system

Abstract

We provide the system. [Solution] A means for users to enter information to set their target running pace, A means for acquiring location information and traffic signal information for multiple running routes in the user's surrounding area, A method for analyzing congestion levels on multiple running routes based on GPS data, A means of selecting and suggesting the optimal running route based on the user's target pace, A means for collecting real-time pace information from a user during running, and for generating and sending encouraging messages to help them achieve their goals, A means of collecting user activity data after a run and providing analysis results and feedback for the next run, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult for a user to maintain their set pace during running. The main reason for this is external factors such as road congestion, the number of traffic lights, and the presence or absence of slopes. Therefore, it becomes a problem to find an appropriate route for a runner to enjoy running comfortably and effectively while maintaining their pace.

Means for Solving the Problems

[0005] This invention provides a system that allows users to input their target running pace and analyzes multiple running routes in the surrounding area based on that information. The system uses map data and real-time GPS data to acquire congestion levels and traffic light locations for each route. It then selects and suggests the running route best suited to the user's goal. In addition, it monitors the user's pace in real time during the run and supports motivation by providing encouragement to help achieve the goal. Furthermore, it solves the aforementioned problems by aggregating activity data after the run and providing feedback to the user, including advice for the next run.

[0006] A "user" refers to an individual who uses this system to set running goals and receive route suggestions.

[0007] A "running route" is geographical information that shows the paths a user can run.

[0008] "Target pace" is an indicator of the running speed that a user tries to maintain while running.

[0009] "Location information" refers to data used to identify the geographical location along a running route.

[0010] "Information about traffic signals" refers to data about the location and status of traffic signals along the running route.

[0011] "GPS data" refers to location measurement data obtained from a global positioning system.

[0012] "Congestion status" refers to information indicating the degree of traffic from other people and vehicles along a running route.

[0013] An "encouragement message" is a notification containing motivational content provided to help users achieve their goals.

[0014] "Activity data" refers to information related to the actions of a user collected during running.

[0015] "Feedback" refers to information related to the results and areas for improvement provided to the user after running.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system that suggests the optimal route for achieving a user-set running goal and provides feedback during and after the run. The system provides services to the user in the following steps:

[0038] First, the user enters information such as their target pace, age, gender, and running experience. The device then sends this information about the user's target pace to the server.

[0039] Next, the server uses map data and real-time GPS data to retrieve multiple running routes in the vicinity based on the user's location. The server analyzes congestion levels and traffic light locations along these routes and selects the route best suited to the user's target pace. This selected route information is then displayed to the user via their device.

[0040] During a run, the device monitors the user's pace in real time and sends the measured data to a server. The server analyzes this data and, if the user is deviating from their target pace, generates an encouraging message and sends it to the device. The device then notifies the user of the message via voice or text to support their activity.

[0041] Once the run is complete, the server compiles all activity data, calculating calories burned, average pace, distance covered, and more. This data is sent to the user's device and provided as feedback, including advice and suggestions for improvement for their next run.

[0042] For example, if a user aims to run 5km in under 30 minutes, the system will suggest a flat, uncongested route with few traffic lights. Furthermore, if the user's pace slows during the run, it will notify them with a message like, "Keep going, almost there!" to maintain their motivation. After the run, it will provide feedback evaluating the user's progress, including whether they burned 500 calories. In this way, the system is designed to help users effectively achieve their goals.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches the application and logs into their account. If it's their first time using the service, they will create an account by entering the required information (name, age, gender, running experience, target pace, etc.) on the registration screen.

[0046] Step 2:

[0047] The device sends the user's set target pace and running objectives (e.g., distance, time, calories burned) to the server.

[0048] Step 3:

[0049] The server uses the user's location information to retrieve nearby running routes from a map database. Furthermore, it uses real-time GPS data to capture congestion information, hills, and traffic light locations for each route.

[0050] Step 4:

[0051] The server analyzes the acquired data and selects the running route best suited to the user's target pace. This selection takes into account factors such as the flatness of the route, the number of traffic lights, and the level of congestion.

[0052] Step 5:

[0053] The server sends detailed information (map, distance, elevation) of the selected optimal running route to the terminal.

[0054] Step 6:

[0055] The device displays the received route information to the user and prompts them to start running.

[0056] Step 7:

[0057] The user checks the displayed route and starts running.

[0058] Step 8:

[0059] While running, the device uses its built-in sensors to measure the user's real-time pace, distance, and time, and sends that data to a server.

[0060] Step 9:

[0061] The server analyzes the received pace data to check if the user is maintaining their target pace. If the user is falling behind the target pace, it generates an encouragement message.

[0062] Step 10:

[0063] The device notifies the user of encouraging messages received from the server. Motivation is supported through voice output or text messages.

[0064] Step 11:

[0065] After the run is finished, the server compiles all the activity data and calculates calories burned, average pace, distance run, and more.

[0066] Step 12:

[0067] The server sends the aggregated data to the terminal and displays feedback to the user as a result of their run.

[0068] Step 13:

[0069] The device provides users with detailed feedback, including advice and suggestions for improvement for future use.

[0070] (Example 1)

[0071] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0072] Conventional exercise support systems have struggled to provide feedback optimized for each user's goals and environment. Furthermore, they often failed to adequately maintain consistent motivation in users during and after exercise. Additionally, they were insufficient in selecting optimal routes that took into account real-time congestion and traffic signals. This system aims to address these challenges.

[0073] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0074] In this invention, the server includes data input means for the user to set a target speed for exercise, means for acquiring location information of multiple exercise routes and information on traffic control devices in the user's surrounding area, and means for analyzing the congestion status of the multiple exercise routes based on location measurement data. This makes it possible to propose individually optimized exercise routes to the user and provide motivation during exercise.

[0075] A "data input method" is an interface that allows users to input their target exercise speed and related information into a digital device.

[0076] A "walking route" is a geographical path selected by the user to perform a specified exercise activity.

[0077] "Location measurement data" refers to data that provides real-time geographical information about users and objects.

[0078] A "traffic control device" is a traffic signal or other control device used in roads or public transportation systems to manage movement.

[0079] "Generative artificial intelligence" refers to an algorithm or system that has the ability to automatically generate new messages or data according to specific conditions.

[0080] An "encouragement message" is a message of audio or text information provided to encourage or motivate a user's actions.

[0081] "Voice output" is a technology that converts digital information into audio and conveys it to the user.

[0082] "Text information output" refers to a format in which information is displayed as text on a screen to visually convey information to the user.

[0083] This invention is a system that proposes the optimal running route to help an athlete achieve a set speed goal, and provides encouragement and feedback during and after the exercise. The system consists of a network-connectable electronic device (hereinafter referred to as "terminal") and a central processing system (hereinafter referred to as "server") that communicates with the terminal.

[0084] First, the user enters information such as their target exercise speed, age, gender, and past exercise experience into the device. This information is entered using a mobile device such as a smartphone or tablet, via a dedicated application installed on that device.

[0085] The terminal sends user input information to the server. This communication utilizes an internet connection, and the data uses a secure protocol (e.g., HTTPS).

[0086] Based on information received from the user, the server uses a map database and real-time location data to obtain a suitable route for the surrounding area. The map database uses a general-purpose geographic information platform (e.g., OpenStreetMap API), and real-time traffic information is also obtained from a similar API. Based on this, the server generates an optimal route that takes into account the congestion status of the route, the location of traffic control devices, and the terrain.

[0087] During exercise, the device monitors the user's pace in real time. This monitoring utilizes the device's built-in GPS sensor and accelerometer. The measured data is then transmitted to a server.

[0088] The server analyzes whether the user is maintaining their target speed during exercise. Using a generative artificial intelligence model, it generates personalized motivational messages for each user. These messages correspond to the input prompts, for example, "Keep it up, almost there!" These messages are sent to the device and provided to the user via voice or text.

[0089] After the exercise is finished, the server compiles all activity data and calculates energy expenditure, average speed, total distance, etc. This analysis is then used to generate advice for the next exercise session, which is then fed back to the user via their device. For example, it might provide specific advice such as, "You burned 500 calories today. Next time, try to maintain a steady pace and cover a slightly longer distance."

[0090] Specific examples of prompt statements are as follows:

[0091] "The user's goal is to run 5km in under 30 minutes. Suggest an optimal route, monitor the user's pace during the exercise, and generate encouraging messages if they deviate from the target speed. After the exercise, provide feedback on calories burned and an evaluation of the achieved goal."

[0092] This invention allows users to receive support optimized for their individual conditions, thereby enhancing the effectiveness of their exercise.

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] Entering user information

[0096] Users enter personal data such as exercise goals, age, gender, and running experience into their device. Based on this input, a dedicated application formats it as structured data and prepares to send it to the server.

[0097] Step 2:

[0098] Sending user data

[0099] The terminal sends data obtained from the user to the server. To do this, the terminal uses an encryption protocol (e.g., HTTPS) to transfer the data, ensuring that personal information is transmitted securely. The output is the user's target information received on the server side.

[0100] Step 3:

[0101] Location information acquisition and route calculation

[0102] The server receives the user's location information and uses a map database and real-time location data to determine the surrounding travel route. Based on the input data, the server uses an algorithm to analyze congestion levels, traffic light locations, and other factors, and calculates the optimal route. The output generated by this process is the proposed optimal route information.

[0103] Step 4:

[0104] Real-time monitoring during running

[0105] The terminal monitors the user's pace in real time using GPS and accelerometer sensors within the device. It processes the sensor data received as input and periodically sends the results to the server. The output is real-time pace information.

[0106] Step 5:

[0107] Data analysis and feedback generation

[0108] The server verifies whether the user is achieving their target speed by comparing and analyzing the received pace with target data. Using a generative AI model, it dynamically generates messages based on prompts that produce motivational messages tailored to the user. The output is the generated motivational message.

[0109] Step 6:

[0110] Data collection and feedback after exercise

[0111] The server aggregates all of the user's exercise data and analyzes the results. It calculates energy consumption, average speed, distance traveled, etc., and generates feedback along with advice for the next time. This result is sent to the terminal, and the output is detailed feedback information for the user.

[0112] This allows users to receive continuous support and opportunities for improvement both during and after exercise.

[0113] (Application Example 1)

[0114] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0115] In modern society, many individuals engage in regular exercise to maintain their health and improve their physical fitness, but there is a need for appropriate support to make these activities more effective. In particular, the lack of a system that can provide the selection of the optimal exercise route, real-time feedback, and effective post-exercise advice tailored to each individual user is a problem.

[0116] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0117] In this invention, the server includes means for inputting information for the user to set a target pace for land-based exercise, means for acquiring location information and road signal information for multiple exercise routes in the user's surrounding area, and means for monitoring pace and heart rate through a home mobile device and providing immediate voice feedback. This enables the user to receive exercise planning optimized to their individual goals and real-time feedback.

[0118] A "user" refers to an individual who uses the system to perform track and field exercises.

[0119] "Track and field exercise" refers to physical activities performed on the ground, such as running and walking.

[0120] "Target pace" refers to the target speed that a user sets when exercising.

[0121] "Information entry method" refers to the interface used by users to input exercise goals and personal information.

[0122] "Surrounding area" refers to the area surrounding the place where the user exercises.

[0123] "Exercise path" refers to the route selected for performing the exercise specified by the user.

[0124] "Location data" refers to data that indicates geographical location information.

[0125] "Traffic signals" refer to signaling devices installed to regulate the flow of traffic.

[0126] "Home-use mobile devices" refer to mobile devices or robots that can be used at home to support exercise.

[0127] "Real-time feedback" refers to information that is instantly provided during exercise based on the situation at that moment.

[0128] "Physical activity data" refers to all data related to physical activity obtained during and after exercise.

[0129] This invention is a system that utilizes a mobile home device to help users perform land-based exercises more effectively. The implementation of this system primarily requires the cooperation of a server, terminals, and a mobile home device.

[0130] The server receives information provided by the user, such as target pace and basic personal profile. Next, the server uses map data and real-time road information to select the optimal exercise route for the user. This route selection process includes analysis of location data and consideration of traffic signal placement. The server then presents the selected exercise route to the user via a home mobile device.

[0131] The home mobile device monitors the user's exercise pace and physical data such as heart rate in real time. This allows the user to check if they are maintaining their target pace and provides voice or text feedback as needed. The feedback from the home mobile device helps the user understand their exercise status in real time, ultimately improving the quality of their workout.

[0132] After exercise, the device receives physical activity data compiled from the server and provides feedback to the user. This feedback includes information on calories burned, average pace, and distance covered. Advice for the next exercise session is also provided. In this process, the server performs a detailed analysis of the exercise's success and areas for improvement, helping the user to consistently achieve their goals.

[0133] For example, if a user wants to take a 20-minute walk near their home, the system will suggest the optimal route that avoids congestion and has fewer traffic lights. During the walk, it will provide verbal encouragement such as, "Your pace is good, keep it up!" After the walk, it will display exercise statistics and suggest ways to further improve.

[0134] An example of a prompt message might be: "Use OpenCV and real-time location data to implement an optimal route and feedback system for home-use mobile devices during exercise."

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] The device receives the user's target exercise pace and personal profile information (age, gender, running experience, etc.) as input. This input data is formatted and sent to the server. The server then receives the user's detailed goal settings and stores them in its database.

[0138] Step 2:

[0139] The server takes the user's location information as input and uses map data and real-time traffic information to search for nearby exercise routes. Considering the location data, traffic light placement, and congestion levels, it selects the optimal route for the user's target pace. The selected route information is then output to a home mobile device.

[0140] Step 3:

[0141] The home mobile device monitors the user's pace and heart rate in real time using sensors, based on the optimal route received from the server. This monitoring data is analyzed as input to determine if the user is maintaining their target pace. If necessary, it outputs encouraging voice messages to provide immediate feedback.

[0142] Step 4:

[0143] The server collects real-time user data transmitted from the home mobile device during exercise and performs data analysis. Using the results of this analysis, it generates specific advice for the user to achieve their exercise goals and suggestions for improvement for their next exercise session, and outputs them to the terminal.

[0144] Step 5:

[0145] The device receives aggregated data from the server after the exercise session ends. This includes calories burned, average pace, and distance covered. This information is compiled and provided to the user as feedback. This helps users review past exercise statistics and use them to plan their next workout.

[0146] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0147] This invention relates to a system that suggests the optimal route for achieving user-defined running goals and recognizes the user's emotions during the run to provide a comfortable running experience. In addition, it analyzes the user's emotional data and provides feedback for use in future runs.

[0148] First, the user launches the application and enters information such as their target running pace. The device then sends this data to the server and prepares for the run.

[0149] Next, the server uses the user's location information to obtain information about surrounding running routes from map data and real-time traffic information, and analyzes congestion levels and traffic light locations. At this time, it selects the optimal running route considering the user's target pace and sends it to the terminal.

[0150] During a run, the system recognizes the user's emotions in real time, using voice and camera data in addition to their pace. The server analyzes this data and generates encouraging messages that match the user's emotions. For example, if the emotion engine detects that the user is feeling tired, it will send an encouraging message from the device such as, "Just a little further, keep going!"

[0151] Once the run is complete, the server compiles activity and emotional data from the entire run, analyzing changes in emotions in addition to calories burned and average pace. These results are sent to the device as feedback for the next run. Based on this, users can receive suggestions for improvement and recommendations that take their emotional changes into account.

[0152] For example, if a user aims to run 5km in 30 minutes, the system will help them complete their run according to plan by providing the optimal route and appropriate encouragement. Post-run feedback will include appropriate rest recommendations if fatigue is observed, and stress reduction strategies for the next run. In this way, the system comprehensively supports the user's running experience.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] After launching the running app and logging in, the user enters their target pace, distance, and other information.

[0156] Step 2:

[0157] The device begins preparing for the run by sending the user's target pace and other information to the server.

[0158] Step 3:

[0159] The server obtains the user's current location and analyzes congestion levels and traffic light locations for each route based on map data of surrounding running routes and real-time traffic information.

[0160] Step 4:

[0161] Based on the analysis results, the server selects the optimal running route for the user's target pace and sends that information to the terminal.

[0162] Step 5:

[0163] The device displays the user the optimal running route it has received and prompts them to start running.

[0164] Step 6:

[0165] During running, the device uses its built-in sensors to measure the user's pace data in real time and transmit it to the server.

[0166] Step 7:

[0167] Furthermore, the device uses microphones and cameras to collect the user's voice and facial expressions and transmits them to the emotion engine.

[0168] Step 8:

[0169] The server receives pace and emotion data transmitted during running and analyzes the user's ability to maintain their target pace and their emotional state.

[0170] Step 9:

[0171] Based on the analysis results, the server generates an encouraging message if the user appears unwell or fatigued.

[0172] Step 10:

[0173] The device notifies the user of encouraging messages sent from the server, either as voice or text, to boost their motivation.

[0174] Step 11:

[0175] After the run is finished, the server compiles all activity and emotional data. It calculates calories burned, average pace, and changes in emotions.

[0176] Step 12:

[0177] The server sends the calculated data to the terminal as feedback, displaying advice and suggestions for improvement for the next run to the user.

[0178] Step 13:

[0179] The device provides users with stress reduction strategies and emotional management advice based on their feedback.

[0180] (Example 2)

[0181] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0182] Modern exercise support systems lack sufficient means to select routes that match the user's goals and to provide appropriate real-time feedback. Furthermore, providing encouraging messages that take into account the user's emotional changes during exercise is difficult, highlighting the need for improved exercise experiences. As a result, there is a lack of effective support for users to achieve their exercise goals.

[0183] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0184] In this invention, the server includes means for acquiring location information and traffic signal information for multiple travel routes in the user's surrounding area, means for analyzing the congestion status of the multiple travel routes based on location measurement data, and means for recognizing the user's emotional state in real time using an emotion analysis engine and generating encouraging messages based on the user's emotions. This enables route selection tailored to the user's goals and real-time feedback based on emotion recognition.

[0185] "Target exercise speed" refers to the target speed that a user sets when exercising, and is information that serves as a basis for exercise planning and performance evaluation.

[0186] "Information input means" refers to an interface or device for users to input their target speed and distance for exercise.

[0187] "Location information" refers to geographical location data used to select a movement route, providing detailed route information within a specific area.

[0188] "Location measurement data" refers to positioning information about a user's current location and movement, and is a collection of data used for analyzing congestion levels and guiding routes.

[0189] An "emotion analysis engine" refers to an analysis tool or algorithm that identifies and processes emotions from a user's voice and facial expressions.

[0190] A "message of encouragement" is a message containing motivation and instructions provided to help users achieve their goals.

[0191] "Activity data" refers to information related to exercise, such as the user's speed, distance, and energy expenditure, recorded during exercise.

[0192] "Feedback" refers to engaging advice and analysis results provided to users after exercise, intended to help them improve their next workout.

[0193] This system provides real-time optimal route selection and emotion-based feedback to improve the user's exercise experience. The embodiments of the invention are described below.

[0194] Before exercising, users launch a dedicated application on their device and set their target speed and distance. This information is entered via an information entry system. The information set by the user is transmitted to the server using a secure communication method.

[0195] Based on the received target information, the server uses location services (e.g., geographic information APIs) to obtain multiple travel routes in the user's surrounding area. The server then uses the location measurement data to analyze the degree of congestion and the location of traffic lights.

[0196] Next, the server uses a generative AI model to select the optimal movement path based on the user's target speed. The selected path is returned to the terminal in real time, and the user can receive navigation on the terminal.

[0197] During exercise, the device uses a microphone and camera to collect the user's voice and video data. This data is sent to a server, which uses an emotion analysis engine to analyze the user's emotional state. The server generates and sends encouraging messages to the device based on the user's emotions. For example, if fatigue is detected, it will generate a message such as, "Just a little further, keep going!"

[0198] After the workout, the server collects the user's activity and emotional data, generates feedback for the next workout, and sends it to the device. This feedback helps the user understand how to improve their next workout and provides guidance for a better exercise experience.

[0199] For example, if a user sets a goal like "run 5km in 30 minutes," the server will provide less congested routes and send appropriate encouraging messages when the user feels fatigued, supporting them in achieving their goal. Another example of a prompt message would be, "Please give me advice on how to improve my pace in my next workout."

[0200] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0201] Step 1:

[0202] The user launches the application on their device and enters information such as their target speed and distance for exercise. This input data is collected through an information entry interface and transmitted to the server using a secure communication channel. As a concrete example of input data, a target of "running 5km in 30 minutes" is set.

[0203] Step 2:

[0204] The server analyzes the received target information and, based on that, calls a geographic information service API to obtain geographic information of the surrounding area. The input data is the user's target setting information, and the output is geographic information necessary for selecting a travel route. The server uses the location measurement data to identify multiple valid routes and obtain detailed information, including congestion levels and the locations of traffic lights.

[0205] Step 3:

[0206] The server analyzes the congestion status of multiple travel routes based on acquired geographic information. Specifically, it integrates location measurement data and real-time traffic information to predict congestion for each route. It takes multiple route information as input and provides optimized travel route suggestions as output.

[0207] Step 4:

[0208] The server uses a generated AI model to select the optimal movement path based on the user's target speed and sends it to the terminal. The server generates prompt messages, which are executed by the AI ​​model to evaluate and determine the optimal path. The selected movement path and its detailed information are provided to the terminal as output data.

[0209] Step 5:

[0210] During exercise, the device continuously records the user's voice and video data using its microphone and camera. The input data consists of the user's biosensors and voice / video feeds, which are sent to a server and used for sentiment analysis.

[0211] Step 6:

[0212] The server uses the received audio and video data to activate an emotion analysis engine, determining the user's emotional state in real time. Based on the data analysis, the user's emotions are detected, leading to the generation of appropriate encouraging messages.

[0213] Step 7:

[0214] The server uses a generative AI model to generate and send encouraging messages to the user's device based on real-time determined emotion data. The output data consists of encouraging messages delivered in both voice and text formats. For example, a message such as "Just a little further, keep going!" might be sent.

[0215] Step 8:

[0216] After the exercise session, the server integrates the activity and emotional data collected during the workout to generate feedback for the next session. The input data consists of a record of the entire workout and the user's emotional changes. The output provides improvement advice and sends it back to the device.

[0217] Step 9:

[0218] Users receive feedback through their devices, which they use to improve their approach to their next workout. Specific suggestions and recommendations are provided to support the user's long-term exercise plan.

[0219] (Application Example 2)

[0220] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0221] In modern society, maintaining health through exercise such as running is important. However, selecting the optimal route for running and maintaining motivation during exercise are not easy. In particular, there is a lack of comprehensive support, including mental support, during running, which means that users do not receive sufficient support to achieve their goals.

[0222] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0223] In this invention, the server includes means for inputting information for the user to set a target running pace, means for acquiring location information and control device information for multiple running routes in the surrounding area, and means for recognizing the user's emotions in real time using emotion analysis means and generating encouraging messages that match those emotions. This enables the user to receive guidance on the optimal route and encouragement that corresponds to their emotions while running, providing the necessary support to achieve their goals.

[0224] An "information entry device" refers to a device that allows users to input their target running pace and related data.

[0225] "Location information" refers to data used to identify the geographical location of a user.

[0226] "Control device information" refers to data that provides information about traffic signals and other traffic control systems.

[0227] A "congestion status analysis device" refers to a device that uses location measurement data to analyze the congestion status of multiple running routes.

[0228] "Real-time pace information" refers to instantaneous data on a user's speed and tempo while running.

[0229] "Encouraging voice messages" refer to motivational messages that are generated and delivered via voice while the user is exercising.

[0230] "Emotional analysis means" refers to a device that analyzes a user's facial expressions and voice to recognize their emotional state and then provides an appropriate message based on that analysis.

[0231] "Activity data" refers to data about a user's exercise recorded during or after running.

[0232] "Geographic data" refers to data that provides information related to maps and topography.

[0233] "Real-time travel information" refers to instantaneous data on current traffic conditions and travel patterns.

[0234] To implement this invention, the user must first input setting information such as target pace and running distance into the terminal using an information input means. After collecting this information, the terminal transmits it to the server. The server selects the optimal running route based on the user's current location. This involves considering congestion and traffic light information using geographic data and real-time movement information.

[0235] Next, the server sends the selected running route back to the terminal. The terminal presents this information to the user visually or audibly. During the run, emotion analysis is used to recognize the user's emotions in real time from their voice and facial expressions, and this information, along with pace information, is sent to the server.

[0236] The server analyzes the collected emotional data and uses a generative AI model to generate encouraging voice messages. These messages are then delivered to the user via their device, allowing them to receive personalized mental support while running.

[0237] Furthermore, after the run is completed, the server collects and analyzes the user's activity data. The analysis results are sent to the device as feedback for the next run, and the user can receive suggested improvements and stress reduction measures. This enhances the user's running experience.

[0238] As a concrete example, consider a scenario where a user aims to run 5km in a park in 30 minutes, receiving real-time encouragement and feedback via their device while running. In this case, the prompt message would be: "I would like advice to help me run. Based on the user's emotional data, please tell me what kind of encouragement I should provide in real time."

[0239] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0240] Step 1:

[0241] The user enters their target running pace and distance using an information entry tool on their device. The entered information is organized into a database format and sent from the device to the server. The server receives this information and prepares for processing in the next step.

[0242] Step 2:

[0243] The server receives the user's location information and analyzes congestion levels and traffic light locations by referencing geographical data and real-time movement information. This analysis uses location data to execute an algorithm that helps the user select the optimal route, determining the most suitable travel path. This result is then transmitted to the terminal.

[0244] Step 3:

[0245] The user receives route information presented by the device visually or audibly. As the run begins, the sentiment analysis system activates, collecting the user's emotional data in real time through the camera and microphone. This data is immediately transmitted to the server.

[0246] Step 4:

[0247] The server analyzes emotional data and uses a generative AI model to generate appropriate encouraging voice messages for the user. Emotional data is used as input, and the generated encouraging message is sent to the device as output. The device provides this voice message to the user to maintain motivation while running.

[0248] Step 5:

[0249] Once the run is complete, the server compiles the user's activity data and performs analysis for the next run. This analysis generates feedback that incorporates the user's pace and emotional changes. The feedback information obtained from the analysis is sent to the device and presented to the user.

[0250] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0251] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0252] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0253] [Second Embodiment]

[0254] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0255] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0256] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0257] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0258] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0259] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0260] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0261] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0262] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0263] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0264] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0265] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0266] This invention relates to a system that suggests the optimal route for achieving a user-set running goal and provides feedback during and after the run. The system provides services to the user in the following steps:

[0267] First, the user enters information such as their target pace, age, gender, and running experience. The device then sends this information about the user's target pace to the server.

[0268] Next, the server uses map data and real-time GPS data to retrieve multiple running routes in the vicinity based on the user's location. The server analyzes congestion levels and traffic light locations along these routes and selects the route best suited to the user's target pace. This selected route information is then displayed to the user via their device.

[0269] During a run, the device monitors the user's pace in real time and sends the measured data to a server. The server analyzes this data and, if the user is deviating from their target pace, generates an encouraging message and sends it to the device. The device then notifies the user of the message via voice or text to support their activity.

[0270] Once the run is complete, the server compiles all activity data, calculating calories burned, average pace, distance covered, and more. This data is sent to the user's device and provided as feedback, including advice and suggestions for improvement for their next run.

[0271] For example, if a user aims to run 5km in under 30 minutes, the system will suggest a flat, uncongested route with few traffic lights. Furthermore, if the user's pace slows during the run, it will notify them with a message like, "Keep going, almost there!" to maintain their motivation. After the run, it will provide feedback evaluating the user's progress, including whether they burned 500 calories. In this way, the system is designed to help users effectively achieve their goals.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] The user launches the application and logs into their account. If it's their first time using the service, they will create an account by entering the required information (name, age, gender, running experience, target pace, etc.) on the registration screen.

[0275] Step 2:

[0276] The terminal sends the target pace and running purpose (e.g., distance, time, calorie consumption) set by the user to the server.

[0277] Step 3:

[0278] Based on the user's location information, the server obtains the surrounding running routes from the map database. Furthermore, using real-time GPS data, it incorporates the congestion status, slopes, and traffic signal location information of each route.

[0279] Step 4:

[0280] The server analyzes the acquired data and selects the running route that is most suitable for the user's target pace. In this selection, the flatness of the route, the scarcity of signals, and the low congestion are considered.

[0281] Step 5:

[0282] The server sends the detailed information (map, distance, elevation difference) of the selected optimal running route to the terminal.

[0283] Step 6:

[0284] The terminal displays the received route information to the user and prompts the start of running.

[0285] Step 7:

[0286] The user checks the displayed route and starts running.

[0287] Step 8:

[0288] During running, the terminal uses the built-in sensors to measure the user's real-time pace, running distance, and time, and sends the data to the server.

[0289] Step 9:

[0290] The server analyzes the received pace data to check if the user is maintaining their target pace. If the user is falling behind the target pace, it generates an encouragement message.

[0291] Step 10:

[0292] The device notifies the user of encouraging messages received from the server. Motivation is supported through voice output or text messages.

[0293] Step 11:

[0294] After the run is finished, the server compiles all the activity data and calculates calories burned, average pace, distance run, and more.

[0295] Step 12:

[0296] The server sends the aggregated data to the terminal and displays feedback to the user as a result of their run.

[0297] Step 13:

[0298] The device provides users with detailed feedback, including advice and suggestions for improvement for future use.

[0299] (Example 1)

[0300] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In conventional exercise support systems, it has been difficult to provide feedback optimized for the goals and environments of individual users. Also, in many cases, consistent motivation maintenance during and after exercise has not been sufficiently achieved. Furthermore, the selection of an optimal route considering real-time congestion and traffic signal effects has also been inadequate. The purpose is to solve such problems.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0303] In this invention, the server includes data input means for a user to set a target speed of exercise, means for acquiring position information of a plurality of exercise routes and information on traffic control devices in the user's surrounding area, and means for analyzing the congestion status of the plurality of exercise routes based on position measurement data. Thereby, it becomes possible to propose an exercise route optimized individually for the user and to provide motivation during exercise.

[0304] The "data input means" is an interface for a user to input the target speed and related information of exercise into a digital device.

[0305] The "exercise route" is a geographical route selected for a user to perform a specified exercise activity.

[0306] The "position measurement data" is data that provides geographical information of a user or an object in real time.

[0307] The "traffic control device" is a signal or other control device in a road or public transportation system, which is a device for managing movement.

[0308] The "generative artificial intelligence" is an algorithm or system having the ability to automatically generate new messages and data according to specific conditions.

[0309] An "encouragement message" is a message of audio or text information provided to encourage or motivate a user's actions.

[0310] "Voice output" is a technology that converts digital information into audio and conveys it to the user.

[0311] "Text information output" refers to a format in which information is displayed as text on a screen to visually convey information to the user.

[0312] This invention is a system that proposes the optimal running route to help an athlete achieve a set speed goal, and provides encouragement and feedback during and after the exercise. The system consists of a network-connectable electronic device (hereinafter referred to as "terminal") and a central processing system (hereinafter referred to as "server") that communicates with the terminal.

[0313] First, the user enters information such as their target exercise speed, age, gender, and past exercise experience into the device. This information is entered using a mobile device such as a smartphone or tablet, via a dedicated application installed on that device.

[0314] The terminal sends user input information to the server. This communication utilizes an internet connection, and the data uses a secure protocol (e.g., HTTPS).

[0315] Based on information received from the user, the server uses a map database and real-time location data to obtain a suitable route for the surrounding area. The map database uses a general-purpose geographic information platform (e.g., OpenStreetMap API), and real-time traffic information is also obtained from a similar API. Based on this, the server generates an optimal route that takes into account the congestion status of the route, the location of traffic control devices, and the terrain.

[0316] During exercise, the device monitors the user's pace in real time. This monitoring utilizes the device's built-in GPS sensor and accelerometer. The measured data is then transmitted to a server.

[0317] The server analyzes whether the user is maintaining their target speed during exercise. Using a generative artificial intelligence model, it generates personalized motivational messages for each user. These messages correspond to the input prompts, for example, "Keep it up, almost there!" These messages are sent to the device and provided to the user via voice or text.

[0318] After the exercise is finished, the server compiles all activity data and calculates energy expenditure, average speed, total distance, etc. This analysis is then used to generate advice for the next exercise session, which is then fed back to the user via their device. For example, it might provide specific advice such as, "You burned 500 calories today. Next time, try to maintain a steady pace and cover a slightly longer distance."

[0319] Specific examples of prompt statements are as follows:

[0320] "The user's goal is to run 5km in under 30 minutes. Suggest an optimal route, monitor the user's pace during the exercise, and generate encouraging messages if they deviate from the target speed. After the exercise, provide feedback on calories burned and an evaluation of the achieved goal."

[0321] This invention allows users to receive support optimized for their individual conditions, thereby enhancing the effectiveness of their exercise.

[0322] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0323] Step 1:

[0324] Entering user information

[0325] Users enter personal data such as exercise goals, age, gender, and running experience into their device. Based on this input, a dedicated application formats it as structured data and prepares to send it to the server.

[0326] Step 2:

[0327] Sending user data

[0328] The terminal sends data obtained from the user to the server. To do this, the terminal uses an encryption protocol (e.g., HTTPS) to transfer the data, ensuring that personal information is transmitted securely. The output is the user's target information received on the server side.

[0329] Step 3:

[0330] Location information acquisition and route calculation

[0331] The server receives the user's location information and uses a map database and real-time location data to determine the surrounding travel route. Based on the input data, the server uses an algorithm to analyze congestion levels, traffic light locations, and other factors, and calculates the optimal route. The output generated by this process is the proposed optimal route information.

[0332] Step 4:

[0333] Real-time monitoring during running

[0334] The terminal monitors the user's pace in real time using GPS and accelerometer sensors within the device. It processes the sensor data received as input and periodically sends the results to the server. The output is real-time pace information.

[0335] Step 5:

[0336] Data analysis and feedback generation

[0337] The server verifies whether the user is achieving their target speed by comparing and analyzing the received pace with target data. Using a generative AI model, it dynamically generates messages based on prompts that produce motivational messages tailored to the user. The output is the generated motivational message.

[0338] Step 6:

[0339] Data collection and feedback after exercise

[0340] The server aggregates all of the user's exercise data and analyzes the results. It calculates energy consumption, average speed, distance traveled, etc., and generates feedback along with advice for the next time. This result is sent to the terminal, and the output is detailed feedback information for the user.

[0341] This allows users to receive continuous support and opportunities for improvement both during and after exercise.

[0342] (Application Example 1)

[0343] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0344] In modern society, many individuals engage in regular exercise to maintain their health and improve their physical fitness, but there is a need for appropriate support to make these activities more effective. In particular, the lack of a system that can provide the selection of the optimal exercise route, real-time feedback, and effective post-exercise advice tailored to each individual user is a problem.

[0345] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0346] In this invention, the server includes means for inputting information for the user to set a target pace for land-based exercise, means for acquiring location information and road signal information for multiple exercise routes in the user's surrounding area, and means for monitoring pace and heart rate through a home mobile device and providing immediate voice feedback. This enables the user to receive exercise planning optimized to their individual goals and real-time feedback.

[0347] A "user" refers to an individual who uses the system to perform track and field exercises.

[0348] "Track and field exercise" refers to physical activities performed on the ground, such as running and walking.

[0349] "Target pace" refers to the target speed that a user sets when exercising.

[0350] "Information entry method" refers to the interface used by users to input exercise goals and personal information.

[0351] "Surrounding area" refers to the area surrounding the place where the user exercises.

[0352] "Exercise path" refers to the route selected for performing the exercise specified by the user.

[0353] "Location data" refers to data that indicates geographical location information.

[0354] "Traffic signals" refer to signaling devices installed to regulate the flow of traffic.

[0355] "Home-use mobile devices" refer to mobile devices or robots that can be used at home to support exercise.

[0356] "Real-time feedback" refers to information that is instantly provided during exercise based on the situation at that moment.

[0357] "Physical activity data" refers to all data related to physical activity obtained during and after exercise.

[0358] This invention is a system that utilizes a mobile home device to help users perform land-based exercises more effectively. The implementation of this system primarily requires the cooperation of a server, terminals, and a mobile home device.

[0359] The server receives information provided by the user, such as target pace and basic personal profile. Next, the server uses map data and real-time road information to select the optimal exercise route for the user. This route selection process includes analysis of location data and consideration of traffic signal placement. The server then presents the selected exercise route to the user via a home mobile device.

[0360] The home mobile device monitors the user's exercise pace and physical data such as heart rate in real time. This allows the user to check if they are maintaining their target pace and provides voice or text feedback as needed. The feedback from the home mobile device helps the user understand their exercise status in real time, ultimately improving the quality of their workout.

[0361] After exercise, the device receives physical activity data compiled from the server and provides feedback to the user. This feedback includes information on calories burned, average pace, and distance covered. Advice for the next exercise session is also provided. In this process, the server performs a detailed analysis of the exercise's success and areas for improvement, helping the user to consistently achieve their goals.

[0362] For example, if a user wants to take a 20-minute walk near their home, the system will suggest the optimal route that avoids congestion and has fewer traffic lights. During the walk, it will provide verbal encouragement such as, "Your pace is good, keep it up!" After the walk, it will display exercise statistics and suggest ways to further improve.

[0363] An example of a prompt message might be: "Use OpenCV and real-time location data to implement an optimal route and feedback system for home-use mobile devices during exercise."

[0364] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0365] Step 1:

[0366] The device receives the user's target exercise pace and personal profile information (age, gender, running experience, etc.) as input. This input data is formatted and sent to the server. The server then receives the user's detailed goal settings and stores them in its database.

[0367] Step 2:

[0368] The server takes the user's location information as input and uses map data and real-time traffic information to search for nearby exercise routes. Considering the location data, traffic light placement, and congestion levels, it selects the optimal route for the user's target pace. The selected route information is then output to a home mobile device.

[0369] Step 3:

[0370] The home mobile device monitors the user's pace and heart rate in real time using sensors, based on the optimal route received from the server. This monitoring data is analyzed as input to determine if the user is maintaining their target pace. If necessary, it outputs encouraging voice messages to provide immediate feedback.

[0371] Step 4:

[0372] The server collects real-time user data transmitted from the home mobile device during exercise and performs data analysis. Using the results of this analysis, it generates specific advice for the user to achieve their exercise goals and suggestions for improvement for their next exercise session, and outputs them to the terminal.

[0373] Step 5:

[0374] The device receives aggregated data from the server after the exercise session ends. This includes calories burned, average pace, and distance covered. This information is compiled and provided to the user as feedback. This helps users review past exercise statistics and use them to plan their next workout.

[0375] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0376] This invention relates to a system that suggests the optimal route for achieving user-defined running goals and recognizes the user's emotions during the run to provide a comfortable running experience. In addition, it analyzes the user's emotional data and provides feedback for use in future runs.

[0377] First, the user launches the application and enters information such as their target running pace. The device then sends this data to the server and prepares for the run.

[0378] Next, the server uses the user's location information to obtain information about surrounding running routes from map data and real-time traffic information, and analyzes congestion levels and traffic light locations. At this time, it selects the optimal running route considering the user's target pace and sends it to the terminal.

[0379] During a run, the system recognizes the user's emotions in real time, using voice and camera data in addition to their pace. The server analyzes this data and generates encouraging messages that match the user's emotions. For example, if the emotion engine detects that the user is feeling tired, it will send an encouraging message from the device such as, "Just a little further, keep going!"

[0380] Once the run is complete, the server compiles activity and emotional data from the entire run, analyzing changes in emotions in addition to calories burned and average pace. These results are sent to the device as feedback for the next run. Based on this, users can receive suggestions for improvement and recommendations that take their emotional changes into account.

[0381] For example, if a user aims to run 5km in 30 minutes, the system will help them complete their run according to plan by providing the optimal route and appropriate encouragement. Post-run feedback will include appropriate rest recommendations if fatigue is observed, and stress reduction strategies for the next run. In this way, the system comprehensively supports the user's running experience.

[0382] The following describes the processing flow.

[0383] Step 1:

[0384] After launching the running app and logging in, the user enters their target pace, distance, and other information.

[0385] Step 2:

[0386] The device begins preparing for the run by sending the user's target pace and other information to the server.

[0387] Step 3:

[0388] The server obtains the user's current location and analyzes congestion levels and traffic light locations for each route based on map data of surrounding running routes and real-time traffic information.

[0389] Step 4:

[0390] Based on the analysis results, the server selects the optimal running route for the user's target pace and sends that information to the terminal.

[0391] Step 5:

[0392] The device displays the user the optimal running route it has received and prompts them to start running.

[0393] Step 6:

[0394] During running, the device uses its built-in sensors to measure the user's pace data in real time and transmit it to the server.

[0395] Step 7:

[0396] Furthermore, the device uses microphones and cameras to collect the user's voice and facial expressions and transmits them to the emotion engine.

[0397] Step 8:

[0398] The server receives pace and emotion data transmitted during running and analyzes the user's ability to maintain their target pace and their emotional state.

[0399] Step 9:

[0400] Based on the analysis results, the server generates an encouraging message if the user appears unwell or fatigued.

[0401] Step 10:

[0402] The device notifies the user of encouraging messages sent from the server, either as voice or text, to boost their motivation.

[0403] Step 11:

[0404] After the run is finished, the server compiles all activity and emotional data. It calculates calories burned, average pace, and changes in emotions.

[0405] Step 12:

[0406] The server sends the calculated data to the terminal as feedback, displaying advice and suggestions for improvement for the next run to the user.

[0407] Step 13:

[0408] The device provides users with stress reduction strategies and emotional management advice based on their feedback.

[0409] (Example 2)

[0410] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0411] Modern exercise support systems lack sufficient means to select routes that match the user's goals and to provide appropriate real-time feedback. Furthermore, providing encouraging messages that take into account the user's emotional changes during exercise is difficult, highlighting the need for improved exercise experiences. As a result, there is a lack of effective support for users to achieve their exercise goals.

[0412] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0413] In this invention, the server includes means for acquiring location information and traffic signal information for multiple travel routes in the user's surrounding area, means for analyzing the congestion status of the multiple travel routes based on location measurement data, and means for recognizing the user's emotional state in real time using an emotion analysis engine and generating encouraging messages based on the user's emotions. This enables route selection tailored to the user's goals and real-time feedback based on emotion recognition.

[0414] "Target exercise speed" refers to the target speed that a user sets when exercising, and is information that serves as a basis for exercise planning and performance evaluation.

[0415] "Information input means" refers to an interface or device for users to input their target speed and distance for exercise.

[0416] "Location information" refers to geographical location data used to select a movement route, providing detailed route information within a specific area.

[0417] "Location measurement data" refers to positioning information about a user's current location and movement, and is a collection of data used for analyzing congestion levels and guiding routes.

[0418] An "emotion analysis engine" refers to an analysis tool or algorithm that identifies and processes emotions from a user's voice and facial expressions.

[0419] A "message of encouragement" is a message containing motivation and instructions provided to help users achieve their goals.

[0420] "Activity data" refers to information related to exercise, such as the user's speed, distance, and energy expenditure, recorded during exercise.

[0421] "Feedback" refers to engaging advice and analysis results provided to users after exercise, intended to help them improve their next workout.

[0422] This system provides real-time optimal route selection and emotion-based feedback to improve the user's exercise experience. The embodiments of the invention are described below.

[0423] Before exercising, users launch a dedicated application on their device and set their target speed and distance. This information is entered via an information entry system. The information set by the user is transmitted to the server using a secure communication method.

[0424] Based on the received target information, the server uses location services (e.g., geographic information APIs) to obtain multiple travel routes in the user's surrounding area. The server then uses the location measurement data to analyze the degree of congestion and the location of traffic lights.

[0425] Next, the server uses a generative AI model to select the optimal movement path based on the user's target speed. The selected path is returned to the terminal in real time, and the user can receive navigation on the terminal.

[0426] During exercise, the device uses a microphone and camera to collect the user's voice and video data. This data is sent to a server, which uses an emotion analysis engine to analyze the user's emotional state. The server generates and sends encouraging messages to the device based on the user's emotions. For example, if fatigue is detected, it will generate a message such as, "Just a little further, keep going!"

[0427] After the workout, the server collects the user's activity and emotional data, generates feedback for the next workout, and sends it to the device. This feedback helps the user understand how to improve their next workout and provides guidance for a better exercise experience.

[0428] For example, if a user sets a goal like "run 5km in 30 minutes," the server will provide less congested routes and send appropriate encouraging messages when the user feels fatigued, supporting them in achieving their goal. Another example of a prompt message would be, "Please give me advice on how to improve my pace in my next workout."

[0429] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0430] Step 1:

[0431] The user launches the application on their device and enters information such as their target speed and distance for exercise. This input data is collected through an information entry interface and transmitted to the server using a secure communication channel. As a concrete example of input data, a target of "running 5km in 30 minutes" is set.

[0432] Step 2:

[0433] The server analyzes the received target information and, based on that, calls a geographic information service API to obtain geographic information of the surrounding area. The input data is the user's target setting information, and the output is geographic information necessary for selecting a travel route. The server uses the location measurement data to identify multiple valid routes and obtain detailed information, including congestion levels and the locations of traffic lights.

[0434] Step 3:

[0435] The server analyzes the congestion status of multiple travel routes based on acquired geographic information. Specifically, it integrates location measurement data and real-time traffic information to predict congestion for each route. It takes multiple route information as input and provides optimized travel route suggestions as output.

[0436] Step 4:

[0437] The server uses a generated AI model to select the optimal movement path based on the user's target speed and sends it to the terminal. The server generates prompt messages, which are executed by the AI ​​model to evaluate and determine the optimal path. The selected movement path and its detailed information are provided to the terminal as output data.

[0438] Step 5:

[0439] During exercise, the device continuously records the user's voice and video data using its microphone and camera. The input data consists of the user's biosensors and voice / video feeds, which are sent to a server and used for sentiment analysis.

[0440] Step 6:

[0441] The server uses the received audio and video data to activate an emotion analysis engine, determining the user's emotional state in real time. Based on the data analysis, the user's emotions are detected, leading to the generation of appropriate encouraging messages.

[0442] Step 7:

[0443] The server uses a generative AI model to generate and send encouraging messages to the user's device based on real-time determined emotion data. The output data consists of encouraging messages delivered in both voice and text formats. For example, a message such as "Just a little further, keep going!" might be sent.

[0444] Step 8:

[0445] After the exercise session, the server integrates the activity and emotional data collected during the workout to generate feedback for the next session. The input data consists of a record of the entire workout and the user's emotional changes. The output provides improvement advice and sends it back to the device.

[0446] Step 9:

[0447] Users receive feedback through their devices, which they use to improve their approach to their next workout. Specific suggestions and recommendations are provided to support the user's long-term exercise plan.

[0448] (Application Example 2)

[0449] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0450] In modern society, maintaining health through exercise such as running is important. However, selecting the optimal route for running and maintaining motivation during exercise are not easy. In particular, there is a lack of comprehensive support, including mental support, during running, which means that users do not receive sufficient support to achieve their goals.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0452] In this invention, the server includes means for inputting information for the user to set a target running pace, means for acquiring location information and control device information for multiple running routes in the surrounding area, and means for recognizing the user's emotions in real time using emotion analysis means and generating encouraging messages that match those emotions. This enables the user to receive guidance on the optimal route and encouragement that corresponds to their emotions while running, providing the necessary support to achieve their goals.

[0453] An "information entry device" refers to a device that allows users to input their target running pace and related data.

[0454] "Location information" refers to data used to identify the geographical location of a user.

[0455] "Control device information" refers to data that provides information about traffic signals and other traffic control systems.

[0456] A "congestion status analysis device" refers to a device that uses location measurement data to analyze the congestion status of multiple running routes.

[0457] "Real-time pace information" refers to instantaneous data on a user's speed and tempo while running.

[0458] "Encouraging voice messages" refer to motivational messages that are generated and delivered via voice while the user is exercising.

[0459] "Emotional analysis means" refers to a device that analyzes a user's facial expressions and voice to recognize their emotional state and then provides an appropriate message based on that analysis.

[0460] "Activity data" refers to data about a user's exercise recorded during or after running.

[0461] "Geographic data" refers to data that provides information related to maps and topography.

[0462] "Real-time travel information" refers to instantaneous data on current traffic conditions and travel patterns.

[0463] To implement this invention, the user must first input setting information such as target pace and running distance into the terminal using an information input means. After collecting this information, the terminal transmits it to the server. The server selects the optimal running route based on the user's current location. This involves considering congestion and traffic light information using geographic data and real-time movement information.

[0464] Next, the server sends the selected running route back to the terminal. The terminal presents this information to the user visually or audibly. During the run, emotion analysis is used to recognize the user's emotions in real time from their voice and facial expressions, and this information, along with pace information, is sent to the server.

[0465] The server analyzes the collected emotional data and uses a generative AI model to generate encouraging voice messages. These messages are then delivered to the user via their device, allowing them to receive personalized mental support while running.

[0466] Furthermore, after the run is completed, the server collects and analyzes the user's activity data. The analysis results are sent to the device as feedback for the next run, and the user can receive suggested improvements and stress reduction measures. This enhances the user's running experience.

[0467] As a concrete example, consider a scenario where a user aims to run 5km in a park in 30 minutes, receiving real-time encouragement and feedback via their device while running. In this case, the prompt message would be: "I would like advice to help me run. Based on the user's emotional data, please tell me what kind of encouragement I should provide in real time."

[0468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0469] Step 1:

[0470] The user enters their target running pace and distance using an information entry tool on their device. The entered information is organized into a database format and sent from the device to the server. The server receives this information and prepares for processing in the next step.

[0471] Step 2:

[0472] The server receives the user's location information and analyzes congestion levels and traffic light locations by referencing geographical data and real-time movement information. This analysis uses location data to execute an algorithm that helps the user select the optimal route, determining the most suitable travel path. This result is then transmitted to the terminal.

[0473] Step 3:

[0474] The user receives route information presented by the device visually or audibly. As the run begins, the sentiment analysis system activates, collecting the user's emotional data in real time through the camera and microphone. This data is immediately transmitted to the server.

[0475] Step 4:

[0476] The server analyzes emotional data and uses a generative AI model to generate appropriate encouraging voice messages for the user. Emotional data is used as input, and the generated encouraging message is sent to the device as output. The device provides this voice message to the user to maintain motivation while running.

[0477] Step 5:

[0478] Once the run is complete, the server compiles the user's activity data and performs analysis for the next run. This analysis generates feedback that incorporates the user's pace and emotional changes. The feedback information obtained from the analysis is sent to the device and presented to the user.

[0479] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0480] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0481] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0482] [Third Embodiment]

[0483] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0484] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0485] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0486] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0487] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0488] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0489] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0490] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0491] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0492] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0493] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0494] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0495] This invention relates to a system that suggests the optimal route for achieving a user-set running goal and provides feedback during and after the run. The system provides services to the user in the following steps:

[0496] First, the user enters information such as their target pace, age, gender, and running experience. The device then sends this information about the user's target pace to the server.

[0497] Next, the server uses map data and real-time GPS data to retrieve multiple running routes in the vicinity based on the user's location. The server analyzes congestion levels and traffic light locations along these routes and selects the route best suited to the user's target pace. This selected route information is then displayed to the user via their device.

[0498] During a run, the device monitors the user's pace in real time and sends the measured data to a server. The server analyzes this data and, if the user is deviating from their target pace, generates an encouraging message and sends it to the device. The device then notifies the user of the message via voice or text to support their activity.

[0499] Once the run is complete, the server compiles all activity data, calculating calories burned, average pace, distance covered, and more. This data is sent to the user's device and provided as feedback, including advice and suggestions for improvement for their next run.

[0500] For example, if a user aims to run 5km in under 30 minutes, the system will suggest a flat, uncongested route with few traffic lights. Furthermore, if the user's pace slows during the run, it will notify them with a message like, "Keep going, almost there!" to maintain their motivation. After the run, it will provide feedback evaluating the user's progress, including whether they burned 500 calories. In this way, the system is designed to help users effectively achieve their goals.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The user launches the application and logs into their account. If it's their first time using the service, they will create an account by entering the required information (name, age, gender, running experience, target pace, etc.) on the registration screen.

[0504] Step 2:

[0505] The device sends the user's set target pace and running objectives (e.g., distance, time, calories burned) to the server.

[0506] Step 3:

[0507] The server uses the user's location information to retrieve nearby running routes from a map database. Furthermore, it uses real-time GPS data to capture congestion information, hills, and traffic light locations for each route.

[0508] Step 4:

[0509] The server analyzes the acquired data and selects the running route best suited to the user's target pace. This selection takes into account factors such as the flatness of the route, the number of traffic lights, and the level of congestion.

[0510] Step 5:

[0511] The server sends detailed information (map, distance, elevation) of the selected optimal running route to the terminal.

[0512] Step 6:

[0513] The device displays the received route information to the user and prompts them to start running.

[0514] Step 7:

[0515] The user checks the displayed route and starts running.

[0516] Step 8:

[0517] While running, the device uses its built-in sensors to measure the user's real-time pace, distance, and time, and sends that data to a server.

[0518] Step 9:

[0519] The server analyzes the received pace data to check if the user is maintaining their target pace. If the user is falling behind the target pace, it generates an encouragement message.

[0520] Step 10:

[0521] The device notifies the user of encouraging messages received from the server. Motivation is supported through voice output or text messages.

[0522] Step 11:

[0523] After the run is finished, the server compiles all the activity data and calculates calories burned, average pace, distance run, and more.

[0524] Step 12:

[0525] The server sends the aggregated data to the terminal and displays feedback to the user as a result of their run.

[0526] Step 13:

[0527] The device provides users with detailed feedback, including advice and suggestions for improvement for future use.

[0528] (Example 1)

[0529] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] Conventional exercise support systems have struggled to provide feedback optimized for each user's goals and environment. Furthermore, they often failed to adequately maintain consistent motivation in users during and after exercise. Additionally, they were insufficient in selecting optimal routes that took into account real-time congestion and traffic signals. This system aims to address these challenges.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0532] In this invention, the server includes data input means for the user to set a target speed for exercise, means for acquiring location information of multiple exercise routes and information on traffic control devices in the user's surrounding area, and means for analyzing the congestion status of the multiple exercise routes based on location measurement data. This makes it possible to propose individually optimized exercise routes to the user and provide motivation during exercise.

[0533] A "data input method" is an interface that allows users to input their target exercise speed and related information into a digital device.

[0534] A "walking route" is a geographical path selected by the user to perform a specified exercise activity.

[0535] "Location measurement data" refers to data that provides real-time geographical information about users and objects.

[0536] A "traffic control device" is a traffic signal or other control device used in roads or public transportation systems to manage movement.

[0537] "Generative artificial intelligence" refers to an algorithm or system that has the ability to automatically generate new messages or data according to specific conditions.

[0538] An "encouragement message" is a message of audio or text information provided to encourage or motivate a user's actions.

[0539] "Voice output" is a technology that converts digital information into audio and conveys it to the user.

[0540] "Text information output" refers to a format in which information is displayed as text on a screen to visually convey information to the user.

[0541] This invention is a system that proposes the optimal running route to help an athlete achieve a set speed goal, and provides encouragement and feedback during and after the exercise. The system consists of a network-connectable electronic device (hereinafter referred to as "terminal") and a central processing system (hereinafter referred to as "server") that communicates with the terminal.

[0542] First, the user enters information such as their target exercise speed, age, gender, and past exercise experience into the device. This information is entered using a mobile device such as a smartphone or tablet, via a dedicated application installed on that device.

[0543] The terminal sends user input information to the server. This communication utilizes an internet connection, and the data uses a secure protocol (e.g., HTTPS).

[0544] Based on information received from the user, the server uses a map database and real-time location data to obtain a suitable route for the surrounding area. The map database uses a general-purpose geographic information platform (e.g., OpenStreetMap API), and real-time traffic information is also obtained from a similar API. Based on this, the server generates an optimal route that takes into account the congestion status of the route, the location of traffic control devices, and the terrain.

[0545] During exercise, the device monitors the user's pace in real time. This monitoring utilizes the device's built-in GPS sensor and accelerometer. The measured data is then transmitted to a server.

[0546] The server analyzes whether the user is maintaining their target speed during exercise. Using a generative artificial intelligence model, it generates personalized motivational messages for each user. These messages correspond to the input prompts, for example, "Keep it up, almost there!" These messages are sent to the device and provided to the user via voice or text.

[0547] After the exercise is finished, the server compiles all activity data and calculates energy expenditure, average speed, total distance, etc. This analysis is then used to generate advice for the next exercise session, which is then fed back to the user via their device. For example, it might provide specific advice such as, "You burned 500 calories today. Next time, try to maintain a steady pace and cover a slightly longer distance."

[0548] Specific examples of prompt statements are as follows:

[0549] "The user's goal is to run 5km in under 30 minutes. Suggest an optimal route, monitor the user's pace during the exercise, and generate encouraging messages if they deviate from the target speed. After the exercise, provide feedback on calories burned and an evaluation of the achieved goal."

[0550] This invention allows users to receive support optimized for their individual conditions, thereby enhancing the effectiveness of their exercise.

[0551] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0552] Step 1:

[0553] Entering user information

[0554] Users enter personal data such as exercise goals, age, gender, and running experience into their device. Based on this input, a dedicated application formats it as structured data and prepares to send it to the server.

[0555] Step 2:

[0556] Sending user data

[0557] The terminal sends data obtained from the user to the server. To do this, the terminal uses an encryption protocol (e.g., HTTPS) to transfer the data, ensuring that personal information is transmitted securely. The output is the user's target information received on the server side.

[0558] Step 3:

[0559] Location information acquisition and route calculation

[0560] The server receives the user's location information and uses a map database and real-time location data to determine the surrounding travel route. Based on the input data, the server uses an algorithm to analyze congestion levels, traffic light locations, and other factors, and calculates the optimal route. The output generated by this process is the proposed optimal route information.

[0561] Step 4:

[0562] Real-time monitoring during running

[0563] The terminal monitors the user's pace in real time using GPS and accelerometer sensors within the device. It processes the sensor data received as input and periodically sends the results to the server. The output is real-time pace information.

[0564] Step 5:

[0565] Data analysis and feedback generation

[0566] The server verifies whether the user is achieving their target speed by comparing and analyzing the received pace with target data. Using a generative AI model, it dynamically generates messages based on prompts that produce motivational messages tailored to the user. The output is the generated motivational message.

[0567] Step 6:

[0568] Data collection and feedback after exercise

[0569] The server aggregates all of the user's exercise data and analyzes the results. It calculates energy consumption, average speed, distance traveled, etc., and generates feedback along with advice for the next time. This result is sent to the terminal, and the output is detailed feedback information for the user.

[0570] This allows users to receive continuous support and opportunities for improvement both during and after exercise.

[0571] (Application Example 1)

[0572] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0573] In modern society, many individuals engage in regular exercise to maintain their health and improve their physical fitness, but there is a need for appropriate support to make these activities more effective. In particular, the lack of a system that can provide the selection of the optimal exercise route, real-time feedback, and effective post-exercise advice tailored to each individual user is a problem.

[0574] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0575] In this invention, the server includes means for inputting information for the user to set a target pace for land-based exercise, means for acquiring location information and road signal information for multiple exercise routes in the user's surrounding area, and means for monitoring pace and heart rate through a home mobile device and providing immediate voice feedback. This enables the user to receive exercise planning optimized to their individual goals and real-time feedback.

[0576] A "user" refers to an individual who uses the system to perform track and field exercises.

[0577] "Track and field exercise" refers to physical activities performed on the ground, such as running and walking.

[0578] "Target pace" refers to the target speed that a user sets when exercising.

[0579] "Information entry method" refers to the interface used by users to input exercise goals and personal information.

[0580] "Surrounding area" refers to the area surrounding the place where the user exercises.

[0581] "Exercise path" refers to the route selected for performing the exercise specified by the user.

[0582] "Location data" refers to data that indicates geographical location information.

[0583] "Traffic signals" refer to signaling devices installed to regulate the flow of traffic.

[0584] "Home-use mobile devices" refer to mobile devices or robots that can be used at home to support exercise.

[0585] "Real-time feedback" refers to information that is instantly provided during exercise based on the situation at that moment.

[0586] "Physical activity data" refers to all data related to physical activity obtained during and after exercise.

[0587] This invention is a system that utilizes a mobile home device to help users perform land-based exercises more effectively. The implementation of this system primarily requires the cooperation of a server, terminals, and a mobile home device.

[0588] The server receives information provided by the user, such as target pace and basic personal profile. Next, the server uses map data and real-time road information to select the optimal exercise route for the user. This route selection process includes analysis of location data and consideration of traffic signal placement. The server then presents the selected exercise route to the user via a home mobile device.

[0589] The home mobile device monitors the user's exercise pace and physical data such as heart rate in real time. This allows the user to check if they are maintaining their target pace and provides voice or text feedback as needed. The feedback from the home mobile device helps the user understand their exercise status in real time, ultimately improving the quality of their workout.

[0590] After exercise, the device receives physical activity data compiled from the server and provides feedback to the user. This feedback includes information on calories burned, average pace, and distance covered. Advice for the next exercise session is also provided. In this process, the server performs a detailed analysis of the exercise's success and areas for improvement, helping the user to consistently achieve their goals.

[0591] For example, if a user wants to take a 20-minute walk near their home, the system will suggest the optimal route that avoids congestion and has fewer traffic lights. During the walk, it will provide verbal encouragement such as, "Your pace is good, keep it up!" After the walk, it will display exercise statistics and suggest ways to further improve.

[0592] An example of a prompt message might be: "Use OpenCV and real-time location data to implement an optimal route and feedback system for home-use mobile devices during exercise."

[0593] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0594] Step 1:

[0595] The device receives the user's target exercise pace and personal profile information (age, gender, running experience, etc.) as input. This input data is formatted and sent to the server. The server then receives the user's detailed goal settings and stores them in its database.

[0596] Step 2:

[0597] The server takes the user's location information as input and uses map data and real-time traffic information to search for nearby exercise routes. Considering the location data, traffic light placement, and congestion levels, it selects the optimal route for the user's target pace. The selected route information is then output to a home mobile device.

[0598] Step 3:

[0599] The home mobile device monitors the user's pace and heart rate in real time using sensors, based on the optimal route received from the server. This monitoring data is analyzed as input to determine if the user is maintaining their target pace. If necessary, it outputs encouraging voice messages to provide immediate feedback.

[0600] Step 4:

[0601] The server collects real-time user data transmitted from the home mobile device during exercise and performs data analysis. Using the results of this analysis, it generates specific advice for the user to achieve their exercise goals and suggestions for improvement for their next exercise session, and outputs them to the terminal.

[0602] Step 5:

[0603] The device receives aggregated data from the server after the exercise session ends. This includes calories burned, average pace, and distance covered. This information is compiled and provided to the user as feedback. This helps users review past exercise statistics and use them to plan their next workout.

[0604] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0605] This invention relates to a system that suggests the optimal route for achieving user-defined running goals and recognizes the user's emotions during the run to provide a comfortable running experience. In addition, it analyzes the user's emotional data and provides feedback for use in future runs.

[0606] First, the user launches the application and enters information such as their target running pace. The device then sends this data to the server and prepares for the run.

[0607] Next, the server uses the user's location information to obtain information about surrounding running routes from map data and real-time traffic information, and analyzes congestion levels and traffic light locations. At this time, it selects the optimal running route considering the user's target pace and sends it to the terminal.

[0608] During a run, the system recognizes the user's emotions in real time, using voice and camera data in addition to their pace. The server analyzes this data and generates encouraging messages that match the user's emotions. For example, if the emotion engine detects that the user is feeling tired, it will send an encouraging message from the device such as, "Just a little further, keep going!"

[0609] Once the run is complete, the server compiles activity and emotional data from the entire run, analyzing changes in emotions in addition to calories burned and average pace. These results are sent to the device as feedback for the next run. Based on this, users can receive suggestions for improvement and recommendations that take their emotional changes into account.

[0610] For example, if a user aims to run 5km in 30 minutes, the system will help them complete their run according to plan by providing the optimal route and appropriate encouragement. Post-run feedback will include appropriate rest recommendations if fatigue is observed, and stress reduction strategies for the next run. In this way, the system comprehensively supports the user's running experience.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] After launching the running app and logging in, the user enters their target pace, distance, and other information.

[0614] Step 2:

[0615] The device begins preparing for the run by sending the user's target pace and other information to the server.

[0616] Step 3:

[0617] The server obtains the user's current location and analyzes congestion levels and traffic light locations for each route based on map data of surrounding running routes and real-time traffic information.

[0618] Step 4:

[0619] Based on the analysis results, the server selects the optimal running route for the user's target pace and sends that information to the terminal.

[0620] Step 5:

[0621] The device displays the user the optimal running route it has received and prompts them to start running.

[0622] Step 6:

[0623] During running, the device uses its built-in sensors to measure the user's pace data in real time and transmit it to the server.

[0624] Step 7:

[0625] Furthermore, the device uses microphones and cameras to collect the user's voice and facial expressions and transmits them to the emotion engine.

[0626] Step 8:

[0627] The server receives pace and emotion data transmitted during running and analyzes the user's ability to maintain their target pace and their emotional state.

[0628] Step 9:

[0629] Based on the analysis results, the server generates an encouraging message if the user appears unwell or fatigued.

[0630] Step 10:

[0631] The device notifies the user of encouraging messages sent from the server, either as voice or text, to boost their motivation.

[0632] Step 11:

[0633] After the run is finished, the server compiles all activity and emotional data. It calculates calories burned, average pace, and changes in emotions.

[0634] Step 12:

[0635] The server sends the calculated data to the terminal as feedback, displaying advice and suggestions for improvement for the next run to the user.

[0636] Step 13:

[0637] The device provides users with stress reduction strategies and emotional management advice based on their feedback.

[0638] (Example 2)

[0639] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0640] Modern exercise support systems lack sufficient means to select routes that match the user's goals and to provide appropriate real-time feedback. Furthermore, providing encouraging messages that take into account the user's emotional changes during exercise is difficult, highlighting the need for improved exercise experiences. As a result, there is a lack of effective support for users to achieve their exercise goals.

[0641] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0642] In this invention, the server includes means for acquiring location information and traffic signal information for multiple travel routes in the user's surrounding area, means for analyzing the congestion status of the multiple travel routes based on location measurement data, and means for recognizing the user's emotional state in real time using an emotion analysis engine and generating encouraging messages based on the user's emotions. This enables route selection tailored to the user's goals and real-time feedback based on emotion recognition.

[0643] "Target exercise speed" refers to the target speed that a user sets when exercising, and is information that serves as a basis for exercise planning and performance evaluation.

[0644] "Information input means" refers to an interface or device for users to input their target speed and distance for exercise.

[0645] "Location information" refers to geographical location data used to select a movement route, providing detailed route information within a specific area.

[0646] "Location measurement data" refers to positioning information about a user's current location and movement, and is a collection of data used for analyzing congestion levels and guiding routes.

[0647] An "emotion analysis engine" refers to an analysis tool or algorithm that identifies and processes emotions from a user's voice and facial expressions.

[0648] A "message of encouragement" is a message containing motivation and instructions provided to help users achieve their goals.

[0649] "Activity data" refers to information related to exercise, such as the user's speed, distance, and energy expenditure, recorded during exercise.

[0650] "Feedback" refers to engaging advice and analysis results provided to users after exercise, intended to help them improve their next workout.

[0651] This system provides real-time optimal route selection and emotion-based feedback to improve the user's exercise experience. The embodiments of the invention are described below.

[0652] Before exercising, users launch a dedicated application on their device and set their target speed and distance. This information is entered via an information entry system. The information set by the user is transmitted to the server using a secure communication method.

[0653] Based on the received target information, the server uses location services (e.g., geographic information APIs) to obtain multiple travel routes in the user's surrounding area. The server then uses the location measurement data to analyze the degree of congestion and the location of traffic lights.

[0654] Next, the server uses a generative AI model to select the optimal movement path based on the user's target speed. The selected path is returned to the terminal in real time, and the user can receive navigation on the terminal.

[0655] During exercise, the device uses a microphone and camera to collect the user's voice and video data. This data is sent to a server, which uses an emotion analysis engine to analyze the user's emotional state. The server generates and sends encouraging messages to the device based on the user's emotions. For example, if fatigue is detected, it will generate a message such as, "Just a little further, keep going!"

[0656] After the workout, the server collects the user's activity and emotional data, generates feedback for the next workout, and sends it to the device. This feedback helps the user understand how to improve their next workout and provides guidance for a better exercise experience.

[0657] For example, if a user sets a goal like "run 5km in 30 minutes," the server will provide less congested routes and send appropriate encouraging messages when the user feels fatigued, supporting them in achieving their goal. Another example of a prompt message would be, "Please give me advice on how to improve my pace in my next workout."

[0658] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0659] Step 1:

[0660] The user launches the application on their device and enters information such as their target speed and distance for exercise. This input data is collected through an information entry interface and transmitted to the server using a secure communication channel. As a concrete example of input data, a target of "running 5km in 30 minutes" is set.

[0661] Step 2:

[0662] The server analyzes the received target information and, based on that, calls a geographic information service API to obtain geographic information of the surrounding area. The input data is the user's target setting information, and the output is geographic information necessary for selecting a travel route. The server uses the location measurement data to identify multiple valid routes and obtain detailed information, including congestion levels and the locations of traffic lights.

[0663] Step 3:

[0664] The server analyzes the congestion status of multiple travel routes based on acquired geographic information. Specifically, it integrates location measurement data and real-time traffic information to predict congestion for each route. It takes multiple route information as input and provides optimized travel route suggestions as output.

[0665] Step 4:

[0666] The server uses a generated AI model to select the optimal movement path based on the user's target speed and sends it to the terminal. The server generates prompt messages, which are executed by the AI ​​model to evaluate and determine the optimal path. The selected movement path and its detailed information are provided to the terminal as output data.

[0667] Step 5:

[0668] During exercise, the device continuously records the user's voice and video data using its microphone and camera. The input data consists of the user's biosensors and voice / video feeds, which are sent to a server and used for sentiment analysis.

[0669] Step 6:

[0670] The server uses the received audio and video data to activate an emotion analysis engine, determining the user's emotional state in real time. Based on the data analysis, the user's emotions are detected, leading to the generation of appropriate encouraging messages.

[0671] Step 7:

[0672] The server uses a generative AI model to generate and send encouraging messages to the user's device based on real-time determined emotion data. The output data consists of encouraging messages delivered in both voice and text formats. For example, a message such as "Just a little further, keep going!" might be sent.

[0673] Step 8:

[0674] After the exercise session, the server integrates the activity and emotional data collected during the workout to generate feedback for the next session. The input data consists of a record of the entire workout and the user's emotional changes. The output provides improvement advice and sends it back to the device.

[0675] Step 9:

[0676] Users receive feedback through their devices, which they use to improve their approach to their next workout. Specific suggestions and recommendations are provided to support the user's long-term exercise plan.

[0677] (Application Example 2)

[0678] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0679] In modern society, maintaining health through exercise such as running is important. However, selecting the optimal route for running and maintaining motivation during exercise are not easy. In particular, there is a lack of comprehensive support, including mental support, during running, which means that users do not receive sufficient support to achieve their goals.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0681] In this invention, the server includes means for inputting information for the user to set a target running pace, means for acquiring location information and control device information for multiple running routes in the surrounding area, and means for recognizing the user's emotions in real time using emotion analysis means and generating encouraging messages that match those emotions. This enables the user to receive guidance on the optimal route and encouragement that corresponds to their emotions while running, providing the necessary support to achieve their goals.

[0682] An "information entry device" refers to a device that allows users to input their target running pace and related data.

[0683] "Location information" refers to data used to identify the geographical location of a user.

[0684] "Control device information" refers to data that provides information about traffic signals and other traffic control systems.

[0685] A "congestion status analysis device" refers to a device that uses location measurement data to analyze the congestion status of multiple running routes.

[0686] "Real-time pace information" refers to instantaneous data on a user's speed and tempo while running.

[0687] "Encouraging voice messages" refer to motivational messages that are generated and delivered via voice while the user is exercising.

[0688] "Emotional analysis means" refers to a device that analyzes a user's facial expressions and voice to recognize their emotional state and then provides an appropriate message based on that analysis.

[0689] "Activity data" refers to data about a user's exercise recorded during or after running.

[0690] "Geographic data" refers to data that provides information related to maps and topography.

[0691] "Real-time travel information" refers to instantaneous data on current traffic conditions and travel patterns.

[0692] To implement this invention, the user must first input setting information such as target pace and running distance into the terminal using an information input means. After collecting this information, the terminal transmits it to the server. The server selects the optimal running route based on the user's current location. This involves considering congestion and traffic light information using geographic data and real-time movement information.

[0693] Next, the server sends the selected running route back to the terminal. The terminal presents this information to the user visually or audibly. During the run, emotion analysis is used to recognize the user's emotions in real time from their voice and facial expressions, and this information, along with pace information, is sent to the server.

[0694] The server analyzes the collected emotional data and uses a generative AI model to generate encouraging voice messages. These messages are then delivered to the user via their device, allowing them to receive personalized mental support while running.

[0695] Furthermore, after the run is completed, the server collects and analyzes the user's activity data. The analysis results are sent to the device as feedback for the next run, and the user can receive suggested improvements and stress reduction measures. This enhances the user's running experience.

[0696] As a concrete example, consider a scenario where a user aims to run 5km in a park in 30 minutes, receiving real-time encouragement and feedback via their device while running. In this case, the prompt message would be: "I would like advice to help me run. Based on the user's emotional data, please tell me what kind of encouragement I should provide in real time."

[0697] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0698] Step 1:

[0699] The user enters their target running pace and distance using an information entry tool on their device. The entered information is organized into a database format and sent from the device to the server. The server receives this information and prepares for processing in the next step.

[0700] Step 2:

[0701] The server receives the user's location information and analyzes congestion levels and traffic light locations by referencing geographical data and real-time movement information. This analysis uses location data to execute an algorithm that helps the user select the optimal route, determining the most suitable travel path. This result is then transmitted to the terminal.

[0702] Step 3:

[0703] The user receives route information presented by the device visually or audibly. As the run begins, the sentiment analysis system activates, collecting the user's emotional data in real time through the camera and microphone. This data is immediately transmitted to the server.

[0704] Step 4:

[0705] The server analyzes emotional data and uses a generative AI model to generate appropriate encouraging voice messages for the user. Emotional data is used as input, and the generated encouraging message is sent to the device as output. The device provides this voice message to the user to maintain motivation while running.

[0706] Step 5:

[0707] Once the run is complete, the server compiles the user's activity data and performs analysis for the next run. This analysis generates feedback that incorporates the user's pace and emotional changes. The feedback information obtained from the analysis is sent to the device and presented to the user.

[0708] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0709] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0711] [Fourth Embodiment]

[0712] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0713] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0714] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0716] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0718] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0719] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0720] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0721] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0722] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0723] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0724] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] This invention relates to a system that suggests the optimal route for achieving a user-set running goal and provides feedback during and after the run. The system provides services to the user in the following steps:

[0726] First, the user enters information such as their target pace, age, gender, and running experience. The device then sends this information about the user's target pace to the server.

[0727] Next, the server uses map data and real-time GPS data to retrieve multiple running routes in the vicinity based on the user's location. The server analyzes congestion levels and traffic light locations along these routes and selects the route best suited to the user's target pace. This selected route information is then displayed to the user via their device.

[0728] During a run, the device monitors the user's pace in real time and sends the measured data to a server. The server analyzes this data and, if the user is deviating from their target pace, generates an encouraging message and sends it to the device. The device then notifies the user of the message via voice or text to support their activity.

[0729] Once the run is complete, the server compiles all activity data, calculating calories burned, average pace, distance covered, and more. This data is sent to the user's device and provided as feedback, including advice and suggestions for improvement for their next run.

[0730] For example, if a user aims to run 5km in under 30 minutes, the system will suggest a flat, uncongested route with few traffic lights. Furthermore, if the user's pace slows during the run, it will notify them with a message like, "Keep going, almost there!" to maintain their motivation. After the run, it will provide feedback evaluating the user's progress, including whether they burned 500 calories. In this way, the system is designed to help users effectively achieve their goals.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The user launches the application and logs into their account. If it's their first time using the service, they will create an account by entering the required information (name, age, gender, running experience, target pace, etc.) on the registration screen.

[0734] Step 2:

[0735] The device sends the user's set target pace and running objectives (e.g., distance, time, calories burned) to the server.

[0736] Step 3:

[0737] The server uses the user's location information to retrieve nearby running routes from a map database. Furthermore, it uses real-time GPS data to capture congestion information, hills, and traffic light locations for each route.

[0738] Step 4:

[0739] The server analyzes the acquired data and selects the running route best suited to the user's target pace. This selection takes into account factors such as the flatness of the route, the number of traffic lights, and the level of congestion.

[0740] Step 5:

[0741] The server sends detailed information (map, distance, elevation) of the selected optimal running route to the terminal.

[0742] Step 6:

[0743] The device displays the received route information to the user and prompts them to start running.

[0744] Step 7:

[0745] The user checks the displayed route and starts running.

[0746] Step 8:

[0747] While running, the device uses its built-in sensors to measure the user's real-time pace, distance, and time, and sends that data to a server.

[0748] Step 9:

[0749] The server analyzes the received pace data to check if the user is maintaining their target pace. If the user is falling behind the target pace, it generates an encouragement message.

[0750] Step 10:

[0751] The device notifies the user of encouraging messages received from the server. Motivation is supported through voice output or text messages.

[0752] Step 11:

[0753] After the run is finished, the server compiles all the activity data and calculates calories burned, average pace, distance run, and more.

[0754] Step 12:

[0755] The server sends the aggregated data to the terminal and displays feedback to the user as a result of their run.

[0756] Step 13:

[0757] The device provides users with detailed feedback, including advice and suggestions for improvement for future use.

[0758] (Example 1)

[0759] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Conventional exercise support systems have struggled to provide feedback optimized for each user's goals and environment. Furthermore, they often failed to adequately maintain consistent motivation in users during and after exercise. Additionally, they were insufficient in selecting optimal routes that took into account real-time congestion and traffic signals. This system aims to address these challenges.

[0761] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0762] In this invention, the server includes data input means for the user to set a target speed for exercise, means for acquiring location information of multiple exercise routes and information on traffic control devices in the user's surrounding area, and means for analyzing the congestion status of the multiple exercise routes based on location measurement data. This makes it possible to propose individually optimized exercise routes to the user and provide motivation during exercise.

[0763] A "data input method" is an interface that allows users to input their target exercise speed and related information into a digital device.

[0764] A "walking route" is a geographical path selected by the user to perform a specified exercise activity.

[0765] "Location measurement data" refers to data that provides real-time geographical information about users and objects.

[0766] A "traffic control device" is a traffic signal or other control device used in roads or public transportation systems to manage movement.

[0767] "Generative artificial intelligence" refers to an algorithm or system that has the ability to automatically generate new messages or data according to specific conditions.

[0768] An "encouragement message" is a message of audio or text information provided to encourage or motivate a user's actions.

[0769] "Voice output" is a technology that converts digital information into audio and conveys it to the user.

[0770] "Text information output" refers to a format in which information is displayed as text on a screen to visually convey information to the user.

[0771] This invention is a system that proposes the optimal running route to help an athlete achieve a set speed goal, and provides encouragement and feedback during and after the exercise. The system consists of a network-connectable electronic device (hereinafter referred to as "terminal") and a central processing system (hereinafter referred to as "server") that communicates with the terminal.

[0772] First, the user enters information such as their target exercise speed, age, gender, and past exercise experience into the device. This information is entered using a mobile device such as a smartphone or tablet, via a dedicated application installed on that device.

[0773] The terminal sends user input information to the server. This communication utilizes an internet connection, and the data uses a secure protocol (e.g., HTTPS).

[0774] Based on information received from the user, the server uses a map database and real-time location data to obtain a suitable route for the surrounding area. The map database uses a general-purpose geographic information platform (e.g., OpenStreetMap API), and real-time traffic information is also obtained from a similar API. Based on this, the server generates an optimal route that takes into account the congestion status of the route, the location of traffic control devices, and the terrain.

[0775] During exercise, the device monitors the user's pace in real time. This monitoring utilizes the device's built-in GPS sensor and accelerometer. The measured data is then transmitted to a server.

[0776] The server analyzes whether the user is maintaining their target speed during exercise. Using a generative artificial intelligence model, it generates personalized motivational messages for each user. These messages correspond to the input prompts, for example, "Keep it up, almost there!" These messages are sent to the device and provided to the user via voice or text.

[0777] After the exercise is finished, the server compiles all activity data and calculates energy expenditure, average speed, total distance, etc. This analysis is then used to generate advice for the next exercise session, which is then fed back to the user via their device. For example, it might provide specific advice such as, "You burned 500 calories today. Next time, try to maintain a steady pace and cover a slightly longer distance."

[0778] Specific examples of prompt statements are as follows:

[0779] "The user's goal is to run 5km in under 30 minutes. Suggest an optimal route, monitor the user's pace during the exercise, and generate encouraging messages if they deviate from the target speed. After the exercise, provide feedback on calories burned and an evaluation of the achieved goal."

[0780] This invention allows users to receive support optimized for their individual conditions, thereby enhancing the effectiveness of their exercise.

[0781] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0782] Step 1:

[0783] Entering user information

[0784] Users enter personal data such as exercise goals, age, gender, and running experience into their device. Based on this input, a dedicated application formats it as structured data and prepares to send it to the server.

[0785] Step 2:

[0786] Sending user data

[0787] The terminal sends data obtained from the user to the server. To do this, the terminal uses an encryption protocol (e.g., HTTPS) to transfer the data, ensuring that personal information is transmitted securely. The output is the user's target information received on the server side.

[0788] Step 3:

[0789] Location information acquisition and route calculation

[0790] The server receives the user's location information and uses a map database and real-time location data to determine the surrounding travel route. Based on the input data, the server uses an algorithm to analyze congestion levels, traffic light locations, and other factors, and calculates the optimal route. The output generated by this process is the proposed optimal route information.

[0791] Step 4:

[0792] Real-time monitoring during running

[0793] The terminal monitors the user's pace in real time using GPS and accelerometer sensors within the device. It processes the sensor data received as input and periodically sends the results to the server. The output is real-time pace information.

[0794] Step 5:

[0795] Data analysis and feedback generation

[0796] The server verifies whether the user is achieving their target speed by comparing and analyzing the received pace with target data. Using a generative AI model, it dynamically generates messages based on prompts that produce motivational messages tailored to the user. The output is the generated motivational message.

[0797] Step 6:

[0798] Data collection and feedback after exercise

[0799] The server aggregates all of the user's exercise data and analyzes the results. It calculates energy consumption, average speed, distance traveled, etc., and generates feedback along with advice for the next time. This result is sent to the terminal, and the output is detailed feedback information for the user.

[0800] This allows users to receive continuous support and opportunities for improvement both during and after exercise.

[0801] (Application Example 1)

[0802] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0803] In modern society, many individuals engage in regular exercise to maintain their health and improve their physical fitness, but there is a need for appropriate support to make these activities more effective. In particular, the lack of a system that can provide the selection of the optimal exercise route, real-time feedback, and effective post-exercise advice tailored to each individual user is a problem.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0805] In this invention, the server includes means for inputting information for the user to set a target pace for land-based exercise, means for acquiring location information and road signal information for multiple exercise routes in the user's surrounding area, and means for monitoring pace and heart rate through a home mobile device and providing immediate voice feedback. This enables the user to receive exercise planning optimized to their individual goals and real-time feedback.

[0806] A "user" refers to an individual who uses the system to perform track and field exercises.

[0807] "Track and field exercise" refers to physical activities performed on the ground, such as running and walking.

[0808] "Target pace" refers to the target speed that a user sets when exercising.

[0809] "Information entry method" refers to the interface used by users to input exercise goals and personal information.

[0810] "Surrounding area" refers to the area surrounding the place where the user exercises.

[0811] "Exercise path" refers to the route selected for performing the exercise specified by the user.

[0812] "Location data" refers to data that indicates geographical location information.

[0813] "Traffic signals" refer to signaling devices installed to regulate the flow of traffic.

[0814] "Home-use mobile devices" refer to mobile devices or robots that can be used at home to support exercise.

[0815] "Real-time feedback" refers to information that is instantly provided during exercise based on the situation at that moment.

[0816] "Physical activity data" refers to all data related to physical activity obtained during and after exercise.

[0817] This invention is a system that utilizes a mobile home device to help users perform land-based exercises more effectively. The implementation of this system primarily requires the cooperation of a server, terminals, and a mobile home device.

[0818] The server receives information provided by the user, such as target pace and basic personal profile. Next, the server uses map data and real-time road information to select the optimal exercise route for the user. This route selection process includes analysis of location data and consideration of traffic signal placement. The server then presents the selected exercise route to the user via a home mobile device.

[0819] The home mobile device monitors the user's exercise pace and physical data such as heart rate in real time. This allows the user to check if they are maintaining their target pace and provides voice or text feedback as needed. The feedback from the home mobile device helps the user understand their exercise status in real time, ultimately improving the quality of their workout.

[0820] After exercise, the device receives physical activity data compiled from the server and provides feedback to the user. This feedback includes information on calories burned, average pace, and distance covered. Advice for the next exercise session is also provided. In this process, the server performs a detailed analysis of the exercise's success and areas for improvement, helping the user to consistently achieve their goals.

[0821] For example, if a user wants to take a 20-minute walk near their home, the system will suggest the optimal route that avoids congestion and has fewer traffic lights. During the walk, it will provide verbal encouragement such as, "Your pace is good, keep it up!" After the walk, it will display exercise statistics and suggest ways to further improve.

[0822] An example of a prompt message might be: "Use OpenCV and real-time location data to implement an optimal route and feedback system for home-use mobile devices during exercise."

[0823] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0824] Step 1:

[0825] The device receives the user's target exercise pace and personal profile information (age, gender, running experience, etc.) as input. This input data is formatted and sent to the server. The server then receives the user's detailed goal settings and stores them in its database.

[0826] Step 2:

[0827] The server takes the user's location information as input and uses map data and real-time traffic information to search for nearby exercise routes. Considering the location data, traffic light placement, and congestion levels, it selects the optimal route for the user's target pace. The selected route information is then output to a home mobile device.

[0828] Step 3:

[0829] The home mobile device monitors the user's pace and heart rate in real time using sensors, based on the optimal route received from the server. This monitoring data is analyzed as input to determine if the user is maintaining their target pace. If necessary, it outputs encouraging voice messages to provide immediate feedback.

[0830] Step 4:

[0831] The server collects real-time user data transmitted from the home mobile device during exercise and performs data analysis. Using the results of this analysis, it generates specific advice for the user to achieve their exercise goals and suggestions for improvement for their next exercise session, and outputs them to the terminal.

[0832] Step 5:

[0833] The device receives aggregated data from the server after the exercise session ends. This includes calories burned, average pace, and distance covered. This information is compiled and provided to the user as feedback. This helps users review past exercise statistics and use them to plan their next workout.

[0834] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0835] This invention relates to a system that suggests the optimal route for achieving user-defined running goals and recognizes the user's emotions during the run to provide a comfortable running experience. In addition, it analyzes the user's emotional data and provides feedback for use in future runs.

[0836] First, the user launches the application and enters information such as their target running pace. The device then sends this data to the server and prepares for the run.

[0837] Next, the server uses the user's location information to obtain information about surrounding running routes from map data and real-time traffic information, and analyzes congestion levels and traffic light locations. At this time, it selects the optimal running route considering the user's target pace and sends it to the terminal.

[0838] During a run, the system recognizes the user's emotions in real time, using voice and camera data in addition to their pace. The server analyzes this data and generates encouraging messages that match the user's emotions. For example, if the emotion engine detects that the user is feeling tired, it will send an encouraging message from the device such as, "Just a little further, keep going!"

[0839] Once the run is complete, the server compiles activity and emotional data from the entire run, analyzing changes in emotions in addition to calories burned and average pace. These results are sent to the device as feedback for the next run. Based on this, users can receive suggestions for improvement and recommendations that take their emotional changes into account.

[0840] For example, if a user aims to run 5km in 30 minutes, the system will help them complete their run according to plan by providing the optimal route and appropriate encouragement. Post-run feedback will include appropriate rest recommendations if fatigue is observed, and stress reduction strategies for the next run. In this way, the system comprehensively supports the user's running experience.

[0841] The following describes the processing flow.

[0842] Step 1:

[0843] After launching the running app and logging in, the user enters their target pace, distance, and other information.

[0844] Step 2:

[0845] The device begins preparing for the run by sending the user's target pace and other information to the server.

[0846] Step 3:

[0847] The server obtains the user's current location and analyzes congestion levels and traffic light locations for each route based on map data of surrounding running routes and real-time traffic information.

[0848] Step 4:

[0849] Based on the analysis results, the server selects the optimal running route for the user's target pace and sends that information to the terminal.

[0850] Step 5:

[0851] The device displays the user the optimal running route it has received and prompts them to start running.

[0852] Step 6:

[0853] During running, the device uses its built-in sensors to measure the user's pace data in real time and transmit it to the server.

[0854] Step 7:

[0855] Furthermore, the device uses microphones and cameras to collect the user's voice and facial expressions and transmits them to the emotion engine.

[0856] Step 8:

[0857] The server receives pace and emotion data transmitted during running and analyzes the user's ability to maintain their target pace and their emotional state.

[0858] Step 9:

[0859] Based on the analysis results, the server generates an encouraging message if the user appears unwell or fatigued.

[0860] Step 10:

[0861] The device notifies the user of encouraging messages sent from the server, either as voice or text, to boost their motivation.

[0862] Step 11:

[0863] After the run is finished, the server compiles all activity and emotional data. It calculates calories burned, average pace, and changes in emotions.

[0864] Step 12:

[0865] The server sends the calculated data to the terminal as feedback, displaying advice and suggestions for improvement for the next run to the user.

[0866] Step 13:

[0867] The device provides users with stress reduction strategies and emotional management advice based on their feedback.

[0868] (Example 2)

[0869] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0870] Modern exercise support systems lack sufficient means to select routes that match the user's goals and to provide appropriate real-time feedback. Furthermore, providing encouraging messages that take into account the user's emotional changes during exercise is difficult, highlighting the need for improved exercise experiences. As a result, there is a lack of effective support for users to achieve their exercise goals.

[0871] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0872] In this invention, the server includes means for acquiring location information and traffic signal information for multiple travel routes in the user's surrounding area, means for analyzing the congestion status of the multiple travel routes based on location measurement data, and means for recognizing the user's emotional state in real time using an emotion analysis engine and generating encouraging messages based on the user's emotions. This enables route selection tailored to the user's goals and real-time feedback based on emotion recognition.

[0873] "Target exercise speed" refers to the target speed that a user sets when exercising, and is information that serves as a basis for exercise planning and performance evaluation.

[0874] "Information input means" refers to an interface or device for users to input their target speed and distance for exercise.

[0875] "Location information" refers to geographical location data used to select a movement route, providing detailed route information within a specific area.

[0876] "Location measurement data" refers to positioning information about a user's current location and movement, and is a collection of data used for analyzing congestion levels and guiding routes.

[0877] An "emotion analysis engine" refers to an analysis tool or algorithm that identifies and processes emotions from a user's voice and facial expressions.

[0878] A "message of encouragement" is a message containing motivation and instructions provided to help users achieve their goals.

[0879] "Activity data" refers to information related to exercise, such as the user's speed, distance, and energy expenditure, recorded during exercise.

[0880] "Feedback" refers to engaging advice and analysis results provided to users after exercise, intended to help them improve their next workout.

[0881] This system provides real-time optimal route selection and emotion-based feedback to improve the user's exercise experience. The embodiments of the invention are described below.

[0882] Before exercising, users launch a dedicated application on their device and set their target speed and distance. This information is entered via an information entry system. The information set by the user is transmitted to the server using a secure communication method.

[0883] Based on the received target information, the server uses location services (e.g., geographic information APIs) to obtain multiple travel routes in the user's surrounding area. The server then uses the location measurement data to analyze the degree of congestion and the location of traffic lights.

[0884] Next, the server uses a generative AI model to select the optimal movement path based on the user's target speed. The selected path is returned to the terminal in real time, and the user can receive navigation on the terminal.

[0885] During exercise, the device uses a microphone and camera to collect the user's voice and video data. This data is sent to a server, which uses an emotion analysis engine to analyze the user's emotional state. The server generates and sends encouraging messages to the device based on the user's emotions. For example, if fatigue is detected, it will generate a message such as, "Just a little further, keep going!"

[0886] After the workout, the server collects the user's activity and emotional data, generates feedback for the next workout, and sends it to the device. This feedback helps the user understand how to improve their next workout and provides guidance for a better exercise experience.

[0887] For example, if a user sets a goal like "run 5km in 30 minutes," the server will provide less congested routes and send appropriate encouraging messages when the user feels fatigued, supporting them in achieving their goal. Another example of a prompt message would be, "Please give me advice on how to improve my pace in my next workout."

[0888] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0889] Step 1:

[0890] The user launches the application on their device and enters information such as their target speed and distance for exercise. This input data is collected through an information entry interface and transmitted to the server using a secure communication channel. As a concrete example of input data, a target of "running 5km in 30 minutes" is set.

[0891] Step 2:

[0892] The server analyzes the received target information and, based on that, calls a geographic information service API to obtain geographic information of the surrounding area. The input data is the user's target setting information, and the output is geographic information necessary for selecting a travel route. The server uses the location measurement data to identify multiple valid routes and obtain detailed information, including congestion levels and the locations of traffic lights.

[0893] Step 3:

[0894] The server analyzes the congestion status of multiple travel routes based on acquired geographic information. Specifically, it integrates location measurement data and real-time traffic information to predict congestion for each route. It takes multiple route information as input and provides optimized travel route suggestions as output.

[0895] Step 4:

[0896] The server uses a generated AI model to select the optimal movement path based on the user's target speed and sends it to the terminal. The server generates prompt messages, which are executed by the AI ​​model to evaluate and determine the optimal path. The selected movement path and its detailed information are provided to the terminal as output data.

[0897] Step 5:

[0898] During exercise, the device continuously records the user's voice and video data using its microphone and camera. The input data consists of the user's biosensors and voice / video feeds, which are sent to a server and used for sentiment analysis.

[0899] Step 6:

[0900] The server uses the received audio and video data to activate an emotion analysis engine, determining the user's emotional state in real time. Based on the data analysis, the user's emotions are detected, leading to the generation of appropriate encouraging messages.

[0901] Step 7:

[0902] The server uses a generative AI model to generate and send encouraging messages to the user's device based on real-time determined emotion data. The output data consists of encouraging messages delivered in both voice and text formats. For example, a message such as "Just a little further, keep going!" might be sent.

[0903] Step 8:

[0904] After the exercise session, the server integrates the activity and emotional data collected during the workout to generate feedback for the next session. The input data consists of a record of the entire workout and the user's emotional changes. The output provides improvement advice and sends it back to the device.

[0905] Step 9:

[0906] Users receive feedback through their devices, which they use to improve their approach to their next workout. Specific suggestions and recommendations are provided to support the user's long-term exercise plan.

[0907] (Application Example 2)

[0908] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0909] In modern society, maintaining health through exercise such as running is important. However, selecting the optimal route for running and maintaining motivation during exercise are not easy. In particular, there is a lack of comprehensive support, including mental support, during running, which means that users do not receive sufficient support to achieve their goals.

[0910] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0911] In this invention, the server includes means for inputting information for the user to set a target running pace, means for acquiring location information and control device information for multiple running routes in the surrounding area, and means for recognizing the user's emotions in real time using emotion analysis means and generating encouraging messages that match those emotions. This enables the user to receive guidance on the optimal route and encouragement that corresponds to their emotions while running, providing the necessary support to achieve their goals.

[0912] An "information entry device" refers to a device that allows users to input their target running pace and related data.

[0913] "Location information" refers to data used to identify the geographical location of a user.

[0914] "Control device information" refers to data that provides information about traffic signals and other traffic control systems.

[0915] A "congestion status analysis device" refers to a device that uses location measurement data to analyze the congestion status of multiple running routes.

[0916] "Real-time pace information" refers to instantaneous data on a user's speed and tempo while running.

[0917] "Encouraging voice messages" refer to motivational messages that are generated and delivered via voice while the user is exercising.

[0918] "Emotional analysis means" refers to a device that analyzes a user's facial expressions and voice to recognize their emotional state and then provides an appropriate message based on that analysis.

[0919] "Activity data" refers to data about a user's exercise recorded during or after running.

[0920] "Geographic data" refers to data that provides information related to maps and topography.

[0921] "Real-time travel information" refers to instantaneous data on current traffic conditions and travel patterns.

[0922] To implement this invention, the user must first input setting information such as target pace and running distance into the terminal using an information input means. After collecting this information, the terminal transmits it to the server. The server selects the optimal running route based on the user's current location. This involves considering congestion and traffic light information using geographic data and real-time movement information.

[0923] Next, the server sends the selected running route back to the terminal. The terminal presents this information to the user visually or audibly. During the run, emotion analysis is used to recognize the user's emotions in real time from their voice and facial expressions, and this information, along with pace information, is sent to the server.

[0924] The server analyzes the collected emotional data and uses a generative AI model to generate encouraging voice messages. These messages are then delivered to the user via their device, allowing them to receive personalized mental support while running.

[0925] Furthermore, after the run is completed, the server collects and analyzes the user's activity data. The analysis results are sent to the device as feedback for the next run, and the user can receive suggested improvements and stress reduction measures. This enhances the user's running experience.

[0926] As a concrete example, consider a scenario where a user aims to run 5km in a park in 30 minutes, receiving real-time encouragement and feedback via their device while running. In this case, the prompt message would be: "I would like advice to help me run. Based on the user's emotional data, please tell me what kind of encouragement I should provide in real time."

[0927] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0928] Step 1:

[0929] The user enters their target running pace and distance using an information entry tool on their device. The entered information is organized into a database format and sent from the device to the server. The server receives this information and prepares for processing in the next step.

[0930] Step 2:

[0931] The server receives the user's location information and analyzes congestion levels and traffic light locations by referencing geographical data and real-time movement information. This analysis uses location data to execute an algorithm that helps the user select the optimal route, determining the most suitable travel path. This result is then transmitted to the terminal.

[0932] Step 3:

[0933] The user receives route information presented by the device visually or audibly. As the run begins, the sentiment analysis system activates, collecting the user's emotional data in real time through the camera and microphone. This data is immediately transmitted to the server.

[0934] Step 4:

[0935] The server analyzes emotional data and uses a generative AI model to generate appropriate encouraging voice messages for the user. Emotional data is used as input, and the generated encouraging message is sent to the device as output. The device provides this voice message to the user to maintain motivation while running.

[0936] Step 5:

[0937] Once the run is complete, the server compiles the user's activity data and performs analysis for the next run. This analysis generates feedback that incorporates the user's pace and emotional changes. The feedback information obtained from the analysis is sent to the device and presented to the user.

[0938] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0939] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0940] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0941] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0942] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0943] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0944] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0945] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0946] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0947] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0948] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0949] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0950] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0951] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0952] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0953] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0954] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0955] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0956] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0957] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0958] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0959] The following is further disclosed regarding the embodiments described above.

[0960] (Claim 1)

[0961] A means for users to enter information to set their target running pace,

[0962] A means for acquiring location information and traffic signal information for multiple running routes in the user's surrounding area,

[0963] A method for analyzing congestion levels on multiple running routes based on GPS data,

[0964] A means of selecting and suggesting the optimal running route based on the user's target pace,

[0965] A means for collecting real-time pace information from a user during running, and for generating and sending encouraging messages to help them achieve their goals,

[0966] A means of collecting user activity data after a run and providing analysis results and feedback for the next run,

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, wherein the encouraging message is provided either by voice output or text message output.

[0970] (Claim 3)

[0971] The system according to claim 1, wherein the location information of the plurality of running routes is generated based on map data and real-time traffic information.

[0972] "Example 1"

[0973] (Claim 1)

[0974] A data input means for the user to set a target speed for exercise,

[0975] Means for acquiring location information of multiple motion paths in the user's surrounding area and information regarding traffic control devices,

[0976] A means for analyzing congestion levels of multiple movement paths based on location measurement data,

[0977] A means for selecting and presenting the optimal movement path based on the user's target speed,

[0978] A means for collecting real-time measurement information of the user during exercise, and for generating and sending motivational messages to help achieve goals,

[0979] A means of collecting user activity data after exercise and providing analysis results and advice for the next time,

[0980] A means of automatically generating messages according to specific user conditions using generative artificial intelligence,

[0981] A system that includes this.

[0982] (Claim 2)

[0983] The system according to claim 1, wherein the aforementioned motivational message is provided by either voice output or text information output.

[0984] (Claim 3)

[0985] The system according to claim 1, wherein the location information of the multiple movement paths is generated based on map data and real-time traffic information and selected to best suit the environmental conditions set by the user.

[0986] "Application Example 1"

[0987] (Claim 1)

[0988] A means for users to enter information to set a target pace for track and field exercises,

[0989] A means for acquiring location information of multiple travel routes and information on road signals in the user's surrounding area,

[0990] A means for analyzing congestion levels of multiple travel routes based on location data,

[0991] A means of selecting and suggesting the optimal exercise path based on the user's target pace,

[0992] A means for collecting real-time pace information from a user during exercise, and for generating and sending improvement messages to help achieve goals,

[0993] A means of collecting user physical activity data after exercise and providing analysis results and feedback for the next time,

[0994] A means of monitoring pace and heart rate through a home mobile device and providing immediate voice feedback,

[0995] A display system that shows exercise statistics data and provides advice for future exercise,

[0996] A system that includes this.

[0997] (Claim 2)

[0998] The system according to claim 1, wherein the improvement message is provided by either voice output or text information output.

[0999] (Claim 3)

[1000] The system according to claim 1, wherein the location information of the plurality of movement paths is generated based on map data and real-time road information.

[1001] "Example 2 of combining an emotion engine"

[1002] (Claim 1)

[1003] A means for users to enter information to set their target speed for exercise,

[1004] A means for acquiring location information of multiple movement paths and information about traffic signals in the user's surrounding area,

[1005] A means for analyzing congestion levels of multiple movement paths based on location measurement data,

[1006] A means for selecting and proposing the optimal movement path based on the user's target speed,

[1007] A means for collecting real-time speed information of a user during exercise, and for generating and sending encouraging messages to help them achieve their goals,

[1008] A means for recognizing the user's emotional state during exercise in real time using an emotion analysis engine and generating encouraging messages based on the user's emotions,

[1009] A means of collecting user activity and emotional data after exercise, and providing analysis results and feedback for the next time,

[1010] A system that includes this.

[1011] (Claim 2)

[1012] The system according to claim 1, wherein the aforementioned support message is provided either by voice output or text information output.

[1013] (Claim 3)

[1014] The system according to claim 1, wherein the location information of the plurality of movement paths is generated based on geographic information data and real-time traffic information.

[1015] "Application example 2 when combining with an emotional engine"

[1016] (Claim 1)

[1017] A means for users to enter information to set their target running pace,

[1018] A means for acquiring location information and control device information for multiple running routes in the user's surrounding area,

[1019] A means of analyzing congestion levels on multiple running routes based on location measurement data,

[1020] A means of selecting and suggesting the optimal running route based on the user's target pace,

[1021] A means for collecting real-time pace information from a user during running, and for generating and transmitting encouraging voice messages to help them achieve their goals,

[1022] A means for recognizing a user's emotions in real time using emotion analysis tools and generating a message of support that matches those emotions,

[1023] A means of collecting user activity data after a run and providing analysis results and feedback for the next run,

[1024] A system that includes this.

[1025] (Claim 2)

[1026] The system according to claim 1, wherein the aforementioned encouraging voice is provided by either voice output or text message output.

[1027] (Claim 3)

[1028] The system according to claim 1, wherein the location information of the plurality of running paths is generated based on geographic data and real-time movement information. [Explanation of Symbols]

[1029] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to enter information to set their target running pace, A means for acquiring location information and traffic signal information for multiple running routes in the user's surrounding area, A method for analyzing congestion levels on multiple running routes based on GPS data, A means of selecting and suggesting the optimal running route based on the user's target pace, A means for collecting real-time pace information from a user during running, and for generating and sending encouraging messages to help them achieve their goals, A means of collecting user activity data after a run and providing analysis results and feedback for the next run, A system that includes this.

2. The system according to claim 1, wherein the aforementioned encouraging message is provided by either voice output or text message output.

3. The system according to claim 1, wherein the location information of the plurality of running routes is generated based on map data and real-time traffic information.

Images