system

Abstract

We provide the system. [Solution] A means of collecting and analyzing diverse data within a region in real time, A method for automatically generating the optimal delivery route based on collected data, A means of efficiently allocating logistics resources among multiple delivery companies, A means to monitor the progress of deliveries in real time and update routes as needed, A system that includes means to achieve environmentally friendly delivery while minimizing energy consumption.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional logistics systems have insufficient optimization of delivery routes, and it is particularly difficult to achieve efficient delivery in sparsely populated areas. In addition, resources are rarely shared among multiple delivery operators, resulting in wasteful costs. Furthermore, since the realization of environmentally friendly delivery has not advanced, there is a problem of high energy consumption. It is necessary to solve these problems and construct a more efficient and sustainable logistics network.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes data in real time to maximize the efficiency of local delivery and logistics, and automatically generates optimized delivery routes. It also incorporates a mechanism to improve overall logistics efficiency by efficiently allocating resources among multiple delivery companies. Furthermore, the system has a function to monitor delivery progress in real time and update delivery routes as needed. It also includes means for implementing environmentally friendly delivery while reducing energy consumption.

[0006] "Within a region" refers to a specific geographical range or area, and the term describes logistics activities within that range.

[0007] "Collecting data in real time" refers to the process of acquiring data the moment it is generated or within a very short period of time.

[0008] "Means of analysis" refers to methods and devices for evaluating and processing collected data and extracting useful information.

[0009] "Automatically generating delivery routes" refers to the process by which a program independently calculates the optimal route using an algorithm.

[0010] "Efficiently allocating logistics resources" refers to allocating available delivery resources, such as vehicles and personnel, in a way that maximizes their efficiency.

[0011] "Monitoring progress in real time" refers to tracking and checking the status of ongoing delivery and logistics processes in real time.

[0012] "Updating the route" refers to recalculating and modifying a pre-set path based on the current situation.

[0013] "Reducing energy consumption" refers to methods and technologies that minimize the amount of energy used to carry out logistics activities.

[0014] "Environmentally friendly delivery" refers to delivery activities that reduce environmental impact by using ecologically conscious methods and technologies. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The system of the present invention utilizes generative AI to collect and analyze data in real time in order to maximize the efficiency of delivery and logistics within a region. Specific embodiments are described below.

[0037] First, the server acquires various data affecting logistics in real time, such as local delivery demand, traffic conditions, and weather information. This data is collected through APIs and various sensors and stored in a database. Based on the collected data, the server automatically generates the optimal delivery route using a generative AI. This route optimization algorithm takes into account traffic congestion and weather conditions to construct a route that maximizes delivery efficiency.

[0038] Next, the server retrieves resource information from multiple delivery companies (e.g., the number of available vehicles and the work status of drivers) and allocates resources efficiently. This allows resources to be shared among delivery companies, resulting in a reduction of unnecessary costs.

[0039] Once delivery begins, the terminal provides the driver with an optimized delivery route. The driver follows the terminal's instructions and performs the delivery based on real-time updated route information. During delivery, the server continuously monitors the progress and modifies the route information in real time as needed, for example, in the event of unexpected traffic congestion or weather changes.

[0040] Furthermore, the server helps reduce energy consumption and enables environmentally friendly delivery. This includes speed control to encourage eco-driving and suggesting optimal rest stops to maximize energy efficiency.

[0041] As a concrete example, consider a case where multiple delivery companies cooperate to deliver goods in a certain area. When a delivery request is made, the server checks the availability of each company's vehicles and drivers and calculates the most efficient route. It also predicts traffic flow and takes into account the delicate delivery needs in sparsely populated areas. Then, instructions are sent to each driver via terminals to support smooth deliveries.

[0042] In this way, the implementation of the invention can be flexibly adapted to different situations and can support the formation of an efficient logistics network.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects delivery request information from each delivery company's order database. This data includes the delivery address, package weight, size, and delivery priority. Furthermore, it obtains real-time traffic and weather data from traffic and weather information services via APIs.

[0046] Step 2:

[0047] The server uses the collected data to optimize delivery routes. The optimization algorithm calculates the shortest route to each delivery destination, taking into account traffic and weather conditions. In this process, a generating AI generates multiple possible routes and selects the most efficient one.

[0048] Step 3:

[0049] The server checks and efficiently allocates vehicle and driver resources across multiple delivery companies. Based on the resources available to each company, it optimizes who is responsible for which route and improves efficiency by utilizing common resources.

[0050] Step 4:

[0051] The terminal sends optimized delivery routes to each driver and displays route information. Drivers can then follow the route instructions displayed on the screen and deliver in the specified order.

[0052] Step 5:

[0053] The server monitors the delivery progress in real time and quickly updates route information if it detects unexpected traffic delays or weather changes. The updated information is immediately sent to the terminal, providing drivers with new instructions.

[0054] Step 6:

[0055] After delivery is complete, the server analyzes all data and generates reports on each vendor's operational efficiency and energy consumption. This allows for feedback to be provided for future operational improvements and energy conservation.

[0056] In this way, each step works in conjunction to maximize the efficiency of delivery and logistics, and enable environmentally conscious operations.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Achieving both maximum delivery efficiency and environmental considerations simultaneously in logistics has been difficult with conventional systems. Furthermore, there were challenges in efficiently allocating resources among multiple carriers and in real-time route updates. This invention aims to overcome these challenges and provide a logistics system that balances efficiency and environmental considerations.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal logistics route based on the collected information, and means for efficiently allocating logistics resources among multiple carriers. This improves the efficiency of logistics and, at the same time, reduces energy consumption, enabling environmentally friendly transportation.

[0062] "Diverse information within the region" refers to various types of data that affect logistics, such as delivery demand, traffic conditions, and weather information.

[0063] "Means of collecting and analyzing data in real time" refers to the process of constantly acquiring the latest information through sensors and APIs, processing it immediately, and deriving useful analytical results.

[0064] "Methods for automatically generating optimal logistics routes" refers to a process that uses a generation AI model to calculate the route that maximizes delivery efficiency based on collected information, taking into account traffic conditions and weather conditions, using an algorithm.

[0065] "Means of efficiently allocating logistics resources" refers to the process of understanding the operational status of transporters' vehicles and drivers, and then scheduling and resource sharing to make optimal use of them.

[0066] "Means of monitoring the progress of transportation in real time and updating the route as needed" refers to the process of using GPS or similar technologies to determine the location of vehicles during delivery and recalculating and updating the route as appropriate in response to conditions such as traffic congestion and weather changes.

[0067] "Means of achieving environmentally friendly transportation while minimizing energy consumption" refers to transportation methods that reduce energy consumption and minimize pollution through measures such as speed control and efficient rest stop timing instructions.

[0068] "Methods for optimizing logistics routes using generative AI models" refer to the process of utilizing machine learning models to plan the optimal route in real time based on collected information.

[0069] "Means of providing electronic devices for the dynamic adjustment of logistics resources" refers to the process of providing instructions and information to carriers and drivers through terminals in order to support the effective and efficient use of resources.

[0070] To implement this invention, three basic elements are utilized: a server, a terminal, and a user.

[0071] First, the server collects and analyzes various types of information in real time, such as local delivery demand, traffic conditions, and weather information. This process involves acquiring information using GPS sensors and APIs and storing it in a database system. Specific software used here includes a database management system and a real-time data processing engine. Furthermore, a generative AI model is utilized to calculate the optimal delivery route based on the collected information. An example of a prompt message is, "Please calculate the optimal delivery route based on local traffic conditions and weather information." This generative AI model takes traffic congestion and weather conditions into account to maximize efficiency.

[0072] The terminal's role is to provide drivers with routes optimized for their needs. Specifically, it uses a map application via a user interface to display route instructions and estimated travel times. If real-time route changes are required, it automatically updates the latest route information based on instructions from the server.

[0073] Furthermore, the driver, as the user, can proceed with deliveries by following the instructions on the terminal. The terminal also displays speed control and rest stop timing advice that are mindful of optimizing energy use, thus promoting eco-driving.

[0074] As a concrete example, consider delivery operations in a specific region. The server continuously collects traffic and weather data for this region and uses generated AI to calculate the optimal route. By providing the latest route information to drivers via terminals and responding to real-time route changes, it enables fast and efficient logistics.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server uses APIs and GPS sensors to collect diverse data in real time, such as local delivery demand, traffic conditions, and weather information. The collected data is stored in a database, making all necessary information available for the next processing step.

[0078] Step 2:

[0079] The server analyzes the information stored in the database and uses a generative AI model to calculate the optimal delivery route. The input consists of collected traffic and weather data. The generative AI operates based on the prompt "Calculate the optimal delivery route based on local traffic and weather information." The output is information on efficient delivery routes.

[0080] Step 3:

[0081] The server retrieves resource information from multiple transportation companies (e.g., the number of available vehicles and driver schedules) and allocates resources optimally. This process uses data provided by each company as input and outputs a resource sharing plan.

[0082] Step 4:

[0083] The terminal receives optimized delivery routes sent from the server as input and displays them to the driver. By using a map application to show specific directions and estimated travel times, the system enables drivers to carry out deliveries efficiently.

[0084] Step 5:

[0085] The server monitors the driver's delivery progress in real time as input. Based on GPS information, it tracks the progress and recalculates the route as needed. An updated, latest delivery route is generated as output. This information is then sent back to the terminal.

[0086] Step 6:

[0087] The terminal provides drivers with advice on optimal speed and rest times for energy-efficient driving. Inputs include current speed information and driving patterns, while output is suggestions to promote energy efficiency.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Current logistics systems suffer from problems such as insufficient real-time data acquisition and rapid recalculation of transportation routes to optimize regional transportation efficiency. Furthermore, while there is a demand for environmentally friendly transportation that reduces energy consumption, the current system fails to meet these needs. In addition, efficient management of internal and external conditions at transportation hubs is difficult, leading to inefficiencies due to delays in rapid route changes and information dissemination.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the acquired information, means for reducing energy consumption and realizing environmentally friendly transportation, means for collecting information on the conditions inside and outside of transportation hubs using mobile terminals and automated equipment and reflecting it in the transportation plan in real time, and means for recalculating the automated route and providing rapid information notifications based on the acquired information. This enables maximizing transportation efficiency, improving energy efficiency, and accelerating logistics operations.

[0093] "Diverse information within the region" refers to traffic conditions, weather information, vehicle conditions, and environmental factors affecting logistics, and it is possible to obtain this information in real time.

[0094] "Automatically generating the optimal transportation route" refers to the process of generating the most efficient transportation route, taking into account traffic congestion and weather changes, based on collected information.

[0095] "Energy-saving and environmentally friendly transportation" refers to transportation methods that improve energy efficiency by providing eco-driving information and giving instructions on optimal rest times and speed control.

[0096] "Collecting information on the internal and external conditions of transportation hubs using mobile devices and automated equipment" means continuously monitoring and collecting information on the physical and environmental conditions at transportation hubs using smartphones and automated equipment.

[0097] "Reflecting in real-time transportation plans" means quickly updating and applying transportation plans based on the latest acquired data.

[0098] "Recalculating routes and providing rapid information based on acquired data" refers to the process of promptly recalculating transportation routes in response to changes in conditions during logistics operations and promptly notifying drivers and other relevant parties of the results.

[0099] This invention is an embodiment of an advanced logistics system utilizing a generative AI model. Its specific configuration and functions are described in detail below.

[0100] The server acquires diverse information in real time, such as local traffic conditions, weather information, and vehicle status, using API interfaces and sensor data. The collected information is stored in a database and analyzed by a generating AI model. This allows for the automatic generation of optimal transportation routes. By considering variable factors such as traffic flow and weather conditions, efficient delivery becomes possible.

[0101] The terminals transmit data collected using smartphones and robots to a server in order to continuously monitor the conditions inside and outside the transportation hub. Based on this information, the server updates the transportation plan in real time and provides drivers with the most efficient routes. The terminals also play a role in providing drivers with eco-driving support information, promoting driving that reduces energy consumption.

[0102] Users can view real-time updated transportation plans using their mobile devices. Whenever new information becomes available, the server recalculates the route and issues rapid route change notifications based on this information. This allows users to stay constantly aware of the transportation progress and achieve efficient operations.

[0103] As a concrete example, consider the operation at a logistics center. Suppose an unexpected road closure occurs at this logistics center due to heavy rain. In this situation, the server immediately recalculates the route and notifies the driver of the optimal alternative route. This minimizes delays in logistics.

[0104] An example of a prompt would be, "Consider today's weather conditions and show the optimal transport route to the specified destination." Using this prompt, the generative AI model can respond with the optimal route based on real-time data.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server acquires diverse information such as traffic conditions, weather information, and vehicle status through API interfaces and sensor data. The input consists of raw data from sensors and APIs, and this data is first stored in a database.

[0108] Step 2:

[0109] The server automatically generates the optimal transportation route using a generated AI model based on the stored information. In this step, the algorithm takes traffic and weather information obtained from the database as input, performs calculations, and selects the optimal route. The output is optimized route information.

[0110] Step 3:

[0111] The terminal receives the optimal transport route provided by the server and displays it to the driver. The input is route information from the server, and the output is visual route guidance. In this step, the terminal navigates the driver through real-time route guidance functionality.

[0112] Step 4:

[0113] The server monitors the ongoing transportation status and recalculates the route based on newly collected information. Recalculations are performed in response to changes in traffic and weather, with newly collected traffic information as input and updated route information as output. This updated information is immediately notified to the terminal.

[0114] Step 5:

[0115] Users use their mobile devices to view real-time updated transportation plans. The devices provide users with priority information, route updates, and eco-driving information from the server. Inputs are various data from the server, and outputs are information notifications to the user.

[0116] Step 6:

[0117] Users can input prompts and extract detailed information using a generated AI model. The input consists of prompts issued by the user to the generated AI model, and the output is the model's suggested optimal transportation strategies and routes.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system that incorporates an emotion engine to improve service quality by recognizing user emotions, in addition to streamlining local delivery and logistics. This system uses generative AI and emotion recognition technology to analyze data in real time and provide optimal delivery and user experience.

[0120] First, the server collects and analyzes logistics data in real time, including delivery demand, traffic conditions, and weather information. During this process, it utilizes AI to automatically calculate the shortest and most efficient delivery route. An algorithm is employed that selects the optimal route, taking into account traffic congestion and weather conditions.

[0121] Next, the server uses an emotion engine to analyze feedback data obtained from users. This feedback is collected via smartphones and other devices and provides insights into user satisfaction and emotional state. The emotion engine has the function of identifying the emotions users have towards the delivery service and adjusting the service accordingly.

[0122] During delivery, the terminal displays optimized delivery routes to the driver, along with special instructions based on user sentiment data. For example, if a user is in a particular hurry, this information is taken into consideration and delivery is prioritized. Conversely, if there is a high probability that the user will be absent, the system considers optimizing redelivery.

[0123] After delivery is complete, the server comprehensively analyzes all data and generates reports for delivery companies to improve operational efficiency and user satisfaction. This helps not only with shipping cost planning but also with improving the overall service.

[0124] As a concrete example, consider a situation where a user is dissatisfied with a delivery delay. The emotion engine recognizes this emotion and sets a higher priority for the next delivery. This allows the delivery company to provide prompt service to the specific user and improve overall customer satisfaction. In this way, the present invention provides an effective means of achieving efficient logistics and customized services for individual users.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] Users request delivery services from their smartphones or devices and submit their order information along with their current mood as a selection or simple input. This sentiment data is used to collect user experience data.

[0128] Step 2:

[0129] The server processes order information and sentiment data received from users as initial input data, and retrieves real-time traffic and weather data from external APIs. This allows it to gather all the information necessary for delivery planning.

[0130] Step 3:

[0131] The server uses a generative AI and emotion engine to optimize delivery routes based on collected order information, traffic information, weather information, and emotion data. The generated routes are designed to maximize efficiency and user satisfaction.

[0132] Step 4:

[0133] The server evaluates the resources of multiple delivery companies and assigns delivery tasks to the most suitable personnel. It also adjusts order priorities, taking into account special needs based on user emotions.

[0134] Step 5:

[0135] The terminal provides drivers with optimized delivery routes and delivery instructions. If any special instructions based on user sentiment data are included, they will also be displayed.

[0136] Step 6:

[0137] The server monitors the progress of deliveries in real time and updates routes and delivery plans as needed. This information is sent to terminals as it progresses, providing drivers with the latest instructions.

[0138] Step 7:

[0139] Once a delivery is complete, the server compiles delivery data and user feedback to create an analytical report for improving service quality. This information is provided to logistics companies and delivery teams and used for future improvements.

[0140] Thus, by incorporating emotion recognition, this invention enables not only more efficient delivery but also the provision of personalized service to each user.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In today's logistics industry, improving delivery efficiency and customer satisfaction are crucial challenges. In particular, formulating optimal transportation routes based on real-time traffic and weather conditions, and providing services that consider user sentiment, are not adequately achieved with conventional systems. Therefore, there is a need for methods that provide reliable and efficient logistics services while reducing environmental impact.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user sentiment data and adjusting the service. This makes it possible to improve logistics efficiency in an environmentally friendly way and enhance the satisfaction of individual users.

[0146] "Diverse information within the region" refers to data such as delivery demand, traffic conditions, and weather information related to logistics, and this data is collected in real time.

[0147] The "optimal transportation route" refers to the most efficient and fastest transportation route, automatically calculated using AI based on collected data.

[0148] "User sentiment data" refers to information that represents users' emotions and satisfaction levels, obtained through user feedback and evaluations.

[0149] "Means of adjusting services" refers to technologies that dynamically optimize the content and order of delivery services based on user sentiment data.

[0150] "Environmentally friendly transport" refers to sustainable transport methods that utilize means to reduce energy consumption and carbon dioxide emissions during the transport process.

[0151] "Methods for creating reports" refers to the process of analyzing all logistics-related data and generating reports that summarize areas for improvement aimed at increasing efficiency and user satisfaction.

[0152] This invention is a system designed to improve the efficiency of logistics operations and enhance user satisfaction. The system broadly comprises functions related to data collection, analysis, and feedback of results. At its core are a generative AI model and sentiment analysis technology, enabling real-time data processing.

[0153] The server collects and manages diverse information within the region. Specifically, it uses network infrastructure to automatically retrieve traffic conditions, weather information, and delivery demand through APIs and external database connections. It also passes the collected data to generative AI models such as Google's Vertex AI and Amazon's SageMaker to calculate the optimal transportation route. An example of a prompt message is, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather."

[0154] The terminal provides delivery drivers with optimized routes and personalized instructions based on user sentiment. For example, a notification will appear on the driver's device indicating that a user is in a particular hurry, and priority delivery instructions will be given to that user.

[0155] Users provide feedback on the delivery service via smartphones or other connected devices. This feedback is analyzed through emotion recognition technology to collect user satisfaction and emotional states. This data is used as a basis for service adjustments to improve the quality of future deliveries.

[0156] Finally, after delivery is complete, the server comprehensively analyzes all data and generates a comprehensive report to improve operational efficiency and user satisfaction. This report serves as important reference material for future transportation planning.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server collects real-time traffic conditions, weather information, and delivery demand within the region. Input data is obtained from APIs and external databases and received over the network. Each piece of information is converted into a unified format within the server and prepared for incorporation into the generating AI model. Specific operations include data filtering and retaining only the most recent information. The output is a clear dataset ready for making optimal transportation decisions.

[0160] Step 2:

[0161] The server generates a prompt message, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather," based on the collected data, and sends it to the AI ​​model to calculate the optimal transportation route. Data processing involves converting the data into a format applicable to the model, and calculations are performed considering conditions such as shortest time and energy efficiency. Specific operations include initializing the calculation process and accelerating results through parallel computing. The output provides multiple optimized route options based on time and efficiency.

[0162] Step 3:

[0163] The terminal displays special instructions to delivery drivers based on the generated delivery route and user sentiment data. Input data consists of route options and sentiment analysis results sent from the server. Based on this, the terminal generates and displays instructions such as "The user is in a hurry, please prioritize this delivery." Specific actions include prioritizing based on date and time, and visual mapping. As output, the driver receives detailed instructions for completing critical deliveries in the shortest possible time.

[0164] Step 4:

[0165] Once a delivery is complete, the server collects all delivery-related information and generates a report to improve efficiency and user satisfaction. Input data includes route information, user feedback, and other relevant data. The data is analyzed to create heatmaps and statistical metrics. Specifically, error checking and pattern recognition are performed to evaluate delivery performance and identify areas for future improvement. The output is a report with detailed and actionable improvement suggestions that will be incorporated into future plans.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In regional logistics and transportation, there is a need to optimize transportation routes by efficiently considering traffic conditions and weather information, while simultaneously improving service quality based on user sentiment. Conventional systems struggle to achieve both efficient transportation routes and increased user satisfaction at the same time; therefore, a system that effectively solves these challenges is necessary.

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

[0170] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user emotional feedback and dynamically adjusting the transportation order. This makes it possible to simultaneously achieve improved efficiency of transportation routes and increased user satisfaction.

[0171] "Diverse information within the region" refers to all dynamic information related to transportation and logistics within the region, including traffic conditions, weather conditions, and user feedback data.

[0172] An "optimal transportation route" is a route that enables efficient and rapid transportation, calculated by taking into account traffic conditions and weather information.

[0173] "Logistical resources" refer to tangible and intangible resources related to logistics and transportation, such as transport vehicles, delivery staff, and warehouse space.

[0174] "Emotional feedback" refers to textual information collected from users, including their satisfaction and dissatisfaction with transportation services.

[0175] "Analyzing user emotional feedback and dynamically adjusting the transport order" refers to the process of analyzing users' emotional responses and changing the order and priority of transports in real time based on the results.

[0176] "Generating promotions based on text analysis" is a method that uses text analysis technology to evaluate user feedback and sentiment data, and then creates promotional activities tailored to the interests of individual users.

[0177] This invention aims to improve the efficiency of logistics and transportation and enhance user satisfaction by realizing a system based on the interaction between servers, terminals, and users.

[0178] The server collects and analyzes various local information in real time, such as traffic conditions, weather conditions, and user feedback data. Using this information, a generative AI model calculates the optimal transportation route. Traffic information is obtained using a common map service API (e.g., Google Maps API), and weather conditions are analyzed using a weather forecast API. Furthermore, to analyze user feedback, an emotion recognition API (e.g., IBM Watson® Emotion Analysis) is used to analyze the emotional state of the feedback.

[0179] The terminal displays optimized route information to transporters, along with special instructions based on emotional feedback. For example, if a user expressed dissatisfaction with their previous transport, the terminal will instruct the transporter to prioritize that user's transport. It can also generate promotions based on text analysis and provide personalized offers to users.

[0180] Users provide feedback via their smartphones, and this information is sent to a server and used to improve future deliveries. If the feedback is negative, the system adds a promotion to the next service to improve user satisfaction.

[0181] As a concrete example, consider a user who orders food delivery during the busy evening hours. The server detects traffic congestion and uses a generated AI model to recalculate an efficient delivery route. At the same time, it also considers the user's past negative feedback and adjusts the delivery priority. An example of a prompt used in this process is, "This user has been dissatisfied with past deliveries. Please suggest the optimal delivery route and additional promotions based on current traffic conditions." In this way, the system achieves improved delivery efficiency and user experience.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server obtains real-time traffic and weather information from traffic information APIs and weather forecast APIs. Based on the input information, a generating AI model calculates the optimal transportation route. In this step, data processing is performed to take into account traffic congestion and bad weather, and as a result, optimal route information is output.

[0185] Step 2:

[0186] Users input feedback about the transportation service via their smartphones. The server receives this feedback and performs text analysis using an emotion recognition API to determine the user's emotional state. This involves data processing to extract emotional attributes from the text data of the feedback, and the output is the user's emotional information.

[0187] Step 3:

[0188] The server uses a generative AI model to adjust transport priorities. Based on the results of sentiment analysis, it identifies urgent transports and revises their priorities. The input is user sentiment information, and the output is a list of adjusted transport priorities.

[0189] Step 4:

[0190] The terminal displays optimized route information and special instructions based on emotional feedback to the transporter. Here, generated route and priority information is input, and clear, organized instructions are output for the transporter.

[0191] Step 5:

[0192] The server generates promotions based on text analysis. It utilizes user sentiment data to generate promotions tailored to specific interests. Here, data processing based on sentiment data is performed, and the resulting promotional information is output.

[0193] Step 6:

[0194] The user receives the promotion via their smartphone. In this step, the server receives promotion information as input and notifies or displays it to the user. The output is the promotion information displayed on the user's device.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0197] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0202] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0207] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0209] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0210] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0211] The system of the present invention utilizes generative AI to collect and analyze data in real time in order to maximize the efficiency of delivery and logistics within a region. Specific embodiments are described below.

[0212] First, the server acquires various data affecting logistics in real time, such as local delivery demand, traffic conditions, and weather information. This data is collected through APIs and various sensors and stored in a database. Based on the collected data, the server automatically generates the optimal delivery route using a generative AI. This route optimization algorithm takes into account traffic congestion and weather conditions to construct a route that maximizes delivery efficiency.

[0213] Next, the server retrieves resource information from multiple delivery companies (e.g., the number of available vehicles and the work status of drivers) and allocates resources efficiently. This allows resources to be shared among delivery companies, resulting in a reduction of unnecessary costs.

[0214] Once delivery begins, the terminal provides the driver with an optimized delivery route. The driver follows the terminal's instructions and performs the delivery based on real-time updated route information. During delivery, the server continuously monitors the progress and modifies the route information in real time as needed, for example, in the event of unexpected traffic congestion or weather changes.

[0215] Furthermore, the server helps reduce energy consumption and enables environmentally friendly delivery. This includes speed control to encourage eco-driving and suggesting optimal rest stops to maximize energy efficiency.

[0216] As a concrete example, consider a case where multiple delivery companies cooperate to deliver goods in a certain area. When a delivery request is made, the server checks the availability of each company's vehicles and drivers and calculates the most efficient route. It also predicts traffic flow and takes into account the delicate delivery needs in sparsely populated areas. Then, instructions are sent to each driver via terminals to support smooth deliveries.

[0217] In this way, the implementation of the invention can be flexibly adapted to different situations and can support the formation of an efficient logistics network.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server collects delivery request information from each delivery company's order database. This data includes the delivery address, package weight, size, and delivery priority. Furthermore, it obtains real-time traffic and weather data from traffic and weather information services via APIs.

[0221] Step 2:

[0222] The server uses the collected data to optimize delivery routes. The optimization algorithm calculates the shortest route to each delivery destination, taking into account traffic and weather conditions. In this process, a generating AI generates multiple possible routes and selects the most efficient one.

[0223] Step 3:

[0224] The server checks and efficiently allocates vehicle and driver resources across multiple delivery companies. Based on the resources available to each company, it optimizes who is responsible for which route and improves efficiency by utilizing common resources.

[0225] Step 4:

[0226] The terminal sends optimized delivery routes to each driver and displays route information. Drivers can then follow the route instructions displayed on the screen and deliver in the specified order.

[0227] Step 5:

[0228] The server monitors the delivery progress in real time and quickly updates route information if it detects unexpected traffic delays or weather changes. The updated information is immediately sent to the terminal, providing drivers with new instructions.

[0229] Step 6:

[0230] After delivery is complete, the server analyzes all data and generates reports on each vendor's operational efficiency and energy consumption. This allows for feedback to be provided for future operational improvements and energy conservation.

[0231] In this way, each step works in conjunction to maximize the efficiency of delivery and logistics, and enable environmentally conscious operations.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] Achieving both maximum delivery efficiency and environmental considerations simultaneously in logistics has been difficult with conventional systems. Furthermore, there were challenges in efficiently allocating resources among multiple carriers and in real-time route updates. This invention aims to overcome these challenges and provide a logistics system that balances efficiency and environmental considerations.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal logistics route based on the collected information, and means for efficiently allocating logistics resources among multiple carriers. This improves the efficiency of logistics and, at the same time, reduces energy consumption, enabling environmentally friendly transportation.

[0237] "Diverse information within the region" refers to various types of data that affect logistics, such as delivery demand, traffic conditions, and weather information.

[0238] "Means of collecting and analyzing data in real time" refers to the process of constantly acquiring the latest information through sensors and APIs, processing it immediately, and deriving useful analytical results.

[0239] "Methods for automatically generating optimal logistics routes" refers to a process that uses a generation AI model to calculate the route that maximizes delivery efficiency based on collected information, taking into account traffic conditions and weather conditions, using an algorithm.

[0240] "Means of efficiently allocating logistics resources" refers to the process of understanding the operational status of transporters' vehicles and drivers, and then scheduling and resource sharing to make optimal use of them.

[0241] "Means of monitoring the progress of transportation in real time and updating the route as needed" refers to the process of using GPS or similar technologies to determine the location of vehicles during delivery and recalculating and updating the route as appropriate in response to conditions such as traffic congestion and weather changes.

[0242] "Means of achieving environmentally friendly transportation while minimizing energy consumption" refers to transportation methods that reduce energy consumption and minimize pollution through measures such as speed control and efficient rest stop timing instructions.

[0243] "Methods for optimizing logistics routes using generative AI models" refer to the process of utilizing machine learning models to plan the optimal route in real time based on collected information.

[0244] "Means of providing electronic devices for the dynamic adjustment of logistics resources" refers to the process of providing instructions and information to carriers and drivers through terminals in order to support the effective and efficient use of resources.

[0245] To implement this invention, three basic elements are utilized: a server, a terminal, and a user.

[0246] First, the server collects and analyzes various types of information in real time, such as local delivery demand, traffic conditions, and weather information. This process involves acquiring information using GPS sensors and APIs and storing it in a database system. Specific software used here includes a database management system and a real-time data processing engine. Furthermore, a generative AI model is utilized to calculate the optimal delivery route based on the collected information. An example of a prompt message is, "Please calculate the optimal delivery route based on local traffic conditions and weather information." This generative AI model takes traffic congestion and weather conditions into account to maximize efficiency.

[0247] The terminal's role is to provide drivers with routes optimized for their needs. Specifically, it uses a map application via a user interface to display route instructions and estimated travel times. If real-time route changes are required, it automatically updates the latest route information based on instructions from the server.

[0248] Furthermore, the driver, as the user, can proceed with deliveries by following the instructions on the terminal. The terminal also displays speed control and rest stop timing advice that are mindful of optimizing energy use, thus promoting eco-driving.

[0249] As a concrete example, consider delivery operations in a specific region. The server continuously collects traffic and weather data for this region and uses generated AI to calculate the optimal route. By providing the latest route information to drivers via terminals and responding to real-time route changes, it enables fast and efficient logistics.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server uses APIs and GPS sensors to collect diverse data in real time, such as local delivery demand, traffic conditions, and weather information. The collected data is stored in a database, making all necessary information available for the next processing step.

[0253] Step 2:

[0254] The server analyzes the information stored in the database and uses a generative AI model to calculate the optimal delivery route. The input consists of collected traffic and weather data. The generative AI operates based on the prompt "Calculate the optimal delivery route based on local traffic and weather information." The output is information on efficient delivery routes.

[0255] Step 3:

[0256] The server retrieves resource information from multiple transportation companies (e.g., the number of available vehicles and driver schedules) and allocates resources optimally. This process uses data provided by each company as input and outputs a resource sharing plan.

[0257] Step 4:

[0258] The terminal receives optimized delivery routes sent from the server as input and displays them to the driver. By using a map application to show specific directions and estimated travel times, the system enables drivers to carry out deliveries efficiently.

[0259] Step 5:

[0260] The server monitors the driver's delivery progress in real time as input. Based on GPS information, it tracks the progress and recalculates the route as needed. An updated, latest delivery route is generated as output. This information is then sent back to the terminal.

[0261] Step 6:

[0262] The terminal provides drivers with advice on optimal speed and rest times for energy-efficient driving. Inputs include current speed information and driving patterns, while output is suggestions to promote energy efficiency.

[0263] (Application Example 1)

[0264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] Current logistics systems suffer from problems such as insufficient real-time data acquisition and rapid recalculation of transportation routes to optimize regional transportation efficiency. Furthermore, while there is a demand for environmentally friendly transportation that reduces energy consumption, the current system fails to meet these needs. In addition, efficient management of internal and external conditions at transportation hubs is difficult, leading to inefficiencies due to delays in rapid route changes and information dissemination.

[0266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0267] In this invention, the server includes means for acquiring and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the acquired information, means for reducing energy consumption and realizing environmentally friendly transportation, means for collecting information on the conditions inside and outside of transportation hubs using mobile terminals and automated equipment and reflecting it in the transportation plan in real time, and means for recalculating the automated route and providing rapid information notifications based on the acquired information. This enables maximizing transportation efficiency, improving energy efficiency, and accelerating logistics operations.

[0268] "Diverse information within the region" refers to traffic conditions, weather information, vehicle conditions, and environmental factors affecting logistics, and it is possible to obtain this information in real time.

[0269] "Automatically generating the optimal transportation route" refers to the process of generating the most efficient transportation route, taking into account traffic congestion and weather changes, based on collected information.

[0270] "Energy-saving and environmentally friendly transportation" refers to transportation methods that improve energy efficiency by providing eco-driving information and giving instructions on optimal rest times and speed control.

[0271] "Collecting information on the internal and external conditions of transportation hubs using mobile devices and automated equipment" means continuously monitoring and collecting information on the physical and environmental conditions at transportation hubs using smartphones and automated equipment.

[0272] "Reflecting in real-time transportation plans" means quickly updating and applying transportation plans based on the latest acquired data.

[0273] "Recalculating routes and providing rapid information based on acquired data" refers to the process of promptly recalculating transportation routes in response to changes in conditions during logistics operations and promptly notifying drivers and other relevant parties of the results.

[0274] This invention is an embodiment of an advanced logistics system utilizing a generative AI model. Its specific configuration and functions are described in detail below.

[0275] The server acquires diverse information in real time, such as local traffic conditions, weather information, and vehicle status, using API interfaces and sensor data. The collected information is stored in a database and analyzed by a generating AI model. This allows for the automatic generation of optimal transportation routes. By considering variable factors such as traffic flow and weather conditions, efficient delivery becomes possible.

[0276] The terminals transmit data collected using smartphones and robots to a server in order to continuously monitor the conditions inside and outside the transportation hub. Based on this information, the server updates the transportation plan in real time and provides drivers with the most efficient routes. The terminals also play a role in providing drivers with eco-driving support information, promoting driving that reduces energy consumption.

[0277] Users can view real-time updated transportation plans using their mobile devices. Whenever new information becomes available, the server recalculates the route and issues rapid route change notifications based on this information. This allows users to stay constantly aware of the transportation progress and achieve efficient operations.

[0278] As a concrete example, consider the operation at a logistics center. Suppose an unexpected road closure occurs at this logistics center due to heavy rain. In this situation, the server immediately recalculates the route and notifies the driver of the optimal alternative route. This minimizes delays in logistics.

[0279] An example of a prompt would be, "Consider today's weather conditions and show the optimal transport route to the specified destination." Using this prompt, the generative AI model can respond with the optimal route based on real-time data.

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The server acquires diverse information such as traffic conditions, weather information, and vehicle status through API interfaces and sensor data. The input consists of raw data from sensors and APIs, and this data is first stored in a database.

[0283] Step 2:

[0284] The server automatically generates an optimal transportation route using a generated AI model based on the stored information. In this step, the algorithm performs calculations and selects an optimal route with the traffic and weather information obtained from the database as input. The output is the optimized route information.

[0285] Step 3:

[0286] The terminal receives the optimal transportation route provided by the server and displays it to the driver. The input is the route information from the server, and the output is a visual route guide. In this step, the terminal navigates the driver through the real-time route guidance function.

[0287] Step 4:

[0288] The server monitors the ongoing transportation situation and recalculates the route according to newly collected information. Recalculation is performed according to changes in traffic and weather. The input is the newly collected traffic information, and the output is the updated route information. This updated information is immediately notified to the terminal.

[0289] Step 5:

[0290] The user uses a mobile terminal to check the transportation plan updated in real time. The terminal provides the user with priority information from the server, route updates, and eco-driving information. The input is various data from the server, and the output is information notification to the user.

[0291] Step 6:

[0292] The user can input a prompt sentence and extract detailed information using the generated AI model. The input at this time is the prompt sentence issued by the user to the generated AI model, and the output is the proposal of the optimal transportation strategy and route provided by the model.

[0293] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0294] This invention is a system that incorporates an emotion engine to improve service quality by recognizing user emotions, in addition to streamlining local delivery and logistics. This system uses generative AI and emotion recognition technology to analyze data in real time and provide optimal delivery and user experience.

[0295] First, the server collects and analyzes logistics data in real time, including delivery demand, traffic conditions, and weather information. During this process, it utilizes AI to automatically calculate the shortest and most efficient delivery route. An algorithm is employed that selects the optimal route, taking into account traffic congestion and weather conditions.

[0296] Next, the server uses an emotion engine to analyze feedback data obtained from users. This feedback is collected via smartphones and other devices and provides insights into user satisfaction and emotional state. The emotion engine has the function of identifying the emotions users have towards the delivery service and adjusting the service accordingly.

[0297] During delivery, the terminal displays optimized delivery routes to the driver, along with special instructions based on user sentiment data. For example, if a user is in a particular hurry, this information is taken into consideration and delivery is prioritized. Conversely, if there is a high probability that the user will be absent, the system considers optimizing redelivery.

[0298] After delivery is complete, the server comprehensively analyzes all data and generates reports for delivery companies to improve operational efficiency and user satisfaction. This helps not only with shipping cost planning but also with improving the overall service.

[0299] As a specific example, consider a situation where a certain user is dissatisfied with a delivery delay. The emotion engine recognizes the emotion and sets a higher priority for the next delivery. As a result, the delivery operator can provide prompt service to a specific user and improve overall customer satisfaction. In this way, the present invention provides an effective means for realizing efficient logistics and customized services for individual users.

[0300] The processing flow will be described below.

[0301] Step 1:

[0302] The user requests a delivery service from a smartphone or terminal and transmits the current mood together with the order information as options or simple input. The emotion data is used for collecting user experiences.

[0303] Step 2:

[0304] The server processes the order information and emotion data received from the user as the first input data and obtains traffic information and weather data from an external API in real time. In this way, all the information necessary for the delivery plan is integrated.

[0305] Step 3:

[0306] The server optimizes the delivery route based on the collected order information, traffic information, weather information, and emotion data using a generation AI and an emotion engine. The generated route is for maximizing efficiency and user satisfaction.

[0307] Step 4:

[0308] The server evaluates the resources of multiple delivery operators and assigns the delivery task to the most appropriate person in charge. Considering the special needs based on the user's emotion, the priority of the order is adjusted.

[0309] Step 5:

[0310] The terminal provides drivers with optimized delivery routes and delivery instructions. If any special instructions based on user sentiment data are included, they will also be displayed.

[0311] Step 6:

[0312] The server monitors the progress of deliveries in real time and updates routes and delivery plans as needed. This information is sent to terminals as it progresses, providing drivers with the latest instructions.

[0313] Step 7:

[0314] Once a delivery is complete, the server compiles delivery data and user feedback to create an analytical report for improving service quality. This information is provided to logistics companies and delivery teams and used for future improvements.

[0315] Thus, by incorporating emotion recognition, this invention enables not only more efficient delivery but also the provision of personalized service to each user.

[0316] (Example 2)

[0317] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0318] In today's logistics industry, improving delivery efficiency and customer satisfaction are crucial challenges. In particular, formulating optimal transportation routes based on real-time traffic and weather conditions, and providing services that consider user sentiment, are not adequately achieved with conventional systems. Therefore, there is a need for methods that provide reliable and efficient logistics services while reducing environmental impact.

[0319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0320] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user sentiment data and adjusting the service. This makes it possible to improve logistics efficiency in an environmentally friendly way and enhance the satisfaction of individual users.

[0321] "Diverse information within the region" refers to data such as delivery demand, traffic conditions, and weather information related to logistics, and this data is collected in real time.

[0322] The "optimal transportation route" refers to the most efficient and fastest transportation route, automatically calculated using AI based on collected data.

[0323] "User sentiment data" refers to information that represents users' emotions and satisfaction levels, obtained through user feedback and evaluations.

[0324] "Means of adjusting services" refers to technologies that dynamically optimize the content and order of delivery services based on user sentiment data.

[0325] "Environmentally friendly transport" refers to sustainable transport methods that utilize means to reduce energy consumption and carbon dioxide emissions during the transport process.

[0326] "Methods for creating reports" refers to the process of analyzing all logistics-related data and generating reports that summarize areas for improvement aimed at increasing efficiency and user satisfaction.

[0327] This invention is a system designed to improve the efficiency of logistics operations and enhance user satisfaction. The system broadly comprises functions related to data collection, analysis, and feedback of results. At its core are a generative AI model and sentiment analysis technology, enabling real-time data processing.

[0328] The server collects and manages diverse information within the region. Specifically, it uses network infrastructure to automatically retrieve traffic conditions, weather information, and delivery demand through APIs and external database connections. It also passes the collected data to generative AI models such as Google's Vertex AI and Amazon's SageMaker to calculate the optimal transportation route. An example of a prompt message would be, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather."

[0329] The terminal provides delivery drivers with optimized routes and personalized instructions based on user sentiment. For example, a notification will appear on the driver's device indicating that a user is in a particular hurry, and priority delivery instructions will be given to that user.

[0330] Users provide feedback on the delivery service via smartphones or other connected devices. This feedback is analyzed through emotion recognition technology to collect user satisfaction and emotional states. This data is used as a basis for service adjustments to improve the quality of future deliveries.

[0331] Finally, after delivery is complete, the server comprehensively analyzes all data and generates a comprehensive report to improve operational efficiency and user satisfaction. This report serves as important reference material for future transportation planning.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The server collects real-time traffic conditions, weather information, and delivery demand within the region. Input data is obtained from APIs and external databases and received over the network. Each piece of information is converted into a unified format within the server and prepared for incorporation into the generating AI model. Specific operations include data filtering and retaining only the most recent information. The output is a clear dataset ready for making optimal transportation decisions.

[0335] Step 2:

[0336] The server generates a prompt message, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather," based on the collected data, and sends it to the AI ​​model to calculate the optimal transportation route. Data processing involves converting the data into a format applicable to the model, and calculations are performed considering conditions such as shortest time and energy efficiency. Specific operations include initializing the calculation process and accelerating results through parallel computing. The output provides multiple optimized route options based on time and efficiency.

[0337] Step 3:

[0338] The terminal displays special instructions to delivery drivers based on the generated delivery route and user sentiment data. Input data consists of route options and sentiment analysis results sent from the server. Based on this, the terminal generates and displays instructions such as "The user is in a hurry, please prioritize this delivery." Specific actions include prioritizing based on date and time, and visual mapping. As output, the driver receives detailed instructions for completing critical deliveries in the shortest possible time.

[0339] Step 4:

[0340] Once a delivery is complete, the server collects all delivery-related information and generates a report to improve efficiency and user satisfaction. Input data includes route information, user feedback, and other relevant data. The data is analyzed to create heatmaps and statistical metrics. Specifically, error checking and pattern recognition are performed to evaluate delivery performance and identify areas for future improvement. The output is a report with detailed and actionable improvement suggestions that will be incorporated into future plans.

[0341] (Application Example 2)

[0342] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0343] In regional logistics and transportation, there is a need to optimize transportation routes by efficiently considering traffic conditions and weather information, while simultaneously improving service quality based on user sentiment. Conventional systems struggle to achieve both efficient transportation routes and increased user satisfaction at the same time; therefore, a system that effectively solves these challenges is necessary.

[0344] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0345] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user emotional feedback and dynamically adjusting the transportation order. This makes it possible to simultaneously achieve improved efficiency of transportation routes and increased user satisfaction.

[0346] "Diverse information within the region" refers to all dynamic information related to transportation and logistics within the region, including traffic conditions, weather conditions, and user feedback data.

[0347] An "optimal transportation route" is a route that enables efficient and rapid transportation, calculated by taking into account traffic conditions and weather information.

[0348] "Logistical resources" refer to tangible and intangible resources related to logistics and transportation, such as transport vehicles, delivery staff, and warehouse space.

[0349] "Emotional feedback" refers to textual information collected from users, including their satisfaction and dissatisfaction with transportation services.

[0350] "Analyzing user emotional feedback and dynamically adjusting the transport order" refers to the process of analyzing users' emotional responses and changing the order and priority of transports in real time based on the results.

[0351] "Generating promotions based on text analysis" is a method that uses text analysis technology to evaluate user feedback and sentiment data, and then creates promotional activities tailored to the interests of individual users.

[0352] This invention aims to improve the efficiency of logistics and transportation and enhance user satisfaction by realizing a system based on the interaction between servers, terminals, and users.

[0353] The server collects and analyzes various local information in real time, such as traffic conditions, weather conditions, and user feedback data. Using this information, a generative AI model calculates the optimal transportation route. Traffic information is obtained using a common map service API (e.g., Google Maps API), and weather forecast APIs are used for weather conditions. Furthermore, to analyze user feedback, an emotion recognition API (e.g., IBM Watson Emotion Analysis) is used to analyze the emotional state of the feedback.

[0354] The terminal displays optimized route information to transporters, along with special instructions based on emotional feedback. For example, if a user expressed dissatisfaction with their previous transport, the terminal will instruct the transporter to prioritize that user's transport. It can also generate promotions based on text analysis and provide personalized offers to users.

[0355] Users provide feedback via their smartphones, and this information is sent to a server and used to improve future deliveries. If the feedback is negative, the system adds a promotion to the next service to improve user satisfaction.

[0356] As a concrete example, consider a user who orders food delivery during the busy evening hours. The server detects traffic congestion and uses a generated AI model to recalculate an efficient delivery route. At the same time, it also considers the user's past negative feedback and adjusts the delivery priority. An example of a prompt used in this process is, "This user has been dissatisfied with past deliveries. Please suggest the optimal delivery route and additional promotions based on current traffic conditions." In this way, the system achieves improved delivery efficiency and user experience.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server obtains real-time traffic and weather information from traffic information APIs and weather forecast APIs. Based on the input information, a generating AI model calculates the optimal transportation route. In this step, data processing is performed to take into account traffic congestion and bad weather, and as a result, optimal route information is output.

[0360] Step 2:

[0361] Users input feedback about the transportation service via their smartphones. The server receives this feedback and performs text analysis using an emotion recognition API to determine the user's emotional state. This involves data processing to extract emotional attributes from the text data of the feedback, and the output is the user's emotional information.

[0362] Step 3:

[0363] The server uses a generative AI model to adjust transport priorities. Based on the results of sentiment analysis, it identifies urgent transports and revises their priorities. The input is user sentiment information, and the output is a list of adjusted transport priorities.

[0364] Step 4:

[0365] The terminal displays optimized route information and special instructions based on emotional feedback to the transporter. Here, generated route and priority information is input, and clear, organized instructions are output for the transporter.

[0366] Step 5:

[0367] The server generates promotions based on text analysis. It utilizes user sentiment data to generate promotions tailored to specific interests. Here, data processing based on sentiment data is performed, and the resulting promotional information is output.

[0368] Step 6:

[0369] The user receives the promotion via their smartphone. In this step, the server receives promotion information as input and notifies or displays it to the user. The output is the promotion information displayed on the user's device.

[0370] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0371] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0372] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0373] [Third Embodiment]

[0374] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0375] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0376] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0377] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0378] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0380] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0381] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0382] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0383] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0384] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0385] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0386] The system of the present invention utilizes generative AI to collect and analyze data in real time in order to maximize the efficiency of delivery and logistics within a region. Specific embodiments are described below.

[0387] First, the server acquires various data affecting logistics in real time, such as local delivery demand, traffic conditions, and weather information. This data is collected through APIs and various sensors and stored in a database. Based on the collected data, the server automatically generates the optimal delivery route using a generative AI. This route optimization algorithm takes into account traffic congestion and weather conditions to construct a route that maximizes delivery efficiency.

[0388] Next, the server retrieves resource information from multiple delivery companies (e.g., the number of available vehicles and the work status of drivers) and allocates resources efficiently. This allows resources to be shared among delivery companies, resulting in a reduction of unnecessary costs.

[0389] Once delivery begins, the terminal provides the driver with an optimized delivery route. The driver follows the terminal's instructions and performs the delivery based on real-time updated route information. During delivery, the server continuously monitors the progress and modifies the route information in real time as needed, for example, in the event of unexpected traffic congestion or weather changes.

[0390] Furthermore, the server helps reduce energy consumption and enables environmentally friendly delivery. This includes speed control to encourage eco-driving and suggesting optimal rest stops to maximize energy efficiency.

[0391] As a concrete example, consider a case where multiple delivery companies cooperate to deliver goods in a certain area. When a delivery request is made, the server checks the availability of each company's vehicles and drivers and calculates the most efficient route. It also predicts traffic flow and takes into account the delicate delivery needs in sparsely populated areas. Then, instructions are sent to each driver via terminals to support smooth deliveries.

[0392] In this way, the implementation of the invention can be flexibly adapted to different situations and can support the formation of an efficient logistics network.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] The server collects delivery request information from each delivery company's order database. This data includes the delivery address, package weight, size, and delivery priority. Furthermore, it obtains real-time traffic and weather data from traffic and weather information services via APIs.

[0396] Step 2:

[0397] The server uses the collected data to optimize delivery routes. The optimization algorithm calculates the shortest route to each delivery destination, taking into account traffic and weather conditions. In this process, a generating AI generates multiple possible routes and selects the most efficient one.

[0398] Step 3:

[0399] The server checks and efficiently allocates vehicle and driver resources across multiple delivery companies. Based on the resources available to each company, it optimizes who is responsible for which route and improves efficiency by utilizing common resources.

[0400] Step 4:

[0401] The terminal sends optimized delivery routes to each driver and displays route information. Drivers can then follow the route instructions displayed on the screen and deliver in the specified order.

[0402] Step 5:

[0403] The server monitors the delivery progress in real time and quickly updates route information if it detects unexpected traffic delays or weather changes. The updated information is immediately sent to the terminal, providing drivers with new instructions.

[0404] Step 6:

[0405] After delivery is complete, the server analyzes all data and generates reports on each vendor's operational efficiency and energy consumption. This allows for feedback to be provided for future operational improvements and energy conservation.

[0406] In this way, each step works in conjunction to maximize the efficiency of delivery and logistics, and enable environmentally conscious operations.

[0407] (Example 1)

[0408] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0409] Achieving both maximum delivery efficiency and environmental considerations simultaneously in logistics has been difficult with conventional systems. Furthermore, there were challenges in efficiently allocating resources among multiple carriers and in real-time route updates. This invention aims to overcome these challenges and provide a logistics system that balances efficiency and environmental considerations.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal logistics route based on the collected information, and means for efficiently allocating logistics resources among multiple carriers. This improves the efficiency of logistics and, at the same time, reduces energy consumption, enabling environmentally friendly transportation.

[0412] "Diverse information within the region" refers to various types of data that affect logistics, such as delivery demand, traffic conditions, and weather information.

[0413] "Means of collecting and analyzing data in real time" refers to the process of constantly acquiring the latest information through sensors and APIs, processing it immediately, and deriving useful analytical results.

[0414] "Methods for automatically generating optimal logistics routes" refers to a process that uses a generation AI model to calculate the route that maximizes delivery efficiency based on collected information, taking into account traffic conditions and weather conditions, using an algorithm.

[0415] "Means of efficiently allocating logistics resources" refers to the process of understanding the operational status of transporters' vehicles and drivers, and then scheduling and resource sharing to make optimal use of them.

[0416] "Means of monitoring the progress of transportation in real time and updating the route as needed" refers to the process of using GPS or similar technologies to determine the location of vehicles during delivery and recalculating and updating the route as appropriate in response to conditions such as traffic congestion and weather changes.

[0417] "Means of achieving environmentally friendly transportation while minimizing energy consumption" refers to transportation methods that reduce energy consumption and minimize pollution through measures such as speed control and efficient rest stop timing instructions.

[0418] "Methods for optimizing logistics routes using generative AI models" refer to the process of utilizing machine learning models to plan the optimal route in real time based on collected information.

[0419] "Means of providing electronic devices for the dynamic adjustment of logistics resources" refers to the process of providing instructions and information to carriers and drivers through terminals in order to support the effective and efficient use of resources.

[0420] To implement this invention, three basic elements are utilized: a server, a terminal, and a user.

[0421] First, the server collects and analyzes various types of information in real time, such as local delivery demand, traffic conditions, and weather information. This process involves acquiring information using GPS sensors and APIs and storing it in a database system. Specific software used here includes a database management system and a real-time data processing engine. Furthermore, a generative AI model is utilized to calculate the optimal delivery route based on the collected information. An example of a prompt message is, "Please calculate the optimal delivery route based on local traffic conditions and weather information." This generative AI model takes traffic congestion and weather conditions into account to maximize efficiency.

[0422] The terminal's role is to provide drivers with routes optimized for their needs. Specifically, it uses a map application via a user interface to display route instructions and estimated travel times. If real-time route changes are required, it automatically updates the latest route information based on instructions from the server.

[0423] Furthermore, the driver, as the user, can proceed with deliveries by following the instructions on the terminal. The terminal also displays speed control and rest stop timing advice that are mindful of optimizing energy use, thus promoting eco-driving.

[0424] As a concrete example, consider delivery operations in a specific region. The server continuously collects traffic and weather data for this region and uses generated AI to calculate the optimal route. By providing the latest route information to drivers via terminals and responding to real-time route changes, it enables fast and efficient logistics.

[0425] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0426] Step 1:

[0427] The server uses APIs and GPS sensors to collect diverse data in real time, such as local delivery demand, traffic conditions, and weather information. The collected data is stored in a database, making all necessary information available for the next processing step.

[0428] Step 2:

[0429] The server analyzes the information stored in the database and uses a generative AI model to calculate the optimal delivery route. The input consists of collected traffic and weather data. The generative AI operates based on the prompt "Calculate the optimal delivery route based on local traffic and weather information." The output is information on efficient delivery routes.

[0430] Step 3:

[0431] The server retrieves resource information from multiple transportation companies (e.g., the number of available vehicles and driver schedules) and allocates resources optimally. This process uses data provided by each company as input and outputs a resource sharing plan.

[0432] Step 4:

[0433] The terminal receives optimized delivery routes sent from the server as input and displays them to the driver. By using a map application to show specific directions and estimated travel times, the system enables drivers to carry out deliveries efficiently.

[0434] Step 5:

[0435] The server monitors the driver's delivery progress in real time as input. Based on GPS information, it tracks the progress and recalculates the route as needed. An updated, latest delivery route is generated as output. This information is then sent back to the terminal.

[0436] Step 6:

[0437] The terminal provides drivers with advice on optimal speed and rest times for energy-efficient driving. Inputs include current speed information and driving patterns, while output is suggestions to promote energy efficiency.

[0438] (Application Example 1)

[0439] Next, we will explain Application Example 1. In the following explanation, 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."

[0440] Current logistics systems suffer from problems such as insufficient real-time data acquisition and rapid recalculation of transportation routes to optimize regional transportation efficiency. Furthermore, while there is a demand for environmentally friendly transportation that reduces energy consumption, the current system fails to meet these needs. In addition, efficient management of internal and external conditions at transportation hubs is difficult, leading to inefficiencies due to delays in rapid route changes and information dissemination.

[0441] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0442] In this invention, the server includes means for acquiring and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the acquired information, means for reducing energy consumption and realizing environmentally friendly transportation, means for collecting information on the conditions inside and outside of transportation hubs using mobile terminals and automated equipment and reflecting it in the transportation plan in real time, and means for recalculating the automated route and providing rapid information notifications based on the acquired information. This enables maximizing transportation efficiency, improving energy efficiency, and accelerating logistics operations.

[0443] "Diverse information within the region" refers to traffic conditions, weather information, vehicle conditions, and environmental factors affecting logistics, and it is possible to obtain this information in real time.

[0444] "Automatically generating the optimal transportation route" refers to the process of generating the most efficient transportation route, taking into account traffic congestion and weather changes, based on collected information.

[0445] "Energy-saving and environmentally friendly transportation" refers to transportation methods that improve energy efficiency by providing eco-driving information and giving instructions on optimal rest times and speed control.

[0446] "Collecting information on the internal and external conditions of transportation hubs using mobile devices and automated equipment" means continuously monitoring and collecting information on the physical and environmental conditions at transportation hubs using smartphones and automated equipment.

[0447] "Reflecting in real-time transportation plans" means quickly updating and applying transportation plans based on the latest acquired data.

[0448] "Recalculating routes and providing rapid information based on acquired data" refers to the process of promptly recalculating transportation routes in response to changes in conditions during logistics operations and promptly notifying drivers and other relevant parties of the results.

[0449] This invention is an embodiment of an advanced logistics system utilizing a generative AI model. Its specific configuration and functions are described in detail below.

[0450] The server acquires diverse information in real time, such as local traffic conditions, weather information, and vehicle status, using API interfaces and sensor data. The collected information is stored in a database and analyzed by a generating AI model. This allows for the automatic generation of optimal transportation routes. By considering variable factors such as traffic flow and weather conditions, efficient delivery becomes possible.

[0451] The terminals transmit data collected using smartphones and robots to a server in order to continuously monitor the conditions inside and outside the transportation hub. Based on this information, the server updates the transportation plan in real time and provides drivers with the most efficient routes. The terminals also play a role in providing drivers with eco-driving support information, promoting driving that reduces energy consumption.

[0452] Users can view real-time updated transportation plans using their mobile devices. Whenever new information becomes available, the server recalculates the route and issues rapid route change notifications based on this information. This allows users to stay constantly aware of the transportation progress and achieve efficient operations.

[0453] As a concrete example, consider the operation at a logistics center. Suppose an unexpected road closure occurs at this logistics center due to heavy rain. In this situation, the server immediately recalculates the route and notifies the driver of the optimal alternative route. This minimizes delays in logistics.

[0454] An example of a prompt would be, "Consider today's weather conditions and show the optimal transport route to the specified destination." Using this prompt, the generative AI model can respond with the optimal route based on real-time data.

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The server acquires diverse information such as traffic conditions, weather information, and vehicle status through API interfaces and sensor data. The input consists of raw data from sensors and APIs, and this data is first stored in a database.

[0458] Step 2:

[0459] The server automatically generates the optimal transportation route using a generated AI model based on the stored information. In this step, the algorithm takes traffic and weather information obtained from the database as input, performs calculations, and selects the optimal route. The output is optimized route information.

[0460] Step 3:

[0461] The terminal receives the optimal transport route provided by the server and displays it to the driver. The input is route information from the server, and the output is visual route guidance. In this step, the terminal navigates the driver through real-time route guidance functionality.

[0462] Step 4:

[0463] The server monitors the ongoing transportation status and recalculates the route based on newly collected information. Recalculations are performed in response to changes in traffic and weather, with newly collected traffic information as input and updated route information as output. This updated information is immediately notified to the terminal.

[0464] Step 5:

[0465] Users use their mobile devices to view real-time updated transportation plans. The devices provide users with priority information, route updates, and eco-driving information from the server. Inputs are various data from the server, and outputs are information notifications to the user.

[0466] Step 6:

[0467] Users can input prompts and extract detailed information using a generated AI model. The input consists of prompts issued by the user to the generated AI model, and the output is the model's suggested optimal transportation strategies and routes.

[0468] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0469] This invention is a system that incorporates an emotion engine to improve service quality by recognizing user emotions, in addition to streamlining local delivery and logistics. This system uses generative AI and emotion recognition technology to analyze data in real time and provide optimal delivery and user experience.

[0470] First, the server collects and analyzes logistics data in real time, including delivery demand, traffic conditions, and weather information. During this process, it utilizes AI to automatically calculate the shortest and most efficient delivery route. An algorithm is employed that selects the optimal route, taking into account traffic congestion and weather conditions.

[0471] Next, the server uses an emotion engine to analyze feedback data obtained from users. This feedback is collected via smartphones and other devices and provides insights into user satisfaction and emotional state. The emotion engine has the function of identifying the emotions users have towards the delivery service and adjusting the service accordingly.

[0472] During delivery, the terminal displays optimized delivery routes to the driver, along with special instructions based on user sentiment data. For example, if a user is in a particular hurry, this information is taken into consideration and delivery is prioritized. Conversely, if there is a high probability that the user will be absent, the system considers optimizing redelivery.

[0473] After delivery is complete, the server comprehensively analyzes all data and generates reports for delivery companies to improve operational efficiency and user satisfaction. This helps not only with shipping cost planning but also with improving the overall service.

[0474] As a concrete example, consider a situation where a user is dissatisfied with a delivery delay. The emotion engine recognizes this emotion and sets a higher priority for the next delivery. This allows the delivery company to provide prompt service to the specific user and improve overall customer satisfaction. In this way, the present invention provides an effective means of achieving efficient logistics and customized services for individual users.

[0475] The following describes the processing flow.

[0476] Step 1:

[0477] Users request delivery services from their smartphones or devices and submit their order information along with their current mood as a selection or simple input. This sentiment data is used to collect user experience data.

[0478] Step 2:

[0479] The server processes order information and sentiment data received from users as initial input data, and retrieves real-time traffic and weather data from external APIs. This allows it to gather all the information necessary for delivery planning.

[0480] Step 3:

[0481] The server uses a generative AI and emotion engine to optimize delivery routes based on collected order information, traffic information, weather information, and emotion data. The generated routes are designed to maximize efficiency and user satisfaction.

[0482] Step 4:

[0483] The server evaluates the resources of multiple delivery companies and assigns delivery tasks to the most suitable personnel. It also adjusts order priorities, taking into account special needs based on user emotions.

[0484] Step 5:

[0485] The terminal provides drivers with optimized delivery routes and delivery instructions. If any special instructions based on user sentiment data are included, they will also be displayed.

[0486] Step 6:

[0487] The server monitors the progress of deliveries in real time and updates routes and delivery plans as needed. This information is sent to terminals as it progresses, providing drivers with the latest instructions.

[0488] Step 7:

[0489] Once a delivery is complete, the server compiles delivery data and user feedback to create an analytical report for improving service quality. This information is provided to logistics companies and delivery teams and used for future improvements.

[0490] Thus, by incorporating emotion recognition, this invention enables not only more efficient delivery but also the provision of personalized service to each user.

[0491] (Example 2)

[0492] Next, we will describe Example 2. 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."

[0493] In today's logistics industry, improving delivery efficiency and customer satisfaction are crucial challenges. In particular, formulating optimal transportation routes based on real-time traffic and weather conditions, and providing services that consider user sentiment, are not adequately achieved with conventional systems. Therefore, there is a need for methods that provide reliable and efficient logistics services while reducing environmental impact.

[0494] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0495] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user sentiment data and adjusting the service. This makes it possible to improve logistics efficiency in an environmentally friendly way and enhance the satisfaction of individual users.

[0496] "Diverse information within the region" refers to data such as delivery demand, traffic conditions, and weather information related to logistics, and this data is collected in real time.

[0497] The "optimal transportation route" refers to the most efficient and fastest transportation route, automatically calculated using AI based on collected data.

[0498] "User sentiment data" refers to information that represents users' emotions and satisfaction levels, obtained through user feedback and evaluations.

[0499] "Means of adjusting services" refers to technologies that dynamically optimize the content and order of delivery services based on user sentiment data.

[0500] "Environmentally friendly transport" refers to sustainable transport methods that utilize means to reduce energy consumption and carbon dioxide emissions during the transport process.

[0501] "Methods for creating reports" refers to the process of analyzing all logistics-related data and generating reports that summarize areas for improvement aimed at increasing efficiency and user satisfaction.

[0502] This invention is a system designed to improve the efficiency of logistics operations and enhance user satisfaction. The system broadly comprises functions related to data collection, analysis, and feedback of results. At its core are a generative AI model and sentiment analysis technology, enabling real-time data processing.

[0503] The server collects and manages diverse information within the region. Specifically, it uses network infrastructure to automatically retrieve traffic conditions, weather information, and delivery demand through APIs and external database connections. It also passes the collected data to generative AI models such as Google's Vertex AI and Amazon's SageMaker to calculate the optimal transportation route. An example of a prompt message would be, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather."

[0504] The terminal provides delivery drivers with optimized routes and personalized instructions based on user sentiment. For example, a notification will appear on the driver's device indicating that a user is in a particular hurry, and priority delivery instructions will be given to that user.

[0505] Users provide feedback on the delivery service via smartphones or other connected devices. This feedback is analyzed through emotion recognition technology to collect user satisfaction and emotional states. This data is used as a basis for service adjustments to improve the quality of future deliveries.

[0506] Finally, after delivery is complete, the server comprehensively analyzes all data and generates a comprehensive report to improve operational efficiency and user satisfaction. This report serves as important reference material for future transportation planning.

[0507] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0508] Step 1:

[0509] The server collects real-time traffic conditions, weather information, and delivery demand within the region. Input data is obtained from APIs and external databases and received over the network. Each piece of information is converted into a unified format within the server and prepared for incorporation into the generating AI model. Specific operations include data filtering and retaining only the most recent information. The output is a clear dataset ready for making optimal transportation decisions.

[0510] Step 2:

[0511] The server generates a prompt message, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather," based on the collected data, and sends it to the AI ​​model to calculate the optimal transportation route. Data processing involves converting the data into a format applicable to the model, and calculations are performed considering conditions such as shortest time and energy efficiency. Specific operations include initializing the calculation process and accelerating results through parallel computing. The output provides multiple optimized route options based on time and efficiency.

[0512] Step 3:

[0513] The terminal displays special instructions to delivery drivers based on the generated delivery route and user sentiment data. Input data consists of route options and sentiment analysis results sent from the server. Based on this, the terminal generates and displays instructions such as "The user is in a hurry, please prioritize this delivery." Specific actions include prioritizing based on date and time, and visual mapping. As output, the driver receives detailed instructions for completing critical deliveries in the shortest possible time.

[0514] Step 4:

[0515] Once a delivery is complete, the server collects all delivery-related information and generates a report to improve efficiency and user satisfaction. Input data includes route information, user feedback, and other relevant data. The data is analyzed to create heatmaps and statistical metrics. Specifically, error checking and pattern recognition are performed to evaluate delivery performance and identify areas for future improvement. The output is a report with detailed and actionable improvement suggestions that will be incorporated into future plans.

[0516] (Application Example 2)

[0517] Next, we will explain Application Example 2. In the following explanation, 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."

[0518] In regional logistics and transportation, there is a need to optimize transportation routes by efficiently considering traffic conditions and weather information, while simultaneously improving service quality based on user sentiment. Conventional systems struggle to achieve both efficient transportation routes and increased user satisfaction at the same time; therefore, a system that effectively solves these challenges is necessary.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0520] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user emotional feedback and dynamically adjusting the transportation order. This makes it possible to simultaneously achieve improved efficiency of transportation routes and increased user satisfaction.

[0521] "Diverse information within the region" refers to all dynamic information related to transportation and logistics within the region, including traffic conditions, weather conditions, and user feedback data.

[0522] An "optimal transportation route" is a route that enables efficient and rapid transportation, calculated by taking into account traffic conditions and weather information.

[0523] "Logistical resources" refer to tangible and intangible resources related to logistics and transportation, such as transport vehicles, delivery staff, and warehouse space.

[0524] "Emotional feedback" refers to textual information collected from users, including their satisfaction and dissatisfaction with transportation services.

[0525] "Analyzing user emotional feedback and dynamically adjusting the transport order" refers to the process of analyzing users' emotional responses and changing the order and priority of transports in real time based on the results.

[0526] "Generating promotions based on text analysis" is a method that uses text analysis technology to evaluate user feedback and sentiment data, and then creates promotional activities tailored to the interests of individual users.

[0527] This invention aims to improve the efficiency of logistics and transportation and enhance user satisfaction by realizing a system based on the interaction between servers, terminals, and users.

[0528] The server collects and analyzes various local information in real time, such as traffic conditions, weather conditions, and user feedback data. Using this information, a generative AI model calculates the optimal transportation route. Traffic information is obtained using a common map service API (e.g., Google Maps API), and weather forecast APIs are used for weather conditions. Furthermore, to analyze user feedback, an emotion recognition API (e.g., IBM Watson Emotion Analysis) is used to analyze the emotional state of the feedback.

[0529] The terminal displays optimized route information to transporters, along with special instructions based on emotional feedback. For example, if a user expressed dissatisfaction with their previous transport, the terminal will instruct the transporter to prioritize that user's transport. It can also generate promotions based on text analysis and provide personalized offers to users.

[0530] Users provide feedback via their smartphones, and this information is sent to a server and used to improve future deliveries. If the feedback is negative, the system adds a promotion to the next service to improve user satisfaction.

[0531] As a concrete example, consider a user who orders food delivery during the busy evening hours. The server detects traffic congestion and uses a generated AI model to recalculate an efficient delivery route. At the same time, it also considers the user's past negative feedback and adjusts the delivery priority. An example of a prompt used in this process is, "This user has been dissatisfied with past deliveries. Please suggest the optimal delivery route and additional promotions based on current traffic conditions." In this way, the system achieves improved delivery efficiency and user experience.

[0532] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0533] Step 1:

[0534] The server obtains real-time traffic and weather information from traffic information APIs and weather forecast APIs. Based on the input information, a generating AI model calculates the optimal transportation route. In this step, data processing is performed to take into account traffic congestion and bad weather, and as a result, optimal route information is output.

[0535] Step 2:

[0536] Users input feedback about the transportation service via their smartphones. The server receives this feedback and performs text analysis using an emotion recognition API to determine the user's emotional state. This involves data processing to extract emotional attributes from the text data of the feedback, and the output is the user's emotional information.

[0537] Step 3:

[0538] The server uses a generative AI model to adjust transport priorities. Based on the results of sentiment analysis, it identifies urgent transports and revises their priorities. The input is user sentiment information, and the output is a list of adjusted transport priorities.

[0539] Step 4:

[0540] The terminal displays optimized route information and special instructions based on emotional feedback to the transporter. Here, generated route and priority information is input, and clear, organized instructions are output for the transporter.

[0541] Step 5:

[0542] The server generates promotions based on text analysis. It utilizes user sentiment data to generate promotions tailored to specific interests. Here, data processing based on sentiment data is performed, and the resulting promotional information is output.

[0543] Step 6:

[0544] The user receives the promotion via their smartphone. In this step, the server receives promotion information as input and notifies or displays it to the user. The output is the promotion information displayed on the user's device.

[0545] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0546] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0547] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0548] [Fourth Embodiment]

[0549] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0550] As shown in Figure 7, the 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.

[0551] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0552] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0553] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0554] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0555] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0556] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0557] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0558] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0559] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0560] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0561] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0562] The system of the present invention utilizes generative AI to collect and analyze data in real time in order to maximize the efficiency of delivery and logistics within a region. Specific embodiments are described below.

[0563] First, the server acquires various data affecting logistics in real time, such as local delivery demand, traffic conditions, and weather information. This data is collected through APIs and various sensors and stored in a database. Based on the collected data, the server automatically generates the optimal delivery route using a generative AI. This route optimization algorithm takes into account traffic congestion and weather conditions to construct a route that maximizes delivery efficiency.

[0564] Next, the server retrieves resource information from multiple delivery companies (e.g., the number of available vehicles and the work status of drivers) and allocates resources efficiently. This allows resources to be shared among delivery companies, resulting in a reduction of unnecessary costs.

[0565] Once delivery begins, the terminal provides the driver with an optimized delivery route. The driver follows the terminal's instructions and performs the delivery based on real-time updated route information. During delivery, the server continuously monitors the progress and modifies the route information in real time as needed, for example, in the event of unexpected traffic congestion or weather changes.

[0566] Furthermore, the server helps reduce energy consumption and enables environmentally friendly delivery. This includes speed control to encourage eco-driving and suggesting optimal rest stops to maximize energy efficiency.

[0567] As a concrete example, consider a case where multiple delivery companies cooperate to deliver goods in a certain area. When a delivery request is made, the server checks the availability of each company's vehicles and drivers and calculates the most efficient route. It also predicts traffic flow and takes into account the delicate delivery needs in sparsely populated areas. Then, instructions are sent to each driver via terminals to support smooth deliveries.

[0568] In this way, the implementation of the invention can be flexibly adapted to different situations and can support the formation of an efficient logistics network.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The server collects delivery request information from each delivery company's order database. This data includes the delivery address, package weight, size, and delivery priority. Furthermore, it obtains real-time traffic and weather data from traffic and weather information services via APIs.

[0572] Step 2:

[0573] The server uses the collected data to optimize delivery routes. The optimization algorithm calculates the shortest route to each delivery destination, taking into account traffic and weather conditions. In this process, a generating AI generates multiple possible routes and selects the most efficient one.

[0574] Step 3:

[0575] The server checks and efficiently allocates vehicle and driver resources across multiple delivery companies. Based on the resources available to each company, it optimizes who is responsible for which route and improves efficiency by utilizing common resources.

[0576] Step 4:

[0577] The terminal sends optimized delivery routes to each driver and displays route information. Drivers can then follow the route instructions displayed on the screen and deliver in the specified order.

[0578] Step 5:

[0579] The server monitors the delivery progress in real time and quickly updates route information if it detects unexpected traffic delays or weather changes. The updated information is immediately sent to the terminal, providing drivers with new instructions.

[0580] Step 6:

[0581] After delivery is complete, the server analyzes all data and generates reports on each vendor's operational efficiency and energy consumption. This allows for feedback to be provided for future operational improvements and energy conservation.

[0582] In this way, each step works in conjunction to maximize the efficiency of delivery and logistics, and enable environmentally conscious operations.

[0583] (Example 1)

[0584] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] Achieving both maximum delivery efficiency and environmental considerations simultaneously in logistics has been difficult with conventional systems. Furthermore, there were challenges in efficiently allocating resources among multiple carriers and in real-time route updates. This invention aims to overcome these challenges and provide a logistics system that balances efficiency and environmental considerations.

[0586] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0587] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal logistics route based on the collected information, and means for efficiently allocating logistics resources among multiple carriers. This improves the efficiency of logistics and, at the same time, reduces energy consumption, enabling environmentally friendly transportation.

[0588] "Diverse information within the region" refers to various types of data that affect logistics, such as delivery demand, traffic conditions, and weather information.

[0589] "Means of collecting and analyzing data in real time" refers to the process of constantly acquiring the latest information through sensors and APIs, processing it immediately, and deriving useful analytical results.

[0590] "Methods for automatically generating optimal logistics routes" refers to a process that uses a generation AI model to calculate the route that maximizes delivery efficiency based on collected information, taking into account traffic conditions and weather conditions, using an algorithm.

[0591] "Means of efficiently allocating logistics resources" refers to the process of understanding the operational status of transporters' vehicles and drivers, and then scheduling and resource sharing to make optimal use of them.

[0592] "Means of monitoring the progress of transportation in real time and updating the route as needed" refers to the process of using GPS or similar technologies to determine the location of vehicles during delivery and recalculating and updating the route as appropriate in response to conditions such as traffic congestion and weather changes.

[0593] "Means of achieving environmentally friendly transportation while minimizing energy consumption" refers to transportation methods that reduce energy consumption and minimize pollution through measures such as speed control and efficient rest stop timing instructions.

[0594] "Methods for optimizing logistics routes using generative AI models" refer to the process of utilizing machine learning models to plan the optimal route in real time based on collected information.

[0595] "Means of providing electronic devices for the dynamic adjustment of logistics resources" refers to the process of providing instructions and information to carriers and drivers through terminals in order to support the effective and efficient use of resources.

[0596] To implement this invention, three basic elements are utilized: a server, a terminal, and a user.

[0597] First, the server collects and analyzes various types of information in real time, such as local delivery demand, traffic conditions, and weather information. This process involves acquiring information using GPS sensors and APIs and storing it in a database system. Specific software used here includes a database management system and a real-time data processing engine. Furthermore, a generative AI model is utilized to calculate the optimal delivery route based on the collected information. An example of a prompt message is, "Please calculate the optimal delivery route based on local traffic conditions and weather information." This generative AI model takes traffic congestion and weather conditions into account to maximize efficiency.

[0598] The terminal's role is to provide drivers with routes optimized for their needs. Specifically, it uses a map application via a user interface to display route instructions and estimated travel times. If real-time route changes are required, it automatically updates the latest route information based on instructions from the server.

[0599] Furthermore, the driver, as the user, can proceed with deliveries by following the instructions on the terminal. The terminal also displays speed control and rest stop timing advice that are mindful of optimizing energy use, thus promoting eco-driving.

[0600] As a concrete example, consider delivery operations in a specific region. The server continuously collects traffic and weather data for this region and uses generated AI to calculate the optimal route. By providing the latest route information to drivers via terminals and responding to real-time route changes, it enables fast and efficient logistics.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] The server uses APIs and GPS sensors to collect diverse data in real time, such as local delivery demand, traffic conditions, and weather information. The collected data is stored in a database, making all necessary information available for the next processing step.

[0604] Step 2:

[0605] The server analyzes the information stored in the database and uses a generative AI model to calculate the optimal delivery route. The input consists of collected traffic and weather data. The generative AI operates based on the prompt "Calculate the optimal delivery route based on local traffic and weather information." The output is information on efficient delivery routes.

[0606] Step 3:

[0607] The server retrieves resource information from multiple transportation companies (e.g., the number of available vehicles and driver schedules) and allocates resources optimally. This process uses data provided by each company as input and outputs a resource sharing plan.

[0608] Step 4:

[0609] The terminal receives optimized delivery routes sent from the server as input and displays them to the driver. By using a map application to show specific directions and estimated travel times, the system enables drivers to carry out deliveries efficiently.

[0610] Step 5:

[0611] The server monitors the driver's delivery progress in real time as input. Based on GPS information, it tracks the progress and recalculates the route as needed. An updated, latest delivery route is generated as output. This information is then sent back to the terminal.

[0612] Step 6:

[0613] The terminal provides drivers with advice on optimal speed and rest times for energy-efficient driving. Inputs include current speed information and driving patterns, while output is suggestions to promote energy efficiency.

[0614] (Application Example 1)

[0615] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] Current logistics systems suffer from problems such as insufficient real-time data acquisition and rapid recalculation of transportation routes to optimize regional transportation efficiency. Furthermore, while there is a demand for environmentally friendly transportation that reduces energy consumption, the current system fails to meet these needs. In addition, efficient management of internal and external conditions at transportation hubs is difficult, leading to inefficiencies due to delays in rapid route changes and information dissemination.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0618] In this invention, the server includes means for acquiring and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the acquired information, means for reducing energy consumption and realizing environmentally friendly transportation, means for collecting information on the conditions inside and outside of transportation hubs using mobile terminals and automated equipment and reflecting it in the transportation plan in real time, and means for recalculating the automated route and providing rapid information notifications based on the acquired information. This enables maximizing transportation efficiency, improving energy efficiency, and accelerating logistics operations.

[0619] "Diverse information within the region" refers to traffic conditions, weather information, vehicle conditions, and environmental factors affecting logistics, and it is possible to obtain this information in real time.

[0620] "Automatically generating the optimal transportation route" refers to the process of generating the most efficient transportation route, taking into account traffic congestion and weather changes, based on collected information.

[0621] "Energy-saving and environmentally friendly transportation" refers to transportation methods that improve energy efficiency by providing eco-driving information and giving instructions on optimal rest times and speed control.

[0622] "Collecting information on the internal and external conditions of transportation hubs using mobile devices and automated equipment" means continuously monitoring and collecting information on the physical and environmental conditions at transportation hubs using smartphones and automated equipment.

[0623] "Reflecting in real-time transportation plans" means quickly updating and applying transportation plans based on the latest acquired data.

[0624] "Recalculating routes and providing rapid information based on acquired data" refers to the process of promptly recalculating transportation routes in response to changes in conditions during logistics operations and promptly notifying drivers and other relevant parties of the results.

[0625] This invention is an embodiment of an advanced logistics system utilizing a generative AI model. Its specific configuration and functions are described in detail below.

[0626] The server acquires diverse information in real time, such as local traffic conditions, weather information, and vehicle status, using API interfaces and sensor data. The collected information is stored in a database and analyzed by a generating AI model. This allows for the automatic generation of optimal transportation routes. By considering variable factors such as traffic flow and weather conditions, efficient delivery becomes possible.

[0627] The terminals transmit data collected using smartphones and robots to a server in order to continuously monitor the conditions inside and outside the transportation hub. Based on this information, the server updates the transportation plan in real time and provides drivers with the most efficient routes. The terminals also play a role in providing drivers with eco-driving support information, promoting driving that reduces energy consumption.

[0628] Users can view real-time updated transportation plans using their mobile devices. Whenever new information becomes available, the server recalculates the route and issues rapid route change notifications based on this information. This allows users to stay constantly aware of the transportation progress and achieve efficient operations.

[0629] As a concrete example, consider the operation at a logistics center. Suppose an unexpected road closure occurs at this logistics center due to heavy rain. In this situation, the server immediately recalculates the route and notifies the driver of the optimal alternative route. This minimizes delays in logistics.

[0630] An example of a prompt would be, "Consider today's weather conditions and show the optimal transport route to the specified destination." Using this prompt, the generative AI model can respond with the optimal route based on real-time data.

[0631] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0632] Step 1:

[0633] The server acquires diverse information such as traffic conditions, weather information, and vehicle status through API interfaces and sensor data. The input consists of raw data from sensors and APIs, and this data is first stored in a database.

[0634] Step 2:

[0635] The server automatically generates the optimal transportation route using a generated AI model based on the stored information. In this step, the algorithm takes traffic and weather information obtained from the database as input, performs calculations, and selects the optimal route. The output is optimized route information.

[0636] Step 3:

[0637] The terminal receives the optimal transport route provided by the server and displays it to the driver. The input is route information from the server, and the output is visual route guidance. In this step, the terminal navigates the driver through real-time route guidance functionality.

[0638] Step 4:

[0639] The server monitors the ongoing transportation status and recalculates the route based on newly collected information. Recalculations are performed in response to changes in traffic and weather, with newly collected traffic information as input and updated route information as output. This updated information is immediately notified to the terminal.

[0640] Step 5:

[0641] Users use their mobile devices to view real-time updated transportation plans. The devices provide users with priority information, route updates, and eco-driving information from the server. Inputs are various data from the server, and outputs are information notifications to the user.

[0642] Step 6:

[0643] Users can input prompts and extract detailed information using a generated AI model. The input consists of prompts issued by the user to the generated AI model, and the output is the model's suggested optimal transportation strategies and routes.

[0644] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0645] This invention is a system that incorporates an emotion engine to improve service quality by recognizing user emotions, in addition to streamlining local delivery and logistics. This system uses generative AI and emotion recognition technology to analyze data in real time and provide optimal delivery and user experience.

[0646] First, the server collects and analyzes logistics data in real time, including delivery demand, traffic conditions, and weather information. During this process, it utilizes AI to automatically calculate the shortest and most efficient delivery route. An algorithm is employed that selects the optimal route, taking into account traffic congestion and weather conditions.

[0647] Next, the server uses an emotion engine to analyze feedback data obtained from users. This feedback is collected via smartphones and other devices and provides insights into user satisfaction and emotional state. The emotion engine has the function of identifying the emotions users have towards the delivery service and adjusting the service accordingly.

[0648] During delivery, the terminal displays optimized delivery routes to the driver, along with special instructions based on user sentiment data. For example, if a user is in a particular hurry, this information is taken into consideration and delivery is prioritized. Conversely, if there is a high probability that the user will be absent, the system considers optimizing redelivery.

[0649] After delivery is complete, the server comprehensively analyzes all data and generates reports for delivery companies to improve operational efficiency and user satisfaction. This helps not only with shipping cost planning but also with improving the overall service.

[0650] As a concrete example, consider a situation where a user is dissatisfied with a delivery delay. The emotion engine recognizes this emotion and sets a higher priority for the next delivery. This allows the delivery company to provide prompt service to the specific user and improve overall customer satisfaction. In this way, the present invention provides an effective means of achieving efficient logistics and customized services for individual users.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] Users request delivery services from their smartphones or devices and submit their order information along with their current mood as a selection or simple input. This sentiment data is used to collect user experience data.

[0654] Step 2:

[0655] The server processes order information and sentiment data received from users as initial input data, and retrieves real-time traffic and weather data from external APIs. This allows it to gather all the information necessary for delivery planning.

[0656] Step 3:

[0657] The server uses a generative AI and emotion engine to optimize delivery routes based on collected order information, traffic information, weather information, and emotion data. The generated routes are designed to maximize efficiency and user satisfaction.

[0658] Step 4:

[0659] The server evaluates the resources of multiple delivery companies and assigns delivery tasks to the most suitable personnel. It also adjusts order priorities, taking into account special needs based on user emotions.

[0660] Step 5:

[0661] The terminal provides drivers with optimized delivery routes and delivery instructions. If any special instructions based on user sentiment data are included, they will also be displayed.

[0662] Step 6:

[0663] The server monitors the progress of deliveries in real time and updates routes and delivery plans as needed. This information is sent to terminals as it progresses, providing drivers with the latest instructions.

[0664] Step 7:

[0665] Once a delivery is complete, the server compiles delivery data and user feedback to create an analytical report for improving service quality. This information is provided to logistics companies and delivery teams and used for future improvements.

[0666] Thus, by incorporating emotion recognition, this invention enables not only more efficient delivery but also the provision of personalized service to each user.

[0667] (Example 2)

[0668] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] In today's logistics industry, improving delivery efficiency and customer satisfaction are crucial challenges. In particular, formulating optimal transportation routes based on real-time traffic and weather conditions, and providing services that consider user sentiment, are not adequately achieved with conventional systems. Therefore, there is a need for methods that provide reliable and efficient logistics services while reducing environmental impact.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0671] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user sentiment data and adjusting the service. This makes it possible to improve logistics efficiency in an environmentally friendly way and enhance the satisfaction of individual users.

[0672] "Diverse information within the region" refers to data such as delivery demand, traffic conditions, and weather information related to logistics, and this data is collected in real time.

[0673] The "optimal transportation route" refers to the most efficient and fastest transportation route, automatically calculated using AI based on collected data.

[0674] "User sentiment data" refers to information that represents users' emotions and satisfaction levels, obtained through user feedback and evaluations.

[0675] "Means of adjusting services" refers to technologies that dynamically optimize the content and order of delivery services based on user sentiment data.

[0676] "Environmentally friendly transport" refers to sustainable transport methods that utilize means to reduce energy consumption and carbon dioxide emissions during the transport process.

[0677] "Methods for creating reports" refers to the process of analyzing all logistics-related data and generating reports that summarize areas for improvement aimed at increasing efficiency and user satisfaction.

[0678] This invention is a system designed to improve the efficiency of logistics operations and enhance user satisfaction. The system broadly comprises functions related to data collection, analysis, and feedback of results. At its core are a generative AI model and sentiment analysis technology, enabling real-time data processing.

[0679] The server collects and manages diverse information within the region. Specifically, it uses network infrastructure to automatically retrieve traffic conditions, weather information, and delivery demand through APIs and external database connections. It also passes the collected data to generative AI models such as Google's Vertex AI and Amazon's SageMaker to calculate the optimal transportation route. An example of a prompt message would be, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather."

[0680] The terminal provides delivery drivers with optimized routes and personalized instructions based on user sentiment. For example, a notification will appear on the driver's device indicating that a user is in a particular hurry, and priority delivery instructions will be given to that user.

[0681] Users provide feedback on the delivery service via smartphones or other connected devices. This feedback is analyzed through emotion recognition technology to collect user satisfaction and emotional states. This data is used as a basis for service adjustments to improve the quality of future deliveries.

[0682] Finally, after delivery is complete, the server comprehensively analyzes all data and generates a comprehensive report to improve operational efficiency and user satisfaction. This report serves as important reference material for future transportation planning.

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The server collects real-time traffic conditions, weather information, and delivery demand within the region. Input data is obtained from APIs and external databases and received over the network. Each piece of information is converted into a unified format within the server and prepared for incorporation into the generating AI model. Specific operations include data filtering and retaining only the most recent information. The output is a clear dataset ready for making optimal transportation decisions.

[0686] Step 2:

[0687] The server generates a prompt message, "Calculate the optimal transportation route to the specified address based on current traffic conditions and weather," based on the collected data, and sends it to the AI ​​model to calculate the optimal transportation route. Data processing involves converting the data into a format applicable to the model, and calculations are performed considering conditions such as shortest time and energy efficiency. Specific operations include initializing the calculation process and accelerating results through parallel computing. The output provides multiple optimized route options based on time and efficiency.

[0688] Step 3:

[0689] The terminal displays special instructions to delivery drivers based on the generated delivery route and user sentiment data. Input data consists of route options and sentiment analysis results sent from the server. Based on this, the terminal generates and displays instructions such as "The user is in a hurry, please prioritize this delivery." Specific actions include prioritizing based on date and time, and visual mapping. As output, the driver receives detailed instructions for completing critical deliveries in the shortest possible time.

[0690] Step 4:

[0691] Once a delivery is complete, the server collects all delivery-related information and generates a report to improve efficiency and user satisfaction. Input data includes route information, user feedback, and other relevant data. The data is analyzed to create heatmaps and statistical metrics. Specifically, error checking and pattern recognition are performed to evaluate delivery performance and identify areas for future improvement. The output is a report with detailed and actionable improvement suggestions that will be incorporated into future plans.

[0692] (Application Example 2)

[0693] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] In regional logistics and transportation, there is a need to optimize transportation routes by efficiently considering traffic conditions and weather information, while simultaneously improving service quality based on user sentiment. Conventional systems struggle to achieve both efficient transportation routes and increased user satisfaction at the same time; therefore, a system that effectively solves these challenges is necessary.

[0695] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0696] In this invention, the server includes means for collecting and analyzing diverse information within a region in real time, means for automatically generating the optimal transportation route based on the collected information, and means for analyzing user emotional feedback and dynamically adjusting the transportation order. This makes it possible to simultaneously achieve improved efficiency of transportation routes and increased user satisfaction.

[0697] "Diverse information within the region" refers to all dynamic information related to transportation and logistics within the region, including traffic conditions, weather conditions, and user feedback data.

[0698] An "optimal transportation route" is a route that enables efficient and rapid transportation, calculated by taking into account traffic conditions and weather information.

[0699] "Logistical resources" refer to tangible and intangible resources related to logistics and transportation, such as transport vehicles, delivery staff, and warehouse space.

[0700] "Emotional feedback" refers to textual information collected from users, including their satisfaction and dissatisfaction with transportation services.

[0701] "Analyzing user emotional feedback and dynamically adjusting the transport order" refers to the process of analyzing users' emotional responses and changing the order and priority of transports in real time based on the results.

[0702] "Generating promotions based on text analysis" is a method that uses text analysis technology to evaluate user feedback and sentiment data, and then creates promotional activities tailored to the interests of individual users.

[0703] This invention aims to improve the efficiency of logistics and transportation and enhance user satisfaction by realizing a system based on the interaction between servers, terminals, and users.

[0704] The server collects and analyzes various local information in real time, such as traffic conditions, weather conditions, and user feedback data. Using this information, a generative AI model calculates the optimal transportation route. Traffic information is obtained using a common map service API (e.g., Google Maps API), and weather forecast APIs are used for weather conditions. Furthermore, to analyze user feedback, an emotion recognition API (e.g., IBM Watson Emotion Analysis) is used to analyze the emotional state of the feedback.

[0705] The terminal displays optimized route information to transporters, along with special instructions based on emotional feedback. For example, if a user expressed dissatisfaction with their previous transport, the terminal will instruct the transporter to prioritize that user's transport. It can also generate promotions based on text analysis and provide personalized offers to users.

[0706] Users provide feedback via their smartphones, and this information is sent to a server and used to improve future deliveries. If the feedback is negative, the system adds a promotion to the next service to improve user satisfaction.

[0707] As a concrete example, consider a user who orders food delivery during the busy evening hours. The server detects traffic congestion and uses a generated AI model to recalculate an efficient delivery route. At the same time, it also considers the user's past negative feedback and adjusts the delivery priority. An example of a prompt used in this process is, "This user has been dissatisfied with past deliveries. Please suggest the optimal delivery route and additional promotions based on current traffic conditions." In this way, the system achieves improved delivery efficiency and user experience.

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The server obtains real-time traffic and weather information from traffic information APIs and weather forecast APIs. Based on the input information, a generating AI model calculates the optimal transportation route. In this step, data processing is performed to take into account traffic congestion and bad weather, and as a result, optimal route information is output.

[0711] Step 2:

[0712] Users input feedback about the transportation service via their smartphones. The server receives this feedback and performs text analysis using an emotion recognition API to determine the user's emotional state. This involves data processing to extract emotional attributes from the text data of the feedback, and the output is the user's emotional information.

[0713] Step 3:

[0714] The server uses a generative AI model to adjust transport priorities. Based on the results of sentiment analysis, it identifies urgent transports and revises their priorities. The input is user sentiment information, and the output is a list of adjusted transport priorities.

[0715] Step 4:

[0716] The terminal displays optimized route information and special instructions based on emotional feedback to the transporter. Here, generated route and priority information is input, and clear, organized instructions are output for the transporter.

[0717] Step 5:

[0718] The server generates promotions based on text analysis. It utilizes user sentiment data to generate promotions tailored to specific interests. Here, data processing based on sentiment data is performed, and the resulting promotional information is output.

[0719] Step 6:

[0720] The user receives the promotion via their smartphone. In this step, the server receives promotion information as input and notifies or displays it to the user. The output is the promotion information displayed on the user's device.

[0721] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0722] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0723] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0724] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0725] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0726] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0727] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0728] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0729] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0730] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0731] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0732] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0733] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0734] 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.

[0735] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0736] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0737] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0738] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0739] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0740] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0741] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0742] The following is further disclosed regarding the embodiments described above.

[0743] (Claim 1)

[0744] A means of collecting and analyzing diverse data within a region in real time,

[0745] A method for automatically generating the optimal delivery route based on collected data,

[0746] A means of efficiently allocating logistics resources among multiple delivery companies,

[0747] A means to monitor the progress of deliveries in real time and update routes as needed,

[0748] A system that includes means to achieve environmentally friendly delivery while minimizing energy consumption.

[0749] (Claim 2)

[0750] The system according to claim 1, comprising an algorithm that takes into account traffic conditions and weather conditions in order to generate an optimal delivery route.

[0751] (Claim 3)

[0752] The system according to claim 1, further comprising means for providing multiple delivery strategies suitable for sparsely populated areas and densely populated areas, respectively.

[0753] "Example 1"

[0754] (Claim 1)

[0755] A means of collecting and analyzing diverse information within a region in real time,

[0756] A means of automatically generating the optimal logistics route based on the collected information,

[0757] A means of efficiently allocating logistics resources among multiple carriers,

[0758] A means to monitor the progress of transportation in real time and update the route as needed,

[0759] Means of achieving environmentally friendly transportation while minimizing energy consumption,

[0760] A means for optimizing logistics routes using a generative AI model,

[0761] Means for providing electronic equipment for the dynamic adjustment of logistics resources,

[0762] A system that includes this.

[0763] (Claim 2)

[0764] The system according to claim 1, comprising an algorithm that takes into account traffic conditions and weather conditions.

[0765] (Claim 3)

[0766] The system according to claim 1, further comprising means for providing multiple transportation strategies suitable for sparsely populated areas and densely populated areas, respectively.

[0767] "Application Example 1"

[0768] (Claim 1)

[0769] A means of acquiring and analyzing diverse information within a region in real time,

[0770] A means for automatically generating the optimal transportation route based on acquired information,

[0771] A means of efficiently allocating logistics resources among multiple transporters,

[0772] A means of monitoring the progress of transportation in real time and updating the route as needed,

[0773] Means to reduce energy consumption and realize environmentally friendly transportation,

[0774] A means of collecting information on the conditions inside and outside of transportation hubs using mobile terminals and automated equipment, and reflecting this information in real time transportation plans,

[0775] A system that includes means for automated route recalculation and rapid information notification based on acquired information.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for providing information that promotes efficient operation based on predictions of pre-formed information.

[0778] (Claim 3)

[0779] The system according to claim 1, further comprising means for displaying an efficient transport plan on a mobile terminal and automated equipment, and for providing rapid route change notifications.

[0780] "Example 2 of combining an emotion engine"

[0781] (Claim 1)

[0782] A means of collecting and analyzing diverse information within a region in real time,

[0783] A means of automatically generating the optimal transportation route based on the collected information,

[0784] Means for efficiently allocating resources among multiple transportation operators,

[0785] A means to monitor the progress of transportation in real time and update the route as needed,

[0786] Means of achieving environmentally friendly transportation while minimizing energy consumption,

[0787] A means of analyzing user sentiment data and adjusting services accordingly,

[0788] Means for providing optimized transportation routes and special instructions based on user sentiment,

[0789] A system that includes means for comprehensively analyzing information after transportation and creating reports to improve operational efficiency and user satisfaction.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising calculation means that takes into account traffic conditions and weather conditions in order to generate an optimal transport route.

[0792] (Claim 3)

[0793] The system according to claim 1, further comprising means for providing multiple transportation strategies suitable for sparsely populated areas and densely populated areas, respectively.

[0794] "Application example 2 when combining with an emotional engine"

[0795] (Claim 1)

[0796] A means of collecting and analyzing diverse information within a region in real time,

[0797] A means of automatically generating the optimal transportation route based on the collected information,

[0798] A means of efficiently allocating logistics resources among multiple transporters,

[0799] A means to monitor the progress of transportation in real time and update the route as needed,

[0800] Means to achieve sustainable transportation while minimizing energy consumption,

[0801] A means of analyzing user emotional feedback and dynamically adjusting the transport order,

[0802] A system that includes means for generating promotions based on text analysis, while taking weather information and traffic information into consideration.

[0803] (Claim 2)

[0804] The system according to claim 1, comprising an algorithm that takes into account traffic conditions and weather conditions in order to generate an optimal transport route.

[0805] (Claim 3)

[0806] The system according to claim 1, further comprising means for providing multiple delivery strategies suitable for sparsely populated areas and densely populated areas, respectively. [Explanation of Symbols]

[0807] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and analyzing diverse data within a region in real time, A method for automatically generating the optimal delivery route based on collected data, A means of efficiently allocating logistics resources among multiple delivery companies, A means to monitor the progress of deliveries in real time and update routes as needed, A system that includes means to achieve environmentally friendly delivery while minimizing energy consumption.

2. The system according to claim 1, comprising an algorithm that takes into account traffic conditions and weather conditions in order to generate an optimal delivery route.

3. The system according to claim 1, further comprising means for providing multiple delivery strategies suitable for sparsely populated areas and densely populated areas, respectively.

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