system

Abstract

We provide the system. [Solution] An input means for the user to input target driving parameters, A means for obtaining location information, An analysis means analyzes the data collected by the aforementioned location identification means and calculates the optimal driving route for a target set by the user, A display means for visually displaying the driving route calculated by the analysis means, A notification system that tracks the user's real-time running status and motivates the user if their running pace does not reach their target, 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 method for controlling a persona chatbot, which is 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional running support system, it is difficult for a user to maintain a pace during running, and pace fluctuations are likely to occur due to external factors such as signals and slopes, making it difficult to obtain an ideal training effect. Also, there is a problem that running is difficult to continue because an optimal route according to each user's goal and environment cannot be provided. To solve such problems, there is a need to provide a technology for selecting a running route that matches the user's goal, managing the running pace in real time, and maintaining appropriate motivation.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system comprising an input means for the user to input target running parameters, a location identification means for acquiring location information, an analysis means for analyzing the collected data and calculating the optimal running route for the target, a display means for displaying the calculated running route to the user, and a notification means for tracking the user's running status and encouraging the user by voice when the running pace falls below the target. This system minimizes obstacles by selecting a running route using environmental data, enabling the user to run while effectively maintaining their pace.

[0006] A "user" is an individual who uses a running support system to achieve their target run.

[0007] "Target running parameters" are specific target values ​​related to running, such as pace, distance, and time, that the user wants to achieve.

[0008] An "input means" is an interface that allows the user to input target driving parameters into the system.

[0009] "Location determination means" refers to methods using technologies such as GPS to determine the user's current location and travel route.

[0010] "Analysis means" refers to a method for calculating the optimal driving route for the user using acquired location information and environmental data.

[0011] A "display means" is a device that visually displays the driving route information calculated by the analysis means to the user.

[0012] "Notification methods" refer to features that provide users with messages to maintain their pace through voice or other means while they are riding.

[0013] "Environmental data" is a general term for data acquired as surrounding information related to the driving route, such as road gradient, traffic light locations, and the degree of crowding. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system designed to help users maintain their target pace while running. First, the user launches a running support app and inputs a specific goal as a target running parameter, such as "run 5km in 30 minutes." The device receives this information via its built-in input device. Additional requirements, such as avoiding hills or minimizing traffic lights, can also be input at this time.

[0036] Next, the device uses GPS-based location tracking to collect the user's current location and surrounding route data. The device then obtains environmental data such as road shape, gradient, traffic light locations, and pedestrian congestion via a map service API. After that, the device sends this information to the server.

[0037] Based on this data, the server calculates the optimal route to help the user achieve their goals. The server then sends the calculated optimal route information back to the terminal. The terminal uses the received information to display the optimal route on a map, allowing the user to receive route guidance that is easy to understand visually on the spot.

[0038] During a run, the device monitors the user's pace and location in real time. If the pace is about to fall below the entered target, it sends a voice message via a notification system. Encouraging voice messages such as "Great pace, keep it up!" or "Almost there!" help maintain the user's motivation. This system allows users to train efficiently and effectively towards achieving their goals.

[0039] After the run is completed, the server analyzes the user's running data and generates feedback such as calories burned, average speed, and achievement level. The terminal displays this feedback information to the user, providing data that will be useful for future runs. Throughout the entire invention, users are supported in achieving their ideal running lifestyle through an experience optimized to their own pace.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches the running support app and enters their target running parameters. They can set goals such as "run 5km in 30 minutes" or additional requirements like "avoid traffic lights."

[0043] Step 2:

[0044] The terminal saves the target driving parameters entered by the user. (1)

[0045] Step 3:

[0046] The device uses GPS to determine the user's current location. Simultaneously, it uses a map service API to acquire environmental data such as surrounding geographical information, road gradients, traffic light locations, and pedestrian congestion levels.

[0047] Step 4:

[0048] The device sends the collected information to the server. This data includes the user's current location and surrounding environment data.

[0049] Step 5:

[0050] The server analyzes the received environmental data and the user's target driving parameters to calculate the optimal driving route. In this process, it prioritizes routes with fewer traffic lights and fewer hills.

[0051] Step 6:

[0052] The server returns optimized route information to the terminal as route guidance.

[0053] Step 7:

[0054] The device displays the optimal route information it receives to the user. It shows the route on a map and displays important points such as traffic lights and hills with icons.

[0055] Step 8:

[0056] The user begins running according to the provided route guidance.

[0057] Step 9:

[0058] The device monitors the user's current location and pace in real time while they are running.

[0059] Step 10:

[0060] The device measures the user's pace, and if it determines that the user is falling below the entered target pace, it sends a voice message to the user via a notification system. This message encourages the user and helps them maintain their goal.

[0061] Step 11:

[0062] After the run is finished, the device sends the run log to the server.

[0063] Step 12:

[0064] The server analyzes the running logs and generates feedback such as calorie consumption, average speed, and goal achievement.

[0065] Step 13:

[0066] The device visualizes and displays the generated feedback to the user, providing reference information for their next run.

[0067] (Example 1)

[0068] 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."

[0069] Modern exercise support devices are insufficient in providing efficient and encouraging support to users towards their goals. In particular, current systems lack real-time feedback and optimization that takes environmental information into account, failing to provide adequate support for effectively achieving user-set goals. This situation needs improvement, and a more effective and motivating exercise support environment for users is required.

[0070] 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.

[0071] In this invention, the server includes an input means for the user to input target running specifications, a position measurement means for acquiring location information, and a calculation means for analyzing surrounding information collected using the position measurement means and calculating the optimal route to the target. This enables the user to select the optimal route utilizing environmental information and to effectively engage in activities toward achieving their goal while receiving real-time exercise support and feedback.

[0072] A "user" refers to an individual who uses this system to set exercise goals and engage in exercise activities.

[0073] "Target running specifications" refer to the target values ​​for exercise set by the user, and may include, for example, distance, time, or exercise efficiency under specific conditions.

[0074] "Input means" refers to a device or interface for the user to communicate target driving specifications or other driving conditions to the system.

[0075] "Location measurement means" refers to technologies and devices used to obtain the user's current location and geographical information of their surroundings, and generally includes location information technologies such as GPS.

[0076] "Surrounding area information" refers to geographical and environmental data of the area related to the exercise, including road information, traffic conditions, and weather conditions.

[0077] "Calculation means" refers to technology that calculates the optimal path and strategy to achieve the user's exercise goals based on the acquired information.

[0078] "Visual display means" refers to a device or interface that visually provides the user with information about the calculated optimal route and movement status.

[0079] A "notification method" is a function that provides notifications to the user, delivering information to the user in the form of audio or visual messages.

[0080] "Analysis means" refers to technology that analyzes the user's exercise data in detail after the exercise is completed and generates feedback regarding exercise efficiency and performance.

[0081] This invention constructs an exercise support system and is equipped with various functions to efficiently achieve user-set goals.

[0082] First, the user inputs their target running specifications using a device such as a mobile terminal. The terminal then receives the exercise goal information from the user through the input method. Here, a specific goal can be set, for example, "run 5km in 30 minutes." The input data is stored within the terminal and used for the next processing step.

[0083] Next, the device uses GPS, a location measurement tool, to obtain the user's current location. Furthermore, it uses a map service API (e.g., a general-purpose map software API) to collect surrounding information such as road information, traffic conditions, and weather information. This information is used by the calculation tool to calculate the optimal route to support the user in achieving their exercise goals.

[0084] The calculated results are displayed as map information on the terminal via a visual display device. Based on this information, the user can select the optimal exercise route and begin exercising.

[0085] During exercise, the device continuously monitors the user's exercise status in real time using notification methods. It also features a function to alert the user with an audio notification if the exercise pace deviates from the set target. This implementation helps users maintain an appropriate pace.

[0086] Once the exercise is complete, the device sends the running data to a server, where an analysis system evaluates it. It generates and provides feedback to the user regarding exercise efficiency, such as calories burned, exercise time, and average speed.

[0087] For example, if a user sets a goal of "running 5km in 30 minutes" and selects a safe route within a park, the device will select the optimal route based on real-time data and provide voice guidance such as "maintain your pace." In this way, users can continue exercising efficiently.

[0088] Examples of prompts for a generative AI model:

[0089] "If a user sets a goal of running 5km in 30 minutes, please explain the steps to suggest the optimal running route that avoids congestion."

[0090] This exercise support system allows users to engage in planned and effective activities toward achieving their goals.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] The user launches a running support app on their device and enters their target running specifications. The user enters a specific goal, such as "run 5km in 30 minutes." The input device receives this information and saves it as input data on the device. The output of this step is the data saved as the target running specifications.

[0094] Step 2:

[0095] The device obtains the user's current location using GPS. The location measurement device receives the current location information and stores it within the device as location data for future use. After the location information is determined, the device uses a map service API to collect surrounding road information, traffic conditions, and weather conditions. This environmental data becomes the input used in the next calculation step.

[0096] Step 3:

[0097] The terminal uses collected location and environmental data to calculate the optimal route for achieving the user's goal. The data processing performed here involves executing a route optimization algorithm that takes into account the user's goal and current environmental conditions. The output is the optimized route information. A server may also assist in this route calculation.

[0098] Step 4:

[0099] The terminal presents the calculated optimal route to the user using a visual display. The user can intuitively understand the route through a visual location display on a map. The output provides a display of the route the user should actually follow.

[0100] Step 5:

[0101] During running, the device monitors the user's location and pace in real time. Through this monitoring, if the user is about to deviate from their target pace, the notification system generates and provides a voice message to the user. Here, the user's pace data is used as input, and the notification message is output. The voice message provides encouragement such as, "Please maintain your pace."

[0102] Step 6:

[0103] After the exercise is completed, the device sends the user's running data to the server. The server uses analysis tools to evaluate this data and generates feedback such as calories burned, average speed, and goal achievement. As output, detailed feedback on the user's exercise efficiency is generated, which can be used to plan future exercise sessions.

[0104] (Application Example 1)

[0105] 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."

[0106] When running or walking, it is essential to provide users with the optimal course for efficient and safe running while maintaining their set target pace, and to maintain motivation during the run. However, conventional systems have difficulty flexibly responding to changes in the environment and individual goals, so further improvements are needed.

[0107] 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.

[0108] In this invention, the server includes an input means for the user to set target driving parameters, a location identification means for acquiring location information, and an analysis means for calculating the optimal driving route based on the collected environmental data. This enables the provision of an optimal driving route tailored to each user's individual goals and real-time pace management during driving.

[0109] A "user" is a person who uses this system to set and achieve their own running or walking goals.

[0110] "Target driving parameters" refer to specific numerical information such as the target time, distance, and pace set by the user for their driving.

[0111] "Input method" refers to an interface for the user to input target driving parameters, and usually refers to a smartphone or computer application.

[0112] "Location determination means" refers to the technology used to determine the user's current location, and generally uses GPS.

[0113] The "analysis means" is a calculation function that calculates the optimal driving route for achieving the user's goals based on collected location information and environmental data.

[0114] "Display means" refers to a method for visually showing the calculated driving route to the user, and typically involves using a display or map application.

[0115] The "notification method" is a function that monitors the user's real-time running pace and encourages the user with voice messages or text messages when that pace falls below the target.

[0116] "Environmental data" refers to data about the user's surroundings, including road shape, gradient, traffic signal information, and congestion levels.

[0117] This system provides users with the optimal running route to achieve their set running or walking goals and manages their pace in real time. Users first launch the application using a mobile device such as a smartphone and input their target running parameters. A touchscreen interface is used for input.

[0118] The device uses its built-in GPS module to determine the user's current location. Next, it collects surrounding environmental data via a map service API and sends it to the server. The server analyzes this data using programming languages ​​such as Python to calculate the optimal driving route. The calculated route information is returned to the device and displayed through the map application.

[0119] Once the user starts running, the device monitors their pace in real time. If the pace falls below the entered target, a notification system will encourage the user through a voice message. Voice messages such as "Almost there!" help maintain the pace and support the user's motivation.

[0120] Finally, after the run is completed, the server analyzes the running data in more detail and provides feedback to the user on calories burned, average speed, and achievement level. This allows the user to identify areas for further improvement and use them in their next run.

[0121] As a concrete example, there is a case where a user received a route suggestion that avoided crowded urban areas and went through a quiet park on a Sunday morning, allowing them to easily achieve their target pace. Behind the realization of such a system lies advanced analytical technology utilizing generative AI models. When using the generative AI model to input the optimal route as a prompt, a specific prompt statement is used: "Generate the optimal route that takes into account the real-time information of the surrounding area, in accordance with the goal set by the user."

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The terminal receives the application from the user and inputs target driving parameters. These parameters include target values ​​such as distance and pace. The terminal then receives this input data and prepares it for the next processing step.

[0125] Step 2:

[0126] The device uses its built-in GPS module to obtain the user's location information. This determines the user's current location and prepares it to collect environmental data about its surroundings.

[0127] Step 3:

[0128] The device collects surrounding environmental data through a map service API. This includes road shapes, traffic signal information, and congestion levels. The device then aggregates this data and generates data packets to send to the server.

[0129] Step 4:

[0130] The server receives data sent from the terminal and begins analyzing the data using an analysis program such as Python. Here, a generative AI model is used to calculate the optimal driving route. Specifically, based on the input target parameters and environmental data, the most efficient route for the user is derived. This calculation result is generated as route data.

[0131] Step 5:

[0132] The server returns the optimal route data, which is the result of its analysis, to the terminal. The terminal receives this data and displays the route visually through a map application. The user then refers to this and prepares to start running.

[0133] Step 6:

[0134] The device monitors the user's real-time pace while they are running. It continuously acquires the user's speed and location and compares it to the entered target pace. If the pace is about to fall below the target, the device uses a notification system to provide encouragement through voice messages.

[0135] Step 7:

[0136] After the ride is complete, the device sends the collected ride data to the server. The server uses this data to perform a detailed analysis and generates feedback that includes calories burned, average speed, and degree of goal achievement.

[0137] Step 8:

[0138] The server sends the generated feedback back to the terminal. The terminal can then display this feedback to the user, which can be used to help with future improvements and goal setting.

[0139] The above outlines the processing steps of the program required to implement this application example.

[0140] 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.

[0141] This invention is a running support system that enables users to run effectively, and in particular, it has a function to monitor and analyze the user's emotional state and maintain motivation. The user first launches a running app and inputs target running parameters (e.g., distance and pace). Based on this information, the device starts providing support in the direction desired by the user.

[0142] This system uses terminal location tracking to determine the user's location in real time and transmits it to a server along with surrounding environment data obtained using a map API. The server uses this information to analyze the optimal driving route, particularly minimizing obstacles such as traffic lights and congestion. The calculated route is sent back to the terminal, visualized on a map, and guided to the user.

[0143] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The terminal is equipped with sensors to read the user's voice and facial expressions, and the emotion engine uses this sensor information to estimate the user's emotions. For example, if it detects anxiety or fatigue, the server sends an appropriate encouraging message to the terminal that takes that state into account.

[0144] During a run, the device monitors the user's pace and sends an encouraging voice message if it falls below the target pace. If the emotion engine determines that the user's motivation is low, it can send a more personalized message such as, "You've come this far, let's keep going a little longer!"

[0145] Once the run is complete, the device sends the emotion engine data and run results to the server. The server performs comprehensive data processing and generates a feedback report based on calorie consumption, emotional changes, running pace, etc., which is then presented to the user. This detailed feedback allows users to make adjustments for future runs and develop more effective exercise plans. In this way, the system enhances the user's running experience and provides motivation management that is tailored to individual emotions.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user launches the running support app and sets their target running parameters. They input information such as target pace, distance, and running time. Additional conditions can also be set, such as selecting a route with fewer traffic lights.

[0149] Step 2:

[0150] The device receives the set target information and uses GPS to determine the user's current location. At the same time, it activates sensors to collect facial and voice data and prepares to send it to the emotion engine.

[0151] Step 3:

[0152] The device obtains surrounding geographical information, road conditions, traffic light locations, and pedestrian density via a map API, and sends all the data to the server.

[0153] Step 4:

[0154] The server analyzes the received data and calculates the most suitable route for achieving the user's goal. At this stage, it selects a route that minimizes obstacles and obstacles.

[0155] Step 5:

[0156] The server sends the analysis results to the terminal, which then displays the optimal route on a map based on these results. The user visually confirms the appropriate terrain by looking at the map and begins running based on the information.

[0157] Step 6:

[0158] During running, the device monitors the user's current location and running pace in real time. It also uses sensors to transmit data on the user's voice and facial expressions to an emotion engine to evaluate their emotional state.

[0159] Step 7:

[0160] The emotion engine analyzes user data and detects emotional changes such as anxiety and fatigue. If necessary, it requests the server to generate an appropriate message based on the user's emotional state.

[0161] Step 8:

[0162] The server generates and sends encouraging messages tailored to the user's emotions. For example, it might create messages like, "You're doing great, keep it up!" or "You're getting tired, but you're almost there!"

[0163] Step 9:

[0164] The terminal delivers messages received from the server to the user via voice output. This allows the user to maintain motivation and continue riding.

[0165] Step 10:

[0166] Once the run is finished, the device sends the run log, user emotion change data, and sensor data to the server.

[0167] Step 11:

[0168] The server comprehensively analyzes the received data and generates feedback reports related to calorie consumption, emotional changes, and running pace.

[0169] Step 12:

[0170] The device informs the user of the generated feedback and presents it as information to help plan their next run. The user can then use this information to further improve their training.

[0171] (Example 2)

[0172] 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".

[0173] A challenge when it comes to providing exercise support that takes into account the impact of a user's emotional state on their motivation while running is to address this issue. Conventional systems are limited to simply tracking running pace and distance, making it difficult to provide appropriate feedback in response to changes in the user's emotions. Therefore, there is a need for a system that can support users in achieving their goals efficiently and comfortably, and in maintaining their motivation.

[0174] 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.

[0175] In this invention, the server includes an input means for the user to input target running parameters, a location identification means for acquiring location information, and an analysis means for analyzing environmental information. This enables personalized feedback that takes into account the user's emotional state and the presentation of the optimal running route.

[0176] The "input method" refers to the interface for the user to input their target running parameters.

[0177] "Location determination means" refers to the technical methods and devices used to obtain the user's current location.

[0178] "Analysis means" refers to a process or device for calculating the optimal driving route based on collected data and environmental information.

[0179] "Display means" refers to a visual device or method for presenting the calculated travel route to the user.

[0180] "Emotional analysis means" refers to technologies and devices for detecting and analyzing a user's emotional state from their voice and facial expressions.

[0181] "Notification means" refers to systems or devices that inform users about their driving status.

[0182] "Data processing means" refers to a technology or process for analyzing integrated emotional data and driving data after a drive and providing it as feedback.

[0183] This invention is a support system to help users run more effectively. Specifically, it begins with the user installing and launching a running app on their device. The device acquires the user's location information in real time using GPS-based location tracking. Furthermore, it collects surrounding environmental data by using APIs from external services via the internet.

[0184] The server has software for analyzing location and environmental data transmitted from the terminal. This analysis calculates the optimal running route, taking into account terrain data and traffic conditions. The calculated route is sent back to the terminal and displayed to the user through a map application.

[0185] The device also features an emotion analysis system that analyzes voice and facial expressions, monitoring the user's emotional state in real time. If the user shows signs of anxiety or fatigue, the server generates an appropriate encouraging message based on the analysis and sends it to the device. For example, a message such as, "You've come this far, let's keep going a little longer!" might be provided.

[0186] Once the run is complete, the device aggregates all data and sends it to a server. The server then analyzes this data comprehensively and generates a feedback report based on calories burned, running pace, and changes in emotional state, which is delivered to the user via the device. This report allows the user to make appropriate plans for their next run.

[0187] As a concrete example, when a user goes for an early morning run, they input information such as "target distance 5 kilometers, pace 6 minutes / km" into their device. During the run, if the user feels fatigued, they receive a customized encouraging message such as, "Relax and focus on your breathing."

[0188] Examples of prompts generated by the AI ​​model include, "Please create a running support message that reflects the user's emotions," and "Please tell me how to generate a running route that takes scenery and safe routes into consideration." In this way, the invention provides practical running support that is tailored to the user's emotions and circumstances.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user launches the running app and enters their target parameters for the run (e.g., distance, pace) into the device. The entered information is provided to the system as the conditions for starting the run. The device then receives this information and prepares for the next necessary processes.

[0192] Step 2:

[0193] The device obtains location information using its built-in GPS. This acquired location information is updated in real time as data indicating the user's current location. Simultaneously, the device uses external APIs to collect surrounding environmental information such as weather, terrain, and traffic conditions. This information serves as input data for calculating the optimal driving route.

[0194] Step 3:

[0195] The device transmits acquired location information and environmental data to the server. Upon receiving this data, the server applies a dedicated analysis algorithm to calculate the optimal driving route. The calculated route takes into account the absence of traffic lights and congestion. The server returns the calculated route to the device, which then displays it on a map.

[0196] Step 4:

[0197] During running, the device uses voice sensors and a camera to detect the user's voice tone and facial expressions. This data is used as input for emotion analysis to estimate the user's emotional state. Emotional states such as optimism or fatigue are estimated.

[0198] Step 5:

[0199] The server receives emotional state information from the device and generates an appropriate encouraging message based on this information. The generated message will be designed to boost the user's motivation. The server sends this message to the device. The device then presents the message to the user via voice or text.

[0200] Step 6:

[0201] Once the run is complete, the device sends run result data and emotional state data to the server. The server receives and integrates this data to generate a feedback report that includes calories burned, running pace, and emotional changes. The generated report is sent to the device, and the user uses it to plan their next run.

[0202] (Application Example 2)

[0203] 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."

[0204] Loss of motivation and emotional fluctuations during running can hinder an optimal exercise experience. Traditional technologies have struggled to analyze a user's emotional state in real time and provide personalized encouragement and guidance based on that analysis. A more sophisticated feedback system is needed to enable users to continue running effectively without interruption.

[0205] 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.

[0206] In this invention, the server includes an input means for the user to input target running criteria, a location identification means for acquiring location information, an analysis means for analyzing the data collected using the location identification means and calculating the optimal running route for the target, and an emotion analysis means for monitoring the user's emotional state and providing emotion-based feedback. This allows the user to receive specific and real-time instructions tailored to their individual emotional state, enabling them to achieve optimal running while maintaining motivation.

[0207] An "input method" is an interface for the user to input their target driving standards into the device.

[0208] "Location identification means" refers to technologies and devices for acquiring a user's location information in real time.

[0209] The "analysis means" is a system that analyzes acquired data to calculate the optimal driving route for the user's goals.

[0210] "Display means" refers to a device or method for visually presenting the travel route and information calculated by the analysis means to the user.

[0211] "Notification means" refers to an audio or visual output device that provides messages to help the user maintain their driving pace.

[0212] "Emotional analysis means" refers to technologies and devices for monitoring a user's emotional state and providing feedback based on that information.

[0213] This invention is a running support system that incorporates various means to improve the user's running experience. The system includes a terminal for the user to input target running criteria and location tracking means for acquiring location information in real time. Specifically, a GPS module is used to accurately determine the user's location. Based on the user's set goals, the server analyzes and calculates the optimal running route. A commercially available map API is used for the analysis to derive a highly accurate route. Furthermore, a camera and microphone equipped on the terminal are used as sensors to monitor the user's emotional state. Data acquired from these sensors is analyzed in real time by an emotion analysis engine.

[0214] The server also tracks the user's running pace and, if that pace falls below the target, provides encouraging notifications via voice and visuals through the device. Voice feedback uses speech synthesis technology to ensure the user receives the most appropriate encouragement. Furthermore, a detailed feedback report is generated at the end of the run, based on the user's performance and emotional changes. This report helps in planning the next run and encourages the user to set new goals.

[0215] For example, if a user starts a morning run and is running at their planned pace but shows signs of fatigue along the way, the device will display an encouraging message such as, "Your current pace is excellent. Keep it up!" This message is based on emotional data generated by the emotion analysis engine. Another example of a prompt message is, "Analyze the user's emotional state during their run, generate an appropriate encouraging message, and output it aloud."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user inputs their target running criteria using a terminal. The data entered includes distance, pace, and running time. This data is sent to the server and used as basic information for achieving the goal.

[0219] Step 2:

[0220] The device utilizes location tracking to obtain the user's real-time location information. The acquired location data is transmitted to a server and used as input information for analysis. The data is provided by a GPS module, resulting in highly accurate location information.

[0221] Step 3:

[0222] The server performs analysis based on the received location information and the user's target driving criteria. Using a map API, it calculates the optimal driving route and generates a route that also takes environmental information into account. The analysis results are then returned to the terminal.

[0223] Step 4:

[0224] The terminal displays the optimal running route received from the server to the user. The route is visually shown on a map, and the user begins running according to it. The terminal's display is used for the display.

[0225] Step 5:

[0226] The device uses a camera and microphone to monitor the user's emotional state in real time. An emotion engine analyzes the user's voice and facial expression data to estimate their emotional state. This analysis result is sent to a server.

[0227] Step 6:

[0228] The server monitors the user's emotional state and running pace. If the user's motivation decreases or their running pace falls below the target, it uses an AI model to generate appropriate encouragement and instructions and sends them to the device.

[0229] Step 7:

[0230] The device provides the user with encouraging messages received from the server via voice output. Based on the generated messages, the user receives encouragement and is supported in achieving their goals.

[0231] Step 8:

[0232] After the run is complete, the terminal sends the run data and emotion analysis results to the server. The server performs comprehensive data processing and creates a feedback report. This report is used to plan the next run.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a system designed to help users maintain their target pace while running. First, the user launches a running support app and inputs a specific goal as a target running parameter, such as "run 5km in 30 minutes." The device receives this information via its built-in input device. Additional requirements, such as avoiding hills or minimizing traffic lights, can also be input at this time.

[0250] Next, the device uses GPS-based location tracking to collect the user's current location and surrounding route data. The device then obtains environmental data such as road shape, gradient, traffic light locations, and pedestrian congestion via a map service API. After that, the device sends this information to the server.

[0251] Based on this data, the server calculates the optimal route to help the user achieve their goals. The server then sends the calculated optimal route information back to the terminal. The terminal uses the received information to display the optimal route on a map, allowing the user to receive route guidance that is easy to understand visually on the spot.

[0252] During a run, the device monitors the user's pace and location in real time. If the pace is about to fall below the entered target, it sends a voice message via a notification system. Encouraging voice messages such as "Great pace, keep it up!" or "Almost there!" help maintain the user's motivation. This system allows users to train efficiently and effectively towards achieving their goals.

[0253] After the run is completed, the server analyzes the user's running data and generates feedback such as calories burned, average speed, and achievement level. The terminal displays this feedback information to the user, providing data that will be useful for future runs. Throughout the entire invention, users are supported in achieving their ideal running lifestyle through an experience optimized to their own pace.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user launches the running support app and enters their target running parameters. They can set goals such as "run 5km in 30 minutes" or additional requirements like "avoid traffic lights."

[0257] Step 2:

[0258] The terminal saves the target driving parameters entered by the user. (1)

[0259] Step 3:

[0260] The device uses GPS to determine the user's current location. Simultaneously, it uses a map service API to acquire environmental data such as surrounding geographical information, road gradients, traffic light locations, and pedestrian congestion levels.

[0261] Step 4:

[0262] The device sends the collected information to the server. This data includes the user's current location and surrounding environment data.

[0263] Step 5:

[0264] The server analyzes the received environmental data and the user's target driving parameters to calculate the optimal driving route. In this process, it prioritizes routes with fewer traffic lights and fewer hills.

[0265] Step 6:

[0266] The server returns optimized route information to the terminal as route guidance.

[0267] Step 7:

[0268] The device displays the optimal route information it receives to the user. It shows the route on a map and displays important points such as traffic lights and hills with icons.

[0269] Step 8:

[0270] The user begins running according to the provided route guidance.

[0271] Step 9:

[0272] The device monitors the user's current location and pace in real time while they are running.

[0273] Step 10:

[0274] The device measures the user's pace, and if it determines that the user is falling below the entered target pace, it sends a voice message to the user via a notification system. This message encourages the user and helps them maintain their goal.

[0275] Step 11:

[0276] After the run is finished, the device sends the run log to the server.

[0277] Step 12:

[0278] The server analyzes the running logs and generates feedback such as calorie consumption, average speed, and goal achievement.

[0279] Step 13:

[0280] The device visualizes and displays the generated feedback to the user, providing reference information for their next run.

[0281] (Example 1)

[0282] 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."

[0283] Modern exercise support devices are insufficient in providing efficient and encouraging support to users towards their goals. In particular, current systems lack real-time feedback and optimization that takes environmental information into account, failing to provide adequate support for effectively achieving user-set goals. This situation needs improvement, and a more effective and motivating exercise support environment for users is required.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes an input means for a user to input a target driving specification, a position measuring means for acquiring position information, and an arithmetic means for analyzing the surrounding information collected using the position measuring means and calculating an optimal route for the target. Thereby, the user can select an optimal route utilizing environmental information and can effectively perform activities towards achieving the target while receiving real-time motion support and feedback.

[0286] The "user" refers to an individual who sets a motion target and performs motion activities using this system.

[0287] The "target driving specification" is a target value of the motion set by the user, and indicates a specification that can include, for example, distance, time, motion efficiency under specific conditions, etc.

[0288] The "input means" refers to a device or interface for the user to convey the target driving specification and other motion conditions to the system.

[0289] The "position measuring means" refers to a technology or device for acquiring the current position of the user and the geographical information around it, and generally includes position information technologies such as GPS.

[0290] The "surrounding information" is geographical and environmental data of the area related to the motion, and includes road information, traffic conditions, weather conditions, etc.

[0291] The "arithmetic means" refers to a technology for calculating a route and strategy for optimally achieving the user's motion target based on the acquired information.

[0292] The "visual display means" refers to a device or interface that visually provides information on the calculated optimal route and motion status to the user.

[0293] A "notification method" is a function that provides notifications to the user, delivering information to the user in the form of audio or visual messages.

[0294] "Analysis means" refers to technology that analyzes the user's exercise data in detail after the exercise is completed and generates feedback regarding exercise efficiency and performance.

[0295] This invention constructs an exercise support system and is equipped with various functions to efficiently achieve user-set goals.

[0296] First, the user inputs their target running specifications using a device such as a mobile terminal. The terminal then receives the exercise goal information from the user through the input method. Here, a specific goal can be set, for example, "run 5km in 30 minutes." The input data is stored within the terminal and used for the next processing step.

[0297] Next, the device uses GPS, a location measurement tool, to obtain the user's current location. Furthermore, it uses a map service API (e.g., a general-purpose map software API) to collect surrounding information such as road information, traffic conditions, and weather information. This information is used by the calculation tool to calculate the optimal route to support the user in achieving their exercise goals.

[0298] The calculated results are displayed as map information on the terminal via a visual display device. Based on this information, the user can select the optimal exercise route and begin exercising.

[0299] During exercise, the device continuously monitors the user's exercise status in real time using notification methods. It also features a function to alert the user with an audio notification if the exercise pace deviates from the set target. This implementation helps users maintain an appropriate pace.

[0300] When the exercise ends, the terminal sends the running data to the server, and the analysis means evaluates it. Feedback regarding exercise efficiency, such as calories burned, exercise time, average speed, etc., is generated and provided to the user.

[0301] As a specific example, when the user sets a goal of "running 5 km in 30 minutes" and selects a route within a safe park, the terminal selects an optimal route based on real-time data and gives guidance such as "Please maintain your pace" by voice. In this way, the user can continue exercising efficiently.

[0302] Example of a prompt sentence for the generation AI model:

[0303] "When the user sets a goal of running 5 km in 30 minutes, please explain the steps for proposing an optimal running route to avoid congestion."

[0304] With this exercise support system, the user can carry out planned and effective activities towards achieving the goal.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The user launches the running support app on the terminal and inputs the target running specifications. What the user inputs is a specific goal such as "running 5 km in 30 minutes", for example. The input means receives this information and stores it in the terminal as input data. The output of this step is the data stored as the target running specifications.

[0308] Step 2:

[0309] The device obtains the user's current location using GPS. The location measurement device receives the current location information and stores it within the device as location data for future use. After the location information is determined, the device uses a map service API to collect surrounding road information, traffic conditions, and weather conditions. This environmental data becomes the input used in the next calculation step.

[0310] Step 3:

[0311] The terminal uses collected location and environmental data to calculate the optimal route for achieving the user's goal. The data processing performed here involves executing a route optimization algorithm that takes into account the user's goal and current environmental conditions. The output is the optimized route information. A server may also assist in this route calculation.

[0312] Step 4:

[0313] The terminal presents the calculated optimal route to the user using a visual display. The user can intuitively understand the route through a visual location display on a map. The output provides a display of the route the user should actually follow.

[0314] Step 5:

[0315] During running, the device monitors the user's location and pace in real time. Through this monitoring, if the user is about to deviate from their target pace, the notification system generates and provides a voice message to the user. Here, the user's pace data is used as input, and the notification message is output. The voice message provides encouragement such as, "Please maintain your pace."

[0316] Step 6:

[0317] After the exercise is completed, the device sends the user's running data to the server. The server uses analysis tools to evaluate this data and generates feedback such as calories burned, average speed, and goal achievement. As output, detailed feedback on the user's exercise efficiency is generated, which can be used to plan future exercise sessions.

[0318] (Application Example 1)

[0319] 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."

[0320] When running or walking, it is essential to provide users with the optimal course for efficient and safe running while maintaining their set target pace, and to maintain motivation during the run. However, conventional systems have difficulty flexibly responding to changes in the environment and individual goals, so further improvements are needed.

[0321] 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.

[0322] In this invention, the server includes an input means for the user to set target driving parameters, a location identification means for acquiring location information, and an analysis means for calculating the optimal driving route based on the collected environmental data. This enables the provision of an optimal driving route tailored to each user's individual goals and real-time pace management during driving.

[0323] A "user" is a person who uses this system to set and achieve their own running or walking goals.

[0324] "Target driving parameters" refer to specific numerical information such as the target time, distance, and pace set by the user for their driving.

[0325] "Input method" refers to an interface for the user to input target driving parameters, and usually refers to a smartphone or computer application.

[0326] "Location determination means" refers to the technology used to determine the user's current location, and generally uses GPS.

[0327] The "analysis means" is a calculation function that calculates the optimal driving route for achieving the user's goals based on collected location information and environmental data.

[0328] "Display means" refers to a method for visually showing the calculated driving route to the user, and typically involves using a display or map application.

[0329] The "notification method" is a function that monitors the user's real-time running pace and encourages the user with voice messages or text messages when that pace falls below the target.

[0330] "Environmental data" refers to data about the user's surroundings, including road shape, gradient, traffic signal information, and congestion levels.

[0331] This system provides users with the optimal running route to achieve their set running or walking goals and manages their pace in real time. Users first launch the application using a mobile device such as a smartphone and input their target running parameters. A touchscreen interface is used for input.

[0332] The device uses its built-in GPS module to determine the user's current location. Next, it collects surrounding environmental data via a map service API and sends it to the server. The server analyzes this data using programming languages ​​such as Python to calculate the optimal driving route. The calculated route information is returned to the device and displayed through the map application.

[0333] Once the user starts running, the device monitors their pace in real time. If the pace falls below the entered target, a notification system will encourage the user through a voice message. Voice messages such as "Almost there!" help maintain the pace and support the user's motivation.

[0334] Finally, after the run is completed, the server analyzes the running data in more detail and provides feedback to the user on calories burned, average speed, and achievement level. This allows the user to identify areas for further improvement and use them in their next run.

[0335] As a concrete example, there is a case where a user received a route suggestion that avoided crowded urban areas and went through a quiet park on a Sunday morning, allowing them to easily achieve their target pace. Behind the realization of such a system lies advanced analytical technology utilizing generative AI models. When using the generative AI model to input the optimal route as a prompt, a specific prompt statement is used: "Generate the optimal route that takes into account the real-time information of the surrounding area, in accordance with the goal set by the user."

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The terminal receives the application from the user and inputs target driving parameters. These parameters include target values ​​such as distance and pace. The terminal then receives this input data and prepares it for the next processing step.

[0339] Step 2:

[0340] The device uses its built-in GPS module to obtain the user's location information. This determines the user's current location and prepares it to collect environmental data about its surroundings.

[0341] Step 3:

[0342] The device collects surrounding environmental data through a map service API. This includes road shapes, traffic signal information, and congestion levels. The device then aggregates this data and generates data packets to send to the server.

[0343] Step 4:

[0344] The server receives data sent from the terminal and begins analyzing the data using an analysis program such as Python. Here, a generative AI model is used to calculate the optimal driving route. Specifically, based on the input target parameters and environmental data, the most efficient route for the user is derived. This calculation result is generated as route data.

[0345] Step 5:

[0346] The server returns the optimal route data, which is the result of its analysis, to the terminal. The terminal receives this data and displays the route visually through a map application. The user then refers to this and prepares to start running.

[0347] Step 6:

[0348] The device monitors the user's real-time pace while they are running. It continuously acquires the user's speed and location and compares it to the entered target pace. If the pace is about to fall below the target, the device uses a notification system to provide encouragement through voice messages.

[0349] Step 7:

[0350] After the ride is complete, the device sends the collected ride data to the server. The server uses this data to perform a detailed analysis and generates feedback that includes calories burned, average speed, and degree of goal achievement.

[0351] Step 8:

[0352] The server sends the generated feedback back to the terminal. The terminal can then display this feedback to the user, which can be used to help with future improvements and goal setting.

[0353] The above outlines the processing steps of the program required to implement this application example.

[0354] 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.

[0355] This invention is a running support system that enables users to run effectively, and in particular, it has a function to monitor and analyze the user's emotional state and maintain motivation. The user first launches a running app and inputs target running parameters (e.g., distance and pace). Based on this information, the device starts providing support in the direction desired by the user.

[0356] This system uses terminal location tracking to determine the user's location in real time and transmits it to a server along with surrounding environment data obtained using a map API. The server uses this information to analyze the optimal driving route, particularly minimizing obstacles such as traffic lights and congestion. The calculated route is sent back to the terminal, visualized on a map, and guided to the user.

[0357] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The terminal is equipped with sensors to read the user's voice and facial expressions, and the emotion engine uses this sensor information to estimate the user's emotions. For example, if it detects anxiety or fatigue, the server sends an appropriate encouraging message to the terminal that takes that state into account.

[0358] During a run, the device monitors the user's pace and sends an encouraging voice message if it falls below the target pace. If the emotion engine determines that the user's motivation is low, it can send a more personalized message such as, "You've come this far, let's keep going a little longer!"

[0359] Once the run is complete, the device sends the emotion engine data and run results to the server. The server performs comprehensive data processing and generates a feedback report based on calorie consumption, emotional changes, running pace, etc., which is then presented to the user. This detailed feedback allows users to make adjustments for future runs and develop more effective exercise plans. In this way, the system enhances the user's running experience and provides motivation management that is tailored to individual emotions.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The user launches the running support app and sets their target running parameters. They input information such as target pace, distance, and running time. Additional conditions can also be set, such as selecting a route with fewer traffic lights.

[0363] Step 2:

[0364] The device receives the set target information and uses GPS to determine the user's current location. At the same time, it activates sensors to collect facial and voice data and prepares to send it to the emotion engine.

[0365] Step 3:

[0366] The device obtains surrounding geographical information, road conditions, traffic light locations, and pedestrian density via a map API, and sends all the data to the server.

[0367] Step 4:

[0368] The server analyzes the received data and calculates the most suitable route for achieving the user's goal. At this stage, it selects a route that minimizes obstacles and obstacles.

[0369] Step 5:

[0370] The server sends the analysis results to the terminal, which then displays the optimal route on a map based on these results. The user visually confirms the appropriate terrain by looking at the map and begins running based on the information.

[0371] Step 6:

[0372] During running, the device monitors the user's current location and running pace in real time. It also uses sensors to transmit data on the user's voice and facial expressions to an emotion engine to evaluate their emotional state.

[0373] Step 7:

[0374] The emotion engine analyzes user data and detects emotional changes such as anxiety and fatigue. If necessary, it requests the server to generate an appropriate message based on the user's emotional state.

[0375] Step 8:

[0376] The server generates and sends encouraging messages tailored to the user's emotions. For example, it might create messages like, "You're doing great, keep it up!" or "You're getting tired, but you're almost there!"

[0377] Step 9:

[0378] The terminal delivers messages received from the server to the user via voice output. This allows the user to maintain motivation and continue riding.

[0379] Step 10:

[0380] Once the run is finished, the device sends the run log, user emotion change data, and sensor data to the server.

[0381] Step 11:

[0382] The server comprehensively analyzes the received data and generates feedback reports related to calorie consumption, emotional changes, and running pace.

[0383] Step 12:

[0384] The device informs the user of the generated feedback and presents it as information to help plan their next run. The user can then use this information to further improve their training.

[0385] (Example 2)

[0386] 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".

[0387] A challenge when it comes to providing exercise support that takes into account the impact of a user's emotional state on their motivation while running is to address this issue. Conventional systems are limited to simply tracking running pace and distance, making it difficult to provide appropriate feedback in response to changes in the user's emotions. Therefore, there is a need for a system that can support users in achieving their goals efficiently and comfortably, and in maintaining their motivation.

[0388] 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.

[0389] In this invention, the server includes an input means for the user to input target running parameters, a location identification means for acquiring location information, and an analysis means for analyzing environmental information. This enables personalized feedback that takes into account the user's emotional state and the presentation of the optimal running route.

[0390] The "input method" refers to the interface for the user to input their target running parameters.

[0391] "Location determination means" refers to the technical methods and devices used to obtain the user's current location.

[0392] "Analysis means" refers to a process or device for calculating the optimal driving route based on collected data and environmental information.

[0393] "Display means" refers to a visual device or method for presenting the calculated travel route to the user.

[0394] "Emotional analysis means" refers to technologies and devices for detecting and analyzing a user's emotional state from their voice and facial expressions.

[0395] "Notification means" refers to systems or devices that inform users about their driving status.

[0396] "Data processing means" refers to a technology or process for analyzing integrated emotional data and driving data after a drive and providing it as feedback.

[0397] This invention is a support system to help users run more effectively. Specifically, it begins with the user installing and launching a running app on their device. The device acquires the user's location information in real time using GPS-based location tracking. Furthermore, it collects surrounding environmental data by using APIs from external services via the internet.

[0398] The server has software for analyzing location and environmental data transmitted from the terminal. This analysis calculates the optimal running route, taking into account terrain data and traffic conditions. The calculated route is sent back to the terminal and displayed to the user through a map application.

[0399] The device also features an emotion analysis system that analyzes voice and facial expressions, monitoring the user's emotional state in real time. If the user shows signs of anxiety or fatigue, the server generates an appropriate encouraging message based on the analysis and sends it to the device. For example, a message such as, "You've come this far, let's keep going a little longer!" might be provided.

[0400] Once the run is complete, the device aggregates all data and sends it to a server. The server then analyzes this data comprehensively and generates a feedback report based on calories burned, running pace, and changes in emotional state, which is delivered to the user via the device. This report allows the user to make appropriate plans for their next run.

[0401] As a concrete example, when a user goes for an early morning run, they input information such as "target distance 5 kilometers, pace 6 minutes / km" into their device. During the run, if the user feels fatigued, they receive a customized encouraging message such as, "Relax and focus on your breathing."

[0402] Examples of prompts generated by the AI ​​model include, "Please create a running support message that reflects the user's emotions," and "Please tell me how to generate a running route that takes scenery and safe routes into consideration." In this way, the invention provides practical running support that is tailored to the user's emotions and circumstances.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] The user launches the running app and enters their target parameters for the run (e.g., distance, pace) into the device. The entered information is provided to the system as the conditions for starting the run. The device then receives this information and prepares for the next necessary processes.

[0406] Step 2:

[0407] The device obtains location information using its built-in GPS. This acquired location information is updated in real time as data indicating the user's current location. Simultaneously, the device uses external APIs to collect surrounding environmental information such as weather, terrain, and traffic conditions. This information serves as input data for calculating the optimal driving route.

[0408] Step 3:

[0409] The device transmits acquired location information and environmental data to the server. Upon receiving this data, the server applies a dedicated analysis algorithm to calculate the optimal driving route. The calculated route takes into account the absence of traffic lights and congestion. The server returns the calculated route to the device, which then displays it on a map.

[0410] Step 4:

[0411] During running, the device uses voice sensors and a camera to detect the user's voice tone and facial expressions. This data is used as input for emotion analysis to estimate the user's emotional state. Emotional states such as optimism or fatigue are estimated.

[0412] Step 5:

[0413] The server receives emotional state information from the device and generates an appropriate encouraging message based on this information. The generated message will be designed to boost the user's motivation. The server sends this message to the device. The device then presents the message to the user via voice or text.

[0414] Step 6:

[0415] Once the run is complete, the device sends run result data and emotional state data to the server. The server receives and integrates this data to generate a feedback report that includes calories burned, running pace, and emotional changes. The generated report is sent to the device, and the user uses it to plan their next run.

[0416] (Application Example 2)

[0417] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0418] Loss of motivation and emotional fluctuations during running can hinder an optimal exercise experience. Traditional technologies have struggled to analyze a user's emotional state in real time and provide personalized encouragement and guidance based on that analysis. A more sophisticated feedback system is needed to enable users to continue running effectively without interruption.

[0419] 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.

[0420] In this invention, the server includes an input means for the user to input target running criteria, a location identification means for acquiring location information, an analysis means for analyzing the data collected using the location identification means and calculating the optimal running route for the target, and an emotion analysis means for monitoring the user's emotional state and providing emotion-based feedback. This allows the user to receive specific and real-time instructions tailored to their individual emotional state, enabling them to achieve optimal running while maintaining motivation.

[0421] An "input method" is an interface for the user to input their target driving standards into the device.

[0422] "Location identification means" refers to technologies and devices for acquiring a user's location information in real time.

[0423] The "analysis means" is a system that analyzes acquired data to calculate the optimal driving route for the user's goals.

[0424] "Display means" refers to a device or method for visually presenting the travel route and information calculated by the analysis means to the user.

[0425] "Notification means" refers to an audio or visual output device that provides messages to help the user maintain their driving pace.

[0426] "Emotional analysis means" refers to technologies and devices for monitoring a user's emotional state and providing feedback based on that information.

[0427] This invention is a running support system that incorporates various means to improve the user's running experience. The system includes a terminal for the user to input target running criteria and location tracking means for acquiring location information in real time. Specifically, a GPS module is used to accurately determine the user's location. Based on the user's set goals, the server analyzes and calculates the optimal running route. A commercially available map API is used for the analysis to derive a highly accurate route. Furthermore, a camera and microphone equipped on the terminal are used as sensors to monitor the user's emotional state. Data acquired from these sensors is analyzed in real time by an emotion analysis engine.

[0428] The server also tracks the user's running pace and, if that pace falls below the target, provides encouraging notifications via voice and visuals through the device. Voice feedback uses speech synthesis technology to ensure the user receives the most appropriate encouragement. Furthermore, a detailed feedback report is generated at the end of the run, based on the user's performance and emotional changes. This report helps in planning the next run and encourages the user to set new goals.

[0429] For example, if a user starts a morning run and is running at their planned pace but shows signs of fatigue along the way, the device will display an encouraging message such as, "Your current pace is excellent. Keep it up!" This message is based on emotional data generated by the emotion analysis engine. Another example of a prompt message is, "Analyze the user's emotional state during their run, generate an appropriate encouraging message, and output it aloud."

[0430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0431] Step 1:

[0432] The user inputs their target running criteria using a terminal. The data entered includes distance, pace, and running time. This data is sent to the server and used as basic information for achieving the goal.

[0433] Step 2:

[0434] The device utilizes location tracking to obtain the user's real-time location information. The acquired location data is transmitted to a server and used as input information for analysis. The data is provided by a GPS module, resulting in highly accurate location information.

[0435] Step 3:

[0436] The server performs analysis based on the received location information and the user's target driving criteria. Using a map API, it calculates the optimal driving route and generates a route that also takes environmental information into account. The analysis results are then returned to the terminal.

[0437] Step 4:

[0438] The terminal displays the optimal running route received from the server to the user. The route is visually shown on a map, and the user begins running according to it. The terminal's display is used for the display.

[0439] Step 5:

[0440] The device uses a camera and microphone to monitor the user's emotional state in real time. An emotion engine analyzes the user's voice and facial expression data to estimate their emotional state. This analysis result is sent to a server.

[0441] Step 6:

[0442] The server monitors the user's emotional state and running pace. If the user's motivation decreases or their running pace falls below the target, it uses an AI model to generate appropriate encouragement and instructions and sends them to the device.

[0443] Step 7:

[0444] The device provides the user with encouraging messages received from the server via voice output. Based on the generated messages, the user receives encouragement and is supported in achieving their goals.

[0445] Step 8:

[0446] After the run is complete, the terminal sends the run data and emotion analysis results to the server. The server performs comprehensive data processing and creates a feedback report. This report is used to plan the next run.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Third Embodiment]

[0451] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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".

[0463] This invention is a system designed to help users maintain their target pace while running. First, the user launches a running support app and inputs a specific goal as a target running parameter, such as "run 5km in 30 minutes." The device receives this information via its built-in input device. Additional requirements, such as avoiding hills or minimizing traffic lights, can also be input at this time.

[0464] Next, the device uses GPS-based location tracking to collect the user's current location and surrounding route data. The device then obtains environmental data such as road shape, gradient, traffic light locations, and pedestrian congestion via a map service API. After that, the device sends this information to the server.

[0465] Based on this data, the server calculates the optimal route to help the user achieve their goals. The server then sends the calculated optimal route information back to the terminal. The terminal uses the received information to display the optimal route on a map, allowing the user to receive route guidance that is easy to understand visually on the spot.

[0466] During a run, the device monitors the user's pace and location in real time. If the pace is about to fall below the entered target, it sends a voice message via a notification system. Encouraging voice messages such as "Great pace, keep it up!" or "Almost there!" help maintain the user's motivation. This system allows users to train efficiently and effectively towards achieving their goals.

[0467] After the run is completed, the server analyzes the user's running data and generates feedback such as calories burned, average speed, and achievement level. The terminal displays this feedback information to the user, providing data that will be useful for future runs. Throughout the entire invention, users are supported in achieving their ideal running lifestyle through an experience optimized to their own pace.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user launches the running support app and enters their target running parameters. They can set goals such as "run 5km in 30 minutes" or additional requirements like "avoid traffic lights."

[0471] Step 2:

[0472] The terminal saves the target driving parameters entered by the user. (1)

[0473] Step 3:

[0474] The device uses GPS to determine the user's current location. Simultaneously, it uses a map service API to acquire environmental data such as surrounding geographical information, road gradients, traffic light locations, and pedestrian congestion levels.

[0475] Step 4:

[0476] The device sends the collected information to the server. This data includes the user's current location and surrounding environment data.

[0477] Step 5:

[0478] The server analyzes the received environmental data and the user's target driving parameters to calculate the optimal driving route. In this process, it prioritizes routes with fewer traffic lights and fewer hills.

[0479] Step 6:

[0480] The server returns optimized route information to the terminal as route guidance.

[0481] Step 7:

[0482] The device displays the optimal route information it receives to the user. It shows the route on a map and displays important points such as traffic lights and hills with icons.

[0483] Step 8:

[0484] The user begins running according to the provided route guidance.

[0485] Step 9:

[0486] The device monitors the user's current location and pace in real time while they are running.

[0487] Step 10:

[0488] The device measures the user's pace, and if it determines that the user is falling below the entered target pace, it sends a voice message to the user via a notification system. This message encourages the user and helps them maintain their goal.

[0489] Step 11:

[0490] After the run is finished, the device sends the run log to the server.

[0491] Step 12:

[0492] The server analyzes the running logs and generates feedback such as calorie consumption, average speed, and goal achievement.

[0493] Step 13:

[0494] The device visualizes and displays the generated feedback to the user, providing reference information for their next run.

[0495] (Example 1)

[0496] 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."

[0497] Modern exercise support devices are insufficient in providing efficient and encouraging support to users towards their goals. In particular, current systems lack real-time feedback and optimization that takes environmental information into account, failing to provide adequate support for effectively achieving user-set goals. This situation needs improvement, and a more effective and motivating exercise support environment for users is required.

[0498] 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.

[0499] In this invention, the server includes an input means for the user to input target running specifications, a position measurement means for acquiring location information, and a calculation means for analyzing surrounding information collected using the position measurement means and calculating the optimal route to the target. This enables the user to select the optimal route utilizing environmental information and to effectively engage in activities toward achieving their goal while receiving real-time exercise support and feedback.

[0500] A "user" refers to an individual who uses this system to set exercise goals and engage in exercise activities.

[0501] "Target running specifications" refer to the target values ​​for exercise set by the user, and may include, for example, distance, time, or exercise efficiency under specific conditions.

[0502] "Input means" refers to a device or interface for the user to communicate target driving specifications or other driving conditions to the system.

[0503] "Location measurement means" refers to technologies and devices used to obtain the user's current location and geographical information of their surroundings, and generally includes location information technologies such as GPS.

[0504] "Surrounding area information" refers to geographical and environmental data of the area related to the exercise, including road information, traffic conditions, and weather conditions.

[0505] "Calculation means" refers to technology that calculates the optimal path and strategy to achieve the user's exercise goals based on the acquired information.

[0506] "Visual display means" refers to a device or interface that visually provides the user with information about the calculated optimal route and movement status.

[0507] A "notification method" is a function that provides notifications to the user, delivering information to the user in the form of audio or visual messages.

[0508] "Analysis means" refers to technology that analyzes the user's exercise data in detail after the exercise is completed and generates feedback regarding exercise efficiency and performance.

[0509] This invention constructs an exercise support system and is equipped with various functions to efficiently achieve user-set goals.

[0510] First, the user inputs their target running specifications using a device such as a mobile terminal. The terminal then receives the exercise goal information from the user through the input method. Here, a specific goal can be set, for example, "run 5km in 30 minutes." The input data is stored within the terminal and used for the next processing step.

[0511] Next, the device uses GPS, a location measurement tool, to obtain the user's current location. Furthermore, it uses a map service API (e.g., a general-purpose map software API) to collect surrounding information such as road information, traffic conditions, and weather information. This information is used by the calculation tool to calculate the optimal route to support the user in achieving their exercise goals.

[0512] The calculated results are displayed as map information on the terminal via a visual display device. Based on this information, the user can select the optimal exercise route and begin exercising.

[0513] During exercise, the device continuously monitors the user's exercise status in real time using notification methods. It also features a function to alert the user with an audio notification if the exercise pace deviates from the set target. This implementation helps users maintain an appropriate pace.

[0514] Once the exercise is complete, the device sends the running data to a server, where an analysis system evaluates it. It generates and provides feedback to the user regarding exercise efficiency, such as calories burned, exercise time, and average speed.

[0515] For example, if a user sets a goal of "running 5km in 30 minutes" and selects a safe route within a park, the device will select the optimal route based on real-time data and provide voice guidance such as "maintain your pace." In this way, users can continue exercising efficiently.

[0516] Examples of prompts for a generative AI model:

[0517] "If a user sets a goal of running 5km in 30 minutes, please explain the steps to suggest the optimal running route that avoids congestion."

[0518] This exercise support system allows users to engage in planned and effective activities toward achieving their goals.

[0519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0520] Step 1:

[0521] The user launches a running support app on their device and enters their target running specifications. The user enters a specific goal, such as "run 5km in 30 minutes." The input device receives this information and saves it as input data on the device. The output of this step is the data saved as the target running specifications.

[0522] Step 2:

[0523] The device obtains the user's current location using GPS. The location measurement device receives the current location information and stores it within the device as location data for future use. After the location information is determined, the device uses a map service API to collect surrounding road information, traffic conditions, and weather conditions. This environmental data becomes the input used in the next calculation step.

[0524] Step 3:

[0525] The terminal uses collected location and environmental data to calculate the optimal route for achieving the user's goal. The data processing performed here involves executing a route optimization algorithm that takes into account the user's goal and current environmental conditions. The output is the optimized route information. A server may also assist in this route calculation.

[0526] Step 4:

[0527] The terminal presents the calculated optimal route to the user using a visual display. The user can intuitively understand the route through a visual location display on a map. The output provides a display of the route the user should actually follow.

[0528] Step 5:

[0529] During running, the device monitors the user's location and pace in real time. Through this monitoring, if the user is about to deviate from their target pace, the notification system generates and provides a voice message to the user. Here, the user's pace data is used as input, and the notification message is output. The voice message provides encouragement such as, "Please maintain your pace."

[0530] Step 6:

[0531] After the exercise is completed, the device sends the user's running data to the server. The server uses analysis tools to evaluate this data and generates feedback such as calories burned, average speed, and goal achievement. As output, detailed feedback on the user's exercise efficiency is generated, which can be used to plan future exercise sessions.

[0532] (Application Example 1)

[0533] 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."

[0534] When running or walking, it is essential to provide users with the optimal course for efficient and safe running while maintaining their set target pace, and to maintain motivation during the run. However, conventional systems have difficulty flexibly responding to changes in the environment and individual goals, so further improvements are needed.

[0535] 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.

[0536] In this invention, the server includes an input means for the user to set target driving parameters, a location identification means for acquiring location information, and an analysis means for calculating the optimal driving route based on the collected environmental data. This enables the provision of an optimal driving route tailored to each user's individual goals and real-time pace management during driving.

[0537] A "user" is a person who uses this system to set and achieve their own running or walking goals.

[0538] "Target driving parameters" refer to specific numerical information such as the target time, distance, and pace set by the user for their driving.

[0539] "Input method" refers to an interface for the user to input target driving parameters, and usually refers to a smartphone or computer application.

[0540] "Location determination means" refers to the technology used to determine the user's current location, and generally uses GPS.

[0541] The "analysis means" is a calculation function that calculates the optimal driving route for achieving the user's goals based on collected location information and environmental data.

[0542] "Display means" refers to a method for visually showing the calculated driving route to the user, and typically involves using a display or map application.

[0543] The "notification method" is a function that monitors the user's real-time running pace and encourages the user with voice messages or text messages when that pace falls below the target.

[0544] "Environmental data" refers to data about the user's surroundings, including road shape, gradient, traffic signal information, and congestion levels.

[0545] This system provides users with the optimal running route to achieve their set running or walking goals and manages their pace in real time. Users first launch the application using a mobile device such as a smartphone and input their target running parameters. A touchscreen interface is used for input.

[0546] The device uses its built-in GPS module to determine the user's current location. Next, it collects surrounding environmental data via a map service API and sends it to the server. The server analyzes this data using programming languages ​​such as Python to calculate the optimal driving route. The calculated route information is returned to the device and displayed through the map application.

[0547] Once the user starts running, the device monitors their pace in real time. If the pace falls below the entered target, a notification system will encourage the user through a voice message. Voice messages such as "Almost there!" help maintain the pace and support the user's motivation.

[0548] Finally, after the run is completed, the server analyzes the running data in more detail and provides feedback to the user on calories burned, average speed, and achievement level. This allows the user to identify areas for further improvement and use them in their next run.

[0549] As a concrete example, there is a case where a user received a route suggestion that avoided crowded urban areas and went through a quiet park on a Sunday morning, allowing them to easily achieve their target pace. Behind the realization of such a system lies advanced analytical technology utilizing generative AI models. When using the generative AI model to input the optimal route as a prompt, a specific prompt statement is used: "Generate the optimal route that takes into account the real-time information of the surrounding area, in accordance with the goal set by the user."

[0550] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0551] Step 1:

[0552] The terminal receives the application from the user and inputs target driving parameters. These parameters include target values ​​such as distance and pace. The terminal then receives this input data and prepares it for the next processing step.

[0553] Step 2:

[0554] The device uses its built-in GPS module to obtain the user's location information. This determines the user's current location and prepares it to collect environmental data about its surroundings.

[0555] Step 3:

[0556] The device collects surrounding environmental data through a map service API. This includes road shapes, traffic signal information, and congestion levels. The device then aggregates this data and generates data packets to send to the server.

[0557] Step 4:

[0558] The server receives data sent from the terminal and begins analyzing the data using an analysis program such as Python. Here, a generative AI model is used to calculate the optimal driving route. Specifically, based on the input target parameters and environmental data, the most efficient route for the user is derived. This calculation result is generated as route data.

[0559] Step 5:

[0560] The server returns the optimal route data, which is the result of its analysis, to the terminal. The terminal receives this data and displays the route visually through a map application. The user then refers to this and prepares to start running.

[0561] Step 6:

[0562] The device monitors the user's real-time pace while they are running. It continuously acquires the user's speed and location and compares it to the entered target pace. If the pace is about to fall below the target, the device uses a notification system to provide encouragement through voice messages.

[0563] Step 7:

[0564] After the ride is complete, the device sends the collected ride data to the server. The server uses this data to perform a detailed analysis and generates feedback that includes calories burned, average speed, and degree of goal achievement.

[0565] Step 8:

[0566] The server sends the generated feedback back to the terminal. The terminal can then display this feedback to the user, which can be used to help with future improvements and goal setting.

[0567] The above outlines the processing steps of the program required to implement this application example.

[0568] 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.

[0569] This invention is a running support system that enables users to run effectively, and in particular, it has a function to monitor and analyze the user's emotional state and maintain motivation. The user first launches a running app and inputs target running parameters (e.g., distance and pace). Based on this information, the device starts providing support in the direction desired by the user.

[0570] This system uses terminal location tracking to determine the user's location in real time and transmits it to a server along with surrounding environment data obtained using a map API. The server uses this information to analyze the optimal driving route, particularly minimizing obstacles such as traffic lights and congestion. The calculated route is sent back to the terminal, visualized on a map, and guided to the user.

[0571] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The terminal is equipped with sensors to read the user's voice and facial expressions, and the emotion engine uses this sensor information to estimate the user's emotions. For example, if it detects anxiety or fatigue, the server sends an appropriate encouraging message to the terminal that takes that state into account.

[0572] During a run, the device monitors the user's pace and sends an encouraging voice message if it falls below the target pace. If the emotion engine determines that the user's motivation is low, it can send a more personalized message such as, "You've come this far, let's keep going a little longer!"

[0573] Once the run is complete, the device sends the emotion engine data and run results to the server. The server performs comprehensive data processing and generates a feedback report based on calorie consumption, emotional changes, running pace, etc., which is then presented to the user. This detailed feedback allows users to make adjustments for future runs and develop more effective exercise plans. In this way, the system enhances the user's running experience and provides motivation management that is tailored to individual emotions.

[0574] The following describes the processing flow.

[0575] Step 1:

[0576] The user launches the running support app and sets their target running parameters. They input information such as target pace, distance, and running time. Additional conditions can also be set, such as selecting a route with fewer traffic lights.

[0577] Step 2:

[0578] The device receives the set target information and uses GPS to determine the user's current location. At the same time, it activates sensors to collect facial and voice data and prepares to send it to the emotion engine.

[0579] Step 3:

[0580] The device obtains surrounding geographical information, road conditions, traffic light locations, and pedestrian density via a map API, and sends all the data to the server.

[0581] Step 4:

[0582] The server analyzes the received data and calculates the most suitable route for achieving the user's goal. At this stage, it selects a route that minimizes obstacles and obstacles.

[0583] Step 5:

[0584] The server sends the analysis results to the terminal, which then displays the optimal route on a map based on these results. The user visually confirms the appropriate terrain by looking at the map and begins running based on the information.

[0585] Step 6:

[0586] During running, the device monitors the user's current location and running pace in real time. It also uses sensors to transmit data on the user's voice and facial expressions to an emotion engine to evaluate their emotional state.

[0587] Step 7:

[0588] The emotion engine analyzes user data and detects emotional changes such as anxiety and fatigue. If necessary, it requests the server to generate an appropriate message based on the user's emotional state.

[0589] Step 8:

[0590] The server generates and sends encouraging messages tailored to the user's emotions. For example, it might create messages like, "You're doing great, keep it up!" or "You're getting tired, but you're almost there!"

[0591] Step 9:

[0592] The terminal delivers messages received from the server to the user via voice output. This allows the user to maintain motivation and continue riding.

[0593] Step 10:

[0594] Once the run is finished, the device sends the run log, user emotion change data, and sensor data to the server.

[0595] Step 11:

[0596] The server comprehensively analyzes the received data and generates feedback reports related to calorie consumption, emotional changes, and running pace.

[0597] Step 12:

[0598] The device informs the user of the generated feedback and presents it as information to help plan their next run. The user can then use this information to further improve their training.

[0599] (Example 2)

[0600] 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."

[0601] A challenge when it comes to providing exercise support that takes into account the impact of a user's emotional state on their motivation while running is to address this issue. Conventional systems are limited to simply tracking running pace and distance, making it difficult to provide appropriate feedback in response to changes in the user's emotions. Therefore, there is a need for a system that can support users in achieving their goals efficiently and comfortably, and in maintaining their motivation.

[0602] 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.

[0603] In this invention, the server includes an input means for the user to input target running parameters, a location identification means for acquiring location information, and an analysis means for analyzing environmental information. This enables personalized feedback that takes into account the user's emotional state and the presentation of the optimal running route.

[0604] The "input method" refers to the interface for the user to input their target running parameters.

[0605] "Location determination means" refers to the technical methods and devices used to obtain the user's current location.

[0606] "Analysis means" refers to a process or device for calculating the optimal driving route based on collected data and environmental information.

[0607] "Display means" refers to a visual device or method for presenting the calculated travel route to the user.

[0608] "Emotional analysis means" refers to technologies and devices for detecting and analyzing a user's emotional state from their voice and facial expressions.

[0609] "Notification means" refers to systems or devices that inform users about their driving status.

[0610] "Data processing means" refers to a technology or process for analyzing integrated emotional data and driving data after a drive and providing it as feedback.

[0611] This invention is a support system to help users run more effectively. Specifically, it begins with the user installing and launching a running app on their device. The device acquires the user's location information in real time using GPS-based location tracking. Furthermore, it collects surrounding environmental data by using APIs from external services via the internet.

[0612] The server has software for analyzing location and environmental data transmitted from the terminal. This analysis calculates the optimal running route, taking into account terrain data and traffic conditions. The calculated route is sent back to the terminal and displayed to the user through a map application.

[0613] The device also features an emotion analysis system that analyzes voice and facial expressions, monitoring the user's emotional state in real time. If the user shows signs of anxiety or fatigue, the server generates an appropriate encouraging message based on the analysis and sends it to the device. For example, a message such as, "You've come this far, let's keep going a little longer!" might be provided.

[0614] Once the run is complete, the device aggregates all data and sends it to a server. The server then analyzes this data comprehensively and generates a feedback report based on calories burned, running pace, and changes in emotional state, which is delivered to the user via the device. This report allows the user to make appropriate plans for their next run.

[0615] As a concrete example, when a user goes for an early morning run, they input information such as "target distance 5 kilometers, pace 6 minutes / km" into their device. During the run, if the user feels fatigued, they receive a customized encouraging message such as, "Relax and focus on your breathing."

[0616] Examples of prompts generated by the AI ​​model include, "Please create a running support message that reflects the user's emotions," and "Please tell me how to generate a running route that takes scenery and safe routes into consideration." In this way, the invention provides practical running support that is tailored to the user's emotions and circumstances.

[0617] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0618] Step 1:

[0619] The user launches the running app and enters their target parameters for the run (e.g., distance, pace) into the device. The entered information is provided to the system as the conditions for starting the run. The device then receives this information and prepares for the next necessary processes.

[0620] Step 2:

[0621] The device obtains location information using its built-in GPS. This acquired location information is updated in real time as data indicating the user's current location. Simultaneously, the device uses external APIs to collect surrounding environmental information such as weather, terrain, and traffic conditions. This information serves as input data for calculating the optimal driving route.

[0622] Step 3:

[0623] The device transmits acquired location information and environmental data to the server. Upon receiving this data, the server applies a dedicated analysis algorithm to calculate the optimal driving route. The calculated route takes into account the absence of traffic lights and congestion. The server returns the calculated route to the device, which then displays it on a map.

[0624] Step 4:

[0625] During running, the device uses voice sensors and a camera to detect the user's voice tone and facial expressions. This data is used as input for emotion analysis to estimate the user's emotional state. Emotional states such as optimism or fatigue are estimated.

[0626] Step 5:

[0627] The server receives emotional state information from the device and generates an appropriate encouraging message based on this information. The generated message will be designed to boost the user's motivation. The server sends this message to the device. The device then presents the message to the user via voice or text.

[0628] Step 6:

[0629] Once the run is complete, the device sends run result data and emotional state data to the server. The server receives and integrates this data to generate a feedback report that includes calories burned, running pace, and emotional changes. The generated report is sent to the device, and the user uses it to plan their next run.

[0630] (Application Example 2)

[0631] 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."

[0632] Loss of motivation and emotional fluctuations during running can hinder an optimal exercise experience. Traditional technologies have struggled to analyze a user's emotional state in real time and provide personalized encouragement and guidance based on that analysis. A more sophisticated feedback system is needed to enable users to continue running effectively without interruption.

[0633] 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.

[0634] In this invention, the server includes an input means for the user to input target running criteria, a location identification means for acquiring location information, an analysis means for analyzing the data collected using the location identification means and calculating the optimal running route for the target, and an emotion analysis means for monitoring the user's emotional state and providing emotion-based feedback. This allows the user to receive specific and real-time instructions tailored to their individual emotional state, enabling them to achieve optimal running while maintaining motivation.

[0635] An "input method" is an interface for the user to input their target driving standards into the device.

[0636] "Location identification means" refers to technologies and devices for acquiring a user's location information in real time.

[0637] The "analysis means" is a system that analyzes acquired data to calculate the optimal driving route for the user's goals.

[0638] "Display means" refers to a device or method for visually presenting the travel route and information calculated by the analysis means to the user.

[0639] "Notification means" refers to an audio or visual output device that provides messages to help the user maintain their driving pace.

[0640] "Emotional analysis means" refers to technologies and devices for monitoring a user's emotional state and providing feedback based on that information.

[0641] This invention is a running support system that incorporates various means to improve the user's running experience. The system includes a terminal for the user to input target running criteria and location tracking means for acquiring location information in real time. Specifically, a GPS module is used to accurately determine the user's location. Based on the user's set goals, the server analyzes and calculates the optimal running route. A commercially available map API is used for the analysis to derive a highly accurate route. Furthermore, a camera and microphone equipped on the terminal are used as sensors to monitor the user's emotional state. Data acquired from these sensors is analyzed in real time by an emotion analysis engine.

[0642] The server also tracks the user's running pace and, if that pace falls below the target, provides encouraging notifications via voice and visuals through the device. Voice feedback uses speech synthesis technology to ensure the user receives the most appropriate encouragement. Furthermore, a detailed feedback report is generated at the end of the run, based on the user's performance and emotional changes. This report helps in planning the next run and encourages the user to set new goals.

[0643] For example, if a user starts a morning run and is running at their planned pace but shows signs of fatigue along the way, the device will display an encouraging message such as, "Your current pace is excellent. Keep it up!" This message is based on emotional data generated by the emotion analysis engine. Another example of a prompt message is, "Analyze the user's emotional state during their run, generate an appropriate encouraging message, and output it aloud."

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The user inputs their target running criteria using a terminal. The data entered includes distance, pace, and running time. This data is sent to the server and used as basic information for achieving the goal.

[0647] Step 2:

[0648] The device utilizes location tracking to obtain the user's real-time location information. The acquired location data is transmitted to a server and used as input information for analysis. The data is provided by a GPS module, resulting in highly accurate location information.

[0649] Step 3:

[0650] The server performs analysis based on the received location information and the user's target driving criteria. Using a map API, it calculates the optimal driving route and generates a route that also takes environmental information into account. The analysis results are then returned to the terminal.

[0651] Step 4:

[0652] The terminal displays the optimal running route received from the server to the user. The route is visually shown on a map, and the user begins running according to it. The terminal's display is used for the display.

[0653] Step 5:

[0654] The device uses a camera and microphone to monitor the user's emotional state in real time. An emotion engine analyzes the user's voice and facial expression data to estimate their emotional state. This analysis result is sent to a server.

[0655] Step 6:

[0656] The server monitors the user's emotional state and running pace. If the user's motivation decreases or their running pace falls below the target, it uses an AI model to generate appropriate encouragement and instructions and sends them to the device.

[0657] Step 7:

[0658] The device provides the user with encouraging messages received from the server via voice output. Based on the generated messages, the user receives encouragement and is supported in achieving their goals.

[0659] Step 8:

[0660] After the run is complete, the terminal sends the run data and emotion analysis results to the server. The server performs comprehensive data processing and creates a feedback report. This report is used to plan the next run.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] [Fourth Embodiment]

[0665] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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".

[0678] This invention is a system designed to help users maintain their target pace while running. First, the user launches a running support app and inputs a specific goal as a target running parameter, such as "run 5km in 30 minutes." The device receives this information via its built-in input device. Additional requirements, such as avoiding hills or minimizing traffic lights, can also be input at this time.

[0679] Next, the device uses GPS-based location tracking to collect the user's current location and surrounding route data. The device then obtains environmental data such as road shape, gradient, traffic light locations, and pedestrian congestion via a map service API. After that, the device sends this information to the server.

[0680] Based on this data, the server calculates the optimal route to help the user achieve their goals. The server then sends the calculated optimal route information back to the terminal. The terminal uses the received information to display the optimal route on a map, allowing the user to receive route guidance that is easy to understand visually on the spot.

[0681] During a run, the device monitors the user's pace and location in real time. If the pace is about to fall below the entered target, it sends a voice message via a notification system. Encouraging voice messages such as "Great pace, keep it up!" or "Almost there!" help maintain the user's motivation. This system allows users to train efficiently and effectively towards achieving their goals.

[0682] After the run is completed, the server analyzes the user's running data and generates feedback such as calories burned, average speed, and achievement level. The terminal displays this feedback information to the user, providing data that will be useful for future runs. Throughout the entire invention, users are supported in achieving their ideal running lifestyle through an experience optimized to their own pace.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The user launches the running support app and enters their target running parameters. They can set goals such as "run 5km in 30 minutes" or additional requirements like "avoid traffic lights."

[0686] Step 2:

[0687] The terminal saves the target driving parameters entered by the user. (1)

[0688] Step 3:

[0689] The device uses GPS to determine the user's current location. Simultaneously, it uses a map service API to acquire environmental data such as surrounding geographical information, road gradients, traffic light locations, and pedestrian congestion levels.

[0690] Step 4:

[0691] The device sends the collected information to the server. This data includes the user's current location and surrounding environment data.

[0692] Step 5:

[0693] The server analyzes the received environmental data and the user's target driving parameters to calculate the optimal driving route. In this process, it prioritizes routes with fewer traffic lights and fewer hills.

[0694] Step 6:

[0695] The server returns optimized route information to the terminal as route guidance.

[0696] Step 7:

[0697] The device displays the optimal route information it receives to the user. It shows the route on a map and displays important points such as traffic lights and hills with icons.

[0698] Step 8:

[0699] The user begins running according to the provided route guidance.

[0700] Step 9:

[0701] The device monitors the user's current location and pace in real time while they are running.

[0702] Step 10:

[0703] The device measures the user's pace, and if it determines that the user is falling below the entered target pace, it sends a voice message to the user via a notification system. This message encourages the user and helps them maintain their goal.

[0704] Step 11:

[0705] After the run is finished, the device sends the run log to the server.

[0706] Step 12:

[0707] The server analyzes the running logs and generates feedback such as calorie consumption, average speed, and goal achievement.

[0708] Step 13:

[0709] The device visualizes and displays the generated feedback to the user, providing reference information for their next run.

[0710] (Example 1)

[0711] 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".

[0712] Modern exercise support devices are insufficient in providing efficient and encouraging support to users towards their goals. In particular, current systems lack real-time feedback and optimization that takes environmental information into account, failing to provide adequate support for effectively achieving user-set goals. This situation needs improvement, and a more effective and motivating exercise support environment for users is required.

[0713] 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.

[0714] In this invention, the server includes an input means for the user to input target running specifications, a position measurement means for acquiring location information, and a calculation means for analyzing surrounding information collected using the position measurement means and calculating the optimal route to the target. This enables the user to select the optimal route utilizing environmental information and to effectively engage in activities toward achieving their goal while receiving real-time exercise support and feedback.

[0715] A "user" refers to an individual who uses this system to set exercise goals and engage in exercise activities.

[0716] "Target running specifications" refer to the target values ​​for exercise set by the user, and may include, for example, distance, time, or exercise efficiency under specific conditions.

[0717] "Input means" refers to a device or interface for the user to communicate target driving specifications or other driving conditions to the system.

[0718] "Location measurement means" refers to technologies and devices used to obtain the user's current location and geographical information of their surroundings, and generally includes location information technologies such as GPS.

[0719] "Surrounding area information" refers to geographical and environmental data of the area related to the exercise, including road information, traffic conditions, and weather conditions.

[0720] "Calculation means" refers to technology that calculates the optimal path and strategy to achieve the user's exercise goals based on the acquired information.

[0721] "Visual display means" refers to a device or interface that visually provides the user with information about the calculated optimal route and movement status.

[0722] A "notification method" is a function that provides notifications to the user, delivering information to the user in the form of audio or visual messages.

[0723] "Analysis means" refers to technology that analyzes the user's exercise data in detail after the exercise is completed and generates feedback regarding exercise efficiency and performance.

[0724] This invention constructs an exercise support system and is equipped with various functions to efficiently achieve user-set goals.

[0725] First, the user inputs their target running specifications using a device such as a mobile terminal. The terminal then receives the exercise goal information from the user through the input method. Here, a specific goal can be set, for example, "run 5km in 30 minutes." The input data is stored within the terminal and used for the next processing step.

[0726] Next, the device uses GPS, a location measurement tool, to obtain the user's current location. Furthermore, it uses a map service API (e.g., a general-purpose map software API) to collect surrounding information such as road information, traffic conditions, and weather information. This information is used by the calculation tool to calculate the optimal route to support the user in achieving their exercise goals.

[0727] The calculated results are displayed as map information on the terminal via a visual display device. Based on this information, the user can select the optimal exercise route and begin exercising.

[0728] During exercise, the device continuously monitors the user's exercise status in real time using notification methods. It also features a function to alert the user with an audio notification if the exercise pace deviates from the set target. This implementation helps users maintain an appropriate pace.

[0729] Once the exercise is complete, the device sends the running data to a server, where an analysis system evaluates it. It generates and provides feedback to the user regarding exercise efficiency, such as calories burned, exercise time, and average speed.

[0730] For example, if a user sets a goal of "running 5km in 30 minutes" and selects a safe route within a park, the device will select the optimal route based on real-time data and provide voice guidance such as "maintain your pace." In this way, users can continue exercising efficiently.

[0731] Examples of prompts for a generative AI model:

[0732] "If a user sets a goal of running 5km in 30 minutes, please explain the steps to suggest the optimal running route that avoids congestion."

[0733] This exercise support system allows users to engage in planned and effective activities toward achieving their goals.

[0734] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0735] Step 1:

[0736] The user launches a running support app on their device and enters their target running specifications. The user enters a specific goal, such as "run 5km in 30 minutes." The input device receives this information and saves it as input data on the device. The output of this step is the data saved as the target running specifications.

[0737] Step 2:

[0738] The device obtains the user's current location using GPS. The location measurement device receives the current location information and stores it within the device as location data for future use. After the location information is determined, the device uses a map service API to collect surrounding road information, traffic conditions, and weather conditions. This environmental data becomes the input used in the next calculation step.

[0739] Step 3:

[0740] The terminal uses collected location and environmental data to calculate the optimal route for achieving the user's goal. The data processing performed here involves executing a route optimization algorithm that takes into account the user's goal and current environmental conditions. The output is the optimized route information. A server may also assist in this route calculation.

[0741] Step 4:

[0742] The terminal presents the calculated optimal route to the user using a visual display. The user can intuitively understand the route through a visual location display on a map. The output provides a display of the route the user should actually follow.

[0743] Step 5:

[0744] During running, the device monitors the user's location and pace in real time. Through this monitoring, if the user is about to deviate from their target pace, the notification system generates and provides a voice message to the user. Here, the user's pace data is used as input, and the notification message is output. The voice message provides encouragement such as, "Please maintain your pace."

[0745] Step 6:

[0746] After the exercise is completed, the device sends the user's running data to the server. The server uses analysis tools to evaluate this data and generates feedback such as calories burned, average speed, and goal achievement. As output, detailed feedback on the user's exercise efficiency is generated, which can be used to plan future exercise sessions.

[0747] (Application Example 1)

[0748] 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".

[0749] When running or walking, it is essential to provide users with the optimal course for efficient and safe running while maintaining their set target pace, and to maintain motivation during the run. However, conventional systems have difficulty flexibly responding to changes in the environment and individual goals, so further improvements are needed.

[0750] 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.

[0751] In this invention, the server includes an input means for the user to set target driving parameters, a location identification means for acquiring location information, and an analysis means for calculating the optimal driving route based on the collected environmental data. This enables the provision of an optimal driving route tailored to each user's individual goals and real-time pace management during driving.

[0752] A "user" is a person who uses this system to set and achieve their own running or walking goals.

[0753] "Target driving parameters" refer to specific numerical information such as the target time, distance, and pace set by the user for their driving.

[0754] "Input method" refers to an interface for the user to input target driving parameters, and usually refers to a smartphone or computer application.

[0755] "Location determination means" refers to the technology used to determine the user's current location, and generally uses GPS.

[0756] The "analysis means" is a calculation function that calculates the optimal driving route for achieving the user's goals based on collected location information and environmental data.

[0757] "Display means" refers to a method for visually showing the calculated driving route to the user, and typically involves using a display or map application.

[0758] The "notification method" is a function that monitors the user's real-time running pace and encourages the user with voice messages or text messages when that pace falls below the target.

[0759] "Environmental data" refers to data about the user's surroundings, including road shape, gradient, traffic signal information, and congestion levels.

[0760] This system provides users with the optimal running route to achieve their set running or walking goals and manages their pace in real time. Users first launch the application using a mobile device such as a smartphone and input their target running parameters. A touchscreen interface is used for input.

[0761] The device uses its built-in GPS module to determine the user's current location. Next, it collects surrounding environmental data via a map service API and sends it to the server. The server analyzes this data using programming languages ​​such as Python to calculate the optimal driving route. The calculated route information is returned to the device and displayed through the map application.

[0762] Once the user starts running, the device monitors their pace in real time. If the pace falls below the entered target, a notification system will encourage the user through a voice message. Voice messages such as "Almost there!" help maintain the pace and support the user's motivation.

[0763] Finally, after the run is completed, the server analyzes the running data in more detail and provides feedback to the user on calories burned, average speed, and achievement level. This allows the user to identify areas for further improvement and use them in their next run.

[0764] As a concrete example, there is a case where a user received a route suggestion that avoided crowded urban areas and went through a quiet park on a Sunday morning, allowing them to easily achieve their target pace. Behind the realization of such a system lies advanced analytical technology utilizing generative AI models. When using the generative AI model to input the optimal route as a prompt, a specific prompt statement is used: "Generate the optimal route that takes into account the real-time information of the surrounding area, in accordance with the goal set by the user."

[0765] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0766] Step 1:

[0767] The terminal receives the application from the user and inputs target driving parameters. These parameters include target values ​​such as distance and pace. The terminal then receives this input data and prepares it for the next processing step.

[0768] Step 2:

[0769] The device uses its built-in GPS module to obtain the user's location information. This determines the user's current location and prepares it to collect environmental data about its surroundings.

[0770] Step 3:

[0771] The device collects surrounding environmental data through a map service API. This includes road shapes, traffic signal information, and congestion levels. The device then aggregates this data and generates data packets to send to the server.

[0772] Step 4:

[0773] The server receives data sent from the terminal and begins analyzing the data using an analysis program such as Python. Here, a generative AI model is used to calculate the optimal driving route. Specifically, based on the input target parameters and environmental data, the most efficient route for the user is derived. This calculation result is generated as route data.

[0774] Step 5:

[0775] The server returns the optimal route data, which is the result of its analysis, to the terminal. The terminal receives this data and displays the route visually through a map application. The user then refers to this and prepares to start running.

[0776] Step 6:

[0777] The device monitors the user's real-time pace while they are running. It continuously acquires the user's speed and location and compares it to the entered target pace. If the pace is about to fall below the target, the device uses a notification system to provide encouragement through voice messages.

[0778] Step 7:

[0779] After the ride is complete, the device sends the collected ride data to the server. The server uses this data to perform a detailed analysis and generates feedback that includes calories burned, average speed, and degree of goal achievement.

[0780] Step 8:

[0781] The server sends the generated feedback back to the terminal. The terminal can then display this feedback to the user, which can be used to help with future improvements and goal setting.

[0782] The above outlines the processing steps of the program required to implement this application example.

[0783] 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.

[0784] This invention is a running support system that enables users to run effectively, and in particular, it has a function to monitor and analyze the user's emotional state and maintain motivation. The user first launches a running app and inputs target running parameters (e.g., distance and pace). Based on this information, the device starts providing support in the direction desired by the user.

[0785] This system uses terminal location tracking to determine the user's location in real time and transmits it to a server along with surrounding environment data obtained using a map API. The server uses this information to analyze the optimal driving route, particularly minimizing obstacles such as traffic lights and congestion. The calculated route is sent back to the terminal, visualized on a map, and guided to the user.

[0786] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The terminal is equipped with sensors to read the user's voice and facial expressions, and the emotion engine uses this sensor information to estimate the user's emotions. For example, if it detects anxiety or fatigue, the server sends an appropriate encouraging message to the terminal that takes that state into account.

[0787] During a run, the device monitors the user's pace and sends an encouraging voice message if it falls below the target pace. If the emotion engine determines that the user's motivation is low, it can send a more personalized message such as, "You've come this far, let's keep going a little longer!"

[0788] Once the run is complete, the device sends the emotion engine data and run results to the server. The server performs comprehensive data processing and generates a feedback report based on calorie consumption, emotional changes, running pace, etc., which is then presented to the user. This detailed feedback allows users to make adjustments for future runs and develop more effective exercise plans. In this way, the system enhances the user's running experience and provides motivation management that is tailored to individual emotions.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The user launches the running support app and sets their target running parameters. They input information such as target pace, distance, and running time. Additional conditions can also be set, such as selecting a route with fewer traffic lights.

[0792] Step 2:

[0793] The device receives the set target information and uses GPS to determine the user's current location. At the same time, it activates sensors to collect facial and voice data and prepares to send it to the emotion engine.

[0794] Step 3:

[0795] The device obtains surrounding geographical information, road conditions, traffic light locations, and pedestrian density via a map API, and sends all the data to the server.

[0796] Step 4:

[0797] The server analyzes the received data and calculates the most suitable route for achieving the user's goal. At this stage, it selects a route that minimizes obstacles and obstacles.

[0798] Step 5:

[0799] The server sends the analysis results to the terminal, which then displays the optimal route on a map based on these results. The user visually confirms the appropriate terrain by looking at the map and begins running based on the information.

[0800] Step 6:

[0801] During running, the device monitors the user's current location and running pace in real time. It also uses sensors to transmit data on the user's voice and facial expressions to an emotion engine to evaluate their emotional state.

[0802] Step 7:

[0803] The emotion engine analyzes user data and detects emotional changes such as anxiety and fatigue. If necessary, it requests the server to generate an appropriate message based on the user's emotional state.

[0804] Step 8:

[0805] The server generates and sends encouraging messages tailored to the user's emotions. For example, it might create messages like, "You're doing great, keep it up!" or "You're getting tired, but you're almost there!"

[0806] Step 9:

[0807] The terminal delivers messages received from the server to the user via voice output. This allows the user to maintain motivation and continue riding.

[0808] Step 10:

[0809] Once the run is finished, the device sends the run log, user emotion change data, and sensor data to the server.

[0810] Step 11:

[0811] The server comprehensively analyzes the received data and generates feedback reports related to calorie consumption, emotional changes, and running pace.

[0812] Step 12:

[0813] The device informs the user of the generated feedback and presents it as information to help plan their next run. The user can then use this information to further improve their training.

[0814] (Example 2)

[0815] 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".

[0816] A challenge when it comes to providing exercise support that takes into account the impact of a user's emotional state on their motivation while running is to address this issue. Conventional systems are limited to simply tracking running pace and distance, making it difficult to provide appropriate feedback in response to changes in the user's emotions. Therefore, there is a need for a system that can support users in achieving their goals efficiently and comfortably, and in maintaining their motivation.

[0817] 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.

[0818] In this invention, the server includes an input means for the user to input target running parameters, a location identification means for acquiring location information, and an analysis means for analyzing environmental information. This enables personalized feedback that takes into account the user's emotional state and the presentation of the optimal running route.

[0819] The "input method" refers to the interface for the user to input their target running parameters.

[0820] "Location determination means" refers to the technical methods and devices used to obtain the user's current location.

[0821] "Analysis means" refers to a process or device for calculating the optimal driving route based on collected data and environmental information.

[0822] "Display means" refers to a visual device or method for presenting the calculated travel route to the user.

[0823] "Emotional analysis means" refers to technologies and devices for detecting and analyzing a user's emotional state from their voice and facial expressions.

[0824] "Notification means" refers to systems or devices that inform users about their driving status.

[0825] "Data processing means" refers to a technology or process for analyzing integrated emotional data and driving data after a drive and providing it as feedback.

[0826] This invention is a support system to help users run more effectively. Specifically, it begins with the user installing and launching a running app on their device. The device acquires the user's location information in real time using GPS-based location tracking. Furthermore, it collects surrounding environmental data by using APIs from external services via the internet.

[0827] The server has software for analyzing location and environmental data transmitted from the terminal. This analysis calculates the optimal running route, taking into account terrain data and traffic conditions. The calculated route is sent back to the terminal and displayed to the user through a map application.

[0828] The device also features an emotion analysis system that analyzes voice and facial expressions, monitoring the user's emotional state in real time. If the user shows signs of anxiety or fatigue, the server generates an appropriate encouraging message based on the analysis and sends it to the device. For example, a message such as, "You've come this far, let's keep going a little longer!" might be provided.

[0829] Once the run is complete, the device aggregates all data and sends it to a server. The server then analyzes this data comprehensively and generates a feedback report based on calories burned, running pace, and changes in emotional state, which is delivered to the user via the device. This report allows the user to make appropriate plans for their next run.

[0830] As a concrete example, when a user goes for an early morning run, they input information such as "target distance 5 kilometers, pace 6 minutes / km" into their device. During the run, if the user feels fatigued, they receive a customized encouraging message such as, "Relax and focus on your breathing."

[0831] Examples of prompts generated by the AI ​​model include, "Please create a running support message that reflects the user's emotions," and "Please tell me how to generate a running route that takes scenery and safe routes into consideration." In this way, the invention provides practical running support that is tailored to the user's emotions and circumstances.

[0832] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0833] Step 1:

[0834] The user launches the running app and enters their target parameters for the run (e.g., distance, pace) into the device. The entered information is provided to the system as the conditions for starting the run. The device then receives this information and prepares for the next necessary processes.

[0835] Step 2:

[0836] The device obtains location information using its built-in GPS. This acquired location information is updated in real time as data indicating the user's current location. Simultaneously, the device uses external APIs to collect surrounding environmental information such as weather, terrain, and traffic conditions. This information serves as input data for calculating the optimal driving route.

[0837] Step 3:

[0838] The device transmits acquired location information and environmental data to the server. Upon receiving this data, the server applies a dedicated analysis algorithm to calculate the optimal driving route. The calculated route takes into account the absence of traffic lights and congestion. The server returns the calculated route to the device, which then displays it on a map.

[0839] Step 4:

[0840] During running, the device uses voice sensors and a camera to detect the user's voice tone and facial expressions. This data is used as input for emotion analysis to estimate the user's emotional state. Emotional states such as optimism or fatigue are estimated.

[0841] Step 5:

[0842] The server receives emotional state information from the device and generates an appropriate encouraging message based on this information. The generated message will be designed to boost the user's motivation. The server sends this message to the device. The device then presents the message to the user via voice or text.

[0843] Step 6:

[0844] Once the run is complete, the device sends run result data and emotional state data to the server. The server receives and integrates this data to generate a feedback report that includes calories burned, running pace, and emotional changes. The generated report is sent to the device, and the user uses it to plan their next run.

[0845] (Application Example 2)

[0846] 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".

[0847] Loss of motivation and emotional fluctuations during running can hinder an optimal exercise experience. Traditional technologies have struggled to analyze a user's emotional state in real time and provide personalized encouragement and guidance based on that analysis. A more sophisticated feedback system is needed to enable users to continue running effectively without interruption.

[0848] 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.

[0849] In this invention, the server includes an input means for the user to input target running criteria, a location identification means for acquiring location information, an analysis means for analyzing the data collected using the location identification means and calculating the optimal running route for the target, and an emotion analysis means for monitoring the user's emotional state and providing emotion-based feedback. This allows the user to receive specific and real-time instructions tailored to their individual emotional state, enabling them to achieve optimal running while maintaining motivation.

[0850] An "input method" is an interface for the user to input their target driving standards into the device.

[0851] "Location identification means" refers to technologies and devices for acquiring a user's location information in real time.

[0852] The "analysis means" is a system that analyzes acquired data to calculate the optimal driving route for the user's goals.

[0853] "Display means" refers to a device or method for visually presenting the travel route and information calculated by the analysis means to the user.

[0854] "Notification means" refers to an audio or visual output device that provides messages to help the user maintain their driving pace.

[0855] "Emotional analysis means" refers to technologies and devices for monitoring a user's emotional state and providing feedback based on that information.

[0856] This invention is a running support system that incorporates various means to improve the user's running experience. The system includes a terminal for the user to input target running criteria and location tracking means for acquiring location information in real time. Specifically, a GPS module is used to accurately determine the user's location. Based on the user's set goals, the server analyzes and calculates the optimal running route. A commercially available map API is used for the analysis to derive a highly accurate route. Furthermore, a camera and microphone equipped on the terminal are used as sensors to monitor the user's emotional state. Data acquired from these sensors is analyzed in real time by an emotion analysis engine.

[0857] The server also tracks the user's running pace and, if that pace falls below the target, provides encouraging notifications via voice and visuals through the device. Voice feedback uses speech synthesis technology to ensure the user receives the most appropriate encouragement. Furthermore, a detailed feedback report is generated at the end of the run, based on the user's performance and emotional changes. This report helps in planning the next run and encourages the user to set new goals.

[0858] For example, if a user starts a morning run and is running at their planned pace but shows signs of fatigue along the way, the device will display an encouraging message such as, "Your current pace is excellent. Keep it up!" This message is based on emotional data generated by the emotion analysis engine. Another example of a prompt message is, "Analyze the user's emotional state during their run, generate an appropriate encouraging message, and output it aloud."

[0859] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0860] Step 1:

[0861] The user inputs their target running criteria using a terminal. The data entered includes distance, pace, and running time. This data is sent to the server and used as basic information for achieving the goal.

[0862] Step 2:

[0863] The device utilizes location tracking to obtain the user's real-time location information. The acquired location data is transmitted to a server and used as input information for analysis. The data is provided by a GPS module, resulting in highly accurate location information.

[0864] Step 3:

[0865] The server performs analysis based on the received location information and the user's target driving criteria. Using a map API, it calculates the optimal driving route and generates a route that also takes environmental information into account. The analysis results are then returned to the terminal.

[0866] Step 4:

[0867] The terminal displays the optimal running route received from the server to the user. The route is visually shown on a map, and the user begins running according to it. The terminal's display is used for the display.

[0868] Step 5:

[0869] The device uses a camera and microphone to monitor the user's emotional state in real time. An emotion engine analyzes the user's voice and facial expression data to estimate their emotional state. This analysis result is sent to a server.

[0870] Step 6:

[0871] The server monitors the user's emotional state and running pace. If the user's motivation decreases or their running pace falls below the target, it uses an AI model to generate appropriate encouragement and instructions and sends them to the device.

[0872] Step 7:

[0873] The device provides the user with encouraging messages received from the server via voice output. Based on the generated messages, the user receives encouragement and is supported in achieving their goals.

[0874] Step 8:

[0875] After the run is complete, the terminal sends the run data and emotion analysis results to the server. The server performs comprehensive data processing and creates a feedback report. This report is used to plan the next run.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0897] The following is further disclosed regarding the embodiments described above.

[0898] (Claim 1)

[0899] An input means for the user to input target driving parameters,

[0900] A means for determining location information,

[0901] An analysis means that analyzes the data collected using the aforementioned location identification means and calculates the optimal driving route for the target,

[0902] A display means for displaying the driving route calculated by the analysis means to the user,

[0903] A system that tracks the user's real-time running status and provides a notification mechanism to encourage the user when their running pace falls below their target,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein the analysis means minimizes obstacles to the travel path using environmental data.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the notification means provides the user with a message to maintain a driving pace using voice output.

[0909] "Example 1"

[0910] (Claim 1)

[0911] An input method for the user to input target driving specifications,

[0912] A means for measuring location information,

[0913] A calculation means that analyzes surrounding information collected using the aforementioned position measurement means and calculates the optimal path to the target,

[0914] A visual display means that displays the route calculated by the calculation means to the user,

[0915] A notification system that monitors the user's real-time exercise status and encourages the user if their exercise pace falls below the target,

[0916] An analytical means for evaluating exercise data after the end of exercise and generating feedback on exercise efficiency,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, wherein the calculation means minimizes obstacles to the path using environmental information.

[0920] (Claim 3)

[0921] The system according to claim 1, wherein the notification means provides guidance to the user on maintaining an exercise pace using voice output.

[0922] "Application Example 1"

[0923] (Claim 1)

[0924] An input means for the user to input target driving parameters,

[0925] A means for obtaining location information,

[0926] An analysis means analyzes the data collected by the aforementioned location identification means and calculates the optimal driving route for a target set by the user,

[0927] A display means for visually displaying the driving route calculated by the analysis means,

[0928] A notification system that tracks the user's real-time running status and motivates the user if their running pace does not reach their target,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, wherein the analysis means uses environmental data to avoid obstacles in the travel path as much as possible.

[0932] (Claim 3)

[0933] The system according to claim 1, wherein the notification means provides a message to the user encouraging them to maintain their driving pace using voice output.

[0934] "Example 2 of combining an emotion engine"

[0935] (Claim 1)

[0936] An input means for the user to input target driving parameters,

[0937] A means for determining location information,

[0938] An analysis means that analyzes data and environmental information collected using the aforementioned location identification means and calculates the optimal driving route for the target,

[0939] A display means that presents the driving route calculated by the analysis means to the user,

[0940] An emotion analysis means that detects the user's emotional state from voice and facial expressions and provides supplementary information,

[0941] A system that monitors the user's real-time driving status and provides a notification mechanism to encourage the user when their driving pace falls below the target,

[0942] A data processing method that integrates emotional data and driving data after the end of the run and provides the analysis results as feedback,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, wherein the analysis means minimizes obstacles to the driving path using environmental data, and the emotion analysis means generates personalized messages based on the user's emotions.

[0946] (Claim 3)

[0947] The system according to claim 1, wherein the notification means provides the user with a message to maintain their running pace using voice output, and adjusts the encouraging message according to the user's emotional state.

[0948] "Application example 2 when combining with an emotional engine"

[0949] (Claim 1)

[0950] An input method for the user to input target driving standards,

[0951] A means for determining location information,

[0952] An analysis means that analyzes the data collected using the aforementioned location identification means and calculates the optimal driving route for the target,

[0953] A display means for displaying the driving route calculated by the analysis means to the user,

[0954] A system that tracks the user's real-time running status and provides a notification mechanism to encourage the user when their running pace falls below their target,

[0955] A sentiment analysis means that monitors the user's emotional state and provides emotion-based feedback,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, wherein the analysis means minimizes obstacles to the travel path using environmental information.

[0959] (Claim 3)

[0960] The system according to claim 1, wherein the notification means provides the user with a message to maintain a driving pace using voice output. [Explanation of Symbols]

[0961] 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. An input means for the user to input target driving parameters, A means for obtaining location information, An analysis means analyzes the data collected by the aforementioned location identification means and calculates the optimal driving route for a target set by the user, A display means for visually displaying the driving route calculated by the analysis means, A notification system that tracks the user's real-time running status and motivates the user if their running pace does not reach their target, A system that includes this.

2. The system according to claim 1, wherein the analysis means uses environmental data to avoid obstacles in the travel path as much as possible.

3. The system according to claim 1, wherein the notification means provides a message to the user encouraging them to maintain their driving pace using voice output.

Images