[1036] The server filters and processes the collected traffic information. Specifically, it extracts only the main congestion information and removes information that does not exceed the speed limit and other noise. For example, if the congestion level exceeds a certain threshold, it retains that information as important data. The input is data from the traffic information API, and the output is filtered traffic information.

[1037] Step 3:

[1038] The server filters and processes the collected weather information. For example, it extracts data on severe weather, such as heavy rain or freezing rain, from the current weather. It keeps only the information that directly affects delivery and removes the rest. The input is data from the weather information API, and the output is the filtered weather information.

[1039] Step 4:

[1040] The server collects and organizes item information from the logistics management system (WMS). Specifically, it obtains and organizes data such as the weight, size, and handling precautions of delivery items. For example, if a specific item requires temperature control, this information is included. The input is item data from the WMS, and the output is organized item information.

[1041] Step 5:

[1042] The server uses a generative AI model to calculate the optimal delivery route based on the filtered and processed traffic, weather, and product information. Specifically, it uses a machine learning algorithm to generate the optimal delivery route that avoids traffic congestion, weather, and product handling requirements. The inputs are traffic, weather, and product information, and the output is the optimal delivery route.

[1043] Step 6:

[1044] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If new traffic congestion or weather changes are detected, the terminal sends the data to the server and requests a recalculation of the route. The server then recalculates the optimal route and sends the data to the terminal. The input is the latest data from the server, and the output is the recalculated delivery route.

[1045] Step 7:

[1046] The user (delivery person) can view the new optimized delivery route on the terminal display. The user interface is intuitive, showing the route highlighted on a map. Voice guidance is also provided, allowing the user to follow instructions and efficiently carry out the delivery. The input is delivery route information from the terminal, and the output is efficient delivery work by the user.

[1047] (Application example 1)

[1048] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1049] Improving delivery efficiency in logistics is difficult due to external factors such as traffic congestion and weather fluctuations. Furthermore, there is a need for rapid response to real-time situation changes and a user interface that delivery personnel can intuitively understand. To solve these issues, it is necessary to develop a system that can quickly and effectively process traffic, weather, and product information, and always provide the optimal delivery route.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1051] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, and means for generating an optimal delivery route using a generative model based on the filtered and processed information. This makes it possible to collect and process information in real time and always provide an optimal delivery route.

[1052] "Traffic conditions" refers to information such as road congestion, speed limits, accidents, and construction, and is real-time data on road driving conditions.

[1053] "Weather information" is data on current and forecasted weather conditions, including environmental factors such as precipitation, temperature, and wind speed.

[1054] "Item information" is detailed data such as the weight, size, and handling precautions of the item to be delivered.

[1055] "Means of collection" refers to a system for obtaining necessary information from external data sources such as traffic information APIs and weather information APIs.

[1056] "Filtering and processing means" refers to the process of narrowing down the collected raw data to the necessary information, and is a system that has the function of removing noise and shaping the data.

[1057] "Means for generating optimal delivery routes using generative models" refers to a system that uses mathematical models such as machine learning algorithms to calculate delivery routes based on collected and processed data.

[1058] "Means for updating delivery routes based on real-time information" refers to a system that periodically checks for new traffic and weather data and recalculates existing delivery routes as needed.

[1059] The "means for displaying on the user device" refers to a display or interface for visually presenting the generated delivery route to the user.

[1060] The "means for providing an intuitive map display" is map software that displays delivery routes and additional information in a format that is easily understood by the user.

[1061] "Means for notifying delivery route updates in real time" refers to a notification system that quickly notifies users when new information is updated during delivery.

[1062] To put this invention into practice, a system is required that collects traffic conditions, weather information, and product information in real time, and generates and updates optimal delivery routes based on this information. A specific embodiment of this system is shown below.

[1063] Hardware and software used

[1064] Traffic Information API: Obtains information such as road congestion, speed limits, accidents, and construction.

[1065] Weather Information API: Obtain weather data such as precipitation, temperature, and wind speed.

[1066] Data filtering and processing system: Filters collected data and extracts only the information you need.

[1067] Machine learning algorithm (generative model): Calculates the optimal delivery route based on the filtered data.

[1068] User Interface (UI): Provides delivery personnel with information such as intuitive map displays.

[1069] Notification system: Real-time delivery route updates.

[1070] Data collection and processing

[1071] The server uses traffic information APIs and weather information APIs to collect real-time traffic and weather information. Item information is also obtained from a local database or JSON file. The server filters this data to extract only important information, such as traffic congestion and worsening weather.

[1072] Delivery route generation

[1073] Based on the filtered information, the server uses a generative model to generate the optimal delivery route. This generative model uses linear regression, a type of machine learning algorithm, to calculate the shortest and most efficient route based on past data. For example, it might suggest a route that avoids urban traffic jams and uses suburban highways.

[1074] Real-time updates

[1075] The device periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, the server recalculates the optimal route and sends the new route information to the device. This ensures that the optimal delivery route is always maintained.

[1076] User Interface

[1077] The delivery driver can view the new optimized delivery route on their device display. The intuitive user interface shows the route highlighted on a map, allowing the delivery driver to follow the instructions efficiently.

[1078] Prompt Sentence Examples

[1079] An example of a prompt to input to a generative AI model is as follows:

[1080] The goal of this program is to create a smartphone application for a logistics center that collects traffic, weather, and product information and generates optimal delivery routes. Write the code based on the following criteria:

[1081] Traffic information is obtained from the traffic information API, and weather information is obtained from the weather information API.

[1082] Item information is read from a local JSON file.

[1083] Filter the data to retain only the key information.

[1084] Calculate optimal delivery routes using machine learning algorithms (linear regression).

[1085] The route is updated in real time and displayed in the user interface.

[1086] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1087] Step 1:

[1088] The server collects real-time data from the traffic and weather APIs. In this step, it sends API requests to retrieve road congestion, speed limits, accident information, and current and forecast weather conditions. The input is the API endpoint, and the output is the retrieved traffic and weather data in JSON format.

[1089] Step 2:

[1090] The server filters and processes the acquired data. For traffic data, it extracts only information where congestion exceeds a certain threshold, and for weather data, it extracts only information about bad weather that will affect deliveries. The input is the JSON data collected in step 1, and the output is the filtered important information. Specifically, it removes unnecessary noise data.

[1091] Step 3:

[1092] The server uses the filtered data to run a machine learning algorithm (linear regression) to generate an optimal delivery route. In this step, past delivery data and current traffic and weather data are input to calculate the shortest and most efficient route. The input is the filtered data, and the output is the generated delivery route. The specific operations are to run the algorithm and calculate the delivery route.

[1093] Step 4:

[1094] The terminal periodically receives the latest traffic and weather data from the server to check whether the current delivery route is appropriate. If there is new congestion or weather changes, it sends the data to the server and requests a route recalculation. The input is the delivery route generated in step 3 and the latest traffic and weather data, and the output is the updated delivery route.

[1095] Step 5:

[1096] The server recalculates the optimal route and sends the new route information to the device. In this process, the machine learning algorithm is run again to calculate the optimal route based on the new traffic and weather conditions. The input is the latest data and current route information, and the output is the recalculated delivery route. The specific operation involves sending data from the server to the device.

[1097] Step 6:

[1098] The user, a delivery person, can view the new optimized delivery route on the terminal display. The user interface is intuitively designed, and the route is highlighted on a map. The input is the updated delivery route sent from the server, and the output is the map display on the user interface. The specific operation is to display the route information on the terminal display.

[1099] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1100] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes that information, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions. A specific description of an embodiment of the present invention will be given below.

[1101] Overall program flow

[1102] Examples of data collection

[1103] The server accesses a traffic information API to obtain current traffic data, including traffic congestion and speed limit information for major roads. The server then accesses a weather information API to obtain current and forecast weather data. For example, if rain is predicted for a particular area within the next few hours, that information is also obtained. The server then obtains detailed information about the shipment from internal systems and external package information APIs, including the delivery address, package weight, size, and handling instructions.

[1104] Examples of data processing

[1105] The server filters the collected traffic data to remove noise and unnecessary information, extracting only important traffic congestion information. Next, the server filters the collected weather data to extract only important information that affects delivery. Finally, the server organizes the package information and identifies the handling requirements for each package, for example, classifying it into the appropriate category based on size and weight.

[1106] Example of delivery route generation

[1107] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to generate optimal delivery routes based on these data points. For example, a route might be calculated that uses suburban highways to avoid city congestion and complete the delivery before bad weather arrives.

[1108] Examples of emotion engines

[1109] The emotion engine built into the device recognizes the user's (delivery worker's) emotions in real time through facial recognition and voice analysis. For example, if the user feels stressed or tired during a delivery, the emotion engine will detect this. The device then sends this emotion data to the server and adjusts the route as needed.

[1110] Real-time update example

[1111] The device periodically obtains the latest traffic, weather, and emotion information from the server to check whether the current delivery route is appropriate. If new information is available, the server recalculates the optimal route based on that information and sends the new route information to the device.

[1112] User interface examples

[1113] The user (delivery worker) sees the new optimized delivery route on the terminal display. The intuitive user interface shows the route highlighted on a map. Furthermore, if the user feels stressed or shows signs of fatigue, rest points are automatically inserted, reducing the user's workload.

[1114] This system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the user's emotional state, improving delivery efficiency and reducing the burden on the user, thereby effectively reducing delivery costs and time.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The server accesses the traffic information API to obtain current traffic situation data, such as real-time congestion information and speed limit data for major roads.

[1118] Step 2:

[1119] The server accesses a weather information API to retrieve current and forecast weather data, such as if rain or strong winds are forecast for a particular area in the next few hours.

[1120] Step 3:

[1121] The server collects shipment details from internal systems and external package information APIs, including the delivery address, package weight, dimensions, and handling instructions.

[1122] Step 4:

[1123] The server filters the collected traffic data to remove noise and unnecessary information, for example, extracting only important traffic congestion information and removing other data.

[1124] Step 5:

[1125] The server filters the collected weather data and extracts only important data that will affect delivery, such as heavy rain or storms forecasted along the delivery route.

[1126] Step 6:

[1127] The server collates the package information and identifies the handling requirements for each package, which includes sorting it into the appropriate category based on its size and weight.

[1128] Step 7:

[1129] The server inputs the filtered and processed traffic, weather, and package data into a generative model, which uses machine learning algorithms to calculate optimal delivery routes, such as routes that avoid city congestion and use highways.

[1130] Step 8:

[1131] The server then validates the generated optimal delivery route to ensure feasibility, making fine adjustments as needed to accommodate traffic and weather conditions.

[1132] Step 9:

[1133] The server then sends the confirmed delivery route information to the terminal, which the delivery person with the terminal receives and confirms the route.

[1134] Step 10:

[1135] The emotion engine built into the device analyzes the facial expressions and voice of the user (delivery worker) in real time to recognize their emotional state. For example, it can detect if the user is feeling stressed during a delivery.

[1136] Step 11:

[1137] The device transmits the recognized user emotion data to the server in real time, and the server receives the data and decides whether to reflect it in the delivery route.

[1138] Step 12:

[1139] The server recalculates the delivery route as needed based on the received user emotion data. For example, if the user is feeling stressed, the server generates a route that includes rest stops to reduce stress.

[1140] Step 13:

[1141] The server sends the recalculated optimal route to the terminal, which receives this information and presents the new route to the user.

[1142] Step 14:

[1143] The terminal displays the new delivery route information to the user (delivery person), and the user interface highlights the optimal route on a map, allowing for visual viewing.

[1144] Step 15:

[1145] The user follows the displayed new route to ensure efficient delivery. When the emotion engine recognizes the user's stress or fatigue, it will suggest appropriate rest points.

[1146] Through these steps, the system responds to real-time changes in the situation and the user's emotional state, and always provides the optimal delivery route, thereby improving delivery efficiency and reducing the user's burden.

[1147] Example 2

[1148] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1149] Conventional delivery systems could calculate optimal routes by taking into account real-time traffic conditions and weather information, but they were unable to take into account the emotional state of the delivery person. As a result, delivery people could become stressed or fatigued, resulting in reduced delivery efficiency. Furthermore, it was difficult to dynamically update routes based on data collected in real time and select optimal routes in a timely manner.

[1150] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting traffic data, weather data, and item data in real time, means for filtering and processing the collected traffic data, weather data, and item data, means for generating an optimal delivery route using a generative model based on the filtered and processed data, means for collecting and processing emotion data of the delivery person, means for adjusting the delivery route based on the emotion data, means for updating the delivery route based on the real-time data, and means for displaying the generated delivery route on a display device. As a result, by generating an optimal delivery route in real time and taking into account the emotional state of the delivery person, it is possible to improve delivery efficiency and reduce the burden on the delivery person.

[1151] "Traffic data" refers to information that indicates traffic conditions and road conditions, and specifically includes congestion information and speed limit information.

[1152] "Weather data" refers to information relating to current weather conditions and future weather forecasts, and specifically includes rainfall, snowfall, wind speed, and the like.

[1153] "Item data" refers to detailed information about the item being delivered, including the delivery address, weight, size, handling precautions, etc.

[1154] "Filtering" refers to the process of extracting necessary information from collected data and removing unnecessary noise.

[1155] A "generative model" is a model built on machine learning algorithms to generate optimal results from given data.

[1156] "Delivery route" refers to the route a delivery vehicle takes to deliver a delivery item.

[1157] "Emotion data" is information that indicates the emotional state of the delivery person, and specifically includes stress, fatigue, etc.

[1158] A "display device" is a device that visually presents generated information to a user, and specifically includes a display and a monitor.

[1159] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[1160] An "interface" refers to the means of connection through which a data sender and receiver exchange information.

[1161] This invention is a system that collects traffic data, weather data, and product data in real time and uses them to generate and update optimal delivery routes. Furthermore, it is characterized by recognizing and taking into account the emotional state of delivery personnel, thereby improving delivery efficiency and user comfort.

[1162] The system mainly consists of a server, a terminal, and a user device. Each element is explained in detail below.

[1163] Server Features

[1164] The server accesses traffic and weather information APIs to obtain necessary data in real time, such as traffic congestion information for major roads, speed limits, current weather data, and forecast data. It also obtains detailed information about deliveries (such as delivery address, package weight, size, and handling precautions) from internal systems and external package information APIs.

[1165] The server filters the acquired data to remove noise and unnecessary information, extracting only important information about traffic congestion and weather that may affect deliveries. It also organizes package information and categorizes it into appropriate categories based on size and weight.

[1166] The server then uses a generative AI model to generate optimal delivery routes based on the filtered data. The generative AI model contains machine learning algorithms that generate optimal results from the input data.

[1167] Device Features

[1168] The device is equipped with an emotion engine that recognizes the emotions of delivery personnel in real time by analyzing their facial expressions and voice. The recognized emotion data is sent from the device to a server and used to readjust the delivery route.

[1169] The device also periodically retrieves the latest traffic, weather, and emotion information from the server. The server recalculates the optimal delivery route based on the newly collected data and sends the results to the device, allowing it to respond to changes in the situation in real time.

[1170] User Interface

[1171] The user (delivery worker) can view the optimized new delivery route on the terminal display. The route is intuitively displayed highlighted on the map. If the user feels stressed or fatigued, the system automatically inserts rest points.

[1172] Specific examples

[1173] For example, when a delivery person is making deliveries in a specific area in the morning, the device collects traffic congestion data and rainfall forecast data and recognizes from the delivery person's facial expression that they are feeling stressed. As a result, the server calculates a new route and sends it to the device, including rest stops to reduce stress.

[1174] Prompt Sentence Examples

[1175] Here are some example prompts to input to the AI ​​model:

[1176] Current traffic conditions: Traffic congestion information

[1177] Current weather information: Rainfall forecast

[1178] Package information: shipping address, weight, size

[1179] Please generate the optimal delivery route taking into consideration transportation conditions such as avoiding traffic congestion and areas where rain is expected.

[1180] In this way, the system uses real-time data to generate optimal delivery routes and takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the workload of the delivery person.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1: Collect data

[1183] The server accesses the traffic information API and weather information API to collect traffic and weather data. The input is a request sent to the API endpoint, and the output is response data in JSON or XML format. Specifically, this includes congestion information and speed limit information obtained from the traffic information API, and rainfall and snowfall forecast information obtained from the weather information API. The server also collects item data from internal systems and external parcel information APIs. Input includes delivery address, weight, size, handling precautions, etc., and this information is stored in the database as a response.

[1184] Step 2: Filtering the data

[1185] The server filters the collected traffic data, weather data, and product data. The input includes the raw data saved in step 1. Specifically, the server filters out noise data and extracts only traffic congestion information and important weather information. For example, it selects areas with serious congestion or areas where rain is expected, and ignores other data. The output is the filtered traffic data, weather data, and classified product data.

[1186] Step 3: Generate delivery routes

[1187] The server uses a generative AI model to generate an optimal delivery route based on the filtered data. Inputs include filtered traffic data, weather data, and product data. The generative model calculates the optimal route based on a prompt provided to it. An example of a prompt is:

[1188] "Generate the optimal delivery route taking into consideration current traffic conditions: traffic congestion information, current weather information: rainfall forecast, package information: delivery address, weight, size, and transportation conditions, avoiding traffic congestion and areas where rainfall is forecast."

[1189] The output is data on the optimal delivery route.

[1190] Step 4: Collect and process emotion data

[1191] The device uses an emotion engine to analyze the delivery person's facial expressions and voice and recognize their emotions in real time. Input includes facial expression and voice data acquired from a camera and microphone. The device's emotion engine analyzes this data and evaluates their stress level and fatigue state. The output is data about the delivery person's emotional state, which is immediately sent to the server.

[1192] Step 5: Real-time route adjustments

[1193] The server receives the delivery person's emotional data and recalculates the delivery route as necessary. The inputs are the latest emotional data, traffic data, and weather data. The server re-runs the generative AI model based on this data and calculates a new optimal route. The output is the adjusted delivery route data. This route data is sent to the terminal in real time.

[1194] Step 6: Route Display and User Interface

[1195] The user checks the latest optimized delivery route on the device display. The input is the new delivery route data sent from the server. The device launches a map app based on this data and displays the highlighted route. If the user feels stressed or tired during delivery, the system automatically inserts rest points and displays that information on the map. This allows the user to intuitively understand the optimal route and rest points.

[1196] The above is the specific processing steps and operational flow of the program of this system.

[1197] (Application example 2)

[1198] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1199] Conventional delivery systems have shown some effectiveness in generating optimal delivery routes based on traffic and weather information, but they do not take into account the emotional state of the delivery person, which means they cannot provide the optimal route even when the delivery person is stressed or tired. Furthermore, the display on the user's device is simple, which means it cannot quickly respond to changes in the delivery person's emotional state. This raises concerns that delivery efficiency will decrease and the burden on the delivery person will increase.

[1200] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1201] In this invention, the server includes means for collecting traffic conditions, weather information, and product information in real time, means for filtering and processing the collected traffic conditions, weather information, and product information, means for generating an optimal delivery route using a generative model based on the filtered and processed information, means for updating the delivery route based on the real-time information, means for displaying the generated delivery route on a user device, and means for adjusting the delivery route based on the user's emotional state using an emotion engine that recognizes the user's emotions, thereby improving delivery efficiency and reducing the burden on delivery personnel.

[1202] "Traffic conditions" is information that indicates the current state of road congestion and traffic flow.

[1203] "Weather information" is information that indicates current and forecast weather conditions.

[1204] "Item information" refers to information about the item to be delivered, including the address, weight, size, handling precautions, etc.

[1205] "Real-time" refers to data and information being collected and updated almost immediately.

[1206] "Gathering means" refers to hardware or software mechanisms for acquiring traffic conditions, weather information, and product information.

[1207] "Filtering" is the process of extracting necessary information from collected data and removing unnecessary information.

[1208] A "processing means" is a hardware or software mechanism for organizing and converting data into a required format.

[1209] A "generative model" is software that has an algorithm for calculating the optimal delivery route based on collected data.

[1210] The "optimal delivery route" is a route that takes into account traffic conditions, weather information, and product information to complete delivery in the shortest time and at the lowest cost.

[1211] An "updating means" is a hardware or software mechanism for recalculating existing delivery routes based on new information and keeping them up to date.

[1212] A "user device" is an electronic device such as a smartphone or tablet carried by a delivery person.

[1213] A "displaying means" is a mechanism for visually presenting information on the screen of a user device.

[1214] An "emotion engine" is software that analyzes a user's facial expressions and voice to recognize their emotional state.

[1215] "Emotional state" refers to the user's psychological and physiological state, including stress, fatigue, and the like.

[1216] This invention combines a system that collects traffic conditions, weather information, and product information in real time, processes them, and generates and updates optimal delivery routes with an emotion engine that recognizes user emotions and adjusts routes based on those emotions. The following describes in detail an embodiment of the present invention.

[1217] System Program

[1218] The system first collects data from the traffic information API, weather information API, and goods information API. The traffic information API retrieves information on traffic congestion and speed limits on major roads. The weather information API retrieves current weather and forecast data, such as if rain is predicted for a specific area within the next few hours. The goods information API retrieves detailed information about the delivery, such as the delivery address, package weight, size, and handling precautions.

[1219] The collected data is filtered by the server to extract only the important information. Traffic data is given special emphasis on traffic congestion information, and weather data is focused on factors that affect delivery. Item information is also organized and appropriately categorized according to the size and weight of the package.

[1220] Generative models use this processed data to calculate optimal delivery routes, using machine learning algorithms to take into account numerous data points and generate routes that, for example, avoid traffic jams in cities and complete deliveries before bad weather arrives.

[1221] Emotion Engine

[1222] The emotion engine recognizes the delivery person's (user's) emotions in real time through facial expression recognition and voice analysis. If the user feels stressed or tired, the device detects this and sends the emotion data to the server. The server then reevaluates the emotion data along with the latest traffic, weather, and product information, and recalculates the optimal route. At this time, rest stops are inserted into the route according to the user's stress level.

[1223] User Interface

[1224] The device displays the new optimized delivery route. The intuitive user interface visually shows the route highlighted on a map. Additionally, rest stops are automatically inserted if the user feels stressed or fatigued, improving delivery efficiency and reducing user stress.

[1225] Hardware and software used

[1226] Hardware: User devices such as smartphones and tablets, and servers connected to the internet

[1227] Software: Traffic information API, weather information API, product information API, machine learning algorithm, emotion engine

[1228] Implementation example

[1229] For example, food delivery drivers can use the app to find the optimal delivery route in real time, and if stress is detected through facial expression analysis, it will automatically provide rest stops, improving delivery efficiency and reducing the driver's burden.

[1230] Prompt Sentence Examples

[1231] "Get current traffic congestion information from a traffic API and upcoming rainfall forecasts from a weather API. Then use an emotion engine to detect the driver's emotional state in real time. Based on this data, generate optimal delivery routes and suggest rest stops if the driver is feeling stressed."

[1232] As described above, this system responds to real-time changes in the situation, always providing the optimal delivery route, and also takes into account the emotional state of the delivery person, thereby improving delivery efficiency and reducing the burden on the delivery person.

[1233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1234] Step 1:

[1235] The server accesses the traffic information API, weather information API, and product information API to collect traffic conditions, weather information, and product information. The input data is the raw data obtained from each API, and the output data is the collected traffic conditions, weather information, and product information. The server temporarily stores this data and passes it to the next step.

[1236] Step 2:

[1237] The server filters the collected traffic data and extracts only important traffic congestion information. The input data is raw traffic data, which contains noise and unnecessary information. The server analyzes this data and selects the important parts. The output data is filtered traffic congestion information.

[1238] Step 3:

[1239] The server filters the collected weather data and extracts only the important information that affects delivery. The input data is raw weather information data, which includes various weather conditions. The server selects the data based on factors that directly affect delivery, and obtains important weather information as output.

[1240] Step 4:

[1241] The server organizes the collected item information and identifies the handling requirements for each package. The input data is raw item information data, including delivery address, package weight, size, handling precautions, etc. The server organizes this information and generates organized item information as output.

[1242] Step 5:

[1243] The server inputs the filtered and processed traffic data, weather data, and item data into a generative model to generate an optimal delivery route. The input data is the filtered traffic data, weather data, and organized item data. The server uses a machine learning algorithm based on this data to calculate the optimal delivery route. The output data is the generated optimal delivery route.

[1244] Step 6:

[1245] The emotion engine recognizes emotions in real time through facial expression recognition and voice analysis of the user. Input data is image and voice data of the user. The device analyzes this data to identify the user's emotional state and sends the recognized emotional data as output to the server.

[1246] Step 7:

[1247] The server re-evaluates the emotion data and the latest traffic, weather, and product information, and re-calculates the optimal route. The input data is the emotion data and the latest traffic, weather, and product information. The server re-evaluates based on this information and adjusts the delivery route as necessary. The output data is the adjusted delivery route.

[1248] Step 8:

[1249] The terminal displays the optimized delivery route on the user device. The input data is the optimized delivery route information sent from the server. The terminal displays this information on the display of the user device, visually showing the route. The output data is the route information displayed to the user.

[1250] Through the above processing steps, the system can respond to real-time changes in the situation, always provide the optimal delivery route, and also take into account the user's emotional state, thereby improving delivery efficiency and reducing the burden on the user.

[1251] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1252] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1253] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1254] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1255] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1256] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1257] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1258] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1259] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1260] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1261] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1262] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1263] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1264] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1265] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1266] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1267] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1268] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1269] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1270] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1271] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1272] The following is further disclosed regarding the above embodiment.

[1273] (Claim 1)

[1274] a means for collecting real-time traffic, weather, and commodity information;

[1275] means for filtering and processing the collected traffic, weather, and commodity information;

[1276] a means for generating an optimal delivery route based on the filtered and processed information using a generative model; and

[1277] means for updating delivery routes based on real-time information;

[1278] means for displaying the generated delivery route on a user device;

[1279] A system including:

[1280] (Claim 2)

[1281] 10. The system of claim 1, wherein the system interfaces with a plurality of external data sources to collect real-time traffic, weather, and product information.

[1282] (Claim 3)

[1283] 10. The system of claim 1, wherein the generative model includes an algorithm that calculates optimal delivery routes that take into account traffic congestion, weather variations, and item handling requirements.

[1284] "Example 1"

[1285] (Claim 1)

[1286] means for collecting traffic information from external data sources;

[1287] a means for collecting weather information;

[1288] A means for collecting product information;

[1289] means for filtering and processing the collected traffic information;

[1290] means for filtering and processing the collected weather information;

[1291] A means for organizing the collected item information;

[1292] a means for calculating an optimal delivery route using a generative model based on the filtered and processed traffic information, weather information, and item information;

[1293] A means to check the suitability of the current delivery route based on real-time information and recalculate the route if necessary;

[1294] means for displaying the generated delivery route on a user device and providing visual and audio guidance;

[1295] A system including:

[1296] (Claim 2)

[1297] 10. The system of claim 1, wherein the system interfaces with a plurality of external data sources for collecting real-time traffic and weather information.

[1298] (Claim 3)

[1299] 10. The system of claim 1, wherein the generative model includes an algorithm that calculates optimal delivery routes that take into account traffic congestion, weather variations, and item handling requirements.

[1300] "Application Example 1"

[1301] (Claim 1)

[1302] a means for collecting real-time traffic, weather, and commodity information;

[1303] means for filtering and processing the collected traffic, weather, and commodity information;

[1304] a means for generating an optimal delivery route based on the filtered and processed information using a generative model; and

[1305] means for updating delivery routes based on real-time information;

[1306] means for displaying the generated delivery route on a user device;

[1307] means for notifying the user of real-time delivery route updates on the user device;

[1308] A system including:

[1309] (Claim 2)

[1310] 10. The system of claim 1, wherein the system interfaces with multiple external data sources to collect real-time traffic, weather, and product information and provides an intuitive map display to a user device.

[1311] (Claim 3)

[1312] 10. The system of claim 1, wherein the generative model includes an algorithm that calculates optimal delivery routes taking into account traffic congestion, weather fluctuations, and item handling requirements, and the generated routes are designed to reflect up-to-date information based on real-time feedback.

[1313] "Example 2: Combining Emotion Engines"

[1314] (Claim 1)

[1315] a means for collecting traffic data, weather data, and commodity data in real time;

[1316] means for filtering and processing the collected traffic, weather, and commodity data;

[1317] a means for generating an optimal delivery route based on the filtered and processed data using a generative model; and

[1318] A means for collecting and processing emotion data of delivery personnel;

[1319] a means for adjusting a delivery route based on the emotion data;

[1320] means for updating delivery routes based on real-time data;

[1321] means for displaying the generated delivery route on a display device;

[1322] A system including:

[1323] (Claim 2)

[1324] 10. The system of claim 1, wherein the system interfaces with a plurality of external sources for collecting traffic data, weather data, and commodity data in real time.

[1325] (Claim 3)

[1326] 2. The system of claim 1, wherein the generative model includes an algorithm that calculates an optimal delivery route taking into account traffic congestion information, weather information, and item handling requirements and the emotional state of the delivery person.

[1327] "Application example 2 when combining emotion engines" (Claim 1)

[1328] a means for collecting real-time traffic, weather, and commodity information;

[1329] means for filtering and processing the collected traffic, weather, and commodity information;

[1330] a means for generating an optimal delivery route based on the filtered and processed information using a generative model; and

[1331] means for updating delivery routes based on real-time information;

[1332] means for displaying the generated delivery route on a user device;

[1333] a means for adjusting a delivery route based on the emotional state of a user using an emotion engine that recognizes the user's emotions;

[1334] A system including:

[1335] (Claim 2)

[1336] 10. The system of claim 1, wherein the system interfaces with a plurality of external data sources to collect real-time traffic, weather, and product information.

[1337] (Claim 3)

[1338] 10. The system of claim 1, wherein the generative model includes an algorithm that calculates an optimal delivery route taking into account traffic congestion, weather fluctuations, and item handling requirements, and further includes an algorithm that incorporates the user's emotional state to dynamically adjust the route. [Explanation of symbols]

[1339] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting real-time traffic, weather, and commodity information; means for filtering and processing the collected traffic, weather, and commodity information; a means for generating an optimal delivery route based on the filtered and processed information using a generative model; and means for updating delivery routes based on real-time information; means for displaying the generated delivery route on a user device; A system including:

2. 10. The system of claim 1, wherein the system interfaces with a plurality of external data sources to collect real-time traffic, weather, and product information.

3. The system of claim 1 , wherein the generative model includes an algorithm that calculates optimal delivery routes that take into account traffic congestion, weather variations, and item handling requirements.

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