System

Abstract

A system is provided.SOLUTION: Means for inputting a distance or time desired by a user and a departure point, means for transmitting the input data to a server, means for generating an optimal walk route using a geographic information system based on the input data, means for transmitting the generated route to a terminal of the user, and means for analyzing past walk data and feedback of the user, the system includes a means for learning the preference of a user, a means for customizing a route based on the learning result, a means for presenting the generated customized route to the user and providing navigation, and a means for collecting feedback from the user and reflecting it on the next route generation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, walking is an important activity for maintaining health and relaxation, yet boredom and staleness caused by walking the same route repeatedly are becoming a problem. Furthermore, the appeal of walking tends to decrease because it is difficult to discover new stores and local attractions. Meanwhile, there is a demand for selecting appropriate walking routes and providing safe and comfortable walking environments. Furthermore, it is not easy to provide walking routes customized to each user's preferences and health condition. The purpose of this invention is to comprehensively solve these problems. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. First, a means is provided for the user to input a desired distance or time and a starting point. Next, a means is provided for transmitting the input data to a server. This server then has a means for generating an optimal walking route using a geographic information system based on the input data. The generated route is sent to the user's device, allowing the user to visually check and use the walking route. The system also provides a means for analyzing the user's past walking data and feedback and learning the user's preferences, thereby customizing the route based on the learning results. The system further has a means for presenting the generated customized route to the user and providing navigation. Finally, a means is provided for collecting user feedback and reflecting it in the next route generation, thereby continuously providing an optimal walking route. Through these means, users can enjoy a safe, comfortable, and refreshing walking experience.

[0006] "User" means an individual or group that uses the System.

[0007] "Distance" refers to the physical distance the user wants to walk during their walk.

[0008] "Time" refers to the elapsed time the user wishes to spend on the walk.

[0009] "Starting point" refers to the location where the user begins their walk.

[0010] "Terminal" refers to a device operated by a user, such as a computer, smartphone, or tablet.

[0011] "Server" refers to a remote computer system that provides the central functionality and processes data for the system.

[0012] "Geographic information system" refers to a system that collects, manages, and analyzes geospatial data.

[0013] A "walking route" refers to a route set for a user to walk.

[0014] "Google Maps API" refers to the application program interface for accessing map data provided by Google.

[0015] "Famous stores and facilities" refers to commercial facilities, tourist attractions, or public facilities that are widely known within the area.

[0016] "Feedback" refers to the data of ratings and comments provided by users after their walking experience.

[0017] "Navigation" refers to the function of providing instructions and guidance to help users accurately navigate a specified route. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time. Below, we will create a program for this system and explain the program's processing in natural language. We will also provide specific examples.

[0040] Overall system overview

[0041] This system is implemented using the user's device and a cloud server. First, the user uses a smartphone app to input walking settings (distance, time, starting point, types of points of interest). The device then sends this setting data to the server. The server uses a geographic information system such as Google Maps API to generate an optimal walking route based on the user's desired conditions. The generated route is sent to the user's device, where the user can visually check the route and use the navigation function to walk. The system also has the function of further customizing future walking routes by collecting and analyzing the user's past walking data and feedback.

[0042] Specific processing of the program

[0043] 1. Enter your user settings

[0044] User: Enters desired distance or time, starting point, and type of points of interest through a smartphone app interface.

[0045] 2. Sending input data

[0046] Device: Sends user-entered configuration data, including distance, time, starting point, and points of interest, to a cloud server.

[0047] 3. Route Generation

[0048] Server: Uses the Google Maps API based on the input data to generate a walking route that best suits the user's desired conditions.

[0049] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0050] Server: Retrieves information about popular spots and attractions from a local database and incorporates it into the route.

[0051] 4. Send and navigate a route

[0052] Server: Sends the generated walking route to the user's device.

[0053] Device: The received route is displayed in map format and presented to the user. The navigation function allows the user to take a walk while receiving route guidance in real time.

[0054] 5. Learning your preferences

[0055] Server: Records the user's past walking data and feedback, and uses machine learning algorithms to learn the user's preferences.

[0056] 6. Customized route suggestions

[0057] Server: Customizes and suggests routes for the next walk based on the user's preferences and past walking history, ensuring the user always has a fresh and interesting walking experience.

[0058] 7. Gathering Feedback

[0059] User: Enter a route rating and comments on the feedback screen provided after the walk is completed.

[0060] Device: Sends feedback data to the server.

[0061] Server: The server accumulates the received feedback and reflects it in subsequent route generation to provide highly accurate walking suggestions.

[0062] Specific examples

[0063] For example, if a user enters a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the specific processing is as follows:

[0064] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0065] Device: Sends configuration data to the cloud server.

[0066] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0067] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[0068] Server: Sends the generated routes to the device.

[0069] Device: Display the received route on a map and begin navigation.

[0070] User: Walk along a route, stopping at a cafe along the way.

[0071] User: Provide feedback by rating and commenting on the route after completing the walk.

[0072] On your device: Send feedback to the server to help improve future route suggestions.

[0073] The above process realizes a system that provides optimal walking routes according to the needs and preferences of each individual user.

[0074] The processing flow will be explained below.

[0075] Step 1: Enter your user settings

[0076] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the dedicated interface of the smartphone app.

[0077] Step 2: Submitting input data

[0078] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0079] Step 3: Generate Routes

[0080] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0081] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0082] Server: At the same time, it retrieves information about famous stores and facilities from a local database and incorporates this information into the walking route.

[0083] Step 4: Analyze historical data

[0084] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[0085] Step 5: Generate customized routes

[0086] Server: Based on the analysis results, the server generates a customized walking route tailored to the user's individual preferences, allowing the user to efficiently visit points of interest rather than simply walking a specified distance or time.

[0087] Step 6: Providing a Route

[0088] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0089] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0090] Step 7: Start Navigation

[0091] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0092] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0093] Step 8: Provide feedback

[0094] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0095] Terminal: Sends user feedback data to the cloud server.

[0096] Step 9: Analyze and store feedback

[0097] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0098] Through these steps, the system can provide the user with the most suitable and fresh walking route tailored to their preferences.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] Conventional walking route suggestion systems have difficulty proposing optimal routes that take into account the user's desired distance, time, and points of interest. They also lack the ability to learn the user's preferences and propose customized routes. This results in the problem that users cannot always have a fresh and interesting walking experience.

[0102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0103] In this invention, the server includes: means for a user to input a desired distance or time and a starting point; means for transmitting the input data to an information processing device; means for generating an optimal walking route using a geographic information system based on the input data; means for transmitting the generated route to the user's mobile communication terminal; means for analyzing the user's past movement data and rating information and learning the user's preferences; means for customizing the route based on the learning results; means for presenting the generated customized route to the user and providing navigation; means for collecting rating information from the user and reflecting it in the next route generation; means for the user to input input items through an interface and transmit the input setting data to the information processing device; means for encoding and transmitting the route information using a fixed protocol; means for linking map display and voice guidance to provide real-time navigation; and means for retrieving information on surrounding facilities from a database and incorporating it into the route. This allows the user to obtain an optimal walking route that includes points of interest, allowing for a fresh and fulfilling walking experience.

[0104] "User" refers to any individual or entity using a particular service or system.

[0105] "Distance" refers to the physical distance from the starting point to the destination point.

[0106] "Time" refers to the time that has elapsed from a specified start point to a specified end point.

[0107] "Starting point" refers to the place where you start your walk or journey.

[0108] "Information processing device" refers to a device for inputting, processing, storing, and outputting data.

[0109] "Geographic information system" means an information system for collecting, managing, analyzing, and displaying geographic data.

[0110] A "walking route" refers to a travel route generated based on conditions specified by the user.

[0111] "Mobile communication terminal" refers to a portable terminal that can connect to the Internet via wireless communication.

[0112] "Past travel data" refers to data including the routes a user has taken in the past and their location at that time.

[0113] "Evaluation information" refers to information that indicates feedback and satisfaction of a user regarding the service received or the proposed route.

[0114] "Interface" refers to the operating screen or device through which a user interacts with a system or application.

[0115] A "fixed protocol" refers to standardized procedures and rules for transmitting data.

[0116] "Encoding" refers to the process of converting data into a particular format or protocol.

[0117] "Real-time navigation" refers to providing instant route guidance based on your current location.

[0118] "Map display" refers to an interface or means for visually displaying geographic data.

[0119] "Voice guidance" refers to a function that provides instructions and information to the user by voice.

[0120] A "database" refers to a collection or system for systematically organizing, storing, and managing data.

[0121] "Information about nearby facilities" refers to information about stores and facilities located near the user.

[0122] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time, starting point, and types of spots they are interested in. This system operates in conjunction with the user's device and a cloud server.

[0123] First, the user uses a smartphone app to input the settings for their walk. For example, the user enters specific requirements such as "a 20-minute walk," "stopping at a cafe," and "starting from home." This input includes operations performed through the user interface, and the configured data is packaged in JSON format.

[0124] Next, the terminal transmits the input setting data to the cloud server securely using a fixed protocol (e.g., HTTPS).

[0125] The cloud server uses a geographic information system (e.g., Google Maps API) based on the received data to generate an optimal walking route. The route is generated using input data (walking distance, time, starting point, points of interest, etc.) and information on nearby facilities obtained from a local database. This allows the system to plan the optimal route that matches the user's requirements.

[0126] The generated route is again encoded in JSON format and sent to the user's device using the HTTPS protocol. The device displays the received route in map format and, if necessary, integrates the voice guidance function to provide real-time navigation.

[0127] Additionally, the cloud server learns user preferences by collecting and analyzing the user's past travel data and rating information. This includes using machine learning algorithms (e.g., Scikit-learn) to model user preferences. The learning results are reflected in the next route generation, providing the user with a more customized route.

[0128] In addition, the feedback (ratings and comments) provided by users after completing a walk is sent from the device to a cloud server and used to suggest routes for future walks. This ensures that the system always suggests the latest walking routes that best suit the user's needs.

[0129] As a concrete example, consider the case where a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way." The system proceeds as follows:

[0130] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0131] Device: Sends configuration data to the cloud server.

[0132] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0133] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[0134] Server: Sends the generated routes to the device.

[0135] Device: Displays the received route on a map, begins navigation, and provides voice instructions if voice guidance is enabled.

[0136] User: Walk along a route, stopping at a cafe along the way.

[0137] User: Provide feedback by rating and commenting on the route after completing the walk.

[0138] Device: Sends feedback to the cloud server, which incorporates this information into its next proposal.

[0139] The following are some examples of prompts that can be input to a generative AI model:

[0140] "A user wants to take a 20-minute walking route and wants to stop at a cafe along the way. Please suggest the best walking route that fits those criteria. The user's starting point is their home."

[0141] In this way, the present invention is a system that provides optimal walking routes tailored to the user's needs, ensuring a constantly fresh and interesting walking experience.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: Enter your user settings

[0144] Users open the smartphone app and enter their walk settings: the desired distance or time of the walk, the starting point, and the types of points of interest.

[0145] Input: Configuration data that the user enters into the app interface (e.g., "20 minutes," "cafe," "starting from home").

[0146] Output: Configuration data stored in the app.

[0147] Step 2: Submitting input data

[0148] The device sends the configuration data entered by the user to the cloud server. Specifically, the configuration data is packaged in JSON format and sent using the HTTPS protocol.

[0149] Input: The configuration data entered by the user.

[0150] Data processing: Encode the input data in JSON format.

[0151] Output: Configuration data sent to the cloud server using the HTTPS protocol.

[0152] Step 3: Generate a walking route

[0153] The server uses the received data to generate the optimal walking route using a geographic information system (e.g., a map service API). Specifically, it searches for a route based on the user's desired conditions and generates route information.

[0154] Input: JSON formatted configuration data sent from the device.

[0155] Data processing: Uses map service APIs to search and generate optimal routes.

[0156] Output: Information about the generated walking route.

[0157] Step 4: Incorporating local information

[0158] When generating a route, the server retrieves information on popular spots and landmarks from a local database and incorporates it into the route, enhancing the user's walking experience.

[0159] Input: Spot information retrieved from a local database.

[0160] Data processing: Incorporate the acquired spot information into the generated route.

[0161] Output: A customized route with spot information embedded.

[0162] Step 5: Send and navigate your route

[0163] The server sends the generated walking route to the user's device. Specifically, the route information is encoded in JSON format and sent via HTTPS protocol.

[0164] The device displays the route in map format for the user and also provides voice guidance for real-time navigation.

[0165] Input: Route information sent by the server.

[0166] Data processing: Decode JSON format route information into map format.

[0167] Output: Navigation map and voice directions displayed on the device.

[0168] Step 6: Learning user preferences

[0169] The server collects users' past travel data and rating information, including past walking routes and feedback information provided by users, and uses machine learning algorithms to learn users' preferences.

[0170] Input: User's historical travel data and feedback information.

[0171] Data processing: Using machine learning algorithms to analyze and learn user preferences.

[0172] Output: User's preferred model.

[0173] Step 7: Customized Route Suggestion

[0174] Based on the learning results, the server will further customize and suggest the next walking route, allowing users to always have a fresh walking experience that meets their individual needs.

[0175] Input: A learned model of the user's preferences.

[0176] Data processing: Generate a new route that reflects the learning results.

[0177] Output: Customized walking route information.

[0178] Step 8: Gather feedback

[0179] After completing a walk, users can enter their route rating and comments on the app's feedback screen.

[0180] The device sends the feedback data to the cloud server by packaging it in JSON format and sending it using the HTTPS protocol.

[0181] The server stores the received feedback in a database and uses it to generate future routes.

[0182] Input: User-provided feedback information.

[0183] Data processing: The feedback information is stored in a database and added to the machine learning training dataset.

[0184] Output: Updated training model and database.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Today, there is a demand for systems that efficiently suggest walking routes and shopping routes in virtual stores. However, until now, it has been difficult to generate customized routes that reflect a user's individual preferences and past behavioral history. Furthermore, there has been a lack of systems that dynamically suggest routes based on the user's interests, not just time and distance. Furthermore, providing efficient shopping routes in virtual stores remains an unsolved problem. The purpose of this invention is to solve these problems and provide a system that suggests walking and shopping routes optimized for each user.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes: means for inputting the user's desired distance or time and starting point; means for transmitting the input data to the server; and means for generating an optimal walking route using a geographic information system based on the input data. This enables route suggestions based on the user's specified walking route and preferences. The server also includes means for transmitting the generated route to the user's terminal, analyzing the user's past walking data and feedback, and learning the user's preferences; means for customizing the route based on the learning results; and means for presenting the generated customized route to the user and providing navigation. This enables route suggestions based on the user's individual preferences. The server also includes means for collecting user feedback and reflecting it in next route generation; means for inputting the user's desired stores and products and generating an optimal shopping route within the virtual store; and means for transmitting the generated shopping route to the user's terminal and navigating the user. This enables efficient and personalized shopping route suggestions within the virtual store.

[0190] "Means for the user to input the desired distance or time and starting point" refers to an interface or function that allows the user to input information such as the desired distance or time required to travel and the starting point into the terminal.

[0191] "Means for transmitting input data to a server" refers to the communication functions and protocols for transferring data input by a user to a cloud server.

[0192] "Means for generating optimal walking routes using a geographic information system" refers to an algorithm that uses geographic information and map data to calculate and generate the most suitable travel route for the user's desired conditions.

[0193] The "means for transmitting the generated route to the user's terminal" is a communication function for transferring the travel route information generated by the server back to the user's device.

[0194] "Means of analyzing users' past walking data and feedback to learn their preferences" refers to a machine learning algorithm that analyzes users' past usage history and impressions to learn the preferences and patterns of individual users.

[0195] "Means for customizing routes based on learning results" refers to a processing function that customizes travel routes based on the user's individual preferences and past history, and provides personalized suggestions.

[0196] The "means for presenting the generated customized route to the user and providing navigation" is a function for displaying the customized travel route on the user's terminal and providing route guidance in real time.

[0197] "Means of collecting feedback from users and reflecting it in the next route generation" refers to a function that collects opinions and evaluation data provided by users and analyzes and saves them for use in proposing the next travel route.

[0198] "A means for generating the optimal shopping route within a virtual store by inputting the store and products desired by the user" is an algorithm that calculates and generates the optimal shopping route within a virtual space by inputting the product and store information desired by the user.

[0199] "Means for sending the generated shopping route to the user's terminal and navigating" refers to a communication function for sending the generated shopping route to the user's terminal and providing route guidance within the virtual store.

[0200] This invention is a system that generates an optimal walking route based on the user's specific distance and time requirements and starting point, and also provides a shopping route within a virtual store. This system is realized using a smartphone application and a cloud server. It learns the user's preferences and past behavior history and provides a customized route based on that. Details of the mode for implementing this invention are as follows.

[0201] 1. Hardware and software configuration

[0202] Hardware

[0203] Smartphone: Used as a mobile device for users to check routes and enter settings.

[0204] Cloud Server: A central server that generates routes, analyzes user data, and sends and receives navigation data.

[0205] software

[0206] Smartphone app: Enter user settings, view routes, navigate in real time, and collect feedback.

[0207] Map service API: Routes are generated using Google Maps API or similar geographic information services.

[0208] Virtual Store API: A custom API used to generate virtual shopping routes.

[0209] 2. Program Processing

[0210] Entering User Preferences

[0211] Using a smartphone app, users can input the distance and time of their desired walk, as well as the starting point, and can also input the stores and products they want to explore in the virtual store. This input data is sent to a cloud server.

[0212] Sending and processing input data

[0213] The smartphone device sends the input data to a cloud server in real time. The server uses the received data to generate an optimal walking route using a geographic information system such as Google Maps API. The virtual store uses the virtual store API to generate an optimal shopping route based on the desired products and stores.

[0214] Route generation and submission

[0215] The generated route is sent from the cloud server to a smartphone, where users can view the route and start real-time navigation. The same applies to the virtual store, where the optimal shopping route is provided.

[0216] Learning user preferences

[0217] The cloud server collects and analyzes the user's past walking data, shopping history, and feedback, and uses machine learning algorithms to learn the user's preferences and customize future walking and shopping routes.

[0218] Gathering feedback and suggesting next routes

[0219] By collecting feedback provided by users after completing a walk or shopping trip and reflecting it in the next route generation, more accurate route suggestions can be made.

[0220] 3. Examples and prompts

[0221] Specific examples

[0222] For example, if a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will occur:

[0223] Users enter "20 minutes," "visit a cafe," and "depart from home" into the smartphone app.

[0224] The device sends the configuration data to the cloud server.

[0225] The server uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0226] The server references the user's past walking data and optimizes the route to suit their preferences.

[0227] The server sends the generated route to the terminal.

[0228] The device displays the received route on a map and begins navigation.

[0229] Users walk along a route and stop at cafes along the way.

[0230] After completing a walk, users can provide feedback by rating and commenting on the route.

[0231] The device sends feedback to the server to help suggest routes for future trips.

[0232] Example prompts to input to the generative AI model

[0233] Generate optimal routes in your virtual store starting from the entrance for users to search for shoes and bags in the fashion section. Use your store's map information and users' shopping patterns to create efficient routes and guide them step-by-step from the starting point.

[0234] The above is an embodiment of the present invention.

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] The user inputs the desired distance or time and starting point through the smartphone app interface. They also input the stores and products in the virtual store they want to explore. This generates the user's desired conditions as JSON format data. Specifically, the information entered by the user is saved in a form in the app.

[0238] Step 2:

[0239] The data entered in step 1 is sent to the cloud server on the device (smartphone). Specifically, the application sends the data via an HTTP POST request. At this time, the input data is sent to the API server's endpoint. The input data includes distance, time, starting point, and desired store and product information.

[0240] Step 3:

[0241] The server analyzes the received input data and calls the Google Maps API and virtual store API to generate walking and shopping routes that best fit the user's desired conditions. The server first calculates the optimal walking route from the starting point using a geographic information system. It then calculates the shortest route to the desired store or product in the virtual store. Specifically, the server sends an API request to the Google Maps API or virtual store API and receives a response.

[0242] Step 4:

[0243] The server sends the generated walking and shopping routes to the user's device. Specifically, the server encodes the generated route data into JSON format and sends it to the user's smartphone as an HTTP response, including map information and navigation information.

[0244] Step 5:

[0245] The device visually displays the received route and initiates real-time navigation. Specifically, the smartphone app analyzes the received route data, displays it on a map view, and provides step-by-step navigation guidance. The user is provided with visual and audio guidance.

[0246] Step 6:

[0247] After the user has finished their walk or shopping, they provide feedback through the smartphone app. Specifically, the app displays a feedback form where the user can enter their route rating and comments. This feedback is saved in JSON format.

[0248] Step 7:

[0249] The device sends the feedback provided by the user to the cloud server. Specifically, the app sends the feedback data to the server via an HTTP POST request. The feedback data includes the rating score and comments.

[0250] Step 8:

[0251] The server analyzes the received feedback data to learn user preferences and patterns. Specifically, the server uses machine learning algorithms to analyze the feedback data and model the user's preferences, which then personalizes the next route suggestion.

[0252] Step 9:

[0253] The next time the server generates a route, it will generate a customized route based on the learning results and suggest it to the user again. Specifically, the server calculates a new route taking into account the user's past behavioral history and feedback, and sends it back to the device. This series of processes provides the user with an optimized walking and shopping route.

[0254] The above are the processing steps of the system for realizing the application example.

[0255] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0256] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. The program for this system will be described in detail below.

[0257] Overall system overview

[0258] This system combines the user's device, a cloud server, and an emotion engine. The user first inputs the walking settings (distance, time, starting point, points of interest) through a smartphone app. The device then sends this setting data to the cloud server. The server uses a geographic information system (e.g., Google Maps API) to generate an optimal walking route based on the user's desired conditions and sends the generated route to the user's device. At the same time, it collects and analyzes the user's past walking data and feedback, and customizes the next walking route based on the results. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and can adjust the walking route based on the emotional data.

[0259] Specific processing of the program

[0260] 1. Enter your user settings

[0261] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[0262] 2. Sending input data

[0263] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0264] 3. Route Generation

[0265] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0266] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0267] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[0268] 4. Emotion Recognition by Emotion Engine

[0269] Server: The emotion engine recognizes the user's current emotional state.

[0270] Example: Identifying emotions (happiness, sadness, stress, etc.) through user voice input or facial recognition.

[0271] 5. Emotion-based route adjustment

[0272] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[0273] For example: If you're feeling stressed, suggest a route through a quiet park.

[0274] 6. Creating customized routes

[0275] Server: Analyzes the user's past walking data and feedback, and generates a customized walking route based on their individual preferences, combined with their emotional state.

[0276] 7. Route provision

[0277] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0278] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0279] 8. Start navigation

[0280] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0281] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0282] 9. Providing Feedback

[0283] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0284] Terminal: Sends user feedback data to the cloud server.

[0285] 10. Analysis and storage of feedback

[0286] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0287] Specific examples

[0288] For example, if a user sets a preference to "take a 20-minute walk and stop at a cafe along the way," and the emotion engine recognizes that the user is feeling stressed, the specific processing will be as follows:

[0289] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0290] Device: Sends configuration data to the cloud server.

[0291] Server: Uses the Google Maps API to generate the optimal route that passes through cafes that are reachable within 20 minutes from home.

[0292] Server: The emotion engine recognizes the user's emotions and adds a route through a quiet park to reduce stress.

[0293] Server: Optimizes customized routes based on past user walking data and feedback.

[0294] Server: Sends the generated customized route to the device.

[0295] Device: Display the received route on a map and begin navigation.

[0296] User: Walk along a route, stopping at a cafe along the way.

[0297] User: Provide feedback by rating and commenting on the route after completing the walk.

[0298] On your device: Sends feedback to the server to help generate future routes.

[0299] Through the above process, the user can obtain the optimal walking route that matches his or her emotional state.

[0300] The processing flow will be explained below.

[0301] Step 1: Enter your user settings

[0302] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[0303] Step 2: Submitting input data

[0304] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0305] Step 3: Generate Routes

[0306] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0307] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0308] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[0309] Step 4: Analyze historical data

[0310] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[0311] Step 5: Emotion Recognition with the Emotion Engine

[0312] Server: The emotion engine recognizes the user's current emotional state.

[0313] Example: Identifying emotions (happiness, sadness, stress, etc.) from the voice of a user speaking to a smartphone app or from facial images captured through a camera.

[0314] Step 6: Emotionally Based Route Adjustments

[0315] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[0316] Example: If the user is feeling stressed, suggest a route through a quiet, natural park.

[0317] Step 7: Generate customized routes

[0318] Server: Based on the analysis of past data and emotion recognition results, it generates a customized walking route tailored to the user's individual preferences.

[0319] Step 8: Providing a Route

[0320] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0321] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0322] Step 9: Start Navigation

[0323] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0324] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0325] Step 10: Provide feedback

[0326] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0327] Terminal: Sends user feedback data to the cloud server.

[0328] Step 11: Analyze and store feedback

[0329] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0330] Specific examples

[0331] If you want to take a 20-minute walk, visit a cafe, and de-stress.

[0332] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0333] Device: After completing the procedure, send the setting data to the cloud server.

[0334] Server: Uses the Google Maps API to search for cafes that can be reached within 20 minutes from home and generate the optimal route.

[0335] Server: If the emotion engine detects stress from the user's speech or facial recognition, include a quiet park in the route.

[0336] Server: Sends optimized and customized routes to the device.

[0337] Device: Display the route on the map and begin navigation.

[0338] User: Starts walking following a route, stops at a cafe, passes through a quiet park.

[0339] Users: Provide feedback after completing a walk, leaving a rating and comments.

[0340] Device: Sends all feedback data to the server.

[0341] Server: Stores the received data and uses it to improve and customize the route next time.

[0342] Through the above process, the user can obtain the optimal walking route according to his / her emotional state.

[0343] Example 2

[0344] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0345] Conventional walking route suggestion systems have difficulty providing routes that fully reflect the user's preferences and emotional state. As a result, they have been unable to improve user satisfaction or maximize the benefits of their walks. It has also been difficult to individually analyze past walking data and feedback and reflect them in the next route.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0347] In this invention, the server includes means for inputting the user's desired distance or time and starting point, means for transmitting the input data to the server, means for generating an optimal walking route using a geographic information system based on the input data, means for transmitting the generated route to the user's terminal, means for analyzing the user's past walking data and feedback and learning the user's preferences, means for customizing the route based on the learning results, means for presenting the generated customized route to the user and providing navigation, means for recognizing the user's emotions, means for adjusting the walking route based on the emotion recognition results, and means for collecting feedback from the user and reflecting it in the next route generation.

[0348] This makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[0349] "User" refers to an individual who uses this system to receive walking route suggestions.

[0350] "Terminal" refers to a device used by a user, including mobile devices such as smartphones and tablets.

[0351] A "server" refers to a computer system that exists on the cloud, processes data received from users, and performs tasks such as generating walking routes and recognizing emotions.

[0352] A "geographic information system" is a system that calculates routes using map data and location information, and includes an application program interface for a map provision service.

[0353] A "walking route" refers to a suggested route for walking, including a starting point, a destination point, and intermediate points specified by the user.

[0354] "Settings Data" is information entered by the user, including distance, time, starting point, and points of interest.

[0355] "Navigation" refers to the function that guides the user along a route when they start a walk.

[0356] "Past walking data" refers to the recorded information of walks the user has taken so far.

[0357] "Feedback" refers to the ratings and comments about the route provided by users after completing a walk.

[0358] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[0359] "Customization" refers to individually optimizing walking routes based on the user's preferences and emotional state.

[0360] "Generative AI models" refer to algorithms or machine learning models that create new walking routes based on data.

[0361] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[0362] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. This system is implemented using the user's device, a cloud server, and the emotion engine.

[0363] System configuration

[0364] User's device

[0365] The user's device is a mobile device such as a smartphone or tablet. The user first installs a dedicated smartphone app and enters the walking settings through this application. The information entered includes the desired walking distance or time, starting point, and points of interest. After entering the setting data, the device sends it to the cloud server.

[0366] Cloud Server

[0367] The cloud server generates an optimal walking route using a geographic information system (e.g., an application program interface of a map providing service) based on the setting data received from the user. The generated route includes intermediate points that can be reached within a specified distance or time from the starting point.

[0368] The server also incorporates an emotion engine that recognizes the user's emotional state through voice input and facial recognition, determining whether they are stressed or happy, and can adjust the walking route based on the recognized emotional data.

[0369] The server also collects and analyzes the user's past walking data and feedback to learn the user's preferences, and generates a customized route based on the learning results and provides it to the device.

[0370] Specific examples

[0371] For example, if a user sets "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will be performed.

[0372] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0373] Device: Sends configuration data to the cloud server.

[0374] Server: Analyzes the received data and uses the API of a map provider to generate the optimal route that passes through a cafe that can be reached within 20 minutes from the user's home.

[0375] Server: The emotion engine recognizes the user's emotions and suggests routes through quiet parks to reduce stress.

[0376] Server: Optimizes customized routes based on past user walking data and feedback.

[0377] Server: Sends the generated customized route to the device.

[0378] Device: Display the received route on a map and begin navigation.

[0379] User: Follows a route, stops at a cafe along the way, and provides feedback on the route with ratings and comments after the walk.

[0380] On your device: Sends feedback to the server to help generate future routes.

[0381] Specific examples of hardware and software used

[0382] Device: Smartphone (Android, iOS)

[0383] Server: Cloud computing services

[0384] Geographic Information Systems: Application Program Interfaces for map services (e.g., Google Maps API)

[0385] Emotion engine: speech recognition software, facial recognition software

[0386] Prompt Sentence Examples

[0387] A specific example of a prompt could be, "Generate a 20-minute walking route and suggest a route that includes a stop at a cafe along the way." This allows the generative AI model to provide the optimal walking route according to the user's request.

[0388] This is the outline of the system. The present invention makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[0389] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0390] Step 1:

[0391] User: The user opens the smartphone app and enters the "walk distance or time," "starting point," and "points of interest."

[0392] Input: distance or time, starting point, points of interest

[0393] Output: Configuration data (distance or time, starting point, points of interest)

[0394] Specific operation: The user sets up the walk using touch input or voice input, and presses the "Settings complete" button in the app.

[0395] Step 2:

[0396] Device: Receives configuration data and sends it to the cloud server.

[0397] Input: Configuration data

[0398] Output: HTTP request to the server

[0399] What happens: The device packages the configuration data and sends it as an HTTP request over the internet to the cloud server.

[0400] Step 3:

[0401] Server: Analyzes the received configuration data and calls the API of the geographic information system to generate the optimal walking route.

[0402] Input: Configuration data

[0403] Output: Walking route

[0404] What happens: The server uses a geographic information system such as the Google Maps API to calculate the optimal walking route that includes the specified starting point and points of interest.

[0405] Step 4:

[0406] Server: Refers to a local database to obtain information on famous stores and facilities, and incorporates it into walking routes.

[0407] Input: Area database, best walking routes

[0408] Output: Walking route including facility information

[0409] Specific operation: The server retrieves local commercial and tourist information from a database and adds it to the optimal walking route.

[0410] Step 5:

[0411] Server: The emotion engine recognizes emotions through the user's voice input and facial recognition.

[0412] Input: User's voice data, face image

[0413] Output: Emotion data

[0414] What it does: The server uses voice and facial recognition software to analyze the user's emotions and identify their emotional state (happiness, sadness, stress, etc.).

[0415] Step 6:

[0416] Server: Adjusts the walking route based on the emotion recognition results.

[0417] Input: Emotion data, walking route including facility information

[0418] Output: Adjusted walking route

[0419] What it does: The server incorporates quiet parks and relaxing spots into the route depending on the user's emotional state.

[0420] Step 7:

[0421] Server: Collects past walk data and feedback, and customizes routes based on user preferences.

[0422] Input: Past walk data, feedback, adjusted walk route

[0423] Output: Customized walking route

[0424] How it works: The server analyzes the user's past data and applies an optimal route generation algorithm to generate an individually optimized walking route.

[0425] Step 8:

[0426] Server: Sends the generated customized route to the user's device.

[0427] Input: Custom walking route

[0428] Output: HTTP response to the user's device

[0429] Specific operation: The server packages the data including the generated customized walking route and sends it to the user's device as an HTTP response.

[0430] Step 9:

[0431] Device: Display the received walking route in map format and begin navigation.

[0432] Input: Custom walking route

[0433] Output: Map display, navigation instructions

[0434] What it does: The device displays the route information it receives on a map and provides turn-by-turn directions and voice guidance for navigation.

[0435] Step 10:

[0436] User: After completing a walk, the user enters a route rating and comments on the feedback screen of the app. The feedback is then sent to the server by the device.

[0437] Input: Route rating, comments

[0438] Output: Feedback data

[0439] Specific operation: The user enters a star rating or text comment on the feedback screen and presses the send button. The device packages this and sends it to the server.

[0440] Step 11:

[0441] Server: Saves the feedback data and uses it to generate future walking routes.

[0442] Input: Feedback data

[0443] Output: Updated generation algorithm

[0444] What it does: The server stores the feedback data in a database and uses it to improve the algorithm. It periodically analyzes the feedback and updates the optimal route generation algorithm.

[0445] (Application example 2)

[0446] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0447] In modern society, there is a demand for autonomous vehicle travel routes that take into account the stress and emotional state of the user. However, conventional systems do not recognize user emotions and adjust routes based on them. As a result, it is difficult for users to enjoy an optimal and comfortable journey. Furthermore, they are unable to effectively utilize past travel data and feedback, which means they are unable to provide personalized services based on the user's preferences and emotional state. To solve these issues, a system is needed that recognizes user emotions and adjusts routes in real time based on them.

[0448] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0449] In this invention, the server includes means for generating an optimal travel route using a geographic information system based on input data, means for adjusting the route based on the emotional data using an emotion engine that recognizes the user's emotional state, and means for analyzing the user's past travel data and feedback to learn the user's preferences, thereby providing the optimal travel route in real time according to the user's emotional state and enabling a personalized and comfortable travel experience.

[0450] "Means for users to input their desired distance or time and starting point" means an interface feature that allows users to use a smartphone or other device to input their desired distance or time to travel and the starting point of the trip.

[0451] "Means for transmitting input data to a server" refers to a function that transmits data input by a user to a server via the Internet.

[0452] A "geographic information system" is a system or application program interface (API) that handles geographic information and generates maps and routes.

[0453] The "means for generating an optimal travel route" refers to a function that uses a geographic information system to calculate and generate an optimal travel route based on input data.

[0454] "Means for sending the generated route to the user's device" refers to the function of sending the route information generated by the server to the user's device, such as a smartphone or head-mounted display.

[0455] "Means for learning user preferences by analyzing user's past travel data and feedback" refers to algorithms or functions that analyze the user's past travel history and feedback provided by the user and learn the user's preferences based on that.

[0456] An "emotion engine" is a technology or system that recognizes emotions from a user's facial expressions, voice, etc., and processes that information.

[0457] "Means for adjusting route based on emotional data" refers to a function that recalculates and adjusts the optimal travel route based on the emotional state of the user recognized by the emotion engine.

[0458] "Means for providing navigation" refers to a function that provides real-time travel guidance to users based on the generated route information.

[0459] "Means for collecting feedback and reflecting it in the next route generation" refers to a function that collects feedback provided by users after traveling and uses that information when generating the next route.

[0460] The present invention combines a system that provides an optimal travel route based on the user's desired distance and time with an emotion engine that recognizes the user's emotions. Detailed programs and embodiments of this system will be specifically described below.

[0461] Overall system configuration

[0462] The system is implemented by combining a user device (such as a smartphone or a head-mounted display), a cloud server, and an emotion engine. The system consists of the following main components:

[0463] 1. User Input Interface

[0464] The user inputs the desired distance, time, starting point, destination, and points of interest via a smartphone or head-mounted display.

[0465] 2. Data Transmission

[0466] The device sends the entered data to a cloud server, including information about the user's desired distance, time, starting point, and points of interest.

[0467] 3. Route generation

[0468] Based on the data received, the server uses a geographic information system (GIS) to generate an optimal travel route. Specifically, the route is calculated using the application program interface (API) of the map provider service.

[0469] 4. Emotion recognition

[0470] The server's emotion engine recognizes the user's current emotional state by analyzing their facial expressions and voice data.

[0471] 5. Route adjustment

[0472] The server's emotion engine adjusts the generated route in real time based on the emotional data it recognizes, suggesting a quieter route or one that passes through a park if the user is feeling stressed, for example.

[0473] 6. Customized Route Generation

[0474] The server analyzes the user's past travel data and feedback to learn their preferences, and then regenerates a personalized route based on that information.

[0475] 7. Route provision

[0476] The server sends the generated customized route to the user's device.

[0477] The device visually presents the received route to the user in map format and begins navigation.

[0478] 8. Feedback Collection

[0479] After traveling, users can enter ratings and comments about the route on the feedback screen of their smartphone or head-mounted display.

[0480] The device sends the feedback to the cloud server, which then reflects it in the next route generation.

[0481] Hardware and software used

[0482] Hardware: smartphone, head-mounted display, in-car camera, microphone, emotion recognition sensor

[0483] Software: Cloud servers, emotion engines (e.g., Emotion API), geographic information systems (e.g., Google Maps API), machine learning algorithms (e.g., TensorFlow)

[0484] Specific examples

[0485] For example, if a user sets a preference on their smartphone to "pass through a park on the way home from their office in Tokyo," and the emotion engine recognizes that the user wants to relax, the following process occurs:

[0486] The user enters "from office to home" and "visit to the park" into a smartphone app.

[0487] The device sends the setting data and emotion data to the cloud server.

[0488] The server uses the Google Maps API to generate the optimal route from the office to the home, adding a route that passes through the park along the way.

[0489] The server uses an emotion engine to recognize the user's state of relaxation and suggests scenic routes.

[0490] The server sends the generated route to a smartphone or head-mounted display.

[0491] The user follows the route and provides feedback after the route.

[0492] The device sends feedback to the server to help generate the next route.

[0493] Prompt Sentence Examples

[0494] If the user wants to relax on their way home, suggest the optimal route. The current starting point is an office in Tokyo, and the destination is home. Pass through a quiet park along the way. Prioritize scenic areas in the suggested route.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] User enters desired distance or time and starting point

[0498] Using a smartphone app or head-mounted display, users input the desired distance or time of travel, starting point, destination, and any detours they wish to make.

[0499] Input: distance, time, starting point, destination, points of interest.

[0500] Output: Input data is sent to the system.

[0501] Step 2:

[0502] Data transmission

[0503] The device sends the input data from the user to the cloud server.

[0504] Input: User's configuration data.

[0505] Output: The server receives the data.

[0506] Step 3:

[0507] Generate travel routes

[0508] The server analyzes the received data and generates the optimal travel route using a geographic information system (such as Google Maps API).

[0509] Input: User configuration data, geographic information.

[0510] Output: Optimal travel route information.

[0511] Specific behavior: Calls the Google Maps API and calculates a route based on the desired criteria.

[0512] Step 4:

[0513] Recognition of emotional states

[0514] The server's emotion engine recognizes the user's emotional state by analyzing facial expressions and voice data acquired from smartphones, head-mounted displays, in-car cameras, and microphones.

[0515] Input: User's facial expression data, voice data.

[0516] Output: The user's emotional state.

[0517] Specific actions: Determine emotions using facial recognition and voice analysis technology.

[0518] Step 5:

[0519] Route Adjustment

[0520] The server adjusts the optimal travel route based on the user's emotional state as recognized by the emotion engine. For example, if the user wants to relax, the server will suggest a route that passes through a quiet park.

[0521] Input: Initial travel route, user's emotional state.

[0522] Output: Adjusted travel route.

[0523] Specific behavior: Compare the generated route information with emotion data and recalculate if necessary.

[0524] Step 6:

[0525] Generate customized travel routes

[0526] The server analyzes the user's past travel data and feedback, and generates a more customized travel route based on the learning results.

[0527] Input: adjusted travel route, historical travel data, feedback.

[0528] Output: A customized travel route.

[0529] What it does: Look at historical data and adapt routes to reflect your individual preferences.

[0530] Step 7:

[0531] Route provision

[0532] The server then sends the final customized route to the user's device.

[0533] Input: A customized travel route.

[0534] Output: Route display in map format on the device.

[0535] Specific operation: The transmitted data is sent to the device in a format that can be displayed in the map app.

[0536] Step 8:

[0537] Providing navigation

[0538] The device will then begin navigation based on the route information received, allowing users to receive real-time travel guidance.

[0539] Input: A customized travel route.

[0540] Output: Real-time travel directions.

[0541] Specific operation: Uses GPS data to navigate while comparing it with the current location.

[0542] Step 9:

[0543] Providing feedback

[0544] After completing their journey, users can enter their ratings and comments about the route on the feedback screen of their smartphone app or head-mounted display.

[0545] Input: Rating and / or Comments.

[0546] Output: Feedback data.

[0547] Specific operation: Receives feedback from users in text format and sends it to the cloud server.

[0548] Step 10:

[0549] Collecting and analyzing feedback

[0550] The server collects the feedback data and analyzes it for use in generating the next route.

[0551] Input: Feedback data.

[0552] Output: Updated training data.

[0553] What it does: Your feedback is stored in a database and used by machine learning algorithms to customize your next route.

[0554] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0555] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0556] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0557] [Second embodiment]

[0558] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0559] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0560] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0561] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0562] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0563] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0564] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0565] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0566] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0567] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0568] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0569] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0570] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time. Below, we will create a program for this system and explain the program's processing in natural language. We will also provide specific examples.

[0571] Overall system overview

[0572] This system is implemented using the user's device and a cloud server. First, the user uses a smartphone app to input walking settings (distance, time, starting point, types of points of interest). The device then sends this setting data to the server. The server uses a geographic information system such as Google Maps API to generate an optimal walking route based on the user's desired conditions. The generated route is sent to the user's device, where the user can visually check the route and use the navigation function to walk. The system also has the function of further customizing future walking routes by collecting and analyzing the user's past walking data and feedback.

[0573] Specific processing of the program

[0574] 1. Enter your user settings

[0575] User: Enters desired distance or time, starting point, and type of points of interest through a smartphone app interface.

[0576] 2. Sending input data

[0577] Device: Sends user-entered configuration data, including distance, time, starting point, and points of interest, to a cloud server.

[0578] 3. Route Generation

[0579] Server: Uses the Google Maps API based on the input data to generate a walking route that best suits the user's desired conditions.

[0580] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0581] Server: Retrieves information about popular spots and attractions from a local database and incorporates it into the route.

[0582] 4. Send and navigate a route

[0583] Server: Sends the generated walking route to the user's device.

[0584] Device: The received route is displayed in map format and presented to the user. The navigation function allows the user to take a walk while receiving route guidance in real time.

[0585] 5. Learning your preferences

[0586] Server: Records the user's past walking data and feedback, and uses machine learning algorithms to learn the user's preferences.

[0587] 6. Customized route suggestions

[0588] Server: Customizes and suggests routes for the next walk based on the user's preferences and past walking history, ensuring the user always has a fresh and interesting walking experience.

[0589] 7. Gathering Feedback

[0590] User: Enter a route rating and comments on the feedback screen provided after the walk is completed.

[0591] Device: Sends feedback data to the server.

[0592] Server: The server accumulates the received feedback and reflects it in subsequent route generation to provide highly accurate walking suggestions.

[0593] Specific examples

[0594] For example, if a user enters a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the specific processing is as follows:

[0595] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0596] Device: Sends configuration data to the cloud server.

[0597] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0598] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[0599] Server: Sends the generated routes to the device.

[0600] Device: Display the received route on a map and begin navigation.

[0601] User: Walk along a route, stopping at a cafe along the way.

[0602] User: Provide feedback by rating and commenting on the route after completing the walk.

[0603] On your device: Send feedback to the server to help improve future route suggestions.

[0604] The above process realizes a system that provides optimal walking routes according to the needs and preferences of each individual user.

[0605] The processing flow will be explained below.

[0606] Step 1: Enter your user settings

[0607] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the dedicated interface of the smartphone app.

[0608] Step 2: Submitting input data

[0609] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0610] Step 3: Generate Routes

[0611] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0612] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0613] Server: At the same time, it retrieves information about famous stores and facilities from a local database and incorporates this information into the walking route.

[0614] Step 4: Analyze historical data

[0615] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[0616] Step 5: Generate customized routes

[0617] Server: Based on the analysis results, the server generates a customized walking route tailored to the user's individual preferences, allowing the user to efficiently visit points of interest rather than simply walking a specified distance or time.

[0618] Step 6: Providing a Route

[0619] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0620] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0621] Step 7: Start Navigation

[0622] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0623] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0624] Step 8: Provide feedback

[0625] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0626] Terminal: Sends user feedback data to the cloud server.

[0627] Step 9: Analyze and store feedback

[0628] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0629] Through these steps, the system can provide the user with the most suitable and fresh walking route tailored to their preferences.

[0630] Example 1

[0631] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] Conventional walking route suggestion systems have difficulty proposing optimal routes that take into account the user's desired distance, time, and points of interest. They also lack the ability to learn the user's preferences and propose customized routes. This results in the problem that users cannot always have a fresh and interesting walking experience.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0634] In this invention, the server includes: means for a user to input a desired distance or time and a starting point; means for transmitting the input data to an information processing device; means for generating an optimal walking route using a geographic information system based on the input data; means for transmitting the generated route to the user's mobile communication terminal; means for analyzing the user's past movement data and rating information and learning the user's preferences; means for customizing the route based on the learning results; means for presenting the generated customized route to the user and providing navigation; means for collecting rating information from the user and reflecting it in the next route generation; means for the user to input input items through an interface and transmit the input setting data to the information processing device; means for encoding and transmitting the route information using a fixed protocol; means for linking map display and voice guidance to provide real-time navigation; and means for retrieving information on surrounding facilities from a database and incorporating it into the route. This allows the user to obtain an optimal walking route that includes points of interest, allowing for a fresh and fulfilling walking experience.

[0635] "User" refers to any individual or entity using a particular service or system.

[0636] "Distance" refers to the physical distance from the starting point to the destination point.

[0637] "Time" refers to the time that has elapsed from a specified start point to a specified end point.

[0638] "Starting point" refers to the place where you start your walk or journey.

[0639] "Information processing device" refers to a device for inputting, processing, storing, and outputting data.

[0640] "Geographic information system" means an information system for collecting, managing, analyzing, and displaying geographic data.

[0641] A "walking route" refers to a travel route generated based on conditions specified by the user.

[0642] "Mobile communication terminal" refers to a portable terminal that can connect to the Internet via wireless communication.

[0643] "Past travel data" refers to data including the routes a user has taken in the past and their location at that time.

[0644] "Evaluation information" refers to information that indicates feedback and satisfaction of a user regarding the service received or the proposed route.

[0645] "Interface" refers to the operating screen or device through which a user interacts with a system or application.

[0646] A "fixed protocol" refers to standardized procedures and rules for transmitting data.

[0647] "Encoding" refers to the process of converting data into a particular format or protocol.

[0648] "Real-time navigation" refers to providing instant route guidance based on your current location.

[0649] "Map display" refers to an interface or means for visually displaying geographic data.

[0650] "Voice guidance" refers to a function that provides instructions and information to the user by voice.

[0651] A "database" refers to a collection or system for systematically organizing, storing, and managing data.

[0652] "Information about nearby facilities" refers to information about stores and facilities located near the user.

[0653] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time, starting point, and types of spots they are interested in. This system operates in conjunction with the user's device and a cloud server.

[0654] First, the user uses a smartphone app to input the settings for their walk. For example, the user enters specific requirements such as "a 20-minute walk," "stopping at a cafe," and "starting from home." This input includes operations performed through the user interface, and the configured data is packaged in JSON format.

[0655] Next, the terminal transmits the input setting data to the cloud server securely using a fixed protocol (e.g., HTTPS).

[0656] The cloud server uses a geographic information system (e.g., Google Maps API) based on the received data to generate an optimal walking route. The route is generated using input data (walking distance, time, starting point, points of interest, etc.) and information on nearby facilities obtained from a local database. This allows the system to plan the optimal route that matches the user's requirements.

[0657] The generated route is again encoded in JSON format and sent to the user's device using the HTTPS protocol. The device displays the received route in map format and, if necessary, integrates the voice guidance function to provide real-time navigation.

[0658] Additionally, the cloud server learns user preferences by collecting and analyzing the user's past travel data and rating information. This includes using machine learning algorithms (e.g., Scikit-learn) to model user preferences. The learning results are reflected in the next route generation, providing the user with a more customized route.

[0659] In addition, the feedback (ratings and comments) provided by users after completing a walk is sent from the device to a cloud server and used to suggest routes for future walks. This ensures that the system always suggests the latest walking routes that best suit the user's needs.

[0660] As a concrete example, consider the case where a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way." The system proceeds as follows:

[0661] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0662] Device: Sends configuration data to the cloud server.

[0663] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0664] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[0665] Server: Sends the generated routes to the device.

[0666] Device: Displays the received route on a map, begins navigation, and provides voice instructions if voice guidance is enabled.

[0667] User: Walk along a route, stopping at a cafe along the way.

[0668] User: Provide feedback by rating and commenting on the route after completing the walk.

[0669] Device: Sends feedback to the cloud server, which incorporates this information into its next proposal.

[0670] The following are some examples of prompts that can be input to a generative AI model:

[0671] "A user wants to take a 20-minute walking route and wants to stop at a cafe along the way. Please suggest the best walking route that fits those criteria. The user's starting point is their home."

[0672] In this way, the present invention is a system that provides optimal walking routes tailored to the user's needs, ensuring a constantly fresh and interesting walking experience.

[0673] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0674] Step 1: Enter your user settings

[0675] Users open the smartphone app and enter their walk settings: the desired distance or time of the walk, the starting point, and the types of points of interest.

[0676] Input: Configuration data that the user enters into the app interface (e.g., "20 minutes," "cafe," "starting from home").

[0677] Output: Configuration data stored in the app.

[0678] Step 2: Submitting input data

[0679] The device sends the configuration data entered by the user to the cloud server. Specifically, the configuration data is packaged in JSON format and sent using the HTTPS protocol.

[0680] Input: The configuration data entered by the user.

[0681] Data processing: Encode the input data in JSON format.

[0682] Output: Configuration data sent to the cloud server using the HTTPS protocol.

[0683] Step 3: Generate a walking route

[0684] The server uses the received data to generate the optimal walking route using a geographic information system (e.g., a map service API). Specifically, it searches for a route based on the user's desired conditions and generates route information.

[0685] Input: JSON formatted configuration data sent from the device.

[0686] Data processing: Uses map service APIs to search and generate optimal routes.

[0687] Output: Information about the generated walking route.

[0688] Step 4: Incorporating local information

[0689] When generating a route, the server retrieves information on popular spots and landmarks from a local database and incorporates it into the route, enhancing the user's walking experience.

[0690] Input: Spot information retrieved from a local database.

[0691] Data processing: Incorporate the acquired spot information into the generated route.

[0692] Output: A customized route with spot information embedded.

[0693] Step 5: Send and navigate your route

[0694] The server sends the generated walking route to the user's device. Specifically, the route information is encoded in JSON format and sent via HTTPS protocol.

[0695] The device displays the route in map format for the user and also provides voice guidance for real-time navigation.

[0696] Input: Route information sent by the server.

[0697] Data processing: Decode JSON format route information into map format.

[0698] Output: Navigation map and voice directions displayed on the device.

[0699] Step 6: Learning user preferences

[0700] The server collects users' past travel data and rating information, including past walking routes and feedback information provided by users, and uses machine learning algorithms to learn users' preferences.

[0701] Input: User's historical travel data and feedback information.

[0702] Data processing: Using machine learning algorithms to analyze and learn user preferences.

[0703] Output: User's preferred model.

[0704] Step 7: Customized Route Suggestion

[0705] Based on the learning results, the server will further customize and suggest the next walking route, allowing users to always have a fresh walking experience that meets their individual needs.

[0706] Input: A learned model of the user's preferences.

[0707] Data processing: Generate a new route that reflects the learning results.

[0708] Output: Customized walking route information.

[0709] Step 8: Gather feedback

[0710] After completing a walk, users can enter their route rating and comments on the app's feedback screen.

[0711] The device sends the feedback data to the cloud server by packaging it in JSON format and sending it using the HTTPS protocol.

[0712] The server stores the received feedback in a database and uses it to generate future routes.

[0713] Input: User-provided feedback information.

[0714] Data processing: The feedback information is stored in a database and added to the machine learning training dataset.

[0715] Output: Updated training model and database.

[0716] (Application example 1)

[0717] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0718] Today, there is a demand for systems that efficiently suggest walking routes and shopping routes in virtual stores. However, until now, it has been difficult to generate customized routes that reflect a user's individual preferences and past behavioral history. Furthermore, there has been a lack of systems that dynamically suggest routes based on the user's interests, not just time and distance. Furthermore, providing efficient shopping routes in virtual stores remains an unsolved problem. The purpose of this invention is to solve these problems and provide a system that suggests walking and shopping routes optimized for each user.

[0719] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0720] In this invention, the server includes: means for inputting the user's desired distance or time and starting point; means for transmitting the input data to the server; and means for generating an optimal walking route using a geographic information system based on the input data. This enables route suggestions based on the user's specified walking route and preferences. The server also includes means for transmitting the generated route to the user's terminal, analyzing the user's past walking data and feedback, and learning the user's preferences; means for customizing the route based on the learning results; and means for presenting the generated customized route to the user and providing navigation. This enables route suggestions based on the user's individual preferences. The server also includes means for collecting user feedback and reflecting it in next route generation; means for inputting the user's desired stores and products and generating an optimal shopping route within the virtual store; and means for transmitting the generated shopping route to the user's terminal and navigating the user. This enables efficient and personalized shopping route suggestions within the virtual store.

[0721] "Means for the user to input the desired distance or time and starting point" refers to an interface or function that allows the user to input information such as the desired distance or time required to travel and the starting point into the terminal.

[0722] "Means for transmitting input data to a server" refers to the communication functions and protocols for transferring data input by a user to a cloud server.

[0723] "Means for generating optimal walking routes using a geographic information system" refers to an algorithm that uses geographic information and map data to calculate and generate the most suitable travel route for the user's desired conditions.

[0724] The "means for transmitting the generated route to the user's terminal" is a communication function for transferring the travel route information generated by the server back to the user's device.

[0725] "Means of analyzing users' past walking data and feedback to learn their preferences" refers to a machine learning algorithm that analyzes users' past usage history and impressions to learn the preferences and patterns of individual users.

[0726] "Means for customizing routes based on learning results" refers to a processing function that customizes travel routes based on the user's individual preferences and past history, and provides personalized suggestions.

[0727] The "means for presenting the generated customized route to the user and providing navigation" is a function for displaying the customized travel route on the user's terminal and providing route guidance in real time.

[0728] "Means of collecting feedback from users and reflecting it in the next route generation" refers to a function that collects opinions and evaluation data provided by users and analyzes and saves them for use in proposing the next travel route.

[0729] "A means for generating the optimal shopping route within a virtual store by inputting the store and products desired by the user" is an algorithm that calculates and generates the optimal shopping route within a virtual space by inputting the product and store information desired by the user.

[0730] "Means for sending the generated shopping route to the user's terminal and navigating" refers to a communication function for sending the generated shopping route to the user's terminal and providing route guidance within the virtual store.

[0731] This invention is a system that generates an optimal walking route based on the user's specific distance and time requirements and starting point, and also provides a shopping route within a virtual store. This system is realized using a smartphone application and a cloud server. It learns the user's preferences and past behavior history and provides a customized route based on that. Details of the mode for implementing this invention are as follows.

[0732] 1. Hardware and software configuration

[0733] Hardware

[0734] Smartphone: Used as a mobile device for users to check routes and enter settings.

[0735] Cloud Server: A central server that generates routes, analyzes user data, and sends and receives navigation data.

[0736] software

[0737] Smartphone app: Enter user settings, view routes, navigate in real time, and collect feedback.

[0738] Map service API: Routes are generated using Google Maps API or similar geographic information services.

[0739] Virtual Store API: A custom API used to generate virtual shopping routes.

[0740] 2. Program Processing

[0741] Entering User Preferences

[0742] Using a smartphone app, users can input the distance and time of their desired walk, as well as the starting point, and can also input the stores and products they want to explore in the virtual store. This input data is sent to a cloud server.

[0743] Sending and processing input data

[0744] The smartphone device sends the input data to a cloud server in real time. The server uses the received data to generate an optimal walking route using a geographic information system such as Google Maps API. The virtual store uses the virtual store API to generate an optimal shopping route based on the desired products and stores.

[0745] Route generation and submission

[0746] The generated route is sent from the cloud server to a smartphone, where users can view the route and start real-time navigation. The same applies to the virtual store, where the optimal shopping route is provided.

[0747] Learning user preferences

[0748] The cloud server collects and analyzes the user's past walking data, shopping history, and feedback, and uses machine learning algorithms to learn the user's preferences and customize future walking and shopping routes.

[0749] Gathering feedback and suggesting next routes

[0750] By collecting feedback provided by users after completing a walk or shopping trip and reflecting it in the next route generation, more accurate route suggestions can be made.

[0751] 3. Examples and prompts

[0752] Specific examples

[0753] For example, if a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will occur:

[0754] Users enter "20 minutes," "visit a cafe," and "depart from home" into the smartphone app.

[0755] The device sends the configuration data to the cloud server.

[0756] The server uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[0757] The server references the user's past walking data and optimizes the route to suit their preferences.

[0758] The server sends the generated route to the terminal.

[0759] The device displays the received route on a map and begins navigation.

[0760] Users walk along a route and stop at cafes along the way.

[0761] After completing a walk, users can provide feedback by rating and commenting on the route.

[0762] The device sends feedback to the server to help suggest routes for future trips.

[0763] Example prompts to input to the generative AI model

[0764] Generate optimal routes in your virtual store starting from the entrance for users to search for shoes and bags in the fashion section. Use your store's map information and users' shopping patterns to create efficient routes and guide them step-by-step from the starting point.

[0765] The above is an embodiment of the present invention.

[0766] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0767] Step 1:

[0768] The user inputs the desired distance or time and starting point through the smartphone app interface. They also input the stores and products in the virtual store they want to explore. This generates the user's desired conditions as JSON format data. Specifically, the information entered by the user is saved in a form in the app.

[0769] Step 2:

[0770] The data entered in step 1 is sent to the cloud server on the device (smartphone). Specifically, the application sends the data via an HTTP POST request. At this time, the input data is sent to the API server's endpoint. The input data includes distance, time, starting point, and desired store and product information.

[0771] Step 3:

[0772] The server analyzes the received input data and calls the Google Maps API and virtual store API to generate walking and shopping routes that best fit the user's desired conditions. The server first calculates the optimal walking route from the starting point using a geographic information system. It then calculates the shortest route to the desired store or product in the virtual store. Specifically, the server sends an API request to the Google Maps API or virtual store API and receives a response.

[0773] Step 4:

[0774] The server sends the generated walking and shopping routes to the user's device. Specifically, the server encodes the generated route data into JSON format and sends it to the user's smartphone as an HTTP response, including map information and navigation information.

[0775] Step 5:

[0776] The device visually displays the received route and initiates real-time navigation. Specifically, the smartphone app analyzes the received route data, displays it on a map view, and provides step-by-step navigation guidance. The user is provided with visual and audio guidance.

[0777] Step 6:

[0778] After the user has finished their walk or shopping, they provide feedback through the smartphone app. Specifically, the app displays a feedback form where the user can enter their route rating and comments. This feedback is saved in JSON format.

[0779] Step 7:

[0780] The device sends the feedback provided by the user to the cloud server. Specifically, the app sends the feedback data to the server via an HTTP POST request. The feedback data includes the rating score and comments.

[0781] Step 8:

[0782] The server analyzes the received feedback data to learn user preferences and patterns. Specifically, the server uses machine learning algorithms to analyze the feedback data and model the user's preferences, which then personalizes the next route suggestion.

[0783] Step 9:

[0784] The next time the server generates a route, it will generate a customized route based on the learning results and suggest it to the user again. Specifically, the server calculates a new route taking into account the user's past behavioral history and feedback, and sends it back to the device. This series of processes provides the user with an optimized walking and shopping route.

[0785] The above are the processing steps of the system for realizing the application example.

[0786] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0787] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. The program for this system will be described in detail below.

[0788] Overall system overview

[0789] This system combines the user's device, a cloud server, and an emotion engine. The user first inputs the walking settings (distance, time, starting point, points of interest) through a smartphone app. The device then sends this setting data to the cloud server. The server uses a geographic information system (e.g., Google Maps API) to generate an optimal walking route based on the user's desired conditions and sends the generated route to the user's device. At the same time, it collects and analyzes the user's past walking data and feedback, and customizes the next walking route based on the results. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and can adjust the walking route based on the emotional data.

[0790] Specific processing of the program

[0791] 1. Enter your user settings

[0792] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[0793] 2. Sending input data

[0794] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0795] 3. Route Generation

[0796] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0797] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0798] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[0799] 4. Emotion Recognition by Emotion Engine

[0800] Server: The emotion engine recognizes the user's current emotional state.

[0801] Example: Identifying emotions (happiness, sadness, stress, etc.) through user voice input or facial recognition.

[0802] 5. Emotion-based route adjustment

[0803] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[0804] For example: If you're feeling stressed, suggest a route through a quiet park.

[0805] 6. Creating customized routes

[0806] Server: Analyzes the user's past walking data and feedback, and generates a customized walking route based on their individual preferences, combined with their emotional state.

[0807] 7. Route provision

[0808] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0809] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0810] 8. Start navigation

[0811] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0812] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0813] 9. Providing Feedback

[0814] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0815] Terminal: Sends user feedback data to the cloud server.

[0816] 10. Analysis and storage of feedback

[0817] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0818] Specific examples

[0819] For example, if a user sets a preference to "take a 20-minute walk and stop at a cafe along the way," and the emotion engine recognizes that the user is feeling stressed, the specific processing will be as follows:

[0820] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0821] Device: Sends configuration data to the cloud server.

[0822] Server: Uses the Google Maps API to generate the optimal route that passes through cafes that are reachable within 20 minutes from home.

[0823] Server: The emotion engine recognizes the user's emotions and adds a route through a quiet park to reduce stress.

[0824] Server: Optimizes customized routes based on past user walking data and feedback.

[0825] Server: Sends the generated customized route to the device.

[0826] Device: Display the received route on a map and begin navigation.

[0827] User: Walk along a route, stopping at a cafe along the way.

[0828] User: Provide feedback by rating and commenting on the route after completing the walk.

[0829] On your device: Sends feedback to the server to help generate future routes.

[0830] Through the above process, the user can obtain the optimal walking route that matches his or her emotional state.

[0831] The processing flow will be explained below.

[0832] Step 1: Enter your user settings

[0833] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[0834] Step 2: Submitting input data

[0835] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[0836] Step 3: Generate Routes

[0837] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[0838] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[0839] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[0840] Step 4: Analyze historical data

[0841] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[0842] Step 5: Emotion Recognition with the Emotion Engine

[0843] Server: The emotion engine recognizes the user's current emotional state.

[0844] Example: Identifying emotions (happiness, sadness, stress, etc.) from the voice of a user speaking to a smartphone app or from facial images captured through a camera.

[0845] Step 6: Emotionally Based Route Adjustments

[0846] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[0847] Example: If the user is feeling stressed, suggest a route through a quiet, natural park.

[0848] Step 7: Generate customized routes

[0849] Server: Based on the analysis of past data and emotion recognition results, it generates a customized walking route tailored to the user's individual preferences.

[0850] Step 8: Providing a Route

[0851] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[0852] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[0853] Step 9: Start Navigation

[0854] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[0855] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[0856] Step 10: Provide feedback

[0857] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[0858] Terminal: Sends user feedback data to the cloud server.

[0859] Step 11: Analyze and store feedback

[0860] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[0861] Specific examples

[0862] If you want to take a 20-minute walk, visit a cafe, and de-stress.

[0863] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0864] Device: After completing the procedure, send the setting data to the cloud server.

[0865] Server: Uses the Google Maps API to search for cafes that can be reached within 20 minutes from home and generate the optimal route.

[0866] Server: If the emotion engine detects stress from the user's speech or facial recognition, include a quiet park in the route.

[0867] Server: Sends optimized and customized routes to the device.

[0868] Device: Display the route on the map and begin navigation.

[0869] User: Starts walking following a route, stops at a cafe, passes through a quiet park.

[0870] Users: Provide feedback after completing a walk, leaving a rating and comments.

[0871] Device: Sends all feedback data to the server.

[0872] Server: Stores the received data and uses it to improve and customize the route next time.

[0873] Through the above process, the user can obtain the optimal walking route according to his / her emotional state.

[0874] Example 2

[0875] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] Conventional walking route suggestion systems have difficulty providing routes that fully reflect the user's preferences and emotional state. As a result, they have been unable to improve user satisfaction or maximize the benefits of their walks. It has also been difficult to individually analyze past walking data and feedback and reflect them in the next route.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0878] In this invention, the server includes means for inputting the user's desired distance or time and starting point, means for transmitting the input data to the server, means for generating an optimal walking route using a geographic information system based on the input data, means for transmitting the generated route to the user's terminal, means for analyzing the user's past walking data and feedback and learning the user's preferences, means for customizing the route based on the learning results, means for presenting the generated customized route to the user and providing navigation, means for recognizing the user's emotions, means for adjusting the walking route based on the emotion recognition results, and means for collecting feedback from the user and reflecting it in the next route generation.

[0879] This makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[0880] "User" refers to an individual who uses this system to receive walking route suggestions.

[0881] "Terminal" refers to a device used by a user, including mobile devices such as smartphones and tablets.

[0882] A "server" refers to a computer system that exists on the cloud, processes data received from users, and performs tasks such as generating walking routes and recognizing emotions.

[0883] A "geographic information system" is a system that calculates routes using map data and location information, and includes an application program interface for a map provision service.

[0884] A "walking route" refers to a suggested route for walking, including a starting point, a destination point, and intermediate points specified by the user.

[0885] "Settings Data" is information entered by the user, including distance, time, starting point, and points of interest.

[0886] "Navigation" refers to the function that guides the user along a route when they start a walk.

[0887] "Past walking data" refers to the recorded information of walks the user has taken so far.

[0888] "Feedback" refers to the ratings and comments about the route provided by users after completing a walk.

[0889] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[0890] "Customization" refers to individually optimizing walking routes based on the user's preferences and emotional state.

[0891] "Generative AI models" refer to algorithms or machine learning models that create new walking routes based on data.

[0892] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[0893] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. This system is implemented using the user's device, a cloud server, and the emotion engine.

[0894] System configuration

[0895] User's device

[0896] The user's device is a mobile device such as a smartphone or tablet. The user first installs a dedicated smartphone app and enters the walking settings through this application. The information entered includes the desired walking distance or time, starting point, and points of interest. After entering the setting data, the device sends it to the cloud server.

[0897] Cloud Server

[0898] The cloud server generates an optimal walking route using a geographic information system (e.g., an application program interface of a map providing service) based on the setting data received from the user. The generated route includes intermediate points that can be reached within a specified distance or time from the starting point.

[0899] The server also incorporates an emotion engine that recognizes the user's emotional state through voice input and facial recognition, determining whether they are stressed or happy, and can adjust the walking route based on the recognized emotional data.

[0900] The server also collects and analyzes the user's past walking data and feedback to learn the user's preferences, and generates a customized route based on the learning results and provides it to the device.

[0901] Specific examples

[0902] For example, if a user sets "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will be performed.

[0903] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[0904] Device: Sends configuration data to the cloud server.

[0905] Server: Analyzes the received data and uses the API of a map provider to generate the optimal route that passes through a cafe that can be reached within 20 minutes from the user's home.

[0906] Server: The emotion engine recognizes the user's emotions and suggests routes through quiet parks to reduce stress.

[0907] Server: Optimizes customized routes based on past user walking data and feedback.

[0908] Server: Sends the generated customized route to the device.

[0909] Device: Display the received route on a map and begin navigation.

[0910] User: Follows a route, stops at a cafe along the way, and provides feedback on the route with ratings and comments after the walk.

[0911] On your device: Sends feedback to the server to help generate future routes.

[0912] Specific examples of hardware and software used

[0913] Device: Smartphone (Android, iOS)

[0914] Server: Cloud computing services

[0915] Geographic Information Systems: Application Program Interfaces for map services (e.g., Google Maps API)

[0916] Emotion engine: speech recognition software, facial recognition software

[0917] Prompt Sentence Examples

[0918] A specific example of a prompt could be, "Generate a 20-minute walking route and suggest a route that includes a stop at a cafe along the way." This allows the generative AI model to provide the optimal walking route according to the user's request.

[0919] This is the outline of the system. The present invention makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[0920] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0921] Step 1:

[0922] User: The user opens the smartphone app and enters the "walk distance or time," "starting point," and "points of interest."

[0923] Input: distance or time, starting point, points of interest

[0924] Output: Configuration data (distance or time, starting point, points of interest)

[0925] Specific operation: The user sets up the walk using touch input or voice input, and presses the "Settings complete" button in the app.

[0926] Step 2:

[0927] Device: Receives configuration data and sends it to the cloud server.

[0928] Input: Configuration data

[0929] Output: HTTP request to the server

[0930] What happens: The device packages the configuration data and sends it as an HTTP request over the internet to the cloud server.

[0931] Step 3:

[0932] Server: Analyzes the received configuration data and calls the API of the geographic information system to generate the optimal walking route.

[0933] Input: Configuration data

[0934] Output: Walking route

[0935] What happens: The server uses a geographic information system such as the Google Maps API to calculate the optimal walking route that includes the specified starting point and points of interest.

[0936] Step 4:

[0937] Server: Refers to a local database to obtain information on famous stores and facilities, and incorporates it into walking routes.

[0938] Input: Area database, best walking routes

[0939] Output: Walking route including facility information

[0940] Specific operation: The server retrieves local commercial and tourist information from a database and adds it to the optimal walking route.

[0941] Step 5:

[0942] Server: The emotion engine recognizes emotions through the user's voice input and facial recognition.

[0943] Input: User's voice data, face image

[0944] Output: Emotion data

[0945] What it does: The server uses voice and facial recognition software to analyze the user's emotions and identify their emotional state (happiness, sadness, stress, etc.).

[0946] Step 6:

[0947] Server: Adjusts the walking route based on the emotion recognition results.

[0948] Input: Emotion data, walking route including facility information

[0949] Output: Adjusted walking route

[0950] What it does: The server incorporates quiet parks and relaxing spots into the route depending on the user's emotional state.

[0951] Step 7:

[0952] Server: Collects past walk data and feedback, and customizes routes based on user preferences.

[0953] Input: Past walk data, feedback, adjusted walk route

[0954] Output: Customized walking route

[0955] How it works: The server analyzes the user's past data and applies an optimal route generation algorithm to generate an individually optimized walking route.

[0956] Step 8:

[0957] Server: Sends the generated customized route to the user's device.

[0958] Input: Custom walking route

[0959] Output: HTTP response to the user's device

[0960] Specific operation: The server packages the data including the generated customized walking route and sends it to the user's device as an HTTP response.

[0961] Step 9:

[0962] Device: Display the received walking route in map format and begin navigation.

[0963] Input: Custom walking route

[0964] Output: Map display, navigation instructions

[0965] What it does: The device displays the route information it receives on a map and provides turn-by-turn directions and voice guidance for navigation.

[0966] Step 10:

[0967] User: After completing a walk, the user enters a route rating and comments on the feedback screen of the app. The feedback is then sent to the server by the device.

[0968] Input: Route rating, comments

[0969] Output: Feedback data

[0970] Specific operation: The user enters a star rating or text comment on the feedback screen and presses the send button. The device packages this and sends it to the server.

[0971] Step 11:

[0972] Server: Saves the feedback data and uses it to generate future walking routes.

[0973] Input: Feedback data

[0974] Output: Updated generation algorithm

[0975] What it does: The server stores the feedback data in a database and uses it to improve the algorithm. It periodically analyzes the feedback and updates the optimal route generation algorithm.

[0976] (Application example 2)

[0977] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0978] In modern society, there is a demand for autonomous vehicle travel routes that take into account the stress and emotional state of the user. However, conventional systems do not recognize user emotions and adjust routes based on them. As a result, it is difficult for users to enjoy an optimal and comfortable journey. Furthermore, they are unable to effectively utilize past travel data and feedback, which means they are unable to provide personalized services based on the user's preferences and emotional state. To solve these issues, a system is needed that recognizes user emotions and adjusts routes in real time based on them.

[0979] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0980] In this invention, the server includes means for generating an optimal travel route using a geographic information system based on input data, means for adjusting the route based on the emotional data using an emotion engine that recognizes the user's emotional state, and means for analyzing the user's past travel data and feedback to learn the user's preferences, thereby providing the optimal travel route in real time according to the user's emotional state and enabling a personalized and comfortable travel experience.

[0981] "Means for users to input their desired distance or time and starting point" means an interface feature that allows users to use a smartphone or other device to input their desired distance or time to travel and the starting point of the trip.

[0982] "Means for transmitting input data to a server" refers to a function that transmits data input by a user to a server via the Internet.

[0983] A "geographic information system" is a system or application program interface (API) that handles geographic information and generates maps and routes.

[0984] The "means for generating an optimal travel route" refers to a function that uses a geographic information system to calculate and generate an optimal travel route based on input data.

[0985] "Means for sending the generated route to the user's device" refers to the function of sending the route information generated by the server to the user's device, such as a smartphone or head-mounted display.

[0986] "Means for learning user preferences by analyzing user's past travel data and feedback" refers to algorithms or functions that analyze the user's past travel history and feedback provided by the user and learn the user's preferences based on that.

[0987] An "emotion engine" is a technology or system that recognizes emotions from a user's facial expressions, voice, etc., and processes that information.

[0988] "Means for adjusting route based on emotional data" refers to a function that recalculates and adjusts the optimal travel route based on the emotional state of the user recognized by the emotion engine.

[0989] "Means for providing navigation" refers to a function that provides real-time travel guidance to users based on the generated route information.

[0990] "Means for collecting feedback and reflecting it in the next route generation" refers to a function that collects feedback provided by users after traveling and uses that information when generating the next route.

[0991] The present invention combines a system that provides an optimal travel route based on the user's desired distance and time with an emotion engine that recognizes the user's emotions. Detailed programs and embodiments of this system will be specifically described below.

[0992] Overall system configuration

[0993] The system is implemented by combining a user device (such as a smartphone or a head-mounted display), a cloud server, and an emotion engine. The system consists of the following main components:

[0994] 1. User Input Interface

[0995] The user inputs the desired distance, time, starting point, destination, and points of interest via a smartphone or head-mounted display.

[0996] 2. Data Transmission

[0997] The device sends the entered data to a cloud server, including information about the user's desired distance, time, starting point, and points of interest.

[0998] 3. Route generation

[0999] Based on the data received, the server uses a geographic information system (GIS) to generate an optimal travel route. Specifically, the route is calculated using the application program interface (API) of the map provider service.

[1000] 4. Emotion recognition

[1001] The server's emotion engine recognizes the user's current emotional state by analyzing their facial expressions and voice data.

[1002] 5. Route adjustment

[1003] The server's emotion engine adjusts the generated route in real time based on the emotional data it recognizes, suggesting a quieter route or one that passes through a park if the user is feeling stressed, for example.

[1004] 6. Customized Route Generation

[1005] The server analyzes the user's past travel data and feedback to learn their preferences, and then regenerates a personalized route based on that information.

[1006] 7. Route provision

[1007] The server sends the generated customized route to the user's device.

[1008] The device visually presents the received route to the user in map format and begins navigation.

[1009] 8. Feedback Collection

[1010] After traveling, users can enter ratings and comments about the route on the feedback screen of their smartphone or head-mounted display.

[1011] The device sends the feedback to the cloud server, which then reflects it in the next route generation.

[1012] Hardware and software used

[1013] Hardware: smartphone, head-mounted display, in-car camera, microphone, emotion recognition sensor

[1014] Software: Cloud servers, emotion engines (e.g., Emotion API), geographic information systems (e.g., Google Maps API), machine learning algorithms (e.g., TensorFlow)

[1015] Specific examples

[1016] For example, if a user sets a preference on their smartphone to "pass through a park on the way home from their office in Tokyo," and the emotion engine recognizes that the user wants to relax, the following process occurs:

[1017] The user enters "from office to home" and "visit to the park" into a smartphone app.

[1018] The device sends the setting data and emotion data to the cloud server.

[1019] The server uses the Google Maps API to generate the optimal route from the office to the home, adding a route that passes through the park along the way.

[1020] The server uses an emotion engine to recognize the user's state of relaxation and suggests scenic routes.

[1021] The server sends the generated route to a smartphone or head-mounted display.

[1022] The user follows the route and provides feedback after the route.

[1023] The device sends feedback to the server to help generate the next route.

[1024] Prompt Sentence Examples

[1025] If the user wants to relax on their way home, suggest the optimal route. The current starting point is an office in Tokyo, and the destination is home. Pass through a quiet park along the way. Prioritize scenic areas in the suggested route.

[1026] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1027] Step 1:

[1028] User enters desired distance or time and starting point

[1029] Using a smartphone app or head-mounted display, users input the desired distance or time of travel, starting point, destination, and any detours they wish to make.

[1030] Input: distance, time, starting point, destination, points of interest.

[1031] Output: Input data is sent to the system.

[1032] Step 2:

[1033] Data transmission

[1034] The device sends the input data from the user to the cloud server.

[1035] Input: User's configuration data.

[1036] Output: The server receives the data.

[1037] Step 3:

[1038] Generate travel routes

[1039] The server analyzes the received data and generates the optimal travel route using a geographic information system (such as Google Maps API).

[1040] Input: User configuration data, geographic information.

[1041] Output: Optimal travel route information.

[1042] Specific behavior: Calls the Google Maps API and calculates a route based on the desired criteria.

[1043] Step 4:

[1044] Recognition of emotional states

[1045] The server's emotion engine recognizes the user's emotional state by analyzing facial expressions and voice data acquired from smartphones, head-mounted displays, in-car cameras, and microphones.

[1046] Input: User's facial expression data, voice data.

[1047] Output: The user's emotional state.

[1048] Specific actions: Determine emotions using facial recognition and voice analysis technology.

[1049] Step 5:

[1050] Route Adjustment

[1051] The server adjusts the optimal travel route based on the user's emotional state as recognized by the emotion engine. For example, if the user wants to relax, the server will suggest a route that passes through a quiet park.

[1052] Input: Initial travel route, user's emotional state.

[1053] Output: Adjusted travel route.

[1054] Specific behavior: Compare the generated route information with emotion data and recalculate if necessary.

[1055] Step 6:

[1056] Generate customized travel routes

[1057] The server analyzes the user's past travel data and feedback, and generates a more customized travel route based on the learning results.

[1058] Input: adjusted travel route, historical travel data, feedback.

[1059] Output: A customized travel route.

[1060] What it does: Look at historical data and adapt routes to reflect your individual preferences.

[1061] Step 7:

[1062] Route provision

[1063] The server then sends the final customized route to the user's device.

[1064] Input: A customized travel route.

[1065] Output: Route display in map format on the device.

[1066] Specific operation: The transmitted data is sent to the device in a format that can be displayed in the map app.

[1067] Step 8:

[1068] Providing navigation

[1069] The device will then begin navigation based on the route information received, allowing users to receive real-time travel guidance.

[1070] Input: A customized travel route.

[1071] Output: Real-time travel directions.

[1072] Specific operation: Uses GPS data to navigate while comparing it with the current location.

[1073] Step 9:

[1074] Providing feedback

[1075] After completing their journey, users can enter their ratings and comments about the route on the feedback screen of their smartphone app or head-mounted display.

[1076] Input: Rating and / or Comments.

[1077] Output: Feedback data.

[1078] Specific operation: Receives feedback from users in text format and sends it to the cloud server.

[1079] Step 10:

[1080] Collecting and analyzing feedback

[1081] The server collects the feedback data and analyzes it for use in generating the next route.

[1082] Input: Feedback data.

[1083] Output: Updated training data.

[1084] What it does: Your feedback is stored in a database and used by machine learning algorithms to customize your next route.

[1085] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1087] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1088] [Third embodiment]

[1089] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1093] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1097] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1099] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1100] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1101] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time. Below, we will create a program for this system and explain the program's processing in natural language. We will also provide specific examples.

[1102] Overall system overview

[1103] This system is implemented using the user's device and a cloud server. First, the user uses a smartphone app to input walking settings (distance, time, starting point, types of points of interest). The device then sends this setting data to the server. The server uses a geographic information system such as Google Maps API to generate an optimal walking route based on the user's desired conditions. The generated route is sent to the user's device, where the user can visually check the route and use the navigation function to walk. The system also has the function of further customizing future walking routes by collecting and analyzing the user's past walking data and feedback.

[1104] Specific processing of the program

[1105] 1. Enter your user settings

[1106] User: Enters desired distance or time, starting point, and type of points of interest through a smartphone app interface.

[1107] 2. Sending input data

[1108] Device: Sends user-entered configuration data, including distance, time, starting point, and points of interest, to a cloud server.

[1109] 3. Route Generation

[1110] Server: Uses the Google Maps API based on the input data to generate a walking route that best suits the user's desired conditions.

[1111] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1112] Server: Retrieves information about popular spots and attractions from a local database and incorporates it into the route.

[1113] 4. Send and navigate a route

[1114] Server: Sends the generated walking route to the user's device.

[1115] Device: The received route is displayed in map format and presented to the user. The navigation function allows the user to take a walk while receiving route guidance in real time.

[1116] 5. Learning your preferences

[1117] Server: Records the user's past walking data and feedback, and uses machine learning algorithms to learn the user's preferences.

[1118] 6. Customized route suggestions

[1119] Server: Customizes and suggests routes for the next walk based on the user's preferences and past walking history, ensuring the user always has a fresh and interesting walking experience.

[1120] 7. Gathering Feedback

[1121] User: Enter a route rating and comments on the feedback screen provided after the walk is completed.

[1122] Device: Sends feedback data to the server.

[1123] Server: The server accumulates the received feedback and reflects it in subsequent route generation to provide highly accurate walking suggestions.

[1124] Specific examples

[1125] For example, if a user enters a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the specific processing is as follows:

[1126] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1127] Device: Sends configuration data to the cloud server.

[1128] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1129] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[1130] Server: Sends the generated routes to the device.

[1131] Device: Display the received route on a map and begin navigation.

[1132] User: Walk along a route, stopping at a cafe along the way.

[1133] User: Provide feedback by rating and commenting on the route after completing the walk.

[1134] On your device: Send feedback to the server to help improve future route suggestions.

[1135] The above process realizes a system that provides optimal walking routes according to the needs and preferences of each individual user.

[1136] The processing flow will be explained below.

[1137] Step 1: Enter your user settings

[1138] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the dedicated interface of the smartphone app.

[1139] Step 2: Submitting input data

[1140] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1141] Step 3: Generate Routes

[1142] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1143] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1144] Server: At the same time, it retrieves information about famous stores and facilities from a local database and incorporates this information into the walking route.

[1145] Step 4: Analyze historical data

[1146] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[1147] Step 5: Generate customized routes

[1148] Server: Based on the analysis results, the server generates a customized walking route tailored to the user's individual preferences, allowing the user to efficiently visit points of interest rather than simply walking a specified distance or time.

[1149] Step 6: Providing a Route

[1150] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1151] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1152] Step 7: Start Navigation

[1153] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1154] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1155] Step 8: Provide feedback

[1156] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1157] Terminal: Sends user feedback data to the cloud server.

[1158] Step 9: Analyze and store feedback

[1159] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1160] Through these steps, the system can provide the user with the most suitable and fresh walking route tailored to their preferences.

[1161] Example 1

[1162] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] Conventional walking route suggestion systems have difficulty proposing optimal routes that take into account the user's desired distance, time, and points of interest. They also lack the ability to learn the user's preferences and propose customized routes. This results in the problem that users cannot always have a fresh and interesting walking experience.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1165] In this invention, the server includes: means for a user to input a desired distance or time and a starting point; means for transmitting the input data to an information processing device; means for generating an optimal walking route using a geographic information system based on the input data; means for transmitting the generated route to the user's mobile communication terminal; means for analyzing the user's past movement data and rating information and learning the user's preferences; means for customizing the route based on the learning results; means for presenting the generated customized route to the user and providing navigation; means for collecting rating information from the user and reflecting it in the next route generation; means for the user to input input items through an interface and transmit the input setting data to the information processing device; means for encoding and transmitting the route information using a fixed protocol; means for linking map display and voice guidance to provide real-time navigation; and means for retrieving information on surrounding facilities from a database and incorporating it into the route. This allows the user to obtain an optimal walking route that includes points of interest, allowing for a fresh and fulfilling walking experience.

[1166] "User" refers to any individual or entity using a particular service or system.

[1167] "Distance" refers to the physical distance from the starting point to the destination point.

[1168] "Time" refers to the time that has elapsed from a specified start point to a specified end point.

[1169] "Starting point" refers to the place where you start your walk or journey.

[1170] "Information processing device" refers to a device for inputting, processing, storing, and outputting data.

[1171] "Geographic information system" means an information system for collecting, managing, analyzing, and displaying geographic data.

[1172] A "walking route" refers to a travel route generated based on conditions specified by the user.

[1173] "Mobile communication terminal" refers to a portable terminal that can connect to the Internet via wireless communication.

[1174] "Past travel data" refers to data including the routes a user has taken in the past and their location at that time.

[1175] "Evaluation information" refers to information that indicates feedback and satisfaction of a user regarding the service received or the proposed route.

[1176] "Interface" refers to the operating screen or device through which a user interacts with a system or application.

[1177] A "fixed protocol" refers to standardized procedures and rules for transmitting data.

[1178] "Encoding" refers to the process of converting data into a particular format or protocol.

[1179] "Real-time navigation" refers to providing instant route guidance based on your current location.

[1180] "Map display" refers to an interface or means for visually displaying geographic data.

[1181] "Voice guidance" refers to a function that provides instructions and information to the user by voice.

[1182] A "database" refers to a collection or system for systematically organizing, storing, and managing data.

[1183] "Information about nearby facilities" refers to information about stores and facilities located near the user.

[1184] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time, starting point, and types of spots they are interested in. This system operates in conjunction with the user's device and a cloud server.

[1185] First, the user uses a smartphone app to input the settings for their walk. For example, the user enters specific requirements such as "a 20-minute walk," "stopping at a cafe," and "starting from home." This input includes operations performed through the user interface, and the configured data is packaged in JSON format.

[1186] Next, the terminal transmits the input setting data to the cloud server securely using a fixed protocol (e.g., HTTPS).

[1187] The cloud server uses a geographic information system (e.g., Google Maps API) based on the received data to generate an optimal walking route. The route is generated using input data (walking distance, time, starting point, points of interest, etc.) and information on nearby facilities obtained from a local database. This allows the system to plan the optimal route that matches the user's requirements.

[1188] The generated route is again encoded in JSON format and sent to the user's device using the HTTPS protocol. The device displays the received route in map format and, if necessary, integrates the voice guidance function to provide real-time navigation.

[1189] Additionally, the cloud server learns user preferences by collecting and analyzing the user's past travel data and rating information. This includes using machine learning algorithms (e.g., Scikit-learn) to model user preferences. The learning results are reflected in the next route generation, providing the user with a more customized route.

[1190] In addition, the feedback (ratings and comments) provided by users after completing a walk is sent from the device to a cloud server and used to suggest routes for future walks. This ensures that the system always suggests the latest walking routes that best suit the user's needs.

[1191] As a concrete example, consider the case where a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way." The system proceeds as follows:

[1192] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1193] Device: Sends configuration data to the cloud server.

[1194] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1195] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[1196] Server: Sends the generated routes to the device.

[1197] Device: Displays the received route on a map, begins navigation, and provides voice instructions if voice guidance is enabled.

[1198] User: Walk along a route, stopping at a cafe along the way.

[1199] User: Provide feedback by rating and commenting on the route after completing the walk.

[1200] Device: Sends feedback to the cloud server, which incorporates this information into its next proposal.

[1201] The following are some examples of prompts that can be input to a generative AI model:

[1202] "A user wants to take a 20-minute walking route and wants to stop at a cafe along the way. Please suggest the best walking route that fits those criteria. The user's starting point is their home."

[1203] In this way, the present invention is a system that provides optimal walking routes tailored to the user's needs, ensuring a constantly fresh and interesting walking experience.

[1204] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1205] Step 1: Enter your user settings

[1206] Users open the smartphone app and enter their walk settings: the desired distance or time of the walk, the starting point, and the types of points of interest.

[1207] Input: Configuration data that the user enters into the app interface (e.g., "20 minutes," "cafe," "starting from home").

[1208] Output: Configuration data stored in the app.

[1209] Step 2: Submitting input data

[1210] The device sends the configuration data entered by the user to the cloud server. Specifically, the configuration data is packaged in JSON format and sent using the HTTPS protocol.

[1211] Input: The configuration data entered by the user.

[1212] Data processing: Encode the input data in JSON format.

[1213] Output: Configuration data sent to the cloud server using the HTTPS protocol.

[1214] Step 3: Generate a walking route

[1215] The server uses the received data to generate the optimal walking route using a geographic information system (e.g., a map service API). Specifically, it searches for a route based on the user's desired conditions and generates route information.

[1216] Input: JSON formatted configuration data sent from the device.

[1217] Data processing: Uses map service APIs to search and generate optimal routes.

[1218] Output: Information about the generated walking route.

[1219] Step 4: Incorporating local information

[1220] When generating a route, the server retrieves information on popular spots and landmarks from a local database and incorporates it into the route, enhancing the user's walking experience.

[1221] Input: Spot information retrieved from a local database.

[1222] Data processing: Incorporate the acquired spot information into the generated route.

[1223] Output: A customized route with spot information embedded.

[1224] Step 5: Send and navigate your route

[1225] The server sends the generated walking route to the user's device. Specifically, the route information is encoded in JSON format and sent via HTTPS protocol.

[1226] The device displays the route in map format for the user and also provides voice guidance for real-time navigation.

[1227] Input: Route information sent by the server.

[1228] Data processing: Decode JSON format route information into map format.

[1229] Output: Navigation map and voice directions displayed on the device.

[1230] Step 6: Learning user preferences

[1231] The server collects users' past travel data and rating information, including past walking routes and feedback information provided by users, and uses machine learning algorithms to learn users' preferences.

[1232] Input: User's historical travel data and feedback information.

[1233] Data processing: Using machine learning algorithms to analyze and learn user preferences.

[1234] Output: User's preferred model.

[1235] Step 7: Customized Route Suggestion

[1236] Based on the learning results, the server will further customize and suggest the next walking route, allowing users to always have a fresh walking experience that meets their individual needs.

[1237] Input: A learned model of the user's preferences.

[1238] Data processing: Generate a new route that reflects the learning results.

[1239] Output: Customized walking route information.

[1240] Step 8: Gather feedback

[1241] After completing a walk, users can enter their route rating and comments on the app's feedback screen.

[1242] The device sends the feedback data to the cloud server by packaging it in JSON format and sending it using the HTTPS protocol.

[1243] The server stores the received feedback in a database and uses it to generate future routes.

[1244] Input: User-provided feedback information.

[1245] Data processing: The feedback information is stored in a database and added to the machine learning training dataset.

[1246] Output: Updated training model and database.

[1247] (Application example 1)

[1248] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1249] Today, there is a demand for systems that efficiently suggest walking routes and shopping routes in virtual stores. However, until now, it has been difficult to generate customized routes that reflect a user's individual preferences and past behavioral history. Furthermore, there has been a lack of systems that dynamically suggest routes based on the user's interests, not just time and distance. Furthermore, providing efficient shopping routes in virtual stores remains an unsolved problem. The purpose of this invention is to solve these problems and provide a system that suggests walking and shopping routes optimized for each user.

[1250] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1251] In this invention, the server includes: means for inputting the user's desired distance or time and starting point; means for transmitting the input data to the server; and means for generating an optimal walking route using a geographic information system based on the input data. This enables route suggestions based on the user's specified walking route and preferences. The server also includes means for transmitting the generated route to the user's terminal, analyzing the user's past walking data and feedback, and learning the user's preferences; means for customizing the route based on the learning results; and means for presenting the generated customized route to the user and providing navigation. This enables route suggestions based on the user's individual preferences. The server also includes means for collecting user feedback and reflecting it in next route generation; means for inputting the user's desired stores and products and generating an optimal shopping route within the virtual store; and means for transmitting the generated shopping route to the user's terminal and navigating the user. This enables efficient and personalized shopping route suggestions within the virtual store.

[1252] "Means for the user to input the desired distance or time and starting point" refers to an interface or function that allows the user to input information such as the desired distance or time required to travel and the starting point into the terminal.

[1253] "Means for transmitting input data to a server" refers to the communication functions and protocols for transferring data input by a user to a cloud server.

[1254] "Means for generating optimal walking routes using a geographic information system" refers to an algorithm that uses geographic information and map data to calculate and generate the most suitable travel route for the user's desired conditions.

[1255] The "means for transmitting the generated route to the user's terminal" is a communication function for transferring the travel route information generated by the server back to the user's device.

[1256] "Means of analyzing users' past walking data and feedback to learn their preferences" refers to a machine learning algorithm that analyzes users' past usage history and impressions to learn the preferences and patterns of individual users.

[1257] "Means for customizing routes based on learning results" refers to a processing function that customizes travel routes based on the user's individual preferences and past history, and provides personalized suggestions.

[1258] The "means for presenting the generated customized route to the user and providing navigation" is a function for displaying the customized travel route on the user's terminal and providing route guidance in real time.

[1259] "Means of collecting feedback from users and reflecting it in the next route generation" refers to a function that collects opinions and evaluation data provided by users and analyzes and saves them for use in proposing the next travel route.

[1260] "A means for generating the optimal shopping route within a virtual store by inputting the store and products desired by the user" is an algorithm that calculates and generates the optimal shopping route within a virtual space by inputting the product and store information desired by the user.

[1261] "Means for sending the generated shopping route to the user's terminal and navigating" refers to a communication function for sending the generated shopping route to the user's terminal and providing route guidance within the virtual store.

[1262] This invention is a system that generates an optimal walking route based on the user's specific distance and time requirements and starting point, and also provides a shopping route within a virtual store. This system is realized using a smartphone application and a cloud server. It learns the user's preferences and past behavior history and provides a customized route based on that. Details of the mode for implementing this invention are as follows.

[1263] 1. Hardware and software configuration

[1264] Hardware

[1265] Smartphone: Used as a mobile device for users to check routes and enter settings.

[1266] Cloud Server: A central server that generates routes, analyzes user data, and sends and receives navigation data.

[1267] software

[1268] Smartphone app: Enter user settings, view routes, navigate in real time, and collect feedback.

[1269] Map service API: Routes are generated using Google Maps API or similar geographic information services.

[1270] Virtual Store API: A custom API used to generate virtual shopping routes.

[1271] 2. Program Processing

[1272] Entering User Preferences

[1273] Using a smartphone app, users can input the distance and time of their desired walk, as well as the starting point, and can also input the stores and products they want to explore in the virtual store. This input data is sent to a cloud server.

[1274] Sending and processing input data

[1275] The smartphone device sends the input data to a cloud server in real time. The server uses the received data to generate an optimal walking route using a geographic information system such as Google Maps API. The virtual store uses the virtual store API to generate an optimal shopping route based on the desired products and stores.

[1276] Route generation and submission

[1277] The generated route is sent from the cloud server to a smartphone, where users can view the route and start real-time navigation. The same applies to the virtual store, where the optimal shopping route is provided.

[1278] Learning user preferences

[1279] The cloud server collects and analyzes the user's past walking data, shopping history, and feedback, and uses machine learning algorithms to learn the user's preferences and customize future walking and shopping routes.

[1280] Gathering feedback and suggesting next routes

[1281] By collecting feedback provided by users after completing a walk or shopping trip and reflecting it in the next route generation, more accurate route suggestions can be made.

[1282] 3. Examples and prompts

[1283] Specific examples

[1284] For example, if a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will occur:

[1285] Users enter "20 minutes," "visit a cafe," and "depart from home" into the smartphone app.

[1286] The device sends the configuration data to the cloud server.

[1287] The server uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1288] The server references the user's past walking data and optimizes the route to suit their preferences.

[1289] The server sends the generated route to the terminal.

[1290] The device displays the received route on a map and begins navigation.

[1291] Users walk along a route and stop at cafes along the way.

[1292] After completing a walk, users can provide feedback by rating and commenting on the route.

[1293] The device sends feedback to the server to help suggest routes for future trips.

[1294] Example prompts to input to the generative AI model

[1295] Generate optimal routes in your virtual store starting from the entrance for users to search for shoes and bags in the fashion section. Use your store's map information and users' shopping patterns to create efficient routes and guide them step-by-step from the starting point.

[1296] The above is an embodiment of the present invention.

[1297] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1298] Step 1:

[1299] The user inputs the desired distance or time and starting point through the smartphone app interface. They also input the stores and products in the virtual store they want to explore. This generates the user's desired conditions as JSON format data. Specifically, the information entered by the user is saved in a form in the app.

[1300] Step 2:

[1301] The data entered in step 1 is sent to the cloud server on the device (smartphone). Specifically, the application sends the data via an HTTP POST request. At this time, the input data is sent to the API server's endpoint. The input data includes distance, time, starting point, and desired store and product information.

[1302] Step 3:

[1303] The server analyzes the received input data and calls the Google Maps API and virtual store API to generate walking and shopping routes that best fit the user's desired conditions. The server first calculates the optimal walking route from the starting point using a geographic information system. It then calculates the shortest route to the desired store or product in the virtual store. Specifically, the server sends an API request to the Google Maps API or virtual store API and receives a response.

[1304] Step 4:

[1305] The server sends the generated walking and shopping routes to the user's device. Specifically, the server encodes the generated route data into JSON format and sends it to the user's smartphone as an HTTP response, including map information and navigation information.

[1306] Step 5:

[1307] The device visually displays the received route and initiates real-time navigation. Specifically, the smartphone app analyzes the received route data, displays it on a map view, and provides step-by-step navigation guidance. The user is provided with visual and audio guidance.

[1308] Step 6:

[1309] After the user has finished their walk or shopping, they provide feedback through the smartphone app. Specifically, the app displays a feedback form where the user can enter their route rating and comments. This feedback is saved in JSON format.

[1310] Step 7:

[1311] The device sends the feedback provided by the user to the cloud server. Specifically, the app sends the feedback data to the server via an HTTP POST request. The feedback data includes the rating score and comments.

[1312] Step 8:

[1313] The server analyzes the received feedback data to learn user preferences and patterns. Specifically, the server uses machine learning algorithms to analyze the feedback data and model the user's preferences, which then personalizes the next route suggestion.

[1314] Step 9:

[1315] The next time the server generates a route, it will generate a customized route based on the learning results and suggest it to the user again. Specifically, the server calculates a new route taking into account the user's past behavioral history and feedback, and sends it back to the device. This series of processes provides the user with an optimized walking and shopping route.

[1316] The above are the processing steps of the system for realizing the application example.

[1317] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1318] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. The program for this system will be described in detail below.

[1319] Overall system overview

[1320] This system combines the user's device, a cloud server, and an emotion engine. The user first inputs the walking settings (distance, time, starting point, points of interest) through a smartphone app. The device then sends this setting data to the cloud server. The server uses a geographic information system (e.g., Google Maps API) to generate an optimal walking route based on the user's desired conditions and sends the generated route to the user's device. At the same time, it collects and analyzes the user's past walking data and feedback, and customizes the next walking route based on the results. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and can adjust the walking route based on the emotional data.

[1321] Specific processing of the program

[1322] 1. Enter your user settings

[1323] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[1324] 2. Sending input data

[1325] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1326] 3. Route Generation

[1327] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1328] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1329] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[1330] 4. Emotion Recognition by Emotion Engine

[1331] Server: The emotion engine recognizes the user's current emotional state.

[1332] Example: Identifying emotions (happiness, sadness, stress, etc.) through user voice input or facial recognition.

[1333] 5. Emotion-based route adjustment

[1334] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[1335] For example: If you're feeling stressed, suggest a route through a quiet park.

[1336] 6. Creating customized routes

[1337] Server: Analyzes the user's past walking data and feedback, and generates a customized walking route based on their individual preferences, combined with their emotional state.

[1338] 7. Route provision

[1339] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1340] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1341] 8. Start navigation

[1342] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1343] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1344] 9. Providing Feedback

[1345] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1346] Terminal: Sends user feedback data to the cloud server.

[1347] 10. Analysis and storage of feedback

[1348] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1349] Specific examples

[1350] For example, if a user sets a preference to "take a 20-minute walk and stop at a cafe along the way," and the emotion engine recognizes that the user is feeling stressed, the specific processing will be as follows:

[1351] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1352] Device: Sends configuration data to the cloud server.

[1353] Server: Uses the Google Maps API to generate the optimal route that passes through cafes that are reachable within 20 minutes from home.

[1354] Server: The emotion engine recognizes the user's emotions and adds a route through a quiet park to reduce stress.

[1355] Server: Optimizes customized routes based on past user walking data and feedback.

[1356] Server: Sends the generated customized route to the device.

[1357] Device: Display the received route on a map and begin navigation.

[1358] User: Walk along a route, stopping at a cafe along the way.

[1359] User: Provide feedback by rating and commenting on the route after completing the walk.

[1360] On your device: Sends feedback to the server to help generate future routes.

[1361] Through the above process, the user can obtain the optimal walking route that matches his or her emotional state.

[1362] The processing flow will be explained below.

[1363] Step 1: Enter your user settings

[1364] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[1365] Step 2: Submitting input data

[1366] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1367] Step 3: Generate Routes

[1368] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1369] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1370] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[1371] Step 4: Analyze historical data

[1372] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[1373] Step 5: Emotion Recognition with the Emotion Engine

[1374] Server: The emotion engine recognizes the user's current emotional state.

[1375] Example: Identifying emotions (happiness, sadness, stress, etc.) from the voice of a user speaking to a smartphone app or from facial images captured through a camera.

[1376] Step 6: Emotionally Based Route Adjustments

[1377] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[1378] Example: If the user is feeling stressed, suggest a route through a quiet, natural park.

[1379] Step 7: Generate customized routes

[1380] Server: Based on the analysis of past data and emotion recognition results, it generates a customized walking route tailored to the user's individual preferences.

[1381] Step 8: Providing a Route

[1382] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1383] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1384] Step 9: Start Navigation

[1385] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1386] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1387] Step 10: Provide feedback

[1388] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1389] Terminal: Sends user feedback data to the cloud server.

[1390] Step 11: Analyze and store feedback

[1391] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1392] Specific examples

[1393] If you want to take a 20-minute walk, visit a cafe, and de-stress.

[1394] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1395] Device: After completing the procedure, send the setting data to the cloud server.

[1396] Server: Uses the Google Maps API to search for cafes that can be reached within 20 minutes from home and generate the optimal route.

[1397] Server: If the emotion engine detects stress from the user's speech or facial recognition, include a quiet park in the route.

[1398] Server: Sends optimized and customized routes to the device.

[1399] Device: Display the route on the map and begin navigation.

[1400] User: Starts walking following a route, stops at a cafe, passes through a quiet park.

[1401] Users: Provide feedback after completing a walk, leaving a rating and comments.

[1402] Device: Sends all feedback data to the server.

[1403] Server: Stores the received data and uses it to improve and customize the route next time.

[1404] Through the above process, the user can obtain the optimal walking route according to his / her emotional state.

[1405] Example 2

[1406] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1407] Conventional walking route suggestion systems have difficulty providing routes that fully reflect the user's preferences and emotional state. As a result, they have been unable to improve user satisfaction or maximize the benefits of their walks. It has also been difficult to individually analyze past walking data and feedback and reflect them in the next route.

[1408] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1409] In this invention, the server includes means for inputting the user's desired distance or time and starting point, means for transmitting the input data to the server, means for generating an optimal walking route using a geographic information system based on the input data, means for transmitting the generated route to the user's terminal, means for analyzing the user's past walking data and feedback and learning the user's preferences, means for customizing the route based on the learning results, means for presenting the generated customized route to the user and providing navigation, means for recognizing the user's emotions, means for adjusting the walking route based on the emotion recognition results, and means for collecting feedback from the user and reflecting it in the next route generation.

[1410] This makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[1411] "User" refers to an individual who uses this system to receive walking route suggestions.

[1412] "Terminal" refers to a device used by a user, including mobile devices such as smartphones and tablets.

[1413] A "server" refers to a computer system that exists on the cloud, processes data received from users, and performs tasks such as generating walking routes and recognizing emotions.

[1414] A "geographic information system" is a system that calculates routes using map data and location information, and includes an application program interface for a map provision service.

[1415] A "walking route" refers to a suggested route for walking, including a starting point, a destination point, and intermediate points specified by the user.

[1416] "Settings Data" is information entered by the user, including distance, time, starting point, and points of interest.

[1417] "Navigation" refers to the function that guides the user along a route when they start a walk.

[1418] "Past walking data" refers to the recorded information of walks the user has taken so far.

[1419] "Feedback" refers to the ratings and comments about the route provided by users after completing a walk.

[1420] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[1421] "Customization" refers to individually optimizing walking routes based on the user's preferences and emotional state.

[1422] "Generative AI models" refer to algorithms or machine learning models that create new walking routes based on data.

[1423] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[1424] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. This system is implemented using the user's device, a cloud server, and the emotion engine.

[1425] System configuration

[1426] User's device

[1427] The user's device is a mobile device such as a smartphone or tablet. The user first installs a dedicated smartphone app and enters the walking settings through this application. The information entered includes the desired walking distance or time, starting point, and points of interest. After entering the setting data, the device sends it to the cloud server.

[1428] Cloud Server

[1429] The cloud server generates an optimal walking route using a geographic information system (e.g., an application program interface of a map providing service) based on the setting data received from the user. The generated route includes intermediate points that can be reached within a specified distance or time from the starting point.

[1430] The server also incorporates an emotion engine that recognizes the user's emotional state through voice input and facial recognition, determining whether they are stressed or happy, and can adjust the walking route based on the recognized emotional data.

[1431] The server also collects and analyzes the user's past walking data and feedback to learn the user's preferences, and generates a customized route based on the learning results and provides it to the device.

[1432] Specific examples

[1433] For example, if a user sets "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will be performed.

[1434] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1435] Device: Sends configuration data to the cloud server.

[1436] Server: Analyzes the received data and uses the API of a map provider to generate the optimal route that passes through a cafe that can be reached within 20 minutes from the user's home.

[1437] Server: The emotion engine recognizes the user's emotions and suggests routes through quiet parks to reduce stress.

[1438] Server: Optimizes customized routes based on past user walking data and feedback.

[1439] Server: Sends the generated customized route to the device.

[1440] Device: Display the received route on a map and begin navigation.

[1441] User: Follows a route, stops at a cafe along the way, and provides feedback on the route with ratings and comments after the walk.

[1442] On your device: Sends feedback to the server to help generate future routes.

[1443] Specific examples of hardware and software used

[1444] Device: Smartphone (Android, iOS)

[1445] Server: Cloud computing services

[1446] Geographic Information Systems: Application Program Interfaces for map services (e.g., Google Maps API)

[1447] Emotion engine: speech recognition software, facial recognition software

[1448] Prompt Sentence Examples

[1449] A specific example of a prompt could be, "Generate a 20-minute walking route and suggest a route that includes a stop at a cafe along the way." This allows the generative AI model to provide the optimal walking route according to the user's request.

[1450] This is the outline of the system. The present invention makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] User: The user opens the smartphone app and enters the "walk distance or time," "starting point," and "points of interest."

[1454] Input: distance or time, starting point, points of interest

[1455] Output: Configuration data (distance or time, starting point, points of interest)

[1456] Specific operation: The user sets up the walk using touch input or voice input, and presses the "Settings complete" button in the app.

[1457] Step 2:

[1458] Device: Receives configuration data and sends it to the cloud server.

[1459] Input: Configuration data

[1460] Output: HTTP request to the server

[1461] What happens: The device packages the configuration data and sends it as an HTTP request over the internet to the cloud server.

[1462] Step 3:

[1463] Server: Analyzes the received configuration data and calls the API of the geographic information system to generate the optimal walking route.

[1464] Input: Configuration data

[1465] Output: Walking route

[1466] What happens: The server uses a geographic information system such as the Google Maps API to calculate the optimal walking route that includes the specified starting point and points of interest.

[1467] Step 4:

[1468] Server: Refers to a local database to obtain information on famous stores and facilities, and incorporates it into walking routes.

[1469] Input: Area database, best walking routes

[1470] Output: Walking route including facility information

[1471] Specific operation: The server retrieves local commercial and tourist information from a database and adds it to the optimal walking route.

[1472] Step 5:

[1473] Server: The emotion engine recognizes emotions through the user's voice input and facial recognition.

[1474] Input: User's voice data, face image

[1475] Output: Emotion data

[1476] What it does: The server uses voice and facial recognition software to analyze the user's emotions and identify their emotional state (happiness, sadness, stress, etc.).

[1477] Step 6:

[1478] Server: Adjusts the walking route based on the emotion recognition results.

[1479] Input: Emotion data, walking route including facility information

[1480] Output: Adjusted walking route

[1481] What it does: The server incorporates quiet parks and relaxing spots into the route depending on the user's emotional state.

[1482] Step 7:

[1483] Server: Collects past walk data and feedback, and customizes routes based on user preferences.

[1484] Input: Past walk data, feedback, adjusted walk route

[1485] Output: Customized walking route

[1486] How it works: The server analyzes the user's past data and applies an optimal route generation algorithm to generate an individually optimized walking route.

[1487] Step 8:

[1488] Server: Sends the generated customized route to the user's device.

[1489] Input: Custom walking route

[1490] Output: HTTP response to the user's device

[1491] Specific operation: The server packages the data including the generated customized walking route and sends it to the user's device as an HTTP response.

[1492] Step 9:

[1493] Device: Display the received walking route in map format and begin navigation.

[1494] Input: Custom walking route

[1495] Output: Map display, navigation instructions

[1496] What it does: The device displays the route information it receives on a map and provides turn-by-turn directions and voice guidance for navigation.

[1497] Step 10:

[1498] User: After completing a walk, the user enters a route rating and comments on the feedback screen of the app. The feedback is then sent to the server by the device.

[1499] Input: Route rating, comments

[1500] Output: Feedback data

[1501] Specific operation: The user enters a star rating or text comment on the feedback screen and presses the send button. The device packages this and sends it to the server.

[1502] Step 11:

[1503] Server: Saves the feedback data and uses it to generate future walking routes.

[1504] Input: Feedback data

[1505] Output: Updated generation algorithm

[1506] What it does: The server stores the feedback data in a database and uses it to improve the algorithm. It periodically analyzes the feedback and updates the optimal route generation algorithm.

[1507] (Application example 2)

[1508] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1509] In modern society, there is a demand for autonomous vehicle travel routes that take into account the stress and emotional state of the user. However, conventional systems do not recognize user emotions and adjust routes based on them. As a result, it is difficult for users to enjoy an optimal and comfortable journey. Furthermore, they are unable to effectively utilize past travel data and feedback, which means they are unable to provide personalized services based on the user's preferences and emotional state. To solve these issues, a system is needed that recognizes user emotions and adjusts routes in real time based on them.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1511] In this invention, the server includes means for generating an optimal travel route using a geographic information system based on input data, means for adjusting the route based on the emotional data using an emotion engine that recognizes the user's emotional state, and means for analyzing the user's past travel data and feedback to learn the user's preferences, thereby providing the optimal travel route in real time according to the user's emotional state and enabling a personalized and comfortable travel experience.

[1512] "Means for users to input their desired distance or time and starting point" means an interface feature that allows users to use a smartphone or other device to input their desired distance or time to travel and the starting point of the trip.

[1513] "Means for transmitting input data to a server" refers to a function that transmits data input by a user to a server via the Internet.

[1514] A "geographic information system" is a system or application program interface (API) that handles geographic information and generates maps and routes.

[1515] The "means for generating an optimal travel route" refers to a function that uses a geographic information system to calculate and generate an optimal travel route based on input data.

[1516] "Means for sending the generated route to the user's device" refers to the function of sending the route information generated by the server to the user's device, such as a smartphone or head-mounted display.

[1517] "Means for learning user preferences by analyzing user's past travel data and feedback" refers to algorithms or functions that analyze the user's past travel history and feedback provided by the user and learn the user's preferences based on that.

[1518] An "emotion engine" is a technology or system that recognizes emotions from a user's facial expressions, voice, etc., and processes that information.

[1519] "Means for adjusting route based on emotional data" refers to a function that recalculates and adjusts the optimal travel route based on the emotional state of the user recognized by the emotion engine.

[1520] "Means for providing navigation" refers to a function that provides real-time travel guidance to users based on the generated route information.

[1521] "Means for collecting feedback and reflecting it in the next route generation" refers to a function that collects feedback provided by users after traveling and uses that information when generating the next route.

[1522] The present invention combines a system that provides an optimal travel route based on the user's desired distance and time with an emotion engine that recognizes the user's emotions. Detailed programs and embodiments of this system will be specifically described below.

[1523] Overall system configuration

[1524] The system is implemented by combining a user device (such as a smartphone or a head-mounted display), a cloud server, and an emotion engine. The system consists of the following main components:

[1525] 1. User Input Interface

[1526] The user inputs the desired distance, time, starting point, destination, and points of interest via a smartphone or head-mounted display.

[1527] 2. Data Transmission

[1528] The device sends the entered data to a cloud server, including information about the user's desired distance, time, starting point, and points of interest.

[1529] 3. Route generation

[1530] Based on the data received, the server uses a geographic information system (GIS) to generate an optimal travel route. Specifically, the route is calculated using the application program interface (API) of the map provider service.

[1531] 4. Emotion recognition

[1532] The server's emotion engine recognizes the user's current emotional state by analyzing their facial expressions and voice data.

[1533] 5. Route adjustment

[1534] The server's emotion engine adjusts the generated route in real time based on the emotional data it recognizes, suggesting a quieter route or one that passes through a park if the user is feeling stressed, for example.

[1535] 6. Customized Route Generation

[1536] The server analyzes the user's past travel data and feedback to learn their preferences, and then regenerates a personalized route based on that information.

[1537] 7. Route provision

[1538] The server sends the generated customized route to the user's device.

[1539] The device visually presents the received route to the user in map format and begins navigation.

[1540] 8. Feedback Collection

[1541] After traveling, users can enter ratings and comments about the route on the feedback screen of their smartphone or head-mounted display.

[1542] The device sends the feedback to the cloud server, which then reflects it in the next route generation.

[1543] Hardware and software used

[1544] Hardware: smartphone, head-mounted display, in-car camera, microphone, emotion recognition sensor

[1545] Software: Cloud servers, emotion engines (e.g., Emotion API), geographic information systems (e.g., Google Maps API), machine learning algorithms (e.g., TensorFlow)

[1546] Specific examples

[1547] For example, if a user sets a preference on their smartphone to "pass through a park on the way home from their office in Tokyo," and the emotion engine recognizes that the user wants to relax, the following process occurs:

[1548] The user enters "from office to home" and "visit to the park" into a smartphone app.

[1549] The device sends the setting data and emotion data to the cloud server.

[1550] The server uses the Google Maps API to generate the optimal route from the office to the home, adding a route that passes through the park along the way.

[1551] The server uses an emotion engine to recognize the user's state of relaxation and suggests scenic routes.

[1552] The server sends the generated route to a smartphone or head-mounted display.

[1553] The user follows the route and provides feedback after the route.

[1554] The device sends feedback to the server to help generate the next route.

[1555] Prompt Sentence Examples

[1556] If the user wants to relax on their way home, suggest the optimal route. The current starting point is an office in Tokyo, and the destination is home. Pass through a quiet park along the way. Prioritize scenic areas in the suggested route.

[1557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1558] Step 1:

[1559] User enters desired distance or time and starting point

[1560] Using a smartphone app or head-mounted display, users input the desired distance or time of travel, starting point, destination, and any detours they wish to make.

[1561] Input: distance, time, starting point, destination, points of interest.

[1562] Output: Input data is sent to the system.

[1563] Step 2:

[1564] Data transmission

[1565] The device sends the input data from the user to the cloud server.

[1566] Input: User's configuration data.

[1567] Output: The server receives the data.

[1568] Step 3:

[1569] Generate travel routes

[1570] The server analyzes the received data and generates the optimal travel route using a geographic information system (such as Google Maps API).

[1571] Input: User configuration data, geographic information.

[1572] Output: Optimal travel route information.

[1573] Specific behavior: Calls the Google Maps API and calculates a route based on the desired criteria.

[1574] Step 4:

[1575] Recognition of emotional states

[1576] The server's emotion engine recognizes the user's emotional state by analyzing facial expressions and voice data acquired from smartphones, head-mounted displays, in-car cameras, and microphones.

[1577] Input: User's facial expression data, voice data.

[1578] Output: The user's emotional state.

[1579] Specific actions: Determine emotions using facial recognition and voice analysis technology.

[1580] Step 5:

[1581] Route Adjustment

[1582] The server adjusts the optimal travel route based on the user's emotional state as recognized by the emotion engine. For example, if the user wants to relax, the server will suggest a route that passes through a quiet park.

[1583] Input: Initial travel route, user's emotional state.

[1584] Output: Adjusted travel route.

[1585] Specific behavior: Compare the generated route information with emotion data and recalculate if necessary.

[1586] Step 6:

[1587] Generate customized travel routes

[1588] The server analyzes the user's past travel data and feedback, and generates a more customized travel route based on the learning results.

[1589] Input: adjusted travel route, historical travel data, feedback.

[1590] Output: A customized travel route.

[1591] What it does: Look at historical data and adapt routes to reflect your individual preferences.

[1592] Step 7:

[1593] Route provision

[1594] The server then sends the final customized route to the user's device.

[1595] Input: A customized travel route.

[1596] Output: Route display in map format on the device.

[1597] Specific operation: The transmitted data is sent to the device in a format that can be displayed in the map app.

[1598] Step 8:

[1599] Providing navigation

[1600] The device will then begin navigation based on the route information received, allowing users to receive real-time travel guidance.

[1601] Input: A customized travel route.

[1602] Output: Real-time travel directions.

[1603] Specific operation: Uses GPS data to navigate while comparing it with the current location.

[1604] Step 9:

[1605] Providing feedback

[1606] After completing their journey, users can enter their ratings and comments about the route on the feedback screen of their smartphone app or head-mounted display.

[1607] Input: Rating and / or Comments.

[1608] Output: Feedback data.

[1609] Specific operation: Receives feedback from users in text format and sends it to the cloud server.

[1610] Step 10:

[1611] Collecting and analyzing feedback

[1612] The server collects the feedback data and analyzes it for use in generating the next route.

[1613] Input: Feedback data.

[1614] Output: Updated training data.

[1615] What it does: Your feedback is stored in a database and used by machine learning algorithms to customize your next route.

[1616] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1617] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1618] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1619] [Fourth embodiment]

[1620] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1621] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1622] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1623] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1624] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1625] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1626] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1627] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1628] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1629] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1630] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1631] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1632] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1633] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time. Below, we will create a program for this system and explain the program's processing in natural language. We will also provide specific examples.

[1634] Overall system overview

[1635] This system is implemented using the user's device and a cloud server. First, the user uses a smartphone app to input walking settings (distance, time, starting point, types of points of interest). The device then sends this setting data to the server. The server uses a geographic information system such as Google Maps API to generate an optimal walking route based on the user's desired conditions. The generated route is sent to the user's device, where the user can visually check the route and use the navigation function to walk. The system also has the function of further customizing future walking routes by collecting and analyzing the user's past walking data and feedback.

[1636] Specific processing of the program

[1637] 1. Enter your user settings

[1638] User: Enters desired distance or time, starting point, and type of points of interest through a smartphone app interface.

[1639] 2. Sending input data

[1640] Device: Sends user-entered configuration data, including distance, time, starting point, and points of interest, to a cloud server.

[1641] 3. Route Generation

[1642] Server: Uses the Google Maps API based on the input data to generate a walking route that best suits the user's desired conditions.

[1643] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1644] Server: Retrieves information about popular spots and attractions from a local database and incorporates it into the route.

[1645] 4. Send and navigate a route

[1646] Server: Sends the generated walking route to the user's device.

[1647] Device: The received route is displayed in map format and presented to the user. The navigation function allows the user to take a walk while receiving route guidance in real time.

[1648] 5. Learning your preferences

[1649] Server: Records the user's past walking data and feedback, and uses machine learning algorithms to learn the user's preferences.

[1650] 6. Customized route suggestions

[1651] Server: Customizes and suggests routes for the next walk based on the user's preferences and past walking history, ensuring the user always has a fresh and interesting walking experience.

[1652] 7. Gathering Feedback

[1653] User: Enter a route rating and comments on the feedback screen provided after the walk is completed.

[1654] Device: Sends feedback data to the server.

[1655] Server: The server accumulates the received feedback and reflects it in subsequent route generation to provide highly accurate walking suggestions.

[1656] Specific examples

[1657] For example, if a user enters a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the specific processing is as follows:

[1658] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1659] Device: Sends configuration data to the cloud server.

[1660] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1661] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[1662] Server: Sends the generated routes to the device.

[1663] Device: Display the received route on a map and begin navigation.

[1664] User: Walk along a route, stopping at a cafe along the way.

[1665] User: Provide feedback by rating and commenting on the route after completing the walk.

[1666] On your device: Send feedback to the server to help improve future route suggestions.

[1667] The above process realizes a system that provides optimal walking routes according to the needs and preferences of each individual user.

[1668] The processing flow will be explained below.

[1669] Step 1: Enter your user settings

[1670] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the dedicated interface of the smartphone app.

[1671] Step 2: Submitting input data

[1672] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1673] Step 3: Generate Routes

[1674] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1675] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1676] Server: At the same time, it retrieves information about famous stores and facilities from a local database and incorporates this information into the walking route.

[1677] Step 4: Analyze historical data

[1678] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[1679] Step 5: Generate customized routes

[1680] Server: Based on the analysis results, the server generates a customized walking route tailored to the user's individual preferences, allowing the user to efficiently visit points of interest rather than simply walking a specified distance or time.

[1681] Step 6: Providing a Route

[1682] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1683] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1684] Step 7: Start Navigation

[1685] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1686] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1687] Step 8: Provide feedback

[1688] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1689] Terminal: Sends user feedback data to the cloud server.

[1690] Step 9: Analyze and store feedback

[1691] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1692] Through these steps, the system can provide the user with the most suitable and fresh walking route tailored to their preferences.

[1693] Example 1

[1694] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1695] Conventional walking route suggestion systems have difficulty proposing optimal routes that take into account the user's desired distance, time, and points of interest. They also lack the ability to learn the user's preferences and propose customized routes. This results in the problem that users cannot always have a fresh and interesting walking experience.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1697] In this invention, the server includes: means for a user to input a desired distance or time and a starting point; means for transmitting the input data to an information processing device; means for generating an optimal walking route using a geographic information system based on the input data; means for transmitting the generated route to the user's mobile communication terminal; means for analyzing the user's past movement data and rating information and learning the user's preferences; means for customizing the route based on the learning results; means for presenting the generated customized route to the user and providing navigation; means for collecting rating information from the user and reflecting it in the next route generation; means for the user to input input items through an interface and transmit the input setting data to the information processing device; means for encoding and transmitting the route information using a fixed protocol; means for linking map display and voice guidance to provide real-time navigation; and means for retrieving information on surrounding facilities from a database and incorporating it into the route. This allows the user to obtain an optimal walking route that includes points of interest, allowing for a fresh and fulfilling walking experience.

[1698] "User" refers to any individual or entity using a particular service or system.

[1699] "Distance" refers to the physical distance from the starting point to the destination point.

[1700] "Time" refers to the time that has elapsed from a specified start point to a specified end point.

[1701] "Starting point" refers to the place where you start your walk or journey.

[1702] "Information processing device" refers to a device for inputting, processing, storing, and outputting data.

[1703] "Geographic information system" means an information system for collecting, managing, analyzing, and displaying geographic data.

[1704] A "walking route" refers to a travel route generated based on conditions specified by the user.

[1705] "Mobile communication terminal" refers to a portable terminal that can connect to the Internet via wireless communication.

[1706] "Past travel data" refers to data including the routes a user has taken in the past and their location at that time.

[1707] "Evaluation information" refers to information that indicates feedback and satisfaction of a user regarding the service received or the proposed route.

[1708] "Interface" refers to the operating screen or device through which a user interacts with a system or application.

[1709] A "fixed protocol" refers to standardized procedures and rules for transmitting data.

[1710] "Encoding" refers to the process of converting data into a particular format or protocol.

[1711] "Real-time navigation" refers to providing instant route guidance based on your current location.

[1712] "Map display" refers to an interface or means for visually displaying geographic data.

[1713] "Voice guidance" refers to a function that provides instructions and information to the user by voice.

[1714] A "database" refers to a collection or system for systematically organizing, storing, and managing data.

[1715] "Information about nearby facilities" refers to information about stores and facilities located near the user.

[1716] This invention is a system that generates and suggests optimal walking routes based on the user's desired distance and time, starting point, and types of spots they are interested in. This system operates in conjunction with the user's device and a cloud server.

[1717] First, the user uses a smartphone app to input the settings for their walk. For example, the user enters specific requirements such as "a 20-minute walk," "stopping at a cafe," and "starting from home." This input includes operations performed through the user interface, and the configured data is packaged in JSON format.

[1718] Next, the terminal transmits the input setting data to the cloud server securely using a fixed protocol (e.g., HTTPS).

[1719] The cloud server uses a geographic information system (e.g., Google Maps API) based on the received data to generate an optimal walking route. The route is generated using input data (walking distance, time, starting point, points of interest, etc.) and information on nearby facilities obtained from a local database. This allows the system to plan the optimal route that matches the user's requirements.

[1720] The generated route is again encoded in JSON format and sent to the user's device using the HTTPS protocol. The device displays the received route in map format and, if necessary, integrates the voice guidance function to provide real-time navigation.

[1721] Additionally, the cloud server learns user preferences by collecting and analyzing the user's past travel data and rating information. This includes using machine learning algorithms (e.g., Scikit-learn) to model user preferences. The learning results are reflected in the next route generation, providing the user with a more customized route.

[1722] In addition, the feedback (ratings and comments) provided by users after completing a walk is sent from the device to a cloud server and used to suggest routes for future walks. This ensures that the system always suggests the latest walking routes that best suit the user's needs.

[1723] As a concrete example, consider the case where a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way." The system proceeds as follows:

[1724] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1725] Device: Sends configuration data to the cloud server.

[1726] Server: Uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1727] Server: Refers to the user's past walking data and optimizes the route based on their preferences.

[1728] Server: Sends the generated routes to the device.

[1729] Device: Displays the received route on a map, begins navigation, and provides voice instructions if voice guidance is enabled.

[1730] User: Walk along a route, stopping at a cafe along the way.

[1731] User: Provide feedback by rating and commenting on the route after completing the walk.

[1732] Device: Sends feedback to the cloud server, which incorporates this information into its next proposal.

[1733] The following are some examples of prompts that can be input to a generative AI model:

[1734] "A user wants to take a 20-minute walking route and wants to stop at a cafe along the way. Please suggest the best walking route that fits those criteria. The user's starting point is their home."

[1735] In this way, the present invention is a system that provides optimal walking routes tailored to the user's needs, ensuring a constantly fresh and interesting walking experience.

[1736] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1737] Step 1: Enter your user settings

[1738] Users open the smartphone app and enter their walk settings: the desired distance or time of the walk, the starting point, and the types of points of interest.

[1739] Input: Configuration data that the user enters into the app interface (e.g., "20 minutes," "cafe," "starting from home").

[1740] Output: Configuration data stored in the app.

[1741] Step 2: Submitting input data

[1742] The device sends the configuration data entered by the user to the cloud server. Specifically, the configuration data is packaged in JSON format and sent using the HTTPS protocol.

[1743] Input: The configuration data entered by the user.

[1744] Data processing: Encode the input data in JSON format.

[1745] Output: Configuration data sent to the cloud server using the HTTPS protocol.

[1746] Step 3: Generate a walking route

[1747] The server uses the received data to generate the optimal walking route using a geographic information system (e.g., a map service API). Specifically, it searches for a route based on the user's desired conditions and generates route information.

[1748] Input: JSON formatted configuration data sent from the device.

[1749] Data processing: Uses map service APIs to search and generate optimal routes.

[1750] Output: Information about the generated walking route.

[1751] Step 4: Incorporating local information

[1752] When generating a route, the server retrieves information on popular spots and landmarks from a local database and incorporates it into the route, enhancing the user's walking experience.

[1753] Input: Spot information retrieved from a local database.

[1754] Data processing: Incorporate the acquired spot information into the generated route.

[1755] Output: A customized route with spot information embedded.

[1756] Step 5: Send and navigate your route

[1757] The server sends the generated walking route to the user's device. Specifically, the route information is encoded in JSON format and sent via HTTPS protocol.

[1758] The device displays the route in map format for the user and also provides voice guidance for real-time navigation.

[1759] Input: Route information sent by the server.

[1760] Data processing: Decode JSON format route information into map format.

[1761] Output: Navigation map and voice directions displayed on the device.

[1762] Step 6: Learning user preferences

[1763] The server collects users' past travel data and rating information, including past walking routes and feedback information provided by users, and uses machine learning algorithms to learn users' preferences.

[1764] Input: User's historical travel data and feedback information.

[1765] Data processing: Using machine learning algorithms to analyze and learn user preferences.

[1766] Output: User's preferred model.

[1767] Step 7: Customized Route Suggestion

[1768] Based on the learning results, the server will further customize and suggest the next walking route, allowing users to always have a fresh walking experience that meets their individual needs.

[1769] Input: A learned model of the user's preferences.

[1770] Data processing: Generate a new route that reflects the learning results.

[1771] Output: Customized walking route information.

[1772] Step 8: Gather feedback

[1773] After completing a walk, users can enter their route rating and comments on the app's feedback screen.

[1774] The device sends the feedback data to the cloud server by packaging it in JSON format and sending it using the HTTPS protocol.

[1775] The server stores the received feedback in a database and uses it to generate future routes.

[1776] Input: User-provided feedback information.

[1777] Data processing: The feedback information is stored in a database and added to the machine learning training dataset.

[1778] Output: Updated training model and database.

[1779] (Application example 1)

[1780] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1781] Today, there is a demand for systems that efficiently suggest walking routes and shopping routes in virtual stores. However, until now, it has been difficult to generate customized routes that reflect a user's individual preferences and past behavioral history. Furthermore, there has been a lack of systems that dynamically suggest routes based on the user's interests, not just time and distance. Furthermore, providing efficient shopping routes in virtual stores remains an unsolved problem. The purpose of this invention is to solve these problems and provide a system that suggests walking and shopping routes optimized for each user.

[1782] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1783] In this invention, the server includes: means for inputting the user's desired distance or time and starting point; means for transmitting the input data to the server; and means for generating an optimal walking route using a geographic information system based on the input data. This enables route suggestions based on the user's specified walking route and preferences. The server also includes means for transmitting the generated route to the user's terminal, analyzing the user's past walking data and feedback, and learning the user's preferences; means for customizing the route based on the learning results; and means for presenting the generated customized route to the user and providing navigation. This enables route suggestions based on the user's individual preferences. The server also includes means for collecting user feedback and reflecting it in next route generation; means for inputting the user's desired stores and products and generating an optimal shopping route within the virtual store; and means for transmitting the generated shopping route to the user's terminal and navigating the user. This enables efficient and personalized shopping route suggestions within the virtual store.

[1784] "Means for the user to input the desired distance or time and starting point" refers to an interface or function that allows the user to input information such as the desired distance or time required to travel and the starting point into the terminal.

[1785] "Means for transmitting input data to a server" refers to the communication functions and protocols for transferring data input by a user to a cloud server.

[1786] "Means for generating optimal walking routes using a geographic information system" refers to an algorithm that uses geographic information and map data to calculate and generate the most suitable travel route for the user's desired conditions.

[1787] The "means for transmitting the generated route to the user's terminal" is a communication function for transferring the travel route information generated by the server back to the user's device.

[1788] "Means of analyzing users' past walking data and feedback to learn their preferences" refers to a machine learning algorithm that analyzes users' past usage history and impressions to learn the preferences and patterns of individual users.

[1789] "Means for customizing routes based on learning results" refers to a processing function that customizes travel routes based on the user's individual preferences and past history, and provides personalized suggestions.

[1790] The "means for presenting the generated customized route to the user and providing navigation" is a function for displaying the customized travel route on the user's terminal and providing route guidance in real time.

[1791] "Means of collecting feedback from users and reflecting it in the next route generation" refers to a function that collects opinions and evaluation data provided by users and analyzes and saves them for use in proposing the next travel route.

[1792] "A means for generating the optimal shopping route within a virtual store by inputting the store and products desired by the user" is an algorithm that calculates and generates the optimal shopping route within a virtual space by inputting the product and store information desired by the user.

[1793] "Means for sending the generated shopping route to the user's terminal and navigating" refers to a communication function for sending the generated shopping route to the user's terminal and providing route guidance within the virtual store.

[1794] This invention is a system that generates an optimal walking route based on the user's specific distance and time requirements and starting point, and also provides a shopping route within a virtual store. This system is realized using a smartphone application and a cloud server. It learns the user's preferences and past behavior history and provides a customized route based on that. Details of the mode for implementing this invention are as follows.

[1795] 1. Hardware and software configuration

[1796] Hardware

[1797] Smartphone: Used as a mobile device for users to check routes and enter settings.

[1798] Cloud Server: A central server that generates routes, analyzes user data, and sends and receives navigation data.

[1799] software

[1800] Smartphone app: Enter user settings, view routes, navigate in real time, and collect feedback.

[1801] Map service API: Routes are generated using Google Maps API or similar geographic information services.

[1802] Virtual Store API: A custom API used to generate virtual shopping routes.

[1803] 2. Program Processing

[1804] Entering User Preferences

[1805] Using a smartphone app, users can input the distance and time of their desired walk, as well as the starting point, and can also input the stores and products they want to explore in the virtual store. This input data is sent to a cloud server.

[1806] Sending and processing input data

[1807] The smartphone device sends the input data to a cloud server in real time. The server uses the received data to generate an optimal walking route using a geographic information system such as Google Maps API. The virtual store uses the virtual store API to generate an optimal shopping route based on the desired products and stores.

[1808] Route generation and submission

[1809] The generated route is sent from the cloud server to a smartphone, where users can view the route and start real-time navigation. The same applies to the virtual store, where the optimal shopping route is provided.

[1810] Learning user preferences

[1811] The cloud server collects and analyzes the user's past walking data, shopping history, and feedback, and uses machine learning algorithms to learn the user's preferences and customize future walking and shopping routes.

[1812] Gathering feedback and suggesting next routes

[1813] By collecting feedback provided by users after completing a walk or shopping trip and reflecting it in the next route generation, more accurate route suggestions can be made.

[1814] 3. Examples and prompts

[1815] Specific examples

[1816] For example, if a user inputs a setting such as "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will occur:

[1817] Users enter "20 minutes," "visit a cafe," and "depart from home" into the smartphone app.

[1818] The device sends the configuration data to the cloud server.

[1819] The server uses the Google Maps API to search for cafes that are reachable within 20 minutes from home and generate the optimal route.

[1820] The server references the user's past walking data and optimizes the route to suit their preferences.

[1821] The server sends the generated route to the terminal.

[1822] The device displays the received route on a map and begins navigation.

[1823] Users walk along a route and stop at cafes along the way.

[1824] After completing a walk, users can provide feedback by rating and commenting on the route.

[1825] The device sends feedback to the server to help suggest routes for future trips.

[1826] Example prompts to input to the generative AI model

[1827] Generate optimal routes in your virtual store starting from the entrance for users to search for shoes and bags in the fashion section. Use your store's map information and users' shopping patterns to create efficient routes and guide them step-by-step from the starting point.

[1828] The above is an embodiment of the present invention.

[1829] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1830] Step 1:

[1831] The user inputs the desired distance or time and starting point through the smartphone app interface. They also input the stores and products in the virtual store they want to explore. This generates the user's desired conditions as JSON format data. Specifically, the information entered by the user is saved in a form in the app.

[1832] Step 2:

[1833] The data entered in step 1 is sent to the cloud server on the device (smartphone). Specifically, the application sends the data via an HTTP POST request. At this time, the input data is sent to the API server's endpoint. The input data includes distance, time, starting point, and desired store and product information.

[1834] Step 3:

[1835] The server analyzes the received input data and calls the Google Maps API and virtual store API to generate walking and shopping routes that best fit the user's desired conditions. The server first calculates the optimal walking route from the starting point using a geographic information system. It then calculates the shortest route to the desired store or product in the virtual store. Specifically, the server sends an API request to the Google Maps API or virtual store API and receives a response.

[1836] Step 4:

[1837] The server sends the generated walking and shopping routes to the user's device. Specifically, the server encodes the generated route data into JSON format and sends it to the user's smartphone as an HTTP response, including map information and navigation information.

[1838] Step 5:

[1839] The device visually displays the received route and initiates real-time navigation. Specifically, the smartphone app analyzes the received route data, displays it on a map view, and provides step-by-step navigation guidance. The user is provided with visual and audio guidance.

[1840] Step 6:

[1841] After the user has finished their walk or shopping, they provide feedback through the smartphone app. Specifically, the app displays a feedback form where the user can enter their route rating and comments. This feedback is saved in JSON format.

[1842] Step 7:

[1843] The device sends the feedback provided by the user to the cloud server. Specifically, the app sends the feedback data to the server via an HTTP POST request. The feedback data includes the rating score and comments.

[1844] Step 8:

[1845] The server analyzes the received feedback data to learn user preferences and patterns. Specifically, the server uses machine learning algorithms to analyze the feedback data and model the user's preferences, which then personalizes the next route suggestion.

[1846] Step 9:

[1847] The next time the server generates a route, it will generate a customized route based on the learning results and suggest it to the user again. Specifically, the server calculates a new route taking into account the user's past behavioral history and feedback, and sends it back to the device. This series of processes provides the user with an optimized walking and shopping route.

[1848] The above are the processing steps of the system for realizing the application example.

[1849] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1850] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. The program for this system will be described in detail below.

[1851] Overall system overview

[1852] This system combines the user's device, a cloud server, and an emotion engine. The user first inputs the walking settings (distance, time, starting point, points of interest) through a smartphone app. The device then sends this setting data to the cloud server. The server uses a geographic information system (e.g., Google Maps API) to generate an optimal walking route based on the user's desired conditions and sends the generated route to the user's device. At the same time, it collects and analyzes the user's past walking data and feedback, and customizes the next walking route based on the results. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and can adjust the walking route based on the emotional data.

[1853] Specific processing of the program

[1854] 1. Enter your user settings

[1855] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[1856] 2. Sending input data

[1857] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1858] 3. Route Generation

[1859] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1860] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1861] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[1862] 4. Emotion Recognition by Emotion Engine

[1863] Server: The emotion engine recognizes the user's current emotional state.

[1864] Example: Identifying emotions (happiness, sadness, stress, etc.) through user voice input or facial recognition.

[1865] 5. Emotion-based route adjustment

[1866] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[1867] For example: If you're feeling stressed, suggest a route through a quiet park.

[1868] 6. Creating customized routes

[1869] Server: Analyzes the user's past walking data and feedback, and generates a customized walking route based on their individual preferences, combined with their emotional state.

[1870] 7. Route provision

[1871] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1872] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1873] 8. Start navigation

[1874] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1875] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1876] 9. Providing Feedback

[1877] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1878] Terminal: Sends user feedback data to the cloud server.

[1879] 10. Analysis and storage of feedback

[1880] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1881] Specific examples

[1882] For example, if a user sets a preference to "take a 20-minute walk and stop at a cafe along the way," and the emotion engine recognizes that the user is feeling stressed, the specific processing will be as follows:

[1883] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1884] Device: Sends configuration data to the cloud server.

[1885] Server: Uses the Google Maps API to generate the optimal route that passes through cafes that are reachable within 20 minutes from home.

[1886] Server: The emotion engine recognizes the user's emotions and adds a route through a quiet park to reduce stress.

[1887] Server: Optimizes customized routes based on past user walking data and feedback.

[1888] Server: Sends the generated customized route to the device.

[1889] Device: Display the received route on a map and begin navigation.

[1890] User: Walk along a route, stopping at a cafe along the way.

[1891] User: Provide feedback by rating and commenting on the route after completing the walk.

[1892] On your device: Sends feedback to the server to help generate future routes.

[1893] Through the above process, the user can obtain the optimal walking route that matches his or her emotional state.

[1894] The processing flow will be explained below.

[1895] Step 1: Enter your user settings

[1896] User: Enter the distance or time of the walk, starting point, and points of interest (e.g., cafes, parks, historical sites, etc.) through the smartphone app interface.

[1897] Step 2: Submitting input data

[1898] Device: Sends user-entered configuration data to a cloud server, including the specified distance or time, starting point, and points of interest.

[1899] Step 3: Generate Routes

[1900] Server: Analyzes the received configuration data and generates the optimal walking route using the Google Maps API based on the configured conditions.

[1901] Example: Search and generate a route that passes through a cafe that can be reached within 20 minutes from the starting point.

[1902] Server: Obtains information about famous stores and facilities from a local database and incorporates this information into walking routes.

[1903] Step 4: Analyze historical data

[1904] Server: Analyzes the user's past walking data and previously provided feedback. Specifically, it learns the user's preferences by using data on which routes the user has preferred to walk in the past, places they want to avoid, and their favorite spots.

[1905] Step 5: Emotion Recognition with the Emotion Engine

[1906] Server: The emotion engine recognizes the user's current emotional state.

[1907] Example: Identifying emotions (happiness, sadness, stress, etc.) from the voice of a user speaking to a smartphone app or from facial images captured through a camera.

[1908] Step 6: Emotionally Based Route Adjustments

[1909] Server: Adjusts a walking route suitable for the user based on the emotions recognized by the emotion engine.

[1910] Example: If the user is feeling stressed, suggest a route through a quiet, natural park.

[1911] Step 7: Generate customized routes

[1912] Server: Based on the analysis of past data and emotion recognition results, it generates a customized walking route tailored to the user's individual preferences.

[1913] Step 8: Providing a Route

[1914] Server: Sends the completed, customized walking route to the user's device. The route information includes the starting point, intermediate points, and end point.

[1915] On the device: The received route is visually presented to the user in map format, allowing the user to view the entire route and each stop on the app.

[1916] Step 9: Start Navigation

[1917] Device: The user launches the navigation function to begin a walk, providing real-time route and waypoint guidance.

[1918] User: Follow the instructions and begin walking along the displayed route, following the app's navigation to move through points of interest.

[1919] Step 10: Provide feedback

[1920] Users: After completing a walk, they can enter their rating and comments about the route on the app's feedback screen. Rating items include "view," "convenience," and "safety."

[1921] Terminal: Sends user feedback data to the cloud server.

[1922] Step 11: Analyze and store feedback

[1923] Server: Stores the received feedback data to help improve and customize your walking route for future visits. Continually adjusts the algorithm based on the collected data.

[1924] Specific examples

[1925] If you want to take a 20-minute walk, visit a cafe, and de-stress.

[1926] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1927] Device: After completing the procedure, send the setting data to the cloud server.

[1928] Server: Uses the Google Maps API to search for cafes that can be reached within 20 minutes from home and generate the optimal route.

[1929] Server: If the emotion engine detects stress from the user's speech or facial recognition, include a quiet park in the route.

[1930] Server: Sends optimized and customized routes to the device.

[1931] Device: Display the route on the map and begin navigation.

[1932] User: Starts walking following a route, stops at a cafe, passes through a quiet park.

[1933] Users: Provide feedback after completing a walk, leaving a rating and comments.

[1934] Device: Sends all feedback data to the server.

[1935] Server: Stores the received data and uses it to improve and customize the route next time.

[1936] Through the above process, the user can obtain the optimal walking route according to his / her emotional state.

[1937] Example 2

[1938] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1939] Conventional walking route suggestion systems have difficulty providing routes that fully reflect the user's preferences and emotional state. As a result, they have been unable to improve user satisfaction or maximize the benefits of their walks. It has also been difficult to individually analyze past walking data and feedback and reflect them in the next route.

[1940] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1941] In this invention, the server includes means for inputting the user's desired distance or time and starting point, means for transmitting the input data to the server, means for generating an optimal walking route using a geographic information system based on the input data, means for transmitting the generated route to the user's terminal, means for analyzing the user's past walking data and feedback and learning the user's preferences, means for customizing the route based on the learning results, means for presenting the generated customized route to the user and providing navigation, means for recognizing the user's emotions, means for adjusting the walking route based on the emotion recognition results, and means for collecting feedback from the user and reflecting it in the next route generation.

[1942] This makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[1943] "User" refers to an individual who uses this system to receive walking route suggestions.

[1944] "Terminal" refers to a device used by a user, including mobile devices such as smartphones and tablets.

[1945] A "server" refers to a computer system that exists on the cloud, processes data received from users, and performs tasks such as generating walking routes and recognizing emotions.

[1946] A "geographic information system" is a system that calculates routes using map data and location information, and includes an application program interface for a map provision service.

[1947] A "walking route" refers to a suggested route for walking, including a starting point, a destination point, and intermediate points specified by the user.

[1948] "Settings Data" is information entered by the user, including distance, time, starting point, and points of interest.

[1949] "Navigation" refers to the function that guides the user along a route when they start a walk.

[1950] "Past walking data" refers to the recorded information of walks the user has taken so far.

[1951] "Feedback" refers to the ratings and comments about the route provided by users after completing a walk.

[1952] "Emotion engine" refers to a system for recognizing and analyzing a user's emotional state.

[1953] "Customization" refers to individually optimizing walking routes based on the user's preferences and emotional state.

[1954] "Generative AI models" refer to algorithms or machine learning models that create new walking routes based on data.

[1955] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[1956] The present invention combines a system that provides the user with an optimal walking route based on the distance and time desired by the user with an emotion engine that recognizes the user's emotions. This system is implemented using the user's device, a cloud server, and the emotion engine.

[1957] System configuration

[1958] User's device

[1959] The user's device is a mobile device such as a smartphone or tablet. The user first installs a dedicated smartphone app and enters the walking settings through this application. The information entered includes the desired walking distance or time, starting point, and points of interest. After entering the setting data, the device sends it to the cloud server.

[1960] Cloud Server

[1961] The cloud server generates an optimal walking route using a geographic information system (e.g., an application program interface of a map providing service) based on the setting data received from the user. The generated route includes intermediate points that can be reached within a specified distance or time from the starting point.

[1962] The server also incorporates an emotion engine that recognizes the user's emotional state through voice input and facial recognition, determining whether they are stressed or happy, and can adjust the walking route based on the recognized emotional data.

[1963] The server also collects and analyzes the user's past walking data and feedback to learn the user's preferences, and generates a customized route based on the learning results and provides it to the device.

[1964] Specific examples

[1965] For example, if a user sets "I would like to take a 20-minute walk and stop at a cafe along the way," the following processing will be performed.

[1966] User: Enters "20 minutes," "visit a cafe," and "leave from home" into the smartphone app.

[1967] Device: Sends configuration data to the cloud server.

[1968] Server: Analyzes the received data and uses the API of a map provider to generate the optimal route that passes through a cafe that can be reached within 20 minutes from the user's home.

[1969] Server: The emotion engine recognizes the user's emotions and suggests routes through quiet parks to reduce stress.

[1970] Server: Optimizes customized routes based on past user walking data and feedback.

[1971] Server: Sends the generated customized route to the device.

[1972] Device: Display the received route on a map and begin navigation.

[1973] User: Follows a route, stops at a cafe along the way, and provides feedback on the route with ratings and comments after the walk.

[1974] On your device: Sends feedback to the server to help generate future routes.

[1975] Specific examples of hardware and software used

[1976] Device: Smartphone (Android, iOS)

[1977] Server: Cloud computing services

[1978] Geographic Information Systems: Application Program Interfaces for map services (e.g., Google Maps API)

[1979] Emotion engine: speech recognition software, facial recognition software

[1980] Prompt Sentence Examples

[1981] A specific example of a prompt could be, "Generate a 20-minute walking route and suggest a route that includes a stop at a cafe along the way." This allows the generative AI model to provide the optimal walking route according to the user's request.

[1982] This is the outline of the system. The present invention makes it possible to provide optimal walking routes that reflect the user's preferences and emotional state, thereby improving user satisfaction and maximizing the benefits of walking.

[1983] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1984] Step 1:

[1985] User: The user opens the smartphone app and enters the "walk distance or time," "starting point," and "points of interest."

[1986] Input: distance or time, starting point, points of interest

[1987] Output: Configuration data (distance or time, starting point, points of interest)

[1988] Specific operation: The user sets up the walk using touch input or voice input, and presses the "Settings complete" button in the app.

[1989] Step 2:

[1990] Device: Receives configuration data and sends it to the cloud server.

[1991] Input: Configuration data

[1992] Output: HTTP request to the server

[1993] What happens: The device packages the configuration data and sends it as an HTTP request over the internet to the cloud server.

[1994] Step 3:

[1995] Server: Analyzes the received configuration data and calls the API of the geographic information system to generate the optimal walking route.

[1996] Input: Configuration data

[1997] Output: Walking route

[1998] What happens: The server uses a geographic information system such as the Google Maps API to calculate the optimal walking route that includes the specified starting point and points of interest.

[1999] Step 4:

[2000] Server: Refers to a local database to obtain information on famous stores and facilities, and incorporates it into walking routes.

[2001] Input: Area database, best walking routes

[2002] Output: Walking route including facility information

[2003] Specific operation: The server retrieves local commercial and tourist information from a database and adds it to the optimal walking route.

[2004] Step 5:

[2005] Server: The emotion engine recognizes emotions through the user's voice input and facial recognition.

[2006] Input: User's voice data, face image

[2007] Output: Emotion data

[2008] What it does: The server uses voice and facial recognition software to analyze the user's emotions and identify their emotional state (happiness, sadness, stress, etc.).

[2009] Step 6:

[2010] Server: Adjusts the walking route based on the emotion recognition results.

[2011] Input: Emotion data, walking route including facility information

[2012] Output: Adjusted walking route

[2013] What it does: The server incorporates quiet parks and relaxing spots into the route depending on the user's emotional state.

[2014] Step 7:

[2015] Server: Collects past walk data and feedback, and customizes routes based on user preferences.

[2016] Input: Past walk data, feedback, adjusted walk route

[2017] Output: Customized walking route

[2018] How it works: The server analyzes the user's past data and applies an optimal route generation algorithm to generate an individually optimized walking route.

[2019] Step 8:

[2020] Server: Sends the generated customized route to the user's device.

[2021] Input: Custom walking route

[2022] Output: HTTP response to the user's device

[2023] Specific operation: The server packages the data including the generated customized walking route and sends it to the user's device as an HTTP response.

[2024] Step 9:

[2025] Device: Display the received walking route in map format and begin navigation.

[2026] Input: Custom walking route

[2027] Output: Map display, navigation instructions

[2028] What it does: The device displays the route information it receives on a map and provides turn-by-turn directions and voice guidance for navigation.

[2029] Step 10:

[2030] User: After completing a walk, the user enters a route rating and comments on the feedback screen of the app. The feedback is then sent to the server by the device.

[2031] Input: Route rating, comments

[2032] Output: Feedback data

[2033] Specific operation: The user enters a star rating or text comment on the feedback screen and presses the send button. The device packages this and sends it to the server.

[2034] Step 11:

[2035] Server: Saves the feedback data and uses it to generate future walking routes.

[2036] Input: Feedback data

[2037] Output: Updated generation algorithm

[2038] What it does: The server stores the feedback data in a database and uses it to improve the algorithm. It periodically analyzes the feedback and updates the optimal route generation algorithm.

[2039] (Application example 2)

[2040] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2041] In modern society, there is a demand for autonomous vehicle travel routes that take into account the stress and emotional state of the user. However, conventional systems do not recognize user emotions and adjust routes based on them. As a result, it is difficult for users to enjoy an optimal and comfortable journey. Furthermore, they are unable to effectively utilize past travel data and feedback, which means they are unable to provide personalized services based on the user's preferences and emotional state. To solve these issues, a system is needed that recognizes user emotions and adjusts routes in real time based on them.

[2042] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2043] In this invention, the server includes means for generating an optimal travel route using a geographic information system based on input data, means for adjusting the route based on the emotional data using an emotion engine that recognizes the user's emotional state, and means for analyzing the user's past travel data and feedback to learn the user's preferences, thereby providing the optimal travel route in real time according to the user's emotional state and enabling a personalized and comfortable travel experience.

[2044] "Means for users to input their desired distance or time and starting point" means an interface feature that allows users to use a smartphone or other device to input their desired distance or time to travel and the starting point of the trip.

[2045] "Means for transmitting input data to a server" refers to a function that transmits data input by a user to a server via the Internet.

[2046] A "geographic information system" is a system or application program interface (API) that handles geographic information and generates maps and routes.

[2047] The "means for generating an optimal travel route" refers to a function that uses a geographic information system to calculate and generate an optimal travel route based on input data.

[2048] "Means for sending the generated route to the user's device" refers to the function of sending the route information generated by the server to the user's device, such as a smartphone or head-mounted display.

[2049] "Means for learning user preferences by analyzing user's past travel data and feedback" refers to algorithms or functions that analyze the user's past travel history and feedback provided by the user and learn the user's preferences based on that.

[2050] An "emotion engine" is a technology or system that recognizes emotions from a user's facial expressions, voice, etc., and processes that information.

[2051] "Means for adjusting route based on emotional data" refers to a function that recalculates and adjusts the optimal travel route based on the emotional state of the user recognized by the emotion engine.

[2052] "Means for providing navigation" refers to a function that provides real-time travel guidance to users based on the generated route information.

[2053] "Means for collecting feedback and reflecting it in the next route generation" refers to a function that collects feedback provided by users after traveling and uses that information when generating the next route.

[2054] The present invention combines a system that provides an optimal travel route based on the user's desired distance and time with an emotion engine that recognizes the user's emotions. Detailed programs and embodiments of this system will be specifically described below.

[2055] Overall system configuration

[2056] The system is implemented by combining a user device (such as a smartphone or a head-mounted display), a cloud server, and an emotion engine. The system consists of the following main components:

[2057] 1. User Input Interface

[2058] The user inputs the desired distance, time, starting point, destination, and points of interest via a smartphone or head-mounted display.

[2059] 2. Data Transmission

[2060] The device sends the entered data to a cloud server, including information about the user's desired distance, time, starting point, and points of interest.

[2061] 3. Route generation

[2062] Based on the data received, the server uses a geographic information system (GIS) to generate an optimal travel route. Specifically, the route is calculated using the application program interface (API) of the map provider service.

[2063] 4. Emotion recognition

[2064] The server's emotion engine recognizes the user's current emotional state by analyzing their facial expressions and voice data.

[2065] 5. Route adjustment

[2066] The server's emotion engine adjusts the generated route in real time based on the emotional data it recognizes, suggesting a quieter route or one that passes through a park if the user is feeling stressed, for example.

[2067] 6. Customized Route Generation

[2068] The server analyzes the user's past travel data and feedback to learn their preferences, and then regenerates a personalized route based on that information.

[2069] 7. Route provision

[2070] The server sends the generated customized route to the user's device.

[2071] The device visually presents the received route to the user in map format and begins navigation.

[2072] 8. Feedback Collection

[2073] After traveling, users can enter ratings and comments about the route on the feedback screen of their smartphone or head-mounted display.

[2074] The device sends the feedback to the cloud server, which then reflects it in the next route generation.

[2075] Hardware and software used

[2076] Hardware: smartphone, head-mounted display, in-car camera, microphone, emotion recognition sensor

[2077] Software: Cloud servers, emotion engines (e.g., Emotion API), geographic information systems (e.g., Google Maps API), machine learning algorithms (e.g., TensorFlow)

[2078] Specific examples

[2079] For example, if a user sets a preference on their smartphone to "pass through a park on the way home from their office in Tokyo," and the emotion engine recognizes that the user wants to relax, the following process occurs:

[2080] The user enters "from office to home" and "visit to the park" into a smartphone app.

[2081] The device sends the setting data and emotion data to the cloud server.

[2082] The server uses the Google Maps API to generate the optimal route from the office to the home, adding a route that passes through the park along the way.

[2083] The server uses an emotion engine to recognize the user's state of relaxation and suggests scenic routes.

[2084] The server sends the generated route to a smartphone or head-mounted display.

[2085] The user follows the route and provides feedback after the route.

[2086] The device sends feedback to the server to help generate the next route.

[2087] Prompt Sentence Examples

[2088] If the user wants to relax on their way home, suggest the optimal route. The current starting point is an office in Tokyo, and the destination is home. Pass through a quiet park along the way. Prioritize scenic areas in the suggested route.

[2089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2090] Step 1:

[2091] User enters desired distance or time and starting point

[2092] Using a smartphone app or head-mounted display, users input the desired distance or time of travel, starting point, destination, and any detours they wish to make.

[2093] Input: distance, time, starting point, destination, points of interest.

[2094] Output: Input data is sent to the system.

[2095] Step 2:

[2096] Data transmission

[2097] The device sends the input data from the user to the cloud server.

[2098] Input: User's configuration data.

[2099] Output: The server receives the data.

[2100] Step 3:

[2101] Generate travel routes

[2102] The server analyzes the received data and generates the optimal travel route using a geographic information system (such as Google Maps API).

[2103] Input: User configuration data, geographic information.

[2104] Output: Optimal travel route information.

[2105] Specific behavior: Calls the Google Maps API and calculates a route based on the desired criteria.

[2106] Step 4:

[2107] Recognition of emotional states

[2108] The server's emotion engine recognizes the user's emotional state by analyzing facial expressions and voice data acquired from smartphones, head-mounted displays, in-car cameras, and microphones.

[2109] Input: User's facial expression data, voice data.

[2110] Output: The user's emotional state.

[2111] Specific actions: Determine emotions using facial recognition and voice analysis technology.

[2112] Step 5:

[2113] Route Adjustment

[2114] The server adjusts the optimal travel route based on the user's emotional state as recognized by the emotion engine. For example, if the user wants to relax, the server will suggest a route that passes through a quiet park.

[2115] Input: Initial travel route, user's emotional state.

[2116] Output: Adjusted travel route.

[2117] Specific behavior: Compare the generated route information with emotion data and recalculate if necessary.

[2118] Step 6:

[2119] Generate customized travel routes

[2120] The server analyzes the user's past travel data and feedback, and generates a more customized travel route based on the learning results.

[2121] Input: adjusted travel route, historical travel data, feedback.

[2122] Output: A customized travel route.

[2123] What it does: Look at historical data and adapt routes to reflect your individual preferences.

[2124] Step 7:

[2125] Route provision

[2126] The server then sends the final customized route to the user's device.

[2127] Input: A customized travel route.

[2128] Output: Route display in map format on the device.

[2129] Specific operation: The transmitted data is sent to the device in a format that can be displayed in the map app.

[2130] Step 8:

[2131] Providing navigation

[2132] The device will then begin navigation based on the route information received, allowing users to receive real-time travel guidance.

[2133] Input: A customized travel route.

[2134] Output: Real-time travel directions.

[2135] Specific operation: Uses GPS data to navigate while comparing it with the current location.

[2136] Step 9:

[2137] Providing feedback

[2138] After completing their journey, users can enter their ratings and comments about the route on the feedback screen of their smartphone app or head-mounted display.

[2139] Input: Rating and / or Comments.

[2140] Output: Feedback data.

[2141] Specific operation: Receives feedback from users in text format and sends it to the cloud server.

[2142] Step 10:

[2143] Collecting and analyzing feedback

[2144] The server collects the feedback data and analyzes it for use in generating the next route.

[2145] Input: Feedback data.

[2146] Output: Updated training data.

[2147] What it does: Your feedback is stored in a database and used by machine learning algorithms to customize your next route.

[2148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2150] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2152] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, au...

Claims

1. means for the user to input a desired distance or time and a starting point; means for transmitting the input data to a server; A means for generating an optimal walking route using a geographic information system based on the input data; a means for transmitting the generated route to the user's device; A means to learn user preferences by analyzing user's past walking data and feedback, A way to customize your route based on your learning results, a means for presenting the generated customized route to the user and providing navigation; A way to collect user feedback and incorporate it into the next route generation. A system including:

2. 2. The system according to claim 1, wherein the geographic information system uses an application program interface of a map providing service.

3. 2. The system according to claim 1, further comprising means for incorporating information on famous local stores and facilities when generating an optimal walking route.

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