system

Abstract

We provide the system. [Solution] Methods for acquiring various data in delivery operations, A method for analyzing acquired data to optimize delivery routes, A means of transmitting an optimized delivery route to a mobile vehicle, A method for obtaining delivery performance data from mobile devices and using it for optimization in the next delivery, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the delivery industry, soaring fuel costs, labor shortages, and redeliveries due to absences are factors that significantly reduce the efficiency of operations. The resulting overwork and increased costs have a profound impact on the working environment and the profitability of enterprises, and there is a strong demand for solving this problem.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that acquires, analyzes, optimizes delivery routes, and utilizes feedback from various data in delivery operations. Specifically, it acquires data such as the experience level of area drivers, traffic conditions, and the time when recipients are home at the delivery destination, and optimizes delivery routes by analyzing this data. Furthermore, it transmits the optimized delivery route to the mobile unit and acquires delivery performance data after its execution to use for optimization in the next run. Through this series of means, it aims to improve the efficiency of fuel costs, reduce redeliveries, and improve the working environment.

[0006] "Various data related to delivery operations" refers to various types of information related to delivery, including area driver experience data, redelivery records of delivery destinations, times when recipients are home, traffic conditions, traffic light change timings, delivery schedules, and time specifications.

[0007] "Acquisition means" refers to the methods and devices used to collect necessary data and incorporate it into the system.

[0008] "Analysis" refers to the process of quantifying or classifying acquired data and extracting useful information from it.

[0009] "Delivery route optimization" refers to the process of calculating the most efficient route for a moving object to reach its intended delivery destination, based on collected data.

[0010] "Mobile entity" refers to the entity that carries out delivery, including the person or vehicle making the delivery, the driver's communication terminal, etc.

[0011] "Transmission means" refers to communication methods and technologies used to send optimized delivery routes to mobile objects.

[0012] "Delivery performance data" refers to information that shows the results of delivery operations after they have been completed, including whether or not the delivery was successful and whether or not redelivery is necessary.

[0013] "Feedback" refers to the process of returning the results of an execution to the system and using them for future analysis and optimization. [Brief explanation of the drawing]

[0014] [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

[0015] 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.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for improving the efficiency of delivery operations. Specifically, it implements a series of processes that acquire and analyze various data in delivery operations, optimize delivery routes, and transmit the results to the mobile vehicle. The processing content of the program as an embodiment is described below.

[0036] First, the server collects a variety of data from multiple perspectives, including area drivers' experience data, redelivery records for delivery destinations, recipients' availability times, traffic conditions, traffic light change timings, delivery schedules, and time specifications. This data collection process involves retrieving information through various sensors and databases.

[0037] Next, the server analyzes the acquired data. Using generative AI technology, it extracts useful patterns and predictions from this data to determine the priority of visits to each delivery destination. It also predicts traffic congestion from traffic information and selects the optimal route, taking into account traffic light change timings.

[0038] The server then calculates an optimized delivery route and sends the results to the terminal. The terminal receives this information in real time and notifies the driver (user). The driver then carries out the delivery according to the instructions provided. Important information and special notes regarding the delivery destination are also conveyed, enabling the driver to perform their duties efficiently.

[0039] Once a delivery is complete, delivery performance data is sent back to the server via the terminal. The server uses this data to analyze the causes of any redeliveries and to provide feedback necessary for optimizing future delivery routes. This feedback loop allows the system to continuously improve and support highly efficient delivery operations.

[0040] As a concrete example, the system predicts the times when customers who have a history of missed deliveries are likely to be home and automatically selects a route to visit during those times. As a result, the risk of redelivery is significantly reduced, and fuel and time are saved. Through such operations, delivery work can be made more streamlined, and the burden on workers can be reduced.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically retrieves data from various databases, including area drivers' experience levels, redelivery records, recipients' availability times, traffic conditions, traffic light timings, delivery schedules, and time-specific delivery requests. The data is updated at regular intervals and managed to maintain the most up-to-date information.

[0044] Step 2:

[0045] The server analyzes the collected data. Using a generative AI algorithm, it evaluates the likelihood of recipients being home and the possibility of redelivery based on past delivery data. It also analyzes road congestion and traffic light waiting times based on traffic data and calculates the impact these factors have on the delivery route.

[0046] Step 3:

[0047] The server calculates the most efficient delivery route based on the analysis results. The route is optimized by considering various factors with the aim of minimizing fuel consumption and shortening delivery time. It derives routes that prioritize times when people are likely to be home and times when roads are less congested.

[0048] Step 4:

[0049] The server sends the calculated optimal delivery route to the terminal. The terminal immediately receives this information and notifies the driver, who is the user. The notification also includes detailed information and notes for each delivery destination, and the driver carries out the delivery work based on this information.

[0050] Step 5:

[0051] The user, acting as the driver, makes deliveries following the optimal route displayed on the terminal. After each delivery is completed, the driver uses the terminal to record the results, entering information such as whether the delivery was successful, the recipient was absent, or if redelivery is necessary.

[0052] Step 6:

[0053] The terminal transmits delivery performance data entered by the driver to the server. The server uses the received feedback data to improve the accuracy of future delivery routes. This continuously improves the overall efficiency of the system.

[0054] (Example 1)

[0055] 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."

[0056] In modern delivery operations, challenges such as an increase in redeliveries and traffic congestion make it difficult to achieve efficient deliveries. This leads to wasted time and fuel, increasing the environmental burden and potentially lowering customer satisfaction. Furthermore, improving delivery efficiency is a challenge due to the difficulty in optimizing delivery routes.

[0057] 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.

[0058] In this invention, the server includes means for collecting information related to delivery operations, means for analyzing the collected information using generation AI technology to optimize delivery routes, and means for transmitting the optimized delivery routes to a communication device. This enables the selection of efficient delivery routes, making it possible to improve delivery operations while minimizing the risk of redelivery and delays due to traffic congestion.

[0059] "Delivery services" refer to a series of activities or processes for transporting goods or packages from one location to another.

[0060] "Information" refers to facts and indicators about a situation or environment, provided in the form of data or knowledge.

[0061] "Means of collection" refers to the methods and techniques used to gather necessary information and data.

[0062] "Generative AI technology" refers to tools and algorithms that use artificial intelligence to perform advanced processing such as data analysis.

[0063] "Analysis" is the process of examining collected information and data in detail to extract patterns and useful information.

[0064] A "delivery route" is the path selected for delivery operations, from the starting point to the destination.

[0065] "Optimization" is the process of adjusting resources and time to use them efficiently and achieve better results.

[0066] A "communication device" is hardware or software used to send and receive information.

[0067] "Means of transmission" refers to the technology or method used to send information to a specific destination.

[0068] "Delivery performance information" refers to records and data related to actual delivery activities.

[0069] The delivery efficiency system in this invention includes a series of processes: information gathering, analysis using AI generation technology, route optimization, and feedback of performance information.

[0070] The server collects a variety of information related to delivery operations. Specifically, it handles data such as traffic conditions, traffic signal control information, past redelivery records, times when residents are home, and time-specific delivery information. This work is performed through API integration with traffic sensors and databases, or via IoT devices.

[0071] Next, the server analyzes the collected information using a generative AI model. This generative AI model is implemented on frameworks such as TENSORFLOW® and PyTorch, and extracts patterns and trends from the data. The results obtained from the analysis are used to optimize delivery routes. For example, it calculates the most efficient delivery route by taking into account traffic congestion and traffic light switching times.

[0072] The calculated optimized route is sent from the server to the terminal. This communication takes place in real time using a mobile network or Wi-Fi. The terminal receives the route information and notifies the driver, who is the user. The driver follows the instructions using visual map displays and voice guidance to complete the delivery.

[0073] Once a delivery is complete, the terminal sends delivery performance information back to the server. This information includes the time of successful delivery, whether redelivery was required, and the time taken for delivery. The server uses this information to optimize the system and analyze causes, thereby improving the overall efficiency of the system.

[0074] As a concrete example, the system identifies customers who have frequently been absent and required redelivery in the past, and uses an AI model to predict their expected time at home. It then automatically selects delivery routes that visit during times when the customer is most likely to be home. This approach improves the efficiency of delivery operations and enhances customer satisfaction.

[0075] An example of a prompt message might be something like, "Based on past redelivery data, predict the times when the recipient is most likely to be home at a specific delivery address, and suggest the optimal delivery route for that time."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects information related to delivery operations. This information includes real-time traffic data obtained through traffic sensors and IoT devices, historical delivery performance data, and customer occupancy time data. The server aggregates the input data and stores it in a database for subsequent processing. As a result, a large amount of delivery-related data is collected.

[0079] Step 2:

[0080] The server begins analyzing the collected data. Using a generative AI model, it predicts visit priorities for each area, the degree of traffic congestion, and the times of day when redelivery is most likely to be required. The data obtained in step 1 is fed into the AI ​​model as input, and data processing and prediction algorithms are applied. This outputs prediction results and patterns, which are used as basic information for optimization processing.

[0081] Step 3:

[0082] The server calculates the optimal delivery route based on the analysis results. Here, it uses the results of the generated AI model, taking into account acquired traffic information and traffic light timings, to calculate the shortest and fastest route. The input includes the analysis results obtained in step 2, and the route optimization algorithm is applied. The output generates the optimal delivery route for the driver.

[0083] Step 4:

[0084] The server sends the optimized delivery route to the terminal. Using data communication technology, route information is transmitted to the terminal in real time. The input includes the optimized delivery route generated in step 3 and is sent to the terminal. This allows the terminal to immediately receive the route information and prepare to notify the user.

[0085] Step 5:

[0086] The terminal notifies the driver, who is the user, of the route information it has received. The notification is given through voice guidance and visual display on a map, providing instructions to efficiently carry out delivery work. The input is the optimized route information sent in step 4, and the output is that the driver receives route guidance visually and audibly.

[0087] Step 6:

[0088] After a delivery is completed, the terminal sends delivery performance information back to the server. This performance information includes whether the delivery was successful, the time taken, and confirmation of receipt. The input consists of performance data recorded by the terminal during the delivery, which is then sent to the server. As output, the server uses this data for optimization and root cause analysis for future deliveries.

[0089] (Application Example 1)

[0090] 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."

[0091] In current delivery operations, the efficiency of delivery routes is a critical issue, particularly the waste of time and resources due to redeliveries. Furthermore, the need for real-time, appropriate decision-making in response to fluctuating traffic conditions is a major challenge. Additionally, insufficient route selection that considers the recipient's availability at the delivery location is contributing to reduced delivery efficiency.

[0092] 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.

[0093] In this invention, the server includes means for collecting various information in delivery operations, means for analyzing the collected information to optimize delivery routes, means for transmitting the optimized delivery routes to transportation equipment, means for acquiring delivery performance information from transportation equipment and using it for future optimization, and means for displaying delivery instructions to employees in real time based on the acquired information. This enables a reduction in redeliveries and efficient delivery that reflects real-time traffic information.

[0094] "Delivery services" refers to all activities involved in transporting packages or goods to their designated destinations.

[0095] "Means of collecting various types of information" refers to a system for collecting various data related to delivery.

[0096] "A means of analyzing collected information to optimize delivery routes" refers to a system that analyzes data to determine the most efficient delivery order and route.

[0097] "Means for transmitting optimized delivery routes to transportation equipment" refers to a system that transmits calculated best route information to the vehicles used for actual delivery.

[0098] "Transportation equipment" refers to vehicles and devices used to physically transport goods or packages.

[0099] "Delivery performance information" refers to data on actual delivery activities and is used to improve future delivery plans.

[0100] "Methods to be used for optimization in the next delivery" refers to a system that further improves future delivery plans based on past delivery data.

[0101] "A means of displaying delivery instructions to employees in real time" refers to a system that provides workers with immediate instructions tailored to the current situation.

[0102] This invention involves a logistics center implementing an information technology-driven system to improve operational efficiency. A server analyzes collected data in real time, calculates the optimal delivery route, and transmits it to the transport equipment. The transport equipment, such as smartphones or tablets, communicates with the Node.js-based server. The server utilizes generative AI models based on TensorFlow and PyTorch to optimize delivery routes based on past delivery data and the latest traffic information. This enables efficient delivery by avoiding congestion and traffic lights.

[0103] The terminal receives optimal route information sent from the server and displays it in an easy-to-understand format for employees. By utilizing the Google® Maps API, the route is displayed visually on a map, allowing users to follow instructions in real time and make deliveries. The terminal also sends delivery performance information back to the server, which is used for future improvements.

[0104] As a concrete example, consider a scenario for efficiently delivering a large volume of packages during the Christmas season. The server receives historical delivery data as input and prompts, "Analyze the delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." Based on this prompt, the generating AI model calculates a route that reduces the risk of redelivery and sends that information to the terminal. This operation can improve the efficiency of delivery operations and reduce costs.

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

[0106] Step 1:

[0107] The server collects various data from multiple perspectives, including traffic conditions, driver experience, and the time when recipients are likely to be home during delivery. It receives data from sensors and databases as input, integrates it, and stores it in a repository. As output, it generates a set of information necessary for delivery planning.

[0108] Step 2:

[0109] The server analyzes the collected data and uses a generated AI model to calculate the optimal delivery route. The information set generated in step 1 is fed into the model as input, along with the prompt "Analyze delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." The model performs data processing and predictive calculations, generating optimized route information as output.

[0110] Step 3:

[0111] The server sends the optimized delivery route to the terminal. It uses the route information obtained in step 2 as input and transmits it to the terminal in real time. As output, the route and instruction information are displayed on the user's terminal in a visually verifiable format.

[0112] Step 4:

[0113] The terminal displays delivery instructions to the user based on the route information it receives. It takes route information from the server as input, and uses the Google Maps API to display the route on a map as output, providing the user with the necessary delivery instructions.

[0114] Step 5:

[0115] The user executes deliveries according to the delivery instructions displayed on the terminal. Delivery performance information collected by the user is resent from the terminal to the server. As input, delivery progress and completion data are collected, and as output, performance data that should be reflected in the next delivery plan is processed.

[0116] Step 6:

[0117] The server analyzes delivery performance information from terminals as feedback to identify areas for improvement needed to optimize the next delivery route. It analyzes the performance data collected in step 5 as input, and generates an improved delivery plan proposal as output. Through this cycle, delivery efficiency continuously improves.

[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 provides a system that improves the efficiency of delivery operations and enhances the customer experience. Specifically, it incorporates an emotion engine to recognize the emotions of users during delivery operations and uses that data to optimize delivery routes and improve service quality. The following describes specific embodiments of this system.

[0120] The server first collects user emotional data using an emotion engine, in addition to the conventional data acquisition process. This emotional data is recognized in real time from the customer's facial expressions and voice when they interact with the delivery driver and transmitted to the server. This makes it possible to understand the customer's emotional state at each delivery location in chronological order.

[0121] The acquired sentiment data is analyzed by a server to evaluate customer satisfaction. This evaluation result is considered as one of the factors in optimizing delivery routes. In some cases, delivery times and methods may be adjusted for delivery destinations where customer satisfaction has decreased. For example, for customers who have repeatedly been absent and require redelivery, it is possible to not only predict times when they are most likely to be home, but also to tailor communication based on the customer's sentiment data.

[0122] The terminal works in conjunction with an emotion engine to monitor interactions with the user in real time and feed the results back to the server. If the system determines that the user was particularly satisfied during delivery, that pattern is accumulated and reflected in the delivery procedure for future deliveries.

[0123] For example, if a user expresses dissatisfaction upon receiving a package, an attempt will be made to identify the reason, making it possible to provide more detailed delivery time notifications or suggest post office hold services for future deliveries to that address. This allows for more customized service for individual customers, leading to an overall improvement in customer satisfaction.

[0124] As described above, the present invention provides an embodiment of a system that utilizes an emotion engine to simultaneously achieve increased efficiency in delivery operations and improved quality of customer service.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server acquires various data necessary for delivery operations. This data includes the experience level of area drivers, the history of redeliveries to delivery destinations, the time when recipients are home, traffic information, traffic light change timings, delivery schedules, and time specifications. In this step, a new emotion engine is also used to collect user emotion data. Emotion data is obtained from facial expressions, tone of voice, and other similar information.

[0128] Step 2:

[0129] The server analyzes the collected data. Based on user sentiment data analyzed by the sentiment engine, it measures customer satisfaction and incorporates the results into the delivery route optimization process. For example, for delivery destinations with low satisfaction in the past, it suggests a new delivery method that takes this into consideration.

[0130] Step 3:

[0131] Based on the analysis results, the server calculates the optimal delivery route and develops a delivery strategy that incorporates customer sentiment data to improve customer satisfaction. This includes suggesting changes to delivery times and implementing special measures for specific customers.

[0132] Step 4:

[0133] The server sends optimized delivery routes and strategies to the terminal. The terminal receives this information immediately and notifies the driver. The driver not only follows the designated route but also makes deliveries while considering customer interactions based on the provided sentiment data.

[0134] Step 5:

[0135] As a user, the driver tracks real-time emotional data through a terminal while making deliveries, and adjusts their communication and response methods with customers accordingly. When positive emotional responses are obtained, the method is recorded to improve the service in the future.

[0136] Step 6:

[0137] The terminal transmits delivery performance data and sentiment data entered by the driver to the server. The server then analyzes the collected feedback data to continuously improve service and optimize future deliveries. This feedback data is a crucial resource for increasing customer satisfaction.

[0138] (Example 2)

[0139] 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".

[0140] In delivery operations, there is a need to optimize delivery routes efficiently while increasing customer satisfaction. Traditional systems optimized routes based on traffic conditions and customer availability, but they failed to improve service quality by considering customer emotions. Therefore, there is a need for a means to improve the delivery experience and provide services tailored to individual customer needs.

[0141] 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.

[0142] In this invention, the server includes means for acquiring various types of information, means for analyzing the acquired information to optimize the travel route, and means for analyzing acquired emotional information to improve the quality of service. This makes it possible to provide delivery services tailored to individual needs, taking into account the emotional information of the user.

[0143] "Various information" refers to all data related to delivery operations, including traffic conditions, the time customers are home at the delivery destination, and customer sentiment information.

[0144] A "travel route" refers to the path a transport device takes to perform delivery tasks, and it is optimized to ensure efficient delivery.

[0145] A "transportation device" refers to a vehicle or means used for delivering goods, which physically moves after receiving information.

[0146] "Delivery result information" refers to performance data obtained when a delivery is made, and includes delivery time, customer feedback, and whether or not a redelivery was required.

[0147] "Emotional information" refers to data related to emotions extracted from the user's facial expressions and voice, and serves as an indicator of customer satisfaction or dissatisfaction.

[0148] To implement this invention, a system is provided in which a server, terminal, and user work in cooperation. First, the server functions as a central processing unit for the integrated management of various types of information. The server is equipped with advanced analytical algorithms and generative AI models to process data streams in real time. This AI model analyzes large amounts of data related to delivery operations to optimize travel routes as well as improve the quality of service based on user sentiment information.

[0149] The server collects emotional information through a terminal. The terminal is a smart device carried by the delivery driver, equipped with a camera and microphone. During delivery, the terminal captures the user's facial expressions and voice, and sends this data to the server. This device performs an initial emotional assessment in real time and uses the results to improve interaction with the user.

[0150] On the other hand, users are important as a source of feedback on their satisfaction and needs through the delivery experience. For example, if a user expresses dissatisfaction with a previous delivery, the server can use that data to improve future delivery plans. This enables a customized delivery service for each individual user.

[0151] As a concrete example of this system, if a user smiles during parcel delivery, that facial expression data is sent from the terminal to the server and stored as an indicator of customer satisfaction. For future deliveries, routes and times are set to match similar conditions for the same user.

[0152] An example of a prompt is, "Analyze the emotions the user is exhibiting during delivery." This prompt is input into an AI model, and the corresponding output is used for emotion data analysis. This system design enables delivery operations that combine efficiency and improved customer satisfaction.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The terminal uses its camera and microphone to collect facial expressions and audio data from the user during the interaction between the delivery driver and the user. The input consists of audio and images captured in real time. This data is initially analyzed by an emotion engine within the terminal and output as the user's emotional state (e.g., joy, surprise, anger). The terminal temporarily stores this output in preparation for proceeding to the next step.

[0156] Step 2:

[0157] The terminal sends the initial analysis results to the server. The input is the user's sentiment data generated in step 1. The server receives this and performs a more detailed analysis using an advanced generative AI model. In this process, the current customer satisfaction is evaluated by comparing it with past sentiment data and outputting a numerical satisfaction score. The server stores this score in a database to help with future improvements.

[0158] Step 3:

[0159] The server optimizes travel routes based on analyzed sentiment data. Inputs include satisfaction scores obtained in step 2 and other logistics information (e.g., traffic conditions, weather data). The server considers this input data to calculate and output the optimal route and time for a specific delivery destination. Based on this information, the server adjusts the delivery schedule.

[0160] Step 4:

[0161] The user receives an actual delivery service and provides feedback on the experience. After the delivery is complete, new emotional data from the user (e.g., judgments based on their facial expressions upon receipt) is collected again through the device, and the cycle continues, returning to step 1. The input is facial and voice data after delivery, and the output is the new initial analysis results from step 1.

[0162] These steps allow the entire system to function as a feedback loop, simultaneously achieving increased efficiency in delivery operations and improved customer satisfaction.

[0163] (Application Example 2)

[0164] 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".

[0165] Traditional delivery systems have shortcomings in terms of improving delivery efficiency and customer satisfaction. In particular, they fail to consider customer emotions during delivery, missing opportunities to improve service quality. Furthermore, they do not adequately address the risk of redelivery, resulting in a lack of improvement in the customer experience.

[0166] 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.

[0167] In this invention, the server includes a device for acquiring various types of information in delivery operations, a device for analyzing the acquired information and optimizing the delivery route, a device for transmitting the optimized delivery route to a moving device, a device for acquiring delivery performance information from the moving device and using it for optimization in the next delivery, a device for recognizing customer sentiment information in real time, and a device for evaluating customer satisfaction based on the recognized sentiment information and using it to improve the delivery service. This makes it possible to simultaneously achieve improved delivery efficiency and customer satisfaction.

[0168] "Delivery services" refer to a series of activities that deliver goods to a location specified by the customer, and include planning delivery routes, transporting packages, and confirming receipt.

[0169] A "device for acquiring various types of information" is a device for collecting data related to deliveries, such as location information, traffic conditions, and customer time spent at home.

[0170] A "device that analyzes acquired information to optimize delivery routes" is a device that calculates delivery routes to improve delivery efficiency based on collected data.

[0171] A "device that transmits optimized delivery routes to moving equipment" is a device that transmits calculated, efficient delivery routes to vehicles, drones, and other equipment that actually carry out deliveries.

[0172] "Mobile equipment" refers to means of transporting deliveries to their destination, including vehicles and drones.

[0173] A "device that acquires delivery performance information and uses it for optimization in the future" is a device that collects delivery records and analyzes them to use in planning future deliveries.

[0174] A "device that recognizes customer emotional information in real time" is a device that analyzes the customer's facial expressions and voice during delivery to understand their emotional state on the spot.

[0175] A "device used to evaluate customer satisfaction and improve delivery services based on recognized emotional information" is a device that utilizes emotional data to measure the quality of service and derive strategies for providing a better customer experience.

[0176] The system for realizing this invention consists of multiple devices and software, aiming to improve the efficiency of delivery operations and enhance the customer experience. It primarily includes a server, terminals, and customer interfaces.

[0177] The server uses devices to acquire various information related to delivery operations, collecting real-time location data, traffic conditions, and customer availability times. This data is processed by devices that optimize delivery routes, calculating efficient delivery routes. The server then transmits the calculated delivery route to the moving devices, reflecting it in the actual delivery process.

[0178] The terminal is equipped with a device that recognizes emotional information such as customer facial expressions and voice in real time during delivery. This uses deep learning technologies such as TensorFlow and Keras to analyze emotions. The recognized emotional data is sent to a server and used to evaluate customer satisfaction.

[0179] The software used includes EmotionEngine and RouteOptimizer, which perform complex data analysis. EmotionEngine directly handles emotional data, while RouteOptimizer combines diverse information to maximize the efficiency of delivery routes.

[0180] As a concrete example, consider its application to a food delivery service. If a customer expresses dissatisfaction upon receiving their food, this emotional data can be analyzed to improve future deliveries. An example of such a prompt could be, "Analyze the customer's emotions (facial and audible) regarding the recently delivered pizza and generate appropriate feedback." This allows for adjustments to delivery routes and times, and even the creation of customized services that take customer emotions into consideration.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server acquires various information necessary for delivery operations. It uses data such as delivery addresses, current traffic conditions, and customer availability times as input. This data is integrated to build a foundational dataset for improving delivery efficiency.

[0184] Step 2:

[0185] The device recognizes the user's facial expressions and voice during delivery and acquires emotional data. It uses audio recordings and captured images from direct interactions with the user as input. Using deep learning technologies such as TensorFlow and Keras, it analyzes emotions in real time from this data and outputs emotional classifications such as positive, negative, and neutral.

[0186] Step 3:

[0187] The server analyzes sentiment data and other delivery information to recalculate the optimal delivery route. The input is the data obtained in steps 1 and 2. Using RouteOptimizer, it considers this data and calculates the route that maximizes delivery efficiency. The output is the optimized delivery route.

[0188] Step 4:

[0189] The user uses the new delivery route sent from the server to head to the next delivery destination. The new route information is fed directly into the GPS navigation system and displayed visually on the user's device. The input is the optimized delivery route, and the output is route information to guide the user's movement.

[0190] Step 5:

[0191] The server retrieves performance data after delivery is completed to prepare for future optimizations. Inputs include delivery time, customer satisfaction rating, and delivery completion report. This data is accumulated, and customer feedback is analyzed using a generative AI model to improve future deliveries. This process ensures continuous improvement of the customer experience.

[0192] 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.

[0193] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] 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.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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".

[0208] This invention provides a system for improving the efficiency of delivery operations. Specifically, it implements a series of processes that acquire and analyze various data in delivery operations, optimize delivery routes, and transmit the results to the mobile vehicle. The processing content of the program as an embodiment is described below.

[0209] First, the server collects a variety of data from multiple perspectives, including area drivers' experience data, redelivery records for delivery destinations, recipients' availability times, traffic conditions, traffic light change timings, delivery schedules, and time specifications. This data collection process involves retrieving information through various sensors and databases.

[0210] Next, the server analyzes the acquired data. Using generative AI technology, it extracts useful patterns and predictions from this data to determine the priority of visits to each delivery destination. It also predicts traffic congestion from traffic information and selects the optimal route, taking into account traffic light change timings.

[0211] The server then calculates an optimized delivery route and sends the results to the terminal. The terminal receives this information in real time and notifies the driver (user). The driver then carries out the delivery according to the instructions provided. Important information and special notes regarding the delivery destination are also conveyed, enabling the driver to perform their duties efficiently.

[0212] Once a delivery is complete, delivery performance data is sent back to the server via the terminal. The server uses this data to analyze the causes of any redeliveries and to provide feedback necessary for optimizing future delivery routes. This feedback loop allows the system to continuously improve and support highly efficient delivery operations.

[0213] As a concrete example, the system predicts the times when customers who have a history of missed deliveries are likely to be home and automatically selects a route to visit during those times. As a result, the risk of redelivery is significantly reduced, and fuel and time are saved. Through such operations, delivery work can be made more streamlined, and the burden on workers can be reduced.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The server automatically retrieves data from various databases, including area drivers' experience levels, redelivery records, recipients' availability times, traffic conditions, traffic light timings, delivery schedules, and time-specific delivery requests. The data is updated at regular intervals and managed to maintain the most up-to-date information.

[0217] Step 2:

[0218] The server analyzes the collected data. Using a generative AI algorithm, it evaluates the likelihood of recipients being home and the possibility of redelivery based on past delivery data. It also analyzes road congestion and traffic light waiting times based on traffic data and calculates the impact these factors have on the delivery route.

[0219] Step 3:

[0220] The server calculates the most efficient delivery route based on the analysis results. The route is optimized by considering various factors with the aim of minimizing fuel consumption and shortening delivery time. It derives routes that prioritize times when people are likely to be home and times when roads are less congested.

[0221] Step 4:

[0222] The server sends the calculated optimal delivery route to the terminal. The terminal immediately receives this information and notifies the driver, who is the user. The notification also includes detailed information and notes for each delivery destination, and the driver carries out the delivery work based on this information.

[0223] Step 5:

[0224] The user, acting as the driver, makes deliveries following the optimal route displayed on the terminal. After each delivery is completed, the driver uses the terminal to record the results, entering information such as whether the delivery was successful, the recipient was absent, or if redelivery is necessary.

[0225] Step 6:

[0226] The terminal transmits delivery performance data entered by the driver to the server. The server uses the received feedback data to improve the accuracy of future delivery routes. This continuously improves the overall efficiency of the system.

[0227] (Example 1)

[0228] 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."

[0229] In modern delivery operations, challenges such as an increase in redeliveries and traffic congestion make it difficult to achieve efficient deliveries. This leads to wasted time and fuel, increasing the environmental burden and potentially lowering customer satisfaction. Furthermore, improving delivery efficiency is a challenge due to the difficulty in optimizing delivery routes.

[0230] 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.

[0231] In this invention, the server includes means for collecting information related to delivery operations, means for analyzing the collected information using generation AI technology to optimize delivery routes, and means for transmitting the optimized delivery routes to a communication device. This enables the selection of efficient delivery routes, making it possible to improve delivery operations while minimizing the risk of redelivery and delays due to traffic congestion.

[0232] "Delivery services" refer to a series of activities or processes for transporting goods or packages from one location to another.

[0233] "Information" refers to facts and indicators about a situation or environment, provided in the form of data or knowledge.

[0234] "Means of collection" refers to the methods and techniques used to gather necessary information and data.

[0235] "Generative AI technology" refers to tools and algorithms that use artificial intelligence to perform advanced processing such as data analysis.

[0236] "Analysis" is the process of examining collected information and data in detail to extract patterns and useful information.

[0237] A "delivery route" is the path selected for delivery operations, from the starting point to the destination.

[0238] "Optimization" is the process of adjusting resources and time to use them efficiently and achieve better results.

[0239] A "communication device" is hardware or software used to send and receive information.

[0240] "Means of transmission" refers to the technology or method used to send information to a specific destination.

[0241] "Delivery performance information" refers to records and data related to actual delivery activities.

[0242] The delivery efficiency system in this invention includes a series of processes: information gathering, analysis using AI generation technology, route optimization, and feedback of performance information.

[0243] The server collects a variety of information related to delivery operations. Specifically, it handles data such as traffic conditions, traffic signal control information, past redelivery records, times when residents are home, and time-specific delivery information. This work is performed through API integration with traffic sensors and databases, or via IoT devices.

[0244] Next, the server analyzes the collected information using a generative AI model. This generative AI model is implemented on frameworks such as TensorFlow and PyTorch, and extracts patterns and trends from the data. The results obtained from the analysis are used to optimize delivery routes. For example, it calculates the most efficient delivery route by taking into account traffic congestion and traffic light switching times.

[0245] The calculated optimized route is sent from the server to the terminal. This communication takes place in real time using a mobile network or Wi-Fi. The terminal receives the route information and notifies the driver, who is the user. The driver follows the instructions using visual map displays and voice guidance to complete the delivery.

[0246] Once a delivery is complete, the terminal sends delivery performance information back to the server. This information includes the time of successful delivery, whether redelivery was required, and the time taken for delivery. The server uses this information to optimize the system and analyze causes, thereby improving the overall efficiency of the system.

[0247] As a concrete example, the system identifies customers who have frequently been absent and required redelivery in the past, and uses an AI model to predict their expected time at home. It then automatically selects delivery routes that visit during times when the customer is most likely to be home. This approach improves the efficiency of delivery operations and enhances customer satisfaction.

[0248] An example of a prompt message might be something like, "Based on past redelivery data, predict the times when the recipient is most likely to be home at a specific delivery address, and suggest the optimal delivery route for that time."

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The server collects information related to delivery operations. This information includes real-time traffic data obtained through traffic sensors and IoT devices, historical delivery performance data, and customer occupancy time data. The server aggregates the input data and stores it in a database for subsequent processing. As a result, a large amount of delivery-related data is collected.

[0252] Step 2:

[0253] The server begins analyzing the collected data. Using a generative AI model, it predicts visit priorities for each area, the degree of traffic congestion, and the times of day when redelivery is most likely to be required. The data obtained in step 1 is fed into the AI ​​model as input, and data processing and prediction algorithms are applied. This outputs prediction results and patterns, which are used as basic information for optimization processing.

[0254] Step 3:

[0255] The server calculates the optimal delivery route based on the analysis results. Here, it uses the results of the generated AI model, taking into account acquired traffic information and traffic light timings, to calculate the shortest and fastest route. The input includes the analysis results obtained in step 2, and the route optimization algorithm is applied. The output generates the optimal delivery route for the driver.

[0256] Step 4:

[0257] The server sends the optimized delivery route to the terminal. Using data communication technology, route information is transmitted to the terminal in real time. The input includes the optimized delivery route generated in step 3 and is sent to the terminal. This allows the terminal to immediately receive the route information and prepare to notify the user.

[0258] Step 5:

[0259] The terminal notifies the driver, who is the user, of the route information it has received. The notification is given through voice guidance and visual display on a map, providing instructions to efficiently carry out delivery work. The input is the optimized route information sent in step 4, and the output is that the driver receives route guidance visually and audibly.

[0260] Step 6:

[0261] After a delivery is completed, the terminal sends delivery performance information back to the server. This performance information includes whether the delivery was successful, the time taken, and confirmation of receipt. The input consists of performance data recorded by the terminal during the delivery, which is then sent to the server. As output, the server uses this data for optimization and root cause analysis for future deliveries.

[0262] (Application Example 1)

[0263] 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."

[0264] In current delivery operations, the efficiency of delivery routes is a critical issue, particularly the waste of time and resources due to redeliveries. Furthermore, the need for real-time, appropriate decision-making in response to fluctuating traffic conditions is a major challenge. Additionally, insufficient route selection that considers the recipient's availability at the delivery location is contributing to reduced delivery efficiency.

[0265] 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.

[0266] In this invention, the server includes means for collecting various information in delivery operations, means for analyzing the collected information to optimize delivery routes, means for transmitting the optimized delivery routes to transportation equipment, means for acquiring delivery performance information from transportation equipment and using it for future optimization, and means for displaying delivery instructions to employees in real time based on the acquired information. This enables a reduction in redeliveries and efficient delivery that reflects real-time traffic information.

[0267] "Delivery services" refers to all activities involved in transporting packages or goods to their designated destinations.

[0268] "Means of collecting various types of information" refers to a system for collecting various data related to delivery.

[0269] "A means of analyzing collected information to optimize delivery routes" refers to a system that analyzes data to determine the most efficient delivery order and route.

[0270] "Means for transmitting optimized delivery routes to transportation equipment" refers to a system that transmits calculated best route information to the vehicles used for actual delivery.

[0271] "Transportation equipment" refers to vehicles and devices used to physically transport goods or packages.

[0272] "Delivery performance information" refers to data on actual delivery activities and is used to improve future delivery plans.

[0273] "Methods to be used for optimization in the next delivery" refers to a system that further improves future delivery plans based on past delivery data.

[0274] "A means of displaying delivery instructions to employees in real time" refers to a system that provides workers with immediate instructions tailored to the current situation.

[0275] This invention involves a logistics center implementing an information technology-driven system to improve operational efficiency. A server analyzes collected data in real time, calculates the optimal delivery route, and transmits it to the transport equipment. The transport equipment, such as smartphones or tablets, communicates with the Node.js-based server. The server utilizes generative AI models based on TensorFlow and PyTorch to optimize delivery routes based on past delivery data and the latest traffic information. This enables efficient delivery by avoiding congestion and traffic lights.

[0276] The terminal receives optimal route information sent from the server and displays it in an easy-to-understand format for employees. By utilizing the Google Maps API, a visual route is displayed on a map, allowing users to follow instructions in real time and make deliveries. The terminal also sends delivery performance information back to the server, which is used for improvement in future deliveries.

[0277] As a specific example, consider a scenario where a large number of packages are efficiently delivered during the Christmas season. The server receives past delivery data as input and executes a prompt saying, "Analyze the delivery data for the past year and propose the visit priorities and routes to reduce redelivery." Based on this prompt, the generative AI model calculates a route that reduces the risk of redelivery and transmits that information to the terminal. By such an operation, it is possible to improve the efficiency of the delivery operation and reduce costs.

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] The server comprehensively collects various data such as traffic conditions, driver experience values, and the time zones when the delivery destinations are at home. It receives data from sensors and databases as input, integrates it, and stores it in a repository. As output, it generates an information set necessary for the delivery plan.

[0281] Step 2:

[0282] The server analyzes the collected data and uses the generative AI model to calculate the optimal delivery route. As input, it inputs the information set generated in Step 1 together with the prompt sentence "Analyze the delivery data for the past year and propose the visit priorities and routes to reduce redelivery" into the model. Data processing and predictive calculations are performed by the model, and optimized route information is generated as output.

[0283] Step 3:

[0284] The server transmits the optimized delivery route to the terminal. Using the route information obtained in Step 2 as input, it transfers it to the terminal in real time. As output, a route and instruction information that can be visually confirmed on the terminal used by the user are displayed.

[0285] Step 4:

[0286] Based on the received route information, the terminal displays a delivery instruction to the user. It receives route information from the server as input, uses the Google Maps API to show the route on a map as output, and provides the necessary delivery instructions to the user.

[0287] Step 5:

[0288] The user executes the delivery according to the delivery instructions displayed on the terminal. The delivery performance information collected by the user is resent from the terminal to the server. As input, it collects the progress status and completion data of the delivery, and processes it as output into performance data to be reflected in the next delivery plan.

[0289] Step 6:

[0290] Based on the delivery performance information from the terminal, the server analyzes the improvements required for the next delivery route optimization as feedback. As input, it analyzes the performance data collected in Step 5, and as output, a proposed improved delivery plan is formed. Through this cycle, the delivery efficiency is continuously improved.

[0291] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0292] The present invention provides a system for improving the efficiency of delivery operations and enhancing the customer experience. Specifically, it incorporates an emotion engine, recognizes the user's emotion in the delivery operation, and uses the data to optimize the delivery route and improve the service quality. In the following, embodiments of this system will be specifically described.

[0293] The server first collects user emotional data using an emotion engine, in addition to the conventional data acquisition process. This emotional data is recognized in real time from the customer's facial expressions and voice when they interact with the delivery driver and transmitted to the server. This makes it possible to understand the customer's emotional state at each delivery location in chronological order.

[0294] The acquired sentiment data is analyzed by a server to evaluate customer satisfaction. This evaluation result is considered as one of the factors in optimizing delivery routes. In some cases, delivery times and methods may be adjusted for delivery destinations where customer satisfaction has decreased. For example, for customers who have repeatedly been absent and require redelivery, it is possible to not only predict times when they are most likely to be home, but also to tailor communication based on the customer's sentiment data.

[0295] The terminal works in conjunction with an emotion engine to monitor interactions with the user in real time and feed the results back to the server. If the system determines that the user was particularly satisfied during delivery, that pattern is accumulated and reflected in the delivery procedure for future deliveries.

[0296] For example, if a user expresses dissatisfaction upon receiving a package, an attempt will be made to identify the reason, making it possible to provide more detailed delivery time notifications or suggest post office hold services for future deliveries to that address. This allows for more customized service for individual customers, leading to an overall improvement in customer satisfaction.

[0297] As described above, the present invention provides an embodiment of a system that utilizes an emotion engine to simultaneously achieve increased efficiency in delivery operations and improved quality of customer service.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The server acquires various data necessary for the delivery service. This data includes the experience values of area drivers, the re-delivery performance of delivery destinations, the home time zones of delivery destinations, traffic information, the switching timings of traffic lights, delivery schedules, and time specifications. Also, in this step, new collection of users' emotion data using an emotion engine is also carried out. The emotion data is acquired from expressions, tones of voice, etc.

[0301] Step 2:

[0302] The server analyzes the collected data. Based on the users' emotion data analyzed by the emotion engine, the customer satisfaction is measured, and the result is incorporated into the optimization process of the delivery route. For example, for delivery destinations with low satisfaction in the past, a new delivery method considered is proposed.

[0303] Step 3:

[0304] The server calculates the optimal delivery route based on the analysis results, and formulates a delivery strategy aiming at improving satisfaction by reflecting the users' emotion data. This includes proposals for time zone changes and special countermeasures for specific customers.

[0305] Step 4:

[0306] The server transmits the optimized delivery route and delivery strategy to the terminal. The terminal immediately receives the information and notifies the driver. The driver not only follows the specified route but also conducts the delivery while considering the customer response based on the provided emotion data.

[0307] Step 5:

[0308] The driver, who is the user, tracks the real-time emotion data through the terminal when conducting the delivery, and timely adjusts the communication and response methods with the customer. At this time, if a good emotional reaction is obtained, the method is recorded for the next service improvement.

[0309] Step 6:

[0310] The terminal transmits delivery performance data and sentiment data entered by the driver to the server. The server then analyzes the collected feedback data to continuously improve service and optimize future deliveries. This feedback data is a crucial resource for increasing customer satisfaction.

[0311] (Example 2)

[0312] 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".

[0313] In delivery operations, there is a need to optimize delivery routes efficiently while increasing customer satisfaction. Traditional systems optimized routes based on traffic conditions and customer availability, but they failed to improve service quality by considering customer emotions. Therefore, there is a need for a means to improve the delivery experience and provide services tailored to individual customer needs.

[0314] 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.

[0315] In this invention, the server includes means for acquiring various types of information, means for analyzing the acquired information to optimize the travel route, and means for analyzing acquired emotional information to improve the quality of service. This makes it possible to provide delivery services tailored to individual needs, taking into account the emotional information of the user.

[0316] "Various information" refers to all data related to delivery operations, including traffic conditions, the time customers are home at the delivery destination, and customer sentiment information.

[0317] A "travel route" refers to the path a transport device takes to perform delivery tasks, and it is optimized to ensure efficient delivery.

[0318] A "transportation device" refers to a vehicle or means used for delivering goods, which physically moves after receiving information.

[0319] "Delivery result information" refers to performance data obtained when a delivery is made, and includes delivery time, customer feedback, and whether or not a redelivery was required.

[0320] "Emotional information" refers to data related to emotions extracted from the user's facial expressions and voice, and serves as an indicator of customer satisfaction or dissatisfaction.

[0321] To implement this invention, a system is provided in which a server, terminal, and user work in cooperation. First, the server functions as a central processing unit for the integrated management of various types of information. The server is equipped with advanced analytical algorithms and generative AI models to process data streams in real time. This AI model analyzes large amounts of data related to delivery operations to optimize travel routes as well as improve the quality of service based on user sentiment information.

[0322] The server collects emotional information through a terminal. The terminal is a smart device carried by the delivery driver, equipped with a camera and microphone. During delivery, the terminal captures the user's facial expressions and voice, and sends this data to the server. This device performs an initial emotional assessment in real time and uses the results to improve interaction with the user.

[0323] On the other hand, users are important as a source of feedback on their satisfaction and needs through the delivery experience. For example, if a user expresses dissatisfaction with a previous delivery, the server can use that data to improve future delivery plans. This enables a customized delivery service for each individual user.

[0324] As a concrete example of this system, if a user smiles during parcel delivery, that facial expression data is sent from the terminal to the server and stored as an indicator of customer satisfaction. For future deliveries, routes and times are set to match similar conditions for the same user.

[0325] An example of a prompt is, "Analyze the emotions the user is exhibiting during delivery." This prompt is input into an AI model, and the corresponding output is used for emotion data analysis. This system design enables delivery operations that combine efficiency and improved customer satisfaction.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The terminal uses its camera and microphone to collect facial expressions and audio data from the user during the interaction between the delivery driver and the user. The input consists of audio and images captured in real time. This data is initially analyzed by an emotion engine within the terminal and output as the user's emotional state (e.g., joy, surprise, anger). The terminal temporarily stores this output in preparation for proceeding to the next step.

[0329] Step 2:

[0330] The terminal sends the initial analysis results to the server. The input is the user's sentiment data generated in step 1. The server receives this and performs a more detailed analysis using an advanced generative AI model. In this process, the current customer satisfaction is evaluated by comparing it with past sentiment data and outputting a numerical satisfaction score. The server stores this score in a database to help with future improvements.

[0331] Step 3:

[0332] The server optimizes travel routes based on analyzed sentiment data. Inputs include satisfaction scores obtained in step 2 and other logistics information (e.g., traffic conditions, weather data). The server considers this input data to calculate and output the optimal route and time for a specific delivery destination. Based on this information, the server adjusts the delivery schedule.

[0333] Step 4:

[0334] The user receives an actual delivery service and provides feedback on the experience. After the delivery is complete, new emotional data from the user (e.g., judgments based on their facial expressions upon receipt) is collected again through the device, and the cycle continues, returning to step 1. The input is facial and voice data after delivery, and the output is the new initial analysis results from step 1.

[0335] These steps allow the entire system to function as a feedback loop, simultaneously achieving increased efficiency in delivery operations and improved customer satisfaction.

[0336] (Application Example 2)

[0337] 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."

[0338] Traditional delivery systems have shortcomings in terms of improving delivery efficiency and customer satisfaction. In particular, they fail to consider customer emotions during delivery, missing opportunities to improve service quality. Furthermore, they do not adequately address the risk of redelivery, resulting in a lack of improvement in the customer experience.

[0339] 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.

[0340] In this invention, the server includes a device for acquiring various types of information in delivery operations, a device for analyzing the acquired information and optimizing the delivery route, a device for transmitting the optimized delivery route to a moving device, a device for acquiring delivery performance information from the moving device and using it for optimization in the next delivery, a device for recognizing customer sentiment information in real time, and a device for evaluating customer satisfaction based on the recognized sentiment information and using it to improve the delivery service. This makes it possible to simultaneously achieve improved delivery efficiency and customer satisfaction.

[0341] "Delivery services" refer to a series of activities that deliver goods to a location specified by the customer, and include planning delivery routes, transporting packages, and confirming receipt.

[0342] A "device for acquiring various types of information" is a device for collecting data related to deliveries, such as location information, traffic conditions, and customer time spent at home.

[0343] A "device that analyzes acquired information to optimize delivery routes" is a device that calculates delivery routes to improve delivery efficiency based on collected data.

[0344] A "device that transmits optimized delivery routes to moving equipment" is a device that transmits calculated, efficient delivery routes to vehicles, drones, and other equipment that actually carry out deliveries.

[0345] "Mobile equipment" refers to means of transporting deliveries to their destination, including vehicles and drones.

[0346] A "device that acquires delivery performance information and uses it for optimization in the future" is a device that collects delivery records and analyzes them to use in planning future deliveries.

[0347] A "device that recognizes customer emotional information in real time" is a device that analyzes the customer's facial expressions and voice during delivery to understand their emotional state on the spot.

[0348] A "device used to evaluate customer satisfaction and improve delivery services based on recognized emotional information" is a device that utilizes emotional data to measure the quality of service and derive strategies for providing a better customer experience.

[0349] The system for realizing this invention consists of multiple devices and software, aiming to improve the efficiency of delivery operations and enhance the customer experience. It primarily includes a server, terminals, and customer interfaces.

[0350] The server uses devices to acquire various information related to delivery operations, collecting real-time location data, traffic conditions, and customer availability times. This data is processed by devices that optimize delivery routes, calculating efficient delivery routes. The server then transmits the calculated delivery route to the moving devices, reflecting it in the actual delivery process.

[0351] The terminal is equipped with a device that recognizes emotional information such as customer facial expressions and voice in real time during delivery. This uses deep learning technologies such as TensorFlow and Keras to analyze emotions. The recognized emotional data is sent to a server and used to evaluate customer satisfaction.

[0352] The software used includes EmotionEngine and RouteOptimizer, which perform complex data analysis. EmotionEngine directly handles emotional data, while RouteOptimizer combines diverse information to maximize the efficiency of delivery routes.

[0353] As a concrete example, consider its application to a food delivery service. If a customer expresses dissatisfaction upon receiving their food, this emotional data can be analyzed to improve future deliveries. An example of such a prompt could be, "Analyze the customer's emotions (facial and audible) regarding the recently delivered pizza and generate appropriate feedback." This allows for adjustments to delivery routes and times, and even the creation of customized services that take customer emotions into consideration.

[0354] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0355] Step 1:

[0356] The server acquires various information necessary for delivery operations. It uses data such as delivery addresses, current traffic conditions, and customer availability times as input. This data is integrated to build a foundational dataset for improving delivery efficiency.

[0357] Step 2:

[0358] The device recognizes the user's facial expressions and voice during delivery and acquires emotional data. It uses audio recordings and captured images from direct interactions with the user as input. Using deep learning technologies such as TensorFlow and Keras, it analyzes emotions in real time from this data and outputs emotional classifications such as positive, negative, and neutral.

[0359] Step 3:

[0360] The server analyzes sentiment data and other delivery information to recalculate the optimal delivery route. The input is the data obtained in steps 1 and 2. Using RouteOptimizer, it considers this data and calculates the route that maximizes delivery efficiency. The output is the optimized delivery route.

[0361] Step 4:

[0362] The user uses the new delivery route sent from the server to head to the next delivery destination. The new route information is fed directly into the GPS navigation system and displayed visually on the user's device. The input is the optimized delivery route, and the output is route information to guide the user's movement.

[0363] Step 5:

[0364] The server retrieves performance data after delivery is completed to prepare for future optimizations. Inputs include delivery time, customer satisfaction rating, and delivery completion report. This data is accumulated, and customer feedback is analyzed using a generative AI model to improve future deliveries. This process ensures continuous improvement of the customer experience.

[0365] 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.

[0366] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] 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.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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".

[0381] This invention provides a system for improving the efficiency of delivery operations. Specifically, it implements a series of processes that acquire and analyze various data in delivery operations, optimize delivery routes, and transmit the results to the mobile vehicle. The processing content of the program as an embodiment is described below.

[0382] First, the server collects a variety of data from multiple perspectives, including area drivers' experience data, redelivery records for delivery destinations, recipients' availability times, traffic conditions, traffic light change timings, delivery schedules, and time specifications. This data collection process involves retrieving information through various sensors and databases.

[0383] Next, the server analyzes the acquired data. Using generative AI technology, it extracts useful patterns and predictions from this data to determine the priority of visits to each delivery destination. It also predicts traffic congestion from traffic information and selects the optimal route, taking into account traffic light change timings.

[0384] The server then calculates an optimized delivery route and sends the results to the terminal. The terminal receives this information in real time and notifies the driver (user). The driver then carries out the delivery according to the instructions provided. Important information and special notes regarding the delivery destination are also conveyed, enabling the driver to perform their duties efficiently.

[0385] Once a delivery is complete, delivery performance data is sent back to the server via the terminal. The server uses this data to analyze the causes of any redeliveries and to provide feedback necessary for optimizing future delivery routes. This feedback loop allows the system to continuously improve and support highly efficient delivery operations.

[0386] As a concrete example, the system predicts the times when customers who have a history of missed deliveries are likely to be home and automatically selects a route to visit during those times. As a result, the risk of redelivery is significantly reduced, and fuel and time are saved. Through such operations, delivery work can be made more streamlined, and the burden on workers can be reduced.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] The server automatically retrieves data from various databases, including area drivers' experience levels, redelivery records, recipients' availability times, traffic conditions, traffic light timings, delivery schedules, and time-specific delivery requests. The data is updated at regular intervals and managed to maintain the most up-to-date information.

[0390] Step 2:

[0391] The server analyzes the collected data. Using a generative AI algorithm, it evaluates the likelihood of recipients being home and the possibility of redelivery based on past delivery data. It also analyzes road congestion and traffic light waiting times based on traffic data and calculates the impact these factors have on the delivery route.

[0392] Step 3:

[0393] The server calculates the most efficient delivery route based on the analysis results. The route is optimized by considering various factors with the aim of minimizing fuel consumption and shortening delivery time. It derives routes that prioritize times when people are likely to be home and times when roads are less congested.

[0394] Step 4:

[0395] The server sends the calculated optimal delivery route to the terminal. The terminal immediately receives this information and notifies the driver, who is the user. The notification also includes detailed information and notes for each delivery destination, and the driver carries out the delivery work based on this information.

[0396] Step 5:

[0397] The user, acting as the driver, makes deliveries following the optimal route displayed on the terminal. After each delivery is completed, the driver uses the terminal to record the results, entering information such as whether the delivery was successful, the recipient was absent, or if redelivery is necessary.

[0398] Step 6:

[0399] The terminal transmits delivery performance data entered by the driver to the server. The server uses the received feedback data to improve the accuracy of future delivery routes. This continuously improves the overall efficiency of the system.

[0400] (Example 1)

[0401] 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."

[0402] In modern delivery operations, challenges such as an increase in redeliveries and traffic congestion make it difficult to achieve efficient deliveries. This leads to wasted time and fuel, increasing the environmental burden and potentially lowering customer satisfaction. Furthermore, improving delivery efficiency is a challenge due to the difficulty in optimizing delivery routes.

[0403] 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.

[0404] In this invention, the server includes means for collecting information related to delivery operations, means for analyzing the collected information using generation AI technology to optimize delivery routes, and means for transmitting the optimized delivery routes to a communication device. This enables the selection of efficient delivery routes, making it possible to improve delivery operations while minimizing the risk of redelivery and delays due to traffic congestion.

[0405] "Delivery services" refer to a series of activities or processes for transporting goods or packages from one location to another.

[0406] "Information" refers to facts and indicators about a situation or environment, provided in the form of data or knowledge.

[0407] "Means of collection" refers to the methods and techniques used to gather necessary information and data.

[0408] "Generative AI technology" refers to tools and algorithms that use artificial intelligence to perform advanced processing such as data analysis.

[0409] "Analysis" is the process of examining collected information and data in detail to extract patterns and useful information.

[0410] A "delivery route" is the path selected for delivery operations, from the starting point to the destination.

[0411] "Optimization" is the process of adjusting resources and time to use them efficiently and achieve better results.

[0412] A "communication device" is hardware or software used to send and receive information.

[0413] "Means of transmission" refers to the technology or method used to send information to a specific destination.

[0414] "Delivery performance information" refers to records and data related to actual delivery activities.

[0415] The delivery efficiency system in this invention includes a series of processes: information gathering, analysis using AI generation technology, route optimization, and feedback of performance information.

[0416] The server collects a variety of information related to delivery operations. Specifically, it handles data such as traffic conditions, traffic signal control information, past redelivery records, times when residents are home, and time-specific delivery information. This work is performed through API integration with traffic sensors and databases, or via IoT devices.

[0417] Next, the server analyzes the collected information using a generative AI model. This generative AI model is implemented on frameworks such as TensorFlow and PyTorch, and extracts patterns and trends from the data. The results obtained from the analysis are used to optimize delivery routes. For example, it calculates the most efficient delivery route by taking into account traffic congestion and traffic light switching times.

[0418] The calculated optimized route is sent from the server to the terminal. This communication takes place in real time using a mobile network or Wi-Fi. The terminal receives the route information and notifies the driver, who is the user. The driver follows the instructions using visual map displays and voice guidance to complete the delivery.

[0419] Once a delivery is complete, the terminal sends delivery performance information back to the server. This information includes the time of successful delivery, whether redelivery was required, and the time taken for delivery. The server uses this information to optimize the system and analyze causes, thereby improving the overall efficiency of the system.

[0420] As a concrete example, the system identifies customers who have frequently been absent and required redelivery in the past, and uses an AI model to predict their expected time at home. It then automatically selects delivery routes that visit during times when the customer is most likely to be home. This approach improves the efficiency of delivery operations and enhances customer satisfaction.

[0421] An example of a prompt message might be something like, "Based on past redelivery data, predict the times when the recipient is most likely to be home at a specific delivery address, and suggest the optimal delivery route for that time."

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The server collects information related to delivery operations. This information includes real-time traffic data obtained through traffic sensors and IoT devices, historical delivery performance data, and customer occupancy time data. The server aggregates the input data and stores it in a database for subsequent processing. As a result, a large amount of delivery-related data is collected.

[0425] Step 2:

[0426] The server begins analyzing the collected data. Using a generative AI model, it predicts visit priorities for each area, the degree of traffic congestion, and the times of day when redelivery is most likely to be required. The data obtained in step 1 is fed into the AI ​​model as input, and data processing and prediction algorithms are applied. This outputs prediction results and patterns, which are used as basic information for optimization processing.

[0427] Step 3:

[0428] The server calculates the optimal delivery route based on the analysis results. Here, it uses the results of the generated AI model, taking into account acquired traffic information and traffic light timings, to calculate the shortest and fastest route. The input includes the analysis results obtained in step 2, and the route optimization algorithm is applied. The output generates the optimal delivery route for the driver.

[0429] Step 4:

[0430] The server sends the optimized delivery route to the terminal. Using data communication technology, route information is transmitted to the terminal in real time. The input includes the optimized delivery route generated in step 3 and is sent to the terminal. This allows the terminal to immediately receive the route information and prepare to notify the user.

[0431] Step 5:

[0432] The terminal notifies the driver, who is the user, of the route information it has received. The notification is given through voice guidance and visual display on a map, providing instructions to efficiently carry out delivery work. The input is the optimized route information sent in step 4, and the output is that the driver receives route guidance visually and audibly.

[0433] Step 6:

[0434] After a delivery is completed, the terminal sends delivery performance information back to the server. This performance information includes whether the delivery was successful, the time taken, and confirmation of receipt. The input consists of performance data recorded by the terminal during the delivery, which is then sent to the server. As output, the server uses this data for optimization and root cause analysis for future deliveries.

[0435] (Application Example 1)

[0436] 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."

[0437] In current delivery operations, the efficiency of delivery routes is a critical issue, particularly the waste of time and resources due to redeliveries. Furthermore, the need for real-time, appropriate decision-making in response to fluctuating traffic conditions is a major challenge. Additionally, insufficient route selection that considers the recipient's availability at the delivery location is contributing to reduced delivery efficiency.

[0438] 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.

[0439] In this invention, the server includes means for collecting various information in delivery operations, means for analyzing the collected information to optimize delivery routes, means for transmitting the optimized delivery routes to transportation equipment, means for acquiring delivery performance information from transportation equipment and using it for future optimization, and means for displaying delivery instructions to employees in real time based on the acquired information. This enables a reduction in redeliveries and efficient delivery that reflects real-time traffic information.

[0440] "Delivery services" refers to all activities involved in transporting packages or goods to their designated destinations.

[0441] "Means of collecting various types of information" refers to a system for collecting various data related to delivery.

[0442] "A means of analyzing collected information to optimize delivery routes" refers to a system that analyzes data to determine the most efficient delivery order and route.

[0443] "Means for transmitting optimized delivery routes to transportation equipment" refers to a system that transmits calculated best route information to the vehicles used for actual delivery.

[0444] "Transportation equipment" refers to vehicles and devices used to physically transport goods or packages.

[0445] "Delivery performance information" refers to data on actual delivery activities and is used to improve future delivery plans.

[0446] "Methods to be used for optimization in the next delivery" refers to a system that further improves future delivery plans based on past delivery data.

[0447] "A means of displaying delivery instructions to employees in real time" refers to a system that provides workers with immediate instructions tailored to the current situation.

[0448] This invention involves a logistics center implementing an information technology-driven system to improve operational efficiency. A server analyzes collected data in real time, calculates the optimal delivery route, and transmits it to the transport equipment. The transport equipment, such as smartphones or tablets, communicates with the Node.js-based server. The server utilizes generative AI models based on TensorFlow and PyTorch to optimize delivery routes based on past delivery data and the latest traffic information. This enables efficient delivery by avoiding congestion and traffic lights.

[0449] The terminal receives optimal route information sent from the server and displays it in an easy-to-understand format for employees. By utilizing the Google Maps API, a visual route is displayed on a map, allowing users to follow instructions in real time and make deliveries. The terminal also sends delivery performance information back to the server, which is used for improvement in future deliveries.

[0450] As a concrete example, consider a scenario for efficiently delivering a large volume of packages during the Christmas season. The server receives historical delivery data as input and prompts, "Analyze the delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." Based on this prompt, the generating AI model calculates a route that reduces the risk of redelivery and sends that information to the terminal. This operation can improve the efficiency of delivery operations and reduce costs.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The server collects various data from multiple perspectives, including traffic conditions, driver experience, and the time when recipients are likely to be home during delivery. It receives data from sensors and databases as input, integrates it, and stores it in a repository. As output, it generates a set of information necessary for delivery planning.

[0454] Step 2:

[0455] The server analyzes the collected data and uses a generated AI model to calculate the optimal delivery route. The information set generated in step 1 is fed into the model as input, along with the prompt "Analyze delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." The model performs data processing and predictive calculations, generating optimized route information as output.

[0456] Step 3:

[0457] The server sends the optimized delivery route to the terminal. It uses the route information obtained in step 2 as input and transmits it to the terminal in real time. As output, the route and instruction information are displayed on the user's terminal in a visually verifiable format.

[0458] Step 4:

[0459] The terminal displays delivery instructions to the user based on the route information it receives. It takes route information from the server as input, and uses the Google Maps API to display the route on a map as output, providing the user with the necessary delivery instructions.

[0460] Step 5:

[0461] The user executes deliveries according to the delivery instructions displayed on the terminal. Delivery performance information collected by the user is resent from the terminal to the server. As input, delivery progress and completion data are collected, and as output, performance data that should be reflected in the next delivery plan is processed.

[0462] Step 6:

[0463] The server analyzes delivery performance information from terminals as feedback to identify areas for improvement needed to optimize the next delivery route. It analyzes the performance data collected in step 5 as input, and generates an improved delivery plan proposal as output. Through this cycle, delivery efficiency continuously improves.

[0464] 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.

[0465] This invention provides a system that improves the efficiency of delivery operations and enhances the customer experience. Specifically, it incorporates an emotion engine to recognize the emotions of users during delivery operations and uses that data to optimize delivery routes and improve service quality. The following describes specific embodiments of this system.

[0466] The server first collects user emotional data using an emotion engine, in addition to the conventional data acquisition process. This emotional data is recognized in real time from the customer's facial expressions and voice when they interact with the delivery driver and transmitted to the server. This makes it possible to understand the customer's emotional state at each delivery location in chronological order.

[0467] The acquired sentiment data is analyzed by a server to evaluate customer satisfaction. This evaluation result is considered as one of the factors in optimizing delivery routes. In some cases, delivery times and methods may be adjusted for delivery destinations where customer satisfaction has decreased. For example, for customers who have repeatedly been absent and require redelivery, it is possible to not only predict times when they are most likely to be home, but also to tailor communication based on the customer's sentiment data.

[0468] The terminal works in conjunction with an emotion engine to monitor interactions with the user in real time and feed the results back to the server. If the system determines that the user was particularly satisfied during delivery, that pattern is accumulated and reflected in the delivery procedure for future deliveries.

[0469] For example, if a user expresses dissatisfaction upon receiving a package, an attempt will be made to identify the reason, making it possible to provide more detailed delivery time notifications or suggest post office hold services for future deliveries to that address. This allows for more customized service for individual customers, leading to an overall improvement in customer satisfaction.

[0470] As described above, the present invention provides an embodiment of a system that utilizes an emotion engine to simultaneously achieve increased efficiency in delivery operations and improved quality of customer service.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The server acquires various data necessary for delivery operations. This data includes the experience level of area drivers, the history of redeliveries to delivery destinations, the time when recipients are home, traffic information, traffic light change timings, delivery schedules, and time specifications. In this step, a new emotion engine is also used to collect user emotion data. Emotion data is obtained from facial expressions, tone of voice, and other similar information.

[0474] Step 2:

[0475] The server analyzes the collected data. Based on user sentiment data analyzed by the sentiment engine, it measures customer satisfaction and incorporates the results into the delivery route optimization process. For example, for delivery destinations with low satisfaction in the past, it suggests a new delivery method that takes this into consideration.

[0476] Step 3:

[0477] Based on the analysis results, the server calculates the optimal delivery route and develops a delivery strategy that incorporates customer sentiment data to improve customer satisfaction. This includes suggesting changes to delivery times and implementing special measures for specific customers.

[0478] Step 4:

[0479] The server sends optimized delivery routes and strategies to the terminal. The terminal receives this information immediately and notifies the driver. The driver not only follows the designated route but also makes deliveries while considering customer interactions based on the provided sentiment data.

[0480] Step 5:

[0481] As a user, the driver tracks real-time emotional data through a terminal while making deliveries, and adjusts their communication and response methods with customers accordingly. When positive emotional responses are obtained, the method is recorded to improve the service in the future.

[0482] Step 6:

[0483] The terminal transmits delivery performance data and sentiment data entered by the driver to the server. The server then analyzes the collected feedback data to continuously improve service and optimize future deliveries. This feedback data is a crucial resource for increasing customer satisfaction.

[0484] (Example 2)

[0485] 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."

[0486] In delivery operations, there is a need to optimize delivery routes efficiently while increasing customer satisfaction. Traditional systems optimized routes based on traffic conditions and customer availability, but they failed to improve service quality by considering customer emotions. Therefore, there is a need for a means to improve the delivery experience and provide services tailored to individual customer needs.

[0487] 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.

[0488] In this invention, the server includes means for acquiring various types of information, means for analyzing the acquired information to optimize the travel route, and means for analyzing acquired emotional information to improve the quality of service. This makes it possible to provide delivery services tailored to individual needs, taking into account the emotional information of the user.

[0489] "Various information" refers to all data related to delivery operations, including traffic conditions, the time customers are home at the delivery destination, and customer sentiment information.

[0490] A "travel route" refers to the path a transport device takes to perform delivery tasks, and it is optimized to ensure efficient delivery.

[0491] A "transportation device" refers to a vehicle or means used for delivering goods, which physically moves after receiving information.

[0492] "Delivery result information" refers to performance data obtained when a delivery is made, and includes delivery time, customer feedback, and whether or not a redelivery was required.

[0493] "Emotional information" refers to data related to emotions extracted from the user's facial expressions and voice, and serves as an indicator of customer satisfaction or dissatisfaction.

[0494] To implement this invention, a system is provided in which a server, terminal, and user work in cooperation. First, the server functions as a central processing unit for the integrated management of various types of information. The server is equipped with advanced analytical algorithms and generative AI models to process data streams in real time. This AI model analyzes large amounts of data related to delivery operations to optimize travel routes as well as improve the quality of service based on user sentiment information.

[0495] The server collects emotional information through a terminal. The terminal is a smart device carried by the delivery driver, equipped with a camera and microphone. During delivery, the terminal captures the user's facial expressions and voice, and sends this data to the server. This device performs an initial emotional assessment in real time and uses the results to improve interaction with the user.

[0496] On the other hand, users are important as a source of feedback on their satisfaction and needs through the delivery experience. For example, if a user expresses dissatisfaction with a previous delivery, the server can use that data to improve future delivery plans. This enables a customized delivery service for each individual user.

[0497] As a concrete example of this system, if a user smiles during parcel delivery, that facial expression data is sent from the terminal to the server and stored as an indicator of customer satisfaction. For future deliveries, routes and times are set to match similar conditions for the same user.

[0498] An example of a prompt is, "Analyze the emotions the user is exhibiting during delivery." This prompt is input into an AI model, and the corresponding output is used for emotion data analysis. This system design enables delivery operations that combine efficiency and improved customer satisfaction.

[0499] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0500] Step 1:

[0501] The terminal uses its camera and microphone to collect facial expressions and audio data from the user during the interaction between the delivery driver and the user. The input consists of audio and images captured in real time. This data is initially analyzed by an emotion engine within the terminal and output as the user's emotional state (e.g., joy, surprise, anger). The terminal temporarily stores this output in preparation for proceeding to the next step.

[0502] Step 2:

[0503] The terminal sends the initial analysis results to the server. The input is the user's sentiment data generated in step 1. The server receives this and performs a more detailed analysis using an advanced generative AI model. In this process, the current customer satisfaction is evaluated by comparing it with past sentiment data and outputting a numerical satisfaction score. The server stores this score in a database to help with future improvements.

[0504] Step 3:

[0505] The server optimizes travel routes based on analyzed sentiment data. Inputs include satisfaction scores obtained in step 2 and other logistics information (e.g., traffic conditions, weather data). The server considers this input data to calculate and output the optimal route and time for a specific delivery destination. Based on this information, the server adjusts the delivery schedule.

[0506] Step 4:

[0507] The user receives an actual delivery service and provides feedback on the experience. After the delivery is complete, new emotional data from the user (e.g., judgments based on their facial expressions upon receipt) is collected again through the device, and the cycle continues, returning to step 1. The input is facial and voice data after delivery, and the output is the new initial analysis results from step 1.

[0508] These steps allow the entire system to function as a feedback loop, simultaneously achieving increased efficiency in delivery operations and improved customer satisfaction.

[0509] (Application Example 2)

[0510] 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."

[0511] Traditional delivery systems have shortcomings in terms of improving delivery efficiency and customer satisfaction. In particular, they fail to consider customer emotions during delivery, missing opportunities to improve service quality. Furthermore, they do not adequately address the risk of redelivery, resulting in a lack of improvement in the customer experience.

[0512] 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.

[0513] In this invention, the server includes a device for acquiring various types of information in delivery operations, a device for analyzing the acquired information and optimizing the delivery route, a device for transmitting the optimized delivery route to a moving device, a device for acquiring delivery performance information from the moving device and using it for optimization in the next delivery, a device for recognizing customer sentiment information in real time, and a device for evaluating customer satisfaction based on the recognized sentiment information and using it to improve the delivery service. This makes it possible to simultaneously achieve improved delivery efficiency and customer satisfaction.

[0514] "Delivery services" refer to a series of activities that deliver goods to a location specified by the customer, and include planning delivery routes, transporting packages, and confirming receipt.

[0515] A "device for acquiring various types of information" is a device for collecting data related to deliveries, such as location information, traffic conditions, and customer time spent at home.

[0516] A "device that analyzes acquired information to optimize delivery routes" is a device that calculates delivery routes to improve delivery efficiency based on collected data.

[0517] A "device that transmits optimized delivery routes to moving equipment" is a device that transmits calculated, efficient delivery routes to vehicles, drones, and other equipment that actually carry out deliveries.

[0518] "Mobile equipment" refers to means of transporting deliveries to their destination, including vehicles and drones.

[0519] A "device that acquires delivery performance information and uses it for optimization in the future" is a device that collects delivery records and analyzes them to use in planning future deliveries.

[0520] A "device that recognizes customer emotional information in real time" is a device that analyzes the customer's facial expressions and voice during delivery to understand their emotional state on the spot.

[0521] A "device used to evaluate customer satisfaction and improve delivery services based on recognized emotional information" is a device that utilizes emotional data to measure the quality of service and derive strategies for providing a better customer experience.

[0522] The system for realizing this invention consists of multiple devices and software, aiming to improve the efficiency of delivery operations and enhance the customer experience. It primarily includes a server, terminals, and customer interfaces.

[0523] The server uses devices to acquire various information related to delivery operations, collecting real-time location data, traffic conditions, and customer availability times. This data is processed by devices that optimize delivery routes, calculating efficient delivery routes. The server then transmits the calculated delivery route to the moving devices, reflecting it in the actual delivery process.

[0524] The terminal is equipped with a device that recognizes emotional information such as customer facial expressions and voice in real time during delivery. This uses deep learning technologies such as TensorFlow and Keras to analyze emotions. The recognized emotional data is sent to a server and used to evaluate customer satisfaction.

[0525] The software used includes EmotionEngine and RouteOptimizer, which perform complex data analysis. EmotionEngine directly handles emotional data, while RouteOptimizer combines diverse information to maximize the efficiency of delivery routes.

[0526] As a concrete example, consider its application to a food delivery service. If a customer expresses dissatisfaction upon receiving their food, this emotional data can be analyzed to improve future deliveries. An example of such a prompt could be, "Analyze the customer's emotions (facial and audible) regarding the recently delivered pizza and generate appropriate feedback." This allows for adjustments to delivery routes and times, and even the creation of customized services that take customer emotions into consideration.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The server acquires various information necessary for delivery operations. It uses data such as delivery addresses, current traffic conditions, and customer availability times as input. This data is integrated to build a foundational dataset for improving delivery efficiency.

[0530] Step 2:

[0531] The device recognizes the user's facial expressions and voice during delivery and acquires emotional data. It uses audio recordings and captured images from direct interactions with the user as input. Using deep learning technologies such as TensorFlow and Keras, it analyzes emotions in real time from this data and outputs emotional classifications such as positive, negative, and neutral.

[0532] Step 3:

[0533] The server analyzes sentiment data and other delivery information to recalculate the optimal delivery route. The input is the data obtained in steps 1 and 2. Using RouteOptimizer, it considers this data and calculates the route that maximizes delivery efficiency. The output is the optimized delivery route.

[0534] Step 4:

[0535] The user uses the new delivery route sent from the server to head to the next delivery destination. The new route information is fed directly into the GPS navigation system and displayed visually on the user's device. The input is the optimized delivery route, and the output is route information to guide the user's movement.

[0536] Step 5:

[0537] The server retrieves performance data after delivery is completed to prepare for future optimizations. Inputs include delivery time, customer satisfaction rating, and delivery completion report. This data is accumulated, and customer feedback is analyzed using a generative AI model to improve future deliveries. This process ensures continuous improvement of the customer experience.

[0538] 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.

[0539] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0540] 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.

[0541] [Fourth Embodiment]

[0542] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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).

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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".

[0555] This invention provides a system for improving the efficiency of delivery operations. Specifically, it implements a series of processes that acquire and analyze various data in delivery operations, optimize delivery routes, and transmit the results to the mobile vehicle. The processing content of the program as an embodiment is described below.

[0556] First, the server collects a variety of data from multiple perspectives, including area drivers' experience data, redelivery records for delivery destinations, recipients' availability times, traffic conditions, traffic light change timings, delivery schedules, and time specifications. This data collection process involves retrieving information through various sensors and databases.

[0557] Next, the server analyzes the acquired data. Using generative AI technology, it extracts useful patterns and predictions from this data to determine the priority of visits to each delivery destination. It also predicts traffic congestion from traffic information and selects the optimal route, taking into account traffic light change timings.

[0558] The server then calculates an optimized delivery route and sends the results to the terminal. The terminal receives this information in real time and notifies the driver (user). The driver then carries out the delivery according to the instructions provided. Important information and special notes regarding the delivery destination are also conveyed, enabling the driver to perform their duties efficiently.

[0559] Once a delivery is complete, delivery performance data is sent back to the server via the terminal. The server uses this data to analyze the causes of any redeliveries and to provide feedback necessary for optimizing future delivery routes. This feedback loop allows the system to continuously improve and support highly efficient delivery operations.

[0560] As a concrete example, the system predicts the times when customers who have a history of missed deliveries are likely to be home and automatically selects a route to visit during those times. As a result, the risk of redelivery is significantly reduced, and fuel and time are saved. Through such operations, delivery work can be made more streamlined, and the burden on workers can be reduced.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] The server automatically retrieves data from various databases, including area drivers' experience levels, redelivery records, recipients' availability times, traffic conditions, traffic light timings, delivery schedules, and time-specific delivery requests. The data is updated at regular intervals and managed to maintain the most up-to-date information.

[0564] Step 2:

[0565] The server analyzes the collected data. Using a generative AI algorithm, it evaluates the likelihood of recipients being home and the possibility of redelivery based on past delivery data. It also analyzes road congestion and traffic light waiting times based on traffic data and calculates the impact these factors have on the delivery route.

[0566] Step 3:

[0567] The server calculates the most efficient delivery route based on the analysis results. The route is optimized by considering various factors with the aim of minimizing fuel consumption and shortening delivery time. It derives routes that prioritize times when people are likely to be home and times when roads are less congested.

[0568] Step 4:

[0569] The server sends the calculated optimal delivery route to the terminal. The terminal immediately receives this information and notifies the driver, who is the user. The notification also includes detailed information and notes for each delivery destination, and the driver carries out the delivery work based on this information.

[0570] Step 5:

[0571] The user, acting as the driver, makes deliveries following the optimal route displayed on the terminal. After each delivery is completed, the driver uses the terminal to record the results, entering information such as whether the delivery was successful, the recipient was absent, or if redelivery is necessary.

[0572] Step 6:

[0573] The terminal transmits delivery performance data entered by the driver to the server. The server uses the received feedback data to improve the accuracy of future delivery routes. This continuously improves the overall efficiency of the system.

[0574] (Example 1)

[0575] 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".

[0576] In modern delivery operations, challenges such as an increase in redeliveries and traffic congestion make it difficult to achieve efficient deliveries. This leads to wasted time and fuel, increasing the environmental burden and potentially lowering customer satisfaction. Furthermore, improving delivery efficiency is a challenge due to the difficulty in optimizing delivery routes.

[0577] 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.

[0578] In this invention, the server includes means for collecting information related to delivery operations, means for analyzing the collected information using generation AI technology to optimize delivery routes, and means for transmitting the optimized delivery routes to a communication device. This enables the selection of efficient delivery routes, making it possible to improve delivery operations while minimizing the risk of redelivery and delays due to traffic congestion.

[0579] "Delivery services" refer to a series of activities or processes for transporting goods or packages from one location to another.

[0580] "Information" refers to facts and indicators about a situation or environment, provided in the form of data or knowledge.

[0581] "Means of collection" refers to the methods and techniques used to gather necessary information and data.

[0582] "Generative AI technology" refers to tools and algorithms that use artificial intelligence to perform advanced processing such as data analysis.

[0583] "Analysis" is the process of examining collected information and data in detail to extract patterns and useful information.

[0584] A "delivery route" is the path selected for delivery operations, from the starting point to the destination.

[0585] "Optimization" is the process of adjusting resources and time to use them efficiently and achieve better results.

[0586] A "communication device" is hardware or software used to send and receive information.

[0587] "Means of transmission" refers to the technology or method used to send information to a specific destination.

[0588] "Delivery performance information" refers to records and data related to actual delivery activities.

[0589] The delivery efficiency system in this invention includes a series of processes: information gathering, analysis using AI generation technology, route optimization, and feedback of performance information.

[0590] The server collects a variety of information related to delivery operations. Specifically, it handles data such as traffic conditions, traffic signal control information, past redelivery records, times when residents are home, and time-specific delivery information. This work is performed through API integration with traffic sensors and databases, or via IoT devices.

[0591] Next, the server analyzes the collected information using a generative AI model. This generative AI model is implemented on frameworks such as TensorFlow and PyTorch, and extracts patterns and trends from the data. The results obtained from the analysis are used to optimize delivery routes. For example, it calculates the most efficient delivery route by taking into account traffic congestion and traffic light switching times.

[0592] The calculated optimized route is sent from the server to the terminal. This communication takes place in real time using a mobile network or Wi-Fi. The terminal receives the route information and notifies the driver, who is the user. The driver follows the instructions using visual map displays and voice guidance to complete the delivery.

[0593] Once a delivery is complete, the terminal sends delivery performance information back to the server. This information includes the time of successful delivery, whether redelivery was required, and the time taken for delivery. The server uses this information to optimize the system and analyze causes, thereby improving the overall efficiency of the system.

[0594] As a concrete example, the system identifies customers who have frequently been absent and required redelivery in the past, and uses an AI model to predict their expected time at home. It then automatically selects delivery routes that visit during times when the customer is most likely to be home. This approach improves the efficiency of delivery operations and enhances customer satisfaction.

[0595] An example of a prompt message might be something like, "Based on past redelivery data, predict the times when the recipient is most likely to be home at a specific delivery address, and suggest the optimal delivery route for that time."

[0596] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0597] Step 1:

[0598] The server collects information related to delivery operations. This information includes real-time traffic data obtained through traffic sensors and IoT devices, historical delivery performance data, and customer occupancy time data. The server aggregates the input data and stores it in a database for subsequent processing. As a result, a large amount of delivery-related data is collected.

[0599] Step 2:

[0600] The server begins analyzing the collected data. Using a generative AI model, it predicts visit priorities for each area, the degree of traffic congestion, and the times of day when redelivery is most likely to be required. The data obtained in step 1 is fed into the AI ​​model as input, and data processing and prediction algorithms are applied. This outputs prediction results and patterns, which are used as basic information for optimization processing.

[0601] Step 3:

[0602] The server calculates the optimal delivery route based on the analysis results. Here, it uses the results of the generated AI model, taking into account acquired traffic information and traffic light timings, to calculate the shortest and fastest route. The input includes the analysis results obtained in step 2, and the route optimization algorithm is applied. The output generates the optimal delivery route for the driver.

[0603] Step 4:

[0604] The server sends the optimized delivery route to the terminal. Using data communication technology, route information is transmitted to the terminal in real time. The input includes the optimized delivery route generated in step 3 and is sent to the terminal. This allows the terminal to immediately receive the route information and prepare to notify the user.

[0605] Step 5:

[0606] The terminal notifies the driver, who is the user, of the route information it has received. The notification is given through voice guidance and visual display on a map, providing instructions to efficiently carry out delivery work. The input is the optimized route information sent in step 4, and the output is that the driver receives route guidance visually and audibly.

[0607] Step 6:

[0608] After a delivery is completed, the terminal sends delivery performance information back to the server. This performance information includes whether the delivery was successful, the time taken, and confirmation of receipt. The input consists of performance data recorded by the terminal during the delivery, which is then sent to the server. As output, the server uses this data for optimization and root cause analysis for future deliveries.

[0609] (Application Example 1)

[0610] 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".

[0611] In current delivery operations, the efficiency of delivery routes is a critical issue, particularly the waste of time and resources due to redeliveries. Furthermore, the need for real-time, appropriate decision-making in response to fluctuating traffic conditions is a major challenge. Additionally, insufficient route selection that considers the recipient's availability at the delivery location is contributing to reduced delivery efficiency.

[0612] 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.

[0613] In this invention, the server includes means for collecting various information in delivery operations, means for analyzing the collected information to optimize delivery routes, means for transmitting the optimized delivery routes to transportation equipment, means for acquiring delivery performance information from transportation equipment and using it for future optimization, and means for displaying delivery instructions to employees in real time based on the acquired information. This enables a reduction in redeliveries and efficient delivery that reflects real-time traffic information.

[0614] "Delivery services" refers to all activities involved in transporting packages or goods to their designated destinations.

[0615] "Means of collecting various types of information" refers to a system for collecting various data related to delivery.

[0616] "A means of analyzing collected information to optimize delivery routes" refers to a system that analyzes data to determine the most efficient delivery order and route.

[0617] "Means for transmitting optimized delivery routes to transportation equipment" refers to a system that transmits calculated best route information to the vehicles used for actual delivery.

[0618] "Transportation equipment" refers to vehicles and devices used to physically transport goods or packages.

[0619] "Delivery performance information" refers to data on actual delivery activities and is used to improve future delivery plans.

[0620] "Methods to be used for optimization in the next delivery" refers to a system that further improves future delivery plans based on past delivery data.

[0621] "A means of displaying delivery instructions to employees in real time" refers to a system that provides workers with immediate instructions tailored to the current situation.

[0622] This invention involves a logistics center implementing an information technology-driven system to improve operational efficiency. A server analyzes collected data in real time, calculates the optimal delivery route, and transmits it to the transport equipment. The transport equipment, such as smartphones or tablets, communicates with the Node.js-based server. The server utilizes generative AI models based on TensorFlow and PyTorch to optimize delivery routes based on past delivery data and the latest traffic information. This enables efficient delivery by avoiding congestion and traffic lights.

[0623] The terminal receives optimal route information sent from the server and displays it in an easy-to-understand format for employees. By utilizing the Google Maps API, a visual route is displayed on a map, allowing users to follow instructions in real time and make deliveries. The terminal also sends delivery performance information back to the server, which is used for improvement in future deliveries.

[0624] As a concrete example, consider a scenario for efficiently delivering a large volume of packages during the Christmas season. The server receives historical delivery data as input and prompts, "Analyze the delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." Based on this prompt, the generating AI model calculates a route that reduces the risk of redelivery and sends that information to the terminal. This operation can improve the efficiency of delivery operations and reduce costs.

[0625] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0626] Step 1:

[0627] The server collects various data from multiple perspectives, including traffic conditions, driver experience, and the time when recipients are likely to be home during delivery. It receives data from sensors and databases as input, integrates it, and stores it in a repository. As output, it generates a set of information necessary for delivery planning.

[0628] Step 2:

[0629] The server analyzes the collected data and uses a generated AI model to calculate the optimal delivery route. The information set generated in step 1 is fed into the model as input, along with the prompt "Analyze delivery data from the past year and suggest visit priorities and routes to reduce redeliveries." The model performs data processing and predictive calculations, generating optimized route information as output.

[0630] Step 3:

[0631] The server sends the optimized delivery route to the terminal. It uses the route information obtained in step 2 as input and transmits it to the terminal in real time. As output, the route and instruction information are displayed on the user's terminal in a visually verifiable format.

[0632] Step 4:

[0633] The terminal displays delivery instructions to the user based on the route information it receives. It takes route information from the server as input, and uses the Google Maps API to display the route on a map as output, providing the user with the necessary delivery instructions.

[0634] Step 5:

[0635] The user executes deliveries according to the delivery instructions displayed on the terminal. Delivery performance information collected by the user is resent from the terminal to the server. As input, delivery progress and completion data are collected, and as output, performance data that should be reflected in the next delivery plan is processed.

[0636] Step 6:

[0637] The server analyzes delivery performance information from terminals as feedback to identify areas for improvement needed to optimize the next delivery route. It analyzes the performance data collected in step 5 as input, and generates an improved delivery plan proposal as output. Through this cycle, delivery efficiency continuously improves.

[0638] 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.

[0639] This invention provides a system that improves the efficiency of delivery operations and enhances the customer experience. Specifically, it incorporates an emotion engine to recognize the emotions of users during delivery operations and uses that data to optimize delivery routes and improve service quality. The following describes specific embodiments of this system.

[0640] The server first collects user emotional data using an emotion engine, in addition to the conventional data acquisition process. This emotional data is recognized in real time from the customer's facial expressions and voice when they interact with the delivery driver and transmitted to the server. This makes it possible to understand the customer's emotional state at each delivery location in chronological order.

[0641] The acquired sentiment data is analyzed by a server to evaluate customer satisfaction. This evaluation result is considered as one of the factors in optimizing delivery routes. In some cases, delivery times and methods may be adjusted for delivery destinations where customer satisfaction has decreased. For example, for customers who have repeatedly been absent and require redelivery, it is possible to not only predict times when they are most likely to be home, but also to tailor communication based on the customer's sentiment data.

[0642] The terminal works in conjunction with an emotion engine to monitor interactions with the user in real time and feed the results back to the server. If the system determines that the user was particularly satisfied during delivery, that pattern is accumulated and reflected in the delivery procedure for future deliveries.

[0643] For example, if a user expresses dissatisfaction upon receiving a package, an attempt will be made to identify the reason, making it possible to provide more detailed delivery time notifications or suggest post office hold services for future deliveries to that address. This allows for more customized service for individual customers, leading to an overall improvement in customer satisfaction.

[0644] As described above, the present invention provides an embodiment of a system that utilizes an emotion engine to simultaneously achieve increased efficiency in delivery operations and improved quality of customer service.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The server acquires various data necessary for delivery operations. This data includes the experience level of area drivers, the history of redeliveries to delivery destinations, the time when recipients are home, traffic information, traffic light change timings, delivery schedules, and time specifications. In this step, a new emotion engine is also used to collect user emotion data. Emotion data is obtained from facial expressions, tone of voice, and other similar information.

[0648] Step 2:

[0649] The server analyzes the collected data. Based on user sentiment data analyzed by the sentiment engine, it measures customer satisfaction and incorporates the results into the delivery route optimization process. For example, for delivery destinations with low satisfaction in the past, it suggests a new delivery method that takes this into consideration.

[0650] Step 3:

[0651] Based on the analysis results, the server calculates the optimal delivery route and develops a delivery strategy that incorporates customer sentiment data to improve customer satisfaction. This includes suggesting changes to delivery times and implementing special measures for specific customers.

[0652] Step 4:

[0653] The server sends optimized delivery routes and strategies to the terminal. The terminal receives this information immediately and notifies the driver. The driver not only follows the designated route but also makes deliveries while considering customer interactions based on the provided sentiment data.

[0654] Step 5:

[0655] As a user, the driver tracks real-time emotional data through a terminal while making deliveries, and adjusts their communication and response methods with customers accordingly. When positive emotional responses are obtained, the method is recorded to improve the service in the future.

[0656] Step 6:

[0657] The terminal transmits delivery performance data and sentiment data entered by the driver to the server. The server then analyzes the collected feedback data to continuously improve service and optimize future deliveries. This feedback data is a crucial resource for increasing customer satisfaction.

[0658] (Example 2)

[0659] 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".

[0660] In delivery operations, there is a need to optimize delivery routes efficiently while increasing customer satisfaction. Traditional systems optimized routes based on traffic conditions and customer availability, but they failed to improve service quality by considering customer emotions. Therefore, there is a need for a means to improve the delivery experience and provide services tailored to individual customer needs.

[0661] 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.

[0662] In this invention, the server includes means for acquiring various types of information, means for analyzing the acquired information to optimize the travel route, and means for analyzing acquired emotional information to improve the quality of service. This makes it possible to provide delivery services tailored to individual needs, taking into account the emotional information of the user.

[0663] "Various information" refers to all data related to delivery operations, including traffic conditions, the time customers are home at the delivery destination, and customer sentiment information.

[0664] A "travel route" refers to the path a transport device takes to perform delivery tasks, and it is optimized to ensure efficient delivery.

[0665] A "transportation device" refers to a vehicle or means used for delivering goods, which physically moves after receiving information.

[0666] "Delivery result information" refers to performance data obtained when a delivery is made, and includes delivery time, customer feedback, and whether or not a redelivery was required.

[0667] "Emotional information" refers to data related to emotions extracted from the user's facial expressions and voice, and serves as an indicator of customer satisfaction or dissatisfaction.

[0668] To implement this invention, a system is provided in which a server, terminal, and user work in cooperation. First, the server functions as a central processing unit for the integrated management of various types of information. The server is equipped with advanced analytical algorithms and generative AI models to process data streams in real time. This AI model analyzes large amounts of data related to delivery operations to optimize travel routes as well as improve the quality of service based on user sentiment information.

[0669] The server collects emotional information through a terminal. The terminal is a smart device carried by the delivery driver, equipped with a camera and microphone. During delivery, the terminal captures the user's facial expressions and voice, and sends this data to the server. This device performs an initial emotional assessment in real time and uses the results to improve interaction with the user.

[0670] On the other hand, users are important as a source of feedback on their satisfaction and needs through the delivery experience. For example, if a user expresses dissatisfaction with a previous delivery, the server can use that data to improve future delivery plans. This enables a customized delivery service for each individual user.

[0671] As a concrete example of this system, if a user smiles during parcel delivery, that facial expression data is sent from the terminal to the server and stored as an indicator of customer satisfaction. For future deliveries, routes and times are set to match similar conditions for the same user.

[0672] An example of a prompt is, "Analyze the emotions the user is exhibiting during delivery." This prompt is input into an AI model, and the corresponding output is used for emotion data analysis. This system design enables delivery operations that combine efficiency and improved customer satisfaction.

[0673] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0674] Step 1:

[0675] The terminal uses its camera and microphone to collect facial expressions and audio data from the user during the interaction between the delivery driver and the user. The input consists of audio and images captured in real time. This data is initially analyzed by an emotion engine within the terminal and output as the user's emotional state (e.g., joy, surprise, anger). The terminal temporarily stores this output in preparation for proceeding to the next step.

[0676] Step 2:

[0677] The terminal sends the initial analysis results to the server. The input is the user's sentiment data generated in step 1. The server receives this and performs a more detailed analysis using an advanced generative AI model. In this process, the current customer satisfaction is evaluated by comparing it with past sentiment data and outputting a numerical satisfaction score. The server stores this score in a database to help with future improvements.

[0678] Step 3:

[0679] The server optimizes travel routes based on analyzed sentiment data. Inputs include satisfaction scores obtained in step 2 and other logistics information (e.g., traffic conditions, weather data). The server considers this input data to calculate and output the optimal route and time for a specific delivery destination. Based on this information, the server adjusts the delivery schedule.

[0680] Step 4:

[0681] The user receives an actual delivery service and provides feedback on the experience. After the delivery is complete, new emotional data from the user (e.g., judgments based on their facial expressions upon receipt) is collected again through the device, and the cycle continues, returning to step 1. The input is facial and voice data after delivery, and the output is the new initial analysis results from step 1.

[0682] These steps allow the entire system to function as a feedback loop, simultaneously achieving increased efficiency in delivery operations and improved customer satisfaction.

[0683] (Application Example 2)

[0684] 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".

[0685] Traditional delivery systems have shortcomings in terms of improving delivery efficiency and customer satisfaction. In particular, they fail to consider customer emotions during delivery, missing opportunities to improve service quality. Furthermore, they do not adequately address the risk of redelivery, resulting in a lack of improvement in the customer experience.

[0686] 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.

[0687] In this invention, the server includes a device for acquiring various types of information in delivery operations, a device for analyzing the acquired information and optimizing the delivery route, a device for transmitting the optimized delivery route to a moving device, a device for acquiring delivery performance information from the moving device and using it for optimization in the next delivery, a device for recognizing customer sentiment information in real time, and a device for evaluating customer satisfaction based on the recognized sentiment information and using it to improve the delivery service. This makes it possible to simultaneously achieve improved delivery efficiency and customer satisfaction.

[0688] "Delivery services" refer to a series of activities that deliver goods to a location specified by the customer, and include planning delivery routes, transporting packages, and confirming receipt.

[0689] A "device for acquiring various types of information" is a device for collecting data related to deliveries, such as location information, traffic conditions, and customer time spent at home.

[0690] A "device that analyzes acquired information to optimize delivery routes" is a device that calculates delivery routes to improve delivery efficiency based on collected data.

[0691] A "device that transmits optimized delivery routes to moving equipment" is a device that transmits calculated, efficient delivery routes to vehicles, drones, and other equipment that actually carry out deliveries.

[0692] "Mobile equipment" refers to means of transporting deliveries to their destination, including vehicles and drones.

[0693] A "device that acquires delivery performance information and uses it for optimization in the future" is a device that collects delivery records and analyzes them to use in planning future deliveries.

[0694] A "device that recognizes customer emotional information in real time" is a device that analyzes the customer's facial expressions and voice during delivery to understand their emotional state on the spot.

[0695] A "device used to evaluate customer satisfaction and improve delivery services based on recognized emotional information" is a device that utilizes emotional data to measure the quality of service and derive strategies for providing a better customer experience.

[0696] The system for realizing this invention consists of multiple devices and software, aiming to improve the efficiency of delivery operations and enhance the customer experience. It primarily includes a server, terminals, and customer interfaces.

[0697] The server uses devices to acquire various information related to delivery operations, collecting real-time location data, traffic conditions, and customer availability times. This data is processed by devices that optimize delivery routes, calculating efficient delivery routes. The server then transmits the calculated delivery route to the moving devices, reflecting it in the actual delivery process.

[0698] The terminal is equipped with a device that recognizes emotional information such as customer facial expressions and voice in real time during delivery. This uses deep learning technologies such as TensorFlow and Keras to analyze emotions. The recognized emotional data is sent to a server and used to evaluate customer satisfaction.

[0699] The software used includes EmotionEngine and RouteOptimizer, which perform complex data analysis. EmotionEngine directly handles emotional data, while RouteOptimizer combines diverse information to maximize the efficiency of delivery routes.

[0700] As a concrete example, consider its application to a food delivery service. If a customer expresses dissatisfaction upon receiving their food, this emotional data can be analyzed to improve future deliveries. An example of such a prompt could be, "Analyze the customer's emotions (facial and audible) regarding the recently delivered pizza and generate appropriate feedback." This allows for adjustments to delivery routes and times, and even the creation of customized services that take customer emotions into consideration.

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The server acquires various information necessary for delivery operations. It uses data such as delivery addresses, current traffic conditions, and customer availability times as input. This data is integrated to build a foundational dataset for improving delivery efficiency.

[0704] Step 2:

[0705] The device recognizes the user's facial expressions and voice during delivery and acquires emotional data. It uses audio recordings and captured images from direct interactions with the user as input. Using deep learning technologies such as TensorFlow and Keras, it analyzes emotions in real time from this data and outputs emotional classifications such as positive, negative, and neutral.

[0706] Step 3:

[0707] The server analyzes sentiment data and other delivery information to recalculate the optimal delivery route. The input is the data obtained in steps 1 and 2. Using RouteOptimizer, it considers this data and calculates the route that maximizes delivery efficiency. The output is the optimized delivery route.

[0708] Step 4:

[0709] The user uses the new delivery route sent from the server to head to the next delivery destination. The new route information is fed directly into the GPS navigation system and displayed visually on the user's device. The input is the optimized delivery route, and the output is route information to guide the user's movement.

[0710] Step 5:

[0711] The server retrieves performance data after delivery is completed to prepare for future optimizations. Inputs include delivery time, customer satisfaction rating, and delivery completion report. This data is accumulated, and customer feedback is analyzed using a generative AI model to improve future deliveries. This process ensures continuous improvement of the customer experience.

[0712] 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.

[0713] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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."

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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 as being incorporated by reference.

[0733] The following is further disclosed regarding the embodiments described above.

[0734] (Claim 1)

[0735] Methods for acquiring various data in delivery operations,

[0736] A method for analyzing acquired data to optimize delivery routes,

[0737] A means of transmitting an optimized delivery route to a mobile vehicle,

[0738] A method for obtaining delivery performance data from mobile devices and using it for optimization in the next delivery,

[0739] A system that includes this.

[0740] (Claim 2)

[0741] The system according to claim 1, which analyzes traffic conditions based on acquired data and optimizes the movement efficiency of a moving object.

[0742] (Claim 3)

[0743] The system according to claim 1, which uses data on the time of day when recipients are home to calculate a delivery route that reduces the risk of redelivery.

[0744] "Example 1"

[0745] (Claim 1)

[0746] Means of collecting information in delivery operations,

[0747] A means of optimizing delivery routes by analyzing collected information using generation AI technology,

[0748] Means for transmitting an optimized delivery route to a communication device,

[0749] A means of obtaining delivery performance information from a communication device and using it for optimization in the next delivery,

[0750] A means for optimizing travel routes by taking into account traffic information and signal control information,

[0751] A system that includes this.

[0752] (Claim 2)

[0753] The system according to claim 1, which uses information on the time residents are at home to calculate the optimal delivery route in order to reduce the risk of repeated deliveries.

[0754] (Claim 3)

[0755] The system according to claim 1, which constructs a feedback loop for the purpose of improving travel efficiency based on collected delivery performance information.

[0756] "Application Example 1"

[0757] (Claim 1)

[0758] Means of collecting various types of information in delivery operations,

[0759] A means of optimizing delivery routes by analyzing the collected information,

[0760] A means for transmitting optimized delivery routes to transport equipment,

[0761] A method for obtaining delivery performance information from transportation equipment and using it for future optimization,

[0762] A means of displaying delivery instructions to employees in real time based on the acquired information,

[0763] ...

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, which analyzes traffic conditions based on acquired information and optimizes the movement efficiency of transportation equipment.

[0767] (Claim 3)

[0768] The system according to claim 1, which uses information on the recipient's availability time to calculate a delivery route that reduces the risk of redelivery and displays it on the employee's terminal.

[0769] "Example 2 of combining an emotion engine"

[0770] (Claim 1)

[0771] Means of obtaining various types of information,

[0772] A means of analyzing acquired information to optimize the travel route,

[0773] Means for transmitting the optimized movement path to the transport device,

[0774] A means of obtaining delivery result information from the conveying device and using it for optimization in the next process,

[0775] Means for obtaining user sentiment information,

[0776] A means of improving service quality by analyzing acquired emotional information,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, which analyzes road conditions based on acquired information and optimizes the movement efficiency of the transport device.

[0780] (Claim 3)

[0781] The system according to claim 1, which uses information on the time of day when the recipient is home to calculate a delivery route that reduces the risk of redelivery.

[0782] "Application example 2 when combining with an emotional engine"

[0783] (Claim 1)

[0784] A device for acquiring various types of information in delivery operations,

[0785] A device that analyzes acquired information to optimize delivery routes,

[0786] A device that transmits an optimized delivery route to a moving vehicle,

[0787] A device that acquires delivery performance information from mobile devices and uses it for optimization in the next delivery,

[0788] A device that recognizes customer emotional information in real time,

[0789] A device used to evaluate customer satisfaction based on recognized emotional information and to improve delivery services,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, which analyzes traffic conditions based on acquired information and optimizes the movement efficiency of moving equipment.

[0793] (Claim 3)

[0794] The system according to claim 1, which uses information on the time when the recipient is home to calculate a delivery route that reduces the risk of redelivery and provides a service that takes into account the customer's feelings. [Explanation of Symbols]

[0795] 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. Methods for acquiring various data in delivery operations, A method for analyzing acquired data to optimize delivery routes, A means of transmitting an optimized delivery route to a mobile vehicle, A method for obtaining delivery performance data from mobile devices and using it for optimization in the next delivery, A system that includes this.

2. The system according to claim 1, which analyzes traffic conditions based on acquired data and optimizes the movement efficiency of a moving object.

3. The system according to claim 1, which uses data on the time of day when recipients are home to calculate a delivery route that reduces the risk of redelivery.

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