system

Abstract

We provide the system. [Solution] A means of acquiring geographic information and plotting multiple visited locations on a map, A means for grouping geographically close destinations and calculating the optimal order of visits, A method for optimizing visiting routes by analyzing past travel history and real-time traffic data, A means of notifying users of optimized visit routes and suggesting schedules, A means of calculating and visualizing the shortest route between destination locations using an information device, A system that includes means for dynamically changing delivery routes in real time according to traffic conditions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The creation of visit routes is the basis of sales activities, but conventionally, there has been a problem that it takes a great deal of time and effort to construct them manually. Therefore, route optimization has been slow, and it has sometimes been impossible to quickly respond to changes in traffic conditions. In addition, there is a lack of flexibility in dealing with different means of transportation and visit frequencies, and an improvement in the efficiency of sales activities has been demanded.

Means for Solving the Problems

[0005] This invention provides a means for acquiring geographic information and plotting visited locations on a map. Next, it includes means for grouping geographically close visited locations and calculating the optimal visiting order. Furthermore, it includes means for analyzing past travel history and real-time traffic data to optimize the visiting route. The invention also includes means for notifying the user of the optimized visiting route and providing schedule suggestions, thereby solving the aforementioned problems. This system enables the rapid and efficient automatic creation of visiting routes and can flexibly accommodate different modes of transportation and visit frequencies.

[0006] "Geographic information" refers to data related to locations on a map, such as visited places and road information.

[0007] "Places to visit" refers to multiple specific geographical locations that the user should visit.

[0008] "Plotting on a map" refers to the process of visually placing visited locations on a map based on geographical information.

[0009] "Grouping" refers to consolidating visiting locations based on geographical proximity or means of transportation.

[0010] "Visit order" refers to the sequence of locations a user should visit, and is determined with efficient travel in mind.

[0011] "Travel history" refers to data on past visits and is used to optimize visit routes.

[0012] "Real-time traffic data" refers to information that shows current traffic conditions, including fluctuations such as congestion and traffic restrictions.

[0013] "Notification" refers to the act of communicating calculated information to the user.

[0014] "Schedule suggestions" refer to providing users with recommendations on when and where they should go, based on optimized travel routes.

Brief Description of the Drawings

[0015] [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 apparatus 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 apparatus 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 apparatus 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 apparatus 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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

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

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

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

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] One embodiment of the present invention is a visit route creation system for supporting sales activities. This system mainly consists of a server, terminals, and users.

[0037] Description of the server process

[0038] The server retrieves geographical information, customer information, historical travel history data, and real-time traffic data from data sources and integrates them into a database. Based on travel history, the server creates a profile to determine visit frequency and effective visit order. Next, the server plots the planned visit locations on a map and groups them based on geographical proximity. The server calculates the optimal visit order from the grouped locations, optimizing the travel route based on priority conditions that take traffic conditions into account (e.g., reducing the number of right turns). The server then sends the optimized visit route information to the terminal.

[0039] Description of terminal processes

[0040] The terminal displays the user with the visit route information received from the server. Furthermore, the terminal provides a schedule suggestion regarding planned travel times and visit order, and offers an interface that allows the user to make adjustments. In addition, the terminal receives traffic data from the server in real time while traveling, recalculates the visit route as needed, and informs the user of the updated route. In this way, it can respond in real time to changes in traffic conditions while traveling.

[0041] Explanation from the user's perspective

[0042] The user enters a list of customer addresses to be visited into the terminal interface and starts the system. The system automatically calculates the visit route and displays the estimated travel time and the order of visits. The user can refer to the presented schedule and adjust visit times as needed. After the visit is completed, the system improves the accuracy of route optimization for future visits by providing feedback on the terminal.

[0043] Thus, this system allows sales representatives to plan visit routes efficiently and quickly, and enables real-time responses, thereby improving the overall efficiency of sales activities. For example, even when there are dozens of locations to visit, the system can propose the optimal route in a short time, supporting swift and effective sales activities.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects and integrates geographical information, customer information, historical travel history, and real-time traffic data from external data sources and internal databases. It regularly updates this information stored in the database to keep it up-to-date.

[0047] Step 2:

[0048] The user enters a list of customer addresses to be visited into the terminal. The terminal then sends the entered address information to the server.

[0049] Step 3:

[0050] The server plots the received address data on a map and identifies geographically close locations to visit. Based on the plotting results, the locations are grouped together.

[0051] Step 4:

[0052] The server calculates the order of visits based on grouped locations. It considers travel time and traffic conditions and applies priority criteria (e.g., reducing the number of right turns, shortest distance) to select the most appropriate route.

[0053] Step 5:

[0054] The server sends the calculated visit order and route information to the terminal. The terminal displays the received information to the user and provides the user with an overview of the visit plan.

[0055] Step 6:

[0056] The user reviews the presented visit plan and adjusts the scheduled times and order of visits as needed. The terminal records the user's changes and sends the updated information to the server.

[0057] Step 7:

[0058] The terminal uses real-time traffic data during visits and provides users with alerts based on changes in traffic conditions. The server recalculates the route as needed and sends the changes to the terminal.

[0059] Step 8:

[0060] After the visit ends, the user enters feedback via a terminal. The terminal sends the feedback to a server, which collects data for future route optimization.

[0061] Through this process, the system provides users with efficient visit routes and continuously improves the service through schedule adjustments and feedback.

[0062] (Example 1)

[0063] 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."

[0064] The present invention aims to improve the efficiency of customer visits and travel in sales activities and fieldwork. In particular, in situations where there are multiple visit locations, there is a need to generate and modify optimal visit routes using past visit history and real-time traffic information. However, conventional methods do not fully utilize this data, resulting in challenges such as difficulty in optimizing routes and responding flexibly.

[0065] 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.

[0066] In this invention, the server includes means for acquiring spatial information and visualizing multiple visit locations, means for classifying spatially adjacent visit locations and calculating the optimal visit order, and means for analyzing past travel history and dynamic traffic data and adjusting the visit route. This enables efficient and flexible optimization of visit routes.

[0067] "Spatial information" refers to data that includes geographical elements and represents information related to a specific location or area.

[0068] A "visiting location" refers to a specific geographical place that a person needs to visit for business or service provision purposes.

[0069] "Visualization" refers to the process of representing data and information visually to make them easier to understand.

[0070] "Classification" is the process of grouping data or information based on certain criteria or attributes.

[0071] "Optimal visiting order" refers to the most rational sequence for efficiently visiting locations.

[0072] "Movement history" refers to information that records the routes and frequency of movement of people and objects in the past.

[0073] "Dynamic traffic data" refers to data that reflects the current traffic situation in real time.

[0074] "Visiting route" refers to a specific travel route set up to visit multiple locations.

[0075] "Adjustment" refers to the act of changing data or plans according to the purpose or conditions.

[0076] The embodiment for carrying out the present invention relates to a visit route creation system aimed at improving the efficiency of sales activities. This system mainly consists of three components: a server, a terminal, and a user.

[0077] The server uses geographic information systems and customer management software to acquire spatial and customer information. Furthermore, it employs a client-server model to analyze past travel history and dynamic traffic data, integrating the data into a dedicated database. For analysis, it utilizes real-time traffic information services such as Google Maps API. Based on this data, the server calculates the optimal route for the user and sends it to their terminal.

[0078] The terminal receives data from the server and visualizes the visit route using a dedicated application. When the user enters the desired locations to visit, the terminal displays them and provides an interface that allows them to adjust the order of visits and travel times as needed. The terminal also incorporates real-time updated traffic information and recalculates the route during travel to provide the latest visit order.

[0079] Users can input customer address lists into this system and adjust their schedules while referring to automatically calculated visit order and estimated time. After each visit, user feedback is sent to the server and used to improve the accuracy of future visit routes.

[0080] For example, a salesperson with multiple destinations who wants to efficiently plan their route can use this system to obtain the optimal route in a short amount of time. Another example of a prompt when using the AI ​​generation model is "Enter a list of destinations to suggest the optimal sales route." In this way, users can effectively utilize their time and resources, enabling them to conduct sales activities more efficiently.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server retrieves spatial and customer information from geographic information systems and customer management software. Inputs include specified geographic regions and specific customer lists. The server stores this data in a database and integrates it while maintaining information consistency, preparing it for subsequent processing.

[0084] Step 2:

[0085] The server analyzes past travel history and dynamic traffic data. It receives past travel patterns and real-time traffic information as input. The server processes this data through an analysis algorithm, calculating the frequency of visits to specific locations and the optimal order of visits to create an efficient profile.

[0086] Step 3:

[0087] The server optimizes the visit route based on the user's profile. The inputs include the geographical coordinates and priority of each visit location, as well as traffic data. The server calculates the optimal route, considering factors such as reducing the number of right turns at intersections, and sends the result to the user's terminal.

[0088] Step 4:

[0089] The terminal displays visit route information received from the server on its interface. Its inputs include the optimized visit order and estimated duration sent from the server. The terminal visualizes this information and provides an interface for the user to adjust the order and timing of visits.

[0090] Step 5:

[0091] The user enters the desired locations into the terminal and adjusts the schedule as needed. During this process, the user refers to the suggested visit order and estimated time, confirms the most efficient route, and approves it.

[0092] Step 6:

[0093] While on the move, the device continuously receives real-time traffic information from the server and recalculates the route as needed. The device flexibly adapts to the situation by recalculating the route based on changes in traffic conditions and informing the user of the updated route.

[0094] Step 7:

[0095] Users enter feedback on their device after their visit ends. The entered feedback data is sent to the server and used to optimize future visit routes. The server uses this data to leverage a generative AI model to continuously improve the accuracy of the routes.

[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] In modern logistics, designing efficient delivery routes is a critical challenge that directly impacts an organization's time and cost savings. However, many current systems are unable to flexibly respond to traffic conditions and real-time changes, and often fail to quickly resolve complex problems. This makes it difficult to maximize delivery efficiency and execute optimal delivery plans.

[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 geographic information and plotting multiple destinations on a map, means for grouping geographically close destinations and calculating the optimal order of visits, and means for analyzing past travel history and real-time traffic data to optimize the visit route. This enables the dynamic modification of the delivery route in real time according to traffic conditions, allowing for the execution of an efficient and flexible delivery plan.

[0101] "Geographic information" refers to data that shows location information and characteristics of specific points on Earth.

[0102] A "visiting location" refers to a place visited for the purpose of carrying out a specific task, such as delivery or sales activities.

[0103] "Plotting on a map" means using geographical information to visually represent visited locations on a map.

[0104] "Visiting order" refers to a plan that determines the order in which to visit locations in order to carry out tasks efficiently.

[0105] "Movement history" refers to data that shows changes in location and routes in the past.

[0106] "Real-time traffic data" refers to information about current traffic conditions that is updated instantly.

[0107] "Optimizing a visit route" means calculating the best possible path to visit locations, while adhering to the minimum time, distance, and other constraints.

[0108] "Notifying the user" means communicating information to the user based on calculation and analysis results.

[0109] "Schedule proposals" refer to providing users with a plan to help them carry out their activities efficiently.

[0110] "Information equipment" refers to electronic devices that process digital data, and generally includes computers.

[0111] The "shortest route" is the path chosen to minimize distance and travel time when visiting different locations.

[0112] "Visualization" refers to the process of representing data and results graphically, making them easy to understand visually.

[0113] "To change dynamically" means to make corrections or adjustments on the fly depending on the conditions or circumstances.

[0114] To implement this invention, three main components are used: a server, a terminal, and a user.

[0115] The server first integrates geographical information and customer visit location information into a single database. This information is used to optimize the order of visits and improve efficiency based on past travel history. The server also has the function to analyze real-time traffic data and optimize visit routes. Programming languages ​​such as Python and JavaScript (registered trademark) can be used for this data processing. The server sends the calculated optimal visit route to the terminal and presents it to the user.

[0116] The terminal visually displays the visit route to the user based on information received from the server. The terminal uses common information devices such as smartphones and tablets. Its interface is intuitive and easy to use, and it includes functions to allow the user to check and adjust their visit schedule. Furthermore, it receives real-time updated traffic information from the server during the visit and suggests dynamic changes to the visit route as needed.

[0117] Users can utilize this visit route creation system by accessing the system and registering their destinations. This allows for efficient travel based on routes suggested by information devices. After the user completes their visit, feedback data is entered, enabling the server to perform machine learning to improve the accuracy of future route optimizations.

[0118] This system assists in efficiently visiting multiple locations, contributing to increased efficiency in logistics centers and sales activities. For example, in a pizza delivery service, it can suggest routes that allow drivers to reach multiple delivery destinations in the shortest possible time.

[0119] As an example of a prompt, you could instruct the system with a message like, "When visiting multiple delivery locations, please create an app that uses real-time traffic information to show the most efficient order of visits."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server retrieves and integrates geographical information, customer visit location information, and past travel history into its database. This information includes latitude and longitude, arrival time, and visit frequency for each visit location, and is used for subsequent data analysis. The server uses a database management system to efficiently store and manage this information.

[0123] Step 2:

[0124] The server receives real-time traffic data from external traffic information services. This data includes current road conditions and predicted congestion information. The server periodically updates this data using APIs via a program. The server then combines past travel history with real-time traffic data to calculate the shortest route between destinations and determine the optimal order of visits. This optimizes the visit route.

[0125] Step 3:

[0126] The server sends the calculated optimal route to the terminal. The server formats the information in a data structure such as JSON and delivers it to the terminal quickly and accurately via a communication protocol. This allows the user to easily view the route details.

[0127] Step 4:

[0128] The terminal visualizes and displays the visit route received by the user on the screen. Using a map service application, the terminal provides information including the estimated arrival time at each destination along the route. The user can refer to this information and adjust their visit schedule as needed.

[0129] Step 5:

[0130] After completing their visit, users provide feedback through an interface on their device. Users record their actual experience, including visit duration and traffic conditions, and this data is then sent back to the server.

[0131] Step 6:

[0132] The server analyzes feedback data sent by the user and uses machine learning algorithms to improve the accuracy of route optimization for future visits. The server utilizes a generative AI model to better reflect user needs and conditions based on the prompt text for creating visit routes.

[0133] 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.

[0134] This invention combines an emotion engine with a visit route optimization system, enabling efficient and less stressful sales activities by flexibly adjusting visit plans based on the user's emotions. The system primarily consists of a server, terminals, and an emotion engine.

[0135] Description of the server process

[0136] The server acquires geographical information, customer information, historical travel history data, and real-time traffic data, and centrally manages them in a database. Using this data, it plots visited locations, performs geographical grouping, and optimizes the order of visits. Furthermore, it sets priority conditions according to the mode of transport and calculates efficient visiting routes. The server sends the visit plan to the terminal and also receives input from the emotion engine to re-evaluate the route and schedule.

[0137] Description of terminal processes

[0138] The terminal receives the visit route and user sentiment data transmitted from the server and displays it to the user. Based on the sentiment data, which is analyzed in real time by the sentiment engine, the terminal dynamically adjusts the order of visits and rest plans. It also provides a function to monitor real-time traffic data while the user is traveling and correct the route as needed.

[0139] Explanation of the Emotion Engine

[0140] The emotion engine acquires and analyzes user emotion data from the device's sensors and external devices, and sends the results to the server. It understands the user's stress levels and fluctuations in comfort, and uses this information to readjust or suggest visit routes. For example, if a user is feeling stressed, the schedule might be restructured to reduce the number of visits or to include breaks.

[0141] Explanation of specific examples

[0142] As a concrete example, if the emotion engine detects that a user is fatigued while visiting multiple clients, the device will use this information to revise the visit schedule and insert short breaks between visits. Furthermore, if it determines that the user is experiencing high stress levels, it will change the order of visits and suggest a less stressful route. In this way, users can continue their visiting activities efficiently and comfortably.

[0143] This system aims not only to optimize visit routes but also to provide flexible visit plans that take into account user emotions. Therefore, it can improve the efficiency of sales activities and increase user satisfaction.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The server retrieves the necessary geographic and customer information from the geographic information system and customer database. This generates an initial list of planned visit locations.

[0147] Step 2:

[0148] The server plots visited locations on a map using past travel history and real-time traffic data, and groups geographically close locations. It then calculates the optimal order of visits and creates an initial route.

[0149] Step 3:

[0150] The user views a list of customers they wish to visit via their device and makes adjustments as needed. The device then sends information to the server based on the user's input.

[0151] Step 4:

[0152] The emotion engine collects user emotional data in real time through sensors built into the device and external devices. This data indicates the user's stress level, fatigue level, and other emotional states.

[0153] Step 5:

[0154] The emotion engine analyzes the collected user emotion data and sends the result of the emotion state to the server. Based on this result, the server recalculates the visit order and break insertions as needed.

[0155] Step 6:

[0156] The terminal notifies the user of updated visit routes from the server and schedule adjustments based on their mood. The user can review the presented plan and give final approval.

[0157] Step 7:

[0158] While the visit is underway, the terminal monitors real-time traffic data and adjusts the route as needed, reflecting continuous sentiment analysis by the emotion engine.

[0159] Step 8:

[0160] After a visit, the user provides brief feedback on their device, and the emotion engine uses this feedback as training data. The server stores this feedback to use for route optimization in the future.

[0161] This process allows the system to streamline user visits and provide a comfortable sales experience by addressing emotional situations.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] In modern sales activities, optimizing visit routes is crucial, but the associated user stress and emotional burden are often overlooked. Traditional systems only optimize based on geographical and traffic information, failing to respond flexibly to the user's emotional state. As a result, it becomes difficult to support users' efficient sales activities, potentially leading to decreased user satisfaction and sales results.

[0165] 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.

[0166] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's biometric data and analyzing their emotional state. This makes it possible to re-evaluate and adjust the visit route and schedule based on the user's emotional state.

[0167] "Geographic information" is a general term for data that includes information about location, topography, and facilities related to a specific place.

[0168] A "place to visit" is location information that indicates a specific place the user should visit.

[0169] "Grouping" is the process of combining multiple objects into a single set based on common attributes or distance.

[0170] The "order of visits" refers to the sequence in which multiple locations are arranged to allow for efficient travel.

[0171] "Past travel history" refers to recorded data about routes the user has traveled or places they have visited in the past.

[0172] "Real-time traffic information" refers to dynamic data that shows the current traffic situation, including information on congestion and accidents.

[0173] "Biometric data" refers to data that indicates the user's physical condition, including heart rate, body temperature, and stress level measurements.

[0174] "Emotional state" refers to the user's psychological state, determined based on biometric data and subjective self-reporting.

[0175] "Re-evaluation" is the process of reviewing and updating the results of an evaluation that has already been conducted.

[0176] "Schedule suggestions" are recommendations for scheduling and time management to support users' efficient activities.

[0177] This invention combines a visit route optimization system with an emotion engine to support efficient and stress-free sales activities for users. The specific implementation of this system is described below.

[0178] The server first obtains geographic information from a Geographic Information System (GIS) and stores it in a database. Next, it receives customer information from a Customer Relationship Management (CRM) system and obtains past travel history and real-time traffic information via an API. Based on this data, it plots visited locations on a map and groups geographically close locations. Then, to optimize the order of visits, it uses algorithms such as the Travel Salesperson Problem (TSP) algorithm. The optimized visit route is notified to the user and sent to the terminal as a schedule suggestion. During this optimization process, it is also possible to consider the type of transportation and set priority conditions.

[0179] The terminal consists of mobile devices such as smartphones and tablets, and displays the visit route and schedule sent from the server to the user. The terminal also acquires the user's biometric data using biosensors, and this data is analyzed by an emotion engine. The emotion engine evaluates the user's emotional state from the acquired data and, based on that, re-evaluates the schedule and adjusts the visit route. To reduce the user's stress and fatigue, it can reduce the number of visits or suggest breaks as needed.

[0180] As a concrete example, consider a scenario where a sales representative visits multiple customers in a single day. If the emotion engine detects user fatigue, the device re-evaluates the schedule to insert a short break before the next visit. Furthermore, if the user's stress level is determined to be high, the system can change the order of visits and suggest a new route that allows for less stressful travel.

[0181] Examples of prompt statements are as follows:

[0182] "Please describe the system for optimizing visit routes. This system utilizes user emotional data to flexibly adjust visit plans. How does it change the order of visits if the user's stress level is high?"

[0183] This system allows for increased efficiency in sales activities and the provision of flexible visit plans tailored to the user's emotions. This, in turn, can improve user satisfaction and sales results.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The server retrieves geographic and customer information. It receives data from geographic information systems (GIS) and customer relationship management (CRM) systems as input. The server stores this data in a database and generates location data for plotting as output.

[0187] Step 2:

[0188] The server plots the visited locations on a map and groups geographically close locations. It uses the location data generated in step 1 as input. Data processing involves grouping the visited locations and calculating the center point of each group. The output is a grouped list of locations, which forms the basis for efficient route planning.

[0189] Step 3:

[0190] The server calculates the order of visits and optimizes the initial route. It uses the list of locations generated in step 2 as input. The server applies the Travel Salesman Problem (TSP) algorithm to calculate the shortest route. The result of the data calculation is output as the optimal order of visits.

[0191] Step 4:

[0192] The terminal receives an optimized visit route sent from the server. The input is optimized visit sequence data from the server. The terminal displays this to the user, presenting it as a visit plan. The output is a route display in a user-readable format.

[0193] Step 5:

[0194] The device acquires biometric data from wearable devices and built-in sensors. Real-time data from sensors is used as input. The device collects the data and analyzes the user's emotional state using an emotion engine. Through data processing, the analysis results are obtained as the user's emotional state.

[0195] Step 6:

[0196] The server receives data from the emotion engine and re-evaluates the visit route. The input is the result of an analysis of the user's emotional state. Based on this, the server re-evaluates the visit route and schedule and outputs an adjusted route suggestion. To reduce stress, it suggests adjusting the number of visits and inserting breaks.

[0197] Step 7:

[0198] The terminal notifies the user of the coordinated visit route and schedule. The input is a coordinated route proposal from the server. The terminal presents it to the user in an easy-to-understand format and provides an updated route as output. This allows the user to conduct visits efficiently while reducing stress.

[0199] (Application Example 2)

[0200] 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 device 14 will be referred to as the "terminal."

[0201] Conventional visit route optimization systems could calculate efficient routes using geographical and traffic information, but they failed to take into account the user's emotional state or stress levels. This meant that while users could complete their visits efficiently, they might also experience emotional distress. Therefore, there was a need for methods to improve user comfort.

[0202] 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.

[0203] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's emotional state and dynamically adjusting the visit route based on that state. This makes it possible to optimize the route while taking the user's emotional state into consideration.

[0204] "Geographic information" refers to information related to specific points or areas on a map, and is data used to identify destinations and calculate routes.

[0205] A "destination" is a place that a visitor intends to reach with a specific purpose.

[0206] "Plotting" is the act of displaying multiple points on a map and visualizing their relative positions.

[0207] "Grouping" is the process of combining geographically close visited locations and treating them as a single visit unit.

[0208] The "optimal visiting order" refers to a calculated sequence of locations to visit efficiently, aiming to minimize time and cost.

[0209] "Past travel history" refers to records of past visits and travels, and is data used for route optimization.

[0210] "Real-time traffic data" refers to the latest information on current road conditions and traffic volume, which is used to dynamically adjust your travel route.

[0211] An "optimized visit route" is a path calculated by taking various conditions into consideration to efficiently carry out a visit.

[0212] "Emotional state" refers to the user's current psychological state and is a concept that includes feelings of happiness and stress levels.

[0213] "Dynamic adjustment" means making adaptive changes in response to real-time changes in the situation.

[0214] "Suggesting a break" means prompting the user to pause at an appropriate time based on their stress level and fatigue level.

[0215] The system for implementing this invention mainly consists of a server and terminals. The server acquires geographic information, plots visited locations on a map, and performs data processing to group geographically close visited locations. Geographic information system (GIS) software and databases are used for this purpose. It is possible to manipulate geographic data and calculate the order of visits using programming languages ​​such as Python and Java (registered trademark).

[0216] The server also receives data from the emotion engine. This emotion engine acquires the user's emotional state and monitors it in real time via sensors. This allows the system to dynamically adjust the visit route and suggest breaks based on the user's stress level. Emotional data is acquired from the user's smartphone, smartwatch, or other devices and analyzed by AI algorithms. Machine learning libraries such as TENSORFLOW® and PyTorch are used for this process.

[0217] The device notifies the user of optimized visit routes and schedule suggestions sent from the server via an application interface. The data is displayed through native applications for Android® or iOS. Users can receive real-time updates to their visit plans and route adjustments through the app.

[0218] As a concrete example, when a delivery driver checks the route information given to them at the morning meeting using a smartphone app, if the user's heart rate increases and stress signs are detected, the emotion engine sends this information to the server and recommends a route that reduces stress by re-evaluating the order of visits. An example of a prompt message at this time could be, "How can we improve the driver's stress on the current route?"

[0219] In this way, in addition to optimizing visit routes, it is possible to provide flexible visit plans that take user emotions into consideration, thereby improving user satisfaction.

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The server acquires geographic information using a Geographic Information System (GIS). The input geographic information is obtained from a database pre-stored on the server, and based on this, the visited locations are plotted on a map. This plotted information becomes the output used in the next step to optimize the visiting route.

[0223] Step 2:

[0224] The server groups geographically close locations based on the plotted visit locations. The input is the location information of the visit locations obtained in step 1, which is then used to perform grouping using a distance algorithm. The output is a visit order designed to optimize visit efficiency.

[0225] Step 3:

[0226] The server analyzes real-time traffic data and historical travel history to optimize visiting routes. Input data includes current traffic conditions obtained from a traffic information API and historical travel history stored in an internal database. This data is combined to execute an optimization algorithm, generating an optimized visiting route as output.

[0227] Step 4:

[0228] A device equipped with an emotion engine collects biosensor data such as the user's heart rate and skin electrical activity in real time. The input is information from the sensors, and by analyzing this data, the user's emotional state is estimated and their stress level is quantified. This numerical data is used to improve the user experience in the next step.

[0229] Step 5:

[0230] The server receives output from the emotion engine and dynamically adjusts suggested visit routes and rest stops. The input is the user's emotion data obtained in step 4, which is used to re-evaluate the current route. The output generates a new visit order and appropriate rest points.

[0231] Step 6:

[0232] The terminal notifies the user of new visit routes and rest stop suggestions sent from the server. The input is data from the server, which is then displayed on the terminal's application interface. The user then uses this information to carry out their visit. The output is a smooth visit activity on the user's end.

[0233] This workflow allows users to conduct their visits efficiently and comfortably.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] One embodiment of the present invention is a visit route creation system for supporting sales activities. This system mainly consists of a server, terminals, and users.

[0251] Description of the server process

[0252] The server retrieves geographical information, customer information, historical travel history data, and real-time traffic data from data sources and integrates them into a database. Based on travel history, the server creates a profile to determine visit frequency and effective visit order. Next, the server plots the planned visit locations on a map and groups them based on geographical proximity. The server calculates the optimal visit order from the grouped locations, optimizing the travel route based on priority conditions that take traffic conditions into account (e.g., reducing the number of right turns). The server then sends the optimized visit route information to the terminal.

[0253] Description of terminal processes

[0254] The terminal displays the user with the visit route information received from the server. Furthermore, the terminal provides a schedule suggestion regarding planned travel times and visit order, and offers an interface that allows the user to make adjustments. In addition, the terminal receives traffic data from the server in real time while traveling, recalculates the visit route as needed, and informs the user of the updated route. In this way, it can respond in real time to changes in traffic conditions while traveling.

[0255] Explanation from the user's perspective

[0256] The user enters a list of customer addresses to be visited into the terminal interface and starts the system. The system automatically calculates the visit route and displays the estimated travel time and the order of visits. The user can refer to the presented schedule and adjust visit times as needed. After the visit is completed, the system improves the accuracy of route optimization for future visits by providing feedback on the terminal.

[0257] Thus, this system allows sales representatives to plan visit routes efficiently and quickly, and enables real-time responses, thereby improving the overall efficiency of sales activities. For example, even when there are dozens of locations to visit, the system can propose the optimal route in a short time, supporting swift and effective sales activities.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The server collects and integrates geographical information, customer information, historical travel history, and real-time traffic data from external data sources and internal databases. It regularly updates this information stored in the database to keep it up-to-date.

[0261] Step 2:

[0262] The user enters a list of customer addresses to be visited into the terminal. The terminal then sends the entered address information to the server.

[0263] Step 3:

[0264] The server plots the received address data on a map and identifies geographically close locations to visit. Based on the plotting results, the locations are grouped together.

[0265] Step 4:

[0266] The server calculates the order of visits based on grouped locations. It considers travel time and traffic conditions and applies priority criteria (e.g., reducing the number of right turns, shortest distance) to select the most appropriate route.

[0267] Step 5:

[0268] The server sends the calculated visit order and route information to the terminal. The terminal displays the received information to the user and provides the user with an overview of the visit plan.

[0269] Step 6:

[0270] The user reviews the presented visit plan and adjusts the scheduled times and order of visits as needed. The terminal records the user's changes and sends the updated information to the server.

[0271] Step 7:

[0272] The terminal uses real-time traffic data during visits and provides users with alerts based on changes in traffic conditions. The server recalculates the route as needed and sends the changes to the terminal.

[0273] Step 8:

[0274] After the visit ends, the user enters feedback via a terminal. The terminal sends the feedback to a server, which collects data for future route optimization.

[0275] Through this process, the system provides users with efficient visit routes and continuously improves the service through schedule adjustments and feedback.

[0276] (Example 1)

[0277] 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".

[0278] The present invention aims to improve the efficiency of customer visits and travel in sales activities and fieldwork. In particular, in situations where there are multiple visit locations, there is a need to generate and modify optimal visit routes using past visit history and real-time traffic information. However, conventional methods do not fully utilize this data, resulting in challenges such as difficulty in optimizing routes and responding flexibly.

[0279] 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.

[0280] In this invention, the server includes means for acquiring spatial information and visualizing a plurality of visit locations, means for classifying spatially proximate visit locations and calculating an optimal visit order, and means for analyzing past movement histories and dynamic traffic data and adjusting visit routes. This enables the optimization of efficient and flexible visit routes.

[0281] "Spatial information" is data that includes geographical elements and represents information related to specific locations or areas.

[0282] "Visit location" refers to a specific geographical location that a person needs to visit for business or service provision.

[0283] "Visualization" refers to the process of visually representing data and information to make it easier to understand.

[0284] "Classification" is an operation of grouping target data or information based on certain criteria or attributes.

[0285] "Optimal visit order" refers to the most reasonable order for efficiently touring visit locations.

[0286] "Movement history" refers to information recording the movement routes and frequencies of people or objects in the past.

[0287] "Dynamic traffic data" refers to data that reflects the current ongoing traffic situation in real time.

[0288] "Visit route" means a specific movement route set for touring a plurality of visit locations.

[0289] "Adjustment" means the act of changing data or plans according to objectives and conditions.

[0290] The embodiment for carrying out the present invention relates to a visit route creation system aimed at improving the efficiency of sales activities. This system mainly consists of three components: a server, a terminal, and a user.

[0291] The server uses geographic information systems and customer management software to acquire spatial and customer information. Furthermore, it employs a client-server model to analyze past travel history and dynamic traffic data, integrating the data into a dedicated database. For analysis, it utilizes real-time traffic information services such as the Google Maps API. Based on this data, the server calculates the optimal route for the user and sends it to their terminal.

[0292] The terminal receives data from the server and visualizes the visit route using a dedicated application. When the user enters the desired locations to visit, the terminal displays them and provides an interface that allows them to adjust the order of visits and travel times as needed. The terminal also incorporates real-time updated traffic information and recalculates the route during travel to provide the latest visit order.

[0293] Users can input customer address lists into this system and adjust their schedules while referring to automatically calculated visit order and estimated time. After each visit, user feedback is sent to the server and used to improve the accuracy of future visit routes.

[0294] For example, a salesperson with multiple destinations who wants to efficiently plan their route can use this system to obtain the optimal route in a short amount of time. Another example of a prompt when using the AI ​​generation model is "Enter a list of destinations to suggest the optimal sales route." In this way, users can effectively utilize their time and resources, enabling them to conduct sales activities more efficiently.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The server retrieves spatial and customer information from geographic information systems and customer management software. Inputs include specified geographic regions and specific customer lists. The server stores this data in a database and integrates it while maintaining information consistency, preparing it for subsequent processing.

[0298] Step 2:

[0299] The server analyzes past travel history and dynamic traffic data. It receives past travel patterns and real-time traffic information as input. The server processes this data through an analysis algorithm, calculating the frequency of visits to specific locations and the optimal order of visits to create an efficient profile.

[0300] Step 3:

[0301] The server optimizes the visit route based on the user's profile. The inputs include the geographical coordinates and priority of each visit location, as well as traffic data. The server calculates the optimal route, considering factors such as reducing the number of right turns at intersections, and sends the result to the user's terminal.

[0302] Step 4:

[0303] The terminal displays visit route information received from the server on its interface. Its inputs include the optimized visit order and estimated duration sent from the server. The terminal visualizes this information and provides an interface for the user to adjust the order and timing of visits.

[0304] Step 5:

[0305] The user enters the desired locations into the terminal and adjusts the schedule as needed. During this process, the user refers to the suggested visit order and estimated time, confirms the most efficient route, and approves it.

[0306] Step 6:

[0307] During movement, the terminal continuously receives real-time traffic information from the server and recalculates the access route as needed. Based on changes in the traffic situation, the terminal recalculates the access route and notifies the user of the updated route, enabling flexible response to the situation.

[0308] Step 7:

[0309] After the visit is completed, the user inputs feedback on the terminal. The input feedback data is sent to the server and used for optimizing the access route for subsequent visits. The server uses this data to continuously improve the accuracy of the route by leveraging the generated AI model.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In modern logistics, the design of an efficient delivery route is an important issue directly related to reducing an organization's time and costs. However, many current systems cannot flexibly respond to traffic conditions and real-time changes, and often cannot quickly solve complex problems. As a result, there is a problem that it is difficult to maximize delivery efficiency and an optimal delivery plan cannot be executed.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes means for acquiring geographic information and plotting multiple destinations on a map, means for grouping geographically close destinations and calculating the optimal order of visits, and means for analyzing past travel history and real-time traffic data to optimize the visit route. This enables the dynamic modification of the delivery route in real time according to traffic conditions, allowing for the execution of an efficient and flexible delivery plan.

[0315] "Geographic information" refers to data that shows location information and characteristics of specific points on Earth.

[0316] A "visiting location" refers to a place visited for the purpose of carrying out a specific task, such as delivery or sales activities.

[0317] "Plotting on a map" means using geographical information to visually represent visited locations on a map.

[0318] "Visiting order" refers to a plan that determines the order in which to visit locations in order to carry out tasks efficiently.

[0319] "Movement history" refers to data that shows changes in location and routes in the past.

[0320] "Real-time traffic data" refers to information about current traffic conditions that is updated instantly.

[0321] "Optimizing a visit route" means calculating the best possible path to visit locations, while adhering to the minimum time, distance, and other constraints.

[0322] "Notifying the user" means communicating information to the user based on calculation and analysis results.

[0323] "Schedule proposals" refer to providing users with a plan to help them carry out their activities efficiently.

[0324] "Information equipment" refers to electronic devices that process digital data, and generally includes computers.

[0325] The "shortest route" is the path chosen to minimize distance and travel time when visiting different locations.

[0326] "Visualization" refers to the process of representing data and results graphically, making them easy to understand visually.

[0327] "To change dynamically" means to make corrections or adjustments on the fly depending on the conditions or circumstances.

[0328] To implement this invention, three main components are used: a server, a terminal, and a user.

[0329] The server first integrates geographical information and customer visit location information into a single database. This information is used to optimize the order of visits and improve efficiency based on past travel history. The server also has the ability to analyze real-time traffic data and optimize visit routes. Programming languages ​​such as Python and JavaScript can be used for this data processing. The server sends the calculated optimal visit route to the terminal and presents it to the user.

[0330] The terminal visually displays the visit route to the user based on information received from the server. The terminal uses common information devices such as smartphones and tablets. Its interface is intuitive and easy to use, and it includes functions to allow the user to check and adjust their visit schedule. Furthermore, it receives real-time updated traffic information from the server during the visit and suggests dynamic changes to the visit route as needed.

[0331] Users can utilize this visit route creation system by accessing the system and registering their destinations. This allows for efficient travel based on routes suggested by information devices. After the user completes their visit, feedback data is entered, enabling the server to perform machine learning to improve the accuracy of future route optimizations.

[0332] This system assists in efficiently visiting multiple locations, contributing to increased efficiency in logistics centers and sales activities. For example, in a pizza delivery service, it can suggest routes that allow drivers to reach multiple delivery destinations in the shortest possible time.

[0333] As an example of a prompt, you could instruct the system with a message like, "When visiting multiple delivery locations, please create an app that uses real-time traffic information to show the most efficient order of visits."

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The server retrieves and integrates geographical information, customer visit location information, and past travel history into its database. This information includes latitude and longitude, arrival time, and visit frequency for each visit location, and is used for subsequent data analysis. The server uses a database management system to efficiently store and manage this information.

[0337] Step 2:

[0338] The server receives real-time traffic data from external traffic information services. This data includes current road conditions and predicted congestion information. The server periodically updates this data using APIs via a program. The server then combines past travel history with real-time traffic data to calculate the shortest route between destinations and determine the optimal order of visits. This optimizes the visit route.

[0339] Step 3:

[0340] The server sends the calculated optimal route to the terminal. The server formats the information in a data structure such as JSON and delivers it to the terminal quickly and accurately via a communication protocol. This allows the user to easily view the route details.

[0341] Step 4:

[0342] The terminal visualizes and displays the visit route received by the user on the screen. Using a map service application, the terminal provides information including the estimated arrival time at each destination along the route. The user can refer to this information and adjust their visit schedule as needed.

[0343] Step 5:

[0344] After completing their visit, users provide feedback through an interface on their device. Users record their actual experience, including visit duration and traffic conditions, and this data is then sent back to the server.

[0345] Step 6:

[0346] The server analyzes feedback data sent by the user and uses machine learning algorithms to improve the accuracy of route optimization for future visits. The server utilizes a generative AI model to better reflect user needs and conditions based on the prompt text for creating visit routes.

[0347] 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.

[0348] This invention combines an emotion engine with a visit route optimization system, enabling efficient and less stressful sales activities by flexibly adjusting visit plans based on the user's emotions. The system primarily consists of a server, terminals, and an emotion engine.

[0349] Description of the server process

[0350] The server acquires geographical information, customer information, historical travel history data, and real-time traffic data, and centrally manages them in a database. Using this data, it plots visited locations, performs geographical grouping, and optimizes the order of visits. Furthermore, it sets priority conditions according to the mode of transport and calculates efficient visiting routes. The server sends the visit plan to the terminal and also receives input from the emotion engine to re-evaluate the route and schedule.

[0351] Description of terminal processes

[0352] The terminal receives the visit route and user sentiment data transmitted from the server and displays it to the user. Based on the sentiment data, which is analyzed in real time by the sentiment engine, the terminal dynamically adjusts the order of visits and rest plans. It also provides a function to monitor real-time traffic data while the user is traveling and correct the route as needed.

[0353] Explanation of the Emotion Engine

[0354] The emotion engine acquires and analyzes user emotion data from the device's sensors and external devices, and sends the results to the server. It understands the user's stress levels and fluctuations in comfort, and uses this information to readjust or suggest visit routes. For example, if a user is feeling stressed, the schedule might be restructured to reduce the number of visits or to include breaks.

[0355] Explanation of specific examples

[0356] As a concrete example, if the emotion engine detects that a user is fatigued while visiting multiple clients, the device will use this information to revise the visit schedule and insert short breaks between visits. Furthermore, if it determines that the user is experiencing high stress levels, it will change the order of visits and suggest a less stressful route. In this way, users can continue their visiting activities efficiently and comfortably.

[0357] This system aims not only to optimize visit routes but also to provide flexible visit plans that take into account user emotions. Therefore, it can improve the efficiency of sales activities and increase user satisfaction.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The server retrieves the necessary geographic and customer information from the geographic information system and customer database. This generates an initial list of planned visit locations.

[0361] Step 2:

[0362] The server plots visited locations on a map using past travel history and real-time traffic data, and groups geographically close locations. It then calculates the optimal order of visits and creates an initial route.

[0363] Step 3:

[0364] The user views a list of customers they wish to visit via their device and makes adjustments as needed. The device then sends information to the server based on the user's input.

[0365] Step 4:

[0366] The emotion engine collects user emotional data in real time through sensors built into the device and external devices. This data indicates the user's stress level, fatigue level, and other emotional states.

[0367] Step 5:

[0368] The emotion engine analyzes the collected user emotion data and sends the result of the emotion state to the server. Based on this result, the server recalculates the visit order and break insertions as needed.

[0369] Step 6:

[0370] The terminal notifies the user of updated visit routes from the server and schedule adjustments based on their mood. The user can review the presented plan and give final approval.

[0371] Step 7:

[0372] While the visit is underway, the terminal monitors real-time traffic data and adjusts the route as needed, reflecting continuous sentiment analysis by the emotion engine.

[0373] Step 8:

[0374] After a visit, the user provides brief feedback on their device, and the emotion engine uses this feedback as training data. The server stores this feedback to use for route optimization in the future.

[0375] This process allows the system to streamline user visits and provide a comfortable sales experience by addressing emotional situations.

[0376] (Example 2)

[0377] 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".

[0378] In modern sales activities, optimizing visit routes is crucial, but the associated user stress and emotional burden are often overlooked. Traditional systems only optimize based on geographical and traffic information, failing to respond flexibly to the user's emotional state. As a result, it becomes difficult to support users' efficient sales activities, potentially leading to decreased user satisfaction and sales results.

[0379] 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.

[0380] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's biometric data and analyzing their emotional state. This makes it possible to re-evaluate and adjust the visit route and schedule based on the user's emotional state.

[0381] "Geographic information" is a general term for data that includes information about location, topography, and facilities related to a specific place.

[0382] A "place to visit" is location information that indicates a specific place the user should visit.

[0383] "Grouping" is the process of combining multiple objects into a single set based on common attributes or distance.

[0384] The "order of visits" refers to the sequence in which multiple locations are arranged to allow for efficient travel.

[0385] "Past travel history" refers to recorded data about routes the user has traveled or places they have visited in the past.

[0386] "Real-time traffic information" refers to dynamic data that shows the current traffic situation, including information on congestion and accidents.

[0387] "Biometric data" refers to data that indicates the user's physical condition, including heart rate, body temperature, and stress level measurements.

[0388] "Emotional state" refers to the user's psychological state, determined based on biometric data and subjective self-reporting.

[0389] "Re-evaluation" is the process of reviewing and updating the results of an evaluation that has already been conducted.

[0390] "Schedule suggestions" are recommendations for scheduling and time management to support users' efficient activities.

[0391] This invention combines a visit route optimization system with an emotion engine to support efficient and stress-free sales activities for users. The specific implementation of this system is described below.

[0392] The server first obtains geographic information from a Geographic Information System (GIS) and stores it in a database. Next, it receives customer information from a Customer Relationship Management (CRM) system and obtains past travel history and real-time traffic information via an API. Based on this data, it plots visited locations on a map and groups geographically close locations. Then, to optimize the order of visits, it uses algorithms such as the Travel Salesperson Problem (TSP) algorithm. The optimized visit route is notified to the user and sent to the terminal as a schedule suggestion. During this optimization process, it is also possible to consider the type of transportation and set priority conditions.

[0393] The terminal consists of mobile devices such as smartphones and tablets, and displays the visit route and schedule sent from the server to the user. The terminal also acquires the user's biometric data using biosensors, and this data is analyzed by an emotion engine. The emotion engine evaluates the user's emotional state from the acquired data and, based on that, re-evaluates the schedule and adjusts the visit route. To reduce the user's stress and fatigue, it can reduce the number of visits or suggest breaks as needed.

[0394] As a concrete example, consider a scenario where a sales representative visits multiple customers in a single day. If the emotion engine detects user fatigue, the device re-evaluates the schedule to insert a short break before the next visit. Furthermore, if the user's stress level is determined to be high, the system can change the order of visits and suggest a new route that allows for less stressful travel.

[0395] Examples of prompt statements are as follows:

[0396] "Please describe the system for optimizing visit routes. This system utilizes user emotional data to flexibly adjust visit plans. How does it change the order of visits if the user's stress level is high?"

[0397] This system allows for increased efficiency in sales activities and the provision of flexible visit plans tailored to the user's emotions. This, in turn, can improve user satisfaction and sales results.

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] The server retrieves geographic and customer information. It receives data from geographic information systems (GIS) and customer relationship management (CRM) systems as input. The server stores this data in a database and generates location data for plotting as output.

[0401] Step 2:

[0402] The server plots the visited locations on a map and groups geographically close locations. It uses the location data generated in step 1 as input. Data processing involves grouping the visited locations and calculating the center point of each group. The output is a grouped list of locations, which forms the basis for efficient route planning.

[0403] Step 3:

[0404] The server calculates the order of visits and optimizes the initial route. It uses the list of locations generated in step 2 as input. The server applies the Travel Salesman Problem (TSP) algorithm to calculate the shortest route. The result of the data calculation is output as the optimal order of visits.

[0405] Step 4:

[0406] The terminal receives an optimized visit route sent from the server. The input is optimized visit sequence data from the server. The terminal displays this to the user, presenting it as a visit plan. The output is a route display in a user-readable format.

[0407] Step 5:

[0408] The device acquires biometric data from wearable devices and built-in sensors. Real-time data from sensors is used as input. The device collects the data and analyzes the user's emotional state using an emotion engine. Through data processing, the analysis results are obtained as the user's emotional state.

[0409] Step 6:

[0410] The server receives data from the emotion engine and re-evaluates the visit route. The input is the result of an analysis of the user's emotional state. Based on this, the server re-evaluates the visit route and schedule and outputs an adjusted route suggestion. To reduce stress, it suggests adjusting the number of visits and inserting breaks.

[0411] Step 7:

[0412] The terminal notifies the user of the coordinated visit route and schedule. The input is a coordinated route proposal from the server. The terminal presents it to the user in an easy-to-understand format and provides an updated route as output. This allows the user to conduct visits efficiently while reducing stress.

[0413] (Application Example 2)

[0414] 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."

[0415] Conventional visit route optimization systems could calculate efficient routes using geographical and traffic information, but they failed to take into account the user's emotional state or stress levels. This meant that while users could complete their visits efficiently, they might also experience emotional distress. Therefore, there was a need for methods to improve user comfort.

[0416] 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.

[0417] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's emotional state and dynamically adjusting the visit route based on that state. This makes it possible to optimize the route while taking the user's emotional state into consideration.

[0418] "Geographic information" refers to information related to specific points or areas on a map, and is data used to identify destinations and calculate routes.

[0419] A "destination" is a place that a visitor intends to reach with a specific purpose.

[0420] "Plotting" is the act of displaying multiple points on a map and visualizing their relative positions.

[0421] "Grouping" is the process of combining geographically close visited locations and treating them as a single visit unit.

[0422] The "optimal visiting order" refers to a calculated sequence of locations to visit efficiently, aiming to minimize time and cost.

[0423] "Past travel history" refers to records of past visits and travels, and is data used for route optimization.

[0424] "Real-time traffic data" refers to the latest information on current road conditions and traffic volume, which is used to dynamically adjust your travel route.

[0425] An "optimized visit route" is a path calculated by taking various conditions into consideration to efficiently carry out a visit.

[0426] "Emotional state" refers to the user's current psychological state and is a concept that includes feelings of happiness and stress levels.

[0427] "Dynamic adjustment" means making adaptive changes in response to real-time changes in the situation.

[0428] "Suggesting a break" means prompting the user to pause at an appropriate time based on their stress level and fatigue level.

[0429] The system for implementing this invention mainly consists of a server and terminals. The server acquires geographic information, plots visited locations on a map, and performs data processing to group geographically close visited locations. Geographic information system (GIS) software and databases are used for this purpose. It is possible to manipulate geographic data and calculate the order of visits using programming languages ​​such as Python and Java.

[0430] The server also receives data from the emotion engine. This emotion engine acquires the user's emotional state and monitors it in real time via sensors. This allows the server to dynamically adjust the visit route and suggest breaks based on the user's stress level. Emotional data is acquired from the user's devices, such as smartphones and smartwatches, and analyzed by AI algorithms. Machine learning libraries such as TensorFlow and PyTorch are used for this process.

[0431] The device notifies the user of optimized visit routes and schedule suggestions sent from the server via an application interface. The data is displayed through a native application for Android or iOS. Users can receive real-time updates to their visit plans and route adjustments through the app.

[0432] As a concrete example, when a delivery driver checks the route information given to them at the morning meeting using a smartphone app, if the user's heart rate increases and stress signs are detected, the emotion engine sends this information to the server and recommends a route that reduces stress by re-evaluating the order of visits. An example of a prompt message at this time could be, "How can we improve the driver's stress on the current route?"

[0433] In this way, in addition to optimizing visit routes, it is possible to provide flexible visit plans that take user emotions into consideration, thereby improving user satisfaction.

[0434] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0435] Step 1:

[0436] The server acquires geographic information using a Geographic Information System (GIS). The input geographic information is obtained from a database pre-stored on the server, and based on this, the visited locations are plotted on a map. This plotted information becomes the output used in the next step to optimize the visiting route.

[0437] Step 2:

[0438] The server groups geographically close locations based on the plotted visit locations. The input is the location information of the visit locations obtained in step 1, which is then used to perform grouping using a distance algorithm. The output is a visit order designed to optimize visit efficiency.

[0439] Step 3:

[0440] The server analyzes real-time traffic data and historical travel history to optimize visiting routes. Input data includes current traffic conditions obtained from a traffic information API and historical travel history stored in an internal database. This data is combined to execute an optimization algorithm, generating an optimized visiting route as output.

[0441] Step 4:

[0442] A device equipped with an emotion engine collects biosensor data such as the user's heart rate and skin electrical activity in real time. The input is information from the sensors, and by analyzing this data, the user's emotional state is estimated and their stress level is quantified. This numerical data is used to improve the user experience in the next step.

[0443] Step 5:

[0444] The server receives output from the emotion engine and dynamically adjusts suggested visit routes and rest stops. The input is the user's emotion data obtained in step 4, which is used to re-evaluate the current route. The output generates a new visit order and appropriate rest points.

[0445] Step 6:

[0446] The terminal notifies the user of new visit routes and rest stop suggestions sent from the server. The input is data from the server, which is then displayed on the terminal's application interface. The user then uses this information to carry out their visit. The output is a smooth visit activity on the user's end.

[0447] This workflow allows users to conduct their visits efficiently and comfortably.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Third Embodiment]

[0452] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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".

[0464] One embodiment of the present invention is a visit route creation system for supporting sales activities. This system mainly consists of a server, terminals, and users.

[0465] Description of the server process

[0466] The server retrieves geographical information, customer information, historical travel history data, and real-time traffic data from data sources and integrates them into a database. Based on travel history, the server creates a profile to determine visit frequency and effective visit order. Next, the server plots the planned visit locations on a map and groups them based on geographical proximity. The server calculates the optimal visit order from the grouped locations, optimizing the travel route based on priority conditions that take traffic conditions into account (e.g., reducing the number of right turns). The server then sends the optimized visit route information to the terminal.

[0467] Description of terminal processes

[0468] The terminal displays the user with the visit route information received from the server. Furthermore, the terminal provides a schedule suggestion regarding planned travel times and visit order, and offers an interface that allows the user to make adjustments. In addition, the terminal receives traffic data from the server in real time while traveling, recalculates the visit route as needed, and informs the user of the updated route. In this way, it can respond in real time to changes in traffic conditions while traveling.

[0469] Explanation from the user's perspective

[0470] The user enters a list of customer addresses to be visited into the terminal interface and starts the system. The system automatically calculates the visit route and displays the estimated travel time and the order of visits. The user can refer to the presented schedule and adjust visit times as needed. After the visit is completed, the system improves the accuracy of route optimization for future visits by providing feedback on the terminal.

[0471] Thus, this system allows sales representatives to plan visit routes efficiently and quickly, and enables real-time responses, thereby improving the overall efficiency of sales activities. For example, even when there are dozens of locations to visit, the system can propose the optimal route in a short time, supporting swift and effective sales activities.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The server collects and integrates geographical information, customer information, historical travel history, and real-time traffic data from external data sources and internal databases. It regularly updates this information stored in the database to keep it up-to-date.

[0475] Step 2:

[0476] The user enters a list of customer addresses to be visited into the terminal. The terminal then sends the entered address information to the server.

[0477] Step 3:

[0478] The server plots the received address data on a map and identifies geographically close locations to visit. Based on the plotting results, the locations are grouped together.

[0479] Step 4:

[0480] The server calculates the order of visits based on grouped locations. It considers travel time and traffic conditions and applies priority criteria (e.g., reducing the number of right turns, shortest distance) to select the most appropriate route.

[0481] Step 5:

[0482] The server sends the calculated visit order and route information to the terminal. The terminal displays the received information to the user and provides the user with an overview of the visit plan.

[0483] Step 6:

[0484] The user reviews the presented visit plan and adjusts the scheduled times and order of visits as needed. The terminal records the user's changes and sends the updated information to the server.

[0485] Step 7:

[0486] The terminal uses real-time traffic data during visits and provides users with alerts based on changes in traffic conditions. The server recalculates the route as needed and sends the changes to the terminal.

[0487] Step 8:

[0488] After the visit ends, the user enters feedback via a terminal. The terminal sends the feedback to a server, which collects data for future route optimization.

[0489] Through this process, the system provides users with efficient visit routes and continuously improves the service through schedule adjustments and feedback.

[0490] (Example 1)

[0491] 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."

[0492] The present invention aims to improve the efficiency of customer visits and travel in sales activities and fieldwork. In particular, in situations where there are multiple visit locations, there is a need to generate and modify optimal visit routes using past visit history and real-time traffic information. However, conventional methods do not fully utilize this data, resulting in challenges such as difficulty in optimizing routes and responding flexibly.

[0493] 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.

[0494] In this invention, the server includes means for acquiring spatial information and visualizing multiple visit locations, means for classifying spatially adjacent visit locations and calculating the optimal visit order, and means for analyzing past travel history and dynamic traffic data and adjusting the visit route. This enables efficient and flexible optimization of visit routes.

[0495] "Spatial information" refers to data that includes geographical elements and represents information related to a specific location or area.

[0496] A "visiting location" refers to a specific geographical place that a person needs to visit for business or service provision purposes.

[0497] "Visualization" refers to the process of representing data and information visually to make them easier to understand.

[0498] "Classification" is the process of grouping data or information based on certain criteria or attributes.

[0499] "Optimal visiting order" refers to the most rational sequence for efficiently visiting locations.

[0500] "Movement history" refers to information that records the routes and frequency of movement of people and objects in the past.

[0501] "Dynamic traffic data" refers to data that reflects the current traffic situation in real time.

[0502] "Visiting route" refers to a specific travel route set up to visit multiple locations.

[0503] "Adjustment" refers to the act of changing data or plans according to the purpose or conditions.

[0504] The embodiment for carrying out the present invention relates to a visit route creation system aimed at improving the efficiency of sales activities. This system mainly consists of three components: a server, a terminal, and a user.

[0505] The server uses geographic information systems and customer management software to acquire spatial and customer information. Furthermore, it employs a client-server model to analyze past travel history and dynamic traffic data, integrating the data into a dedicated database. For analysis, it utilizes real-time traffic information services such as the Google Maps API. Based on this data, the server calculates the optimal route for the user and sends it to their terminal.

[0506] The terminal receives data from the server and visualizes the visit route using a dedicated application. When the user enters the desired locations to visit, the terminal displays them and provides an interface that allows them to adjust the order of visits and travel times as needed. The terminal also incorporates real-time updated traffic information and recalculates the route during travel to provide the latest visit order.

[0507] Users can input customer address lists into this system and adjust their schedules while referring to automatically calculated visit order and estimated time. After each visit, user feedback is sent to the server and used to improve the accuracy of future visit routes.

[0508] For example, a salesperson with multiple destinations who wants to efficiently plan their route can use this system to obtain the optimal route in a short amount of time. Another example of a prompt when using the AI ​​generation model is "Enter a list of destinations to suggest the optimal sales route." In this way, users can effectively utilize their time and resources, enabling them to conduct sales activities more efficiently.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] The server retrieves spatial and customer information from geographic information systems and customer management software. Inputs include specified geographic regions and specific customer lists. The server stores this data in a database and integrates it while maintaining information consistency, preparing it for subsequent processing.

[0512] Step 2:

[0513] The server analyzes past travel history and dynamic traffic data. It receives past travel patterns and real-time traffic information as input. The server processes this data through an analysis algorithm, calculating the frequency of visits to specific locations and the optimal order of visits to create an efficient profile.

[0514] Step 3:

[0515] The server optimizes the visit route based on the user's profile. The inputs include the geographical coordinates and priority of each visit location, as well as traffic data. The server calculates the optimal route, considering factors such as reducing the number of right turns at intersections, and sends the result to the user's terminal.

[0516] Step 4:

[0517] The terminal displays visit route information received from the server on its interface. Its inputs include the optimized visit order and estimated duration sent from the server. The terminal visualizes this information and provides an interface for the user to adjust the order and timing of visits.

[0518] Step 5:

[0519] The user enters the desired locations into the terminal and adjusts the schedule as needed. During this process, the user refers to the suggested visit order and estimated time, confirms the most efficient route, and approves it.

[0520] Step 6:

[0521] While on the move, the device continuously receives real-time traffic information from the server and recalculates the route as needed. The device flexibly adapts to the situation by recalculating the route based on changes in traffic conditions and informing the user of the updated route.

[0522] Step 7:

[0523] Users enter feedback on their device after their visit ends. The entered feedback data is sent to the server and used to optimize future visit routes. The server uses this data to leverage a generative AI model to continuously improve the accuracy of the routes.

[0524] (Application Example 1)

[0525] 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."

[0526] In modern logistics, designing efficient delivery routes is a critical challenge that directly impacts an organization's time and cost savings. However, many current systems are unable to flexibly respond to traffic conditions and real-time changes, and often fail to quickly resolve complex problems. This makes it difficult to maximize delivery efficiency and execute optimal delivery plans.

[0527] 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.

[0528] In this invention, the server includes means for acquiring geographic information and plotting multiple destinations on a map, means for grouping geographically close destinations and calculating the optimal order of visits, and means for analyzing past travel history and real-time traffic data to optimize the visit route. This enables the dynamic modification of the delivery route in real time according to traffic conditions, allowing for the execution of an efficient and flexible delivery plan.

[0529] "Geographic information" refers to data that shows location information and characteristics of specific points on Earth.

[0530] A "visiting location" refers to a place visited for the purpose of carrying out a specific task, such as delivery or sales activities.

[0531] "Plotting on a map" means using geographical information to visually represent visited locations on a map.

[0532] "Visiting order" refers to a plan that determines the order in which to visit locations in order to carry out tasks efficiently.

[0533] "Movement history" refers to data that shows changes in location and routes in the past.

[0534] "Real-time traffic data" refers to information about current traffic conditions that is updated instantly.

[0535] "Optimizing a visit route" means calculating the best possible path to visit locations, while adhering to the minimum time, distance, and other constraints.

[0536] "Notifying the user" means communicating information to the user based on calculation and analysis results.

[0537] "Schedule proposals" refer to providing users with a plan to help them carry out their activities efficiently.

[0538] "Information equipment" refers to electronic devices that process digital data, and generally includes computers.

[0539] The "shortest route" is the path chosen to minimize distance and travel time when visiting different locations.

[0540] "Visualization" refers to the process of representing data and results graphically, making them easy to understand visually.

[0541] "To change dynamically" means to make corrections or adjustments on the fly depending on the conditions or circumstances.

[0542] To implement this invention, three main components are used: a server, a terminal, and a user.

[0543] The server first integrates geographical information and customer visit location information into a single database. This information is used to optimize the order of visits and improve efficiency based on past travel history. The server also has the ability to analyze real-time traffic data and optimize visit routes. Programming languages ​​such as Python and JavaScript can be used for this data processing. The server sends the calculated optimal visit route to the terminal and presents it to the user.

[0544] The terminal visually displays the visit route to the user based on information received from the server. The terminal uses common information devices such as smartphones and tablets. Its interface is intuitive and easy to use, and it includes functions to allow the user to check and adjust their visit schedule. Furthermore, it receives real-time updated traffic information from the server during the visit and suggests dynamic changes to the visit route as needed.

[0545] Users can utilize this visit route creation system by accessing the system and registering their destinations. This allows for efficient travel based on routes suggested by information devices. After the user completes their visit, feedback data is entered, enabling the server to perform machine learning to improve the accuracy of future route optimizations.

[0546] This system assists in efficiently visiting multiple locations, contributing to increased efficiency in logistics centers and sales activities. For example, in a pizza delivery service, it can suggest routes that allow drivers to reach multiple delivery destinations in the shortest possible time.

[0547] As an example of a prompt, you could instruct the system with a message like, "When visiting multiple delivery locations, please create an app that uses real-time traffic information to show the most efficient order of visits."

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The server retrieves and integrates geographical information, customer visit location information, and past travel history into its database. This information includes latitude and longitude, arrival time, and visit frequency for each visit location, and is used for subsequent data analysis. The server uses a database management system to efficiently store and manage this information.

[0551] Step 2:

[0552] The server receives real-time traffic data from external traffic information services. This data includes current road conditions and predicted congestion information. The server periodically updates this data using APIs via a program. The server then combines past travel history with real-time traffic data to calculate the shortest route between destinations and determine the optimal order of visits. This optimizes the visit route.

[0553] Step 3:

[0554] The server sends the calculated optimal route to the terminal. The server formats the information in a data structure such as JSON and delivers it to the terminal quickly and accurately via a communication protocol. This allows the user to easily view the route details.

[0555] Step 4:

[0556] The terminal visualizes and displays the visit route received by the user on the screen. Using a map service application, the terminal provides information including the estimated arrival time at each destination along the route. The user can refer to this information and adjust their visit schedule as needed.

[0557] Step 5:

[0558] After completing their visit, users provide feedback through an interface on their device. Users record their actual experience, including visit duration and traffic conditions, and this data is then sent back to the server.

[0559] Step 6:

[0560] The server analyzes feedback data sent by the user and uses machine learning algorithms to improve the accuracy of route optimization for future visits. The server utilizes a generative AI model to better reflect user needs and conditions based on the prompt text for creating visit routes.

[0561] 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.

[0562] This invention combines an emotion engine with a visit route optimization system, enabling efficient and less stressful sales activities by flexibly adjusting visit plans based on the user's emotions. The system primarily consists of a server, terminals, and an emotion engine.

[0563] Description of the server process

[0564] The server acquires geographical information, customer information, historical travel history data, and real-time traffic data, and centrally manages them in a database. Using this data, it plots visited locations, performs geographical grouping, and optimizes the order of visits. Furthermore, it sets priority conditions according to the mode of transport and calculates efficient visiting routes. The server sends the visit plan to the terminal and also receives input from the emotion engine to re-evaluate the route and schedule.

[0565] Description of terminal processes

[0566] The terminal receives the visit route and user sentiment data transmitted from the server and displays it to the user. Based on the sentiment data, which is analyzed in real time by the sentiment engine, the terminal dynamically adjusts the order of visits and rest plans. It also provides a function to monitor real-time traffic data while the user is traveling and correct the route as needed.

[0567] Explanation of the Emotion Engine

[0568] The emotion engine acquires and analyzes user emotion data from the device's sensors and external devices, and sends the results to the server. It understands the user's stress levels and fluctuations in comfort, and uses this information to readjust or suggest visit routes. For example, if a user is feeling stressed, the schedule might be restructured to reduce the number of visits or to include breaks.

[0569] Explanation of specific examples

[0570] As a concrete example, if the emotion engine detects that a user is fatigued while visiting multiple clients, the device will use this information to revise the visit schedule and insert short breaks between visits. Furthermore, if it determines that the user is experiencing high stress levels, it will change the order of visits and suggest a less stressful route. In this way, users can continue their visiting activities efficiently and comfortably.

[0571] This system aims not only to optimize visit routes but also to provide flexible visit plans that take into account user emotions. Therefore, it can improve the efficiency of sales activities and increase user satisfaction.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The server retrieves the necessary geographic and customer information from the geographic information system and customer database. This generates an initial list of planned visit locations.

[0575] Step 2:

[0576] The server plots visited locations on a map using past travel history and real-time traffic data, and groups geographically close locations. It then calculates the optimal order of visits and creates an initial route.

[0577] Step 3:

[0578] The user views a list of customers they wish to visit via their device and makes adjustments as needed. The device then sends information to the server based on the user's input.

[0579] Step 4:

[0580] The emotion engine collects user emotional data in real time through sensors built into the device and external devices. This data indicates the user's stress level, fatigue level, and other emotional states.

[0581] Step 5:

[0582] The emotion engine analyzes the collected user emotion data and sends the result of the emotion state to the server. Based on this result, the server recalculates the visit order and break insertions as needed.

[0583] Step 6:

[0584] The terminal notifies the user of updated visit routes from the server and schedule adjustments based on their mood. The user can review the presented plan and give final approval.

[0585] Step 7:

[0586] While the visit is underway, the terminal monitors real-time traffic data and adjusts the route as needed, reflecting continuous sentiment analysis by the emotion engine.

[0587] Step 8:

[0588] After a visit, the user provides brief feedback on their device, and the emotion engine uses this feedback as training data. The server stores this feedback to use for route optimization in the future.

[0589] This process allows the system to streamline user visits and provide a comfortable sales experience by addressing emotional situations.

[0590] (Example 2)

[0591] 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."

[0592] In modern sales activities, optimizing visit routes is crucial, but the associated user stress and emotional burden are often overlooked. Traditional systems only optimize based on geographical and traffic information, failing to respond flexibly to the user's emotional state. As a result, it becomes difficult to support users' efficient sales activities, potentially leading to decreased user satisfaction and sales results.

[0593] 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.

[0594] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's biometric data and analyzing their emotional state. This makes it possible to re-evaluate and adjust the visit route and schedule based on the user's emotional state.

[0595] "Geographic information" is a general term for data that includes information about location, topography, and facilities related to a specific place.

[0596] A "place to visit" is location information that indicates a specific place the user should visit.

[0597] "Grouping" is the process of combining multiple objects into a single set based on common attributes or distance.

[0598] The "order of visits" refers to the sequence in which multiple locations are arranged to allow for efficient travel.

[0599] "Past travel history" refers to recorded data about routes the user has traveled or places they have visited in the past.

[0600] "Real-time traffic information" refers to dynamic data that shows the current traffic situation, including information on congestion and accidents.

[0601] "Biometric data" refers to data that indicates the user's physical condition, including heart rate, body temperature, and stress level measurements.

[0602] "Emotional state" refers to the user's psychological state, determined based on biometric data and subjective self-reporting.

[0603] "Re-evaluation" is the process of reviewing and updating the results of an evaluation that has already been conducted.

[0604] "Schedule suggestions" are recommendations for scheduling and time management to support users' efficient activities.

[0605] This invention combines a visit route optimization system with an emotion engine to support efficient and stress-free sales activities for users. The specific implementation of this system is described below.

[0606] The server first obtains geographic information from a Geographic Information System (GIS) and stores it in a database. Next, it receives customer information from a Customer Relationship Management (CRM) system and obtains past travel history and real-time traffic information via an API. Based on this data, it plots visited locations on a map and groups geographically close locations. Then, to optimize the order of visits, it uses algorithms such as the Travel Salesperson Problem (TSP) algorithm. The optimized visit route is notified to the user and sent to the terminal as a schedule suggestion. During this optimization process, it is also possible to consider the type of transportation and set priority conditions.

[0607] The terminal consists of mobile devices such as smartphones and tablets, and displays the visit route and schedule sent from the server to the user. The terminal also acquires the user's biometric data using biosensors, and this data is analyzed by an emotion engine. The emotion engine evaluates the user's emotional state from the acquired data and, based on that, re-evaluates the schedule and adjusts the visit route. To reduce the user's stress and fatigue, it can reduce the number of visits or suggest breaks as needed.

[0608] As a concrete example, consider a scenario where a sales representative visits multiple customers in a single day. If the emotion engine detects user fatigue, the device re-evaluates the schedule to insert a short break before the next visit. Furthermore, if the user's stress level is determined to be high, the system can change the order of visits and suggest a new route that allows for less stressful travel.

[0609] Examples of prompt statements are as follows:

[0610] "Please describe the system for optimizing visit routes. This system utilizes user emotional data to flexibly adjust visit plans. How does it change the order of visits if the user's stress level is high?"

[0611] This system allows for increased efficiency in sales activities and the provision of flexible visit plans tailored to the user's emotions. This, in turn, can improve user satisfaction and sales results.

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] The server retrieves geographic and customer information. It receives data from geographic information systems (GIS) and customer relationship management (CRM) systems as input. The server stores this data in a database and generates location data for plotting as output.

[0615] Step 2:

[0616] The server plots the visited locations on a map and groups geographically close locations. It uses the location data generated in step 1 as input. Data processing involves grouping the visited locations and calculating the center point of each group. The output is a grouped list of locations, which forms the basis for efficient route planning.

[0617] Step 3:

[0618] The server calculates the order of visits and optimizes the initial route. It uses the list of locations generated in step 2 as input. The server applies the Travel Salesman Problem (TSP) algorithm to calculate the shortest route. The result of the data calculation is output as the optimal order of visits.

[0619] Step 4:

[0620] The terminal receives an optimized visit route sent from the server. The input is optimized visit sequence data from the server. The terminal displays this to the user, presenting it as a visit plan. The output is a route display in a user-readable format.

[0621] Step 5:

[0622] The device acquires biometric data from wearable devices and built-in sensors. Real-time data from sensors is used as input. The device collects the data and analyzes the user's emotional state using an emotion engine. Through data processing, the analysis results are obtained as the user's emotional state.

[0623] Step 6:

[0624] The server receives data from the emotion engine and re-evaluates the visit route. The input is the result of an analysis of the user's emotional state. Based on this, the server re-evaluates the visit route and schedule and outputs an adjusted route suggestion. To reduce stress, it suggests adjusting the number of visits and inserting breaks.

[0625] Step 7:

[0626] The terminal notifies the user of the coordinated visit route and schedule. The input is a coordinated route proposal from the server. The terminal presents it to the user in an easy-to-understand format and provides an updated route as output. This allows the user to conduct visits efficiently while reducing stress.

[0627] (Application Example 2)

[0628] 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."

[0629] Conventional visit route optimization systems could calculate efficient routes using geographical and traffic information, but they failed to take into account the user's emotional state or stress levels. This meant that while users could complete their visits efficiently, they might also experience emotional distress. Therefore, there was a need for methods to improve user comfort.

[0630] 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.

[0631] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's emotional state and dynamically adjusting the visit route based on that state. This makes it possible to optimize the route while taking the user's emotional state into consideration.

[0632] "Geographic information" refers to information related to specific points or areas on a map, and is data used to identify destinations and calculate routes.

[0633] A "destination" is a place that a visitor intends to reach with a specific purpose.

[0634] "Plotting" is the act of displaying multiple points on a map and visualizing their relative positions.

[0635] "Grouping" is the process of combining geographically close visited locations and treating them as a single visit unit.

[0636] The "optimal visiting order" refers to a calculated sequence of locations to visit efficiently, aiming to minimize time and cost.

[0637] "Past travel history" refers to records of past visits and travels, and is data used for route optimization.

[0638] "Real-time traffic data" refers to the latest information on current road conditions and traffic volume, which is used to dynamically adjust your travel route.

[0639] An "optimized visit route" is a path calculated by taking various conditions into consideration to efficiently carry out a visit.

[0640] "Emotional state" refers to the user's current psychological state and is a concept that includes feelings of happiness and stress levels.

[0641] "Dynamic adjustment" means making adaptive changes in response to real-time changes in the situation.

[0642] "Suggesting a break" means prompting the user to pause at an appropriate time based on their stress level and fatigue level.

[0643] The system for implementing this invention mainly consists of a server and terminals. The server acquires geographic information, plots visited locations on a map, and performs data processing to group geographically close visited locations. Geographic information system (GIS) software and databases are used for this purpose. It is possible to manipulate geographic data and calculate the order of visits using programming languages ​​such as Python and Java.

[0644] The server also receives data from the emotion engine. This emotion engine acquires the user's emotional state and monitors it in real time via sensors. This allows the server to dynamically adjust the visit route and suggest breaks based on the user's stress level. Emotional data is acquired from the user's devices, such as smartphones and smartwatches, and analyzed by AI algorithms. Machine learning libraries such as TensorFlow and PyTorch are used for this process.

[0645] The device notifies the user of optimized visit routes and schedule suggestions sent from the server via an application interface. The data is displayed through a native application for Android or iOS. Users can receive real-time updates to their visit plans and route adjustments through the app.

[0646] As a concrete example, when a delivery driver checks the route information given to them at the morning meeting using a smartphone app, if the user's heart rate increases and stress signs are detected, the emotion engine sends this information to the server and recommends a route that reduces stress by re-evaluating the order of visits. An example of a prompt message at this time could be, "How can we improve the driver's stress on the current route?"

[0647] In this way, in addition to optimizing visit routes, it is possible to provide flexible visit plans that take user emotions into consideration, thereby improving user satisfaction.

[0648] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0649] Step 1:

[0650] The server acquires geographic information using a Geographic Information System (GIS). The input geographic information is obtained from a database pre-stored on the server, and based on this, the visited locations are plotted on a map. This plotted information becomes the output used in the next step to optimize the visiting route.

[0651] Step 2:

[0652] The server groups geographically close locations based on the plotted visit locations. The input is the location information of the visit locations obtained in step 1, which is then used to perform grouping using a distance algorithm. The output is a visit order designed to optimize visit efficiency.

[0653] Step 3:

[0654] The server analyzes real-time traffic data and historical travel history to optimize visiting routes. Input data includes current traffic conditions obtained from a traffic information API and historical travel history stored in an internal database. This data is combined to execute an optimization algorithm, generating an optimized visiting route as output.

[0655] Step 4:

[0656] A device equipped with an emotion engine collects biosensor data such as the user's heart rate and skin electrical activity in real time. The input is information from the sensors, and by analyzing this data, the user's emotional state is estimated and their stress level is quantified. This numerical data is used to improve the user experience in the next step.

[0657] Step 5:

[0658] The server receives output from the emotion engine and dynamically adjusts suggested visit routes and rest stops. The input is the user's emotion data obtained in step 4, which is used to re-evaluate the current route. The output generates a new visit order and appropriate rest points.

[0659] Step 6:

[0660] The terminal notifies the user of new visit routes and rest stop suggestions sent from the server. The input is data from the server, which is then displayed on the terminal's application interface. The user then uses this information to carry out their visit. The output is a smooth visit activity on the user's end.

[0661] This workflow allows users to conduct their visits efficiently and comfortably.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] [Fourth Embodiment]

[0666] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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).

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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".

[0679] One embodiment of the present invention is a visit route creation system for supporting sales activities. This system mainly consists of a server, terminals, and users.

[0680] Description of the server process

[0681] The server retrieves geographical information, customer information, historical travel history data, and real-time traffic data from data sources and integrates them into a database. Based on travel history, the server creates a profile to determine visit frequency and effective visit order. Next, the server plots the planned visit locations on a map and groups them based on geographical proximity. The server calculates the optimal visit order from the grouped locations, optimizing the travel route based on priority conditions that take traffic conditions into account (e.g., reducing the number of right turns). The server then sends the optimized visit route information to the terminal.

[0682] Description of terminal processes

[0683] The terminal displays the user with the visit route information received from the server. Furthermore, the terminal provides a schedule suggestion regarding planned travel times and visit order, and offers an interface that allows the user to make adjustments. In addition, the terminal receives traffic data from the server in real time while traveling, recalculates the visit route as needed, and informs the user of the updated route. In this way, it can respond in real time to changes in traffic conditions while traveling.

[0684] Explanation from the user's perspective

[0685] The user enters a list of customer addresses to be visited into the terminal interface and starts the system. The system automatically calculates the visit route and displays the estimated travel time and the order of visits. The user can refer to the presented schedule and adjust visit times as needed. After the visit is completed, the system improves the accuracy of route optimization for future visits by providing feedback on the terminal.

[0686] Thus, this system allows sales representatives to plan visit routes efficiently and quickly, and enables real-time responses, thereby improving the overall efficiency of sales activities. For example, even when there are dozens of locations to visit, the system can propose the optimal route in a short time, supporting swift and effective sales activities.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server collects and integrates geographical information, customer information, historical travel history, and real-time traffic data from external data sources and internal databases. It regularly updates this information stored in the database to keep it up-to-date.

[0690] Step 2:

[0691] The user enters a list of customer addresses to be visited into the terminal. The terminal then sends the entered address information to the server.

[0692] Step 3:

[0693] The server plots the received address data on a map and identifies geographically close locations to visit. Based on the plotting results, the locations are grouped together.

[0694] Step 4:

[0695] The server calculates the order of visits based on grouped locations. It considers travel time and traffic conditions and applies priority criteria (e.g., reducing the number of right turns, shortest distance) to select the most appropriate route.

[0696] Step 5:

[0697] The server sends the calculated visit order and route information to the terminal. The terminal displays the received information to the user and provides the user with an overview of the visit plan.

[0698] Step 6:

[0699] The user reviews the presented visit plan and adjusts the scheduled times and order of visits as needed. The terminal records the user's changes and sends the updated information to the server.

[0700] Step 7:

[0701] The terminal uses real-time traffic data during visits and provides users with alerts based on changes in traffic conditions. The server recalculates the route as needed and sends the changes to the terminal.

[0702] Step 8:

[0703] After the visit ends, the user enters feedback via a terminal. The terminal sends the feedback to a server, which collects data for future route optimization.

[0704] Through this process, the system provides users with efficient visit routes and continuously improves the service through schedule adjustments and feedback.

[0705] (Example 1)

[0706] 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".

[0707] The present invention aims to improve the efficiency of customer visits and travel in sales activities and fieldwork. In particular, in situations where there are multiple visit locations, there is a need to generate and modify optimal visit routes using past visit history and real-time traffic information. However, conventional methods do not fully utilize this data, resulting in challenges such as difficulty in optimizing routes and responding flexibly.

[0708] 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.

[0709] In this invention, the server includes means for acquiring spatial information and visualizing multiple visit locations, means for classifying spatially adjacent visit locations and calculating the optimal visit order, and means for analyzing past travel history and dynamic traffic data and adjusting the visit route. This enables efficient and flexible optimization of visit routes.

[0710] "Spatial information" refers to data that includes geographical elements and represents information related to a specific location or area.

[0711] A "visiting location" refers to a specific geographical place that a person needs to visit for business or service provision purposes.

[0712] "Visualization" refers to the process of representing data and information visually to make them easier to understand.

[0713] "Classification" is the process of grouping data or information based on certain criteria or attributes.

[0714] "Optimal visiting order" refers to the most rational sequence for efficiently visiting locations.

[0715] "Movement history" refers to information that records the routes and frequency of movement of people and objects in the past.

[0716] "Dynamic traffic data" refers to data that reflects the current traffic situation in real time.

[0717] "Visiting route" refers to a specific travel route set up to visit multiple locations.

[0718] "Adjustment" refers to the act of changing data or plans according to the purpose or conditions.

[0719] The embodiment for carrying out the present invention relates to a visit route creation system aimed at improving the efficiency of sales activities. This system mainly consists of three components: a server, a terminal, and a user.

[0720] The server uses geographic information systems and customer management software to acquire spatial and customer information. Furthermore, it employs a client-server model to analyze past travel history and dynamic traffic data, integrating the data into a dedicated database. For analysis, it utilizes real-time traffic information services such as the Google Maps API. Based on this data, the server calculates the optimal route for the user and sends it to their terminal.

[0721] The terminal receives data from the server and visualizes the visit route using a dedicated application. When the user enters the desired locations to visit, the terminal displays them and provides an interface that allows them to adjust the order of visits and travel times as needed. The terminal also incorporates real-time updated traffic information and recalculates the route during travel to provide the latest visit order.

[0722] Users can input customer address lists into this system and adjust their schedules while referring to automatically calculated visit order and estimated time. After each visit, user feedback is sent to the server and used to improve the accuracy of future visit routes.

[0723] For example, a salesperson with multiple destinations who wants to efficiently plan their route can use this system to obtain the optimal route in a short amount of time. Another example of a prompt when using the AI ​​generation model is "Enter a list of destinations to suggest the optimal sales route." In this way, users can effectively utilize their time and resources, enabling them to conduct sales activities more efficiently.

[0724] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0725] Step 1:

[0726] The server retrieves spatial and customer information from geographic information systems and customer management software. Inputs include specified geographic regions and specific customer lists. The server stores this data in a database and integrates it while maintaining information consistency, preparing it for subsequent processing.

[0727] Step 2:

[0728] The server analyzes past travel history and dynamic traffic data. It receives past travel patterns and real-time traffic information as input. The server processes this data through an analysis algorithm, calculating the frequency of visits to specific locations and the optimal order of visits to create an efficient profile.

[0729] Step 3:

[0730] The server optimizes the visit route based on the user's profile. The inputs include the geographical coordinates and priority of each visit location, as well as traffic data. The server calculates the optimal route, considering factors such as reducing the number of right turns at intersections, and sends the result to the user's terminal.

[0731] Step 4:

[0732] The terminal displays visit route information received from the server on its interface. Its inputs include the optimized visit order and estimated duration sent from the server. The terminal visualizes this information and provides an interface for the user to adjust the order and timing of visits.

[0733] Step 5:

[0734] The user enters the desired locations into the terminal and adjusts the schedule as needed. During this process, the user refers to the suggested visit order and estimated time, confirms the most efficient route, and approves it.

[0735] Step 6:

[0736] While on the move, the device continuously receives real-time traffic information from the server and recalculates the route as needed. The device flexibly adapts to the situation by recalculating the route based on changes in traffic conditions and informing the user of the updated route.

[0737] Step 7:

[0738] Users enter feedback on their device after their visit ends. The entered feedback data is sent to the server and used to optimize future visit routes. The server uses this data to leverage a generative AI model to continuously improve the accuracy of the routes.

[0739] (Application Example 1)

[0740] 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".

[0741] In modern logistics, designing efficient delivery routes is a critical challenge that directly impacts an organization's time and cost savings. However, many current systems are unable to flexibly respond to traffic conditions and real-time changes, and often fail to quickly resolve complex problems. This makes it difficult to maximize delivery efficiency and execute optimal delivery plans.

[0742] 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.

[0743] In this invention, the server includes means for acquiring geographic information and plotting multiple destinations on a map, means for grouping geographically close destinations and calculating the optimal order of visits, and means for analyzing past travel history and real-time traffic data to optimize the visit route. This enables the dynamic modification of the delivery route in real time according to traffic conditions, allowing for the execution of an efficient and flexible delivery plan.

[0744] "Geographic information" refers to data that shows location information and characteristics of specific points on Earth.

[0745] A "visiting location" refers to a place visited for the purpose of carrying out a specific task, such as delivery or sales activities.

[0746] "Plotting on a map" means using geographical information to visually represent visited locations on a map.

[0747] "Visiting order" refers to a plan that determines the order in which to visit locations in order to carry out tasks efficiently.

[0748] "Movement history" refers to data that shows changes in location and routes in the past.

[0749] "Real-time traffic data" refers to information about current traffic conditions that is updated instantly.

[0750] "Optimizing a visit route" means calculating the best possible path to visit locations, while adhering to the minimum time, distance, and other constraints.

[0751] "Notifying the user" means communicating information to the user based on calculation and analysis results.

[0752] "Schedule proposals" refer to providing users with a plan to help them carry out their activities efficiently.

[0753] "Information equipment" refers to electronic devices that process digital data, and generally includes computers.

[0754] The "shortest route" is the path chosen to minimize distance and travel time when visiting different locations.

[0755] "Visualization" refers to the process of representing data and results graphically, making them easy to understand visually.

[0756] "To change dynamically" means to make corrections or adjustments on the fly depending on the conditions or circumstances.

[0757] To implement this invention, three main components are used: a server, a terminal, and a user.

[0758] The server first integrates geographical information and customer visit location information into a single database. This information is used to optimize the order of visits and improve efficiency based on past travel history. The server also has the ability to analyze real-time traffic data and optimize visit routes. Programming languages ​​such as Python and JavaScript can be used for this data processing. The server sends the calculated optimal visit route to the terminal and presents it to the user.

[0759] The terminal visually displays the visit route to the user based on information received from the server. The terminal uses common information devices such as smartphones and tablets. Its interface is intuitive and easy to use, and it includes functions to allow the user to check and adjust their visit schedule. Furthermore, it receives real-time updated traffic information from the server during the visit and suggests dynamic changes to the visit route as needed.

[0760] Users can utilize this visit route creation system by accessing the system and registering their destinations. This allows for efficient travel based on routes suggested by information devices. After the user completes their visit, feedback data is entered, enabling the server to perform machine learning to improve the accuracy of future route optimizations.

[0761] This system assists in efficiently visiting multiple locations, contributing to increased efficiency in logistics centers and sales activities. For example, in a pizza delivery service, it can suggest routes that allow drivers to reach multiple delivery destinations in the shortest possible time.

[0762] As an example of a prompt, you could instruct the system with a message like, "When visiting multiple delivery locations, please create an app that uses real-time traffic information to show the most efficient order of visits."

[0763] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0764] Step 1:

[0765] The server retrieves and integrates geographical information, customer visit location information, and past travel history into its database. This information includes latitude and longitude, arrival time, and visit frequency for each visit location, and is used for subsequent data analysis. The server uses a database management system to efficiently store and manage this information.

[0766] Step 2:

[0767] The server receives real-time traffic data from external traffic information services. This data includes current road conditions and predicted congestion information. The server periodically updates this data using APIs via a program. The server then combines past travel history with real-time traffic data to calculate the shortest route between destinations and determine the optimal order of visits. This optimizes the visit route.

[0768] Step 3:

[0769] The server sends the calculated optimal route to the terminal. The server formats the information in a data structure such as JSON and delivers it to the terminal quickly and accurately via a communication protocol. This allows the user to easily view the route details.

[0770] Step 4:

[0771] The terminal visualizes and displays the visit route received by the user on the screen. Using a map service application, the terminal provides information including the estimated arrival time at each destination along the route. The user can refer to this information and adjust their visit schedule as needed.

[0772] Step 5:

[0773] After completing their visit, users provide feedback through an interface on their device. Users record their actual experience, including visit duration and traffic conditions, and this data is then sent back to the server.

[0774] Step 6:

[0775] The server analyzes feedback data sent by the user and uses machine learning algorithms to improve the accuracy of route optimization for future visits. The server utilizes a generative AI model to better reflect user needs and conditions based on the prompt text for creating visit routes.

[0776] 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.

[0777] This invention combines an emotion engine with a visit route optimization system, enabling efficient and less stressful sales activities by flexibly adjusting visit plans based on the user's emotions. The system primarily consists of a server, terminals, and an emotion engine.

[0778] Description of the server process

[0779] The server acquires geographical information, customer information, historical travel history data, and real-time traffic data, and centrally manages them in a database. Using this data, it plots visited locations, performs geographical grouping, and optimizes the order of visits. Furthermore, it sets priority conditions according to the mode of transport and calculates efficient visiting routes. The server sends the visit plan to the terminal and also receives input from the emotion engine to re-evaluate the route and schedule.

[0780] Description of terminal processes

[0781] The terminal receives the visit route and user sentiment data transmitted from the server and displays it to the user. Based on the sentiment data, which is analyzed in real time by the sentiment engine, the terminal dynamically adjusts the order of visits and rest plans. It also provides a function to monitor real-time traffic data while the user is traveling and correct the route as needed.

[0782] Explanation of the Emotion Engine

[0783] The emotion engine acquires and analyzes user emotion data from the device's sensors and external devices, and sends the results to the server. It understands the user's stress levels and fluctuations in comfort, and uses this information to readjust or suggest visit routes. For example, if a user is feeling stressed, the schedule might be restructured to reduce the number of visits or to include breaks.

[0784] Explanation of specific examples

[0785] As a concrete example, if the emotion engine detects that a user is fatigued while visiting multiple clients, the device will use this information to revise the visit schedule and insert short breaks between visits. Furthermore, if it determines that the user is experiencing high stress levels, it will change the order of visits and suggest a less stressful route. In this way, users can continue their visiting activities efficiently and comfortably.

[0786] This system aims not only to optimize visit routes but also to provide flexible visit plans that take into account user emotions. Therefore, it can improve the efficiency of sales activities and increase user satisfaction.

[0787] The following describes the processing flow.

[0788] Step 1:

[0789] The server retrieves the necessary geographic and customer information from the geographic information system and customer database. This generates an initial list of planned visit locations.

[0790] Step 2:

[0791] The server plots visited locations on a map using past travel history and real-time traffic data, and groups geographically close locations. It then calculates the optimal order of visits and creates an initial route.

[0792] Step 3:

[0793] The user views a list of customers they wish to visit via their device and makes adjustments as needed. The device then sends information to the server based on the user's input.

[0794] Step 4:

[0795] The emotion engine collects user emotional data in real time through sensors built into the device and external devices. This data indicates the user's stress level, fatigue level, and other emotional states.

[0796] Step 5:

[0797] The emotion engine analyzes the collected user emotion data and sends the result of the emotion state to the server. Based on this result, the server recalculates the visit order and break insertions as needed.

[0798] Step 6:

[0799] The terminal notifies the user of updated visit routes from the server and schedule adjustments based on their mood. The user can review the presented plan and give final approval.

[0800] Step 7:

[0801] While the visit is underway, the terminal monitors real-time traffic data and adjusts the route as needed, reflecting continuous sentiment analysis by the emotion engine.

[0802] Step 8:

[0803] After a visit, the user provides brief feedback on their device, and the emotion engine uses this feedback as training data. The server stores this feedback to use for route optimization in the future.

[0804] This process allows the system to streamline user visits and provide a comfortable sales experience by addressing emotional situations.

[0805] (Example 2)

[0806] 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".

[0807] In modern sales activities, optimizing visit routes is crucial, but the associated user stress and emotional burden are often overlooked. Traditional systems only optimize based on geographical and traffic information, failing to respond flexibly to the user's emotional state. As a result, it becomes difficult to support users' efficient sales activities, potentially leading to decreased user satisfaction and sales results.

[0808] 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.

[0809] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's biometric data and analyzing their emotional state. This makes it possible to re-evaluate and adjust the visit route and schedule based on the user's emotional state.

[0810] "Geographic information" is a general term for data that includes information about location, topography, and facilities related to a specific place.

[0811] A "place to visit" is location information that indicates a specific place the user should visit.

[0812] "Grouping" is the process of combining multiple objects into a single set based on common attributes or distance.

[0813] The "order of visits" refers to the sequence in which multiple locations are arranged to allow for efficient travel.

[0814] "Past travel history" refers to recorded data about routes the user has traveled or places they have visited in the past.

[0815] "Real-time traffic information" refers to dynamic data that shows the current traffic situation, including information on congestion and accidents.

[0816] "Biometric data" refers to data that indicates the user's physical condition, including heart rate, body temperature, and stress level measurements.

[0817] "Emotional state" refers to the user's psychological state, determined based on biometric data and subjective self-reporting.

[0818] "Re-evaluation" is the process of reviewing and updating the results of an evaluation that has already been conducted.

[0819] "Schedule suggestions" are recommendations for scheduling and time management to support users' efficient activities.

[0820] This invention combines a visit route optimization system with an emotion engine to support efficient and stress-free sales activities for users. The specific implementation of this system is described below.

[0821] The server first obtains geographic information from a Geographic Information System (GIS) and stores it in a database. Next, it receives customer information from a Customer Relationship Management (CRM) system and obtains past travel history and real-time traffic information via an API. Based on this data, it plots visited locations on a map and groups geographically close locations. Then, to optimize the order of visits, it uses algorithms such as the Travel Salesperson Problem (TSP) algorithm. The optimized visit route is notified to the user and sent to the terminal as a schedule suggestion. During this optimization process, it is also possible to consider the type of transportation and set priority conditions.

[0822] The terminal consists of mobile devices such as smartphones and tablets, and displays the visit route and schedule sent from the server to the user. The terminal also acquires the user's biometric data using biosensors, and this data is analyzed by an emotion engine. The emotion engine evaluates the user's emotional state from the acquired data and, based on that, re-evaluates the schedule and adjusts the visit route. To reduce the user's stress and fatigue, it can reduce the number of visits or suggest breaks as needed.

[0823] As a concrete example, consider a scenario where a sales representative visits multiple customers in a single day. If the emotion engine detects user fatigue, the device re-evaluates the schedule to insert a short break before the next visit. Furthermore, if the user's stress level is determined to be high, the system can change the order of visits and suggest a new route that allows for less stressful travel.

[0824] Examples of prompt statements are as follows:

[0825] "Please describe the system for optimizing visit routes. This system utilizes user emotional data to flexibly adjust visit plans. How does it change the order of visits if the user's stress level is high?"

[0826] This system allows for increased efficiency in sales activities and the provision of flexible visit plans tailored to the user's emotions. This, in turn, can improve user satisfaction and sales results.

[0827] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0828] Step 1:

[0829] The server retrieves geographic and customer information. It receives data from geographic information systems (GIS) and customer relationship management (CRM) systems as input. The server stores this data in a database and generates location data for plotting as output.

[0830] Step 2:

[0831] The server plots the visited locations on a map and groups geographically close locations. It uses the location data generated in step 1 as input. Data processing involves grouping the visited locations and calculating the center point of each group. The output is a grouped list of locations, which forms the basis for efficient route planning.

[0832] Step 3:

[0833] The server calculates the order of visits and optimizes the initial route. It uses the list of locations generated in step 2 as input. The server applies the Travel Salesman Problem (TSP) algorithm to calculate the shortest route. The result of the data calculation is output as the optimal order of visits.

[0834] Step 4:

[0835] The terminal receives an optimized visit route sent from the server. The input is optimized visit sequence data from the server. The terminal displays this to the user, presenting it as a visit plan. The output is a route display in a user-readable format.

[0836] Step 5:

[0837] The device acquires biometric data from wearable devices and built-in sensors. Real-time data from sensors is used as input. The device collects the data and analyzes the user's emotional state using an emotion engine. Through data processing, the analysis results are obtained as the user's emotional state.

[0838] Step 6:

[0839] The server receives data from the emotion engine and re-evaluates the visit route. The input is the result of an analysis of the user's emotional state. Based on this, the server re-evaluates the visit route and schedule and outputs an adjusted route suggestion. To reduce stress, it suggests adjusting the number of visits and inserting breaks.

[0840] Step 7:

[0841] The terminal notifies the user of the coordinated visit route and schedule. The input is a coordinated route proposal from the server. The terminal presents it to the user in an easy-to-understand format and provides an updated route as output. This allows the user to conduct visits efficiently while reducing stress.

[0842] (Application Example 2)

[0843] 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".

[0844] Conventional visit route optimization systems could calculate efficient routes using geographical and traffic information, but they failed to take into account the user's emotional state or stress levels. This meant that while users could complete their visits efficiently, they might also experience emotional distress. Therefore, there was a need for methods to improve user comfort.

[0845] 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.

[0846] In this invention, the server includes means for acquiring geographic information and plotting multiple visit locations on a map, means for grouping geographically close visit locations and calculating the optimal visit order, and means for acquiring the user's emotional state and dynamically adjusting the visit route based on that state. This makes it possible to optimize the route while taking the user's emotional state into consideration.

[0847] "Geographic information" refers to information related to specific points or areas on a map, and is data used to identify destinations and calculate routes.

[0848] A "destination" is a place that a visitor intends to reach with a specific purpose.

[0849] "Plotting" is the act of displaying multiple points on a map and visualizing their relative positions.

[0850] "Grouping" is the process of combining geographically close visited locations and treating them as a single visit unit.

[0851] The "optimal visiting order" refers to a calculated sequence of locations to visit efficiently, aiming to minimize time and cost.

[0852] "Past travel history" refers to records of past visits and travels, and is data used for route optimization.

[0853] "Real-time traffic data" refers to the latest information on current road conditions and traffic volume, which is used to dynamically adjust your travel route.

[0854] An "optimized visit route" is a path calculated by taking various conditions into consideration to efficiently carry out a visit.

[0855] "Emotional state" refers to the user's current psychological state and is a concept that includes feelings of happiness and stress levels.

[0856] "Dynamic adjustment" means making adaptive changes in response to real-time changes in the situation.

[0857] "Suggesting a break" means prompting the user to pause at an appropriate time based on their stress level and fatigue level.

[0858] The system for implementing this invention mainly consists of a server and terminals. The server acquires geographic information, plots visited locations on a map, and performs data processing to group geographically close visited locations. Geographic information system (GIS) software and databases are used for this purpose. It is possible to manipulate geographic data and calculate the order of visits using programming languages ​​such as Python and Java.

[0859] The server also receives data from the emotion engine. This emotion engine acquires the user's emotional state and monitors it in real time via sensors. This allows the server to dynamically adjust the visit route and suggest breaks based on the user's stress level. Emotional data is acquired from the user's devices, such as smartphones and smartwatches, and analyzed by AI algorithms. Machine learning libraries such as TensorFlow and PyTorch are used for this process.

[0860] The device notifies the user of optimized visit routes and schedule suggestions sent from the server via an application interface. The data is displayed through a native application for Android or iOS. Users can receive real-time updates to their visit plans and route adjustments through the app.

[0861] As a concrete example, when a delivery driver checks the route information given to them at the morning meeting using a smartphone app, if the user's heart rate increases and stress signs are detected, the emotion engine sends this information to the server and recommends a route that reduces stress by re-evaluating the order of visits. An example of a prompt message at this time could be, "How can we improve the driver's stress on the current route?"

[0862] In this way, in addition to optimizing visit routes, it is possible to provide flexible visit plans that take user emotions into consideration, thereby improving user satisfaction.

[0863] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0864] Step 1:

[0865] The server acquires geographic information using a Geographic Information System (GIS). The input geographic information is obtained from a database pre-stored on the server, and based on this, the visited locations are plotted on a map. This plotted information becomes the output used in the next step to optimize the visiting route.

[0866] Step 2:

[0867] The server groups geographically close locations based on the plotted visit locations. The input is the location information of the visit locations obtained in step 1, which is then used to perform grouping using a distance algorithm. The output is a visit order designed to optimize visit efficiency.

[0868] Step 3:

[0869] The server analyzes real-time traffic data and historical travel history to optimize visiting routes. Input data includes current traffic conditions obtained from a traffic information API and historical travel history stored in an internal database. This data is combined to execute an optimization algorithm, generating an optimized visiting route as output.

[0870] Step 4:

[0871] A device equipped with an emotion engine collects biosensor data such as the user's heart rate and skin electrical activity in real time. The input is information from the sensors, and by analyzing this data, the user's emotional state is estimated and their stress level is quantified. This numerical data is used to improve the user experience in the next step.

[0872] Step 5:

[0873] The server receives output from the emotion engine and dynamically adjusts suggested visit routes and rest stops. The input is the user's emotion data obtained in step 4, which is used to re-evaluate the current route. The output generates a new visit order and appropriate rest points.

[0874] Step 6:

[0875] The terminal notifies the user of new visit routes and rest stop suggestions sent from the server. The input is data from the server, which is then displayed on the terminal's application interface. The user then uses this information to carry out their visit. The output is a smooth visit activity on the user's end.

[0876] This workflow allows users to conduct their visits efficiently and comfortably.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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."

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0898] The following is further disclosed regarding the embodiments described above.

[0899] (Claim 1)

[0900] A means of acquiring geographic information and plotting multiple visited locations on a map,

[0901] A means for grouping geographically close destinations and calculating the optimal order of visits,

[0902] A method for optimizing visiting routes by analyzing past travel history and real-time traffic data,

[0903] A system that includes means for notifying users of optimized visit routes and providing schedule suggestions.

[0904] (Claim 2)

[0905] The system according to claim 1, which sets priority conditions in route calculation according to the type of mode of transport.

[0906] (Claim 3)

[0907] The system according to claim 1, which collects user feedback data after a visit and learns from it to improve the accuracy of route suggestions for the next visit.

[0908] "Example 1"

[0909] (Claim 1)

[0910] A means of acquiring spatial information and visualizing multiple visited locations,

[0911] A means for classifying spatially adjacent visit locations and calculating the optimal visit order,

[0912] A means of adjusting visit routes by analyzing past travel history and dynamic traffic data,

[0913] A means of notifying users of the adjusted visit route and presenting a time plan,

[0914] A means to update traffic information in real time and recalculate the route to visit,

[0915] A means of collecting feedback data from users and learning to improve the accuracy of the next path calculation,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, which sets priority conditions in route calculation according to the type of means of transportation.

[0919] (Claim 3)

[0920] The system according to claim 1, which uses a generative AI model to propose the optimal visit route.

[0921] "Application Example 1"

[0922] (Claim 1)

[0923] A means of acquiring geographic information and plotting multiple visited locations on a map,

[0924] A means for grouping geographically close destinations and calculating the optimal order of visits,

[0925] A method for optimizing visiting routes by analyzing past travel history and real-time traffic data,

[0926] A means of notifying users of optimized visit routes and suggesting schedules,

[0927] A means of calculating and visualizing the shortest route between destination locations using an information device,

[0928] A system that includes means for dynamically changing delivery routes in real time according to traffic conditions.

[0929] (Claim 2)

[0930] The system according to claim 1, which sets priority conditions in route calculation according to the type of mode of transport.

[0931] (Claim 3)

[0932] The system according to claim 1, which collects user feedback data after a visit and learns from it to improve the accuracy of route suggestions for the next visit.

[0933] "Example 2 of combining an emotion engine"

[0934] (Claim 1)

[0935] A means of acquiring geographic information and plotting multiple visited locations on a map,

[0936] A means for grouping geographically close destinations and calculating the optimal order of visits,

[0937] A means of optimizing visiting routes by analyzing past travel history and real-time traffic information,

[0938] A means of acquiring user biometric data and analyzing their emotional state,

[0939] A means of re-evaluating and adjusting visit routes and schedules based on the user's emotional state,

[0940] A system that includes means for notifying the user of optimized and re-evaluated visit routes and presenting a schedule including rest suggestions.

[0941] (Claim 2)

[0942] The system according to claim 1, which sets priority conditions in route calculation according to the type of transportation, and further performs dynamic adjustments according to the user's emotional state.

[0943] (Claim 3)

[0944] The system according to claim 1, which collects user feedback information after a visit, learns to improve the accuracy of route suggestions for the next visit, and optimizes by combining sentiment data and feedback.

[0945] "Application example 2 when combining with an emotional engine"

[0946] (Claim 1)

[0947] A means of acquiring geographic information and plotting multiple visited locations on a map,

[0948] A means for grouping geographically close destinations and calculating the optimal order of visits,

[0949] A method for optimizing visiting routes by analyzing past travel history and real-time traffic data,

[0950] A means of notifying users of optimized visit routes and suggesting schedules,

[0951] A means of acquiring the user's emotional state and dynamically adjusting the visit route based on that emotion,

[0952] A system that includes means for suggesting breaks during delivery activities.

[0953] (Claim 2)

[0954] The system according to claim 1, which sets priority conditions in route calculation according to the type of mode of transport.

[0955] (Claim 3)

[0956] The system according to claim 1, which collects user feedback data after a visit and learns from it to improve the accuracy of route suggestions for the next visit. [Explanation of Symbols]

[0957] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring geographic information and plotting multiple visited locations on a map, A means for grouping geographically close destinations and calculating the optimal order of visits, A method for optimizing visiting routes by analyzing past travel history and real-time traffic data, A means of notifying users of optimized visit routes and suggesting schedules, A means of calculating and visualizing the shortest route between destination locations using an information device, A system that includes means for dynamically changing delivery routes in real time according to traffic conditions.

2. The system according to claim 1, which sets priority conditions in route calculation according to the type of mode of transport.

3. The system according to claim 1, which collects user feedback data after a visit and learns from it to improve the accuracy of route suggestions for the next visit.

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