system

Abstract

We provide the system. [Solution] Means of obtaining information about the destination, A method for plotting destinations using map information and grouping them by area, A method for generating the optimal visiting route using traffic information, A method for predicting congestion based on the visit route and optimizing the order of visits, A means of collecting visit history and user input feedback and reflecting it in route generation for future visits, A visit route generation system that includes this feature.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 The creation of visit routes is an important factor that greatly affects the efficiency of sales activities, but the conventional methods have problems of requiring a huge amount of time and labor. Specifically, efficient area division of visit destinations, optimization of routes, prediction of congestion, etc. are often performed manually, and improvement in accuracy is required. In addition, it is necessary to respond to route adjustment according to the choice of transportation means and schedule adjustment for each visit destination, but the systems for realizing this are not sufficient. In view of such problems, it is an object to provide a method for automatically generating visit routes efficiently and supporting sales activities. 【Means for Solving the Problems】 【0005】 This invention provides means for acquiring destination information, plotting destinations using map information, and grouping them by area. Furthermore, it includes means for generating an optimal visit route using traffic information, and means for predicting congestion based on the visit route and optimizing the order of visits. In addition, by having means for collecting visit history and user input feedback and reflecting them in the generation of subsequent routes, the efficiency of visit routes can be improved. This makes it possible to reduce time and improve work efficiency, and effectively support sales activities. 【0006】 "Visiting location information" refers to the address, name, and other related data of locations scheduled to be visited during sales activities. 【0007】 "Map information" refers to digital or physical map data that includes information such as geographical location, roads, and transportation routes. 【0008】 "To plot" refers to the process of using coordinate information to accurately indicate a specific point on a map. 【0009】 "Grouping by area" refers to consolidating visit destinations based on geographical proximity or business relevance. 【0010】 "Traffic information" refers to information necessary for efficient travel, such as road congestion, traffic restrictions, and public transportation service status. 【0011】 A "visiting route" refers to a set of paths or routes designed to effectively visit multiple locations. 【0012】 "Congestion forecasting" refers to predicting traffic volume and the degree of crowd concentration at specific times and under specific conditions. 【0013】 "Optimizing the order of visits" refers to arranging the order in which visits are made most effective, taking into account factors such as travel time to each location and overall efficiency. 【0014】 "Access history" refers to information that retains past access records and includes data such as access date and time, destination, and purpose. 【0015】 "User input feedback" refers to opinions and information provided by users who have utilized the system, based on their actual experiences and evaluations. 【Brief Description of Drawings】 【0016】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0017】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0018】 First, the terms used in the following description will be explained. 【0019】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0020】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc. 【0022】 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). 【0023】 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." 【0024】 [First Embodiment] 【0025】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0026】 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. 【0027】 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). 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0033】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0034】 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. 【0035】 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. 【0036】 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". 【0037】 This invention is a system that enables the automatic creation of visit routes and supports efficient sales activities by integrating various data. This system has a function in which a server acquires visit destination information, map information, and traffic information, and automatically generates visit routes based on them. 【0038】 First, the server retrieves destination information from the user's planned visit list. This information includes the destination's address, priority, and purpose of visit. Next, the server uses map information to plot the destinations on a map and groups nearby destinations by area. This forms the basis for efficient visit planning. 【0039】 Next, the server uses the latest traffic information to generate the optimal route. In this process, it considers travel time, distance, and traffic conditions between destinations, minimizing right turns and selecting routes that avoid congestion if traveling by car. If using public transport, it also optimizes the route by taking transfer times and fares into consideration. 【0040】 Furthermore, the visit route incorporates congestion prediction, and the server suggests routes that avoid expected congestion during specific time periods based on the date, time, and event information. This allows users to complete their visits efficiently and safely. In addition, visit history and user feedback information are utilized to improve the accuracy of the algorithm for generating future visit routes. 【0041】 For example, when a sales staff member plans visits in multiple cities, the terminal displays the optimal route provided by the server. This includes the order of visits, estimated arrival times, and necessary transportation. The user then proceeds with the visits according to the plan and can further improve the system's accuracy by providing feedback based on the results and actual traffic conditions. 【0042】 This invention makes it possible to significantly reduce the time and effort required for visit planning, while also improving the accuracy and safety of routes. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 Users enter a list of planned visits into the system. This includes information such as the address of the destination, priority, date and time, and purpose of the visit. 【0046】 Step 2: 【0047】 The server receives the entered list of scheduled visits and retrieves information about the destinations. This retrieved information is stored in an internal database and used for subsequent processing. 【0048】 Step 3: 【0049】 The server retrieves the latest map information via an external API. This data, including geographic coordinates and road information, is used to plot the location of each destination on the map. 【0050】 Step 4: 【0051】 The server groups the plotted destinations on the map. This is a process that takes into account the geographical proximity of the destinations and groups them into smaller areas. 【0052】 Step 5: 【0053】 The server retrieves real-time traffic information from traffic information providers. This includes road congestion, traffic jam information, and the operating status of public transportation. 【0054】 Step 6: 【0055】 The server generates the optimal route, taking into account the distance between destinations, travel time, and traffic conditions. For those traveling by car, it selects a route that minimizes right turns and avoids congestion. 【0056】 Step 7: 【0057】 The server uses date, time, and event information to predict congestion. Based on the prediction results, it adjusts the order and timing of visits to create an optimal schedule. 【0058】 Step 8: 【0059】 The terminal displays the visit route and schedule provided by the server to the user. It also visualizes the route on a map, clearly indicating the order of visits and travel time. 【0060】 Step 9: 【0061】 After visiting locations, users input feedback into the system regarding their actual visit results and perceived congestion levels. 【0062】 Step 10: 【0063】 The server receives user feedback and updates its model through data analysis. This improves the accuracy of route suggestions for future visits. 【0064】 (Example 1) 【0065】 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." 【0066】 In today's business environment, sales staff and delivery personnel need to create efficient routes. However, traditional methods have made it difficult to quickly generate optimal routes that take into account the priority of destinations and real-time traffic conditions. This results in wasted time and resources, and a loss of economic effectiveness. 【0067】 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. 【0068】 In this invention, the server includes means for acquiring destination information and storing that data, means for visually arranging destinations using geographic data and classifying them by region, and means for formulating optimized visit routes using transportation data. This enables the generation of efficient visit routes in real time based on the latest traffic information and destination priorities, thereby reducing travel time and expenses. 【0069】 "Place information" refers to data about the location the user plans to visit, including address, priority, and purpose of visit. 【0070】 "Geographic data" refers to data used to place visited locations on a map and represent their physical location and distance. 【0071】 "Transportation data" refers to information about travel between destinations, including elements such as travel time, distance, and traffic conditions. 【0072】 A "generative AI model" refers to an algorithm that uses artificial intelligence to generate the optimal visit route in real time from input data. 【0073】 "Route planning" is the process of determining an efficient route to visit a location based on the information that has been gathered. 【0074】 "Real-time" refers to a temporal property where information is processed with virtually no delay after it is generated. 【0075】 To implement this invention, it is necessary to construct a visit route generation system and assume that the server, terminal, and user work together in coordination. The specific form of this system is described below. 【0076】 The server retrieves destination information from the database. This information includes the destination's address, priority, and purpose of visit. The server uses a database management system (DBMS) to efficiently process the data and retrieve destination information. 【0077】 Next, the server uses map information to plot the visited locations on a map. This process leverages the API of a geographic data provider (e.g., a map API) to visually position the visited locations. This allows the server to analyze the spatial relationships between locations and classify them into regions. 【0078】 Furthermore, the server uses transportation data to optimize the visit route. Real-time transportation data is obtained through traffic information providers (e.g., traffic APIs), and the optimal route using vehicles and public transport is formulated. In this process, a generative AI model is used to generate an efficient route that minimizes the number of left and right turns and waiting at traffic lights. 【0079】 As a concrete example, when a user visits multiple cities in one day, the system provided by the server generates a real-time schedule that reflects the priority of the destinations and the current traffic conditions. Because this schedule is based on the predicted order of visits and travel times, it becomes more efficient. 【0080】 The terminal displays visit route information generated by the server to the user. The terminal's interface clearly displays the order of visits, estimated arrival times, and modes of transportation to aid user understanding. The user proceeds with their visits based on this information and provides feedback via the terminal after each visit. 【0081】 This feedback information is sent to the server and used as data to improve the generation of future visit routes. This allows the system to adapt to user needs, improve accuracy, and continue to provide optimal visit plans. 【0082】 For example, a possible prompt for a generative AI model might be: "Based on a list of multiple destinations in Tokyo and Osaka, please suggest the optimal order of visits, taking into account current traffic conditions." This prompt would prompt the AI ​​model to calculate the most efficient route at that moment and provide it to the user. 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 The server receives a list of planned visits as input and retrieves destination information from the database. Specifically, it uses SQL queries to retrieve the addresses, priorities, and purposes of visits specified by the user from the database. The output provides detailed information for each destination. This information is important data used for subsequent geographic plotting and route generation. 【0086】 Step 2: 【0087】 The server uses the API of a geographic data provider to plot the visited locations on a map based on the acquired destination information. The input is the address data of each visited location, which is converted into geographic data. Specifically, it calculates the latitude and longitude and places pins on the map. The output is a visualization of the visited locations on the map, and by color-coding these locations by area, the basis for efficient visiting routes is formed. 【0088】 Step 3: 【0089】 The server acquires the latest traffic data and generates visiting routes based on that data. This process takes real-time traffic information from a traffic API and calculates the optimal route using a generation AI model. The input is the distance between destinations and the current traffic conditions, and the output is information on the shortest travel time and routes that avoid congestion. Specifically, for cars, it reduces right turns, and when using public transportation, it selects a route that takes transfers and fares into consideration. 【0090】 Step 4: 【0091】 The server performs congestion predictions based on the generated visit route information. This process takes date, time, and event information as input data and analyzes the likelihood of congestion using a generating AI model. The output is recommended visit times and order to avoid congestion. Specifically, it proposes a visit plan that takes into account traffic increases and decreases due to specific events. 【0092】 Step 5: 【0093】 The terminal presents the user with the final information on the visit route generated by the server. This includes displaying the order of visits, estimated arrival times, and recommended modes of transportation obtained from the server. The output information is presented clearly through a specific user interface to aid user understanding. In terms of specific operation, it is designed to be flexible enough to accommodate schedule changes and real-time changes in traffic conditions. 【0094】 Step 6: 【0095】 After completing a visit, users input feedback into a terminal based on actual traffic conditions and visit results. This feedback consists of the user's subjective evaluation and performance data. This results in output that provides hints for improvement and data that influences future route generation. Specifically, this data is automatically sent from the terminal to the server, where it is used to improve the accuracy of the next route generation algorithm. 【0096】 (Application Example 1) 【0097】 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." 【0098】 As the need for delivery efficiency in the logistics industry increases, there is a demand for both optimized delivery routes and flexible route changes based on traffic conditions. However, existing systems do not adequately optimize routes considering real-time traffic conditions, hindering efficient delivery. In addition, there is a lack of mechanisms to incorporate user feedback into future route generation. 【0099】 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. 【0100】 In this invention, the server includes means for acquiring information about the destination, means for visualizing the destination using location information and classifying it by area, means for generating a route including the optimal visit route using movement status information, and means for calculating and displaying an efficient delivery route based on delivery destination information and real-time information. This makes it possible to present the optimal delivery route that takes traffic conditions into account in real time and to reflect user feedback in the next route calculation. 【0101】 "Information about the destination" refers to detailed information about the place you plan to visit, such as its address and name. 【0102】 "Location information" refers to data that indicates the geographical location of a place to visit, and is used to pinpoint a location on a map. 【0103】 "Mobility information" refers to dynamic situational data that includes traffic flow and congestion information. 【0104】 "Visit history" refers to records of places and dates visited in the past, and is data used to improve routes. 【0105】 "User input feedback" refers to evaluation and opinion data provided by system users, which is used to improve the service. 【0106】 "Delivery information" refers to detailed information about the planned delivery location, such as the address and recipient. 【0107】 "Real-time information" refers to data that is updated in real time regarding current traffic conditions and travel status. 【0108】 This invention consists of a system that generates efficient delivery routes in real time, enabling drivers and logistics personnel to follow the optimal path. Embodiments of the invention include the following processes: 【0109】 The server retrieves information about delivery destinations and plots the location of each destination on map data. This enables visualization of delivery destinations and classification by region. The server also collects real-time movement information and uses it to generate the optimal delivery route. Traffic congestion and accident information are taken into consideration in the specific route calculation. 【0110】 The terminal visually displays the optimal delivery route provided by the server on the driver's smartphone. The terminal also dynamically recalculates the route as needed based on real-time information and current travel status, and immediately provides updated information to the driver. This helps to reduce travel time and fuel consumption. 【0111】 After delivery is complete, users provide feedback on the actual travel status and traffic information. This feedback data is used to improve the accuracy of future delivery route generation. 【0112】 The system's software obtains map and traffic information using the Google® Maps API, and optimization algorithms are implemented using programming languages ​​such as Python. Frameworks such as React Native are used for displaying the information on the user's device. 【0113】 For example, if a logistics driver needs a route to efficiently deliver to five destinations, the system calculates the optimal route based on current traffic information and displays it on the terminal. Based on this, the driver can deliver using the shortest route. 【0114】 An example of a prompt to input into the generating AI model is: "Please provide the optimal route for a logistics driver to efficiently visit five delivery destinations. Please optimize the route to minimize fuel consumption while taking into account current traffic information and congestion forecasts." 【0115】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0116】 Step 1: 【0117】 The server retrieves the delivery address list from the management system. This input provides address and priority data for each delivery destination. Based on this data, the server prepares to retrieve geographical location information. 【0118】 Step 2: 【0119】 The server uses the Google Maps API to retrieve the location information of delivery destinations and plots it on a map. This plotting of location data visualizes the geographical relationships between delivery destinations. This output serves as the basis for route generation. 【0120】 Step 3: 【0121】 The server collects real-time traffic data. Input data includes traffic information such as current road congestion and accident information. Based on this information, it prepares to calculate the optimal delivery route. 【0122】 Step 4: 【0123】 The server generates the optimal delivery route using a Python algorithm based on acquired location information and traffic conditions. It uses location information and traffic data as input and generates route data as output. The algorithm operates with the aim of minimizing right turns and reducing travel time. 【0124】 Step 5: 【0125】 The server sends the generated optimal route to the terminal. Based on the received route information, the terminal visually displays the route on the delivery driver's smartphone. At this stage, an appropriate map display and estimated arrival time are provided. 【0126】 Step 6: 【0127】 The user performs a delivery and, upon completion, enters feedback into the terminal regarding the travel status and actual changes in traffic. This feedback information is sent to the server as input and is reflected in the next route calculation, so the output is an improvement to the algorithm in the future. 【0128】 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. 【0129】 This invention supports more effective sales activities by combining a visit route generation system with an emotion engine that recognizes the user's emotions. In addition to the conventional automatic visit route creation function, this system aims to reduce user stress and fatigue by dynamically optimizing routes and schedules while considering the user's psychological state. 【0130】 First, the user enters their visit schedule into the system. This includes the address of the destination, priority, and scheduled date and time. Crucially, the terminal acquires the user's emotional state in real time through sensors and input devices. Based on this data, an emotion engine built into the server analyzes the user's emotions and evaluates their stress level and motivation. 【0131】 In addition to conventional route generation procedures, the server optimizes routes by considering the user's emotional state. For example, if a user is tired, it can suggest a route with fewer destinations and less burden. Furthermore, if a user is under high stress, it can select a route that avoids peak hours, making travel between destinations smoother. 【0132】 The generated visit routes and schedules are provided to the user on their device, allowing them to perform their tasks while reducing emotional burden. Furthermore, user feedback is obtained after each visit, and this feedback is incorporated into optimizing future routes, ensuring continuous system improvement. 【0133】 For example, when a sales staff member plans a visit, the server generates a basic route that takes traffic conditions into account, but at the same time, it adjusts the order and timing of visits based on the user's emotional data. This enables flexible and efficient visits tailored to the user's situation. 【0134】 Thus, the present invention is an innovative system that improves the effectiveness of sales activities by incorporating an emotion engine to personalize visit schedules. 【0135】 The following describes the processing flow. 【0136】 Step 1: 【0137】 The user enters their visit schedule into the system. This information includes the address of the destination, the date and time of the visit, and the priority level. The entered data is sent to the server via the terminal. 【0138】 Step 2: 【0139】 The device measures the user's emotional state in real time using sensors (e.g., wearable devices, smartphone cameras). Emotional data includes stress and fatigue levels obtained through facial analysis and pulse measurement. 【0140】 Step 3: 【0141】 The server retrieves destination information and plots the destinations on a map using map data. Geographically close destinations are grouped by area to establish a foundation for efficient visits. 【0142】 Step 4: 【0143】 The server uses external APIs to collect up-to-date traffic information, including road congestion, traffic restrictions, and public transport schedules. 【0144】 Step 5: 【0145】 The server uses an emotion engine to analyze the user's emotional data. Based on this analysis, if the user is in a high-stress state, it re-evaluates the priority of destinations and generates a visit route that reduces the user's psychological and physical burden. 【0146】 Step 6: 【0147】 The server generates the optimal visiting route and order based on traffic information and sentiment analysis. For example, to reduce the burden, it suggests routes that can be traveled in a short time and schedules that reduce the number of visits. 【0148】 Step 7: 【0149】 The device displays the generated visit route and schedule to the user. This information includes the order of visits, travel routes, and estimated arrival times. 【0150】 Step 8: 【0151】 Users complete their visits and provide feedback through the system. This feedback includes information about the outcome of the visit, route evaluation, and stress levels during the visit. 【0152】 Step 9: 【0153】 The server incorporates user feedback and sentiment data to update its algorithms. This allows for more accurate suggestions when generating future visit routes. 【0154】 (Example 2) 【0155】 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 will be referred to as the "terminal." 【0156】 In today's business environment, efficient and effective selection of destinations and optimization of visit routes are crucial when conducting on-site visits. However, conventional technologies struggle to dynamically optimize visit schedules while considering the user's psychological state, potentially causing excessive stress and fatigue. Furthermore, there has been a lack of mechanisms to continuously improve the system by incorporating visit history and feedback into future visit plans. 【0157】 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. 【0158】 In this invention, the server includes means for acquiring basic information about the destination, means for displaying the destination using map data and classifying it by area, means for generating an optimal visit route using movement information, means for analyzing the user's psychological state and dynamically adjusting the visit order considering psychological burden, and means for collecting visit experience and user-provided feedback and utilizing it for route generation in the future. This makes it possible to generate flexible and efficient visit routes and continuously improve the system while reducing the psychological burden on the user. 【0159】 "Basic information about the place to visit" refers to information related to the place to be visited, such as the address, priority, and scheduled date and time. 【0160】 "Map data" refers to a dataset used to visually display geographical information and is used to indicate the location of a place to visit. 【0161】 "Mobility information" refers to data related to travel, including traffic conditions, modes of transport, and travel time. 【0162】 "User's psychological state" refers to the user's current mental and emotional condition, including their current feelings, stress levels, and motivation. 【0163】 "Dynamic adjustment of visit order" refers to the process of changing the order and schedule of visits in order to reduce the psychological burden on the user. 【0164】 "Visit experience" refers to data and insights based on past visit records, which will be used to plan future visits. 【0165】 "User-provided feedback" refers to information such as comments and suggestions for improvement that users provide to the system after visiting it. 【0166】 "Utilizing route generation for future visits" refers to the process of generating more optimized visit routes based on collected visit history and feedback. 【0167】 This invention aims to dynamically optimize visit routes in a visit route generation system, taking into account the user's psychological state. The server utilizes an emotion engine and a generative AI model to process basic information and psychological data received from the user, thereby flexibly adjusting the visit schedule. 【0168】 First, the user enters basic information about their destination (address, priority, scheduled date and time, etc.) through the terminal. The terminal is equipped with sensors that detect the user's mental state in real time and send the information to the server. 【0169】 Next, the server uses a generative AI model to analyze the collected psychological data. This model assesses the user's stress level and motivation, and dynamically optimizes the visit route based on this. It uses travel information and takes into account traffic conditions, visit history, and user feedback to generate the route. 【0170】 The generated visit route is sent to the terminal and provided to the user. This allows the user to conduct visits efficiently with reduced stress. After the visit is completed, the user's feedback is entered into the system and used to optimize the next route, thus continuously improving the system. 【0171】 As a concrete example, when a sales staff member executes a visit plan, the server can combine traffic information and psychological data to generate a route that avoids congestion and adjusts the order of visits. This process allows the sales staff member to enjoy a flexible and appropriate visit plan. 【0172】 An example of a prompt might be, "Optimize the visiting route and suggest relaxation points when the user's stress level is high." This prompt is used by the generative AI model to analyze the user's psychological state and suggest an appropriate visiting route. 【0173】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0174】 Step 1: 【0175】 The user enters basic information about the destination via their device. Specifically, they enter the destination's address, priority, and scheduled date and time on an input screen. This information is saved on the device and prepared for transmission to the server. 【0176】 Step 2: 【0177】 The device uses sensors to acquire the user's emotional state in real time. It collects heart rate and facial expression information from wearable devices and cameras, and sends this data, which indicates stress and motivation, to a server. This emotional data is then analyzed by the server. 【0178】 Step 3: 【0179】 The server receives basic information and emotional data about the visited location as input and analyzes the data using a generative AI model. The model evaluates stress levels and motivation, quantifying the user's psychological state. This process provides the foundational information needed to create optimal visiting routes for each individual user. 【0180】 Step 4: 【0181】 The server optimizes the visit route based on the collected information, taking travel information into consideration. Specifically, it generates a route that reduces travel time by reflecting traffic conditions, adjusts the order of visits, and reduces the psychological burden on the user. The output is the optimized visit route. 【0182】 Step 5: 【0183】 The optimized visit route is sent from the server to the terminal and provided to the user. The user can review this route and schedule on the terminal screen and make adjustments if necessary. This prepares the user to begin their visit activities with an optimized schedule. 【0184】 Step 6: 【0185】 After a visit is completed, the user enters feedback into the terminal. This includes information such as the success of the visit, any challenges, and suggestions for improvement. This feedback is sent to the server as important data for optimizing the next visit route and is used to improve the system. 【0186】 (Application Example 2) 【0187】 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". 【0188】 In optimizing visit schedules, conventional route generation systems only determine the order of visits based on traffic information and visit history, without considering changes in the user's emotional state, leading to the problem of accumulated stress and fatigue. Similarly, in delivery operations, the psychological burden on delivery personnel can affect customer satisfaction. Therefore, flexible route optimization that takes the user's emotional state into account is required. 【0189】 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. 【0190】 In this invention, the server includes means for acquiring destination information, means for plotting destinations using map information and grouping them by area, means for recognizing emotional states and analyzing the data thereof, means for generating an optimal visit route using traffic information, means for dynamically optimizing the visit route according to the user's psychological state based on the analyzed emotional data, and means for collecting visit history and user input feedback and reflecting them in route generation for subsequent visits. This makes it possible to reduce user stress and fatigue and provide an efficient visit schedule with less psychological burden. 【0191】 "Means of obtaining information on places to visit" refers to methods of importing information such as the address and name of the planned place to visit into the system. 【0192】 "A method for plotting destinations using map information and grouping them by area" refers to a method that displays destinations on a map and classifies them into geographically adjacent areas, thereby supporting the setting of efficient visiting routes. 【0193】 "A method for generating the optimal visiting route using traffic information" refers to a method for selecting the most efficient visiting route by considering real-time traffic conditions and forecasts. 【0194】 "Means for recognizing emotional states" refers to means of collecting and recognizing a user's psychological and emotional state through sensors and input devices. 【0195】 "A means of dynamically optimizing visit routes according to the user's psychological state based on analyzed emotional data" refers to a method of reducing the burden on the user by analyzing the user's emotional data and using the results to adjust the order and timing of visits. 【0196】 "Means for collecting visit history and user input feedback and reflecting them in route generation for future visits" refers to methods for collecting past visit records and user opinions and evaluations, and reflecting them in the next visit plan to continuously improve the system. 【0197】 This invention is a system for optimizing visit schedules in a way that reduces the psychological burden on the user. The system mainly consists of a server and terminals. 【0198】 The server retrieves destination information sent from the user's device, plots the destinations using map data, and groups them by area. Next, it generates a basic travel route using real-time traffic information. Crucially, the device uses cameras and sensors to capture the user's emotional state in real time. This information is sent to the server, where an emotion engine analyzes the data to evaluate the user's stress and motivation levels. 【0199】 The server dynamically optimizes the visit route based on the analyzed emotional data. Specifically, if the user is fatigued, it can suggest a route that reduces the number of destinations and alleviates psychological and physical burden. Furthermore, if a high level of stress is detected, the server selects a route that avoids congestion, ensuring a smoother journey. 【0200】 The device provides users with optimized visit routes and schedules, reducing their workload and supporting efficient activities. As a result, users can have a better visit experience while minimizing emotional stress. Users can provide feedback after each visit, which is used to improve future route generation. 【0201】 As a concrete example, when a delivery person visits multiple delivery locations, the system detects their stress level at that time and prioritizes routes with shorter travel distances, thereby improving work efficiency and reducing mental burden. 【0202】 An example of a prompt to input into a generative AI model is: "Based on my current emotional state, please suggest the optimal route to visit. The area to visit is urban, and the emotional state to consider is high levels of fatigue." 【0203】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0204】 Step 1: 【0205】 The terminal receives destination information from the user as input. This includes the destination's address, priority, and scheduled date and time. This data is then prepared for transmission to the geographic information system. 【0206】 Step 2: 【0207】 The device acquires the user's emotional state using a camera and various sensors, and transmits this emotional data to a server in real time. The data collected by the sensors is used to infer the user's psychological state from facial expressions and voice tone. 【0208】 Step 3: 【0209】 The server uses map data to plot destinations based on destination information received from terminals and groups them by area. This process involves calculating geographic coordinates to associate with destinations and grouping them using a clustering algorithm. 【0210】 Step 4: 【0211】 The server takes real-time traffic data as input and generates a basic visit route. It uses a traffic information API to analyze congestion information and passable routes to calculate an efficient route. 【0212】 Step 5: 【0213】 The server uses an emotion engine to analyze the user's emotional data and evaluate their stress level and motivation. The emotional data is then calculated as a numerical stress score using a machine learning model. 【0214】 Step 6: 【0215】 The server uses the analyzed emotional data and the generated base route as input to dynamically optimize the visit route according to the user's psychological state. Specifically, it prioritizes shorter distances when fatigue levels are high, and adjusts the route to avoid congestion when stress levels are high. 【0216】 Step 7: 【0217】 The device provides an optimized visit route and schedule as output. The user then begins their visit according to this and checks the interface displaying the suggested route and schedule. 【0218】 Step 8: 【0219】 After the visit ends, the user enters feedback using a terminal. This feedback is sent to the server and used as a reference for optimizing routes in the future. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 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). 【0227】 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. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 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". 【0236】 This invention is a system that enables the automatic creation of visit routes and supports efficient sales activities by integrating various data. This system has a function in which a server acquires visit destination information, map information, and traffic information, and automatically generates visit routes based on them. 【0237】 First, the server retrieves destination information from the user's planned visit list. This information includes the destination's address, priority, and purpose of visit. Next, the server uses map information to plot the destinations on a map and groups nearby destinations by area. This forms the basis for efficient visit planning. 【0238】 Next, the server uses the latest traffic information to generate the optimal route. In this process, it considers travel time, distance, and traffic conditions between destinations, minimizing right turns and selecting routes that avoid congestion if traveling by car. If using public transport, it also optimizes the route by taking transfer times and fares into consideration. 【0239】 Furthermore, the visit route incorporates congestion prediction, and the server suggests routes that avoid expected congestion during specific time periods based on the date, time, and event information. This allows users to complete their visits efficiently and safely. In addition, visit history and user feedback information are utilized to improve the accuracy of the algorithm for generating future visit routes. 【0240】 For example, when a sales staff member plans visits in multiple cities, the terminal displays the optimal route provided by the server. This includes the order of visits, estimated arrival times, and necessary transportation. The user then proceeds with the visits according to the plan and can further improve the system's accuracy by providing feedback based on the results and actual traffic conditions. 【0241】 This invention makes it possible to significantly reduce the time and effort required for visit planning, while also improving the accuracy and safety of routes. 【0242】 The following describes the processing flow. 【0243】 Step 1: 【0244】 Users enter a list of planned visits into the system. This includes information such as the address of the destination, priority, date and time, and purpose of the visit. 【0245】 Step 2: 【0246】 The server receives the entered list of scheduled visits and retrieves information about the destinations. This retrieved information is stored in an internal database and used for subsequent processing. 【0247】 Step 3: 【0248】 The server retrieves the latest map information via an external API. This data, including geographic coordinates and road information, is used to plot the location of each destination on the map. 【0249】 Step 4: 【0250】 The server groups the plotted destinations on the map. This is a process that takes into account the geographical proximity of the destinations and groups them into smaller areas. 【0251】 Step 5: 【0252】 The server retrieves real-time traffic information from traffic information providers. This includes road congestion, traffic jam information, and the operating status of public transportation. 【0253】 Step 6: 【0254】 The server generates the optimal route, taking into account the distance between destinations, travel time, and traffic conditions. For those traveling by car, it selects a route that minimizes right turns and avoids congestion. 【0255】 Step 7: 【0256】 The server uses date, time, and event information to predict congestion. Based on the prediction results, it adjusts the order and timing of visits to create an optimal schedule. 【0257】 Step 8: 【0258】 The terminal displays the visit route and schedule provided by the server to the user. It also visualizes the route on a map, clearly indicating the order of visits and travel time. 【0259】 Step 9: 【0260】 After visiting locations, users input feedback into the system regarding their actual visit results and perceived congestion levels. 【0261】 Step 10: 【0262】 The server receives user feedback and updates its model through data analysis. This improves the accuracy of route suggestions for future visits. 【0263】 (Example 1) 【0264】 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." 【0265】 In today's business environment, sales staff and delivery personnel need to create efficient routes. However, traditional methods have made it difficult to quickly generate optimal routes that take into account the priority of destinations and real-time traffic conditions. This results in wasted time and resources, and a loss of economic effectiveness. 【0266】 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. 【0267】 In this invention, the server includes means for acquiring destination information and storing that data, means for visually arranging destinations using geographic data and classifying them by region, and means for formulating optimized visit routes using transportation data. This enables the generation of efficient visit routes in real time based on the latest traffic information and destination priorities, thereby reducing travel time and expenses. 【0268】 "Place information" refers to data about the location the user plans to visit, including address, priority, and purpose of visit. 【0269】 "Geographic data" refers to data used to place visited locations on a map and represent their physical location and distance. 【0270】 "Transportation data" refers to information about travel between destinations, including elements such as travel time, distance, and traffic conditions. 【0271】 A "generative AI model" refers to an algorithm that uses artificial intelligence to generate the optimal visit route in real time from input data. 【0272】 "Route planning" is the process of determining an efficient route to visit a location based on the information that has been gathered. 【0273】 "Real-time" refers to a temporal property where information is processed with virtually no delay after it is generated. 【0274】 To implement this invention, it is necessary to construct a visit route generation system and assume that the server, terminal, and user work together in coordination. The specific form of this system is described below. 【0275】 The server retrieves destination information from the database. This information includes the destination's address, priority, and purpose of visit. The server uses a database management system (DBMS) to efficiently process the data and retrieve destination information. 【0276】 Next, the server uses map information to plot the visited locations on a map. This process leverages the API of a geographic data provider (e.g., a map API) to visually position the visited locations. This allows the server to analyze the spatial relationships between locations and classify them into regions. 【0277】 Furthermore, the server uses transportation data to optimize the visit route. Real-time transportation data is obtained through traffic information providers (e.g., traffic APIs), and the optimal route using vehicles and public transport is formulated. In this process, a generative AI model is used to generate an efficient route that minimizes the number of left and right turns and waiting at traffic lights. 【0278】 As a concrete example, when a user visits multiple cities in one day, the system provided by the server generates a real-time schedule that reflects the priority of the destinations and the current traffic conditions. Because this schedule is based on the predicted order of visits and travel times, it becomes more efficient. 【0279】 The terminal displays visit route information generated by the server to the user. The terminal's interface clearly displays the order of visits, estimated arrival times, and modes of transportation to aid user understanding. The user proceeds with their visits based on this information and provides feedback via the terminal after each visit. 【0280】 This feedback information is sent to the server and used as data to improve the generation of future visit routes. This allows the system to adapt to user needs, improve accuracy, and continue to provide optimal visit plans. 【0281】 For example, a possible prompt for a generative AI model might be: "Based on a list of multiple destinations in Tokyo and Osaka, please suggest the optimal order of visits, taking into account current traffic conditions." This prompt would prompt the AI ​​model to calculate the most efficient route at that moment and provide it to the user. 【0282】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0283】 Step 1: 【0284】 The server receives the visit schedule list as input and retrieves the destination information from the database. Specifically, it extracts the address, priority, and purpose of the visit specified by the user from the database using an SQL query. As output, detailed information for each destination is obtained. This information is important data used for subsequent geographical plotting and route generation. 【0285】 Step 2: 【0286】 Based on the obtained destination information, the server plots the visit locations on the map using the API of the geographical data provider. The input is the address data of each destination, which is converted into geographical data. As a specific operation, it calculates the latitude and longitude and places pins on the map. As output, the destinations visualized on the map are obtained, and by coloring them by area unit, the basis for an efficient visit route is formed. 【0287】 Step 3: 【0288】 The server obtains the latest traffic data and generates a visit route based on the transportation data. In this process, it incorporates real-time traffic information from the traffic API and calculates the optimal route using a generation AI model. The input is the distance between visit locations and the current traffic situation, and the output is route information such as the shortest travel time and avoiding congestion. As a specific operation, in the case of a car, it reduces right turns, and when using public transportation, it selects a route considering transfers and fares. 【0289】 Step 4: 【0290】 The server performs congestion prediction based on the generated visit route information. In this process, it takes in date and time and event information as input data and analyzes the possibility of congestion using a generation AI model. The output is the recommended visit time and order to avoid congestion. Specifically, it proposes a visit plan considering traffic increases and decreases due to specific events. 【0291】 <​​​ The terminal presents the user with the final information on the visit route generated by the server. This includes displaying the order of visits, estimated arrival times, and recommended modes of transportation obtained from the server. The output information is presented clearly through a specific user interface to aid user understanding. In terms of specific operation, it is designed to be flexible enough to accommodate schedule changes and real-time changes in traffic conditions. 【0293】 Step 6: 【0294】 After completing a visit, users input feedback into a terminal based on actual traffic conditions and visit results. This feedback consists of the user's subjective evaluation and performance data. This results in output that provides hints for improvement and data that influences future route generation. Specifically, this data is automatically sent from the terminal to the server, where it is used to improve the accuracy of the next route generation algorithm. 【0295】 (Application Example 1) 【0296】 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." 【0297】 As the need for delivery efficiency in the logistics industry increases, there is a demand for both optimized delivery routes and flexible route changes based on traffic conditions. However, existing systems do not adequately optimize routes considering real-time traffic conditions, hindering efficient delivery. In addition, there is a lack of mechanisms to incorporate user feedback into future route generation. 【0298】 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. 【0299】 In this invention, the server includes means for acquiring information about the destination, means for visualizing the destination using location information and classifying it by region, means for generating a route including an optimal visit route using movement status information, and means for calculating and displaying an efficient delivery route based on delivery destination information and real-time information. As a result, it becomes possible to present an optimal delivery route considering traffic conditions in real time and reflect feedback from users in the next route calculation. 【0300】 "Information about the destination" refers to detailed information such as the address and name of the place to be visited. 【0301】 "Location information" refers to data indicating the geographical location of the destination and is used for location identification on a map. 【0302】 "Movement status information" refers to dynamic status data including traffic flow and congestion information. 【0303】 "Visit history" refers to records of places visited and dates and times in the past and is data useful for improving routes. 【0304】 "User input feedback" refers to data of evaluations and opinions provided by system users and is information used for service improvement. 【0305】 "Delivery destination information" refers to detailed information such as the address and recipient regarding the delivery destination. 【0306】 "Real-time information" refers to data that is updated immediately regarding the current traffic conditions and movement status. 【0307】 This invention consists of a system that generates an efficient delivery route in real time so that drivers and logistics personnel can execute an optimal route. In an embodiment of the invention, the following processes are included. 【0308】 The server retrieves information about delivery destinations and plots the location of each destination on map data. This enables visualization of delivery destinations and classification by region. The server also collects real-time movement information and uses it to generate the optimal delivery route. Traffic congestion and accident information are taken into consideration in the specific route calculation. 【0309】 The terminal visually displays the optimal delivery route provided by the server on the driver's smartphone. The terminal also dynamically recalculates the route as needed based on real-time information and current travel status, and immediately provides updated information to the driver. This helps to reduce travel time and fuel consumption. 【0310】 After delivery is complete, users provide feedback on the actual travel status and traffic information. This feedback data is used to improve the accuracy of future delivery route generation. 【0311】 The system's software obtains map and traffic information using the Google Maps API, and optimization algorithms are implemented using programming languages ​​such as Python. Frameworks such as React Native are used for displaying the information on the user's device. 【0312】 For example, if a logistics driver needs a route to efficiently deliver to five destinations, the system calculates the optimal route based on current traffic information and displays it on the terminal. Based on this, the driver can deliver using the shortest route. 【0313】 An example of a prompt to input into the generating AI model is: "Please provide the optimal route for a logistics driver to efficiently visit five delivery destinations. Please optimize the route to minimize fuel consumption while taking into account current traffic information and congestion forecasts." 【0314】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0315】 Step 1: 【0316】 The server retrieves the delivery address list from the management system. This input provides address and priority data for each delivery destination. Based on this data, the server prepares to retrieve geographical location information. 【0317】 Step 2: 【0318】 The server uses the Google Maps API to retrieve the location information of delivery destinations and plots it on a map. This plotting of location data visualizes the geographical relationships between delivery destinations. This output serves as the basis for route generation. 【0319】 Step 3: 【0320】 The server collects real-time traffic data. Input data includes traffic information such as current road congestion and accident information. Based on this information, it prepares to calculate the optimal delivery route. 【0321】 Step 4: 【0322】 The server generates the optimal delivery route using a Python algorithm based on acquired location information and traffic conditions. It uses location information and traffic data as input and generates route data as output. The algorithm operates with the aim of minimizing right turns and reducing travel time. 【0323】 Step 5: 【0324】 The server sends the generated optimal route to the terminal. Based on the received route information, the terminal visually displays the route on the delivery driver's smartphone. At this stage, an appropriate map display and estimated arrival time are provided. 【0325】 Step 6: 【0326】 The user performs a delivery and, upon completion, enters feedback into the terminal regarding the travel status and actual changes in traffic. This feedback information is sent to the server as input and is reflected in the next route calculation, so the output is an improvement to the algorithm in the future. 【0327】 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. 【0328】 This invention supports more effective sales activities by combining a visit route generation system with an emotion engine that recognizes the user's emotions. In addition to the conventional automatic visit route creation function, this system aims to reduce user stress and fatigue by dynamically optimizing routes and schedules while considering the user's psychological state. 【0329】 First, the user enters their visit schedule into the system. This includes the address of the destination, priority, and scheduled date and time. Crucially, the terminal acquires the user's emotional state in real time through sensors and input devices. Based on this data, an emotion engine built into the server analyzes the user's emotions and evaluates their stress level and motivation. 【0330】 In addition to conventional route generation procedures, the server optimizes routes by considering the user's emotional state. For example, if a user is tired, it can suggest a route with fewer destinations and less burden. Furthermore, if a user is under high stress, it can select a route that avoids peak hours, making travel between destinations smoother. 【0331】 The generated visit routes and schedules are provided to the user on their device, allowing them to perform their tasks while reducing emotional burden. Furthermore, user feedback is obtained after each visit, and this feedback is incorporated into optimizing future routes, ensuring continuous system improvement. 【0332】 For example, when a sales staff member plans a visit, the server generates a basic route that takes traffic conditions into account, but at the same time, it adjusts the order and timing of visits based on the user's emotional data. This enables flexible and efficient visits tailored to the user's situation. 【0333】 Thus, the present invention is an innovative system that improves the effectiveness of sales activities by incorporating an emotion engine to personalize visit schedules. 【0334】 The following describes the processing flow. 【0335】 Step 1: 【0336】 The user enters their visit schedule into the system. This information includes the address of the destination, the date and time of the visit, and the priority level. The entered data is sent to the server via the terminal. 【0337】 Step 2: 【0338】 The device measures the user's emotional state in real time using sensors (e.g., wearable devices, smartphone cameras). Emotional data includes stress and fatigue levels obtained through facial analysis and pulse measurement. 【0339】 Step 3: 【0340】 The server retrieves destination information and plots the destinations on a map using map data. Geographically close destinations are grouped by area to establish a foundation for efficient visits. 【0341】 Step 4: 【0342】 The server uses external APIs to collect up-to-date traffic information, including road congestion, traffic restrictions, and public transport schedules. 【0343】 Step 5: 【0344】 The server uses an emotion engine to analyze the user's emotional data. Based on this analysis, if the user is in a high-stress state, it re-evaluates the priority of destinations and generates a visit route that reduces the user's psychological and physical burden. 【0345】 Step 6: 【0346】 The server generates the optimal visiting route and order based on traffic information and sentiment analysis. For example, to reduce the burden, it suggests routes that can be traveled in a short time and schedules that reduce the number of visits. 【0347】 Step 7: 【0348】 The device displays the generated visit route and schedule to the user. This information includes the order of visits, travel routes, and estimated arrival times. 【0349】 Step 8: 【0350】 Users complete their visits and provide feedback through the system. This feedback includes information about the outcome of the visit, route evaluation, and stress levels during the visit. 【0351】 Step 9: 【0352】 The server incorporates user feedback and sentiment data to update its algorithms. This allows for more accurate suggestions when generating future visit routes. 【0353】 (Example 2) 【0354】 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". 【0355】 In today's business environment, efficient and effective selection of destinations and optimization of visit routes are crucial when conducting on-site visits. However, conventional technologies struggle to dynamically optimize visit schedules while considering the user's psychological state, potentially causing excessive stress and fatigue. Furthermore, there has been a lack of mechanisms to continuously improve the system by incorporating visit history and feedback into future visit plans. 【0356】 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. 【0357】 In this invention, the server includes means for acquiring basic information about the destination, means for displaying the destination using map data and classifying it by area, means for generating an optimal visit route using movement information, means for analyzing the user's psychological state and dynamically adjusting the visit order considering psychological burden, and means for collecting visit experience and user-provided feedback and utilizing it for route generation in the future. This makes it possible to generate flexible and efficient visit routes and continuously improve the system while reducing the psychological burden on the user. 【0358】 "Basic information about the place to visit" refers to information related to the place to be visited, such as the address, priority, and scheduled date and time. 【0359】 "Map data" refers to a dataset used to visually display geographical information and is used to indicate the location of a place to visit. 【0360】 "Mobility information" refers to data related to travel, including traffic conditions, modes of transport, and travel time. 【0361】 "User's psychological state" refers to the user's current mental and emotional condition, including their current feelings, stress levels, and motivation. 【0362】 "Dynamic adjustment of visit order" refers to the process of changing the order and schedule of visits in order to reduce the psychological burden on the user. 【0363】 "Visit experience" refers to data and insights based on past visit records, which will be used to plan future visits. 【0364】 "User-provided feedback" refers to information such as comments and suggestions for improvement that users provide to the system after visiting it. 【0365】 "Utilizing route generation for future visits" refers to the process of generating more optimized visit routes based on collected visit history and feedback. 【0366】 This invention aims to dynamically optimize visit routes in a visit route generation system, taking into account the user's psychological state. The server utilizes an emotion engine and a generative AI model to process basic information and psychological data received from the user, thereby flexibly adjusting the visit schedule. 【0367】 First, the user enters basic information about their destination (address, priority, scheduled date and time, etc.) through the terminal. The terminal is equipped with sensors that detect the user's mental state in real time and send the information to the server. 【0368】 Next, the server uses a generative AI model to analyze the collected psychological data. This model assesses the user's stress level and motivation, and dynamically optimizes the visit route based on this. It uses travel information and takes into account traffic conditions, visit history, and user feedback to generate the route. 【0369】 The generated visit route is sent to the terminal and provided to the user. This allows the user to conduct visits efficiently with reduced stress. After the visit is completed, the user's feedback is entered into the system and used to optimize the next route, thus continuously improving the system. 【0370】 As a concrete example, when a sales staff member executes a visit plan, the server can combine traffic information and psychological data to generate a route that avoids congestion and adjusts the order of visits. This process allows the sales staff member to enjoy a flexible and appropriate visit plan. 【0371】 An example of a prompt might be, "Optimize the visiting route and suggest relaxation points when the user's stress level is high." This prompt is used by the generative AI model to analyze the user's psychological state and suggest an appropriate visiting route. 【0372】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0373】 Step 1: 【0374】 The user enters basic information about the destination via their device. Specifically, they enter the destination's address, priority, and scheduled date and time on an input screen. This information is saved on the device and prepared for transmission to the server. 【0375】 Step 2: 【0376】 The device uses sensors to acquire the user's emotional state in real time. It collects heart rate and facial expression information from wearable devices and cameras, and sends this data, which indicates stress and motivation, to a server. This emotional data is then analyzed by the server. 【0377】 Step 3: 【0378】 The server receives basic information and emotional data about the visited location as input and analyzes the data using a generative AI model. The model evaluates stress levels and motivation, quantifying the user's psychological state. This process provides the foundational information needed to create optimal visiting routes for each individual user. 【0379】 Step 4: 【0380】 The server optimizes the visit route based on the collected information, taking travel information into consideration. Specifically, it generates a route that reduces travel time by reflecting traffic conditions, adjusts the order of visits, and reduces the psychological burden on the user. The output is the optimized visit route. 【0381】 Step 5: 【0382】 The optimized visit route is sent from the server to the terminal and provided to the user. The user can review this route and schedule on the terminal screen and make adjustments if necessary. This prepares the user to begin their visit activities with an optimized schedule. 【0383】 Step 6: 【0384】 After a visit is completed, the user enters feedback into the terminal. This includes information such as the success of the visit, any challenges, and suggestions for improvement. This feedback is sent to the server as important data for optimizing the next visit route and is used to improve the system. 【0385】 (Application Example 2) 【0386】 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." 【0387】 In optimizing visit schedules, conventional route generation systems only determine the order of visits based on traffic information and visit history, without considering changes in the user's emotional state, leading to the problem of accumulated stress and fatigue. Similarly, in delivery operations, the psychological burden on delivery personnel can affect customer satisfaction. Therefore, flexible route optimization that takes the user's emotional state into account is required. 【0388】 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. 【0389】 In this invention, the server includes means for acquiring destination information, means for plotting destinations using map information and grouping them by area, means for recognizing emotional states and analyzing the data thereof, means for generating an optimal visit route using traffic information, means for dynamically optimizing the visit route according to the user's psychological state based on the analyzed emotional data, and means for collecting visit history and user input feedback and reflecting them in route generation for subsequent visits. This makes it possible to reduce user stress and fatigue and provide an efficient visit schedule with less psychological burden. 【0390】 "Means of obtaining information on places to visit" refers to methods of importing information such as the address and name of the planned place to visit into the system. 【0391】 "A method for plotting destinations using map information and grouping them by area" refers to a method that displays destinations on a map and classifies them into geographically adjacent areas, thereby supporting the setting of efficient visiting routes. 【0392】 "A method for generating the optimal visiting route using traffic information" refers to a method for selecting the most efficient visiting route by considering real-time traffic conditions and forecasts. 【0393】 "Means for recognizing emotional states" refers to means of collecting and recognizing a user's psychological and emotional state through sensors and input devices. 【0394】 "A means of dynamically optimizing visit routes according to the user's psychological state based on analyzed emotional data" refers to a method of reducing the burden on the user by analyzing the user's emotional data and using the results to adjust the order and timing of visits. 【0395】 "Means for collecting visit history and user input feedback and reflecting them in route generation for future visits" refers to methods for collecting past visit records and user opinions and evaluations, and reflecting them in the next visit plan to continuously improve the system. 【0396】 This invention is a system for optimizing visit schedules in a way that reduces the psychological burden on the user. The system mainly consists of a server and terminals. 【0397】 The server retrieves destination information sent from the user's device, plots the destinations using map data, and groups them by area. Next, it generates a basic travel route using real-time traffic information. Crucially, the device uses cameras and sensors to capture the user's emotional state in real time. This information is sent to the server, where an emotion engine analyzes the data to evaluate the user's stress and motivation levels. 【0398】 The server dynamically optimizes the visit route based on the analyzed emotional data. Specifically, if the user is fatigued, it can suggest a route that reduces the number of destinations and alleviates psychological and physical burden. Furthermore, if a high level of stress is detected, the server selects a route that avoids congestion, ensuring a smoother journey. 【0399】 The device provides users with optimized visit routes and schedules, reducing their workload and supporting efficient activities. As a result, users can have a better visit experience while minimizing emotional stress. Users can provide feedback after each visit, which is used to improve future route generation. 【0400】 As a concrete example, when a delivery person visits multiple delivery locations, the system detects their stress level at that time and prioritizes routes with shorter travel distances, thereby improving work efficiency and reducing mental burden. 【0401】 An example of a prompt to input into a generative AI model is: "Based on my current emotional state, please suggest the optimal route to visit. The area to visit is urban, and the emotional state to consider is high levels of fatigue." 【0402】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0403】 Step 1: 【0404】 The terminal receives destination information from the user as input. This includes the destination's address, priority, and scheduled date and time. This data is then prepared for transmission to the geographic information system. 【0405】 Step 2: 【0406】 The device acquires the user's emotional state using a camera and various sensors, and transmits this emotional data to a server in real time. The data collected by the sensors is used to infer the user's psychological state from facial expressions and voice tone. 【0407】 Step 3: 【0408】 The server uses map data to plot destinations based on destination information received from terminals and groups them by area. This process involves calculating geographic coordinates to associate with destinations and grouping them using a clustering algorithm. 【0409】 Step 4: 【0410】 The server takes real-time traffic data as input and generates a basic visit route. It uses a traffic information API to analyze congestion information and passable routes to calculate an efficient route. 【0411】 Step 5: 【0412】 The server uses an emotion engine to analyze the user's emotional data and evaluate their stress level and motivation. The emotional data is then calculated as a numerical stress score using a machine learning model. 【0413】 Step 6: 【0414】 The server uses the analyzed emotional data and the generated base route as input to dynamically optimize the visit route according to the user's psychological state. Specifically, it prioritizes shorter distances when fatigue levels are high, and adjusts the route to avoid congestion when stress levels are high. 【0415】 Step 7: 【0416】 The device provides an optimized visit route and schedule as output. The user then begins their visit according to this and checks the interface displaying the suggested route and schedule. 【0417】 Step 8: 【0418】 After the visit ends, the user enters feedback using a terminal. This feedback is sent to the server and used as a reference for optimizing routes in the future. 【0419】 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. 【0420】 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. 【0421】 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. 【0422】 [Third Embodiment] 【0423】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0424】 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. 【0425】 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). 【0426】 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. 【0427】 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. 【0428】 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). 【0429】 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. 【0430】 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. 【0431】 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. 【0432】 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. 【0433】 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. 【0434】 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". 【0435】 This invention is a system that enables the automatic creation of visit routes and supports efficient sales activities by integrating various data. This system has a function in which a server acquires visit destination information, map information, and traffic information, and automatically generates visit routes based on them. 【0436】 First, the server retrieves destination information from the user's planned visit list. This information includes the destination's address, priority, and purpose of visit. Next, the server uses map information to plot the destinations on a map and groups nearby destinations by area. This forms the basis for efficient visit planning. 【0437】 Next, the server uses the latest traffic information to generate the optimal route. In this process, it considers travel time, distance, and traffic conditions between destinations, minimizing right turns and selecting routes that avoid congestion if traveling by car. If using public transport, it also optimizes the route by taking transfer times and fares into consideration. 【0438】 Furthermore, the visit route incorporates congestion prediction, and the server suggests routes that avoid expected congestion during specific time periods based on the date, time, and event information. This allows users to complete their visits efficiently and safely. In addition, visit history and user feedback information are utilized to improve the accuracy of the algorithm for generating future visit routes. 【0439】 For example, when a sales staff member plans visits in multiple cities, the terminal displays the optimal route provided by the server. This includes the order of visits, estimated arrival times, and necessary transportation. The user then proceeds with the visits according to the plan and can further improve the system's accuracy by providing feedback based on the results and actual traffic conditions. 【0440】 This invention makes it possible to significantly reduce the time and effort required for visit planning, while also improving the accuracy and safety of routes. 【0441】 The following describes the processing flow. 【0442】 Step 1: 【0443】 Users enter a list of planned visits into the system. This includes information such as the address of the destination, priority, date and time, and purpose of the visit. 【0444】 Step 2: 【0445】 The server receives the entered list of scheduled visits and retrieves information about the destinations. This retrieved information is stored in an internal database and used for subsequent processing. 【0446】 Step 3: 【0447】 The server retrieves the latest map information via an external API. This data, including geographic coordinates and road information, is used to plot the location of each destination on the map. 【0448】 Step 4: 【0449】 The server groups the plotted destinations on the map. This is a process that takes into account the geographical proximity of the destinations and groups them into smaller areas. 【0450】 Step 5: 【0451】 The server retrieves real-time traffic information from traffic information providers. This includes road congestion, traffic jam information, and the operating status of public transportation. 【0452】 Step 6: 【0453】 The server generates the optimal route, taking into account the distance between destinations, travel time, and traffic conditions. For those traveling by car, it selects a route that minimizes right turns and avoids congestion. 【0454】 Step 7: 【0455】 The server uses date, time, and event information to predict congestion. Based on the prediction results, it adjusts the order and timing of visits to create an optimal schedule. 【0456】 Step 8: 【0457】 The terminal displays the visit route and schedule provided by the server to the user. It also visualizes the route on a map, clearly indicating the order of visits and travel time. 【0458】 Step 9: 【0459】 After visiting locations, users input feedback into the system regarding their actual visit results and perceived congestion levels. 【0460】 Step 10: 【0461】 The server receives user feedback and updates its model through data analysis. This improves the accuracy of route suggestions for future visits. 【0462】 (Example 1) 【0463】 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." 【0464】 In today's business environment, sales staff and delivery personnel need to create efficient routes. However, traditional methods have made it difficult to quickly generate optimal routes that take into account the priority of destinations and real-time traffic conditions. This results in wasted time and resources, and a loss of economic effectiveness. 【0465】 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. 【0466】 In this invention, the server includes means for acquiring destination information and storing that data, means for visually arranging destinations using geographic data and classifying them by region, and means for formulating optimized visit routes using transportation data. This enables the generation of efficient visit routes in real time based on the latest traffic information and destination priorities, thereby reducing travel time and expenses. 【0467】 "Place information" refers to data about the location the user plans to visit, including address, priority, and purpose of visit. 【0468】 "Geographic data" refers to data used to place visited locations on a map and represent their physical location and distance. 【0469】 "Transportation data" refers to information about travel between destinations, including elements such as travel time, distance, and traffic conditions. 【0470】 A "generative AI model" refers to an algorithm that uses artificial intelligence to generate the optimal visit route in real time from input data. 【0471】 "Route planning" is the process of determining an efficient route to visit a location based on the information that has been gathered. 【0472】 "Real-time" refers to a temporal property where information is processed with virtually no delay after it is generated. 【0473】 To implement this invention, it is necessary to construct a visit route generation system and assume that the server, terminal, and user work together in coordination. The specific form of this system is described below. 【0474】 The server retrieves destination information from the database. This information includes the destination's address, priority, and purpose of visit. The server uses a database management system (DBMS) to efficiently process the data and retrieve destination information. 【0475】 Next, the server uses map information to plot the visited locations on a map. This process leverages the API of a geographic data provider (e.g., a map API) to visually position the visited locations. This allows the server to analyze the spatial relationships between locations and classify them into regions. 【0476】 Furthermore, the server uses transportation data to optimize the visit route. Real-time transportation data is obtained through traffic information providers (e.g., traffic APIs), and the optimal route using vehicles and public transport is formulated. In this process, a generative AI model is used to generate an efficient route that minimizes the number of left and right turns and waiting at traffic lights. 【0477】 As a concrete example, when a user visits multiple cities in one day, the system provided by the server generates a real-time schedule that reflects the priority of the destinations and the current traffic conditions. Because this schedule is based on the predicted order of visits and travel times, it becomes more efficient. 【0478】 The terminal displays visit route information generated by the server to the user. The terminal's interface clearly displays the order of visits, estimated arrival times, and modes of transportation to aid user understanding. The user proceeds with their visits based on this information and provides feedback via the terminal after each visit. 【0479】 This feedback information is sent to the server and used as data to improve the generation of future visit routes. This allows the system to adapt to user needs, improve accuracy, and continue to provide optimal visit plans. 【0480】 For example, a possible prompt for a generative AI model might be: "Based on a list of multiple destinations in Tokyo and Osaka, please suggest the optimal order of visits, taking into account current traffic conditions." This prompt would prompt the AI ​​model to calculate the most efficient route at that moment and provide it to the user. 【0481】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0482】 Step 1: 【0483】 The server receives a list of planned visits as input and retrieves destination information from the database. Specifically, it uses SQL queries to retrieve the addresses, priorities, and purposes of visits specified by the user from the database. The output provides detailed information for each destination. This information is important data used for subsequent geographic plotting and route generation. 【0484】 Step 2: 【0485】 The server uses the API of a geographic data provider to plot the visited locations on a map based on the acquired destination information. The input is the address data of each visited location, which is converted into geographic data. Specifically, it calculates the latitude and longitude and places pins on the map. The output is a visualization of the visited locations on the map, and by color-coding these locations by area, the basis for efficient visiting routes is formed. 【0486】 Step 3: 【0487】 The server acquires the latest traffic data and generates visiting routes based on that data. This process takes real-time traffic information from a traffic API and calculates the optimal route using a generation AI model. The input is the distance between destinations and the current traffic conditions, and the output is information on the shortest travel time and routes that avoid congestion. Specifically, for cars, it reduces right turns, and when using public transportation, it selects a route that takes transfers and fares into consideration. 【0488】 Step 4: 【0489】 The server performs congestion predictions based on the generated visit route information. This process takes date, time, and event information as input data and analyzes the likelihood of congestion using a generating AI model. The output is recommended visit times and order to avoid congestion. Specifically, it proposes a visit plan that takes into account traffic increases and decreases due to specific events. 【0490】 Step 5: 【0491】 The terminal presents the user with the final information on the visit route generated by the server. This includes displaying the order of visits, estimated arrival times, and recommended modes of transportation obtained from the server. The output information is presented clearly through a specific user interface to aid user understanding. In terms of specific operation, it is designed to be flexible enough to accommodate schedule changes and real-time changes in traffic conditions. 【0492】 Step 6: 【0493】 After completing a visit, users input feedback into a terminal based on actual traffic conditions and visit results. This feedback consists of the user's subjective evaluation and performance data. This results in output that provides hints for improvement and data that influences future route generation. Specifically, this data is automatically sent from the terminal to the server, where it is used to improve the accuracy of the next route generation algorithm. 【0494】 (Application Example 1) 【0495】 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." 【0496】 As the need for delivery efficiency in the logistics industry increases, there is a demand for both optimized delivery routes and flexible route changes based on traffic conditions. However, existing systems do not adequately optimize routes considering real-time traffic conditions, hindering efficient delivery. In addition, there is a lack of mechanisms to incorporate user feedback into future route generation. 【0497】 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. 【0498】 In this invention, the server includes means for acquiring information about the destination, means for visualizing the destination using location information and classifying it by area, means for generating a route including the optimal visit route using movement status information, and means for calculating and displaying an efficient delivery route based on delivery destination information and real-time information. This makes it possible to present the optimal delivery route that takes traffic conditions into account in real time and to reflect user feedback in the next route calculation. 【0499】 "Information about the destination" refers to detailed information about the place you plan to visit, such as its address and name. 【0500】 "Location information" refers to data that indicates the geographical location of a place to visit, and is used to pinpoint a location on a map. 【0501】 "Mobility information" refers to dynamic situational data that includes traffic flow and congestion information. 【0502】 "Visit history" refers to records of places and dates visited in the past, and is data used to improve routes. 【0503】 "User input feedback" refers to evaluation and opinion data provided by system users, which is used to improve the service. 【0504】 "Delivery information" refers to detailed information about the planned delivery location, such as the address and recipient. 【0505】 "Real-time information" refers to data that is updated in real time regarding current traffic conditions and travel status. 【0506】 This invention consists of a system that generates efficient delivery routes in real time, enabling drivers and logistics personnel to follow the optimal path. Embodiments of the invention include the following processes: 【0507】 The server retrieves information about delivery destinations and plots the location of each destination on map data. This enables visualization of delivery destinations and classification by region. The server also collects real-time movement information and uses it to generate the optimal delivery route. Traffic congestion and accident information are taken into consideration in the specific route calculation. 【0508】 The terminal visually displays the optimal delivery route provided by the server on the driver's smartphone. The terminal also dynamically recalculates the route as needed based on real-time information and current travel status, and immediately provides updated information to the driver. This helps to reduce travel time and fuel consumption. 【0509】 After delivery is complete, users provide feedback on the actual travel status and traffic information. This feedback data is used to improve the accuracy of future delivery route generation. 【0510】 The system's software obtains map and traffic information using the Google Maps API, and optimization algorithms are implemented using programming languages ​​such as Python. Frameworks such as React Native are used for displaying the information on the user's device. 【0511】 For example, if a logistics driver needs a route to efficiently deliver to five destinations, the system calculates the optimal route based on current traffic information and displays it on the terminal. Based on this, the driver can deliver using the shortest route. 【0512】 An example of a prompt to input into the generating AI model is: "Please provide the optimal route for a logistics driver to efficiently visit five delivery destinations. Please optimize the route to minimize fuel consumption while taking into account current traffic information and congestion forecasts." 【0513】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0514】 Step 1: 【0515】 The server retrieves the delivery address list from the management system. This input provides address and priority data for each delivery destination. Based on this data, the server prepares to retrieve geographical location information. 【0516】 Step 2: 【0517】 The server uses the Google Maps API to retrieve the location information of delivery destinations and plots it on a map. This plotting of location data visualizes the geographical relationships between delivery destinations. This output serves as the basis for route generation. 【0518】 Step 3: 【0519】 The server collects real-time traffic data. Input data includes traffic information such as current road congestion and accident information. Based on this information, it prepares to calculate the optimal delivery route. 【0520】 Step 4: 【0521】 The server generates the optimal delivery route using a Python algorithm based on acquired location information and traffic conditions. It uses location information and traffic data as input and generates route data as output. The algorithm operates with the aim of minimizing right turns and reducing travel time. 【0522】 Step 5: 【0523】 The server sends the generated optimal route to the terminal. Based on the received route information, the terminal visually displays the route on the delivery driver's smartphone. At this stage, an appropriate map display and estimated arrival time are provided. 【0524】 Step 6: 【0525】 The user performs a delivery and, upon completion, enters feedback into the terminal regarding the travel status and actual changes in traffic. This feedback information is sent to the server as input and is reflected in the next route calculation, so the output is an improvement to the algorithm in the future. 【0526】 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. 【0527】 This invention supports more effective sales activities by combining a visit route generation system with an emotion engine that recognizes the user's emotions. In addition to the conventional automatic visit route creation function, this system aims to reduce user stress and fatigue by dynamically optimizing routes and schedules while considering the user's psychological state. 【0528】 First, the user enters their visit schedule into the system. This includes the address of the destination, priority, and scheduled date and time. Crucially, the terminal acquires the user's emotional state in real time through sensors and input devices. Based on this data, an emotion engine built into the server analyzes the user's emotions and evaluates their stress level and motivation. 【0529】 In addition to conventional route generation procedures, the server optimizes routes by considering the user's emotional state. For example, if a user is tired, it can suggest a route with fewer destinations and less burden. Furthermore, if a user is under high stress, it can select a route that avoids peak hours, making travel between destinations smoother. 【0530】 The generated visit routes and schedules are provided to the user on their device, allowing them to perform their tasks while reducing emotional burden. Furthermore, user feedback is obtained after each visit, and this feedback is incorporated into optimizing future routes, ensuring continuous system improvement. 【0531】 For example, when a sales staff member plans a visit, the server generates a basic route that takes traffic conditions into account, but at the same time, it adjusts the order and timing of visits based on the user's emotional data. This enables flexible and efficient visits tailored to the user's situation. 【0532】 Thus, the present invention is an innovative system that improves the effectiveness of sales activities by incorporating an emotion engine to personalize visit schedules. 【0533】 The following describes the processing flow. 【0534】 Step 1: 【0535】 The user enters their visit schedule into the system. This information includes the address of the destination, the date and time of the visit, and the priority level. The entered data is sent to the server via the terminal. 【0536】 Step 2: 【0537】 The device measures the user's emotional state in real time using sensors (e.g., wearable devices, smartphone cameras). Emotional data includes stress and fatigue levels obtained through facial analysis and pulse measurement. 【0538】 Step 3: 【0539】 The server retrieves destination information and plots the destinations on a map using map data. Geographically close destinations are grouped by area to establish a foundation for efficient visits. 【0540】 Step 4: 【0541】 The server uses external APIs to collect up-to-date traffic information, including road congestion, traffic restrictions, and public transport schedules. 【0542】 Step 5: 【0543】 The server uses an emotion engine to analyze the user's emotional data. Based on this analysis, if the user is in a high-stress state, it re-evaluates the priority of destinations and generates a visit route that reduces the user's psychological and physical burden. 【0544】 Step 6: 【0545】 The server generates the optimal visiting route and order based on traffic information and sentiment analysis. For example, to reduce the burden, it suggests routes that can be traveled in a short time and schedules that reduce the number of visits. 【0546】 Step 7: 【0547】 The device displays the generated visit route and schedule to the user. This information includes the order of visits, travel routes, and estimated arrival times. 【0548】 Step 8: 【0549】 Users complete their visits and provide feedback through the system. This feedback includes information about the outcome of the visit, route evaluation, and stress levels during the visit. 【0550】 Step 9: 【0551】 The server incorporates user feedback and sentiment data to update its algorithms. This allows for more accurate suggestions when generating future visit routes. 【0552】 (Example 2) 【0553】 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." 【0554】 In today's business environment, efficient and effective selection of destinations and optimization of visit routes are crucial when conducting on-site visits. However, conventional technologies struggle to dynamically optimize visit schedules while considering the user's psychological state, potentially causing excessive stress and fatigue. Furthermore, there has been a lack of mechanisms to continuously improve the system by incorporating visit history and feedback into future visit plans. 【0555】 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. 【0556】 In this invention, the server includes means for acquiring basic information about the destination, means for displaying the destination using map data and classifying it by area, means for generating an optimal visit route using movement information, means for analyzing the user's psychological state and dynamically adjusting the visit order considering psychological burden, and means for collecting visit experience and user-provided feedback and utilizing it for route generation in the future. This makes it possible to generate flexible and efficient visit routes and continuously improve the system while reducing the psychological burden on the user. 【0557】 "Basic information about the place to visit" refers to information related to the place to be visited, such as the address, priority, and scheduled date and time. 【0558】 "Map data" refers to a dataset used to visually display geographical information and is used to indicate the location of a place to visit. 【0559】 "Mobility information" refers to data related to travel, including traffic conditions, modes of transport, and travel time. 【0560】 "User's psychological state" refers to the user's current mental and emotional condition, including their current feelings, stress levels, and motivation. 【0561】 "Dynamic adjustment of visit order" refers to the process of changing the order and schedule of visits in order to reduce the psychological burden on the user. 【0562】 "Visit experience" refers to data and insights based on past visit records, which will be used to plan future visits. 【0563】 "User-provided feedback" refers to information such as comments and suggestions for improvement that users provide to the system after visiting it. 【0564】 "Utilizing route generation for future visits" refers to the process of generating more optimized visit routes based on collected visit history and feedback. 【0565】 This invention aims to dynamically optimize visit routes in a visit route generation system, taking into account the user's psychological state. The server utilizes an emotion engine and a generative AI model to process basic information and psychological data received from the user, thereby flexibly adjusting the visit schedule. 【0566】 First, the user enters basic information about their destination (address, priority, scheduled date and time, etc.) through the terminal. The terminal is equipped with sensors that detect the user's mental state in real time and send the information to the server. 【0567】 Next, the server uses a generative AI model to analyze the collected psychological data. This model assesses the user's stress level and motivation, and dynamically optimizes the visit route based on this. It uses travel information and takes into account traffic conditions, visit history, and user feedback to generate the route. 【0568】 The generated visit route is sent to the terminal and provided to the user. This allows the user to conduct visits efficiently with reduced stress. After the visit is completed, the user's feedback is entered into the system and used to optimize the next route, thus continuously improving the system. 【0569】 As a concrete example, when a sales staff member executes a visit plan, the server can combine traffic information and psychological data to generate a route that avoids congestion and adjusts the order of visits. This process allows the sales staff member to enjoy a flexible and appropriate visit plan. 【0570】 An example of a prompt might be, "Optimize the visiting route and suggest relaxation points when the user's stress level is high." This prompt is used by the generative AI model to analyze the user's psychological state and suggest an appropriate visiting route. 【0571】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0572】 Step 1: 【0573】 The user enters basic information about the destination via their device. Specifically, they enter the destination's address, priority, and scheduled date and time on an input screen. This information is saved on the device and prepared for transmission to the server. 【0574】 Step 2: 【0575】 The device uses sensors to acquire the user's emotional state in real time. It collects heart rate and facial expression information from wearable devices and cameras, and sends this data, which indicates stress and motivation, to a server. This emotional data is then analyzed by the server. 【0576】 Step 3: 【0577】 The server receives basic information and emotional data about the visited location as input and analyzes the data using a generative AI model. The model evaluates stress levels and motivation, quantifying the user's psychological state. This process provides the foundational information needed to create optimal visiting routes for each individual user. 【0578】 Step 4: 【0579】 The server optimizes the visit route based on the collected information, taking travel information into consideration. Specifically, it generates a route that reduces travel time by reflecting traffic conditions, adjusts the order of visits, and reduces the psychological burden on the user. The output is the optimized visit route. 【0580】 Step 5: 【0581】 The optimized visit route is sent from the server to the terminal and provided to the user. The user can review this route and schedule on the terminal screen and make adjustments if necessary. This prepares the user to begin their visit activities with an optimized schedule. 【0582】 Step 6: 【0583】 After a visit is completed, the user enters feedback into the terminal. This includes information such as the success of the visit, any challenges, and suggestions for improvement. This feedback is sent to the server as important data for optimizing the next visit route and is used to improve the system. 【0584】 (Application Example 2) 【0585】 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." 【0586】 In optimizing visit schedules, conventional route generation systems only determine the order of visits based on traffic information and visit history, without considering changes in the user's emotional state, leading to the problem of accumulated stress and fatigue. Similarly, in delivery operations, the psychological burden on delivery personnel can affect customer satisfaction. Therefore, flexible route optimization that takes the user's emotional state into account is required. 【0587】 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. 【0588】 In this invention, the server includes means for acquiring destination information, means for plotting destinations using map information and grouping them by area, means for recognizing emotional states and analyzing the data thereof, means for generating an optimal visit route using traffic information, means for dynamically optimizing the visit route according to the user's psychological state based on the analyzed emotional data, and means for collecting visit history and user input feedback and reflecting them in route generation for subsequent visits. This makes it possible to reduce user stress and fatigue and provide an efficient visit schedule with less psychological burden. 【0589】 "Means of obtaining information on places to visit" refers to methods of importing information such as the address and name of the planned place to visit into the system. 【0590】 "A method for plotting destinations using map information and grouping them by area" refers to a method that displays destinations on a map and classifies them into geographically adjacent areas, thereby supporting the setting of efficient visiting routes. 【0591】 "A method for generating the optimal visiting route using traffic information" refers to a method for selecting the most efficient visiting route by considering real-time traffic conditions and forecasts. 【0592】 "Means for recognizing emotional states" refers to means of collecting and recognizing a user's psychological and emotional state through sensors and input devices. 【0593】 "A means of dynamically optimizing visit routes according to the user's psychological state based on analyzed emotional data" refers to a method of reducing the burden on the user by analyzing the user's emotional data and using the results to adjust the order and timing of visits. 【0594】 "Means for collecting visit history and user input feedback and reflecting them in route generation for future visits" refers to methods for collecting past visit records and user opinions and evaluations, and reflecting them in the next visit plan to continuously improve the system. 【0595】 This invention is a system for optimizing visit schedules in a way that reduces the psychological burden on the user. The system mainly consists of a server and terminals. 【0596】 The server retrieves destination information sent from the user's device, plots the destinations using map data, and groups them by area. Next, it generates a basic travel route using real-time traffic information. Crucially, the device uses cameras and sensors to capture the user's emotional state in real time. This information is sent to the server, where an emotion engine analyzes the data to evaluate the user's stress and motivation levels. 【0597】 The server dynamically optimizes the visit route based on the analyzed emotional data. Specifically, if the user is fatigued, it can suggest a route that reduces the number of destinations and alleviates psychological and physical burden. Furthermore, if a high level of stress is detected, the server selects a route that avoids congestion, ensuring a smoother journey. 【0598】 The device provides users with optimized visit routes and schedules, reducing their workload and supporting efficient activities. As a result, users can have a better visit experience while minimizing emotional stress. Users can provide feedback after each visit, which is used to improve future route generation. 【0599】 As a concrete example, when a delivery person visits multiple delivery locations, the system detects their stress level at that time and prioritizes routes with shorter travel distances, thereby improving work efficiency and reducing mental burden. 【0600】 An example of a prompt to input into a generative AI model is: "Based on my current emotional state, please suggest the optimal route to visit. The area to visit is urban, and the emotional state to consider is high levels of fatigue." 【0601】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0602】 Step 1: 【0603】 The terminal receives destination information from the user as input. This includes the destination's address, priority, and scheduled date and time. This data is then prepared for transmission to the geographic information system. 【0604】 Step 2: 【0605】 The device acquires the user's emotional state using a camera and various sensors, and transmits this emotional data to a server in real time. The data collected by the sensors is used to infer the user's psychological state from facial expressions and voice tone. 【0606】 Step 3: 【0607】 The server uses map data to plot destinations based on destination information received from terminals and groups them by area. This process involves calculating geographic coordinates to associate with destinations and grouping them using a clustering algorithm. 【0608】 Step 4: 【0609】 The server takes real-time traffic data as input and generates a basic visit route. It uses a traffic information API to analyze congestion information and passable routes to calculate an efficient route. 【0610】 Step 5: 【0611】 The server uses an emotion engine to analyze the user's emotional data and evaluate their stress level and motivation. The emotional data is then calculated as a numerical stress score using a machine learning model. 【0612】 Step 6: 【0613】 The server uses the analyzed emotional data and the generated base route as input to dynamically optimize the visit route according to the user's psychological state. Specifically, it prioritizes shorter distances when fatigue levels are high, and adjusts the route to avoid congestion when stress levels are high. 【0614】 Step 7: 【0615】 The device provides an optimized visit route and schedule as output. The user then begins their visit according to this and checks the interface displaying the suggested route and schedule. 【0616】 Step 8: 【0617】 After the visit ends, the user enters feedback using a terminal. This feedback is sent to the server and used as a reference for optimizing routes in the future. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 [Fourth Embodiment] 【0622】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0623】 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. 【0624】 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). 【0625】 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. 【0626】 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. 【0627】 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). 【0628】 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. 【0629】 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. 【0630】 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. 【0631】 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. 【0632】 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. 【0633】 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. 【0634】 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". 【0635】 This invention is a system that enables the automatic creation of visit routes and supports efficient sales activities by integrating various data. This system has a function in which a server acquires visit destination information, map information, and traffic information, and automatically generates visit routes based on them. 【0636】 First, the server retrieves destination information from the user's planned visit list. This information includes the destination's address, priority, and purpose of visit. Next, the server uses map information to plot the destinations on a map and groups nearby destinations by area. This forms the basis for efficient visit planning. 【0637】 Next, the server uses the latest traffic information to generate the optimal route. In this process, it considers travel time, distance, and traffic conditions between destinations, minimizing right turns and selecting routes that avoid congestion if traveling by car. If using public transport, it also optimizes the route by taking transfer times and fares into consideration. 【0638】 Furthermore, the visit route incorporates congestion prediction, and the server suggests routes that avoid expected congestion during specific time periods based on the date, time, and event information. This allows users to complete their visits efficiently and safely. In addition, visit history and user feedback information are utilized to improve the accuracy of the algorithm for generating future visit routes. 【0639】 For example, when a sales staff member plans visits in multiple cities, the terminal displays the optimal route provided by the server. This includes the order of visits, estimated arrival times, and necessary transportation. The user then proceeds with the visits according to the plan and can further improve the system's accuracy by providing feedback based on the results and actual traffic conditions. 【0640】 This invention makes it possible to significantly reduce the time and effort required for visit planning, while also improving the accuracy and safety of routes. 【0641】 The following describes the processing flow. 【0642】 Step 1: 【0643】 Users enter a list of planned visits into the system. This includes information such as the address of the destination, priority, date and time, and purpose of the visit. 【0644】 Step 2: 【0645】 The server receives the entered list of scheduled visits and retrieves information about the destinations. This retrieved information is stored in an internal database and used for subsequent processing. 【0646】 Step 3: 【0647】 The server retrieves the latest map information via an external API. This data, including geographic coordinates and road information, is used to plot the location of each destination on the map. 【0648】 Step 4: 【0649】 The server groups the plotted destinations on the map. This is a process that takes into account the geographical proximity of the destinations and groups them into smaller areas. 【0650】 Step 5: 【0651】 The server retrieves real-time traffic information from traffic information providers. This includes road congestion, traffic jam information, and the operating status of public transportation. 【0652】 Step 6: 【0653】 The server generates the optimal route, taking into account the distance between destinations, travel time, and traffic conditions. For those traveling by car, it selects a route that minimizes right turns and avoids congestion. 【0654】 Step 7: 【0655】 The server uses date, time, and event information to predict congestion. Based on the prediction results, it adjusts the order and timing of visits to create an optimal schedule. 【0656】 Step 8: 【0657】 The terminal displays the visit route and schedule provided by the server to the user. It also visualizes the route on a map, clearly indicating the order of visits and travel time. 【0658】 Step 9: 【0659】 After visiting locations, users input feedback into the system regarding their actual visit results and perceived congestion levels. 【0660】 Step 10: 【0661】 The server receives user feedback and updates its model through data analysis. This improves the accuracy of route suggestions for future visits. 【0662】 (Example 1) 【0663】 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". 【0664】 In today's business environment, sales staff and delivery personnel need to create efficient routes. However, traditional methods have made it difficult to quickly generate optimal routes that take into account the priority of destinations and real-time traffic conditions. This results in wasted time and resources, and a loss of economic effectiveness. 【0665】 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. 【0666】 In this invention, the server includes means for acquiring destination information and storing that data, means for visually arranging destinations using geographic data and classifying them by region, and means for formulating optimized visit routes using transportation data. This enables the generation of efficient visit routes in real time based on the latest traffic information and destination priorities, thereby reducing travel time and expenses. 【0667】 "Place information" refers to data about the location the user plans to visit, including address, priority, and purpose of visit. 【0668】 "Geographic data" refers to data used to place visited locations on a map and represent their physical location and distance. 【0669】 "Transportation data" refers to information about travel between destinations, including elements such as travel time, distance, and traffic conditions. 【0670】 A "generative AI model" refers to an algorithm that uses artificial intelligence to generate the optimal visit route in real time from input data. 【0671】 "Route planning" is the process of determining an efficient route to visit a location based on the information that has been gathered. 【0672】 "Real-time" refers to a temporal property where information is processed with virtually no delay after it is generated. 【0673】 To implement this invention, it is necessary to construct a visit route generation system and assume that the server, terminal, and user work together in coordination. The specific form of this system is described below. 【0674】 The server retrieves destination information from the database. This information includes the destination's address, priority, and purpose of visit. The server uses a database management system (DBMS) to efficiently process the data and retrieve destination information. 【0675】 Next, the server uses map information to plot the visited locations on a map. This process leverages the API of a geographic data provider (e.g., a map API) to visually position the visited locations. This allows the server to analyze the spatial relationships between locations and classify them into regions. 【0676】 Furthermore, the server uses transportation data to optimize the visit route. Real-time transportation data is obtained through traffic information providers (e.g., traffic APIs), and the optimal route using vehicles and public transport is formulated. In this process, a generative AI model is used to generate an efficient route that minimizes the number of left and right turns and waiting at traffic lights. 【0677】 As a concrete example, when a user visits multiple cities in one day, the system provided by the server generates a real-time schedule that reflects the priority of the destinations and the current traffic conditions. Because this schedule is based on the predicted order of visits and travel times, it becomes more efficient. 【0678】 The terminal displays visit route information generated by the server to the user. The terminal's interface clearly displays the order of visits, estimated arrival times, and modes of transportation to aid user understanding. The user proceeds with their visits based on this information and provides feedback via the terminal after each visit. 【0679】 This feedback information is sent to the server and used as data to improve the generation of future visit routes. This allows the system to adapt to user needs, improve accuracy, and continue to provide optimal visit plans. 【0680】 For example, a possible prompt for a generative AI model might be: "Based on a list of multiple destinations in Tokyo and Osaka, please suggest the optimal order of visits, taking into account current traffic conditions." This prompt would prompt the AI ​​model to calculate the most efficient route at that moment and provide it to the user. 【0681】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0682】 Step 1: 【0683】 The server receives a list of planned visits as input and retrieves destination information from the database. Specifically, it uses SQL queries to retrieve the addresses, priorities, and purposes of visits specified by the user from the database. The output provides detailed information for each destination. This information is important data used for subsequent geographic plotting and route generation. 【0684】 Step 2: 【0685】 The server uses the API of a geographic data provider to plot the visited locations on a map based on the acquired destination information. The input is the address data of each visited location, which is converted into geographic data. Specifically, it calculates the latitude and longitude and places pins on the map. The output is a visualization of the visited locations on the map, and by color-coding these locations by area, the basis for efficient visiting routes is formed. 【0686】 Step 3: 【0687】 The server acquires the latest traffic data and generates visiting routes based on that data. This process takes real-time traffic information from a traffic API and calculates the optimal route using a generation AI model. The input is the distance between destinations and the current traffic conditions, and the output is information on the shortest travel time and routes that avoid congestion. Specifically, for cars, it reduces right turns, and when using public transportation, it selects a route that takes transfers and fares into consideration. 【0688】 Step 4: 【0689】 The server performs congestion predictions based on the generated visit route information. This process takes date, time, and event information as input data and analyzes the likelihood of congestion using a generating AI model. The output is recommended visit times and order to avoid congestion. Specifically, it proposes a visit plan that takes into account traffic increases and decreases due to specific events. 【0690】 Step 5: 【0691】 The terminal presents the user with the final information on the visit route generated by the server. This includes displaying the order of visits, estimated arrival times, and recommended modes of transportation obtained from the server. The output information is presented clearly through a specific user interface to aid user understanding. In terms of specific operation, it is designed to be flexible enough to accommodate schedule changes and real-time changes in traffic conditions. 【0692】 Step 6: 【0693】 After completing a visit, users input feedback into a terminal based on actual traffic conditions and visit results. This feedback consists of the user's subjective evaluation and performance data. This results in output that provides hints for improvement and data that influences future route generation. Specifically, this data is automatically sent from the terminal to the server, where it is used to improve the accuracy of the next route generation algorithm. 【0694】 (Application Example 1) 【0695】 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". 【0696】 As the need for delivery efficiency in the logistics industry increases, there is a demand for both optimized delivery routes and flexible route changes based on traffic conditions. However, existing systems do not adequately optimize routes considering real-time traffic conditions, hindering efficient delivery. In addition, there is a lack of mechanisms to incorporate user feedback into future route generation. 【0697】 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. 【0698】 In this invention, the server includes means for acquiring information about the destination, means for visualizing the destination using location information and classifying it by area, means for generating a route including the optimal visit route using movement status information, and means for calculating and displaying an efficient delivery route based on delivery destination information and real-time information. This makes it possible to present the optimal delivery route that takes traffic conditions into account in real time and to reflect user feedback in the next route calculation. 【0699】 "Information about the destination" refers to detailed information about the place you plan to visit, such as its address and name. 【0700】 "Location information" refers to data that indicates the geographical location of a place to visit, and is used to pinpoint a location on a map. 【0701】 "Mobility information" refers to dynamic situational data that includes traffic flow and congestion information. 【0702】 "Visit history" refers to records of places and dates visited in the past, and is data used to improve routes. 【0703】 "User input feedback" refers to evaluation and opinion data provided by system users, which is used to improve the service. 【0704】 "Delivery information" refers to detailed information about the planned delivery location, such as the address and recipient. 【0705】 "Real-time information" refers to data that is updated in real time regarding current traffic conditions and travel status. 【0706】 This invention consists of a system that generates efficient delivery routes in real time, enabling drivers and logistics personnel to follow the optimal path. Embodiments of the invention include the following processes: 【0707】 The server retrieves information about delivery destinations and plots the location of each destination on map data. This enables visualization of delivery destinations and classification by region. The server also collects real-time movement information and uses it to generate the optimal delivery route. Traffic congestion and accident information are taken into consideration in the specific route calculation. 【0708】 The terminal visually displays the optimal delivery route provided by the server on the driver's smartphone. The terminal also dynamically recalculates the route as needed based on real-time information and current travel status, and immediately provides updated information to the driver. This helps to reduce travel time and fuel consumption. 【0709】 After delivery is complete, users provide feedback on the actual travel status and traffic information. This feedback data is used to improve the accuracy of future delivery route generation. 【0710】 The system's software obtains map and traffic information using the Google Maps API, and optimization algorithms are implemented using programming languages ​​such as Python. Frameworks such as React Native are used for displaying the information on the user's device. 【0711】 For example, if a logistics driver needs a route to efficiently deliver to five destinations, the system calculates the optimal route based on current traffic information and displays it on the terminal. Based on this, the driver can deliver using the shortest route. 【0712】 An example of a prompt to input into the generating AI model is: "Please provide the optimal route for a logistics driver to efficiently visit five delivery destinations. Please optimize the route to minimize fuel consumption while taking into account current traffic information and congestion forecasts." 【0713】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0714】 Step 1: 【0715】 The server retrieves the delivery address list from the management system. This input provides address and priority data for each delivery destination. Based on this data, the server prepares to retrieve geographical location information. 【0716】 Step 2: 【0717】 The server uses the Google Maps API to retrieve the location information of delivery destinations and plots it on a map. This plotting of location data visualizes the geographical relationships between delivery destinations. This output serves as the basis for route generation. 【0718】 Step 3: 【0719】 The server collects real-time traffic data. Input data includes traffic information such as current road congestion and accident information. Based on this information, it prepares to calculate the optimal delivery route. 【0720】 Step 4: 【0721】 The server generates the optimal delivery route using a Python algorithm based on acquired location information and traffic conditions. It uses location information and traffic data as input and generates route data as output. The algorithm operates with the aim of minimizing right turns and reducing travel time. 【0722】 Step 5: 【0723】 The server sends the generated optimal route to the terminal. Based on the received route information, the terminal visually displays the route on the delivery driver's smartphone. At this stage, an appropriate map display and estimated arrival time are provided. 【0724】 Step 6: 【0725】 The user performs a delivery and, upon completion, enters feedback into the terminal regarding the travel status and actual changes in traffic. This feedback information is sent to the server as input and is reflected in the next route calculation, so the output is an improvement to the algorithm in the future. 【0726】 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. 【0727】 This invention supports more effective sales activities by combining a visit route generation system with an emotion engine that recognizes the user's emotions. In addition to the conventional automatic visit route creation function, this system aims to reduce user stress and fatigue by dynamically optimizing routes and schedules while considering the user's psychological state. 【0728】 First, the user enters their visit schedule into the system. This includes the address of the destination, priority, and scheduled date and time. Crucially, the terminal acquires the user's emotional state in real time through sensors and input devices. Based on this data, an emotion engine built into the server analyzes the user's emotions and evaluates their stress level and motivation. 【0729】 In addition to conventional route generation procedures, the server optimizes routes by considering the user's emotional state. For example, if a user is tired, it can suggest a route with fewer destinations and less burden. Furthermore, if a user is under high stress, it can select a route that avoids peak hours, making travel between destinations smoother. 【0730】 The generated visit routes and schedules are provided to the user on their device, allowing them to perform their tasks while reducing emotional burden. Furthermore, user feedback is obtained after each visit, and this feedback is incorporated into optimizing future routes, ensuring continuous system improvement. 【0731】 For example, when a sales staff member plans a visit, the server generates a basic route that takes traffic conditions into account, but at the same time, it adjusts the order and timing of visits based on the user's emotional data. This enables flexible and efficient visits tailored to the user's situation. 【0732】 Thus, the present invention is an innovative system that improves the effectiveness of sales activities by incorporating an emotion engine to personalize visit schedules. 【0733】 The following describes the processing flow. 【0734】 Step 1: 【0735】 The user enters their visit schedule into the system. This information includes the address of the destination, the date and time of the visit, and the priority level. The entered data is sent to the server via the terminal. 【0736】 Step 2: 【0737】 The device measures the user's emotional state in real time using sensors (e.g., wearable devices, smartphone cameras). Emotional data includes stress and fatigue levels obtained through facial analysis and pulse measurement. 【0738】 Step 3: 【0739】 The server retrieves destination information and plots the destinations on a map using map data. Geographically close destinations are grouped by area to establish a foundation for efficient visits. 【0740】 Step 4: 【0741】 The server uses external APIs to collect up-to-date traffic information, including road congestion, traffic restrictions, and public transport schedules. 【0742】 Step 5: 【0743】 The server uses an emotion engine to analyze the user's emotional data. Based on this analysis, if the user is in a high-stress state, it re-evaluates the priority of destinations and generates a visit route that reduces the user's psychological and physical burden. 【0744】 Step 6: 【0745】 The server generates the optimal visiting route and order based on traffic information and sentiment analysis. For example, to reduce the burden, it suggests routes that can be traveled in a short time and schedules that reduce the number of visits. 【0746】 Step 7: 【0747】 The device displays the generated visit route and schedule to the user. This information includes the order of visits, travel routes, and estimated arrival times. 【0748】 Step 8: 【0749】 Users complete their visits and provide feedback through the system. This feedback includes information about the outcome of the visit, route evaluation, and stress levels during the visit. 【0750】 Step 9: 【0751】 The server incorporates user feedback and sentiment data to update its algorithms. This allows for more accurate suggestions when generating future visit routes. 【0752】 (Example 2) 【0753】 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". 【0754】 In today's business environment, efficient and effective selection of destinations and optimization of visit routes are crucial when conducting on-site visits. However, conventional technologies struggle to dynamically optimize visit schedules while considering the user's psychological state, potentially causing excessive stress and fatigue. Furthermore, there has been a lack of mechanisms to continuously improve the system by incorporating visit history and feedback into future visit plans. 【0755】 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. 【0756】 In this invention, the server includes means for acquiring basic information about the destination, means for displaying the destination using map data and classifying it by area, means for generating an optimal visit route using movement information, means for analyzing the user's psychological state and dynamically adjusting the visit order considering psychological burden, and means for collecting visit experience and user-provided feedback and utilizing it for route generation in the future. This makes it possible to generate flexible and efficient visit routes and continuously improve the system while reducing the psychological burden on the user. 【0757】 "Basic information about the place to visit" refers to information related to the place to be visited, such as the address, priority, and scheduled date and time. 【0758】 "Map data" refers to a dataset used to visually display geographical information and is used to indicate the location of a place to visit. 【0759】 "Mobility information" refers to data related to travel, including traffic conditions, modes of transport, and travel time. 【0760】 "User's psychological state" refers to the user's current mental and emotional condition, including their current feelings, stress levels, and motivation. 【0761】 "Dynamic adjustment of visit order" refers to the process of changing the order and schedule of visits in order to reduce the psychological burden on the user. 【0762】 "Visit experience" refers to data and insights based on past visit records, which will be used to plan future visits. 【0763】 "User-provided feedback" refers to information such as comments and suggestions for improvement that users provide to the system after visiting it. 【0764】 "Utilizing route generation for future visits" refers to the process of generating more optimized visit routes based on collected visit history and feedback. 【0765】 This invention aims to dynamically optimize visit routes in a visit route generation system, taking into account the user's psychological state. The server utilizes an emotion engine and a generative AI model to process basic information and psychological data received from the user, thereby flexibly adjusting the visit schedule. 【0766】 First, the user enters basic information about their destination (address, priority, scheduled date and time, etc.) through the terminal. The terminal is equipped with sensors that detect the user's mental state in real time and send the information to the server. 【0767】 Next, the server uses a generative AI model to analyze the collected psychological data. This model assesses the user's stress level and motivation, and dynamically optimizes the visit route based on this. It uses travel information and takes into account traffic conditions, visit history, and user feedback to generate the route. 【0768】 The generated visit route is sent to the terminal and provided to the user. This allows the user to conduct visits efficiently with reduced stress. After the visit is completed, the user's feedback is entered into the system and used to optimize the next route, thus continuously improving the system. 【0769】 As a concrete example, when a sales staff member executes a visit plan, the server can combine traffic information and psychological data to generate a route that avoids congestion and adjusts the order of visits. This process allows the sales staff member to enjoy a flexible and appropriate visit plan. 【0770】 An example of a prompt might be, "Optimize the visiting route and suggest relaxation points when the user's stress level is high." This prompt is used by the generative AI model to analyze the user's psychological state and suggest an appropriate visiting route. 【0771】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0772】 Step 1: 【0773】 The user enters basic information about the destination via their device. Specifically, they enter the destination's address, priority, and scheduled date and time on an input screen. This information is saved on the device and prepared for transmission to the server. 【0774】 Step 2: 【0775】 The device uses sensors to acquire the user's emotional state in real time. It collects heart rate and facial expression information from wearable devices and cameras, and sends this data, which indicates stress and motivation, to a server. This emotional data is then analyzed by the server. 【0776】 Step 3: 【0777】 The server receives basic information and emotional data about the visited location as input and analyzes the data using a generative AI model. The model evaluates stress levels and motivation, quantifying the user's psychological state. This process provides the foundational information needed to create optimal visiting routes for each individual user. 【0778】 Step 4: 【0779】 The server optimizes the visit route based on the collected information, taking travel information into consideration. Specifically, it generates a route that reduces travel time by reflecting traffic conditions, adjusts the order of visits, and reduces the psychological burden on the user. The output is the optimized visit route. 【0780】 Step 5: 【0781】 The optimized visit route is sent from the server to the terminal and provided to the user. The user can review this route and schedule on the terminal screen and make adjustments if necessary. This prepares the user to begin their visit activities with an optimized schedule. 【0782】 Step 6: 【0783】 After a visit is completed, the user enters feedback into the terminal. This includes information such as the success of the visit, any challenges, and suggestions for improvement. This feedback is sent to the server as important data for optimizing the next visit route and is used to improve the system. 【0784】 (Application Example 2) 【0785】 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". 【0786】 In optimizing visit schedules, conventional route generation systems only determine the order of visits based on traffic information and visit history, without considering changes in the user's emotional state, leading to the problem of accumulated stress and fatigue. Similarly, in delivery operations, the psychological burden on delivery personnel can affect customer satisfaction. Therefore, flexible route optimization that takes the user's emotional state into account is required. 【0787】 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. 【0788】 In this invention, the server includes means for acquiring destination information, means for plotting destinations using map information and grouping them by area, means for recognizing emotional states and analyzing the data thereof, means for generating an optimal visit route using traffic information, means for dynamically optimizing the visit route according to the user's psychological state based on the analyzed emotional data, and means for collecting visit history and user input feedback and reflecting them in route generation for subsequent visits. This makes it possible to reduce user stress and fatigue and provide an efficient visit schedule with less psychological burden. 【0789】 "Means of obtaining information on places to visit" refers to methods of importing information such as the address and name of the planned place to visit into the system. 【0790】 "A method for plotting destinations using map information and grouping them by area" refers to a method that displays destinations on a map and classifies them into geographically adjacent areas, thereby supporting the setting of efficient visiting routes. 【0791】 "A method for generating the optimal visiting route using traffic information" refers to a method for selecting the most efficient visiting route by considering real-time traffic conditions and forecasts. 【0792】 "Means for recognizing emotional states" refers to means of collecting and recognizing a user's psychological and emotional state through sensors and input devices. 【0793】 "A means of dynamically optimizing visit routes according to the user's psychological state based on analyzed emotional data" refers to a method of reducing the burden on the user by analyzing the user's emotional data and using the results to adjust the order and timing of visits. 【0794】 "Means for collecting visit history and user input feedback and reflecting them in route generation for future visits" refers to methods for collecting past visit records and user opinions and evaluations, and reflecting them in the next visit plan to continuously improve the system. 【0795】 This invention is a system for optimizing visit schedules in a way that reduces the psychological burden on the user. The system mainly consists of a server and terminals. 【0796】 The server retrieves destination information sent from the user's device, plots the destinations using map data, and groups them by area. Next, it generates a basic travel route using real-time traffic information. Crucially, the device uses cameras and sensors to capture the user's emotional state in real time. This information is sent to the server, where an emotion engine analyzes the data to evaluate the user's stress and motivation levels. 【0797】 The server dynamically optimizes the visit route based on the analyzed emotional data. Specifically, if the user is fatigued, it can suggest a route that reduces the number of destinations and alleviates psychological and physical burden. Furthermore, if a high level of stress is detected, the server selects a route that avoids congestion, ensuring a smoother journey. 【0798】 The device provides users with optimized visit routes and schedules, reducing their workload and supporting efficient activities. As a result, users can have a better visit experience while minimizing emotional stress. Users can provide feedback after each visit, which is used to improve future route generation. 【0799】 As a concrete example, when a delivery person visits multiple delivery locations, the system detects their stress level at that time and prioritizes routes with shorter travel distances, thereby improving work efficiency and reducing mental burden. 【0800】 An example of a prompt to input into a generative AI model is: "Based on my current emotional state, please suggest the optimal route to visit. The area to visit is urban, and the emotional state to consider is high levels of fatigue." 【0801】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0802】 Step 1: 【0803】 The terminal receives destination information from the user as input. This includes the destination's address, priority, and scheduled date and time. This data is then prepared for transmission to the geographic information system. 【0804】 Step 2: 【0805】 The device acquires the user's emotional state using a camera and various sensors, and transmits this emotional data to a server in real time. The data collected by the sensors is used to infer the user's psychological state from facial expressions and voice tone. 【0806】 Step 3: 【0807】 The server uses map data to plot destinations based on destination information received from terminals and groups them by area. This process involves calculating geographic coordinates to associate with destinations and grouping them using a clustering algorithm. 【0808】 Step 4: 【0809】 The server takes real-time traffic data as input and generates a basic visit route. It uses a traffic information API to analyze congestion information and passable routes to calculate an efficient route. 【0810】 Step 5: 【0811】 The server uses an emotion engine to analyze the user's emotional data and evaluate their stress level and motivation. The emotional data is then calculated as a numerical stress score using a machine learning model. 【0812】 Step 6: 【0813】 The server uses the analyzed emotional data and the generated base route as input to dynamically optimize the visit route according to the user's psychological state. Specifically, it prioritizes shorter distances when fatigue levels are high, and adjusts the route to avoid congestion when stress levels are high. 【0814】 Step 7: 【0815】 The device provides an optimized visit route and schedule as output. The user then begins their visit according to this and checks the interface displaying the suggested route and schedule. 【0816】 Step 8: 【0817】 After the visit ends, the user enters feedback using a terminal. This feedback is sent to the server and used as a reference for optimizing routes in the future. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 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." 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 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. 【0831】 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. 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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. 【0836】 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. 【0837】 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. 【0838】 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. 【0839】 The following is further disclosed regarding the embodiments described above. 【0840】 (Claim 1) 【0841】 Means of obtaining information about the destination, 【0842】 A method for plotting destinations using map information and grouping them by area, 【0843】 A method for generating the optimal visiting route using traffic information, 【0844】 A method for predicting congestion based on the visit route and optimizing the order of visits, 【0845】 A means of collecting visit history and user input feedback and reflecting it in route generation for future visits, 【0846】 A visit route generation system that includes this feature. 【0847】 (Claim 2) 【0848】 The visit route generation system according to claim 1, further comprising means for determining the order of visits based on the priority of the destinations to be visited. 【0849】 (Claim 3) 【0850】 The visit route generation system according to claim 1, further comprising means for providing a route that minimizes right turns and waiting at traffic lights depending on the choice of mode of transport. 【0851】 "Example 1" 【0852】 (Claim 1) 【0853】 A means of obtaining information about visited locations and storing that data, 【0854】 A method for visually arranging visited locations using geographic data and classifying them by region, 【0855】 A means of formulating optimized visit routes using transportation data, 【0856】 A means of predicting congestion based on route information and optimizing the order of visits, 【0857】 A means of accumulating visit history and user input information and utilizing it for future route planning, 【0858】 A method for proposing routes that take traffic conditions into account in real time using a generative AI model, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, further comprising means for optimizing the order of visits according to the importance of the destinations. 【0862】 (Claim 3) 【0863】 The system according to claim 1, further comprising means for providing a route that minimizes changes of direction and waiting times based on the selection of a transport method. 【0864】 "Application Example 1" 【0865】 (Claim 1) 【0866】 Means of obtaining information about the destination, 【0867】 A method for visualizing visited locations using location information and classifying them by area, 【0868】 A means for generating a route including the optimal visit route using travel status information, 【0869】 A means for predicting congestion and optimizing the order of visits based on the visit route, 【0870】 A means of collecting visit history and user input feedback and reflecting it in future route generation, 【0871】 A means of calculating and displaying an efficient delivery route based on delivery destination information and real-time information, 【0872】 A system that includes this. 【0873】 (Claim 2) 【0874】 The system according to claim 1, further comprising means for determining the order of visits based on the priority of the destinations to be visited. 【0875】 (Claim 3) 【0876】 The system according to claim 1, further comprising means for providing a route that minimizes changes of direction and waiting at traffic lights depending on the choice of means of transport. 【0877】 "Example 2 of combining an emotion engine" 【0878】 (Claim 1) 【0879】 Means of obtaining basic information about the place to visit, 【0880】 A method for displaying destinations using map data and classifying them by region, 【0881】 A means of generating the optimal visit route using travel information, 【0882】 A means to analyze the user's psychological state and dynamically adjust the order of visits, taking into account their psychological burden, 【0883】 A means of collecting visit experiences and user-provided feedback and using it to generate routes for future visits, 【0884】 A system that includes this. 【0885】 (Claim 2) 【0886】 The system according to claim 1, further comprising means for determining the order of visits based on the importance of the destinations. 【0887】 (Claim 3) 【0888】 The system according to claim 1, further comprising means for providing a route that minimizes turns and traffic light waits depending on the choice of means of transport. 【0889】 "Application example 2 when combining with an emotional engine" 【0890】 (Claim 1) 【0891】 Means of obtaining information about the destination, 【0892】 A method for plotting destinations using map information and grouping them by area, 【0893】 A method for generating the optimal visiting route using traffic information, 【0894】 Means for recognizing emotional states and means for analyzing that data, 【0895】 A means to dynamically optimize the visit route according to the user's psychological state based on the analyzed emotional data, 【0896】 A means of collecting visit history and user input feedback and reflecting it in route generation for future visits, 【0897】 A system that includes this. 【0898】 (Claim 2) 【0899】 The system according to claim 1, further comprising means for determining the order of visits based on the priority of the destinations to be visited. 【0900】 (Claim 3) 【0901】 The system according to claim 1, further comprising means for providing a route that minimizes right turns and waiting at traffic lights depending on the choice of mode of transport. [Explanation of symbols] 【0902】 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

[Claim 1] Means of obtaining information about the destination, A method for plotting destinations using map information and grouping them by area, A method for generating the optimal visiting route using traffic information, A method for predicting congestion based on the visit route and optimizing the order of visits, A means of collecting visit history and user input feedback and reflecting it in route generation for future visits, A visit route generation system that includes this feature. [Claim 2] The visit route generation system according to claim 1, further comprising means for determining the order of visits based on the priority of the destinations to be visited. [Claim 3] The visit route generation system according to claim 1, further comprising means for providing a route that minimizes right turns and waiting at traffic lights depending on the choice of mode of transport.

Images