System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving setting data by a user and transmitting the setting data to a server; means for collecting position information of the user and transmitting the position information to the server; means for analyzing a past walking history and the setting data of the user and generating an optimal walk course by the server; means for transmitting the generated walk course to a user terminal and presenting the walk course to the user; and means for calculating calorie consumption after the end of a walk and notifying the user of a result of the calculation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Walking around the home usually involves walking the same route every day, resulting in little new discoveries and a tendency to become monotonous. This problem can lead to the walk becoming increasingly boring and a loss of motivation to continue. Another problem is that it can be difficult to effectively manage daily exercise volume and calorie expenditure. The purpose of this invention is to solve these problems and enable users to enjoy a new walking route every day. [Means for solving the problem]

[0005] The present invention is a system including the following means.

[0006] 1. A means of receiving user configuration data and sending it to the server.

[0007] 2. A means of collecting user location information and transmitting that data to a server.

[0008] 3. A means for the server to analyze past walking history and user settings data to generate the optimal walking course.

[0009] 4. A means for transmitting the generated walking course to the user's terminal and presenting it to the user.

[0010] 5. A means of calculating calories burned after a walk and notifying the user of the results.

[0011] The server adjusts the walking course based on the user's preferences and calorie consumption goal, and selects the optimal route from the multiple routes generated. This allows the user to enjoy a new walking course every day while effectively burning calories and living a healthy life.

[0012] "Setting data" is information about preferred locations and target calories burned that the user inputs through the application.

[0013] The "server" is a computer system that receives and analyzes setting data, location information, and walking history, and generates and transmits the optimal walking course.

[0014] "Location information" refers to GPS data as the user moves in real time.

[0015] "Walking history" refers to data on the routes the user has walked in the past and the associated time and distance.

[0016] A "walking course" is a walking route generated by the server and provided to the user.

[0017] "Calories burned" is data that calculates the amount of energy consumed when the user goes for a walk or stroll.

[0018] A "user terminal" is a device such as a smartphone used by a user, which has functions such as inputting setting data, displaying walking courses, and obtaining location information. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system operates mainly using a user terminal, a server, and GPS data.

[0041] Overall system configuration

[0042] 1. User Device

[0043] It is a smartphone or tablet used by the user to input setting data, collect location information, and display walking courses.

[0044] Users input their preferred location and target calorie consumption through the application.

[0045] 2. Server

[0046] The server is a computer system that receives and analyzes the setting data, location information, and walking history.

[0047] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0048] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0049] System Operation

[0050] Initial Setup

[0051] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0052] Collecting walking data

[0053] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0054] Generate new walking routes

[0055] The server uses an AI model to generate a new walking course based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0056] Walking course presentation and selection

[0057] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0058] Calorie burn calculation and feedback

[0059] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0060] Specific examples

[0061] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0062] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user starts the application and first inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). The device temporarily stores this setting data. Then, it sends the setting data to the server. The server saves the received data in the user profile.

[0066] Step 2:

[0067] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device continues to record location information at regular intervals. When the user finishes their walk, the device sends the collected GPS data to the server. The server receives the location data and saves it as the user's walking history.

[0068] Step 3:

[0069] The server analyzes the user's settings and walking history, and generates a new walking course using an AI model. Based on the user's preferences and calorie consumption goals, the server prioritizes routes that have not been walked before or have not been used for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0070] Step 4:

[0071] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a route that goes around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0072] Step 5:

[0073] The user takes a walk according to the new walking course selected. After the walk is over, the device collects GPS data again and sends it to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user, "You've achieved your calorie goal! Congratulations!"

[0074] In this way, the roles of the user, the terminal, and the server are clearly defined in each step, allowing the user to effectively enjoy a new walking course while simultaneously achieving the target calorie consumption.

[0075] Example 1

[0076] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0077] With the increasing health consciousness in today's society, there is a demand for support systems that can help people maintain a fun daily exercise habit. Conventional pedometers and simple course suggestion systems have difficulty generating appropriate walking courses that match the individual preferences and goals of the user, making it difficult to encourage continued use. In response to this, a system is needed that can automatically suggest optimal walking courses that take into account the user's specific wishes and health goals.

[0078] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0079] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, and a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course using a generative AI model. This makes it possible to provide an optimal walking course based on the user's individual preferences and goals, and to encourage continuous use through calculation of calories burned and feedback.

[0080] "User setting data" refers to information such as a favorite location or target calorie consumption that the user inputs through the application.

[0081] The "server" is a computer system that receives and analyzes setting data, location information, and walking history.

[0082] "User location information" is geographical data obtained using the GPS sensor of the user terminal.

[0083] "Walking history" refers to a record of the routes the user has walked in the past and the GPS data from those routes.

[0084] A "generative AI model" is an artificial intelligence algorithm that generates optimal walking courses based on the user's settings and walking history data.

[0085] The "optimal walking course" is the optimal walking route for the user, suggested by the generative AI model based on the user's preferences and goals.

[0086] A "user device" is a device used by a user, such as a smartphone or tablet, which is used to input setting data, collect location information, and display walking courses.

[0087] "GPS data" refers to location information obtained by the GPS sensor of the user terminal.

[0088] "Calories burned" is the amount of energy consumed by the user while walking.

[0089] "Feedback" refers to information such as a calorie burn calculation result and an encouraging message that is provided to the user after the walk is completed.

[0090] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. This system operates mainly using a user terminal, a server, and GPS data.

[0091] Overall system configuration

[0092] 1. User Device

[0093] It is a smart device (smartphone or tablet) used by the user to input setting data, collect location information, and display walking courses. Users input their preferred locations and target calorie consumption through the application.

[0094] Hardware used: smartphone, tablet

[0095] Software used: Application

[0096] 2. Server

[0097] The server is a computer system that receives and analyzes setting data, location information, and walking history. The server is equipped with a generative AI model that generates an optimal walking course based on the user's preferences and goals. It calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0098] Hardware used: Server

[0099] Software used: Generative AI model, data analysis software

[0100] System Operation

[0101] Initial Setup

[0102] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0103] Collecting walking data

[0104] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0105] Generate new walking routes

[0106] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0107] Walking course presentation and selection

[0108] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0109] Calorie burn calculation and feedback

[0110] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0111] Specific examples

[0112] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0113] Prompt Sentence Examples

[0114] "Generate new walking itineraries based on the user's preferred locations and calorie goals. For example, suggest a route that includes a park and a cafe."

[0115] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] When a user launches the application for the first time, a setting screen is displayed. The user inputs their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server.

[0119] Input: User-entered preferred location, target calories burned

[0120] Data processing: This data is temporarily stored on the device and converted into a format for sending to the server.

[0121] Output: Configuration data sent to the server

[0122] Step 2:

[0123] The server receives the user's settings and stores them in a database, which is later used to generate walking routes.

[0124] Input: Configuration data sent from the terminal

[0125] Data processing: Analyzes the setting data and saves it in a database

[0126] Output: Saved user configuration data

[0127] Step 3:

[0128] When a user starts a walk, the device activates the GPS and collects location information at regular intervals. The collected data is temporarily stored on the device and sent to the server after the walk ends.

[0129] Input: Real-time location information from a GPS sensor

[0130] Data processing: Location information is recorded at regular intervals and temporarily saved

[0131] Output: GPS data sent to the server after the walk ends

[0132] Step 4:

[0133] The server analyzes the received location information and saves it as the user's walking history. This data is used to generate the next walking route.

[0134] Input: GPS data sent from the device

[0135] Data processing: Analyze location information and register it in a database as walking history

[0136] Output: Saved user walking history data

[0137] Step 5:

[0138] The server uses past walking history and configuration data to generate new walking routes using a generative AI model, which is designed to prioritize places that have not been walked before and routes that have not been visited in a while.

[0139] Input: Walking history data, setting data

[0140] Data processing: Generative AI model generates new walking routes

[0141] Output: Generated walking course data

[0142] Step 6:

[0143] The generated walking course is sent to the user terminal and presented to the user, who then checks the proposed walking course and approves it if necessary.

[0144] Input: Walking course data sent from the server

[0145] Data processing: Convert the walking course into a display format and present it to the user

[0146] Output: Walking itinerary that the user approves or modifies

[0147] Step 7:

[0148] When the user finishes their walk, the GPS data is collected again and sent to the server, which then calculates the calories burned and notifies the user.

[0149] Input: GPS data after the walk

[0150] Data processing: Calculate calories burned based on location information

[0151] Output: Calculation result (calories burned) and feedback message (e.g., "You've reached your calorie goal! Congratulations!")

[0152] In this way, each processing step works in conjunction with one another to realize a system that enriches the user's walking experience.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] Conventional walking support systems can generate walking courses based on the user's location information and walking history and calculate calories burned, but they lack the perspective of making the walk enjoyable. They also lack real-time information provision based on the user's interests and preferences. This can lead to a monotonous walking experience, which can decrease motivation to continue. Furthermore, the walking courses are not presented visually in an easy-to-understand manner, which can lead to users getting lost.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course, a means for transmitting the generated walking course to the user's terminal and presenting it to the user, a means for calculating calories burned after the walk and notifying the user of the result, a means for displaying the specified walking course using AR technology, and a means for displaying nearby landmarks and recommended spots in real time during the walk. This allows the user to be visually guided along a walking course based on their interests and preferences while receiving real-time information about nearby landmarks and recommended spots during the walk. This makes the walking experience more enjoyable, fresh, and helps maintain motivation. Furthermore, the visual clarity allows the user to continue their walk without getting lost.

[0158] "User setting data" is information necessary for the system to generate a walking course, such as the user's favorite places and target calorie consumption.

[0159] "User location information" is information for identifying the user's current location using GPS data or the like.

[0160] "Past walking history" is history data such as the routes and distances of walks the user has taken in the past.

[0161] An "optimal walking course" is a route suitable for walking that is generated based on the user's preferences and goals.

[0162] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on real-world scenery.

[0163] "Sightseeing spots and recommended spots" are interesting places along the walking route and places worth visiting.

[0164] "Means for displaying in real time" refers to a technology that instantly displays the information required at the user's current location.

[0165] The "means for calculating calories burned" is a method for accurately calculating the amount of calories burned based on the user's walking data.

[0166] "Navigation" refers to guiding the user to move along a walking course set by the user.

[0167] "Visually easy to understand" means presenting information in an easily recognizable form so that the user can understand the information at a glance.

[0168] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system mainly operates using a user terminal, a server, and GPS data. Specific embodiments for realizing this are described below.

[0169] Overall system configuration

[0170] The system consists of the following main components:

[0171] 1. User Device

[0172] The smartphone or tablet used by the user.

[0173] Enters configuration data, collects location information, and displays walking courses.

[0174] Users input their preferred location and target calorie consumption through the application.

[0175] 2. Server

[0176] It is a computer system that receives and analyzes setting data, location information, and walking history.

[0177] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0178] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0179] 3. Augmented Reality Technology

[0180] Used to visually indicate designated walking routes.

[0181] It uses devices such as smart glasses and head-mounted displays.

[0182] System Operation

[0183] Initial Setup

[0184] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0185] Collecting walking data

[0186] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0187] Generate new walking routes

[0188] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0189] Walking course presentation and selection

[0190] When a user opens the application, the user's device displays a new walking route. This is done using AR technology to visually present the walking route. A message such as "How about a route that goes around a new park and cafe?" is displayed, and if the user accepts the suggestion, the walking route is set as the next route.

[0191] Calorie burn calculation and feedback

[0192] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided.

[0193] Hardware and software used

[0194] Hardware:

[0195] Smart glasses (with AR function)

[0196] Smartphone

[0197] software:

[0198] ARKit (Apple's AR development framework)

[0199] TensorFlow (AI model building and prediction)

[0200] Firebase (cloud data management)

[0201] Specific examples

[0202] User A starts the application for the first time and sets the criteria: "I like cafes and parks" and "I want to burn 500 calories a day." The application uses AR to guide the user through their walking route, allowing them to check the calories burned in real time. Next, after the walk is over, a new recommended route is suggested.

[0203] Prompt Sentence Examples

[0204] User preferences: parks, cafes

[0205] Target calorie consumption: 500kcal

[0206] Current location: [35.6895, 139.6917]

[0207] Walking frequency: 3 times a week

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] User configuration data entry

[0211] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their goal for calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server, making it easier for the server to understand the user's preferences and goals.

[0212] Input: User settings data (preferred location, target calories burned)

[0213] Output: Configuration data sent to the server

[0214] Step 2:

[0215] Start collecting user location information

[0216] When a user starts a walk, the device uses GPS to collect the user's location information. This location information is recorded at regular intervals and updated in real time, allowing the device to accurately track the user's movements.

[0217] Input: GPS data (user location information)

[0218] Output: Location information recorded on the device

[0219] Step 3:

[0220] Sending data to the server

[0221] After the walk, the device sends the collected location information to the server, which then stores the data as the user's walking history and uses it to generate the next walking route.

[0222] Input: Recorded location information

[0223] Output: Location data sent to the server

[0224] Step 4:

[0225] Generate new walking routes

[0226] The server uses a generative AI model to generate a new walking course based on the user's settings and past walking history data. The AI ​​model selects the optimal route, taking into account the user's preferences and goals.

[0227] Input: Setting data, walking history data

[0228] Output: Generated walking course data

[0229] Step 5:

[0230] Walking course information

[0231] The generated walking course data is sent to the user's device, which then uses AR technology to visually display the walking course, allowing the user to walk while checking the new walking course in real time.

[0232] Input: New walking course data

[0233] Output: Walking route displayed on the terminal

[0234] Step 6:

[0235] Real-time information provision

[0236] While walking, the device displays real-time information about nearby attractions and recommended spots based on the user's current location, allowing the user to enjoy their walk even more.

[0237] Input: Current location

[0238] Output: Displayed surrounding information

[0239] Step 7:

[0240] Calorie burn calculation and feedback

[0241] After the walk, the GPS data is sent to the server again, and the server calculates the calories burned based on this data. The calculation results are sent to the device and feedback is provided to the user.

[0242] Input: Last location information

[0243] Output: Calorie consumption calculation and feedback

[0244] Prompt Sentence Examples

[0245] User preferences: parks, cafes

[0246] Target calorie consumption: 500kcal

[0247] Current location: [35.6895, 139.6917]

[0248] Walking frequency: 3 times a week

[0249] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0250] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[0251] Overall system configuration

[0252] 1. User Device

[0253] The user's smartphone or tablet is used to input setting data, collect location information, display walking routes, and collect emotional data.

[0254] Users input their preferred location and target calorie consumption through the application.

[0255] The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[0256] 2. Server

[0257] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history.

[0258] The server is equipped with an AI model that generates optimal walking routes based on this data.

[0259] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0260] 3. Emotion Engine

[0261] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state.

[0262] Emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[0263] System Operation

[0264] Initial Setup

[0265] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0266] Walking data collection and emotion recognition

[0267] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0268] Generate new walking routes

[0269] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0270] Walking course presentation and selection

[0271] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0272] Calorie burn calculation and feedback

[0273] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[0274] Specific examples

[0275] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[0276] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The user launches the application and inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0280] Step 2:

[0281] When a user starts walking, the device activates its GPS function to collect location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals.

[0282] Step 3:

[0283] When the user finishes their walk, the device sends the collected GPS data and emotional data to the server. The server receives this data and stores it as the user's walking history and emotional information. The server analyzes this data to understand the user's current emotional state.

[0284] Step 4:

[0285] The server uses an AI model to generate a new walking course based on the received setting data, walking history data, and emotional data. The server considers the user's preferences and target calorie consumption, and also selects an appropriate walking course based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize a route that includes a park where the user can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0286] Step 5:

[0287] When the user opens the application again, the device displays the new walking route received from the server. The device displays a message to the user such as, "How about a relaxing route around a new park and cafe?" The user reviews the proposed walking route and taps the "Accept" button to select the next walking route.

[0288] Step 6:

[0289] The user then takes a walk according to the new walking course selected by the user. After the walk is completed, the device again sends GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback is also provided according to the user's emotional state; for example, if stress is reduced, a message such as "You feel relaxed!" is displayed.

[0290] In this way, users can keep their daily walks fresh and enjoyable, optimizing their health goals and emotional state.

[0291] Example 2

[0292] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] Conventional walking course generation systems provide walking courses based on the user's preferences and calorie consumption goals, but lack the functionality to propose appropriate courses taking into account the user's emotional state, making it difficult to support the user's mental health.

[0294] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving setting data by the user and transmitting it to the server, a means for collecting location information of the user and transmitting the data to the server, and a means for collecting emotional data of the user and transmitting the data to the server. This makes it possible to generate an optimal walking course that takes into consideration not only the user's preferences and target calorie consumption but also their emotional state.

[0295] "Setting data" refers to data such as a preferred location and target calorie consumption input by the user.

[0296] "Location information" is information indicating a geographical location, such as GPS data acquired by a user's terminal.

[0297] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[0298] The "server" is a computer system that receives and analyzes setting data, location information, emotion data, and walking history data.

[0299] A "walking course" is a recommended walking route that is generated by the server and presented to the user.

[0300] "Calories burned" is a numerical value that indicates the amount of energy consumed by the user while walking.

[0301] A "user terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0302] The "emotion engine" is software that analyzes the user's facial expressions and voice to recognize their emotional state.

[0303] An "AI model" is an artificial intelligence algorithm that runs on a server and generates optimal walking routes based on data.

[0304] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[0305] Overall system configuration

[0306] 1. User Device

[0307] The smartphone or tablet used by the user inputs setting data, collects location information, displays walking routes, and collects emotional data. Users input their preferred locations and target calorie consumption through the application. The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[0308] 2. Server

[0309] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history. The server is equipped with an AI model that generates an optimal walking course based on this data. It calculates calories burned based on location information after the walk and sends the results to the user's device.

[0310] 3. Emotion Engine

[0311] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state. The emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[0312] System Operation Overview

[0313] Initial Setup

[0314] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0315] Walking data collection and emotion recognition

[0316] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0317] Generate new walking routes

[0318] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0319] Walking course presentation and selection

[0320] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0321] Calorie burn calculation and feedback

[0322] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[0323] Specific examples

[0324] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[0325] Example prompts to input to the generative AI model

[0326] "Generate the best walking route for you, taking into account your current emotional state."

[0327] "Suggest new walking routes based on user preferences and emotional data."

[0328] "Provide a relaxing walking route to a user who is feeling stressed."

[0329] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1: Collect and submit initial setup data

[0332] When a user launches the application for the first time, they input their favorite places and target calorie consumption through the application interface. As a result, their favorite places (e.g., cafes, parks) and target calorie consumption (e.g., 500 kilocalories per day) are temporarily saved as setting data on the device. The device then sends this setting data to the server. The input is the user's favorite places and target calorie consumption, and the output is sent to the server as setting data.

[0333] Step 2: Save the configuration data

[0334] The server receives the sent setting data and saves it in the database as a user profile. The server analyzes the setting data and checks its validity. The input is the setting data, and the output is saving it in the database as a user profile. Specifically, the server checks the consistency of the data and saves it in the database.

[0335] Step 3: Collect location and emotion data at the start of the walk

[0336] The user starts a walk and taps the "Start Walk" button on the device. This activates the device's GPS function, collecting location information in real time. The device's built-in camera and microphone also detect the user's facial expressions and voice, which the emotion engine analyzes. The input is the user's behavior and biometric data, and the output is location information and emotion data recorded in real time.

[0337] Step 4: Record emotional data and location information

[0338] The device records emotion data and location information at regular intervals and sends this data to the server after the walk ends. The input is the continuously collected emotion data and location information, and the output is the recorded data sent to the server. Specifically, the device batch processes the location information and emotion data at regular intervals and uploads them to the server.

[0339] Step 5: Data analysis and generation of new walking routes

[0340] The server uses an AI model to generate a new walking course based on the setting data, emotion data, and walking history data it receives. The server analyzes the emotion data and selects an appropriate course based on the user's emotional state. The input is past walking history, setting data, and real-time emotion data, and the output is the generated new walking course. Specifically, the AI ​​model on the server analyzes the data and generates the optimal route.

[0341] Step 6: Present and select a walking route

[0342] The server sends the generated walking course data to the user's device, and when the user opens the application, a new walking course is presented. (For example, a message such as "How about a relaxing route around new parks and cafes?" is displayed.) The user checks the proposed walking course and taps the approval button. The input is the generated walking course data, and the output is the course selected by the user. Specifically, the device presents the course to the user through a rich interface.

[0343] Step 7: Calorie Calculation and Feedback

[0344] After the walk is over, the user's device again sends the GPS data and emotional data to the server. The server calculates the calories burned based on this data and sends the result to the device. The input is the GPS data and emotional data collected during the walk, and the output is the calculated calories burned and a feedback message. Specifically, the server calculates the calories burned and sends the result to the device as a notification. The device notifies the user, "You've achieved your calorie goal! Congratulations!" and also provides feedback according to the user's emotional state (e.g., "You felt relaxed!").

[0345] (Application example 2)

[0346] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] In recent years, walking and strolling have become popular for the purpose of improving health. However, simply suggesting walking courses does not sufficiently increase user satisfaction. Furthermore, providing appropriate content according to a user's emotional state would further improve the user experience. However, the current challenge is that services based on the user's emotions using emotion recognition technology have not yet been realized.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user setting data and transmitting it to the server, means for collecting user location information and transmitting the data to the server, means for the server to analyze the past walking history and the user setting data and generate an optimal walking course, means for transmitting the generated walking course to the user terminal and presenting it to the user, means for calculating calories burned after the walk and notifying the user of the result, means for recognizing the user's emotional state in real time and transmitting emotional data to the server, and means for the server to recommend optimal content based on the user's emotional state and transmit the recommended content to the user terminal. This enables the user to achieve their health goals and enjoy content suited to their emotional state.

[0349] "Means for receiving user-specified data and transmitting it to the server" refers to hardware and software that has the function of receiving data entered by the user through the application (such as preferred locations and target calories burned) and transmitting that data to the server.

[0350] "Means for collecting user location information and transmitting that data to a server" refers to hardware and software that has the function of collecting location information from devices (smartphones or devices with GPS functionality) used by users while they are walking and transmitting that data to a server.

[0351] "Means for the server to analyze the user's past walking history and setting data and generate the optimal walking course" refers to a function in which the server uses AI models and algorithms to generate the optimal walking course based on the user's past walking history and setting data.

[0352] "Means for sending the generated walking course to the user terminal and presenting it to the user" refers to a function that sends the walking course data generated by the server to the device used by the user (such as a smartphone or tablet) and displays the walking course on that device.

[0353] "Means for calculating calories burned after a walk ends and notifying the user of the results" is a function that calculates calories burned based on collected location information data after the user finishes a walk and notifies the user of the results.

[0354] "Means for recognizing the user's emotional state in real time and transmitting emotional data to a server" refers to a function that uses the camera and microphone of the device used by the user to analyze facial expressions and voice, recognizes the user's emotional state in real time, and transmits that data to a server.

[0355] "Means for the server to recommend optimal content based on the user's emotional state and send it to the user's device" refers to a function that uses AI models and algorithms to recommend optimal content (videos, music, articles, etc.) based on the emotional data received by the server, and sends that content to the user's device.

[0356] This invention is a system that recognizes a user's emotions and provides appropriate content based on the user's emotional state. This system is implemented using a user terminal such as a smartphone, smart glasses, or head-mounted display, a server, and an emotion engine.

[0357] Overall system configuration

[0358] User terminal

[0359] The user device is a smartphone, smart glasses, or head-mounted display used by the user. These devices have the following capabilities:

[0360] Input of setting data: The user inputs their preferred location and target calorie consumption through the application. This setting data is temporarily stored on the device and then sent to the server.

[0361] Location information collection: During a walk, the user's location information is collected in real time using the GPS function.

[0362] Emotion data collection: The device uses a camera and microphone to analyze the user's facial expressions and voice, and the emotion engine recognizes their emotional state. The recognized data is sent to the server at regular intervals.

[0363] Emotion Engine: The emotion engine uses a deep learning model to analyze emotions and transmits emotional data from the device to the server in real time.

[0364] server

[0365] The server receives and analyzes the setting data, location information, and emotion data sent by the user. Specifically, it has the following functions:

[0366] Data analysis: The server uses an AI model to analyze the user's past walking history and emotional data, and generates optimal walking courses and content.

[0367] Generating walking courses: The server generates the optimal walking course for the user based on the location information and emotion data, and sends it to the user's device.

[0368] Content recommendation: Based on emotional data, AI models are used to generate content such as videos, music, and articles that are optimal for each user, and the URL or information is sent to the user's device.

[0369] System operation example

[0370] Initial Setup

[0371] When a user launches the application for the first time, they set their preferred location (e.g., park, cafe) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and sent to the server. The server then saves the received data in the user profile.

[0372] Walking data collection and emotion recognition

[0373] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0374] Generate and display walking courses

[0375] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects a walking course taking into account the user's preferences, target calorie consumption, and current emotional state. The server selects the optimal route from the multiple generated routes and sends this data to the user's device. When the user opens the application, the device displays the new walking course received from the server.

[0376] Emotion-based content recommendation

[0377] After the user finishes their walk, the device again sends the GPS data and emotional data to the server. The server then calculates the calories burned based on this data and sends the results to the device. At the same time, the AI ​​model selects the most suitable content (movies, music, articles, etc.) based on the emotional data and sends that information to the user's device.

[0378] Examples and prompts

[0379] For example, suppose a user starts a walk and the emotion "happy" is recognized along the way. In this case, the server recommends entertainment-related videos and music that the user tends to like when they are happy. In this case, an example of a prompt for the generative AI model is as follows:

[0380] Example prompt sentence:

[0381] The user is currently in the "happy" emotional state. Recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories.

[0382] This allows the user to enjoy the most suitable content that matches his or her own feelings, thereby increasing satisfaction.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] When a user launches the application for the first time, they set their preferred locations and target calorie consumption. These settings are entered into the user's device, temporarily saved, and then sent to the server. The input data is the user's preferred locations (e.g., parks, cafes) and target calorie consumption (e.g., 500 kcal per day). The server receives this data and saves it in the user's profile.

[0386] Step 2:

[0387] When a user starts walking, the device activates its GPS function and collects location information in real time. The collected location data is periodically sent from the device to a server. The input data is the real-time location information, and the output data is the location data sent to the server. The server receives this and saves it as the user's walking history.

[0388] Step 3:

[0389] During a walk, the user's facial expressions and voice are analyzed in real time using a camera and microphone installed on the user's device, and the emotion engine recognizes emotional data. The recognized emotional data is sent to the server at regular intervals. The input data is the facial expressions and voice captured on the device, and the output data is the emotional state (e.g., happy, sad, angry, relaxed). The server receives the emotional data and stores it in the user's profile.

[0390] Step 4:

[0391] The server uses an AI model to generate a new walking course based on the received setting data, location information, and emotion data. The data processing performed by the server combines past walking history with current location information and emotion data to generate the optimal walking course. The output is the generated walking course data.

[0392] Step 5:

[0393] The generated walking course data is sent from the server to the user terminal. The user terminal presents the received walking course to the user, and a message such as "How about a relaxing route around a new park and cafe?" is displayed to the user. The input data is the generated walking course data, and the output data is the walking course displayed on the screen of the user terminal.

[0394] Step 6:

[0395] After the user finishes their walk, the device again sends the GPS data and emotion data to the server. The server analyzes these data and calculates the calories burned. The input data are the GPS data and emotion data at the end, and the output data is the calorie burned result. The server then sends the calculation results to the user device.

[0396] Step 7:

[0397] The server uses the AI ​​model to recommend content that matches the user's emotional state based on the emotional data sent at the end. For example, if the user is in a "happy" emotional state, it will recommend entertaining videos and fun music. An example of a prompt is as follows:

[0398] "The user is currently in the emotional state of 'happy'. Please recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories." The input data is the emotional state, and the output data is the URL and information of the recommended content. The recommended content is sent to the user's device and provided to the user.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0400] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0402] [Second embodiment]

[0403] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0404] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0405] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0407] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0409] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0410] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0411] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0414] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0415] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system operates mainly using a user terminal, a server, and GPS data.

[0416] Overall system configuration

[0417] 1. User Device

[0418] It is a smartphone or tablet used by the user to input setting data, collect location information, and display walking courses.

[0419] Users input their preferred location and target calorie consumption through the application.

[0420] 2. Server

[0421] The server is a computer system that receives and analyzes the setting data, location information, and walking history.

[0422] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0423] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0424] System Operation

[0425] Initial Setup

[0426] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0427] Collecting walking data

[0428] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0429] Generate new walking routes

[0430] The server uses an AI model to generate a new walking course based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0431] Walking course presentation and selection

[0432] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0433] Calorie burn calculation and feedback

[0434] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0435] Specific examples

[0436] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0437] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0438] The processing flow will be explained below.

[0439] Step 1:

[0440] The user starts the application and first inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). The device temporarily stores this setting data. Then, it sends the setting data to the server. The server saves the received data in the user profile.

[0441] Step 2:

[0442] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device continues to record location information at regular intervals. When the user finishes their walk, the device sends the collected GPS data to the server. The server receives the location data and saves it as the user's walking history.

[0443] Step 3:

[0444] The server analyzes the user's settings and walking history, and generates a new walking course using an AI model. Based on the user's preferences and calorie consumption goals, the server prioritizes routes that have not been walked before or have not been used for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0445] Step 4:

[0446] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a route that goes around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0447] Step 5:

[0448] The user takes a walk according to the new walking course selected. After the walk is over, the device collects GPS data again and sends it to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user, "You've achieved your calorie goal! Congratulations!"

[0449] In this way, the roles of the user, the terminal, and the server are clearly defined in each step, allowing the user to effectively enjoy a new walking course while simultaneously achieving the target calorie consumption.

[0450] Example 1

[0451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0452] With the increasing health consciousness in today's society, there is a demand for support systems that can help people maintain a fun daily exercise habit. Conventional pedometers and simple course suggestion systems have difficulty generating appropriate walking courses that match the individual preferences and goals of the user, making it difficult to encourage continued use. In response to this, a system is needed that can automatically suggest optimal walking courses that take into account the user's specific wishes and health goals.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0454] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, and a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course using a generative AI model. This makes it possible to provide an optimal walking course based on the user's individual preferences and goals, and to encourage continuous use through calculation of calories burned and feedback.

[0455] "User setting data" refers to information such as a favorite location or target calorie consumption that the user inputs through the application.

[0456] The "server" is a computer system that receives and analyzes setting data, location information, and walking history.

[0457] "User location information" is geographical data obtained using the GPS sensor of the user terminal.

[0458] "Walking history" refers to a record of the routes the user has walked in the past and the GPS data from those routes.

[0459] A "generative AI model" is an artificial intelligence algorithm that generates optimal walking courses based on the user's settings and walking history data.

[0460] The "optimal walking course" is the optimal walking route for the user, suggested by the generative AI model based on the user's preferences and goals.

[0461] A "user device" is a device used by a user, such as a smartphone or tablet, which is used to input setting data, collect location information, and display walking courses.

[0462] "GPS data" refers to location information obtained by the GPS sensor of the user terminal.

[0463] "Calories burned" is the amount of energy consumed by the user while walking.

[0464] "Feedback" refers to information such as a calorie burn calculation result and an encouraging message that is provided to the user after the walk is completed.

[0465] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. This system operates mainly using a user terminal, a server, and GPS data.

[0466] Overall system configuration

[0467] 1. User Device

[0468] It is a smart device (smartphone or tablet) used by the user to input setting data, collect location information, and display walking courses. Users input their preferred locations and target calorie consumption through the application.

[0469] Hardware used: smartphone, tablet

[0470] Software used: Application

[0471] 2. Server

[0472] The server is a computer system that receives and analyzes setting data, location information, and walking history. The server is equipped with a generative AI model that generates an optimal walking course based on the user's preferences and goals. It calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0473] Hardware used: Server

[0474] Software used: Generative AI model, data analysis software

[0475] System Operation

[0476] Initial Setup

[0477] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0478] Collecting walking data

[0479] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0480] Generate new walking routes

[0481] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0482] Walking course presentation and selection

[0483] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0484] Calorie burn calculation and feedback

[0485] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0486] Specific examples

[0487] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0488] Prompt Sentence Examples

[0489] "Generate new walking itineraries based on the user's preferred locations and calorie goals. For example, suggest a route that includes a park and a cafe."

[0490] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0492] Step 1:

[0493] When a user launches the application for the first time, a setting screen is displayed. The user inputs their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server.

[0494] Input: User-entered preferred location, target calories burned

[0495] Data processing: This data is temporarily stored on the device and converted into a format for sending to the server.

[0496] Output: Configuration data sent to the server

[0497] Step 2:

[0498] The server receives the user's settings and stores them in a database, which is later used to generate walking routes.

[0499] Input: Configuration data sent from the terminal

[0500] Data processing: Analyzes the setting data and saves it in a database

[0501] Output: Saved user configuration data

[0502] Step 3:

[0503] When a user starts a walk, the device activates the GPS and collects location information at regular intervals. The collected data is temporarily stored on the device and sent to the server after the walk ends.

[0504] Input: Real-time location information from a GPS sensor

[0505] Data processing: Location information is recorded at regular intervals and temporarily saved

[0506] Output: GPS data sent to the server after the walk ends

[0507] Step 4:

[0508] The server analyzes the received location information and saves it as the user's walking history. This data is used to generate the next walking route.

[0509] Input: GPS data sent from the device

[0510] Data processing: Analyze location information and register it in a database as walking history

[0511] Output: Saved user walking history data

[0512] Step 5:

[0513] The server uses past walking history and configuration data to generate new walking routes using a generative AI model, which is designed to prioritize places that have not been walked before and routes that have not been visited in a while.

[0514] Input: Walking history data, setting data

[0515] Data processing: Generative AI model generates new walking routes

[0516] Output: Generated walking course data

[0517] Step 6:

[0518] The generated walking course is sent to the user terminal and presented to the user, who then checks the proposed walking course and approves it if necessary.

[0519] Input: Walking course data sent from the server

[0520] Data processing: Convert the walking course into a display format and present it to the user

[0521] Output: Walking itinerary that the user approves or modifies

[0522] Step 7:

[0523] When the user finishes their walk, the GPS data is collected again and sent to the server, which then calculates the calories burned and notifies the user.

[0524] Input: GPS data after the walk

[0525] Data processing: Calculate calories burned based on location information

[0526] Output: Calculation result (calories burned) and feedback message (e.g., "You've reached your calorie goal! Congratulations!")

[0527] In this way, each processing step works in conjunction with one another to realize a system that enriches the user's walking experience.

[0528] (Application example 1)

[0529] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0530] Conventional walking support systems can generate walking courses based on the user's location information and walking history and calculate calories burned, but they lack the perspective of making the walk enjoyable. They also lack real-time information provision based on the user's interests and preferences. This can lead to a monotonous walking experience, which can decrease motivation to continue. Furthermore, the walking courses are not presented visually in an easy-to-understand manner, which can lead to users getting lost.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0532] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course, a means for transmitting the generated walking course to the user's terminal and presenting it to the user, a means for calculating calories burned after the walk and notifying the user of the result, a means for displaying the specified walking course using AR technology, and a means for displaying nearby landmarks and recommended spots in real time during the walk. This allows the user to be visually guided along a walking course based on their interests and preferences while receiving real-time information about nearby landmarks and recommended spots during the walk. This makes the walking experience more enjoyable, fresh, and helps maintain motivation. Furthermore, the visual clarity allows the user to continue their walk without getting lost.

[0533] "User setting data" is information necessary for the system to generate a walking course, such as the user's favorite places and target calorie consumption.

[0534] "User location information" is information for identifying the user's current location using GPS data or the like.

[0535] "Past walking history" is history data such as the routes and distances of walks the user has taken in the past.

[0536] An "optimal walking course" is a route suitable for walking that is generated based on the user's preferences and goals.

[0537] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on real-world scenery.

[0538] "Sightseeing spots and recommended spots" are interesting places along the walking route and places worth visiting.

[0539] "Means for displaying in real time" refers to a technology that instantly displays the information required at the user's current location.

[0540] The "means for calculating calories burned" is a method for accurately calculating the amount of calories burned based on the user's walking data.

[0541] "Navigation" refers to guiding the user to move along a walking course set by the user.

[0542] "Visually easy to understand" means presenting information in an easily recognizable form so that the user can understand the information at a glance.

[0543] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system mainly operates using a user terminal, a server, and GPS data. Specific embodiments for realizing this are described below.

[0544] Overall system configuration

[0545] The system consists of the following main components:

[0546] 1. User Device

[0547] The smartphone or tablet used by the user.

[0548] Enters configuration data, collects location information, and displays walking courses.

[0549] Users input their preferred location and target calorie consumption through the application.

[0550] 2. Server

[0551] It is a computer system that receives and analyzes setting data, location information, and walking history.

[0552] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0553] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0554] 3. Augmented Reality Technology

[0555] Used to visually indicate designated walking routes.

[0556] It uses devices such as smart glasses and head-mounted displays.

[0557] System Operation

[0558] Initial Setup

[0559] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0560] Collecting walking data

[0561] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0562] Generate new walking routes

[0563] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0564] Walking course presentation and selection

[0565] When a user opens the application, the user's device displays a new walking route. This is done using AR technology to visually present the walking route. A message such as "How about a route that goes around a new park and cafe?" is displayed, and if the user accepts the suggestion, the walking route is set as the next route.

[0566] Calorie burn calculation and feedback

[0567] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided.

[0568] Hardware and software used

[0569] Hardware:

[0570] Smart glasses (with AR function)

[0571] Smartphone

[0572] software:

[0573] ARKit (Apple's AR development framework)

[0574] TensorFlow (AI model building and prediction)

[0575] Firebase (cloud data management)

[0576] Specific examples

[0577] User A starts the application for the first time and sets the criteria: "I like cafes and parks" and "I want to burn 500 calories a day." The application uses AR to guide the user through their walking route, allowing them to check the calories burned in real time. Next, after the walk is over, a new recommended route is suggested.

[0578] Prompt Sentence Examples

[0579] User preferences: parks, cafes

[0580] Target calorie consumption: 500kcal

[0581] Current location: [35.6895, 139.6917]

[0582] Walking frequency: 3 times a week

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] User configuration data entry

[0586] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their goal for calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server, making it easier for the server to understand the user's preferences and goals.

[0587] Input: User settings data (preferred location, target calories burned)

[0588] Output: Configuration data sent to the server

[0589] Step 2:

[0590] Start collecting user location information

[0591] When a user starts a walk, the device uses GPS to collect the user's location information. This location information is recorded at regular intervals and updated in real time, allowing the device to accurately track the user's movements.

[0592] Input: GPS data (user location information)

[0593] Output: Location information recorded on the device

[0594] Step 3:

[0595] Sending data to the server

[0596] After the walk, the device sends the collected location information to the server, which then stores the data as the user's walking history and uses it to generate the next walking route.

[0597] Input: Recorded location information

[0598] Output: Location data sent to the server

[0599] Step 4:

[0600] Generate new walking routes

[0601] The server uses a generative AI model to generate a new walking course based on the user's settings and past walking history data. The AI ​​model selects the optimal route, taking into account the user's preferences and goals.

[0602] Input: Setting data, walking history data

[0603] Output: Generated walking course data

[0604] Step 5:

[0605] Walking course information

[0606] The generated walking course data is sent to the user's device, which then uses AR technology to visually display the walking course, allowing the user to walk while checking the new walking course in real time.

[0607] Input: New walking course data

[0608] Output: Walking route displayed on the terminal

[0609] Step 6:

[0610] Real-time information provision

[0611] While walking, the device displays real-time information about nearby attractions and recommended spots based on the user's current location, allowing the user to enjoy their walk even more.

[0612] Input: Current location

[0613] Output: Displayed surrounding information

[0614] Step 7:

[0615] Calorie burn calculation and feedback

[0616] After the walk, the GPS data is sent to the server again, and the server calculates the calories burned based on this data. The calculation results are sent to the device and feedback is provided to the user.

[0617] Input: Last location information

[0618] Output: Calorie consumption calculation and feedback

[0619] Prompt Sentence Examples

[0620] User preferences: parks, cafes

[0621] Target calorie consumption: 500kcal

[0622] Current location: [35.6895, 139.6917]

[0623] Walking frequency: 3 times a week

[0624] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0625] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[0626] Overall system configuration

[0627] 1. User Device

[0628] The user's smartphone or tablet is used to input setting data, collect location information, display walking routes, and collect emotional data.

[0629] Users input their preferred location and target calorie consumption through the application.

[0630] The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[0631] 2. Server

[0632] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history.

[0633] The server is equipped with an AI model that generates optimal walking routes based on this data.

[0634] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0635] 3. Emotion Engine

[0636] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state.

[0637] Emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[0638] System Operation

[0639] Initial Setup

[0640] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0641] Walking data collection and emotion recognition

[0642] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0643] Generate new walking routes

[0644] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0645] Walking course presentation and selection

[0646] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0647] Calorie burn calculation and feedback

[0648] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[0649] Specific examples

[0650] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[0651] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] The user launches the application and inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0655] Step 2:

[0656] When a user starts walking, the device activates its GPS function to collect location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals.

[0657] Step 3:

[0658] When the user finishes their walk, the device sends the collected GPS data and emotional data to the server. The server receives this data and stores it as the user's walking history and emotional information. The server analyzes this data to understand the user's current emotional state.

[0659] Step 4:

[0660] The server uses an AI model to generate a new walking course based on the received setting data, walking history data, and emotional data. The server considers the user's preferences and target calorie consumption, and also selects an appropriate walking course based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize a route that includes a park where the user can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0661] Step 5:

[0662] When the user opens the application again, the device displays the new walking route received from the server. The device displays a message to the user such as, "How about a relaxing route around a new park and cafe?" The user reviews the proposed walking route and taps the "Accept" button to select the next walking route.

[0663] Step 6:

[0664] The user then takes a walk according to the new walking course selected by the user. After the walk is completed, the device again sends GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback is also provided according to the user's emotional state; for example, if stress is reduced, a message such as "You feel relaxed!" is displayed.

[0665] In this way, users can keep their daily walks fresh and enjoyable, optimizing their health goals and emotional state.

[0666] Example 2

[0667] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional walking course generation systems provide walking courses based on the user's preferences and calorie consumption goals, but lack the functionality to propose appropriate courses taking into account the user's emotional state, making it difficult to support the user's mental health.

[0669] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving setting data by the user and transmitting it to the server, a means for collecting location information of the user and transmitting the data to the server, and a means for collecting emotional data of the user and transmitting the data to the server. This makes it possible to generate an optimal walking course that takes into consideration not only the user's preferences and target calorie consumption but also their emotional state.

[0670] "Setting data" refers to data such as a preferred location and target calorie consumption input by the user.

[0671] "Location information" is information indicating a geographical location, such as GPS data acquired by a user's terminal.

[0672] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[0673] The "server" is a computer system that receives and analyzes setting data, location information, emotion data, and walking history data.

[0674] A "walking course" is a recommended walking route that is generated by the server and presented to the user.

[0675] "Calories burned" is a numerical value that indicates the amount of energy consumed by the user while walking.

[0676] A "user terminal" is an electronic device used by a user, such as a smartphone or tablet.

[0677] The "emotion engine" is software that analyzes the user's facial expressions and voice to recognize their emotional state.

[0678] An "AI model" is an artificial intelligence algorithm that runs on a server and generates optimal walking routes based on data.

[0679] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[0680] Overall system configuration

[0681] 1. User Device

[0682] The smartphone or tablet used by the user inputs setting data, collects location information, displays walking routes, and collects emotional data. Users input their preferred locations and target calorie consumption through the application. The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[0683] 2. Server

[0684] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history. The server is equipped with an AI model that generates an optimal walking course based on this data. It calculates calories burned based on location information after the walk and sends the results to the user's device.

[0685] 3. Emotion Engine

[0686] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state. The emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[0687] System Operation Overview

[0688] Initial Setup

[0689] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[0690] Walking data collection and emotion recognition

[0691] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0692] Generate new walking routes

[0693] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0694] Walking course presentation and selection

[0695] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0696] Calorie burn calculation and feedback

[0697] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[0698] Specific examples

[0699] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[0700] Example prompts to input to the generative AI model

[0701] "Generate the best walking route for you, taking into account your current emotional state."

[0702] "Suggest new walking routes based on user preferences and emotional data."

[0703] "Provide a relaxing walking route to a user who is feeling stressed."

[0704] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[0705] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0706] Step 1: Collect and submit initial setup data

[0707] When a user launches the application for the first time, they input their favorite places and target calorie consumption through the application interface. As a result, their favorite places (e.g., cafes, parks) and target calorie consumption (e.g., 500 kilocalories per day) are temporarily saved as setting data on the device. The device then sends this setting data to the server. The input is the user's favorite places and target calorie consumption, and the output is sent to the server as setting data.

[0708] Step 2: Save the configuration data

[0709] The server receives the sent setting data and saves it in the database as a user profile. The server analyzes the setting data and checks its validity. The input is the setting data, and the output is saving it in the database as a user profile. Specifically, the server checks the consistency of the data and saves it in the database.

[0710] Step 3: Collect location and emotion data at the start of the walk

[0711] The user starts a walk and taps the "Start Walk" button on the device. This activates the device's GPS function, collecting location information in real time. The device's built-in camera and microphone also detect the user's facial expressions and voice, which the emotion engine analyzes. The input is the user's behavior and biometric data, and the output is location information and emotion data recorded in real time.

[0712] Step 4: Record emotional data and location information

[0713] The device records emotion data and location information at regular intervals and sends this data to the server after the walk ends. The input is the continuously collected emotion data and location information, and the output is the recorded data sent to the server. Specifically, the device batch processes the location information and emotion data at regular intervals and uploads them to the server.

[0714] Step 5: Data analysis and generation of new walking routes

[0715] The server uses an AI model to generate a new walking course based on the setting data, emotion data, and walking history data it receives. The server analyzes the emotion data and selects an appropriate course based on the user's emotional state. The input is past walking history, setting data, and real-time emotion data, and the output is the generated new walking course. Specifically, the AI ​​model on the server analyzes the data and generates the optimal route.

[0716] Step 6: Present and select a walking route

[0717] The server sends the generated walking course data to the user's device, and when the user opens the application, a new walking course is presented. (For example, a message such as "How about a relaxing route around new parks and cafes?" is displayed.) The user checks the proposed walking course and taps the approval button. The input is the generated walking course data, and the output is the course selected by the user. Specifically, the device presents the course to the user through a rich interface.

[0718] Step 7: Calorie Calculation and Feedback

[0719] After the walk is over, the user's device again sends the GPS data and emotional data to the server. The server calculates the calories burned based on this data and sends the result to the device. The input is the GPS data and emotional data collected during the walk, and the output is the calculated calories burned and a feedback message. Specifically, the server calculates the calories burned and sends the result to the device as a notification. The device notifies the user, "You've achieved your calorie goal! Congratulations!" and also provides feedback according to the user's emotional state (e.g., "You felt relaxed!").

[0720] (Application example 2)

[0721] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0722] In recent years, walking and strolling have become popular for the purpose of improving health. However, simply suggesting walking courses does not sufficiently increase user satisfaction. Furthermore, providing appropriate content according to a user's emotional state would further improve the user experience. However, the current challenge is that services based on the user's emotions using emotion recognition technology have not yet been realized.

[0723] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user setting data and transmitting it to the server, means for collecting user location information and transmitting the data to the server, means for the server to analyze the past walking history and the user setting data and generate an optimal walking course, means for transmitting the generated walking course to the user terminal and presenting it to the user, means for calculating calories burned after the walk and notifying the user of the result, means for recognizing the user's emotional state in real time and transmitting emotional data to the server, and means for the server to recommend optimal content based on the user's emotional state and transmit the recommended content to the user terminal. This enables the user to achieve their health goals and enjoy content suited to their emotional state.

[0724] "Means for receiving user-specified data and transmitting it to the server" refers to hardware and software that has the function of receiving data entered by the user through the application (such as preferred locations and target calories burned) and transmitting that data to the server.

[0725] "Means for collecting user location information and transmitting that data to a server" refers to hardware and software that has the function of collecting location information from devices (smartphones or devices with GPS functionality) used by users while they are walking and transmitting that data to a server.

[0726] "Means for the server to analyze the user's past walking history and setting data and generate the optimal walking course" refers to a function in which the server uses AI models and algorithms to generate the optimal walking course based on the user's past walking history and setting data.

[0727] "Means for sending the generated walking course to the user terminal and presenting it to the user" refers to a function that sends the walking course data generated by the server to the device used by the user (such as a smartphone or tablet) and displays the walking course on that device.

[0728] "Means for calculating calories burned after a walk ends and notifying the user of the results" is a function that calculates calories burned based on collected location information data after the user finishes a walk and notifies the user of the results.

[0729] "Means for recognizing the user's emotional state in real time and transmitting emotional data to a server" refers to a function that uses the camera and microphone of the device used by the user to analyze facial expressions and voice, recognizes the user's emotional state in real time, and transmits that data to a server.

[0730] "Means for the server to recommend optimal content based on the user's emotional state and send it to the user's device" refers to a function that uses AI models and algorithms to recommend optimal content (videos, music, articles, etc.) based on the emotional data received by the server, and sends that content to the user's device.

[0731] This invention is a system that recognizes a user's emotions and provides appropriate content based on the user's emotional state. This system is implemented using a user terminal such as a smartphone, smart glasses, or head-mounted display, a server, and an emotion engine.

[0732] Overall system configuration

[0733] User terminal

[0734] The user device is a smartphone, smart glasses, or head-mounted display used by the user. These devices have the following capabilities:

[0735] Input of setting data: The user inputs their preferred location and target calorie consumption through the application. This setting data is temporarily stored on the device and then sent to the server.

[0736] Location information collection: During a walk, the user's location information is collected in real time using the GPS function.

[0737] Emotion data collection: The device uses a camera and microphone to analyze the user's facial expressions and voice, and the emotion engine recognizes their emotional state. The recognized data is sent to the server at regular intervals.

[0738] Emotion Engine: The emotion engine uses a deep learning model to analyze emotions and transmits emotional data from the device to the server in real time.

[0739] server

[0740] The server receives and analyzes the setting data, location information, and emotion data sent by the user. Specifically, it has the following functions:

[0741] Data analysis: The server uses an AI model to analyze the user's past walking history and emotional data, and generates optimal walking courses and content.

[0742] Generating walking courses: The server generates the optimal walking course for the user based on the location information and emotion data, and sends it to the user's device.

[0743] Content recommendation: Based on emotional data, AI models are used to generate content such as videos, music, and articles that are optimal for each user, and the URL or information is sent to the user's device.

[0744] System operation example

[0745] Initial Setup

[0746] When a user launches the application for the first time, they set their preferred location (e.g., park, cafe) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and sent to the server. The server then saves the received data in the user profile.

[0747] Walking data collection and emotion recognition

[0748] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[0749] Generate and display walking courses

[0750] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects a walking course taking into account the user's preferences, target calorie consumption, and current emotional state. The server selects the optimal route from the multiple generated routes and sends this data to the user's device. When the user opens the application, the device displays the new walking course received from the server.

[0751] Emotion-based content recommendation

[0752] After the user finishes their walk, the device again sends the GPS data and emotional data to the server. The server then calculates the calories burned based on this data and sends the results to the device. At the same time, the AI ​​model selects the most suitable content (movies, music, articles, etc.) based on the emotional data and sends that information to the user's device.

[0753] Examples and prompts

[0754] For example, suppose a user starts a walk and the emotion "happy" is recognized along the way. In this case, the server recommends entertainment-related videos and music that the user tends to like when they are happy. In this case, an example of a prompt for the generative AI model is as follows:

[0755] Example prompt sentence:

[0756] The user is currently in the "happy" emotional state. Recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories.

[0757] This allows the user to enjoy the most suitable content that matches his or her own feelings, thereby increasing satisfaction.

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Step 1:

[0760] When a user launches the application for the first time, they set their preferred locations and target calorie consumption. These settings are entered into the user's device, temporarily saved, and then sent to the server. The input data is the user's preferred locations (e.g., parks, cafes) and target calorie consumption (e.g., 500 kcal per day). The server receives this data and saves it in the user's profile.

[0761] Step 2:

[0762] When a user starts walking, the device activates its GPS function and collects location information in real time. The collected location data is periodically sent from the device to a server. The input data is the real-time location information, and the output data is the location data sent to the server. The server receives this and saves it as the user's walking history.

[0763] Step 3:

[0764] During a walk, the user's facial expressions and voice are analyzed in real time using a camera and microphone installed on the user's device, and the emotion engine recognizes emotional data. The recognized emotional data is sent to the server at regular intervals. The input data is the facial expressions and voice captured on the device, and the output data is the emotional state (e.g., happy, sad, angry, relaxed). The server receives the emotional data and stores it in the user's profile.

[0765] Step 4:

[0766] The server uses an AI model to generate a new walking course based on the received setting data, location information, and emotion data. The data processing performed by the server combines past walking history with current location information and emotion data to generate the optimal walking course. The output is the generated walking course data.

[0767] Step 5:

[0768] The generated walking course data is sent from the server to the user terminal. The user terminal presents the received walking course to the user, and a message such as "How about a relaxing route around a new park and cafe?" is displayed to the user. The input data is the generated walking course data, and the output data is the walking course displayed on the screen of the user terminal.

[0769] Step 6:

[0770] After the user finishes their walk, the device again sends the GPS data and emotion data to the server. The server analyzes these data and calculates the calories burned. The input data are the GPS data and emotion data at the end, and the output data is the calorie burned result. The server then sends the calculation results to the user device.

[0771] Step 7:

[0772] The server uses the AI ​​model to recommend content that matches the user's emotional state based on the emotional data sent at the end. For example, if the user is in a "happy" emotional state, it will recommend entertaining videos and fun music. An example of a prompt is as follows:

[0773] "The user is currently in the emotional state of 'happy'. Please recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories." The input data is the emotional state, and the output data is the URL and information of the recommended content. The recommended content is sent to the user's device and provided to the user.

[0774] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0775] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0777] [Third embodiment]

[0778] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0779] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0780] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0781] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0782] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0783] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0784] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0785] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0786] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0787] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0788] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0789] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0790] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system operates mainly using a user terminal, a server, and GPS data.

[0791] Overall system configuration

[0792] 1. User Device

[0793] It is a smartphone or tablet used by the user to input setting data, collect location information, and display walking courses.

[0794] Users input their preferred location and target calorie consumption through the application.

[0795] 2. Server

[0796] The server is a computer system that receives and analyzes the setting data, location information, and walking history.

[0797] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0798] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0799] System Operation

[0800] Initial Setup

[0801] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0802] Collecting walking data

[0803] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0804] Generate new walking routes

[0805] The server uses an AI model to generate a new walking course based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0806] Walking course presentation and selection

[0807] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0808] Calorie burn calculation and feedback

[0809] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0810] Specific examples

[0811] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0812] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0813] The processing flow will be explained below.

[0814] Step 1:

[0815] The user starts the application and first inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). The device temporarily stores this setting data. Then, it sends the setting data to the server. The server saves the received data in the user profile.

[0816] Step 2:

[0817] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device continues to record location information at regular intervals. When the user finishes their walk, the device sends the collected GPS data to the server. The server receives the location data and saves it as the user's walking history.

[0818] Step 3:

[0819] The server analyzes the user's settings and walking history, and generates a new walking course using an AI model. Based on the user's preferences and calorie consumption goals, the server prioritizes routes that have not been walked before or have not been used for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[0820] Step 4:

[0821] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a route that goes around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[0822] Step 5:

[0823] The user takes a walk according to the new walking course selected. After the walk is over, the device collects GPS data again and sends it to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user, "You've achieved your calorie goal! Congratulations!"

[0824] In this way, the roles of the user, the terminal, and the server are clearly defined in each step, allowing the user to effectively enjoy a new walking course while simultaneously achieving the target calorie consumption.

[0825] Example 1

[0826] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0827] With the increasing health consciousness in today's society, there is a demand for support systems that can help people maintain a fun daily exercise habit. Conventional pedometers and simple course suggestion systems have difficulty generating appropriate walking courses that match the individual preferences and goals of the user, making it difficult to encourage continued use. In response to this, a system is needed that can automatically suggest optimal walking courses that take into account the user's specific wishes and health goals.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0829] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, and a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course using a generative AI model. This makes it possible to provide an optimal walking course based on the user's individual preferences and goals, and to encourage continuous use through calculation of calories burned and feedback.

[0830] "User setting data" refers to information such as a favorite location or target calorie consumption that the user inputs through the application.

[0831] The "server" is a computer system that receives and analyzes setting data, location information, and walking history.

[0832] "User location information" is geographical data obtained using the GPS sensor of the user terminal.

[0833] "Walking history" refers to a record of the routes the user has walked in the past and the GPS data from those routes.

[0834] A "generative AI model" is an artificial intelligence algorithm that generates optimal walking courses based on the user's settings and walking history data.

[0835] The "optimal walking course" is the optimal walking route for the user, suggested by the generative AI model based on the user's preferences and goals.

[0836] A "user device" is a device used by a user, such as a smartphone or tablet, which is used to input setting data, collect location information, and display walking courses.

[0837] "GPS data" refers to location information obtained by the GPS sensor of the user terminal.

[0838] "Calories burned" is the amount of energy consumed by the user while walking.

[0839] "Feedback" refers to information such as a calorie burn calculation result and an encouraging message that is provided to the user after the walk is completed.

[0840] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. This system operates mainly using a user terminal, a server, and GPS data.

[0841] Overall system configuration

[0842] 1. User Device

[0843] It is a smart device (smartphone or tablet) used by the user to input setting data, collect location information, and display walking courses. Users input their preferred locations and target calorie consumption through the application.

[0844] Hardware used: smartphone, tablet

[0845] Software used: Application

[0846] 2. Server

[0847] The server is a computer system that receives and analyzes setting data, location information, and walking history. The server is equipped with a generative AI model that generates an optimal walking course based on the user's preferences and goals. It calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0848] Hardware used: Server

[0849] Software used: Generative AI model, data analysis software

[0850] System Operation

[0851] Initial Setup

[0852] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0853] Collecting walking data

[0854] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0855] Generate new walking routes

[0856] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0857] Walking course presentation and selection

[0858] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[0859] Calorie burn calculation and feedback

[0860] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[0861] Specific examples

[0862] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[0863] Prompt Sentence Examples

[0864] "Generate new walking itineraries based on the user's preferred locations and calorie goals. For example, suggest a route that includes a park and a cafe."

[0865] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[0866] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0867] Step 1:

[0868] When a user launches the application for the first time, a setting screen is displayed. The user inputs their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server.

[0869] Input: User-entered preferred location, target calories burned

[0870] Data processing: This data is temporarily stored on the device and converted into a format for sending to the server.

[0871] Output: Configuration data sent to the server

[0872] Step 2:

[0873] The server receives the user's settings and stores them in a database, which is later used to generate walking routes.

[0874] Input: Configuration data sent from the terminal

[0875] Data processing: Analyzes the setting data and saves it in a database

[0876] Output: Saved user configuration data

[0877] Step 3:

[0878] When a user starts a walk, the device activates the GPS and collects location information at regular intervals. The collected data is temporarily stored on the device and sent to the server after the walk ends.

[0879] Input: Real-time location information from a GPS sensor

[0880] Data processing: Location information is recorded at regular intervals and temporarily saved

[0881] Output: GPS data sent to the server after the walk ends

[0882] Step 4:

[0883] The server analyzes the received location information and saves it as the user's walking history. This data is used to generate the next walking route.

[0884] Input: GPS data sent from the device

[0885] Data processing: Analyze location information and register it in a database as walking history

[0886] Output: Saved user walking history data

[0887] Step 5:

[0888] The server uses past walking history and configuration data to generate new walking routes using a generative AI model, which is designed to prioritize places that have not been walked before and routes that have not been visited in a while.

[0889] Input: Walking history data, setting data

[0890] Data processing: Generative AI model generates new walking routes

[0891] Output: Generated walking course data

[0892] Step 6:

[0893] The generated walking course is sent to the user terminal and presented to the user, who then checks the proposed walking course and approves it if necessary.

[0894] Input: Walking course data sent from the server

[0895] Data processing: Convert the walking course into a display format and present it to the user

[0896] Output: Walking itinerary that the user approves or modifies

[0897] Step 7:

[0898] When the user finishes their walk, the GPS data is collected again and sent to the server, which then calculates the calories burned and notifies the user.

[0899] Input: GPS data after the walk

[0900] Data processing: Calculate calories burned based on location information

[0901] Output: Calculation result (calories burned) and feedback message (e.g., "You've reached your calorie goal! Congratulations!")

[0902] In this way, each processing step works in conjunction with one another to realize a system that enriches the user's walking experience.

[0903] (Application example 1)

[0904] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0905] Conventional walking support systems can generate walking courses based on the user's location information and walking history and calculate calories burned, but they lack the perspective of making the walk enjoyable. They also lack real-time information provision based on the user's interests and preferences. This can lead to a monotonous walking experience, which can decrease motivation to continue. Furthermore, the walking courses are not presented visually in an easy-to-understand manner, which can lead to users getting lost.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0907] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course, a means for transmitting the generated walking course to the user's terminal and presenting it to the user, a means for calculating calories burned after the walk and notifying the user of the result, a means for displaying the specified walking course using AR technology, and a means for displaying nearby landmarks and recommended spots in real time during the walk. This allows the user to be visually guided along a walking course based on their interests and preferences while receiving real-time information about nearby landmarks and recommended spots during the walk. This makes the walking experience more enjoyable, fresh, and helps maintain motivation. Furthermore, the visual clarity allows the user to continue their walk without getting lost.

[0908] "User setting data" is information necessary for the system to generate a walking course, such as the user's favorite places and target calorie consumption.

[0909] "User location information" is information for identifying the user's current location using GPS data or the like.

[0910] "Past walking history" is history data such as the routes and distances of walks the user has taken in the past.

[0911] An "optimal walking course" is a route suitable for walking that is generated based on the user's preferences and goals.

[0912] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on real-world scenery.

[0913] "Sightseeing spots and recommended spots" are interesting places along the walking route and places worth visiting.

[0914] "Means for displaying in real time" refers to a technology that instantly displays the information required at the user's current location.

[0915] The "means for calculating calories burned" is a method for accurately calculating the amount of calories burned based on the user's walking data.

[0916] "Navigation" refers to guiding the user to move along a walking course set by the user.

[0917] "Visually easy to understand" means presenting information in an easily recognizable form so that the user can understand the information at a glance.

[0918] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system mainly operates using a user terminal, a server, and GPS data. Specific embodiments for realizing this are described below.

[0919] Overall system configuration

[0920] The system consists of the following main components:

[0921] 1. User Device

[0922] The smartphone or tablet used by the user.

[0923] Enters configuration data, collects location information, and displays walking courses.

[0924] Users input their preferred location and target calorie consumption through the application.

[0925] 2. Server

[0926] It is a computer system that receives and analyzes setting data, location information, and walking history.

[0927] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[0928] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[0929] 3. Augmented Reality Technology

[0930] Used to visually indicate designated walking routes.

[0931] It uses devices such as smart glasses and head-mounted displays.

[0932] System Operation

[0933] Initial Setup

[0934] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[0935] Collecting walking data

[0936] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[0937] Generate new walking routes

[0938] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[0939] Walking course presentation and selection

[0940] When a user opens the application, the user's device displays a new walking route. This is done using AR technology to visually present the walking route. A message such as "How about a route that goes around a new park and cafe?" is displayed, and if the user accepts the suggestion, the walking route is set as the next route.

[0941] Calorie burn calculation and feedback

[0942] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided.

[0943] Hardware and software used

[0944] Hardware:

[0945] Smart glasses (with AR function)

[0946] Smartphone

[0947] software:

[0948] ARKit (Apple's AR development framework)

[0949] TensorFlow (AI model building and prediction)

[0950] Firebase (cloud data management)

[0951] Specific examples

[0952] User A starts the application for the first time and sets the criteria: "I like cafes and parks" and "I want to burn 500 calories a day." The application uses AR to guide the user through their walking route, allowing them to check the calories burned in real time. Next, after the walk is over, a new recommended route is suggested.

[0953] Prompt Sentence Examples

[0954] User preferences: parks, cafes

[0955] Target calorie consumption: 500kcal

[0956] Current location: [35.6895, 139.6917]

[0957] Walking frequency: 3 times a week

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] User configuration data entry

[0961] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their goal for calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server, making it easier for the server to understand the user's preferences and goals.

[0962] Input: User settings data (preferred location, target calories burned)

[0963] Output: Configuration data sent to the server

[0964] Step 2:

[0965] Start collecting user location information

[0966] When a user starts a walk, the device uses GPS to collect the user's location information. This location information is recorded at regular intervals and updated in real time, allowing the device to accurately track the user's movements.

[0967] Input: GPS data (user location information)

[0968] Output: Location information recorded on the device

[0969] Step 3:

[0970] Sending data to the server

[0971] After the walk, the device sends the collected location information to the server, which then stores the data as the user's walking history and uses it to generate the next walking route.

[0972] Input: Recorded location information

[0973] Output: Location data sent to the server

[0974] Step 4:

[0975] Generate new walking routes

[0976] The server uses a generative AI model to generate a new walking course based on the user's settings and past walking history data. The AI ​​model selects the optimal route, taking into account the user's preferences and goals.

[0977] Input: Setting data, walking history data

[0978] Output: Generated walking course data

[0979] Step 5:

[0980] Walking course information

[0981] The generated walking course data is sent to the user's device, which then uses AR technology to visually display the walking course, allowing the user to walk while checking the new walking course in real time.

[0982] Input: New walking course data

[0983] Output: Walking route displayed on the terminal

[0984] Step 6:

[0985] Real-time information provision

[0986] While walking, the device displays real-time information about nearby attractions and recommended spots based on the user's current location, allowing the user to enjoy their walk even more.

[0987] Input: Current location

[0988] Output: Displayed surrounding information

[0989] Step 7:

[0990] Calorie burn calculation and feedback

[0991] After the walk, the GPS data is sent to the server again, and the server calculates the calories burned based on this data. The calculation results are sent to the device and feedback is provided to the user.

[0992] Input: Last location information

[0993] Output: Calorie consumption calculation and feedback

[0994] Prompt Sentence Examples

[0995] User preferences: parks, cafes

[0996] Target calorie consumption: 500kcal

[0997] Current location: [35.6895, 139.6917]

[0998] Walking frequency: 3 times a week

[0999] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1000] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[1001] Overall system configuration

[1002] 1. User Device

[1003] The user's smartphone or tablet is used to input setting data, collect location information, display walking routes, and collect emotional data.

[1004] Users input their preferred location and target calorie consumption through the application.

[1005] The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[1006] 2. Server

[1007] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history.

[1008] The server is equipped with an AI model that generates optimal walking routes based on this data.

[1009] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[1010] 3. Emotion Engine

[1011] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state.

[1012] Emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[1013] System Operation

[1014] Initial Setup

[1015] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1016] Walking data collection and emotion recognition

[1017] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1018] Generate new walking routes

[1019] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1020] Walking course presentation and selection

[1021] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[1022] Calorie burn calculation and feedback

[1023] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[1024] Specific examples

[1025] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[1026] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user launches the application and inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1030] Step 2:

[1031] When a user starts walking, the device activates its GPS function to collect location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals.

[1032] Step 3:

[1033] When the user finishes their walk, the device sends the collected GPS data and emotional data to the server. The server receives this data and stores it as the user's walking history and emotional information. The server analyzes this data to understand the user's current emotional state.

[1034] Step 4:

[1035] The server uses an AI model to generate a new walking course based on the received setting data, walking history data, and emotional data. The server considers the user's preferences and target calorie consumption, and also selects an appropriate walking course based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize a route that includes a park where the user can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1036] Step 5:

[1037] When the user opens the application again, the device displays the new walking route received from the server. The device displays a message to the user such as, "How about a relaxing route around a new park and cafe?" The user reviews the proposed walking route and taps the "Accept" button to select the next walking route.

[1038] Step 6:

[1039] The user then takes a walk according to the new walking course selected by the user. After the walk is completed, the device again sends GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback is also provided according to the user's emotional state; for example, if stress is reduced, a message such as "You feel relaxed!" is displayed.

[1040] In this way, users can keep their daily walks fresh and enjoyable, optimizing their health goals and emotional state.

[1041] Example 2

[1042] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1043] Conventional walking course generation systems provide walking courses based on the user's preferences and calorie consumption goals, but lack the functionality to propose appropriate courses taking into account the user's emotional state, making it difficult to support the user's mental health.

[1044] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving setting data by the user and transmitting it to the server, a means for collecting location information of the user and transmitting the data to the server, and a means for collecting emotional data of the user and transmitting the data to the server. This makes it possible to generate an optimal walking course that takes into consideration not only the user's preferences and target calorie consumption but also their emotional state.

[1045] "Setting data" refers to data such as a preferred location and target calorie consumption input by the user.

[1046] "Location information" is information indicating a geographical location, such as GPS data acquired by a user's terminal.

[1047] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1048] The "server" is a computer system that receives and analyzes setting data, location information, emotion data, and walking history data.

[1049] A "walking course" is a recommended walking route that is generated by the server and presented to the user.

[1050] "Calories burned" is a numerical value that indicates the amount of energy consumed by the user while walking.

[1051] A "user terminal" is an electronic device used by a user, such as a smartphone or tablet.

[1052] The "emotion engine" is software that analyzes the user's facial expressions and voice to recognize their emotional state.

[1053] An "AI model" is an artificial intelligence algorithm that runs on a server and generates optimal walking routes based on data.

[1054] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[1055] Overall system configuration

[1056] 1. User Device

[1057] The smartphone or tablet used by the user inputs setting data, collects location information, displays walking routes, and collects emotional data. Users input their preferred locations and target calorie consumption through the application. The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[1058] 2. Server

[1059] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history. The server is equipped with an AI model that generates an optimal walking course based on this data. It calculates calories burned based on location information after the walk and sends the results to the user's device.

[1060] 3. Emotion Engine

[1061] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state. The emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[1062] System Operation Overview

[1063] Initial Setup

[1064] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1065] Walking data collection and emotion recognition

[1066] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1067] Generate new walking routes

[1068] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1069] Walking course presentation and selection

[1070] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[1071] Calorie burn calculation and feedback

[1072] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[1073] Specific examples

[1074] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[1075] Example prompts to input to the generative AI model

[1076] "Generate the best walking route for you, taking into account your current emotional state."

[1077] "Suggest new walking routes based on user preferences and emotional data."

[1078] "Provide a relaxing walking route to a user who is feeling stressed."

[1079] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[1080] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1081] Step 1: Collect and submit initial setup data

[1082] When a user launches the application for the first time, they input their favorite places and target calorie consumption through the application interface. As a result, their favorite places (e.g., cafes, parks) and target calorie consumption (e.g., 500 kilocalories per day) are temporarily saved as setting data on the device. The device then sends this setting data to the server. The input is the user's favorite places and target calorie consumption, and the output is sent to the server as setting data.

[1083] Step 2: Save the configuration data

[1084] The server receives the sent setting data and saves it in the database as a user profile. The server analyzes the setting data and checks its validity. The input is the setting data, and the output is saving it in the database as a user profile. Specifically, the server checks the consistency of the data and saves it in the database.

[1085] Step 3: Collect location and emotion data at the start of the walk

[1086] The user starts a walk and taps the "Start Walk" button on the device. This activates the device's GPS function, collecting location information in real time. The device's built-in camera and microphone also detect the user's facial expressions and voice, which the emotion engine analyzes. The input is the user's behavior and biometric data, and the output is location information and emotion data recorded in real time.

[1087] Step 4: Record emotional data and location information

[1088] The device records emotion data and location information at regular intervals and sends this data to the server after the walk ends. The input is the continuously collected emotion data and location information, and the output is the recorded data sent to the server. Specifically, the device batch processes the location information and emotion data at regular intervals and uploads them to the server.

[1089] Step 5: Data analysis and generation of new walking routes

[1090] The server uses an AI model to generate a new walking course based on the setting data, emotion data, and walking history data it receives. The server analyzes the emotion data and selects an appropriate course based on the user's emotional state. The input is past walking history, setting data, and real-time emotion data, and the output is the generated new walking course. Specifically, the AI ​​model on the server analyzes the data and generates the optimal route.

[1091] Step 6: Present and select a walking route

[1092] The server sends the generated walking course data to the user's device, and when the user opens the application, a new walking course is presented. (For example, a message such as "How about a relaxing route around new parks and cafes?" is displayed.) The user checks the proposed walking course and taps the approval button. The input is the generated walking course data, and the output is the course selected by the user. Specifically, the device presents the course to the user through a rich interface.

[1093] Step 7: Calorie Calculation and Feedback

[1094] After the walk is over, the user's device again sends the GPS data and emotional data to the server. The server calculates the calories burned based on this data and sends the result to the device. The input is the GPS data and emotional data collected during the walk, and the output is the calculated calories burned and a feedback message. Specifically, the server calculates the calories burned and sends the result to the device as a notification. The device notifies the user, "You've achieved your calorie goal! Congratulations!" and also provides feedback according to the user's emotional state (e.g., "You felt relaxed!").

[1095] (Application example 2)

[1096] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1097] In recent years, walking and strolling have become popular for the purpose of improving health. However, simply suggesting walking courses does not sufficiently increase user satisfaction. Furthermore, providing appropriate content according to a user's emotional state would further improve the user experience. However, the current challenge is that services based on the user's emotions using emotion recognition technology have not yet been realized.

[1098] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user setting data and transmitting it to the server, means for collecting user location information and transmitting the data to the server, means for the server to analyze the past walking history and the user setting data and generate an optimal walking course, means for transmitting the generated walking course to the user terminal and presenting it to the user, means for calculating calories burned after the walk and notifying the user of the result, means for recognizing the user's emotional state in real time and transmitting emotional data to the server, and means for the server to recommend optimal content based on the user's emotional state and transmit the recommended content to the user terminal. This enables the user to achieve their health goals and enjoy content suited to their emotional state.

[1099] "Means for receiving user-specified data and transmitting it to the server" refers to hardware and software that has the function of receiving data entered by the user through the application (such as preferred locations and target calories burned) and transmitting that data to the server.

[1100] "Means for collecting user location information and transmitting that data to a server" refers to hardware and software that has the function of collecting location information from devices (smartphones or devices with GPS functionality) used by users while they are walking and transmitting that data to a server.

[1101] "Means for the server to analyze the user's past walking history and setting data and generate the optimal walking course" refers to a function in which the server uses AI models and algorithms to generate the optimal walking course based on the user's past walking history and setting data.

[1102] "Means for sending the generated walking course to the user terminal and presenting it to the user" refers to a function that sends the walking course data generated by the server to the device used by the user (such as a smartphone or tablet) and displays the walking course on that device.

[1103] "Means for calculating calories burned after a walk ends and notifying the user of the results" is a function that calculates calories burned based on collected location information data after the user finishes a walk and notifies the user of the results.

[1104] "Means for recognizing the user's emotional state in real time and transmitting emotional data to a server" refers to a function that uses the camera and microphone of the device used by the user to analyze facial expressions and voice, recognizes the user's emotional state in real time, and transmits that data to a server.

[1105] "Means for the server to recommend optimal content based on the user's emotional state and send it to the user's device" refers to a function that uses AI models and algorithms to recommend optimal content (videos, music, articles, etc.) based on the emotional data received by the server, and sends that content to the user's device.

[1106] This invention is a system that recognizes a user's emotions and provides appropriate content based on the user's emotional state. This system is implemented using a user terminal such as a smartphone, smart glasses, or head-mounted display, a server, and an emotion engine.

[1107] Overall system configuration

[1108] User terminal

[1109] The user device is a smartphone, smart glasses, or head-mounted display used by the user. These devices have the following capabilities:

[1110] Input of setting data: The user inputs their preferred location and target calorie consumption through the application. This setting data is temporarily stored on the device and then sent to the server.

[1111] Location information collection: During a walk, the user's location information is collected in real time using the GPS function.

[1112] Emotion data collection: The device uses a camera and microphone to analyze the user's facial expressions and voice, and the emotion engine recognizes their emotional state. The recognized data is sent to the server at regular intervals.

[1113] Emotion Engine: The emotion engine uses a deep learning model to analyze emotions and transmits emotional data from the device to the server in real time.

[1114] server

[1115] The server receives and analyzes the setting data, location information, and emotion data sent by the user. Specifically, it has the following functions:

[1116] Data analysis: The server uses an AI model to analyze the user's past walking history and emotional data, and generates optimal walking courses and content.

[1117] Generating walking courses: The server generates the optimal walking course for the user based on the location information and emotion data, and sends it to the user's device.

[1118] Content recommendation: Based on emotional data, AI models are used to generate content such as videos, music, and articles that are optimal for each user, and the URL or information is sent to the user's device.

[1119] System operation example

[1120] Initial Setup

[1121] When a user launches the application for the first time, they set their preferred location (e.g., park, cafe) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and sent to the server. The server then saves the received data in the user profile.

[1122] Walking data collection and emotion recognition

[1123] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1124] Generate and display walking courses

[1125] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects a walking course taking into account the user's preferences, target calorie consumption, and current emotional state. The server selects the optimal route from the multiple generated routes and sends this data to the user's device. When the user opens the application, the device displays the new walking course received from the server.

[1126] Emotion-based content recommendation

[1127] After the user finishes their walk, the device again sends the GPS data and emotional data to the server. The server then calculates the calories burned based on this data and sends the results to the device. At the same time, the AI ​​model selects the most suitable content (movies, music, articles, etc.) based on the emotional data and sends that information to the user's device.

[1128] Examples and prompts

[1129] For example, suppose a user starts a walk and the emotion "happy" is recognized along the way. In this case, the server recommends entertainment-related videos and music that the user tends to like when they are happy. In this case, an example of a prompt for the generative AI model is as follows:

[1130] Example prompt sentence:

[1131] The user is currently in the "happy" emotional state. Recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories.

[1132] This allows the user to enjoy the most suitable content that matches his or her own feelings, thereby increasing satisfaction.

[1133] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1134] Step 1:

[1135] When a user launches the application for the first time, they set their preferred locations and target calorie consumption. These settings are entered into the user's device, temporarily saved, and then sent to the server. The input data is the user's preferred locations (e.g., parks, cafes) and target calorie consumption (e.g., 500 kcal per day). The server receives this data and saves it in the user's profile.

[1136] Step 2:

[1137] When a user starts walking, the device activates its GPS function and collects location information in real time. The collected location data is periodically sent from the device to a server. The input data is the real-time location information, and the output data is the location data sent to the server. The server receives this and saves it as the user's walking history.

[1138] Step 3:

[1139] During a walk, the user's facial expressions and voice are analyzed in real time using a camera and microphone installed on the user's device, and the emotion engine recognizes emotional data. The recognized emotional data is sent to the server at regular intervals. The input data is the facial expressions and voice captured on the device, and the output data is the emotional state (e.g., happy, sad, angry, relaxed). The server receives the emotional data and stores it in the user's profile.

[1140] Step 4:

[1141] The server uses an AI model to generate a new walking course based on the received setting data, location information, and emotion data. The data processing performed by the server combines past walking history with current location information and emotion data to generate the optimal walking course. The output is the generated walking course data.

[1142] Step 5:

[1143] The generated walking course data is sent from the server to the user terminal. The user terminal presents the received walking course to the user, and a message such as "How about a relaxing route around a new park and cafe?" is displayed to the user. The input data is the generated walking course data, and the output data is the walking course displayed on the screen of the user terminal.

[1144] Step 6:

[1145] After the user finishes their walk, the device again sends the GPS data and emotion data to the server. The server analyzes these data and calculates the calories burned. The input data are the GPS data and emotion data at the end, and the output data is the calorie burned result. The server then sends the calculation results to the user device.

[1146] Step 7:

[1147] The server uses the AI ​​model to recommend content that matches the user's emotional state based on the emotional data sent at the end. For example, if the user is in a "happy" emotional state, it will recommend entertaining videos and fun music. An example of a prompt is as follows:

[1148] "The user is currently in the emotional state of 'happy'. Please recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories." The input data is the emotional state, and the output data is the URL and information of the recommended content. The recommended content is sent to the user's device and provided to the user.

[1149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1151] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1152] [Fourth embodiment]

[1153] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1157] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1162] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1164] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1165] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1166] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system operates mainly using a user terminal, a server, and GPS data.

[1167] Overall system configuration

[1168] 1. User Device

[1169] It is a smartphone or tablet used by the user to input setting data, collect location information, and display walking courses.

[1170] Users input their preferred location and target calorie consumption through the application.

[1171] 2. Server

[1172] The server is a computer system that receives and analyzes the setting data, location information, and walking history.

[1173] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[1174] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[1175] System Operation

[1176] Initial Setup

[1177] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[1178] Collecting walking data

[1179] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[1180] Generate new walking routes

[1181] The server uses an AI model to generate a new walking course based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[1182] Walking course presentation and selection

[1183] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[1184] Calorie burn calculation and feedback

[1185] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[1186] Specific examples

[1187] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[1188] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[1189] The processing flow will be explained below.

[1190] Step 1:

[1191] The user starts the application and first inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). The device temporarily stores this setting data. Then, it sends the setting data to the server. The server saves the received data in the user profile.

[1192] Step 2:

[1193] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device continues to record location information at regular intervals. When the user finishes their walk, the device sends the collected GPS data to the server. The server receives the location data and saves it as the user's walking history.

[1194] Step 3:

[1195] The server analyzes the user's settings and walking history, and generates a new walking course using an AI model. Based on the user's preferences and calorie consumption goals, the server prioritizes routes that have not been walked before or have not been used for a while. The server selects the optimal route from the multiple routes generated and sends this data to the user's device.

[1196] Step 4:

[1197] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a route that goes around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[1198] Step 5:

[1199] The user takes a walk according to the new walking course selected. After the walk is over, the device collects GPS data again and sends it to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user, "You've achieved your calorie goal! Congratulations!"

[1200] In this way, the roles of the user, the terminal, and the server are clearly defined in each step, allowing the user to effectively enjoy a new walking course while simultaneously achieving the target calorie consumption.

[1201] Example 1

[1202] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1203] With the increasing health consciousness in today's society, there is a demand for support systems that can help people maintain a fun daily exercise habit. Conventional pedometers and simple course suggestion systems have difficulty generating appropriate walking courses that match the individual preferences and goals of the user, making it difficult to encourage continued use. In response to this, a system is needed that can automatically suggest optimal walking courses that take into account the user's specific wishes and health goals.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1205] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, and a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course using a generative AI model. This makes it possible to provide an optimal walking course based on the user's individual preferences and goals, and to encourage continuous use through calculation of calories burned and feedback.

[1206] "User setting data" refers to information such as a favorite location or target calorie consumption that the user inputs through the application.

[1207] The "server" is a computer system that receives and analyzes setting data, location information, and walking history.

[1208] "User location information" is geographical data obtained using the GPS sensor of the user terminal.

[1209] "Walking history" refers to a record of the routes the user has walked in the past and the GPS data from those routes.

[1210] A "generative AI model" is an artificial intelligence algorithm that generates optimal walking courses based on the user's settings and walking history data.

[1211] The "optimal walking course" is the optimal walking route for the user, suggested by the generative AI model based on the user's preferences and goals.

[1212] A "user device" is a device used by a user, such as a smartphone or tablet, which is used to input setting data, collect location information, and display walking courses.

[1213] "GPS data" refers to location information obtained by the GPS sensor of the user terminal.

[1214] "Calories burned" is the amount of energy consumed by the user while walking.

[1215] "Feedback" refers to information such as a calorie burn calculation result and an encouraging message that is provided to the user after the walk is completed.

[1216] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. This system operates mainly using a user terminal, a server, and GPS data.

[1217] Overall system configuration

[1218] 1. User Device

[1219] It is a smart device (smartphone or tablet) used by the user to input setting data, collect location information, and display walking courses. Users input their preferred locations and target calorie consumption through the application.

[1220] Hardware used: smartphone, tablet

[1221] Software used: Application

[1222] 2. Server

[1223] The server is a computer system that receives and analyzes setting data, location information, and walking history. The server is equipped with a generative AI model that generates an optimal walking course based on the user's preferences and goals. It calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[1224] Hardware used: Server

[1225] Software used: Generative AI model, data analysis software

[1226] System Operation

[1227] Initial Setup

[1228] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[1229] Collecting walking data

[1230] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[1231] Generate new walking routes

[1232] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1233] Walking course presentation and selection

[1234] When the user opens the application, the device displays a new walking route, with a message such as, "How about a route that includes a new park and cafe?" If the user accepts the suggestion, the route is set as the next route.

[1235] Calorie burn calculation and feedback

[1236] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided to the user.

[1237] Specific examples

[1238] User A starts the application for the first time and sets his / her preferences, "I like cafes and parks" and "I want to burn 500 kilocalories a day." User A then starts a walk, visiting cafes and parks near his / her home. During this time, the user's device collects GPS data and sends it to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User A. User A then selects the suggested walking course and goes for a walk. The calories burned are calculated and the user is notified that their goal has been achieved.

[1239] Prompt Sentence Examples

[1240] "Generate new walking itineraries based on the user's preferred locations and calorie goals. For example, suggest a route that includes a park and a cafe."

[1241] In this way, users can keep their daily walks fresh and enjoyable and achieve their health goals.

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] When a user launches the application for the first time, a setting screen is displayed. The user inputs their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server.

[1245] Input: User-entered preferred location, target calories burned

[1246] Data processing: This data is temporarily stored on the device and converted into a format for sending to the server.

[1247] Output: Configuration data sent to the server

[1248] Step 2:

[1249] The server receives the user's settings and stores them in a database, which is later used to generate walking routes.

[1250] Input: Configuration data sent from the terminal

[1251] Data processing: Analyzes the setting data and saves it in a database

[1252] Output: Saved user configuration data

[1253] Step 3:

[1254] When a user starts a walk, the device activates the GPS and collects location information at regular intervals. The collected data is temporarily stored on the device and sent to the server after the walk ends.

[1255] Input: Real-time location information from a GPS sensor

[1256] Data processing: Location information is recorded at regular intervals and temporarily saved

[1257] Output: GPS data sent to the server after the walk ends

[1258] Step 4:

[1259] The server analyzes the received location information and saves it as the user's walking history. This data is used to generate the next walking route.

[1260] Input: GPS data sent from the device

[1261] Data processing: Analyze location information and register it in a database as walking history

[1262] Output: Saved user walking history data

[1263] Step 5:

[1264] The server uses past walking history and configuration data to generate new walking routes using a generative AI model, which is designed to prioritize places that have not been walked before and routes that have not been visited in a while.

[1265] Input: Walking history data, setting data

[1266] Data processing: Generative AI model generates new walking routes

[1267] Output: Generated walking course data

[1268] Step 6:

[1269] The generated walking course is sent to the user terminal and presented to the user, who then checks the proposed walking course and approves it if necessary.

[1270] Input: Walking course data sent from the server

[1271] Data processing: Convert the walking course into a display format and present it to the user

[1272] Output: Walking itinerary that the user approves or modifies

[1273] Step 7:

[1274] When the user finishes their walk, the GPS data is collected again and sent to the server, which then calculates the calories burned and notifies the user.

[1275] Input: GPS data after the walk

[1276] Data processing: Calculate calories burned based on location information

[1277] Output: Calculation result (calories burned) and feedback message (e.g., "You've reached your calorie goal! Congratulations!")

[1278] In this way, each processing step works in conjunction with one another to realize a system that enriches the user's walking experience.

[1279] (Application example 1)

[1280] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1281] Conventional walking support systems can generate walking courses based on the user's location information and walking history and calculate calories burned, but they lack the perspective of making the walk enjoyable. They also lack real-time information provision based on the user's interests and preferences. This can lead to a monotonous walking experience, which can decrease motivation to continue. Furthermore, the walking courses are not presented visually in an easy-to-understand manner, which can lead to users getting lost.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1283] In this invention, the server includes a means for receiving user-specified data and transmitting it to the server, a means for collecting user location information and transmitting that data to the server, a means for the server to analyze the user's past walking history and the user's specified data and generate an optimal walking course, a means for transmitting the generated walking course to the user's terminal and presenting it to the user, a means for calculating calories burned after the walk and notifying the user of the result, a means for displaying the specified walking course using AR technology, and a means for displaying nearby landmarks and recommended spots in real time during the walk. This allows the user to be visually guided along a walking course based on their interests and preferences while receiving real-time information about nearby landmarks and recommended spots during the walk. This makes the walking experience more enjoyable, fresh, and helps maintain motivation. Furthermore, the visual clarity allows the user to continue their walk without getting lost.

[1284] "User setting data" is information necessary for the system to generate a walking course, such as the user's favorite places and target calorie consumption.

[1285] "User location information" is information for identifying the user's current location using GPS data or the like.

[1286] "Past walking history" is history data such as the routes and distances of walks the user has taken in the past.

[1287] An "optimal walking course" is a route suitable for walking that is generated based on the user's preferences and goals.

[1288] "AR technology" stands for augmented reality technology, which displays virtual information overlaid on real-world scenery.

[1289] "Sightseeing spots and recommended spots" are interesting places along the walking route and places worth visiting.

[1290] "Means for displaying in real time" refers to a technology that instantly displays the information required at the user's current location.

[1291] The "means for calculating calories burned" is a method for accurately calculating the amount of calories burned based on the user's walking data.

[1292] "Navigation" refers to guiding the user to move along a walking course set by the user.

[1293] "Visually easy to understand" means presenting information in an easily recognizable form so that the user can understand the information at a glance.

[1294] This invention is a system for making users' walking experiences more enjoyable and supporting healthy lifestyles. The system mainly operates using a user terminal, a server, and GPS data. Specific embodiments for realizing this are described below.

[1295] Overall system configuration

[1296] The system consists of the following main components:

[1297] 1. User Device

[1298] The smartphone or tablet used by the user.

[1299] Enters configuration data, collects location information, and displays walking courses.

[1300] Users input their preferred location and target calorie consumption through the application.

[1301] 2. Server

[1302] It is a computer system that receives and analyzes setting data, location information, and walking history.

[1303] The server is equipped with an AI model that generates optimal walking routes based on the user's preferences and goals.

[1304] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[1305] 3. Augmented Reality Technology

[1306] Used to visually indicate designated walking routes.

[1307] It uses devices such as smart glasses and head-mounted displays.

[1308] System Operation

[1309] Initial Setup

[1310] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their target calorie consumption (e.g., 500 kcal per day). These setting data are temporarily stored on the device and then sent to the server.

[1311] Collecting walking data

[1312] When a user starts a walk, the user's device starts collecting GPS data. This location information is recorded at regular intervals and sent to the server after the walk ends. The server analyzes this location information and saves it as the user's walking history.

[1313] Generate new walking routes

[1314] The server generates a new walking course using a generative AI model based on the received setting data and walking history data. Based on the user's preferences and goals, the server prioritizes routes that have not been walked before or have not been traveled for a while. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1315] Walking course presentation and selection

[1316] When a user opens the application, the user's device displays a new walking route. This is done using AR technology to visually present the walking route. A message such as "How about a route that goes around a new park and cafe?" is displayed, and if the user accepts the suggestion, the walking route is set as the next route.

[1317] Calorie burn calculation and feedback

[1318] After the walk, the user device sends the GPS data to the server again. The server calculates the calories burned based on this data. The calculation results are sent to the user device, and feedback such as "You've achieved your calorie goal! Congratulations!" is provided.

[1319] Hardware and software used

[1320] Hardware:

[1321] Smart glasses (with AR function)

[1322] Smartphone

[1323] software:

[1324] ARKit (Apple's AR development framework)

[1325] TensorFlow (AI model building and prediction)

[1326] Firebase (cloud data management)

[1327] Specific examples

[1328] User A starts the application for the first time and sets the criteria: "I like cafes and parks" and "I want to burn 500 calories a day." The application uses AR to guide the user through their walking route, allowing them to check the calories burned in real time. Next, after the walk is over, a new recommended route is suggested.

[1329] Prompt Sentence Examples

[1330] User preferences: parks, cafes

[1331] Target calorie consumption: 500kcal

[1332] Current location: [35.6895, 139.6917]

[1333] Walking frequency: 3 times a week

[1334] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1335] Step 1:

[1336] User configuration data entry

[1337] When a user launches the application for the first time, they input their preferred location (e.g., a park or a cafe) and their goal for calorie consumption (e.g., 500 kcal per day). The input data is temporarily stored on the device and then sent to the server, making it easier for the server to understand the user's preferences and goals.

[1338] Input: User settings data (preferred location, target calories burned)

[1339] Output: Configuration data sent to the server

[1340] Step 2:

[1341] Start collecting user location information

[1342] When a user starts a walk, the device uses GPS to collect the user's location information. This location information is recorded at regular intervals and updated in real time, allowing the device to accurately track the user's movements.

[1343] Input: GPS data (user location information)

[1344] Output: Location information recorded on the device

[1345] Step 3:

[1346] Sending data to the server

[1347] After the walk, the device sends the collected location information to the server, which then stores the data as the user's walking history and uses it to generate the next walking route.

[1348] Input: Recorded location information

[1349] Output: Location data sent to the server

[1350] Step 4:

[1351] Generate new walking routes

[1352] The server uses a generative AI model to generate a new walking course based on the user's settings and past walking history data. The AI ​​model selects the optimal route, taking into account the user's preferences and goals.

[1353] Input: Setting data, walking history data

[1354] Output: Generated walking course data

[1355] Step 5:

[1356] Walking course information

[1357] The generated walking course data is sent to the user's device, which then uses AR technology to visually display the walking course, allowing the user to walk while checking the new walking course in real time.

[1358] Input: New walking course data

[1359] Output: Walking route displayed on the terminal

[1360] Step 6:

[1361] Real-time information provision

[1362] While walking, the device displays real-time information about nearby attractions and recommended spots based on the user's current location, allowing the user to enjoy their walk even more.

[1363] Input: Current location

[1364] Output: Displayed surrounding information

[1365] Step 7:

[1366] Calorie burn calculation and feedback

[1367] After the walk, the GPS data is sent to the server again, and the server calculates the calories burned based on this data. The calculation results are sent to the device and feedback is provided to the user.

[1368] Input: Last location information

[1369] Output: Calorie consumption calculation and feedback

[1370] Prompt Sentence Examples

[1371] User preferences: parks, cafes

[1372] Target calorie consumption: 500kcal

[1373] Current location: [35.6895, 139.6917]

[1374] Walking frequency: 3 times a week

[1375] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1376] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[1377] Overall system configuration

[1378] 1. User Device

[1379] The user's smartphone or tablet is used to input setting data, collect location information, display walking routes, and collect emotional data.

[1380] Users input their preferred location and target calorie consumption through the application.

[1381] The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[1382] 2. Server

[1383] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history.

[1384] The server is equipped with an AI model that generates optimal walking routes based on this data.

[1385] The system calculates the calories burned based on the location information after the walk and sends the results to the user's device.

[1386] 3. Emotion Engine

[1387] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state.

[1388] Emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[1389] System Operation

[1390] Initial Setup

[1391] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1392] Walking data collection and emotion recognition

[1393] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1394] Generate new walking routes

[1395] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1396] Walking course presentation and selection

[1397] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[1398] Calorie burn calculation and feedback

[1399] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[1400] Specific examples

[1401] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[1402] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[1403] The processing flow will be explained below.

[1404] Step 1:

[1405] The user launches the application and inputs their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1406] Step 2:

[1407] When a user starts walking, the device activates its GPS function to collect location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals.

[1408] Step 3:

[1409] When the user finishes their walk, the device sends the collected GPS data and emotional data to the server. The server receives this data and stores it as the user's walking history and emotional information. The server analyzes this data to understand the user's current emotional state.

[1410] Step 4:

[1411] The server uses an AI model to generate a new walking course based on the received setting data, walking history data, and emotional data. The server considers the user's preferences and target calorie consumption, and also selects an appropriate walking course based on the user's emotional state. For example, if the user is feeling stressed, it will prioritize a route that includes a park where the user can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1412] Step 5:

[1413] When the user opens the application again, the device displays the new walking route received from the server. The device displays a message to the user such as, "How about a relaxing route around a new park and cafe?" The user reviews the proposed walking route and taps the "Accept" button to select the next walking route.

[1414] Step 6:

[1415] The user then takes a walk according to the new walking course selected by the user. After the walk is completed, the device again sends GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback is also provided according to the user's emotional state; for example, if stress is reduced, a message such as "You feel relaxed!" is displayed.

[1416] In this way, users can keep their daily walks fresh and enjoyable, optimizing their health goals and emotional state.

[1417] Example 2

[1418] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1419] Conventional walking course generation systems provide walking courses based on the user's preferences and calorie consumption goals, but lack the functionality to propose appropriate courses taking into account the user's emotional state, making it difficult to support the user's mental health.

[1420] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving setting data by the user and transmitting it to the server, a means for collecting location information of the user and transmitting the data to the server, and a means for collecting emotional data of the user and transmitting the data to the server. This makes it possible to generate an optimal walking course that takes into consideration not only the user's preferences and target calorie consumption but also their emotional state.

[1421] "Setting data" refers to data such as a preferred location and target calorie consumption input by the user.

[1422] "Location information" is information indicating a geographical location, such as GPS data acquired by a user's terminal.

[1423] "Emotion data" is data that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1424] The "server" is a computer system that receives and analyzes setting data, location information, emotion data, and walking history data.

[1425] A "walking course" is a recommended walking route that is generated by the server and presented to the user.

[1426] "Calories burned" is a numerical value that indicates the amount of energy consumed by the user while walking.

[1427] A "user terminal" is an electronic device used by a user, such as a smartphone or tablet.

[1428] The "emotion engine" is software that analyzes the user's facial expressions and voice to recognize their emotional state.

[1429] An "AI model" is an artificial intelligence algorithm that runs on a server and generates optimal walking routes based on data.

[1430] This invention is a system that improves the user experience and provides appropriate walking courses by combining an emotion engine that recognizes the user's emotions. The system operates mainly using the user's terminal, a server, GPS data, and the emotion engine.

[1431] Overall system configuration

[1432] 1. User Device

[1433] The smartphone or tablet used by the user inputs setting data, collects location information, displays walking routes, and collects emotional data. Users input their preferred locations and target calorie consumption through the application. The device is equipped with an emotion engine that uses a camera and microphone to analyze emotions from the user's facial expressions and voice.

[1434] 2. Server

[1435] The server is a computer system that receives and analyzes setting data, location information, emotion data, and walking history. The server is equipped with an AI model that generates an optimal walking course based on this data. It calculates calories burned based on location information after the walk and sends the results to the user's device.

[1436] 3. Emotion Engine

[1437] The emotion engine analyzes data such as the user's facial expressions and voice to recognize the user's emotional state. The emotion data is sent from the device to the server in real time and is taken into consideration when generating walking courses.

[1438] System Operation Overview

[1439] Initial Setup

[1440] When a user launches the application for the first time, they input their preferred location (e.g., cafe, park) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and then sent to the server. The server saves the received data in the user profile.

[1441] Walking data collection and emotion recognition

[1442] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1443] Generate new walking routes

[1444] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects an appropriate walking course based on the user's current emotional state, taking into account the user's preferences and calorie consumption goals. For example, if the user is feeling stressed, it will prioritize a route that includes a park where they can relax. The server selects the optimal route from the multiple generated routes and sends this data to the user's device.

[1445] Walking course presentation and selection

[1446] When the user opens the application, the device displays the new walking route received from the server. The user is presented with a message such as, "How about a relaxing route around a new park and cafe?" The user can review the proposed walking route and tap the "Approve" button to select the next walking route.

[1447] Calorie burn calculation and feedback

[1448] After the walk, the user's device again sends the GPS data and emotional data to the server. The server uses this data to calculate the calories burned. The calculation results are sent to the device, which then notifies the user by saying, "You've achieved your calorie goal! Congratulations!" Feedback based on the user's emotional state is also provided.

[1449] Specific examples

[1450] User B starts the application for the first time and sets his / her preferences, such as "I like cafes and parks" and "I want to burn 500 calories a day." User B then starts a walk, visiting cafes and parks near his / her home, with the emotion engine recognizing his / her emotions as he / she walks. During this time, the user's device collects GPS data and emotion data, and sends this to the server after the walk is over. The server uses this data to generate a new walking course and suggests it to User B. User B then selects the suggested walking course and goes for a walk. The calories burned are calculated, and the user is notified that their goal has been achieved. Furthermore, if stress is reduced, feedback such as "You felt relaxed!" is displayed.

[1451] Example prompts to input to the generative AI model

[1452] "Generate the best walking route for you, taking into account your current emotional state."

[1453] "Suggest new walking routes based on user preferences and emotional data."

[1454] "Provide a relaxing walking route to a user who is feeling stressed."

[1455] In this way, users can make their daily walks fresh and enjoyable, not only achieving their health goals but also maintaining an optimal emotional state.

[1456] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1457] Step 1: Collect and submit initial setup data

[1458] When a user launches the application for the first time, they input their favorite places and target calorie consumption through the application interface. As a result, their favorite places (e.g., cafes, parks) and target calorie consumption (e.g., 500 kilocalories per day) are temporarily saved as setting data on the device. The device then sends this setting data to the server. The input is the user's favorite places and target calorie consumption, and the output is sent to the server as setting data.

[1459] Step 2: Save the configuration data

[1460] The server receives the sent setting data and saves it in the database as a user profile. The server analyzes the setting data and checks its validity. The input is the setting data, and the output is saving it in the database as a user profile. Specifically, the server checks the consistency of the data and saves it in the database.

[1461] Step 3: Collect location and emotion data at the start of the walk

[1462] The user starts a walk and taps the "Start Walk" button on the device. This activates the device's GPS function, collecting location information in real time. The device's built-in camera and microphone also detect the user's facial expressions and voice, which the emotion engine analyzes. The input is the user's behavior and biometric data, and the output is location information and emotion data recorded in real time.

[1463] Step 4: Record emotional data and location information

[1464] The device records emotion data and location information at regular intervals and sends this data to the server after the walk ends. The input is the continuously collected emotion data and location information, and the output is the recorded data sent to the server. Specifically, the device batch processes the location information and emotion data at regular intervals and uploads them to the server.

[1465] Step 5: Data analysis and generation of new walking routes

[1466] The server uses an AI model to generate a new walking course based on the setting data, emotion data, and walking history data it receives. The server analyzes the emotion data and selects an appropriate course based on the user's emotional state. The input is past walking history, setting data, and real-time emotion data, and the output is the generated new walking course. Specifically, the AI ​​model on the server analyzes the data and generates the optimal route.

[1467] Step 6: Present and select a walking route

[1468] The server sends the generated walking course data to the user's device, and when the user opens the application, a new walking course is presented. (For example, a message such as "How about a relaxing route around new parks and cafes?" is displayed.) The user checks the proposed walking course and taps the approval button. The input is the generated walking course data, and the output is the course selected by the user. Specifically, the device presents the course to the user through a rich interface.

[1469] Step 7: Calorie Calculation and Feedback

[1470] After the walk is over, the user's device again sends the GPS data and emotional data to the server. The server calculates the calories burned based on this data and sends the result to the device. The input is the GPS data and emotional data collected during the walk, and the output is the calculated calories burned and a feedback message. Specifically, the server calculates the calories burned and sends the result to the device as a notification. The device notifies the user, "You've achieved your calorie goal! Congratulations!" and also provides feedback according to the user's emotional state (e.g., "You felt relaxed!").

[1471] (Application example 2)

[1472] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1473] In recent years, walking and strolling have become popular for the purpose of improving health. However, simply suggesting walking courses does not sufficiently increase user satisfaction. Furthermore, providing appropriate content according to a user's emotional state would further improve the user experience. However, the current challenge is that services based on the user's emotions using emotion recognition technology have not yet been realized.

[1474] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user setting data and transmitting it to the server, means for collecting user location information and transmitting the data to the server, means for the server to analyze the past walking history and the user setting data and generate an optimal walking course, means for transmitting the generated walking course to the user terminal and presenting it to the user, means for calculating calories burned after the walk and notifying the user of the result, means for recognizing the user's emotional state in real time and transmitting emotional data to the server, and means for the server to recommend optimal content based on the user's emotional state and transmit the recommended content to the user terminal. This enables the user to achieve their health goals and enjoy content suited to their emotional state.

[1475] "Means for receiving user-specified data and transmitting it to the server" refers to hardware and software that has the function of receiving data entered by the user through the application (such as preferred locations and target calories burned) and transmitting that data to the server.

[1476] "Means for collecting user location information and transmitting that data to a server" refers to hardware and software that has the function of collecting location information from devices (smartphones or devices with GPS functionality) used by users while they are walking and transmitting that data to a server.

[1477] "Means for the server to analyze the user's past walking history and setting data and generate the optimal walking course" refers to a function in which the server uses AI models and algorithms to generate the optimal walking course based on the user's past walking history and setting data.

[1478] "Means for sending the generated walking course to the user terminal and presenting it to the user" refers to a function that sends the walking course data generated by the server to the device used by the user (such as a smartphone or tablet) and displays the walking course on that device.

[1479] "Means for calculating calories burned after a walk ends and notifying the user of the results" is a function that calculates calories burned based on collected location information data after the user finishes a walk and notifies the user of the results.

[1480] "Means for recognizing the user's emotional state in real time and transmitting emotional data to a server" refers to a function that uses the camera and microphone of the device used by the user to analyze facial expressions and voice, recognizes the user's emotional state in real time, and transmits that data to a server.

[1481] "Means for the server to recommend optimal content based on the user's emotional state and send it to the user's device" refers to a function that uses AI models and algorithms to recommend optimal content (videos, music, articles, etc.) based on the emotional data received by the server, and sends that content to the user's device.

[1482] This invention is a system that recognizes a user's emotions and provides appropriate content based on the user's emotional state. This system is implemented using a user terminal such as a smartphone, smart glasses, or head-mounted display, a server, and an emotion engine.

[1483] Overall system configuration

[1484] User terminal

[1485] The user device is a smartphone, smart glasses, or head-mounted display used by the user. These devices have the following capabilities:

[1486] Input of setting data: The user inputs their preferred location and target calorie consumption through the application. This setting data is temporarily stored on the device and then sent to the server.

[1487] Location information collection: During a walk, the user's location information is collected in real time using the GPS function.

[1488] Emotion data collection: The device uses a camera and microphone to analyze the user's facial expressions and voice, and the emotion engine recognizes their emotional state. The recognized data is sent to the server at regular intervals.

[1489] Emotion Engine: The emotion engine uses a deep learning model to analyze emotions and transmits emotional data from the device to the server in real time.

[1490] server

[1491] The server receives and analyzes the setting data, location information, and emotion data sent by the user. Specifically, it has the following functions:

[1492] Data analysis: The server uses an AI model to analyze the user's past walking history and emotional data, and generates optimal walking courses and content.

[1493] Generating walking courses: The server generates the optimal walking course for the user based on the location information and emotion data, and sends it to the user's device.

[1494] Content recommendation: Based on emotional data, AI models are used to generate content such as videos, music, and articles that are optimal for each user, and the URL or information is sent to the user's device.

[1495] System operation example

[1496] Initial Setup

[1497] When a user launches the application for the first time, they set their preferred location (e.g., park, cafe) and target calorie consumption (e.g., 500 kcal per day). This setting data is temporarily stored on the device and sent to the server. The server then saves the received data in the user profile.

[1498] Walking data collection and emotion recognition

[1499] When a user starts a walk, the device activates its GPS function and collects location information in real time. The device's built-in emotion engine also analyzes the user's facial expressions and voice to collect emotional data. This data is recorded at regular intervals and sent to a server after the walk ends. The server analyzes this data and saves it as the user's walking history and emotional information.

[1500] Generate and display walking courses

[1501] The server uses an AI model to generate a new walking course based on the received setting data, emotion data, and walking history data. The server selects a walking course taking into account the user's preferences, target calorie consumption, and current emotional state. The server selects the optimal route from the multiple generated routes and sends this data to the user's device. When the user opens the application, the device displays the new walking course received from the server.

[1502] Emotion-based content recommendation

[1503] After the user finishes their walk, the device again sends the GPS data and emotional data to the server. The server then calculates the calories burned based on this data and sends the results to the device. At the same time, the AI ​​model selects the most suitable content (movies, music, articles, etc.) based on the emotional data and sends that information to the user's device.

[1504] Examples and prompts

[1505] For example, suppose a user starts a walk and the emotion "happy" is recognized along the way. In this case, the server recommends entertainment-related videos and music that the user tends to like when they are happy. In this case, an example of a prompt for the generative AI model is as follows:

[1506] Example prompt sentence:

[1507] The user is currently in the "happy" emotional state. Recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories.

[1508] This allows the user to enjoy the most suitable content that matches his or her own feelings, thereby increasing satisfaction.

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1:

[1511] When a user launches the application for the first time, they set their preferred locations and target calorie consumption. These settings are entered into the user's device, temporarily saved, and then sent to the server. The input data is the user's preferred locations (e.g., parks, cafes) and target calorie consumption (e.g., 500 kcal per day). The server receives this data and saves it in the user's profile.

[1512] Step 2:

[1513] When a user starts walking, the device activates its GPS function and collects location information in real time. The collected location data is periodically sent from the device to a server. The input data is the real-time location information, and the output data is the location data sent to the server. The server receives this and saves it as the user's walking history.

[1514] Step 3:

[1515] During a walk, the user's facial expressions and voice are analyzed in real time using a camera and microphone installed on the user's device, and the emotion engine recognizes emotional data. The recognized emotional data is sent to the server at regular intervals. The input data is the facial expressions and voice captured on the device, and the output data is the emotional state (e.g., happy, sad, angry, relaxed). The server receives the emotional data and stores it in the user's profile.

[1516] Step 4:

[1517] The server uses an AI model to generate a new walking course based on the received setting data, location information, and emotion data. The data processing performed by the server combines past walking history with current location information and emotion data to generate the optimal walking course. The output is the generated walking course data.

[1518] Step 5:

[1519] The generated walking course data is sent from the server to the user terminal. The user terminal presents the received walking course to the user, and a message such as "How about a relaxing route around a new park and cafe?" is displayed to the user. The input data is the generated walking course data, and the output data is the walking course displayed on the screen of the user terminal.

[1520] Step 6:

[1521] After the user finishes their walk, the device again sends the GPS data and emotion data to the server. The server analyzes these data and calculates the calories burned. The input data are the GPS data and emotion data at the end, and the output data is the calorie burned result. The server then sends the calculation results to the user device.

[1522] Step 7:

[1523] The server uses the AI ​​model to recommend content that matches the user's emotional state based on the emotional data sent at the end. For example, if the user is in a "happy" emotional state, it will recommend entertaining videos and fun music. An example of a prompt is as follows:

[1524] "The user is currently in the emotional state of 'happy'. Please recommend videos, movies, music, or articles that best fit this emotional state. Please also provide genres and categories." The input data is the emotional state, and the output data is the URL and information of the recommended content. The recommended content is sent to the user's device and provided to the user.

[1525] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1526] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1527] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1528] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1529] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1530] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1531] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1532] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1533] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1534] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1535] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1536] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1537] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1538] 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.

[1539] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1540] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1541] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1542] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1543] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1544] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1545] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1546] The following is further disclosed regarding the above embodiment.

[1547] (Claim 1)

[1548] means for receiving user configuration data and transmitting it to a server;

[1549] means for collecting user location information and transmitting the data to a server;

[1550] A means for the server to analyze the past walking history and the user's setting data and generate an optimal walking course;

[1551] means for transmitting the generated walking course to a user terminal and presenting it to the user;

[1552] A means for calculating the calories burned after the walk and notifying the user of the result;

[1553] A system including:

[1554] (Claim 2)

[1555] 2. The system according to claim 1, wherein the server selects the optimum route from the plurality of routes generated.

[1556] (Claim 3)

[1557] 10. The system of claim 1, wherein the server adjusts the walking course based on the user's preferences and calorie consumption goals.

[1558] "Example 1"

[1559] (Claim 1)

[1560] means for receiving user configuration data and transmitting it to a server;

[1561] means for collecting user location information and transmitting the data to a server;

[1562] A means for the server to analyze the past walking history and the user's setting data and generate an optimal walking course using a generation AI model;

[1563] means for transmitting the generated walking course to a user terminal and presenting it to the user;

[1564] After the walk, the system calculates the calories burned based on the GPS data and notifies the user of the results.

[1565] A system including:

[1566] (Claim 2)

[1567] 2. The system according to claim 1, wherein the server selects the optimum route from the plurality of routes generated.

[1568] (Claim 3)

[1569] 10. The system of claim 1, wherein the server adjusts the walking course based on the user's preferences and calorie consumption goals.

[1570] "Application Example 1"

[1571] Rewritten claims

[1572] (Claim 1)

[1573] means for receiving user configuration data and transmitting it to a server;

[1574] means for collecting user location information and transmitting the data to a server;

[1575] A means for the server to analyze the past walking history and the user's setting data and generate an optimal walking course;

[1576] means for transmitting the generated walking course to a user terminal and presenting it to the user;

[1577] A means for calculating the calories burned after the walk and notifying the user of the result;

[1578] A means for displaying a designated walking course using AR technology;

[1579] A way to display nearby attractions and recommended spots in real time while you're walking,

[1580] A system including:

[1581] (Claim 2)

[1582] 2. The system according to claim 1, wherein the server selects the optimum route from the plurality of routes generated.

[1583] (Claim 3)

[1584] 10. The system of claim 1, wherein the server adjusts the walking course based on the user's preferences and calorie consumption goals.

[1585] "Example 2: Combining Emotion Engines"

[1586] (Claim 1)

[1587] means for receiving user configuration data and transmitting it to a server;

[1588] means for collecting user location information and transmitting the data to a server;

[1589] means for collecting user emotion data and transmitting the data to a server;

[1590] A means for the server to analyze the past walking history, the user's setting data, and the emotion data, and generate an optimal walking course;

[1591] means for transmitting the generated walking course to a user terminal and presenting it to the user;

[1592] A means for the server to calculate the calories burned after the walk ends and notify the user of the result;

[1593] A system including:

[1594] (Claim 2)

[1595] 2. The system according to claim 1, wherein the server selects the optimum route from the plurality of routes generated.

[1596] (Claim 3)

[1597] 10. The system of claim 1, wherein the server adjusts the walking course based on the user's preferences, calorie consumption goals, and emotional state.

[1598] "Application example 2 when combining emotion engines"

[1599] (Claim 1)

[1600] means for receiving user configuration data and transmitting it to a server;

[1601] means for collecting user location information and transmitting the data to a server;

[1602] A means for the server to analyze the past walking history and the user's setting data and generate an optimal walking course;

[1603] means for transmitting the generated walking course to a user terminal and presenting it to the user;

[1604] A means for calculating the calories burned after the walk and notifying the user of the result;

[1605] means for recognizing the emotional state of a user in real time and transmitting the emotional data to a server;

[1606] A server recommends optimal content based on the user's emotional state and transmits the content to the user terminal;

[1607] A system including:

[1608] (Claim 2)

[1609] A means for selecting an optimal route from a plurality of routes generated by the server;

[1610] 2. The system according to claim 1, wherein the system analyzes the user's emotional data and recommends optimal content.

[1611] (Claim 3)

[1612] A means for the server to adjust a walking course based on the user's preferences and target calorie consumption;

[1613] 10. The system of claim 1, wherein the system recommends content individually based on emotion data. [Explanation of symbols]

[1614] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user configuration data and transmitting it to a server; means for collecting user location information and transmitting the data to a server; A means for the server to analyze the past walking history and the user's setting data and generate an optimal walking course; means for transmitting the generated walking course to a user terminal and presenting it to the user; A means for calculating the calories burned after the walk and notifying the user of the result; A system including:

2. 2. The system according to claim 1, wherein the server selects the optimum route from a plurality of routes generated.

3. The system of claim 1, wherein the server adjusts the walking course based on the user's preferences and calorie consumption goals.

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