System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting location information and traffic conditions for each transportation vehicle; means for analyzing the collected AI using generated information and calculating an expected delivery time for a package; and means for notifying a user of the calculated expected delivery time.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 the modern logistics industry, redelivery is a factor that increases the workload of drivers. In particular, because the scheduled delivery time is uncertain, it is difficult for users to wait at home, and deliveries are often made when the user is not at home. This problem leads to a labor shortage of truck drivers, worsening working conditions, and even increased transportation costs. The purpose of this invention is to solve this redelivery problem and realize an efficient delivery system. [Means for solving the problem]

[0005] The present invention is a system that includes a means for collecting location information and traffic conditions of each transport vehicle, a means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, and a means for notifying the user of the calculated estimated delivery time. This system allows users to know the estimated delivery time with high accuracy in advance, thereby improving the rate of people being at home and reducing missed deliveries. Furthermore, by including a means for notifying users of updates to the estimated delivery time in real time and a means for calculating the estimated delivery time taking weather forecast data into account, it is possible to provide an even more accurate estimated delivery time.

[0006] A "transport vehicle" is a vehicle such as an automobile or truck used to transport cargo.

[0007] "Location information" is geographic coordinate data such as the current latitude and longitude of the transport vehicle.

[0008] "Traffic conditions" refers to data relating to road traffic conditions, such as road congestion information, construction information, and traffic accident information.

[0009] "Generative AI" is a system that uses artificial intelligence technology to analyze data and calculate optimal transportation routes and estimated delivery times.

[0010] A "user" is a recipient such as an individual or corporation who receives a delivered package.

[0011] The "estimated delivery time" is the specific time when the package is expected to arrive at the user's location.

[0012] A "notification" is an act or message that conveys information to a user via email or a dedicated app.

[0013] "Weather forecast data" is forecast information regarding future weather in a particular area. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0036] A system for implementing the present invention operates as follows.

[0037] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the vehicle's current latitude and longitude. The server then obtains current traffic conditions via a traffic information API. Traffic conditions include road congestion, construction, and traffic accident information.

[0038] The server then uses a weather API to collect weather forecast data, which contains information about future weather in a particular location.

[0039] Once this data is collected, a server-based AI analyzes it and calculates the optimal route for each vehicle. The AI ​​uses machine learning algorithms to calculate highly accurate delivery times, taking into account current traffic conditions and weather forecasts.

[0040] The server then notifies the user of the calculated estimated delivery time. This notification can be sent by email to the email address registered by the user or by push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time.

[0041] When users receive the notification, they can either be at home at the scheduled delivery time or be ready to receive the package at the specified location, which reduces the number of missed deliveries and reduces the workload of drivers due to redelivery.

[0042] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. According to this data, delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is forecast.

[0043] Based on this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates that delivery vehicle A will arrive at destination B at 9:30. The server then sends an email to the user at destination B saying, "Your package is expected to arrive at 9:30," and also sends a similar notification via a dedicated app.

[0044] The device (user's smartphone) receives this notification and displays it on the screen. The user confirms the notification and waits at home until 9:30. If the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and notifies the user again that "the new delivery time is 10:00."

[0045] This allows users to know the exact delivery time and receive their packages efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The server collects real-time location information from the GPS devices installed in the delivery vehicles, which is the current latitude and longitude of the delivery vehicles and is periodically retrieved through a REST API.

[0049] Step 2:

[0050] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. For example, it can obtain this information using the Google Maps API.

[0051] Step 3:

[0052] The server collects weather forecast data for the target area from the weather forecast API, which includes weather information for specific time periods.

[0053] Step 4:

[0054] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to calculate the optimal shipping route and estimated delivery time for each package.

[0055] Step 5:

[0056] The server obtains the estimated delivery time calculated by the generation AI and sends a notification to the user's registered email address or a dedicated app based on that time. The notification includes information about the estimated delivery time and delivery destination.

[0057] Step 6:

[0058] The device (such as the user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0059] Step 7:

[0060] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0061] Step 8:

[0062] The server will then notify the user again of the reanalyzed estimated delivery time via email or a dedicated app, just like the first time.

[0063] Step 9:

[0064] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0065] Step 10:

[0066] After the delivery is completed, the server stops monitoring the location information of the delivery vehicle and starts analyzing a new route based on the data of the next delivery destination.

[0067] Example 1

[0068] 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."

[0069] In conventional delivery systems, it was difficult to notify users of the exact scheduled delivery time, taking into account the location information of the transport vehicle and traffic conditions. Furthermore, even when the scheduled delivery time was delayed due to weather changes or traffic congestion, there was a lack of a way to provide users with real-time updated information, which led to frequent redelivery and reduced efficiency of logistics operations.

[0070] 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.

[0071] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the optimal transport route and scheduled delivery time of the package with high accuracy, means for collecting weather forecast data and taking it into consideration when calculating the transport route and scheduled delivery time, means for notifying the user of the calculated scheduled delivery time, and means for displaying the notification on the user terminal. This enables the notification of the scheduled delivery time with high accuracy and allows the user to take appropriate action according to the delivery time, thereby reducing the need for redelivery and improving the efficiency of logistics operations.

[0072] "Transportation vehicle" means any vehicle used to transport cargo.

[0073] "Location information" refers to geographic coordinate data of the current location of the transport vehicle, specifically, latitude and longitude data.

[0074] "Traffic conditions" refers to data about current road conditions, such as road congestion information, construction information, and traffic accident information.

[0075] "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze collected data and calculate optimal transportation routes and estimated delivery times.

[0076] "Weather Forecast Data" means information about future weather conditions in a particular geographic area, including the probability of precipitation, temperature, and wind speed.

[0077] "Estimated Delivery Time" means the time when the package is scheduled to arrive at the specified delivery address.

[0078] "User" refers to the person who receives the package.

[0079] "Notification" refers to the means by which the user is notified of estimated delivery times and updates, including email and push notifications.

[0080] "Terminal" refers to the information device used by the user, specifically including smartphones and tablets.

[0081] "Collection methods" refers to the methods or technologies used to obtain specific information.

[0082] "Means of analysis" refers to methods and techniques for performing analysis and calculations based on collected data.

[0083] "Means of notification" refers to the methods and techniques used to convey information to users.

[0084] "Display means" refers to the method or technology for displaying information on a terminal.

[0085] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0086] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. For example, if the location of delivery vehicle A is identified as latitude 35.6895 and longitude 139.6917, this information is sent to the server. The server also obtains the current traffic conditions using a traffic information API (e.g., Google Maps API or Here API). Traffic conditions include information on road congestion, construction work, and traffic accidents. For example, if there is congestion in section X, that data is obtained.

[0087] Next, the server uses a weather forecast API (e.g., OpenWeatherMap API) to collect weather forecast data. Weather forecast data contains information about future weather in a specific area. For example, if rain is forecast for a specific area, that information is retrieved.

[0088] Once this data is collected, a generative AI installed on the server analyzes it. The generative AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate highly accurate estimated delivery times, taking into account current traffic conditions and weather forecasts. For example, if delivery vehicle A takes the optimal route to point B, it will calculate an estimated arrival time of 9:30.

[0089] The calculated estimated delivery time is then notified to the user by the server. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. The device (such as the user's smartphone) receives these notifications and displays them on the screen to inform the user. For example, the notification may say, "Your package is scheduled to arrive at 9:30."

[0090] When the user receives the notification, they can either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. This reduces missed deliveries and reduces the workload on the driver due to redelivery. Furthermore, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives later than 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and re-notifies the user that "the new delivery time is 10:00." The device receives the re-notification and displays it on the screen. In this way, the user can always keep up to date with the latest delivery information.

[0091] Specific examples

[0092] For example, at 8:00 AM, based on the GPS data of delivery vehicle A, the server recognizes that "delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917)." Next, the server uses a traffic information API to obtain data that "traffic congestion is occurring in section X." It also uses a weather forecast API to confirm that "rain is forecast for this area." Based on this, the generation AI calculates that "delivery vehicle A will take the optimal route and is scheduled to arrive at point B at 9:30."

[0093] The server notifies the user by email or app, saying, "The package is expected to arrive at 9:30." The user's smartphone receives the notification and informs the user of the information. The user confirms this and prepares to wait at home until 9:30. If the logistics is delayed, the user will be notified again, "The new delivery time is 10:00."

[0094] This system will significantly reduce the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0095] Prompt Sentence Examples

[0096] "Please explain in detail the operating process of the system that generates the optimal transportation route and highly accurate scheduled delivery time based on the delivery vehicle's current location information, traffic conditions, and weather forecast."

[0097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0098] Step 1: Collect location information

[0099] The server collects real-time location information from the GPS devices installed in the delivery vehicles. As input, latitude and longitude data obtained from the GPS devices are provided. For example, if delivery vehicle A is located at latitude 35.6895 and longitude 139.6917, this information is received as input. As output, these location information data are stored on the server.

[0100] Step 2: Obtaining traffic conditions

[0101] The server obtains the current traffic conditions through the traffic information API. As input, traffic information for the target area is provided based on the API request. Specifically, this includes road congestion information, construction information, accident information, etc. For example, if there is congestion in section X, data related to this is obtained. As output, the obtained traffic information data is saved on the server.

[0102] Step 3: Get the weather forecast

[0103] The server uses a weather forecast API to collect weather forecast data. As input, the API provides information about future weather conditions for a specified region. Specifically, this includes data such as the probability of precipitation, temperature, and wind speed. For example, if rain is forecast for a specific region, this information is retrieved. As output, the collected weather forecast data is stored on the server.

[0104] Step 4: Data analysis and calculation of optimal route and estimated delivery time

[0105] The generation AI installed on the server analyzes location information, traffic conditions, and weather forecast data. The previously collected location information, traffic information, and weather forecast data are used as input. The generation AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate the optimal transportation route and highly accurate estimated delivery time. As output, the estimated arrival time at each delivery destination is saved on the server. For example, the optimal route for delivery vehicle A is calculated, and the estimated arrival time at point B is calculated as "9:30."

[0106] Step 5: Notify users

[0107] The server notifies the user of the calculated estimated delivery time. The calculated estimated delivery time and the user's contact information (email address, app ID, etc.) are used as input. Specific actions include sending an email or a push notification. Examples of outputs include notifications being sent to the user's device. For example, an email or app notification saying, "Your package is scheduled to arrive at 9:30."

[0108] Step 6: Display notifications on your device

[0109] The terminal (user's smartphone) receives the notification from the server and displays it on the screen. The input is the received notification data (estimated delivery time). The output is a message on the user's smartphone screen saying "Your package is scheduled to arrive at 9:30." Specific operations include an action to confirm the notification.

[0110] Step 7: User Action

[0111] The user checks the notification displayed on the device and responds according to the specified scheduled delivery time. The input is the notification information displayed on the smartphone. The output is the user being at home or preparing to receive the package at the specified location. Specifically, the user will wait at home or head to the package collection location at the scheduled delivery time. For example, the user will wait at home to receive the package at 9:30.

[0112] This makes it possible to notify users of the scheduled delivery time with high accuracy, allowing them to take appropriate action depending on the delivery time, reducing the need for redelivery and improving the efficiency of logistics operations.

[0113] (Application example 1)

[0114] 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."

[0115] When delivering food, it is necessary to constantly keep track of the optimal delivery route, taking into account the delivery person's current location, traffic conditions, and weather forecasts, and to notify the user of the exact scheduled delivery time.However, currently, delivery errors and delays occur frequently.In addition, there are issues such as the uncertainty of notifications and the need for redelivery, which hinder the efficiency of food delivery operations.

[0116] 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.

[0117] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, means for notifying the user of the calculated estimated delivery time, and means for improving the efficiency and accuracy of food delivery based on the estimated delivery time. This allows the user to know the accurate estimated delivery time, reduces the need for redelivery, and enables the efficiency of food delivery operations and improves user satisfaction.

[0118] A "transport vehicle" is a vehicle used to transport luggage or goods.

[0119] "Location information" is data indicating the current location of the transport vehicle, and includes coordinate data of latitude and longitude.

[0120] "Traffic conditions" refers to information that indicates the state of the roads on which the vehicle is traveling, such as information about road congestion, construction work, and traffic accidents.

[0121] "Generative AI" refers to artificial intelligence that uses machine learning algorithms to analyze data and calculate optimal results.

[0122] The "scheduled delivery time" is the time when the delivery vehicle is scheduled to arrive at the specified location.

[0123] "User" refers to the person or business receiving the package or food delivery.

[0124] "Food delivery" is a service that delivers food and drinks to a location specified by the customer.

[0125] "Efficiency" refers to achieving goals with less effort and time, and means improving business productivity.

[0126] "Accuracy" refers to the degree to which the results of a prediction or calculation match the actual situation.

[0127] The system for implementing this invention collects location information, traffic conditions, and weather forecast data for each transport vehicle, and based on this, a generation AI calculates the optimal transport route and estimated delivery time with high accuracy. This system consists of three main components: a server, a terminal, and a user.

[0128] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. In addition, the server obtains current traffic conditions via a traffic information API. Traffic conditions include information on road congestion, construction, and traffic accidents. Next, the server collects weather forecast data using a weather forecast API. The weather forecast data includes information on future weather in a specific area.

[0129] Once this data is collected, a generation AI installed on the server analyzes the data and calculates the optimal transportation route for each delivery vehicle. The generation AI uses a machine learning algorithm to calculate a highly accurate estimated delivery time, taking into account current traffic conditions and weather forecasts. The calculated estimated delivery time is then notified to the user from the server. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time. Upon receiving the notification, the user either waits at home at the specified scheduled delivery time or prepares to receive the package at the specified location.

[0130] As a specific example, the server obtains GPS data for a delivery vehicle at 8:00 AM. According to this data, the delivery vehicle is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is predicted. Based on this data, the generation AI performs analysis and calculates the optimal route for the delivery vehicle and the estimated arrival time for each delivery destination.

[0131] For example, the delivery location is calculated as "9:30 AM expected arrival." The server sends an email to the user at location B saying, "Your package is expected to arrive at 9:30 AM," and also sends a similar notification via a dedicated app. The device (the user's smartphone) receives this notification and displays it on its screen. The user checks the notification and waits at home until 9:30 AM. Also, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00 AM, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user that "The new delivery time is 10:00 AM."

[0132] Example prompt sentence:

[0133] "Current location: Latitude 35.6895, Longitude 139.6917. Destination: Shibuya, Tokyo. Current traffic conditions: Traffic jams, construction information. Weather forecast: Rain. Please calculate the optimal route and delivery time."

[0134] This allows users to know the exact delivery time and receive their parcels or food deliveries efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on drivers.

[0135] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0136] Step 1:

[0137] The server receives real-time location information from the GPS devices installed in the delivery vehicles. The input of this processing step is the latitude and longitude coordinate data sent from the GPS devices of the delivery vehicles, and the output is the current location information of the delivery vehicles stored on the server. Specifically, the server periodically requests location information from each vehicle and stores the received data in a database.

[0138] Step 2:

[0139] The server obtains the current traffic conditions via the traffic information API. The input to this processing step is the location information specified by the server, and the output is traffic condition data for that location (traffic congestion information, construction information, traffic accident information, etc.). Specifically, the server sends an HTTP request to each API, analyzes the traffic data returned as a response, and saves it.

[0140] Step 3:

[0141] The server uses weather forecast APIs to collect weather forecast data. The input for this processing step is location information along the delivery route, and the output is weather forecast data for that area. Specifically, the server sends an HTTP request to each weather forecast API, analyzes the retrieved weather forecast data, and saves it.

[0142] Step 4:

[0143] The generative AI model installed on the server analyzes location information, traffic conditions, and weather forecast data to calculate the optimal transport route and estimated delivery time. The inputs to this processing step are location information, traffic conditions data, and weather forecast data, and the output is the optimal transport route and estimated delivery time. Specifically, the generative AI inputs this data in the form of a prompt statement, and the model performs the calculations and returns the optimal result.

[0144] Step 5:

[0145] The server notifies the user of the calculated estimated delivery time. The input to this processing step is the estimated delivery time and the user's contact information, and the output is a notification message to the user. Specifically, the server sends an email to the specified email address or a push notification via the smartphone app.

[0146] Step 6:

[0147] The terminal displays the received notification on its screen and notifies the user of the estimated delivery time. The input to this processing step is the notification message sent from the server, and the output is the estimated delivery time displayed on the terminal screen. Specifically, the terminal receives the notification in the background and displays it on the user interface.

[0148] Step 7:

[0149] When the user receives the notification, they will either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. The input to this processing step is the scheduled delivery time displayed on the terminal, and the output is the user's action (being at home or preparing to receive the package at the specified location). Specifically, the user checks the content of the notification and takes action according to the situation.

[0150] The above are the specific processing steps of the system.

[0151] 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.

[0152] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0153] A system for implementing the present invention operates as follows.

[0154] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. Next, the server uses a traffic information API to collect current traffic conditions, including information on road congestion, construction, and traffic accidents. Finally, the server collects weather forecast data from a weather forecast API to obtain information on future weather in a specific area.

[0155] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0156] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0157] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0158] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0159] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it may obtain a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "estimated arrival time" at delivery destination B as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!"

[0160] The device (user's smartphone) receives this notification and displays it on the screen. The user checks the notification and waits at home until 9:30. If the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0161] This system will virtually eliminate the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. In addition, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0162] The processing flow will be explained below.

[0163] Step 1:

[0164] The server collects real-time location information from GPS devices installed in delivery vehicles, including the vehicle's current latitude and longitude, periodically retrieved through a REST API.

[0165] Step 2:

[0166] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, traffic accident information, etc. For example, it obtains data using the Google Maps API.

[0167] Step 3:

[0168] The server uses a weather API to collect weather forecast data, which contains information about future weather in a particular region.

[0169] Step 4:

[0170] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0171] Step 5:

[0172] The server receives the estimated delivery time calculated by the generation AI and uses an emotion engine to analyze the user's emotional data, which is collected based on the user's past reactions and current situation.

[0173] Step 6:

[0174] The server notifies the user of the estimated delivery time, including a message generated by the emotion engine, either by sending an email to the email address registered by the user or by sending a push notification via a dedicated smartphone app.

[0175] Step 7:

[0176] The device (user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0177] Step 8:

[0178] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0179] Step 9:

[0180] The server then notifies the user of the recalculated estimated delivery time by generating an appropriate message using the emotion engine and sending the notification via email or a dedicated app.

[0181] Step 10:

[0182] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0183] Step 11:

[0184] After the delivery is completed, the server collects user sentiment data and evaluates the user's satisfaction with the delivery experience, which can be used to improve the service in the future.

[0185] Through these efforts, this system eliminates the need for redelivery, improves logistics efficiency, and reduces the burden on truck drivers. Furthermore, the introduction of an emotion engine improves user satisfaction and enables the provision of personalized services.

[0186] Example 2

[0187] 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."

[0188] In conventional delivery systems, it is common to calculate the estimated delivery time by taking into account only the location information of the delivery vehicle and traffic conditions, and notify the user of the estimated delivery time. However, this method does not fully consider the risk of delivery delays due to sudden changes in traffic conditions or weather changes, making it difficult to notify the user of the accurate estimated delivery time. In addition, users only receive notifications of the delivery time, which can lead to frustration and stress about the delivery.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0190] In this invention, the server includes means for collecting location information of each transport vehicle, means for collecting traffic condition data, means for collecting weather forecast data, means for analyzing the collected location information, traffic condition data, and weather forecast data and calculating an estimated delivery time of the package using a generation AI, means for predicting a user's current emotion based on the user's past emotion data and generating a message corresponding to the emotion, and means for notifying the user of the calculated estimated delivery time and a message corresponding to the emotion. This makes it possible to reduce stress associated with delivery and improve user satisfaction by notifying the user of a more accurate and personalized estimated delivery time and sending a message corresponding to the user's emotion.

[0191] "Transportation vehicle" means any vehicle used to transport cargo or passengers to a particular destination.

[0192] "Location information" refers to data indicating the current latitude and longitude of a transport vehicle.

[0193] "Traffic condition data" refers to all information that affects traffic flow, such as road congestion, construction information, and accident information.

[0194] "Weather forecast data" refers to information that predicts future weather conditions in a particular region.

[0195] "Generative AI" refers to systems that use artificial intelligence algorithms to analyze data and perform calculations to solve specific problems.

[0196] "Estimated Delivery Time" means the time when a package is expected to arrive at the specified delivery destination.

[0197] "User" refers to the person or organization receiving the delivery.

[0198] "Emotional data" refers to information about a user's past reactions and current emotional state.

[0199] An "emotion-appropriate message" refers to a notification message that is composed of content that matches the user's emotional state.

[0200] "Notification means" refers to a method or tool for conveying a calculated estimated delivery time or a message corresponding to the user's emotion.

[0201] The system of the present invention operates primarily in cooperation between a server, a terminal, and a user. The server uses a GPS device installed in the delivery vehicle, a traffic information API, a weather forecast API, and a generation AI engine. The terminal primarily refers to the user's smartphone or PC, and the user is the person or organization that receives the package.

[0202] First, the server collects real-time location information of each delivery vehicle through the GPS device installed in the delivery vehicle. This location information includes the vehicle's latitude and longitude and is periodically obtained using a REST API. Next, the server collects current traffic condition data using a traffic information API. This data includes information on road congestion, construction, and traffic accidents. The server also collects weather forecast data from a weather forecast API to obtain future weather information for a specific area.

[0203] Based on the collected location information, traffic data, and weather forecast data, the generation AI installed on the server analyzes the data. The generation AI uses machine learning algorithms to calculate the optimal transportation route and highly accurate estimated delivery time for each package. For example, it determines that delivery vehicle A's location is latitude 35.6895, longitude 139.6917, and based on this data, calculates that it will arrive at delivery destination B at 9:30.

[0204] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. For example, a message might say, "Your package is scheduled to arrive at 9:30. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen.

[0205] The user checks the notification and prepares to be home by the specified scheduled delivery time. The server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The new scheduled delivery time is notified again, and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is generated. This message is notified at an appropriate time using the emotion engine, taking into account the user's current situation.

[0206] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0207] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). The server then obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. Rain is forecast for the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, the estimated arrival time for delivery destination B is calculated as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Additionally, if the delivery vehicle is delayed due to traffic congestion or other factors, for example if it arrives later than 10:00, the server will re-analyze the data, calculate a new estimated delivery time, and re-notify the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0208] Examples of prompts include:

[0209] "Based on the current location of delivery vehicle A, check the traffic situation at coordinates (latitude 35.6895, longitude 139.6917). If rain is forecast at 8:20, calculate the optimal delivery route and estimated delivery time. Also, if the user's emotion is positive, notify them with a message that corresponds to that emotion."

[0210] This system is expected to improve delivery efficiency and accuracy, as well as increase user satisfaction.

[0211] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0212] Step 1:

[0213] GPS data collection

[0214] The server obtains real-time location information from the GPS devices installed in the delivery vehicles. The input is latitude and longitude data sent from the delivery vehicles. For example, the location information for delivery vehicle A is received as latitude 35.6895 and longitude 139.6917. This data is periodically retrieved via a REST API. The output is real-time location data that is stored on the server side.

[0215] Step 2:

[0216] Traffic and weather data collection

[0217] The server uses a traffic information API to collect current traffic condition data. This data includes road congestion information, construction information, traffic accident information, etc. For example, it sends the API request "https: / / api.traffic.com / status?location=35.6895,139.6917". The input is the latitude and longitude of the target. The output is traffic condition data. Similarly, the server uses a weather forecast API to collect future weather forecast data. For example, it requests "https: / / api.weather.com / forecast?location=35.6895,139.6917", and the output is stored as weather forecast data.

[0218] Step 3:

[0219] Analyzing data and calculating estimated delivery times

[0220] The server uses a generation AI to analyze the collected location information, traffic data, and weather forecast data. The generation AI receives this data as input and uses a machine learning algorithm to calculate the optimal delivery route and estimated delivery time. For example, it calculates "9:30 a.m." as the estimated arrival time for destination B. The output is estimated delivery time data for each destination.

[0221] Step 4:

[0222] Notification of estimated delivery time

[0223] The server notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message according to that emotion. The inputs are the estimated delivery time and the user's past reaction data. The output is a notification message generated according to the emotion. For example, a message such as "Your package is scheduled to arrive at 9:30. Please look forward to it!" is sent via a smartphone app or email.

[0224] Step 5:

[0225] Timing adjustment and re-notification

[0226] The server monitors the location information of delivery vehicles in real time, and if a major delay or route change occurs, it reanalyzes and calculates a new estimated delivery time. The input is the latest collected location information and traffic condition data. The generative AI is used to recalculate the optimal estimated delivery time. The output is a new estimated delivery time data and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is re-announced.

[0227] Step 6:

[0228] User satisfaction rating after delivery completion

[0229] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction based on that data. The input is the user's emotional data at the time of delivery completion. The output is a user satisfaction rating. This data will be used to improve the service in the future and to refine the notification mechanism.

[0230] (Application example 2)

[0231] 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."

[0232] In modern delivery systems, the accuracy of estimated delivery times is insufficient, leading to stressful waiting times for users. Furthermore, insufficient re-notifications due to changes in delivery status often result in low user satisfaction. Furthermore, delivery notifications are uniform and do not take into account individual user feelings, potentially further reducing the user experience. Therefore, there is a need for more accurate calculations of estimated delivery times and for appropriate notifications that reflect the user's feelings.

[0233] 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.

[0234] In this invention, the server includes a means for collecting location information and traffic conditions for each delivery vehicle, a means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, a means for notifying the user of the calculated estimated delivery time, a means for recognizing the user's emotions at the time of notification and generating a personalized message according to the emotions, and a means for evaluating the user's satisfaction after the delivery is completed. This allows for highly accurate calculation of the estimated delivery time based on real-time information on delivery vehicles, traffic conditions, and weather forecasts, and for appropriate notifications according to the user's emotions. This reduces user stress and provides a high level of satisfaction. It also enables appropriate follow-up through re-notification, which reduces redeliveries and improves the efficiency of logistics operations.

[0235] "Transport vehicle location information" is data indicating the current location of a delivery vehicle, and is typically obtained by a GPS device.

[0236] "Traffic conditions" refers to information that indicates current road conditions, such as road congestion, construction information, and accident information.

[0237] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze various data and generate specific outputs.

[0238] The "estimated delivery time" is the time when the package is expected to be delivered to the user.

[0239] The "means of notifying the user" refers to a method for notifying the user of the estimated delivery time and update information, such as push notification or email.

[0240] The "emotion engine" is an engine that predicts a user's current emotions based on their past reaction data and generates a message that corresponds to that emotion.

[0241] "User satisfaction" is a measure of how well a service meets users' expectations and requirements.

[0242] "Weather forecast data" is data that indicates information about future weather in a specific area.

[0243] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0244] The server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. In addition, the server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. The server also collects weather forecast data from a weather forecast API to obtain information about future weather in a specific area.

[0245] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0246] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0247] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0248] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0249] Specific examples

[0250] The server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it obtains a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "9:30 AM arrival" at delivery destination B, and the emotion engine recognizes the user's emotion and generates a message such as "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Furthermore, if the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and sends a message like, "The new delivery time is 10:00. Thank you for your understanding!" This system virtually eliminates the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. Furthermore, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0251] An example of a prompt is as follows:

[0252] "Create an app that notifies users of food delivery times with high accuracy and changes the message content depending on the user's emotions. The goal is to reduce stress and increase satisfaction by adjusting the timing and content of notifications based on the user's emotions. Required APIs include GPS data, traffic information, and weather forecast data, and an emotion engine should also be used."

[0253] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0254] Step 1:

[0255] The server receives real-time location information (latitude and longitude) from the GPS devices installed in the delivery vehicles, which are periodically retrieved through a REST API. The input is the ID of the delivery vehicle, and the output is the current latitude and longitude of the delivery vehicle.

[0256] Step 2:

[0257] The server uses the traffic information API to collect traffic conditions such as current road congestion information, construction information, traffic accident information, etc. The input is the API key and location information, and the output is the collected traffic condition data.

[0258] Step 3:

[0259] The server collects weather forecast data from a weather forecast API and obtains future weather information for a specific region. The input is an API key and location information, and the output is future weather forecast data.

[0260] Step 4:

[0261] The server uses AI to analyze the collected location information, traffic conditions, and weather forecast data. This analysis allows it to accurately calculate the optimal transport route and estimated delivery time for each package. The input is the collected data (location information, traffic conditions, weather forecast), and the output is the estimated delivery time and route information.

[0262] Step 5:

[0263] The server notifies the user of the calculated estimated delivery time. At this time, it uses an emotion engine to infer the user's current emotion from past reaction data and generates a message according to that emotion. The input is the calculated estimated delivery time and the user's past reaction data, and the output is an appropriate notification message.

[0264] Step 6:

[0265] The device (user's smartphone) receives the notification and displays it on the screen. The input is the notification message sent from the server, and the output is the notification message displayed on the device screen.

[0266] Step 7:

[0267] The user checks the notification and prepares to be at home at the specified scheduled delivery time. The input is the notification message displayed on the terminal, and the output is the user's behavior.

[0268] Step 8:

[0269] The server monitors the location information of delivery vehicles in real time, and if there is a major delay or route change, it reanalyzes and calculates a new estimated delivery time. The input is the latest location information and traffic condition data, and the output is the recalculated estimated delivery time.

[0270] Step 9:

[0271] The server notifies the user of the recalculated estimated delivery time and sends a re-notification at an appropriate time using the emotion engine. The input is the recalculated estimated delivery time and the user's emotion data, and the output is an appropriate re-notification message.

[0272] Step 10:

[0273] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction. The input is the user's reaction data after the delivery is completed, and the output is the evaluation of the user's satisfaction. This will be useful for future service improvements and refinement of the notification mechanism.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] [Second embodiment]

[0278] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0279] 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.

[0280] 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).

[0281] 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.

[0282] 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.

[0283] 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).

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0289] 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."

[0290] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0291] A system for implementing the present invention operates as follows.

[0292] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the vehicle's current latitude and longitude. The server then obtains current traffic conditions via a traffic information API. Traffic conditions include road congestion, construction, and traffic accident information.

[0293] The server then uses a weather API to collect weather forecast data, which contains information about future weather in a particular location.

[0294] Once this data is collected, a server-based AI analyzes it and calculates the optimal route for each vehicle. The AI ​​uses machine learning algorithms to calculate highly accurate delivery times, taking into account current traffic conditions and weather forecasts.

[0295] The server then notifies the user of the calculated estimated delivery time. This notification can be sent by email to the email address registered by the user or by push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time.

[0296] When users receive the notification, they can either be at home at the scheduled delivery time or be ready to receive the package at the specified location, which reduces the number of missed deliveries and reduces the workload of drivers due to redelivery.

[0297] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. According to this data, delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is forecast.

[0298] Based on this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates that delivery vehicle A will arrive at destination B at 9:30. The server then sends an email to the user at destination B saying, "Your package is expected to arrive at 9:30," and also sends a similar notification via a dedicated app.

[0299] The device (user's smartphone) receives this notification and displays it on the screen. The user confirms the notification and waits at home until 9:30. If the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and notifies the user again that "the new delivery time is 10:00."

[0300] This allows users to know the exact delivery time and receive their packages efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0301] The processing flow will be explained below.

[0302] Step 1:

[0303] The server collects real-time location information from the GPS devices installed in the delivery vehicles, which is the current latitude and longitude of the delivery vehicles and is periodically retrieved through a REST API.

[0304] Step 2:

[0305] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. For example, it can obtain this information using the Google Maps API.

[0306] Step 3:

[0307] The server collects weather forecast data for the target area from the weather forecast API, which includes weather information for specific time periods.

[0308] Step 4:

[0309] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to calculate the optimal shipping route and estimated delivery time for each package.

[0310] Step 5:

[0311] The server obtains the estimated delivery time calculated by the generation AI and sends a notification to the user's registered email address or a dedicated app based on that time. The notification includes information about the estimated delivery time and delivery destination.

[0312] Step 6:

[0313] The device (such as the user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0314] Step 7:

[0315] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0316] Step 8:

[0317] The server will then notify the user again of the reanalyzed estimated delivery time via email or a dedicated app, just like the first time.

[0318] Step 9:

[0319] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0320] Step 10:

[0321] After the delivery is completed, the server stops monitoring the location information of the delivery vehicle and starts analyzing a new route based on the data of the next delivery destination.

[0322] Example 1

[0323] 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."

[0324] In conventional delivery systems, it was difficult to notify users of the exact scheduled delivery time, taking into account the location information of the transport vehicle and traffic conditions. Furthermore, even when the scheduled delivery time was delayed due to weather changes or traffic congestion, there was a lack of a way to provide users with real-time updated information, which led to frequent redelivery and reduced efficiency of logistics operations.

[0325] 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.

[0326] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the optimal transport route and scheduled delivery time of the package with high accuracy, means for collecting weather forecast data and taking it into consideration when calculating the transport route and scheduled delivery time, means for notifying the user of the calculated scheduled delivery time, and means for displaying the notification on the user terminal. This enables the notification of the scheduled delivery time with high accuracy and allows the user to take appropriate action according to the delivery time, thereby reducing the need for redelivery and improving the efficiency of logistics operations.

[0327] "Transportation vehicle" means any vehicle used to transport cargo.

[0328] "Location information" refers to geographic coordinate data of the current location of the transport vehicle, specifically, latitude and longitude data.

[0329] "Traffic conditions" refers to data about current road conditions, such as road congestion information, construction information, and traffic accident information.

[0330] "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze collected data and calculate optimal transportation routes and estimated delivery times.

[0331] "Weather Forecast Data" means information about future weather conditions in a particular geographic area, including the probability of precipitation, temperature, and wind speed.

[0332] "Estimated Delivery Time" means the time when the package is scheduled to arrive at the specified delivery address.

[0333] "User" refers to the person who receives the package.

[0334] "Notification" refers to the means by which the user is notified of estimated delivery times and updates, including email and push notifications.

[0335] "Terminal" refers to the information device used by the user, specifically including smartphones and tablets.

[0336] "Collection methods" refers to the methods or technologies used to obtain specific information.

[0337] "Means of analysis" refers to methods and techniques for performing analysis and calculations based on collected data.

[0338] "Means of notification" refers to the methods and techniques used to convey information to users.

[0339] "Display means" refers to the method or technology for displaying information on a terminal.

[0340] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0341] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. For example, if the location of delivery vehicle A is identified as latitude 35.6895 and longitude 139.6917, this information is sent to the server. The server also obtains the current traffic conditions using a traffic information API (e.g., Google Maps API or Here API). Traffic conditions include information on road congestion, construction work, and traffic accidents. For example, if there is congestion in section X, that data is obtained.

[0342] Next, the server uses a weather forecast API (e.g., OpenWeatherMap API) to collect weather forecast data. Weather forecast data contains information about future weather in a specific area. For example, if rain is forecast for a specific area, that information is retrieved.

[0343] Once this data is collected, a generative AI installed on the server analyzes it. The generative AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate highly accurate estimated delivery times, taking into account current traffic conditions and weather forecasts. For example, if delivery vehicle A takes the optimal route to point B, it will calculate an estimated arrival time of 9:30.

[0344] The calculated estimated delivery time is then notified to the user by the server. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. The device (such as the user's smartphone) receives these notifications and displays them on the screen to inform the user. For example, the notification may say, "Your package is scheduled to arrive at 9:30."

[0345] When the user receives the notification, they can either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. This reduces missed deliveries and reduces the workload on the driver due to redelivery. Furthermore, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives later than 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and re-notifies the user that "the new delivery time is 10:00." The device receives the re-notification and displays it on the screen. In this way, the user can always keep up to date with the latest delivery information.

[0346] Specific examples

[0347] For example, at 8:00 AM, based on the GPS data of delivery vehicle A, the server recognizes that "delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917)." Next, the server uses a traffic information API to obtain data that "traffic congestion is occurring in section X." It also uses a weather forecast API to confirm that "rain is forecast for this area." Based on this, the generation AI calculates that "delivery vehicle A will take the optimal route and is scheduled to arrive at point B at 9:30."

[0348] The server notifies the user by email or app, saying, "The package is expected to arrive at 9:30." The user's smartphone receives the notification and informs the user of the information. The user confirms this and prepares to wait at home until 9:30. If the logistics is delayed, the user will be notified again, "The new delivery time is 10:00."

[0349] This system will significantly reduce the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0350] Prompt Sentence Examples

[0351] "Please explain in detail the operating process of the system that generates the optimal transportation route and highly accurate scheduled delivery time based on the delivery vehicle's current location information, traffic conditions, and weather forecast."

[0352] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0353] Step 1: Collect location information

[0354] The server collects real-time location information from the GPS devices installed in the delivery vehicles. As input, latitude and longitude data obtained from the GPS devices are provided. For example, if delivery vehicle A is located at latitude 35.6895 and longitude 139.6917, this information is received as input. As output, these location information data are stored on the server.

[0355] Step 2: Obtaining traffic conditions

[0356] The server obtains the current traffic conditions through the traffic information API. As input, traffic information for the target area is provided based on the API request. Specifically, this includes road congestion information, construction information, accident information, etc. For example, if there is congestion in section X, data related to this is obtained. As output, the obtained traffic information data is saved on the server.

[0357] Step 3: Get the weather forecast

[0358] The server uses a weather forecast API to collect weather forecast data. As input, the API provides information about future weather conditions for a specified region. Specifically, this includes data such as the probability of precipitation, temperature, and wind speed. For example, if rain is forecast for a specific region, this information is retrieved. As output, the collected weather forecast data is stored on the server.

[0359] Step 4: Data analysis and calculation of optimal route and estimated delivery time

[0360] The generation AI installed on the server analyzes location information, traffic conditions, and weather forecast data. The previously collected location information, traffic information, and weather forecast data are used as input. The generation AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate the optimal transportation route and highly accurate estimated delivery time. As output, the estimated arrival time at each delivery destination is saved on the server. For example, the optimal route for delivery vehicle A is calculated, and the estimated arrival time at point B is calculated as "9:30."

[0361] Step 5: Notify users

[0362] The server notifies the user of the calculated estimated delivery time. The calculated estimated delivery time and the user's contact information (email address, app ID, etc.) are used as input. Specific actions include sending an email or a push notification. Examples of outputs include notifications being sent to the user's device. For example, an email or app notification saying, "Your package is scheduled to arrive at 9:30."

[0363] Step 6: Display notifications on your device

[0364] The terminal (user's smartphone) receives the notification from the server and displays it on the screen. The input is the received notification data (estimated delivery time). The output is a message on the user's smartphone screen saying "Your package is scheduled to arrive at 9:30." Specific operations include an action to confirm the notification.

[0365] Step 7: User Action

[0366] The user checks the notification displayed on the device and responds according to the specified scheduled delivery time. The input is the notification information displayed on the smartphone. The output is the user being at home or preparing to receive the package at the specified location. Specifically, the user will wait at home or head to the package collection location at the scheduled delivery time. For example, the user will wait at home to receive the package at 9:30.

[0367] This makes it possible to notify users of the scheduled delivery time with high accuracy, allowing them to take appropriate action depending on the delivery time, reducing the need for redelivery and improving the efficiency of logistics operations.

[0368] (Application example 1)

[0369] 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."

[0370] When delivering food, it is necessary to constantly keep track of the optimal delivery route, taking into account the delivery person's current location, traffic conditions, and weather forecasts, and to notify the user of the exact scheduled delivery time.However, currently, delivery errors and delays occur frequently.In addition, there are issues such as the uncertainty of notifications and the need for redelivery, which hinder the efficiency of food delivery operations.

[0371] 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.

[0372] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, means for notifying the user of the calculated estimated delivery time, and means for improving the efficiency and accuracy of food delivery based on the estimated delivery time. This allows the user to know the accurate estimated delivery time, reduces the need for redelivery, and enables the efficiency of food delivery operations and improves user satisfaction.

[0373] A "transport vehicle" is a vehicle used to transport luggage or goods.

[0374] "Location information" is data indicating the current location of the transport vehicle, and includes coordinate data of latitude and longitude.

[0375] "Traffic conditions" refers to information that indicates the state of the roads on which the vehicle is traveling, such as information about road congestion, construction work, and traffic accidents.

[0376] "Generative AI" refers to artificial intelligence that uses machine learning algorithms to analyze data and calculate optimal results.

[0377] The "scheduled delivery time" is the time when the delivery vehicle is scheduled to arrive at the specified location.

[0378] "User" refers to the person or business receiving the package or food delivery.

[0379] "Food delivery" is a service that delivers food and drinks to a location specified by the customer.

[0380] "Efficiency" refers to achieving goals with less effort and time, and means improving business productivity.

[0381] "Accuracy" refers to the degree to which the results of a prediction or calculation match the actual situation.

[0382] The system for implementing this invention collects location information, traffic conditions, and weather forecast data for each transport vehicle, and based on this, a generation AI calculates the optimal transport route and estimated delivery time with high accuracy. This system consists of three main components: a server, a terminal, and a user.

[0383] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. In addition, the server obtains current traffic conditions via a traffic information API. Traffic conditions include information on road congestion, construction, and traffic accidents. Next, the server collects weather forecast data using a weather forecast API. The weather forecast data includes information on future weather in a specific area.

[0384] Once this data is collected, a generation AI installed on the server analyzes the data and calculates the optimal transportation route for each delivery vehicle. The generation AI uses a machine learning algorithm to calculate a highly accurate estimated delivery time, taking into account current traffic conditions and weather forecasts. The calculated estimated delivery time is then notified to the user from the server. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time. Upon receiving the notification, the user either waits at home at the specified scheduled delivery time or prepares to receive the package at the specified location.

[0385] As a specific example, the server obtains GPS data for a delivery vehicle at 8:00 AM. According to this data, the delivery vehicle is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is predicted. Based on this data, the generation AI performs analysis and calculates the optimal route for the delivery vehicle and the estimated arrival time for each delivery destination.

[0386] For example, the delivery location is calculated as "9:30 AM expected arrival." The server sends an email to the user at location B saying, "Your package is expected to arrive at 9:30 AM," and also sends a similar notification via a dedicated app. The device (the user's smartphone) receives this notification and displays it on its screen. The user checks the notification and waits at home until 9:30 AM. Also, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00 AM, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user that "The new delivery time is 10:00 AM."

[0387] Example prompt sentence:

[0388] "Current location: Latitude 35.6895, Longitude 139.6917. Destination: Shibuya, Tokyo. Current traffic conditions: Traffic jams, construction information. Weather forecast: Rain. Please calculate the optimal route and delivery time."

[0389] This allows users to know the exact delivery time and receive their parcels or food deliveries efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on drivers.

[0390] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0391] Step 1:

[0392] The server receives real-time location information from the GPS devices installed in the delivery vehicles. The input of this processing step is the latitude and longitude coordinate data sent from the GPS devices of the delivery vehicles, and the output is the current location information of the delivery vehicles stored on the server. Specifically, the server periodically requests location information from each vehicle and stores the received data in a database.

[0393] Step 2:

[0394] The server obtains the current traffic conditions via the traffic information API. The input to this processing step is the location information specified by the server, and the output is traffic condition data for that location (traffic congestion information, construction information, traffic accident information, etc.). Specifically, the server sends an HTTP request to each API, analyzes the traffic data returned as a response, and saves it.

[0395] Step 3:

[0396] The server uses weather forecast APIs to collect weather forecast data. The input for this processing step is location information along the delivery route, and the output is weather forecast data for that area. Specifically, the server sends an HTTP request to each weather forecast API, analyzes the retrieved weather forecast data, and saves it.

[0397] Step 4:

[0398] The generative AI model installed on the server analyzes location information, traffic conditions, and weather forecast data to calculate the optimal transport route and estimated delivery time. The inputs to this processing step are location information, traffic conditions data, and weather forecast data, and the output is the optimal transport route and estimated delivery time. Specifically, the generative AI inputs this data in the form of a prompt statement, and the model performs the calculations and returns the optimal result.

[0399] Step 5:

[0400] The server notifies the user of the calculated estimated delivery time. The input to this processing step is the estimated delivery time and the user's contact information, and the output is a notification message to the user. Specifically, the server sends an email to the specified email address or a push notification via the smartphone app.

[0401] Step 6:

[0402] The terminal displays the received notification on its screen and notifies the user of the estimated delivery time. The input to this processing step is the notification message sent from the server, and the output is the estimated delivery time displayed on the terminal screen. Specifically, the terminal receives the notification in the background and displays it on the user interface.

[0403] Step 7:

[0404] When the user receives the notification, they will either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. The input to this processing step is the scheduled delivery time displayed on the terminal, and the output is the user's action (being at home or preparing to receive the package at the specified location). Specifically, the user checks the content of the notification and takes action according to the situation.

[0405] The above are the specific processing steps of the system.

[0406] 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.

[0407] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0408] A system for implementing the present invention operates as follows.

[0409] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. Next, the server uses a traffic information API to collect current traffic conditions, including information on road congestion, construction, and traffic accidents. Finally, the server collects weather forecast data from a weather forecast API to obtain information on future weather in a specific area.

[0410] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0411] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0412] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0413] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0414] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it may obtain a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "estimated arrival time" at delivery destination B as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!"

[0415] The device (user's smartphone) receives this notification and displays it on the screen. The user checks the notification and waits at home until 9:30. If the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0416] This system will virtually eliminate the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. In addition, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0417] The processing flow will be explained below.

[0418] Step 1:

[0419] The server collects real-time location information from GPS devices installed in delivery vehicles, including the vehicle's current latitude and longitude, periodically retrieved through a REST API.

[0420] Step 2:

[0421] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, traffic accident information, etc. For example, it obtains data using the Google Maps API.

[0422] Step 3:

[0423] The server uses a weather API to collect weather forecast data, which contains information about future weather in a particular region.

[0424] Step 4:

[0425] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0426] Step 5:

[0427] The server receives the estimated delivery time calculated by the generation AI and uses an emotion engine to analyze the user's emotional data, which is collected based on the user's past reactions and current situation.

[0428] Step 6:

[0429] The server notifies the user of the estimated delivery time, including a message generated by the emotion engine, either by sending an email to the email address registered by the user or by sending a push notification via a dedicated smartphone app.

[0430] Step 7:

[0431] The device (user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0432] Step 8:

[0433] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0434] Step 9:

[0435] The server then notifies the user of the recalculated estimated delivery time by generating an appropriate message using the emotion engine and sending the notification via email or a dedicated app.

[0436] Step 10:

[0437] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0438] Step 11:

[0439] After the delivery is completed, the server collects user sentiment data and evaluates the user's satisfaction with the delivery experience, which can be used to improve the service in the future.

[0440] Through these efforts, this system eliminates the need for redelivery, improves logistics efficiency, and reduces the burden on truck drivers. Furthermore, the introduction of an emotion engine improves user satisfaction and enables the provision of personalized services.

[0441] Example 2

[0442] 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."

[0443] In conventional delivery systems, it is common to calculate the estimated delivery time by taking into account only the location information of the delivery vehicle and traffic conditions, and notify the user of the estimated delivery time. However, this method does not fully consider the risk of delivery delays due to sudden changes in traffic conditions or weather changes, making it difficult to notify the user of the accurate estimated delivery time. In addition, users only receive notifications of the delivery time, which can lead to frustration and stress about the delivery.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0445] In this invention, the server includes means for collecting location information of each transport vehicle, means for collecting traffic condition data, means for collecting weather forecast data, means for analyzing the collected location information, traffic condition data, and weather forecast data and calculating an estimated delivery time of the package using a generation AI, means for predicting a user's current emotion based on the user's past emotion data and generating a message corresponding to the emotion, and means for notifying the user of the calculated estimated delivery time and a message corresponding to the emotion. This makes it possible to reduce stress associated with delivery and improve user satisfaction by notifying the user of a more accurate and personalized estimated delivery time and sending a message corresponding to the user's emotion.

[0446] "Transportation vehicle" means any vehicle used to transport cargo or passengers to a particular destination.

[0447] "Location information" refers to data indicating the current latitude and longitude of a transport vehicle.

[0448] "Traffic condition data" refers to all information that affects traffic flow, such as road congestion, construction information, and accident information.

[0449] "Weather forecast data" refers to information that predicts future weather conditions in a particular region.

[0450] "Generative AI" refers to systems that use artificial intelligence algorithms to analyze data and perform calculations to solve specific problems.

[0451] "Estimated Delivery Time" means the time when a package is expected to arrive at the specified delivery destination.

[0452] "User" refers to the person or organization receiving the delivery.

[0453] "Emotional data" refers to information about a user's past reactions and current emotional state.

[0454] An "emotion-appropriate message" refers to a notification message that is composed of content that matches the user's emotional state.

[0455] "Notification means" refers to a method or tool for conveying a calculated estimated delivery time or a message corresponding to the user's emotion.

[0456] The system of the present invention operates primarily in cooperation between a server, a terminal, and a user. The server uses a GPS device installed in the delivery vehicle, a traffic information API, a weather forecast API, and a generation AI engine. The terminal primarily refers to the user's smartphone or PC, and the user is the person or organization that receives the package.

[0457] First, the server collects real-time location information of each delivery vehicle through the GPS device installed in the delivery vehicle. This location information includes the vehicle's latitude and longitude and is periodically obtained using a REST API. Next, the server collects current traffic condition data using a traffic information API. This data includes information on road congestion, construction, and traffic accidents. The server also collects weather forecast data from a weather forecast API to obtain future weather information for a specific area.

[0458] Based on the collected location information, traffic data, and weather forecast data, the generation AI installed on the server analyzes the data. The generation AI uses machine learning algorithms to calculate the optimal transportation route and highly accurate estimated delivery time for each package. For example, it determines that delivery vehicle A's location is latitude 35.6895, longitude 139.6917, and based on this data, calculates that it will arrive at delivery destination B at 9:30.

[0459] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. For example, a message might say, "Your package is scheduled to arrive at 9:30. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen.

[0460] The user checks the notification and prepares to be home by the specified scheduled delivery time. The server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The new scheduled delivery time is notified again, and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is generated. This message is notified at an appropriate time using the emotion engine, taking into account the user's current situation.

[0461] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0462] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). The server then obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. Rain is forecast for the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, the estimated arrival time for delivery destination B is calculated as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Additionally, if the delivery vehicle is delayed due to traffic congestion or other factors, for example if it arrives later than 10:00, the server will re-analyze the data, calculate a new estimated delivery time, and re-notify the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0463] Examples of prompts include:

[0464] "Based on the current location of delivery vehicle A, check the traffic situation at coordinates (latitude 35.6895, longitude 139.6917). If rain is forecast at 8:20, calculate the optimal delivery route and estimated delivery time. Also, if the user's emotion is positive, notify them with a message that corresponds to that emotion."

[0465] This system is expected to improve delivery efficiency and accuracy, as well as increase user satisfaction.

[0466] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0467] Step 1:

[0468] GPS data collection

[0469] The server obtains real-time location information from the GPS devices installed in the delivery vehicles. The input is latitude and longitude data sent from the delivery vehicles. For example, the location information for delivery vehicle A is received as latitude 35.6895 and longitude 139.6917. This data is periodically retrieved via a REST API. The output is real-time location data that is stored on the server side.

[0470] Step 2:

[0471] Traffic and weather data collection

[0472] The server uses a traffic information API to collect current traffic condition data. This data includes road congestion information, construction information, traffic accident information, etc. For example, it sends the API request "https: / / api.traffic.com / status?location=35.6895,139.6917". The input is the latitude and longitude of the target. The output is traffic condition data. Similarly, the server uses a weather forecast API to collect future weather forecast data. For example, it requests "https: / / api.weather.com / forecast?location=35.6895,139.6917", and the output is stored as weather forecast data.

[0473] Step 3:

[0474] Analyzing data and calculating estimated delivery times

[0475] The server uses a generation AI to analyze the collected location information, traffic data, and weather forecast data. The generation AI receives this data as input and uses a machine learning algorithm to calculate the optimal delivery route and estimated delivery time. For example, it calculates "9:30 a.m." as the estimated arrival time for destination B. The output is estimated delivery time data for each destination.

[0476] Step 4:

[0477] Notification of estimated delivery time

[0478] The server notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message according to that emotion. The inputs are the estimated delivery time and the user's past reaction data. The output is a notification message generated according to the emotion. For example, a message such as "Your package is scheduled to arrive at 9:30. Please look forward to it!" is sent via a smartphone app or email.

[0479] Step 5:

[0480] Timing adjustment and re-notification

[0481] The server monitors the location information of delivery vehicles in real time, and if a major delay or route change occurs, it reanalyzes and calculates a new estimated delivery time. The input is the latest collected location information and traffic condition data. The generative AI is used to recalculate the optimal estimated delivery time. The output is a new estimated delivery time data and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is re-announced.

[0482] Step 6:

[0483] User satisfaction rating after delivery completion

[0484] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction based on that data. The input is the user's emotional data at the time of delivery completion. The output is a user satisfaction rating. This data will be used to improve the service in the future and to refine the notification mechanism.

[0485] (Application example 2)

[0486] 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."

[0487] In modern delivery systems, the accuracy of estimated delivery times is insufficient, leading to stressful waiting times for users. Furthermore, insufficient re-notifications due to changes in delivery status often result in low user satisfaction. Furthermore, delivery notifications are uniform and do not take into account individual user feelings, potentially further reducing the user experience. Therefore, there is a need for more accurate calculations of estimated delivery times and for appropriate notifications that reflect the user's feelings.

[0488] 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.

[0489] In this invention, the server includes a means for collecting location information and traffic conditions for each delivery vehicle, a means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, a means for notifying the user of the calculated estimated delivery time, a means for recognizing the user's emotions at the time of notification and generating a personalized message according to the emotions, and a means for evaluating the user's satisfaction after the delivery is completed. This allows for highly accurate calculation of the estimated delivery time based on real-time information on delivery vehicles, traffic conditions, and weather forecasts, and for appropriate notifications according to the user's emotions. This reduces user stress and provides a high level of satisfaction. It also enables appropriate follow-up through re-notification, which reduces redeliveries and improves the efficiency of logistics operations.

[0490] "Transport vehicle location information" is data indicating the current location of a delivery vehicle, and is typically obtained by a GPS device.

[0491] "Traffic conditions" refers to information that indicates current road conditions, such as road congestion, construction information, and accident information.

[0492] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze various data and generate specific outputs.

[0493] The "estimated delivery time" is the time when the package is expected to be delivered to the user.

[0494] The "means of notifying the user" refers to a method for notifying the user of the estimated delivery time and update information, such as push notification or email.

[0495] The "emotion engine" is an engine that predicts a user's current emotions based on their past reaction data and generates a message that corresponds to that emotion.

[0496] "User satisfaction" is a measure of how well a service meets users' expectations and requirements.

[0497] "Weather forecast data" is data that indicates information about future weather in a specific area.

[0498] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0499] The server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. In addition, the server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. The server also collects weather forecast data from a weather forecast API to obtain information about future weather in a specific area.

[0500] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0501] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0502] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0503] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0504] Specific examples

[0505] The server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it obtains a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "9:30 AM arrival" at delivery destination B, and the emotion engine recognizes the user's emotion and generates a message such as "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Furthermore, if the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and sends a message like, "The new delivery time is 10:00. Thank you for your understanding!" This system virtually eliminates the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. Furthermore, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0506] An example of a prompt is as follows:

[0507] "Create an app that notifies users of food delivery times with high accuracy and changes the message content depending on the user's emotions. The goal is to reduce stress and increase satisfaction by adjusting the timing and content of notifications based on the user's emotions. Required APIs include GPS data, traffic information, and weather forecast data, and an emotion engine should also be used."

[0508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0509] Step 1:

[0510] The server receives real-time location information (latitude and longitude) from the GPS devices installed in the delivery vehicles, which are periodically retrieved through a REST API. The input is the ID of the delivery vehicle, and the output is the current latitude and longitude of the delivery vehicle.

[0511] Step 2:

[0512] The server uses the traffic information API to collect traffic conditions such as current road congestion information, construction information, traffic accident information, etc. The input is the API key and location information, and the output is the collected traffic condition data.

[0513] Step 3:

[0514] The server collects weather forecast data from a weather forecast API and obtains future weather information for a specific region. The input is an API key and location information, and the output is future weather forecast data.

[0515] Step 4:

[0516] The server uses AI to analyze the collected location information, traffic conditions, and weather forecast data. This analysis allows it to accurately calculate the optimal transport route and estimated delivery time for each package. The input is the collected data (location information, traffic conditions, weather forecast), and the output is the estimated delivery time and route information.

[0517] Step 5:

[0518] The server notifies the user of the calculated estimated delivery time. At this time, it uses an emotion engine to infer the user's current emotion from past reaction data and generates a message according to that emotion. The input is the calculated estimated delivery time and the user's past reaction data, and the output is an appropriate notification message.

[0519] Step 6:

[0520] The device (user's smartphone) receives the notification and displays it on the screen. The input is the notification message sent from the server, and the output is the notification message displayed on the device screen.

[0521] Step 7:

[0522] The user checks the notification and prepares to be at home at the specified scheduled delivery time. The input is the notification message displayed on the terminal, and the output is the user's behavior.

[0523] Step 8:

[0524] The server monitors the location information of delivery vehicles in real time, and if there is a major delay or route change, it reanalyzes and calculates a new estimated delivery time. The input is the latest location information and traffic condition data, and the output is the recalculated estimated delivery time.

[0525] Step 9:

[0526] The server notifies the user of the recalculated estimated delivery time and sends a re-notification at an appropriate time using the emotion engine. The input is the recalculated estimated delivery time and the user's emotion data, and the output is an appropriate re-notification message.

[0527] Step 10:

[0528] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction. The input is the user's reaction data after the delivery is completed, and the output is the evaluation of the user's satisfaction. This will be useful for future service improvements and refinement of the notification mechanism.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] [Third embodiment]

[0533] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0534] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0535] 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).

[0536] 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.

[0537] 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.

[0538] 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).

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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."

[0545] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0546] A system for implementing the present invention operates as follows.

[0547] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the vehicle's current latitude and longitude. The server then obtains current traffic conditions via a traffic information API. Traffic conditions include road congestion, construction, and traffic accident information.

[0548] The server then uses a weather API to collect weather forecast data, which contains information about future weather in a particular location.

[0549] Once this data is collected, a server-based AI analyzes it and calculates the optimal route for each vehicle. The AI ​​uses machine learning algorithms to calculate highly accurate delivery times, taking into account current traffic conditions and weather forecasts.

[0550] The server then notifies the user of the calculated estimated delivery time. This notification can be sent by email to the email address registered by the user or by push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time.

[0551] When users receive the notification, they can either be at home at the scheduled delivery time or be ready to receive the package at the specified location, which reduces the number of missed deliveries and reduces the workload of drivers due to redelivery.

[0552] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. According to this data, delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is forecast.

[0553] Based on this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates that delivery vehicle A will arrive at destination B at 9:30. The server then sends an email to the user at destination B saying, "Your package is expected to arrive at 9:30," and also sends a similar notification via a dedicated app.

[0554] The device (user's smartphone) receives this notification and displays it on the screen. The user confirms the notification and waits at home until 9:30. If the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and notifies the user again that "the new delivery time is 10:00."

[0555] This allows users to know the exact delivery time and receive their packages efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] The server collects real-time location information from the GPS devices installed in the delivery vehicles, which is the current latitude and longitude of the delivery vehicles and is periodically retrieved through a REST API.

[0559] Step 2:

[0560] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. For example, it can obtain this information using the Google Maps API.

[0561] Step 3:

[0562] The server collects weather forecast data for the target area from the weather forecast API, which includes weather information for specific time periods.

[0563] Step 4:

[0564] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to calculate the optimal shipping route and estimated delivery time for each package.

[0565] Step 5:

[0566] The server obtains the estimated delivery time calculated by the generation AI and sends a notification to the user's registered email address or a dedicated app based on that time. The notification includes information about the estimated delivery time and delivery destination.

[0567] Step 6:

[0568] The device (such as the user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0569] Step 7:

[0570] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0571] Step 8:

[0572] The server will then notify the user again of the reanalyzed estimated delivery time via email or a dedicated app, just like the first time.

[0573] Step 9:

[0574] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0575] Step 10:

[0576] After the delivery is completed, the server stops monitoring the location information of the delivery vehicle and starts analyzing a new route based on the data of the next delivery destination.

[0577] Example 1

[0578] 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."

[0579] In conventional delivery systems, it was difficult to notify users of the exact scheduled delivery time, taking into account the location information of the transport vehicle and traffic conditions. Furthermore, even when the scheduled delivery time was delayed due to weather changes or traffic congestion, there was a lack of a way to provide users with real-time updated information, which led to frequent redelivery and reduced efficiency of logistics operations.

[0580] 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.

[0581] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the optimal transport route and scheduled delivery time of the package with high accuracy, means for collecting weather forecast data and taking it into consideration when calculating the transport route and scheduled delivery time, means for notifying the user of the calculated scheduled delivery time, and means for displaying the notification on the user terminal. This enables the notification of the scheduled delivery time with high accuracy and allows the user to take appropriate action according to the delivery time, thereby reducing the need for redelivery and improving the efficiency of logistics operations.

[0582] "Transportation vehicle" means any vehicle used to transport cargo.

[0583] "Location information" refers to geographic coordinate data of the current location of the transport vehicle, specifically, latitude and longitude data.

[0584] "Traffic conditions" refers to data about current road conditions, such as road congestion information, construction information, and traffic accident information.

[0585] "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze collected data and calculate optimal transportation routes and estimated delivery times.

[0586] "Weather Forecast Data" means information about future weather conditions in a particular geographic area, including the probability of precipitation, temperature, and wind speed.

[0587] "Estimated Delivery Time" means the time when the package is scheduled to arrive at the specified delivery address.

[0588] "User" refers to the person who receives the package.

[0589] "Notification" refers to the means by which the user is notified of estimated delivery times and updates, including email and push notifications.

[0590] "Terminal" refers to the information device used by the user, specifically including smartphones and tablets.

[0591] "Collection methods" refers to the methods or technologies used to obtain specific information.

[0592] "Means of analysis" refers to methods and techniques for performing analysis and calculations based on collected data.

[0593] "Means of notification" refers to the methods and techniques used to convey information to users.

[0594] "Display means" refers to the method or technology for displaying information on a terminal.

[0595] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0596] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. For example, if the location of delivery vehicle A is identified as latitude 35.6895 and longitude 139.6917, this information is sent to the server. The server also obtains the current traffic conditions using a traffic information API (e.g., Google Maps API or Here API). Traffic conditions include information on road congestion, construction work, and traffic accidents. For example, if there is congestion in section X, that data is obtained.

[0597] Next, the server uses a weather forecast API (e.g., OpenWeatherMap API) to collect weather forecast data. Weather forecast data contains information about future weather in a specific area. For example, if rain is forecast for a specific area, that information is retrieved.

[0598] Once this data is collected, a generative AI installed on the server analyzes it. The generative AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate highly accurate estimated delivery times, taking into account current traffic conditions and weather forecasts. For example, if delivery vehicle A takes the optimal route to point B, it will calculate an estimated arrival time of 9:30.

[0599] The calculated estimated delivery time is then notified to the user by the server. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. The device (such as the user's smartphone) receives these notifications and displays them on the screen to inform the user. For example, the notification may say, "Your package is scheduled to arrive at 9:30."

[0600] When the user receives the notification, they can either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. This reduces missed deliveries and reduces the workload on the driver due to redelivery. Furthermore, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives later than 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and re-notifies the user that "the new delivery time is 10:00." The device receives the re-notification and displays it on the screen. In this way, the user can always keep up to date with the latest delivery information.

[0601] Specific examples

[0602] For example, at 8:00 AM, based on the GPS data of delivery vehicle A, the server recognizes that "delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917)." Next, the server uses a traffic information API to obtain data that "traffic congestion is occurring in section X." It also uses a weather forecast API to confirm that "rain is forecast for this area." Based on this, the generation AI calculates that "delivery vehicle A will take the optimal route and is scheduled to arrive at point B at 9:30."

[0603] The server notifies the user by email or app, saying, "The package is expected to arrive at 9:30." The user's smartphone receives the notification and informs the user of the information. The user confirms this and prepares to wait at home until 9:30. If the logistics is delayed, the user will be notified again, "The new delivery time is 10:00."

[0604] This system will significantly reduce the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0605] Prompt Sentence Examples

[0606] "Please explain in detail the operating process of the system that generates the optimal transportation route and highly accurate scheduled delivery time based on the delivery vehicle's current location information, traffic conditions, and weather forecast."

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] Step 1: Collect location information

[0609] The server collects real-time location information from the GPS devices installed in the delivery vehicles. As input, latitude and longitude data obtained from the GPS devices are provided. For example, if delivery vehicle A is located at latitude 35.6895 and longitude 139.6917, this information is received as input. As output, these location information data are stored on the server.

[0610] Step 2: Obtaining traffic conditions

[0611] The server obtains the current traffic conditions through the traffic information API. As input, traffic information for the target area is provided based on the API request. Specifically, this includes road congestion information, construction information, accident information, etc. For example, if there is congestion in section X, data related to this is obtained. As output, the obtained traffic information data is saved on the server.

[0612] Step 3: Get the weather forecast

[0613] The server uses a weather forecast API to collect weather forecast data. As input, the API provides information about future weather conditions for a specified region. Specifically, this includes data such as the probability of precipitation, temperature, and wind speed. For example, if rain is forecast for a specific region, this information is retrieved. As output, the collected weather forecast data is stored on the server.

[0614] Step 4: Data analysis and calculation of optimal route and estimated delivery time

[0615] The generation AI installed on the server analyzes location information, traffic conditions, and weather forecast data. The previously collected location information, traffic information, and weather forecast data are used as input. The generation AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate the optimal transportation route and highly accurate estimated delivery time. As output, the estimated arrival time at each delivery destination is saved on the server. For example, the optimal route for delivery vehicle A is calculated, and the estimated arrival time at point B is calculated as "9:30."

[0616] Step 5: Notify users

[0617] The server notifies the user of the calculated estimated delivery time. The calculated estimated delivery time and the user's contact information (email address, app ID, etc.) are used as input. Specific actions include sending an email or a push notification. Examples of outputs include notifications being sent to the user's device. For example, an email or app notification saying, "Your package is scheduled to arrive at 9:30."

[0618] Step 6: Display notifications on your device

[0619] The terminal (user's smartphone) receives the notification from the server and displays it on the screen. The input is the received notification data (estimated delivery time). The output is a message on the user's smartphone screen saying "Your package is scheduled to arrive at 9:30." Specific operations include an action to confirm the notification.

[0620] Step 7: User Action

[0621] The user checks the notification displayed on the device and responds according to the specified scheduled delivery time. The input is the notification information displayed on the smartphone. The output is the user being at home or preparing to receive the package at the specified location. Specifically, the user will wait at home or head to the package collection location at the scheduled delivery time. For example, the user will wait at home to receive the package at 9:30.

[0622] This makes it possible to notify users of the scheduled delivery time with high accuracy, allowing them to take appropriate action depending on the delivery time, reducing the need for redelivery and improving the efficiency of logistics operations.

[0623] (Application example 1)

[0624] 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."

[0625] When delivering food, it is necessary to constantly keep track of the optimal delivery route, taking into account the delivery person's current location, traffic conditions, and weather forecasts, and to notify the user of the exact scheduled delivery time.However, currently, delivery errors and delays occur frequently.In addition, there are issues such as the uncertainty of notifications and the need for redelivery, which hinder the efficiency of food delivery operations.

[0626] 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.

[0627] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, means for notifying the user of the calculated estimated delivery time, and means for improving the efficiency and accuracy of food delivery based on the estimated delivery time. This allows the user to know the accurate estimated delivery time, reduces the need for redelivery, and enables the efficiency of food delivery operations and improves user satisfaction.

[0628] A "transport vehicle" is a vehicle used to transport luggage or goods.

[0629] "Location information" is data indicating the current location of the transport vehicle, and includes coordinate data of latitude and longitude.

[0630] "Traffic conditions" refers to information that indicates the state of the roads on which the vehicle is traveling, such as information about road congestion, construction work, and traffic accidents.

[0631] "Generative AI" refers to artificial intelligence that uses machine learning algorithms to analyze data and calculate optimal results.

[0632] The "scheduled delivery time" is the time when the delivery vehicle is scheduled to arrive at the specified location.

[0633] "User" refers to the person or business receiving the package or food delivery.

[0634] "Food delivery" is a service that delivers food and drinks to a location specified by the customer.

[0635] "Efficiency" refers to achieving goals with less effort and time, and means improving business productivity.

[0636] "Accuracy" refers to the degree to which the results of a prediction or calculation match the actual situation.

[0637] The system for implementing this invention collects location information, traffic conditions, and weather forecast data for each transport vehicle, and based on this, a generation AI calculates the optimal transport route and estimated delivery time with high accuracy. This system consists of three main components: a server, a terminal, and a user.

[0638] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. In addition, the server obtains current traffic conditions via a traffic information API. Traffic conditions include information on road congestion, construction, and traffic accidents. Next, the server collects weather forecast data using a weather forecast API. The weather forecast data includes information on future weather in a specific area.

[0639] Once this data is collected, a generation AI installed on the server analyzes the data and calculates the optimal transportation route for each delivery vehicle. The generation AI uses a machine learning algorithm to calculate a highly accurate estimated delivery time, taking into account current traffic conditions and weather forecasts. The calculated estimated delivery time is then notified to the user from the server. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time. Upon receiving the notification, the user either waits at home at the specified scheduled delivery time or prepares to receive the package at the specified location.

[0640] As a specific example, the server obtains GPS data for a delivery vehicle at 8:00 AM. According to this data, the delivery vehicle is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is predicted. Based on this data, the generation AI performs analysis and calculates the optimal route for the delivery vehicle and the estimated arrival time for each delivery destination.

[0641] For example, the delivery location is calculated as "9:30 AM expected arrival." The server sends an email to the user at location B saying, "Your package is expected to arrive at 9:30 AM," and also sends a similar notification via a dedicated app. The device (the user's smartphone) receives this notification and displays it on its screen. The user checks the notification and waits at home until 9:30 AM. Also, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00 AM, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user that "The new delivery time is 10:00 AM."

[0642] Example prompt sentence:

[0643] "Current location: Latitude 35.6895, Longitude 139.6917. Destination: Shibuya, Tokyo. Current traffic conditions: Traffic jams, construction information. Weather forecast: Rain. Please calculate the optimal route and delivery time."

[0644] This allows users to know the exact delivery time and receive their parcels or food deliveries efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on drivers.

[0645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0646] Step 1:

[0647] The server receives real-time location information from the GPS devices installed in the delivery vehicles. The input of this processing step is the latitude and longitude coordinate data sent from the GPS devices of the delivery vehicles, and the output is the current location information of the delivery vehicles stored on the server. Specifically, the server periodically requests location information from each vehicle and stores the received data in a database.

[0648] Step 2:

[0649] The server obtains the current traffic conditions via the traffic information API. The input to this processing step is the location information specified by the server, and the output is traffic condition data for that location (traffic congestion information, construction information, traffic accident information, etc.). Specifically, the server sends an HTTP request to each API, analyzes the traffic data returned as a response, and saves it.

[0650] Step 3:

[0651] The server uses weather forecast APIs to collect weather forecast data. The input for this processing step is location information along the delivery route, and the output is weather forecast data for that area. Specifically, the server sends an HTTP request to each weather forecast API, analyzes the retrieved weather forecast data, and saves it.

[0652] Step 4:

[0653] The generative AI model installed on the server analyzes location information, traffic conditions, and weather forecast data to calculate the optimal transport route and estimated delivery time. The inputs to this processing step are location information, traffic conditions data, and weather forecast data, and the output is the optimal transport route and estimated delivery time. Specifically, the generative AI inputs this data in the form of a prompt statement, and the model performs the calculations and returns the optimal result.

[0654] Step 5:

[0655] The server notifies the user of the calculated estimated delivery time. The input to this processing step is the estimated delivery time and the user's contact information, and the output is a notification message to the user. Specifically, the server sends an email to the specified email address or a push notification via the smartphone app.

[0656] Step 6:

[0657] The terminal displays the received notification on its screen and notifies the user of the estimated delivery time. The input to this processing step is the notification message sent from the server, and the output is the estimated delivery time displayed on the terminal screen. Specifically, the terminal receives the notification in the background and displays it on the user interface.

[0658] Step 7:

[0659] When the user receives the notification, they will either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. The input to this processing step is the scheduled delivery time displayed on the terminal, and the output is the user's action (being at home or preparing to receive the package at the specified location). Specifically, the user checks the content of the notification and takes action according to the situation.

[0660] The above are the specific processing steps of the system.

[0661] 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.

[0662] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0663] A system for implementing the present invention operates as follows.

[0664] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. Next, the server uses a traffic information API to collect current traffic conditions, including information on road congestion, construction, and traffic accidents. Finally, the server collects weather forecast data from a weather forecast API to obtain information on future weather in a specific area.

[0665] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0666] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0667] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0668] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0669] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it may obtain a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "estimated arrival time" at delivery destination B as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!"

[0670] The device (user's smartphone) receives this notification and displays it on the screen. The user checks the notification and waits at home until 9:30. If the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0671] This system will virtually eliminate the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. In addition, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] The server collects real-time location information from GPS devices installed in delivery vehicles, including the vehicle's current latitude and longitude, periodically retrieved through a REST API.

[0675] Step 2:

[0676] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, traffic accident information, etc. For example, it obtains data using the Google Maps API.

[0677] Step 3:

[0678] The server uses a weather API to collect weather forecast data, which contains information about future weather in a particular region.

[0679] Step 4:

[0680] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0681] Step 5:

[0682] The server receives the estimated delivery time calculated by the generation AI and uses an emotion engine to analyze the user's emotional data, which is collected based on the user's past reactions and current situation.

[0683] Step 6:

[0684] The server notifies the user of the estimated delivery time, including a message generated by the emotion engine, either by sending an email to the email address registered by the user or by sending a push notification via a dedicated smartphone app.

[0685] Step 7:

[0686] The device (user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0687] Step 8:

[0688] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0689] Step 9:

[0690] The server then notifies the user of the recalculated estimated delivery time by generating an appropriate message using the emotion engine and sending the notification via email or a dedicated app.

[0691] Step 10:

[0692] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0693] Step 11:

[0694] After the delivery is completed, the server collects user sentiment data and evaluates the user's satisfaction with the delivery experience, which can be used to improve the service in the future.

[0695] Through these efforts, this system eliminates the need for redelivery, improves logistics efficiency, and reduces the burden on truck drivers. Furthermore, the introduction of an emotion engine improves user satisfaction and enables the provision of personalized services.

[0696] Example 2

[0697] 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."

[0698] In conventional delivery systems, it is common to calculate the estimated delivery time by taking into account only the location information of the delivery vehicle and traffic conditions, and notify the user of the estimated delivery time. However, this method does not fully consider the risk of delivery delays due to sudden changes in traffic conditions or weather changes, making it difficult to notify the user of the accurate estimated delivery time. In addition, users only receive notifications of the delivery time, which can lead to frustration and stress about the delivery.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0700] In this invention, the server includes means for collecting location information of each transport vehicle, means for collecting traffic condition data, means for collecting weather forecast data, means for analyzing the collected location information, traffic condition data, and weather forecast data and calculating an estimated delivery time of the package using a generation AI, means for predicting a user's current emotion based on the user's past emotion data and generating a message corresponding to the emotion, and means for notifying the user of the calculated estimated delivery time and a message corresponding to the emotion. This makes it possible to reduce stress associated with delivery and improve user satisfaction by notifying the user of a more accurate and personalized estimated delivery time and sending a message corresponding to the user's emotion.

[0701] "Transportation vehicle" means any vehicle used to transport cargo or passengers to a particular destination.

[0702] "Location information" refers to data indicating the current latitude and longitude of a transport vehicle.

[0703] "Traffic condition data" refers to all information that affects traffic flow, such as road congestion, construction information, and accident information.

[0704] "Weather forecast data" refers to information that predicts future weather conditions in a particular region.

[0705] "Generative AI" refers to systems that use artificial intelligence algorithms to analyze data and perform calculations to solve specific problems.

[0706] "Estimated Delivery Time" means the time when a package is expected to arrive at the specified delivery destination.

[0707] "User" refers to the person or organization receiving the delivery.

[0708] "Emotional data" refers to information about a user's past reactions and current emotional state.

[0709] An "emotion-appropriate message" refers to a notification message that is composed of content that matches the user's emotional state.

[0710] "Notification means" refers to a method or tool for conveying a calculated estimated delivery time or a message corresponding to the user's emotion.

[0711] The system of the present invention operates primarily in cooperation between a server, a terminal, and a user. The server uses a GPS device installed in the delivery vehicle, a traffic information API, a weather forecast API, and a generation AI engine. The terminal primarily refers to the user's smartphone or PC, and the user is the person or organization that receives the package.

[0712] First, the server collects real-time location information of each delivery vehicle through the GPS device installed in the delivery vehicle. This location information includes the vehicle's latitude and longitude and is periodically obtained using a REST API. Next, the server collects current traffic condition data using a traffic information API. This data includes information on road congestion, construction, and traffic accidents. The server also collects weather forecast data from a weather forecast API to obtain future weather information for a specific area.

[0713] Based on the collected location information, traffic data, and weather forecast data, the generation AI installed on the server analyzes the data. The generation AI uses machine learning algorithms to calculate the optimal transportation route and highly accurate estimated delivery time for each package. For example, it determines that delivery vehicle A's location is latitude 35.6895, longitude 139.6917, and based on this data, calculates that it will arrive at delivery destination B at 9:30.

[0714] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. For example, a message might say, "Your package is scheduled to arrive at 9:30. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen.

[0715] The user checks the notification and prepares to be home by the specified scheduled delivery time. The server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The new scheduled delivery time is notified again, and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is generated. This message is notified at an appropriate time using the emotion engine, taking into account the user's current situation.

[0716] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0717] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). The server then obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. Rain is forecast for the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, the estimated arrival time for delivery destination B is calculated as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Additionally, if the delivery vehicle is delayed due to traffic congestion or other factors, for example if it arrives later than 10:00, the server will re-analyze the data, calculate a new estimated delivery time, and re-notify the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0718] Examples of prompts include:

[0719] "Based on the current location of delivery vehicle A, check the traffic situation at coordinates (latitude 35.6895, longitude 139.6917). If rain is forecast at 8:20, calculate the optimal delivery route and estimated delivery time. Also, if the user's emotion is positive, notify them with a message that corresponds to that emotion."

[0720] This system is expected to improve delivery efficiency and accuracy, as well as increase user satisfaction.

[0721] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0722] Step 1:

[0723] GPS data collection

[0724] The server obtains real-time location information from the GPS devices installed in the delivery vehicles. The input is latitude and longitude data sent from the delivery vehicles. For example, the location information for delivery vehicle A is received as latitude 35.6895 and longitude 139.6917. This data is periodically retrieved via a REST API. The output is real-time location data that is stored on the server side.

[0725] Step 2:

[0726] Traffic and weather data collection

[0727] The server uses a traffic information API to collect current traffic condition data. This data includes road congestion information, construction information, traffic accident information, etc. For example, it sends the API request "https: / / api.traffic.com / status?location=35.6895,139.6917". The input is the latitude and longitude of the target. The output is traffic condition data. Similarly, the server uses a weather forecast API to collect future weather forecast data. For example, it requests "https: / / api.weather.com / forecast?location=35.6895,139.6917", and the output is stored as weather forecast data.

[0728] Step 3:

[0729] Analyzing data and calculating estimated delivery times

[0730] The server uses a generation AI to analyze the collected location information, traffic data, and weather forecast data. The generation AI receives this data as input and uses a machine learning algorithm to calculate the optimal delivery route and estimated delivery time. For example, it calculates "9:30 a.m." as the estimated arrival time for destination B. The output is estimated delivery time data for each destination.

[0731] Step 4:

[0732] Notification of estimated delivery time

[0733] The server notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message according to that emotion. The inputs are the estimated delivery time and the user's past reaction data. The output is a notification message generated according to the emotion. For example, a message such as "Your package is scheduled to arrive at 9:30. Please look forward to it!" is sent via a smartphone app or email.

[0734] Step 5:

[0735] Timing adjustment and re-notification

[0736] The server monitors the location information of delivery vehicles in real time, and if a major delay or route change occurs, it reanalyzes and calculates a new estimated delivery time. The input is the latest collected location information and traffic condition data. The generative AI is used to recalculate the optimal estimated delivery time. The output is a new estimated delivery time data and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is re-announced.

[0737] Step 6:

[0738] User satisfaction rating after delivery completion

[0739] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction based on that data. The input is the user's emotional data at the time of delivery completion. The output is a user satisfaction rating. This data will be used to improve the service in the future and to refine the notification mechanism.

[0740] (Application example 2)

[0741] 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."

[0742] In modern delivery systems, the accuracy of estimated delivery times is insufficient, leading to stressful waiting times for users. Furthermore, insufficient re-notifications due to changes in delivery status often result in low user satisfaction. Furthermore, delivery notifications are uniform and do not take into account individual user feelings, potentially further reducing the user experience. Therefore, there is a need for more accurate calculations of estimated delivery times and for appropriate notifications that reflect the user's feelings.

[0743] 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.

[0744] In this invention, the server includes a means for collecting location information and traffic conditions for each delivery vehicle, a means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, a means for notifying the user of the calculated estimated delivery time, a means for recognizing the user's emotions at the time of notification and generating a personalized message according to the emotions, and a means for evaluating the user's satisfaction after the delivery is completed. This allows for highly accurate calculation of the estimated delivery time based on real-time information on delivery vehicles, traffic conditions, and weather forecasts, and for appropriate notifications according to the user's emotions. This reduces user stress and provides a high level of satisfaction. It also enables appropriate follow-up through re-notification, which reduces redeliveries and improves the efficiency of logistics operations.

[0745] "Transport vehicle location information" is data indicating the current location of a delivery vehicle, and is typically obtained by a GPS device.

[0746] "Traffic conditions" refers to information that indicates current road conditions, such as road congestion, construction information, and accident information.

[0747] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze various data and generate specific outputs.

[0748] The "estimated delivery time" is the time when the package is expected to be delivered to the user.

[0749] The "means of notifying the user" refers to a method for notifying the user of the estimated delivery time and update information, such as push notification or email.

[0750] The "emotion engine" is an engine that predicts a user's current emotions based on their past reaction data and generates a message that corresponds to that emotion.

[0751] "User satisfaction" is a measure of how well a service meets users' expectations and requirements.

[0752] "Weather forecast data" is data that indicates information about future weather in a specific area.

[0753] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0754] The server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. In addition, the server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. The server also collects weather forecast data from a weather forecast API to obtain information about future weather in a specific area.

[0755] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0756] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0757] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0758] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0759] Specific examples

[0760] The server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it obtains a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "9:30 AM arrival" at delivery destination B, and the emotion engine recognizes the user's emotion and generates a message such as "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Furthermore, if the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and sends a message like, "The new delivery time is 10:00. Thank you for your understanding!" This system virtually eliminates the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. Furthermore, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0761] An example of a prompt is as follows:

[0762] "Create an app that notifies users of food delivery times with high accuracy and changes the message content depending on the user's emotions. The goal is to reduce stress and increase satisfaction by adjusting the timing and content of notifications based on the user's emotions. Required APIs include GPS data, traffic information, and weather forecast data, and an emotion engine should also be used."

[0763] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0764] Step 1:

[0765] The server receives real-time location information (latitude and longitude) from the GPS devices installed in the delivery vehicles, which are periodically retrieved through a REST API. The input is the ID of the delivery vehicle, and the output is the current latitude and longitude of the delivery vehicle.

[0766] Step 2:

[0767] The server uses the traffic information API to collect traffic conditions such as current road congestion information, construction information, traffic accident information, etc. The input is the API key and location information, and the output is the collected traffic condition data.

[0768] Step 3:

[0769] The server collects weather forecast data from a weather forecast API and obtains future weather information for a specific region. The input is an API key and location information, and the output is future weather forecast data.

[0770] Step 4:

[0771] The server uses AI to analyze the collected location information, traffic conditions, and weather forecast data. This analysis allows it to accurately calculate the optimal transport route and estimated delivery time for each package. The input is the collected data (location information, traffic conditions, weather forecast), and the output is the estimated delivery time and route information.

[0772] Step 5:

[0773] The server notifies the user of the calculated estimated delivery time. At this time, it uses an emotion engine to infer the user's current emotion from past reaction data and generates a message according to that emotion. The input is the calculated estimated delivery time and the user's past reaction data, and the output is an appropriate notification message.

[0774] Step 6:

[0775] The device (user's smartphone) receives the notification and displays it on the screen. The input is the notification message sent from the server, and the output is the notification message displayed on the device screen.

[0776] Step 7:

[0777] The user checks the notification and prepares to be at home at the specified scheduled delivery time. The input is the notification message displayed on the terminal, and the output is the user's behavior.

[0778] Step 8:

[0779] The server monitors the location information of delivery vehicles in real time, and if there is a major delay or route change, it reanalyzes and calculates a new estimated delivery time. The input is the latest location information and traffic condition data, and the output is the recalculated estimated delivery time.

[0780] Step 9:

[0781] The server notifies the user of the recalculated estimated delivery time and sends a re-notification at an appropriate time using the emotion engine. The input is the recalculated estimated delivery time and the user's emotion data, and the output is an appropriate re-notification message.

[0782] Step 10:

[0783] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction. The input is the user's reaction data after the delivery is completed, and the output is the evaluation of the user's satisfaction. This will be useful for future service improvements and refinement of the notification mechanism.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] [Fourth embodiment]

[0788] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0789] 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.

[0790] 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).

[0791] 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.

[0792] 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.

[0793] 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).

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0802] A system for implementing the present invention operates as follows.

[0803] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the vehicle's current latitude and longitude. The server then obtains current traffic conditions via a traffic information API. Traffic conditions include road congestion, construction, and traffic accident information.

[0804] The server then uses a weather API to collect weather forecast data, which contains information about future weather in a particular location.

[0805] Once this data is collected, a server-based AI analyzes it and calculates the optimal route for each vehicle. The AI ​​uses machine learning algorithms to calculate highly accurate delivery times, taking into account current traffic conditions and weather forecasts.

[0806] The server then notifies the user of the calculated estimated delivery time. This notification can be sent by email to the email address registered by the user or by push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time.

[0807] When users receive the notification, they can either be at home at the scheduled delivery time or be ready to receive the package at the specified location, which reduces the number of missed deliveries and reduces the workload of drivers due to redelivery.

[0808] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. According to this data, delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is forecast.

[0809] Based on this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates that delivery vehicle A will arrive at destination B at 9:30. The server then sends an email to the user at destination B saying, "Your package is expected to arrive at 9:30," and also sends a similar notification via a dedicated app.

[0810] The device (user's smartphone) receives this notification and displays it on the screen. The user confirms the notification and waits at home until 9:30. If the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and notifies the user again that "the new delivery time is 10:00."

[0811] This allows users to know the exact delivery time and receive their packages efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0812] The processing flow will be explained below.

[0813] Step 1:

[0814] The server collects real-time location information from the GPS devices installed in the delivery vehicles, which is the current latitude and longitude of the delivery vehicles and is periodically retrieved through a REST API.

[0815] Step 2:

[0816] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. For example, it can obtain this information using the Google Maps API.

[0817] Step 3:

[0818] The server collects weather forecast data for the target area from the weather forecast API, which includes weather information for specific time periods.

[0819] Step 4:

[0820] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to calculate the optimal shipping route and estimated delivery time for each package.

[0821] Step 5:

[0822] The server obtains the estimated delivery time calculated by the generation AI and sends a notification to the user's registered email address or a dedicated app based on that time. The notification includes information about the estimated delivery time and delivery destination.

[0823] Step 6:

[0824] The device (such as the user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0825] Step 7:

[0826] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0827] Step 8:

[0828] The server will then notify the user again of the reanalyzed estimated delivery time via email or a dedicated app, just like the first time.

[0829] Step 9:

[0830] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0831] Step 10:

[0832] After the delivery is completed, the server stops monitoring the location information of the delivery vehicle and starts analyzing a new route based on the data of the next delivery destination.

[0833] Example 1

[0834] 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."

[0835] In conventional delivery systems, it was difficult to notify users of the exact scheduled delivery time, taking into account the location information of the transport vehicle and traffic conditions. Furthermore, even when the scheduled delivery time was delayed due to weather changes or traffic congestion, there was a lack of a way to provide users with real-time updated information, which led to frequent redelivery and reduced efficiency of logistics operations.

[0836] 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.

[0837] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the optimal transport route and scheduled delivery time of the package with high accuracy, means for collecting weather forecast data and taking it into consideration when calculating the transport route and scheduled delivery time, means for notifying the user of the calculated scheduled delivery time, and means for displaying the notification on the user terminal. This enables the notification of the scheduled delivery time with high accuracy and allows the user to take appropriate action according to the delivery time, thereby reducing the need for redelivery and improving the efficiency of logistics operations.

[0838] "Transportation vehicle" means any vehicle used to transport cargo.

[0839] "Location information" refers to geographic coordinate data of the current location of the transport vehicle, specifically, latitude and longitude data.

[0840] "Traffic conditions" refers to data about current road conditions, such as road congestion information, construction information, and traffic accident information.

[0841] "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze collected data and calculate optimal transportation routes and estimated delivery times.

[0842] "Weather Forecast Data" means information about future weather conditions in a particular geographic area, including the probability of precipitation, temperature, and wind speed.

[0843] "Estimated Delivery Time" means the time when the package is scheduled to arrive at the specified delivery address.

[0844] "User" refers to the person who receives the package.

[0845] "Notification" refers to the means by which the user is notified of estimated delivery times and updates, including email and push notifications.

[0846] "Terminal" refers to the information device used by the user, specifically including smartphones and tablets.

[0847] "Collection methods" refers to the methods or technologies used to obtain specific information.

[0848] "Means of analysis" refers to methods and techniques for performing analysis and calculations based on collected data.

[0849] "Means of notification" refers to the methods and techniques used to convey information to users.

[0850] "Display means" refers to the method or technology for displaying information on a terminal.

[0851] The system of the present invention collects location information and traffic conditions of each transport vehicle, and based on this information, the generation AI calculates the optimal transport route and estimated delivery time of the package with high accuracy. The calculated estimated delivery time is also notified to the user, which improves delivery efficiency.

[0852] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. For example, if the location of delivery vehicle A is identified as latitude 35.6895 and longitude 139.6917, this information is sent to the server. The server also obtains the current traffic conditions using a traffic information API (e.g., Google Maps API or Here API). Traffic conditions include information on road congestion, construction work, and traffic accidents. For example, if there is congestion in section X, that data is obtained.

[0853] Next, the server uses a weather forecast API (e.g., OpenWeatherMap API) to collect weather forecast data. Weather forecast data contains information about future weather in a specific area. For example, if rain is forecast for a specific area, that information is retrieved.

[0854] Once this data is collected, a generative AI installed on the server analyzes it. The generative AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate highly accurate estimated delivery times, taking into account current traffic conditions and weather forecasts. For example, if delivery vehicle A takes the optimal route to point B, it will calculate an estimated arrival time of 9:30.

[0855] The calculated estimated delivery time is then notified to the user by the server. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. The device (such as the user's smartphone) receives these notifications and displays them on the screen to inform the user. For example, the notification may say, "Your package is scheduled to arrive at 9:30."

[0856] When the user receives the notification, they can either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. This reduces missed deliveries and reduces the workload on the driver due to redelivery. Furthermore, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives later than 10:00, the server reanalyzes the data, calculates a new scheduled delivery time, and re-notifies the user that "the new delivery time is 10:00." The device receives the re-notification and displays it on the screen. In this way, the user can always keep up to date with the latest delivery information.

[0857] Specific examples

[0858] For example, at 8:00 AM, based on the GPS data of delivery vehicle A, the server recognizes that "delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917)." Next, the server uses a traffic information API to obtain data that "traffic congestion is occurring in section X." It also uses a weather forecast API to confirm that "rain is forecast for this area." Based on this, the generation AI calculates that "delivery vehicle A will take the optimal route and is scheduled to arrive at point B at 9:30."

[0859] The server notifies the user by email or app, saying, "The package is expected to arrive at 9:30." The user's smartphone receives the notification and informs the user of the information. The user confirms this and prepares to wait at home until 9:30. If the logistics is delayed, the user will be notified again, "The new delivery time is 10:00."

[0860] This system will significantly reduce the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers.

[0861] Prompt Sentence Examples

[0862] "Please explain in detail the operating process of the system that generates the optimal transportation route and highly accurate scheduled delivery time based on the delivery vehicle's current location information, traffic conditions, and weather forecast."

[0863] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0864] Step 1: Collect location information

[0865] The server collects real-time location information from the GPS devices installed in the delivery vehicles. As input, latitude and longitude data obtained from the GPS devices are provided. For example, if delivery vehicle A is located at latitude 35.6895 and longitude 139.6917, this information is received as input. As output, these location information data are stored on the server.

[0866] Step 2: Obtaining traffic conditions

[0867] The server obtains the current traffic conditions through the traffic information API. As input, traffic information for the target area is provided based on the API request. Specifically, this includes road congestion information, construction information, accident information, etc. For example, if there is congestion in section X, data related to this is obtained. As output, the obtained traffic information data is saved on the server.

[0868] Step 3: Get the weather forecast

[0869] The server uses a weather forecast API to collect weather forecast data. As input, the API provides information about future weather conditions for a specified region. Specifically, this includes data such as the probability of precipitation, temperature, and wind speed. For example, if rain is forecast for a specific region, this information is retrieved. As output, the collected weather forecast data is stored on the server.

[0870] Step 4: Data analysis and calculation of optimal route and estimated delivery time

[0871] The generation AI installed on the server analyzes location information, traffic conditions, and weather forecast data. The previously collected location information, traffic information, and weather forecast data are used as input. The generation AI uses machine learning algorithms (e.g., recurrent neural networks and regression analysis models) to calculate the optimal transportation route and highly accurate estimated delivery time. As output, the estimated arrival time at each delivery destination is saved on the server. For example, the optimal route for delivery vehicle A is calculated, and the estimated arrival time at point B is calculated as "9:30."

[0872] Step 5: Notify users

[0873] The server notifies the user of the calculated estimated delivery time. The calculated estimated delivery time and the user's contact information (email address, app ID, etc.) are used as input. Specific actions include sending an email or a push notification. Examples of outputs include notifications being sent to the user's device. For example, an email or app notification saying, "Your package is scheduled to arrive at 9:30."

[0874] Step 6: Display notifications on your device

[0875] The terminal (user's smartphone) receives the notification from the server and displays it on the screen. The input is the received notification data (estimated delivery time). The output is a message on the user's smartphone screen saying "Your package is scheduled to arrive at 9:30." Specific operations include an action to confirm the notification.

[0876] Step 7: User Action

[0877] The user checks the notification displayed on the device and responds according to the specified scheduled delivery time. The input is the notification information displayed on the smartphone. The output is the user being at home or preparing to receive the package at the specified location. Specifically, the user will wait at home or head to the package collection location at the scheduled delivery time. For example, the user will wait at home to receive the package at 9:30.

[0878] This makes it possible to notify users of the scheduled delivery time with high accuracy, allowing them to take appropriate action depending on the delivery time, reducing the need for redelivery and improving the efficiency of logistics operations.

[0879] (Application example 1)

[0880] 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."

[0881] When delivering food, it is necessary to constantly keep track of the optimal delivery route, taking into account the delivery person's current location, traffic conditions, and weather forecasts, and to notify the user of the exact scheduled delivery time.However, currently, delivery errors and delays occur frequently.In addition, there are issues such as the uncertainty of notifications and the need for redelivery, which hinder the efficiency of food delivery operations.

[0882] 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.

[0883] In this invention, the server includes means for collecting location information and traffic conditions of each transport vehicle, means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, means for notifying the user of the calculated estimated delivery time, and means for improving the efficiency and accuracy of food delivery based on the estimated delivery time. This allows the user to know the accurate estimated delivery time, reduces the need for redelivery, and enables the efficiency of food delivery operations and improves user satisfaction.

[0884] A "transport vehicle" is a vehicle used to transport luggage or goods.

[0885] "Location information" is data indicating the current location of the transport vehicle, and includes coordinate data of latitude and longitude.

[0886] "Traffic conditions" refers to information that indicates the state of the roads on which the vehicle is traveling, such as information about road congestion, construction work, and traffic accidents.

[0887] "Generative AI" refers to artificial intelligence that uses machine learning algorithms to analyze data and calculate optimal results.

[0888] The "scheduled delivery time" is the time when the delivery vehicle is scheduled to arrive at the specified location.

[0889] "User" refers to the person or business receiving the package or food delivery.

[0890] "Food delivery" is a service that delivers food and drinks to a location specified by the customer.

[0891] "Efficiency" refers to achieving goals with less effort and time, and means improving business productivity.

[0892] "Accuracy" refers to the degree to which the results of a prediction or calculation match the actual situation.

[0893] The system for implementing this invention collects location information, traffic conditions, and weather forecast data for each transport vehicle, and based on this, a generation AI calculates the optimal transport route and estimated delivery time with high accuracy. This system consists of three main components: a server, a terminal, and a user.

[0894] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. This location information is geographic coordinate data such as the current latitude and longitude of the delivery vehicle. In addition, the server obtains current traffic conditions via a traffic information API. Traffic conditions include information on road congestion, construction, and traffic accidents. Next, the server collects weather forecast data using a weather forecast API. The weather forecast data includes information on future weather in a specific area.

[0895] Once this data is collected, a generation AI installed on the server analyzes the data and calculates the optimal transportation route for each delivery vehicle. The generation AI uses a machine learning algorithm to calculate a highly accurate estimated delivery time, taking into account current traffic conditions and weather forecasts. The calculated estimated delivery time is then notified to the user from the server. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (such as the user's smartphone) displays the received notification on its screen and informs the user of the estimated delivery time. Upon receiving the notification, the user either waits at home at the specified scheduled delivery time or prepares to receive the package at the specified location.

[0896] As a specific example, the server obtains GPS data for a delivery vehicle at 8:00 AM. According to this data, the delivery vehicle is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from the traffic information API. For example, it receives information that there is congestion in section X. It then checks the weather forecast for the same area and finds that rain is predicted. Based on this data, the generation AI performs analysis and calculates the optimal route for the delivery vehicle and the estimated arrival time for each delivery destination.

[0897] For example, the delivery location is calculated as "9:30 AM expected arrival." The server sends an email to the user at location B saying, "Your package is expected to arrive at 9:30 AM," and also sends a similar notification via a dedicated app. The device (the user's smartphone) receives this notification and displays it on its screen. The user checks the notification and waits at home until 9:30 AM. Also, if the delivery vehicle is caught in traffic and cannot make the scheduled time, for example, if it arrives at 10:00 AM, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user that "The new delivery time is 10:00 AM."

[0898] Example prompt sentence:

[0899] "Current location: Latitude 35.6895, Longitude 139.6917. Destination: Shibuya, Tokyo. Current traffic conditions: Traffic jams, construction information. Weather forecast: Rain. Please calculate the optimal route and delivery time."

[0900] This allows users to know the exact delivery time and receive their parcels or food deliveries efficiently. This system significantly reduces the need for redelivery, optimizing logistics operations and reducing the burden on drivers.

[0901] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0902] Step 1:

[0903] The server receives real-time location information from the GPS devices installed in the delivery vehicles. The input of this processing step is the latitude and longitude coordinate data sent from the GPS devices of the delivery vehicles, and the output is the current location information of the delivery vehicles stored on the server. Specifically, the server periodically requests location information from each vehicle and stores the received data in a database.

[0904] Step 2:

[0905] The server obtains the current traffic conditions via the traffic information API. The input to this processing step is the location information specified by the server, and the output is traffic condition data for that location (traffic congestion information, construction information, traffic accident information, etc.). Specifically, the server sends an HTTP request to each API, analyzes the traffic data returned as a response, and saves it.

[0906] Step 3:

[0907] The server uses weather forecast APIs to collect weather forecast data. The input for this processing step is location information along the delivery route, and the output is weather forecast data for that area. Specifically, the server sends an HTTP request to each weather forecast API, analyzes the retrieved weather forecast data, and saves it.

[0908] Step 4:

[0909] The generative AI model installed on the server analyzes location information, traffic conditions, and weather forecast data to calculate the optimal transport route and estimated delivery time. The inputs to this processing step are location information, traffic conditions data, and weather forecast data, and the output is the optimal transport route and estimated delivery time. Specifically, the generative AI inputs this data in the form of a prompt statement, and the model performs the calculations and returns the optimal result.

[0910] Step 5:

[0911] The server notifies the user of the calculated estimated delivery time. The input to this processing step is the estimated delivery time and the user's contact information, and the output is a notification message to the user. Specifically, the server sends an email to the specified email address or a push notification via the smartphone app.

[0912] Step 6:

[0913] The terminal displays the received notification on its screen and notifies the user of the estimated delivery time. The input to this processing step is the notification message sent from the server, and the output is the estimated delivery time displayed on the terminal screen. Specifically, the terminal receives the notification in the background and displays it on the user interface.

[0914] Step 7:

[0915] When the user receives the notification, they will either be at home at the specified scheduled delivery time or be ready to receive the package at the specified location. The input to this processing step is the scheduled delivery time displayed on the terminal, and the output is the user's action (being at home or preparing to receive the package at the specified location). Specifically, the user checks the content of the notification and takes action according to the situation.

[0916] The above are the specific processing steps of the system.

[0917] 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.

[0918] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[0919] A system for implementing the present invention operates as follows.

[0920] First, the server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. Next, the server uses a traffic information API to collect current traffic conditions, including information on road congestion, construction, and traffic accidents. Finally, the server collects weather forecast data from a weather forecast API to obtain information on future weather in a specific area.

[0921] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0922] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[0923] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[0924] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0925] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it may obtain a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "estimated arrival time" at delivery destination B as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!"

[0926] The device (user's smartphone) receives this notification and displays it on the screen. The user checks the notification and waits at home until 9:30. If the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and re-notifies the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0927] This system will virtually eliminate the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. In addition, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The server collects real-time location information from GPS devices installed in delivery vehicles, including the vehicle's current latitude and longitude, periodically retrieved through a REST API.

[0931] Step 2:

[0932] The server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, traffic accident information, etc. For example, it obtains data using the Google Maps API.

[0933] Step 3:

[0934] The server uses a weather API to collect weather forecast data, which contains information about future weather in a particular region.

[0935] Step 4:

[0936] The server inputs the collected location, traffic, and weather data into the Generator AI, which uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[0937] Step 5:

[0938] The server receives the estimated delivery time calculated by the generation AI and uses an emotion engine to analyze the user's emotional data, which is collected based on the user's past reactions and current situation.

[0939] Step 6:

[0940] The server notifies the user of the estimated delivery time, including a message generated by the emotion engine, either by sending an email to the email address registered by the user or by sending a push notification via a dedicated smartphone app.

[0941] Step 7:

[0942] The device (user's smartphone) receives the notification from the server and displays it on the screen. The user checks the notification and prepares to be at home at the specified scheduled delivery time.

[0943] Step 8:

[0944] The server continuously monitors the location of delivery vehicles in real time, and if there are any major delays or route changes, it reanalyzes the data and calculates a new estimated delivery time.

[0945] Step 9:

[0946] The server then notifies the user of the recalculated estimated delivery time by generating an appropriate message using the emotion engine and sending the notification via email or a dedicated app.

[0947] Step 10:

[0948] The user continues to adjust their presence based on the newly notified estimated delivery time, thereby ensuring that the package can be received at the final delivery time.

[0949] Step 11:

[0950] After the delivery is completed, the server collects user sentiment data and evaluates the user's satisfaction with the delivery experience, which can be used to improve the service in the future.

[0951] Through these efforts, this system eliminates the need for redelivery, improves logistics efficiency, and reduces the burden on truck drivers. Furthermore, the introduction of an emotion engine improves user satisfaction and enables the provision of personalized services.

[0952] Example 2

[0953] 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."

[0954] In conventional delivery systems, it is common to calculate the estimated delivery time by taking into account only the location information of the delivery vehicle and traffic conditions, and notify the user of the estimated delivery time. However, this method does not fully consider the risk of delivery delays due to sudden changes in traffic conditions or weather changes, making it difficult to notify the user of the accurate estimated delivery time. In addition, users only receive notifications of the delivery time, which can lead to frustration and stress about the delivery.

[0955] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0956] In this invention, the server includes means for collecting location information of each transport vehicle, means for collecting traffic condition data, means for collecting weather forecast data, means for analyzing the collected location information, traffic condition data, and weather forecast data and calculating an estimated delivery time of the package using a generation AI, means for predicting a user's current emotion based on the user's past emotion data and generating a message corresponding to the emotion, and means for notifying the user of the calculated estimated delivery time and a message corresponding to the emotion. This makes it possible to reduce stress associated with delivery and improve user satisfaction by notifying the user of a more accurate and personalized estimated delivery time and sending a message corresponding to the user's emotion.

[0957] "Transportation vehicle" means any vehicle used to transport cargo or passengers to a particular destination.

[0958] "Location information" refers to data indicating the current latitude and longitude of a transport vehicle.

[0959] "Traffic condition data" refers to all information that affects traffic flow, such as road congestion, construction information, and accident information.

[0960] "Weather forecast data" refers to information that predicts future weather conditions in a particular region.

[0961] "Generative AI" refers to systems that use artificial intelligence algorithms to analyze data and perform calculations to solve specific problems.

[0962] "Estimated Delivery Time" means the time when a package is expected to arrive at the specified delivery destination.

[0963] "User" refers to the person or organization receiving the delivery.

[0964] "Emotional data" refers to information about a user's past reactions and current emotional state.

[0965] An "emotion-appropriate message" refers to a notification message that is composed of content that matches the user's emotional state.

[0966] "Notification means" refers to a method or tool for conveying a calculated estimated delivery time or a message corresponding to the user's emotion.

[0967] The system of the present invention operates primarily in cooperation between a server, a terminal, and a user. The server uses a GPS device installed in the delivery vehicle, a traffic information API, a weather forecast API, and a generation AI engine. The terminal primarily refers to the user's smartphone or PC, and the user is the person or organization that receives the package.

[0968] First, the server collects real-time location information of each delivery vehicle through the GPS device installed in the delivery vehicle. This location information includes the vehicle's latitude and longitude and is periodically obtained using a REST API. Next, the server collects current traffic condition data using a traffic information API. This data includes information on road congestion, construction, and traffic accidents. The server also collects weather forecast data from a weather forecast API to obtain future weather information for a specific area.

[0969] Based on the collected location information, traffic data, and weather forecast data, the generation AI installed on the server analyzes the data. The generation AI uses machine learning algorithms to calculate the optimal transportation route and highly accurate estimated delivery time for each package. For example, it determines that delivery vehicle A's location is latitude 35.6895, longitude 139.6917, and based on this data, calculates that it will arrive at delivery destination B at 9:30.

[0970] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user, or sending a push notification via a dedicated smartphone app. For example, a message might say, "Your package is scheduled to arrive at 9:30. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen.

[0971] The user checks the notification and prepares to be home by the specified scheduled delivery time. The server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The new scheduled delivery time is notified again, and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is generated. This message is notified at an appropriate time using the emotion engine, taking into account the user's current situation.

[0972] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[0973] As a specific example, the server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). The server then obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. Rain is forecast for the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, the estimated arrival time for delivery destination B is calculated as "9:30 AM." If necessary, the emotion engine recognizes the user's emotions and generates a message along the lines of, "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Additionally, if the delivery vehicle is delayed due to traffic congestion or other factors, for example if it arrives later than 10:00, the server will re-analyze the data, calculate a new estimated delivery time, and re-notify the user with a message such as, "The new delivery time is 10:00. Thank you for your understanding!" This allows the user to know the exact delivery time and receive their package without stress.

[0974] Examples of prompts include:

[0975] "Based on the current location of delivery vehicle A, check the traffic situation at coordinates (latitude 35.6895, longitude 139.6917). If rain is forecast at 8:20, calculate the optimal delivery route and estimated delivery time. Also, if the user's emotion is positive, notify them with a message that corresponds to that emotion."

[0976] This system is expected to improve delivery efficiency and accuracy, as well as increase user satisfaction.

[0977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0978] Step 1:

[0979] GPS data collection

[0980] The server obtains real-time location information from the GPS devices installed in the delivery vehicles. The input is latitude and longitude data sent from the delivery vehicles. For example, the location information for delivery vehicle A is received as latitude 35.6895 and longitude 139.6917. This data is periodically retrieved via a REST API. The output is real-time location data that is stored on the server side.

[0981] Step 2:

[0982] Traffic and weather data collection

[0983] The server uses a traffic information API to collect current traffic condition data. This data includes road congestion information, construction information, traffic accident information, etc. For example, it sends the API request "https: / / api.traffic.com / status?location=35.6895,139.6917". The input is the latitude and longitude of the target. The output is traffic condition data. Similarly, the server uses a weather forecast API to collect future weather forecast data. For example, it requests "https: / / api.weather.com / forecast?location=35.6895,139.6917", and the output is stored as weather forecast data.

[0984] Step 3:

[0985] Analyzing data and calculating estimated delivery times

[0986] The server uses a generation AI to analyze the collected location information, traffic data, and weather forecast data. The generation AI receives this data as input and uses a machine learning algorithm to calculate the optimal delivery route and estimated delivery time. For example, it calculates "9:30 a.m." as the estimated arrival time for destination B. The output is estimated delivery time data for each destination.

[0987] Step 4:

[0988] Notification of estimated delivery time

[0989] The server notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message according to that emotion. The inputs are the estimated delivery time and the user's past reaction data. The output is a notification message generated according to the emotion. For example, a message such as "Your package is scheduled to arrive at 9:30. Please look forward to it!" is sent via a smartphone app or email.

[0990] Step 5:

[0991] Timing adjustment and re-notification

[0992] The server monitors the location information of delivery vehicles in real time, and if a major delay or route change occurs, it reanalyzes and calculates a new estimated delivery time. The input is the latest collected location information and traffic condition data. The generative AI is used to recalculate the optimal estimated delivery time. The output is a new estimated delivery time data and a message such as "The new delivery time is 10:00. Thank you for your understanding!" is re-announced.

[0993] Step 6:

[0994] User satisfaction rating after delivery completion

[0995] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction based on that data. The input is the user's emotional data at the time of delivery completion. The output is a user satisfaction rating. This data will be used to improve the service in the future and to refine the notification mechanism.

[0996] (Application example 2)

[0997] 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."

[0998] In modern delivery systems, the accuracy of estimated delivery times is insufficient, leading to stressful waiting times for users. Furthermore, insufficient re-notifications due to changes in delivery status often result in low user satisfaction. Furthermore, delivery notifications are uniform and do not take into account individual user feelings, potentially further reducing the user experience. Therefore, there is a need for more accurate calculations of estimated delivery times and for appropriate notifications that reflect the user's feelings.

[0999] 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.

[1000] In this invention, the server includes a means for collecting location information and traffic conditions for each delivery vehicle, a means for analyzing the collected data using a generation AI and calculating the estimated delivery time of the package, a means for notifying the user of the calculated estimated delivery time, a means for recognizing the user's emotions at the time of notification and generating a personalized message according to the emotions, and a means for evaluating the user's satisfaction after the delivery is completed. This allows for highly accurate calculation of the estimated delivery time based on real-time information on delivery vehicles, traffic conditions, and weather forecasts, and for appropriate notifications according to the user's emotions. This reduces user stress and provides a high level of satisfaction. It also enables appropriate follow-up through re-notification, which reduces redeliveries and improves the efficiency of logistics operations.

[1001] "Transport vehicle location information" is data indicating the current location of a delivery vehicle, and is typically obtained by a GPS device.

[1002] "Traffic conditions" refers to information that indicates current road conditions, such as road congestion, construction information, and accident information.

[1003] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze various data and generate specific outputs.

[1004] The "estimated delivery time" is the time when the package is expected to be delivered to the user.

[1005] The "means of notifying the user" refers to a method for notifying the user of the estimated delivery time and update information, such as push notification or email.

[1006] The "emotion engine" is an engine that predicts a user's current emotions based on their past reaction data and generates a message that corresponds to that emotion.

[1007] "User satisfaction" is a measure of how well a service meets users' expectations and requirements.

[1008] "Weather forecast data" is data that indicates information about future weather in a specific area.

[1009] The system of the present invention collects location information and traffic conditions for each delivery vehicle, uses generation AI to calculate highly accurate estimated delivery times, and notifies the user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and has the ability to generate individual messages based on the user's emotions and adjust the timing of delivery notifications and re-notifications. It also includes a means to evaluate user satisfaction after delivery is completed.

[1010] The server receives real-time location information from the GPS device installed in the delivery vehicle. The location information is the current latitude and longitude of the delivery vehicle and is periodically obtained through a REST API. In addition, the server uses a traffic information API to collect current traffic conditions, including road congestion information, construction information, and traffic accident information. The server also collects weather forecast data from a weather forecast API to obtain information about future weather in a specific area.

[1011] Based on the collected location information, traffic conditions, and weather forecast data, the server-based AI analyzes this data and uses machine learning algorithms to accurately calculate the optimal transportation route and estimated delivery time for each package.

[1012] The server then notifies the user of the calculated estimated delivery time. At this time, the emotion engine infers the user's current emotion based on the user's past reaction data and generates a message that corresponds to that emotion. Notification methods include sending an email to the email address registered by the user or a push notification via a dedicated smartphone app. The device (user's smartphone) receives the notification and displays it on its screen.

[1013] The user checks the notification and prepares to be home at the specified scheduled delivery time. In addition, the server monitors the delivery vehicle's location information in real time, and if there is a major delay or route change, it reanalyzes and calculates a new scheduled delivery time. The user is notified of the recalculated scheduled delivery time again, and the emotion engine sends notifications at an appropriate time taking into account the user's current situation. This increases the user's willingness to receive the delivery and reduces stress.

[1014] After the delivery is completed, the server collects user emotion data to evaluate user satisfaction, which will be used to improve future services and create a more effective notification mechanism.

[1015] Specific examples

[1016] The server obtains GPS data for delivery vehicle A at 8:00 AM. This data reveals that delivery vehicle A is located at coordinates (latitude 35.6895, longitude 139.6917). Next, the server obtains traffic condition data as of 8:20 AM from a traffic information API and a weather forecast from a weather forecast API. For example, it obtains a forecast of rain in the target area. Using this data, the generation AI performs analysis and calculates the optimal route for delivery vehicle A and the estimated arrival time for each delivery destination. For example, it calculates "9:30 AM arrival" at delivery destination B, and the emotion engine recognizes the user's emotion and generates a message such as "Your package is scheduled to arrive at 9:30 AM. Please look forward to it!" The device (the user's smartphone) receives this notification and displays it on its screen. The user confirms the notification and waits at home until 9:30 AM. Furthermore, if the delivery vehicle is delayed due to traffic congestion or other factors, for example, if it arrives late at 10:00, the server reanalyzes the data, calculates a new estimated delivery time, and sends a message like, "The new delivery time is 10:00. Thank you for your understanding!" This system virtually eliminates the need for redelivery, optimizing logistics operations and reducing the burden on truck drivers. Furthermore, the introduction of an emotion engine will improve user satisfaction and enable the provision of more personalized services.

[1017] An example of a prompt is as follows:

[1018] "Create an app that notifies users of food delivery times with high accuracy and changes the message content depending on the user's emotions. The goal is to reduce stress and increase satisfaction by adjusting the timing and content of notifications based on the user's emotions. Required APIs include GPS data, traffic information, and weather forecast data, and an emotion engine should also be used."

[1019] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1020] Step 1:

[1021] The server receives real-time location information (latitude and longitude) from the GPS devices installed in the delivery vehicles, which are periodically retrieved through a REST API. The input is the ID of the delivery vehicle, and the output is the current latitude and longitude of the delivery vehicle.

[1022] Step 2:

[1023] The server uses the traffic information API to collect traffic conditions such as current road congestion information, construction information, traffic accident information, etc. The input is the API key and location information, and the output is the collected traffic condition data.

[1024] Step 3:

[1025] The server collects weather forecast data from a weather forecast API and obtains future weather information for a specific region. The input is an API key and location information, and the output is future weather forecast data.

[1026] Step 4:

[1027] The server uses AI to analyze the collected location information, traffic conditions, and weather forecast data. This analysis allows it to accurately calculate the optimal transport route and estimated delivery time for each package. The input is the collected data (location information, traffic conditions, weather forecast), and the output is the estimated delivery time and route information.

[1028] Step 5:

[1029] The server notifies the user of the calculated estimated delivery time. At this time, it uses an emotion engine to infer the user's current emotion from past reaction data and generates a message according to that emotion. The input is the calculated estimated delivery time and the user's past reaction data, and the output is an appropriate notification message.

[1030] Step 6:

[1031] The device (user's smartphone) receives the notification and displays it on the screen. The input is the notification message sent from the server, and the output is the notification message displayed on the device screen.

[1032] Step 7:

[1033] The user checks the notification and prepares to be at home at the specified scheduled delivery time. The input is the notification message displayed on the terminal, and the output is the user's behavior.

[1034] Step 8:

[1035] The server monitors the location information of delivery vehicles in real time, and if there is a major delay or route change, it reanalyzes and calculates a new estimated delivery time. The input is the latest location information and traffic condition data, and the output is the recalculated estimated delivery time.

[1036] Step 9:

[1037] The server notifies the user of the recalculated estimated delivery time and sends a re-notification at an appropriate time using the emotion engine. The input is the recalculated estimated delivery time and the user's emotion data, and the output is an appropriate re-notification message.

[1038] Step 10:

[1039] After the delivery is completed, the server collects the user's emotional data and evaluates the user's satisfaction. The input is the user's reaction data after the delivery is completed, and the output is the evaluation of the user's satisfaction. This will be useful for future service improvements and refinement of the notification mechanism.

[1040] 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.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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).

[1047] 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.

[1048] 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."

[1049] 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.

[1050] 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).

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] The following is further disclosed regarding the above embodiment.

[1062] (Claim 1)

[1063] a means for collecting location information and traffic conditions of each transport vehicle;

[1064] A means for analyzing the collected data using generative AI and calculating the estimated delivery time of the package;

[1065] means for notifying a user of the calculated estimated delivery time;

[1066] A system including:

[1067] (Claim 2)

[1068] 10. The system of claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time.

[1069] (Claim 3)

[1070] 10. The system of claim 1, further comprising means for collecting weather forecast data and factoring it into calculating estimated package delivery times.

[1071] "Example 1"

[1072] (Claim 1)

[1073] a means for collecting location information and traffic conditions of each transport vehicle;

[1074] A method for analyzing collected data using generative AI to accurately calculate optimal transport routes and estimated delivery times for packages;

[1075] a means for collecting weather forecast data and taking it into account in calculating transportation routes and estimated delivery times;

[1076] means for notifying a user of the calculated estimated delivery time;

[1077] means for displaying a notification on a user terminal;

[1078] A system including:

[1079] (Claim 2)

[1080] 10. The system of claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time.

[1081] (Claim 3)

[1082] 2. The system according to claim 1, further comprising means for allowing a user to check the estimated delivery time.

[1083] "Application Example 1"

[1084] (Claim 1)

[1085] a means for collecting location information and traffic conditions of each transport vehicle;

[1086] A means for analyzing the collected data using generative AI and calculating the estimated delivery time of the package;

[1087] means for notifying a user of the calculated estimated delivery time;

[1088] A means to improve the efficiency and accuracy of food delivery based on estimated delivery times;

[1089] A system including:

[1090] (Claim 2)

[1091] 10. The system of claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time.

[1092] (Claim 3)

[1093] 10. The system of claim 1, further comprising means for collecting weather forecast data and factoring it into calculating estimated package delivery times.

[1094] "Example 2: Combining Emotion Engines"

[1095] (Claim 1)

[1096] means for collecting location information of each transport vehicle;

[1097] a means for collecting traffic condition data;

[1098] a means for collecting weather forecast data;

[1099] A means for analyzing the collected location information, traffic condition data, and weather forecast data and using a generating AI to calculate the estimated delivery time of the package;

[1100] A means for predicting a user's current emotion based on past emotion data and generating a message according to the emotion;

[1101] means for notifying the user of the calculated estimated delivery time and a message according to the emotion;

[1102] A system including:

[1103] (Claim 2)

[1104] 2. The system according to claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time, and for re-notifying the user at an appropriate timing taking into account the current situation of the user using an emotion engine.

[1105] (Claim 3)

[1106] 2. The system of claim 1, further comprising means for collecting user emotion data after delivery completion and evaluating user satisfaction.

[1107] "Application example 2 when combining emotion engines"

[1108] (Claim 1)

[1109] a means for collecting location information and traffic conditions of each transport vehicle;

[1110] A means for analyzing the collected data using generative AI and calculating the estimated delivery time of the package;

[1111] means for notifying a user of the calculated estimated delivery time;

[1112] a means for recognizing a user's emotion at the time of notification and generating a personalized message according to the emotion;

[1113] a means for assessing user satisfaction after delivery is completed; and

[1114] A system including:

[1115] (Claim 2)

[1116] 10. The system of claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time.

[1117] (Claim 3)

[1118] 10. The system of claim 1, further comprising means for collecting weather forecast data and factoring it into calculating estimated package delivery times. [Explanation of symbols]

[1119] 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 location information and traffic conditions of each transport vehicle; A means for analyzing the collected data using generative AI and calculating the estimated delivery time of the package; means for notifying a user of the calculated estimated delivery time; A system including:

2. 10. The system of claim 1, further comprising means for notifying the user of updates to the estimated delivery time in real time based on the estimated delivery time.

3. 10. The system of claim 1, further comprising means for collecting weather forecast data and factoring it into calculating estimated package delivery times.

Images