system

Abstract

This system provides walking routes generated based on individual user profiles, promoting enjoyable and efficient exercise. [Solution] A system comprising: a means for registering the user's basic information and hobbies and preferences using a personal information input means; a means for measuring the number of steps based on the user's daily activities using a step counting means; a means for generating individual walking routes using the user's registered information and location information using a generation means; a means for suggesting exercise time based on the user's free time using a scheduling means; a means for awarding points and offering rewards for the user achieving their goals using a reward provision means; a means for displaying advertisements for specific stores to the user on the walking route using an advertisement display means; and a means for aggregating and analyzing the user's behavior information using a data aggregation means.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern lifestyles, lack of exercise among adults poses a significant risk to health. In contrast, conventional means for promoting walking have difficulty continuously attracting the interest of users and cannot provide exercise management adapted to daily life. Furthermore, since related health promotion services lack a sustainable business model for generating revenue, there are limitations to achieving commercial success.

Means for Solving the Problems

[0005] This invention provides a system that promotes enjoyable and efficient exercise by offering walking routes generated based on individual user profiles. It also motivates users by awarding points based on their goal achievement and secures new revenue streams by promoting customer traffic to specific stores through location-based advertising. Furthermore, by providing anonymized user step count data and health information as big data to external parties, it builds a sustainable business model that symbolizes both health awareness and commercial profit.

[0006] "Personal information input means" refers to devices or methods for registering a user's basic information, hobbies, and preferences.

[0007] "Step counting means" refers to devices or technologies for measuring the number of steps taken by a user based on their daily activities.

[0008] "Generation means" refers to devices or algorithms for generating individual walking routes using user registration information and location information.

[0009] A "scheduling means" refers to a device or method for suggesting exercise time while taking into account the user's free time.

[0010] A "reward provision method" refers to a device or system that awards points and offers rewards to users for achieving their goals.

[0011] "Advertising display means" refers to devices or software used to display advertisements for specific stores to users along their walking routes.

[0012] "Data aggregation means" refers to devices and methods for aggregating and analyzing user behavior information. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] ​​​​​​​​​​​​​​​​​​First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface that includes a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention begins with a process in which the user registers basic information and preferences using a personal information input device. The device sends this information to a server, which stores it in a database. The user can measure their steps in real time during their daily activities using a step counting device. This measurement data is frequently sent to the server and recorded as the user's exercise pattern.

[0035] Next, the server uses a generation mechanism to create a personalized walking route based on the user's registration information and current location. This route is optimized to include scenic views and points of interest. The generated route is sent to the device, and a schedule is created based on the user's available time.

[0036] The scheduling mechanism notifies the user of the appropriate timing for exercise via the device. This allows the user to walk at the optimal time in their daily routine and continue exercising efficiently.

[0037] When a user achieves their set step goal using the reward system, points are awarded. These points can be easily checked on the device, and rewards are presented. This helps users maintain motivation.

[0038] Furthermore, the server's advertising display system will show information about specific stores along the user's walking route on their device. The advertisements will include promotional information about the stores, encouraging users to visit them.

[0039] In addition, the server uses data aggregation methods to analyze behavioral data collected from all users. This data is anonymized and provided to external parties in a lawful manner.

[0040] Specific example:

[0041] For example, user B sets a daily step goal of 7,000 steps in the app at 8 AM. The server generates a walking route that includes cafes and parks based on B's current location and their break time at 1 PM. At 1 PM, B receives a notification on their device saying, "Let's start walking." In addition to the 4,000 steps B took in the morning, B walks another 3,000 steps along this route. Once B achieves their goal, points are awarded, and promotional information for cafes is displayed on their device.

[0042] This invention enables users to maintain their health in their daily lives while finding and continuing to enjoy themselves. The aim is to provide new value in both health and business aspects.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user downloads the app to their device and enters basic information and preferences on a personal information input screen. The device then sends this information to the server.

[0046] Step 2:

[0047] The server receives user information and stores it in the database. Specifically, it creates an individual profile for each user.

[0048] Step 3:

[0049] The user sets a daily step goal on their device. The set goal is then sent from the device to the server.

[0050] Step 4:

[0051] The device uses a step counting device to measure the user's steps in real time. This data is transmitted to the server at regular intervals.

[0052] Step 5:

[0053] The server generates an appropriate walking route using a generation method based on the user's location information and profile. During this process, the server optimizes the route by taking into account the user's hobbies and preferred time of day.

[0054] Step 6:

[0055] The server generates a walking route and sends the recommended exercise time to the device. The device then notifies the user of this information.

[0056] Step 7:

[0057] The user receives a notification and starts walking at the designated time. The device continuously measures steps and reports progress to the server.

[0058] Step 8:

[0059] The server evaluates the user's step count progress and awards points as a reward when the goal is achieved. The awarded points can be viewed on the device.

[0060] Step 9:

[0061] The server uses the user's location information to access store data and generate advertisements. These advertisements are then sent to the user's device and displayed to them.

[0062] Step 10:

[0063] The server aggregates and analyzes behavioral data collected from all users. This data is anonymized and then provided to external parties.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In modern society, people are required to ensure they get an appropriate amount of exercise to maintain their health. However, the busyness of daily life makes it difficult to continue exercising. Furthermore, there is a lack of strategies to make exercise enjoyable while maintaining motivation. In addition, there is a need for efficient advertising display and data utilization methods that use location information.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for registering the user's basic information and hobbies and preferences via personal information input means, means for generating suggestions based on the user's hobbies and preferences via generation means, and means for notifying the user's terminal of the optimal activity timing via notification means. This enables the user to obtain an efficient and enjoyable way to maintain an appropriate level of activity in their daily life. Furthermore, a reward system that promotes motivation and effective advertising display are realized.

[0069] "Personal information input means" refers to methods or devices for receiving and registering a user's basic information, hobbies, and preferences.

[0070] A "step counting device" refers to a device or method capable of measuring the amount of activity a user engages in during their daily activities and acquiring that data.

[0071] "Generation method" refers to a technical method for creating optimal routes and suggestions based on user registration information and location information.

[0072] A "scheduling method" is a method for suggesting appropriate activity times, taking into account the user's inactivity time.

[0073] A "reward provision system" is a system that provides evaluation and offers rewards when users achieve the goals they have set.

[0074] "Advertising display means" refers to technology that displays advertising information about specific facilities to users along their travel route.

[0075] An "information aggregation tool" is a system for combining and analyzing behavioral data obtained from multiple users.

[0076] A "notification method" is a way of informing a user's device of the optimal timing and information for their activities.

[0077] This invention is a system aimed at promoting user health and maintaining motivation, achieving its effects by coordinating multiple means. The system mainly consists of three components: a server, a terminal, and a user.

[0078] The server receives users' personal information and location data and securely stores it in a database. Based on the registered information, the server uses a generative AI model to generate optimal routes and activity suggestions for the user. These generation processes employ profiling techniques based on each user's hobbies and preferences, providing personalized results. For example, by inputting a prompt such as, "Generate the optimal walking route based on the user's basic information and current location," the generative AI model will formulate a route suitable for the user.

[0079] The device functions as an easy-to-use interface for the user. Through the device, the user inputs personal information and target steps, and manages their daily activity plan. The device also uses built-in sensors to acquire the user's daily activity data, such as steps taken, in real time and transmits it to the server.

[0080] Based on the information provided on the device, users can choose actions to incorporate into their health management and daily lives. For example, the device's notification function allows them to know the right time to "start walking" and exercise accordingly. When goals are achieved, the device displays reward points and promotional information about nearby facilities, making daily exercise more enjoyable.

[0081] Throughout the entire system, the server aggregates and analyzes all data. This data is anonymized and securely provided externally, where it can be used for new health services and commercial strategies. This invention enables users to manage their health efficiently and enjoyably, and also offers new business opportunities for companies.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user opens the application screen on their device and enters their basic information and interests. This information includes their name, age, and places of interest. The device transmits this information to the server via a secure protocol such as SSL. The data is encrypted and securely stored on the server.

[0085] Step 2:

[0086] Before saving received user information to the database, the server verifies the validity and integrity of the data. This checks for duplicate information and ensures the format is correct. Information that has been properly verified is saved to the database and becomes available for subsequent processing.

[0087] Step 3:

[0088] The user measures their steps during daily activities using sensors built into the device. The device acquires step count data in real time and periodically sends it to the server. The user's actual step count data is provided to the server as input, forming the basis for data processing on the server side.

[0089] Step 4:

[0090] The server uses a generative AI model to generate individual walking routes based on the user's basic information and current location. It designs the optimal route while considering location and points of interest. The generated route can be obtained by inputting the prompt message "Generate the optimal walking route based on the user's basic information and current location" into the generative AI model.

[0091] Step 5:

[0092] The server uses the generated route information to schedule exercise time that matches the user's inactive period. The device then sends a notification to the user saying, "Let's start walking," based on this schedule information. Upon receiving the notification, the device prompts the user to take action to begin moving.

[0093] Step 6:

[0094] The server monitors the user's progress toward their exercise goals and awards points as rewards based on achievement. The terminal displays reward information in real time for goals achieved by the user to maintain motivation. Points are awarded and displayed in this manner.

[0095] Step 7:

[0096] The server selects the most relevant advertising information based on the user's individual browsing history and sends it to the device. The device then displays this advertisement on its screen, promoting specific stores or services. This gives the user an opportunity to discover new interests.

[0097] Step 8:

[0098] The server collects behavioral data from all users and analyzes it using data aggregation tools. The data is anonymized, and the analysis results are used to improve health promotion services and commercial strategies. This process involves calculations to extract useful patterns from large amounts of data.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In modern society, promoting health management and effective exercise habits among users is crucial. However, many individuals find it difficult to find appropriate exercise opportunities amidst their busy daily lives. Furthermore, a lack of motivation to continue exercising makes it difficult to improve health. In addition, there is a need to effectively utilize the time spent exercising and provide added value through advertising and promotions.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for registering the user's basic information and interests using personal information input means, means for measuring the amount of exercise based on the user's daily activities using exercise measurement means, and means for providing the user with information that helps with efficient exercise and health management based on the travel route. As a result, the user can easily manage their health by being offered individually optimized exercise routes and timings. Furthermore, the user's motivation can be improved by providing rewards according to the results of their exercise. In addition, value-added experiences can be provided to the user through specific advertising information, contributing to the promotion of health in society as a whole.

[0104] A "personal information input method" is a function that allows users to register their basic information and interests in the system.

[0105] "Exercise measurement means" refers to a function that measures the amount of exercise a user performs based on their daily activities.

[0106] The "generation means" refers to a function that uses collected user information and location information to create personalized travel routes for users.

[0107] A "scheduling method" is a function that suggests the optimal timing for exercise based on the user's available time.

[0108] A "reward provision mechanism" is a function that provides benefits or rewards to users when they achieve their exercise goals.

[0109] "Advertising display means" refers to a function that displays advertisements from specific businesses located along the user's travel route on the screen.

[0110] A "data aggregation method" is a function for integrating and analyzing behavioral information collected from users.

[0111] "A means of providing users with information that helps them exercise efficiently and manage their health based on their travel routes" refers to a function that provides useful information in real time to promote users' exercise.

[0112] This system is designed as a comprehensive solution to provide users with healthy exercise habits. The server aggregates diverse user information and provides individually optimized exercise plans.

[0113] First, users input their basic information and interests using a smartphone or other device. This information is transmitted to the server via a personal information input device. Next, a movement measurement device with step counting and GPS functionality collects the user's daily activity level and location information in real time. This collected data is analyzed by a generation device to design travel routes that match each user's individual interests and free time.

[0114] The server then uses a scheduling mechanism to suggest the optimal exercise time to the user. This suggestion is notified on the device, and the user can start exercising at the recommended time. Upon achieving the goal, points are awarded through a reward system, and reward information is displayed on the user's device.

[0115] The advertising display system provides users with information about nearby businesses while they are on the move and presents promotional information that will encourage them to visit. Furthermore, the exercise data collected from users by the data aggregation system is anonymized, analyzed, and then provided to relevant businesses as pattern information.

[0116] As a concrete example, consider a scenario where a user, who works as a lunch delivery driver, sets a daily exercise goal. Before leaving, this user sets a goal of 7,000 steps using the app. Based on this information, the server creates an exercise route tailored to the user's current location and route, and suggests optimal stopping points. When a specific number of steps is achieved during the exercise, a notification appears on the user's smartphone saying, "Congratulations! You have a reward you can use on your next visit," providing additional motivation.

[0117] An example of a prompt using a generative AI model is: "As a feature for riders in a food delivery app, please suggest health promotion measures based on delivery routes. Riders should be able to track their steps and earn rewards for achieving specific goals."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input their basic information and interests using a terminal. This input information is transmitted to the server via a personal information input system. This information is stored in the server's database and used as the design basis for individual plans generated by the system.

[0121] Step 2:

[0122] The device's exercise measurement system collects the user's exercise volume and location information in real time during their daily activities. This data is periodically transmitted to a server. The server analyzes the received exercise data and performs data processing to extract temporal trends and location patterns.

[0123] Step 3:

[0124] The server uses a generation mechanism to generate individual travel routes based on collected motion data and user interest information. This process uses an optimization algorithm to calculate how to incorporate user-interesting spots into the route.

[0125] Step 4:

[0126] The server uses a scheduling mechanism to suggest an exercise time that takes into account the user's available time based on the generated travel route. The suggested exercise time is sent as a notification to the user's device.

[0127] Step 5:

[0128] When a user actually starts exercising and reaches their step goal, the device sends that information back to the server. The server calculates points based on the user's achievement level via a reward system and sends them to the device. The user checks their points on the device and receives information about available rewards.

[0129] Step 6:

[0130] The advertising display method configured on the device provides advertisements from nearby businesses along the user's travel route. In this process, promotional information received from the server is displayed in a customized format based on the user's interests.

[0131] Step 7:

[0132] The server uses data aggregation to collect and analyze anonymized exercise data from all users. The analysis results are processed for presentation to vendors and provided as data to help users improve their exercise habits.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a system that utilizes emotion recognition means to evaluate a user's emotional state and provides an individually optimized exercise program based on that evaluation. The system works by having the user input personal information via a terminal and recording daily emotional fluctuations using an emotion engine. The emotion engine analyzes biometric data such as facial expressions, voice, and heart rate through sensors built into the device to detect the user's emotional state.

[0135] The device sends this data to the server, which uses a generation mechanism to create a walking route based on the user's current emotional state. In this route generation process, a more challenging route is suggested when the emotional state is positive, and a route aimed at relaxation is suggested when the emotional state is negative.

[0136] The scheduling system also considers emotional data and available time to recommend the optimal time for users to walk. Furthermore, a reward system awards points for achieving goals, which are provided as rewards that can be viewed on the device.

[0137] The advertising display method aims to attract customers to stores while soothing the user's emotions by displaying store advertisements tailored to the user's emotional state and presenting useful promotions based on walking routes and location information.

[0138] Furthermore, the server's data aggregation method securely collects and analyzes user step counts and emotional data, anonymizes it, and provides it externally, thereby contributing to the health management of a wider range of people.

[0139] Specific example:

[0140] For example, user C opens the app at 9 AM using their device, and the emotion engine detects a state of "slight fatigue." Based on this information, the server suggests a short, scenic, and leisurely walking route, primarily around a nearby park. In the afternoon, the device notifies C with a walking guide including relaxing music, and as C begins walking this route, it provides a naturally refreshing experience. After completing the walk, C achieves their goal and earns points, and an advertisement for a discount promotion at a nearby cafe is displayed on the device.

[0141] This invention provides an exercise experience that takes into account not only physical health but also mental state, proposing a new value in fitness that meets individual needs.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user launches the app using their device and enters personal and emotional information. The device collects this information and sends it to the server.

[0145] Step 2:

[0146] The server stores the user's basic information and emotional data in a database and analyzes the user's current emotional state using an emotion recognition system.

[0147] Step 3:

[0148] The emotion engine acquires biometric data from the device's sensors and analyzes facial expressions, voice, heart rate, etc., to evaluate the user's emotional state in real time.

[0149] Step 4:

[0150] The server uses a generation method to create individual walking routes based on the user's emotional state and location information. The route is optimized for relaxation or challenge depending on the emotional state.

[0151] Step 5:

[0152] The scheduling system calculates the optimal walking time, taking into account the generated walking route and the user's free time, and notifies the user from their device.

[0153] Step 6:

[0154] The user starts walking at a specified time, and the device measures the number of steps. The results are sent to a server, and the progress is monitored.

[0155] Step 7:

[0156] The server evaluates the user's goal achievement and awards points through reward programs. This information is reported to the user on their device, and reward details are also displayed.

[0157] Step 8:

[0158] Based on the user's emotional state, the server generates location-based store advertisements and displays them on the device at the appropriate time.

[0159] Step 9:

[0160] The server aggregates and analyzes user step count and sentiment data, anonymizes it, and then provides the data to external parties.

[0161] These steps enable the system to provide users with emotionally sensitive exercise plans and offer a valuable fitness experience that benefits both physical and mental well-being.

[0162] (Example 2)

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

[0164] In modern society, there is a growing demand for both physical health management and mental well-being. However, conventional health management systems tend to focus too much on the physical aspects of exercise and fail to provide optimal exercise programs that take into account the user's emotional state. As a result, exercise suggestions tailored to the mood and condition of individual users are insufficient, and there is a lack of motivation to continue exercising.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes means for registering the user's basic information and habitual information via personal information input means, means for collecting the user's facial expressions, voice, and heart rate data using sensors via emotion recognition means and analyzing their emotional state, and means for generating individual walking routes based on the user's registered information, location information, and emotional state via generation means. This makes it possible to provide exercise programs that take the user's emotional state into consideration.

[0167] A "means for inputting personal information" refers to a method for registering a user's basic information and information about their habits into the system.

[0168] A "step counting device" is a means of measuring the number of steps taken in order to record the amount of exercise a user takes on a daily basis.

[0169] "Emotion recognition means" refers to a method of collecting a user's biometric information using sensors, analyzing it, and identifying their emotional state.

[0170] "Generation method" refers to a method for creating personalized walking routes and other content based on the user's registration information, location information, and emotional state.

[0171] A "scheduling method" is a means of suggesting the optimal exercise time, taking into account the user's free time and emotional state.

[0172] A "reward provision method" is a means of awarding points and displaying rewards to users when they achieve their exercise goals.

[0173] "Advertising display means" refers to means of displaying appropriate store advertisements based on the user's emotional state and location information.

[0174] "Data aggregation means" refers to methods for collecting, analyzing, and aggregating user behavioral information and emotional data.

[0175] This invention is a system that evaluates the user's emotional state and proposes an optimal exercise program tailored to that state. This allows the user to receive personalized health management and mental care simultaneously.

[0176] The user first enters personal information using the device. The device incorporates a means for entering personal information to record the user's basic information and daily habits. The device also uses built-in sensors such as a camera, microphone, and heart rate monitor to collect the user's facial expressions, voice, and heart rate data in real time. Based on this, the device's emotion recognition means analyzes the collected biometric data to identify the user's emotional state.

[0177] The analyzed emotional data is securely encrypted and sent to the server. The server uses a generative AI model to generate a personalized walking route based on the user's current emotional state, location, and registered personal information. For example, if the user is feeling "slightly tired," the server will suggest a relaxing route that passes through a quiet park.

[0178] Regarding exercise time, the server's scheduling mechanism considers the user's free time and emotional state to recommend the optimal exercise start time. If the user achieves their set goals as a result of exercising, points are awarded through the device's reward system. This motivates the user to continue exercising.

[0179] Furthermore, the device displays advertisements for relevant stores based on the user's emotional state, location information, and walking route. This advertising method aims to provide information that is sensitive to the user's emotions, thereby creating a sense of calm and relaxation.

[0180] Finally, the server's data aggregation system securely collects and analyzes user behavior and emotional data. This data is provided externally in an anonymized form and used to improve the health management of the entire community.

[0181] For example, if a user opens the app in the morning and the emotion engine determines they are "somewhat tired," the server will suggest a course that includes a nearby scenic park and then provide a notification with relaxing music in the afternoon, offering the user an exercise experience that helps relieve stress. An example of a prompt message would be, "Recognize the user's emotional state and suggest an exercise route that is best suited to their current mood."

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Users enter basic personal information using their devices. This information includes data such as age, gender, and daily habits. The entered information is stored on the device and forms the user's basic profile. Based on this, the system obtains the basic data to provide a personalized experience.

[0185] Step 2:

[0186] The device uses built-in sensors to collect biometric data necessary to assess the user's current emotional state. This includes facial expression capture via camera, voice analysis via microphone, and heart rate measurement via heart rate monitor. For emotion recognition, this data is analyzed by an AI algorithm to output the user's emotional state (e.g., joy, stress, fatigue).

[0187] Step 3:

[0188] The device sends the analyzed emotional data to the server. This communication is encrypted, protecting the user's privacy. The server uses the received data to understand the emotional state and prepares it for use in the next step.

[0189] Step 4:

[0190] The server uses a generative AI model to generate an optimal walking route based on the user's emotional state, location information, and pre-entered basic information. By combining the input data, it creates an exercise plan tailored to the user's individual needs and condition. The output is detailed walking route information.

[0191] Step 5:

[0192] The generated walking route information is sent back to the device. The device displays this information to the user, providing details of the suggested route. The user confirms the suggested route on the device screen and begins exercising according to it.

[0193] Step 6:

[0194] The device provides relaxation guides and music to enrich the user experience while walking. This is to stabilize the user's emotional state and promote an enjoyable exercise experience. It also provides timely notifications and voice guidance according to the walking progress.

[0195] Step 7:

[0196] When a user finishes their walk, the device evaluates their achievement of the goal based on route information and step count. Points are then awarded through a reward system. The awarded points and reward information are displayed to the user on the device.

[0197] Step 8:

[0198] The server's data aggregation method collects and analyzes emotional and activity data after exercise. This provides insights to further improve suggestions for future users. The analyzed data may be anonymized and provided externally for use in health research.

[0199] (Application Example 2)

[0200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0201] In modern society, providing exercise and dietary recommendations based on individual lifestyles and emotional states is a crucial health management issue. However, mechanisms for selecting optimized exercise routes and dining facilities that take into account individual user emotions and health conditions are not adequately provided. As a result, many individuals miss opportunities for exercise and healthy eating, and are not receiving appropriate services.

[0202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0203] In this invention, the server includes means for evaluating the user's emotional state using emotion recognition means, means for providing an exercise route optimized for the user using generation means, and means for selecting the optimal meal provision facility based on location information and emotional data. This makes it possible to optimize exercise and meal provision according to the individual's emotional state and health condition.

[0204] A "personal information input method" is a function that allows users to register their basic information and preferences.

[0205] "Activity measurement means" refers to a function that measures the number of steps and amount of exercise based on the user's daily activities.

[0206] The "generation means" refers to a function that generates individually optimized exercise routes using the user's registration information and location information.

[0207] The "scheduling method" is a function that suggests appropriate exercise time based on the user's available time.

[0208] A "reward provision method" is a function that awards points to users for achieving their goals and then presents them with rewards as a result.

[0209] "Advertising display means" refers to a function that displays advertisements for specific facilities or services according to the exercise route or the user's emotional state.

[0210] An "emotion recognition method" is a function that evaluates a user's emotional state from facial expressions, voice, and biometric data.

[0211] A "data aggregation method" is a function for collecting user behavior data and obtaining analysis results from that data.

[0212] A system implementing this invention includes means for inputting personal information, means for measuring activity, means for generating data, means for scheduling, means for providing rewards, means for displaying advertisements, means for recognizing emotions, and means for aggregating data.

[0213] The server processes personal information and activity data received from the user to generate exercise routes and meal facilities optimized for that user. This takes into account the user's location and emotional state. The emotion recognition means is software that analyzes the user's facial expressions, voice, and heart rate using hardware such as the camera, microphone, and heart rate sensor of the smart glasses worn by the user. The emotional data obtained from this analysis is sent to the server and reflected in exercise route and facility suggestions tailored to the user's individual needs.

[0214] The user's device notifies them of suggested exercise routes and dining facilities, and the advertising display system selects and displays advertisements appropriate to the user's state. These advertisements take into account emotional state and location information, enabling effective customer acquisition.

[0215] For example, if the emotion engine determines that the user needs to relax, the server will suggest a route to a quiet cafe, input a prompt into the AI ​​model, and prepare suggestions tailored to the user's current needs. By using a prompt such as, "User's emotional data has been collected. Evaluate the user's current emotional state and generate the optimal food delivery suggestion based on it," it becomes possible to provide emotionally adaptive exercise and meal suggestions.

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

[0217] Step 1:

[0218] The user enters basic information and preferences into the terminal using a personal information input method. This entered data is sent to the server. The server receives the entered data and processes it by storing it in a database. As a result, a user profile is formed.

[0219] Step 2:

[0220] The user wears smart glasses, and the emotion recognition system collects biometric information such as facial expressions, voice, and heart rate. This data is transmitted in real time to a server via the device. The server uses an emotion recognition algorithm to evaluate the user's emotional state based on this biometric information. This evaluation result becomes the main input data for the next processing step.

[0221] Step 3:

[0222] The server uses a generation mechanism to generate the optimal exercise route and food delivery facilities based on user profile and emotional state data. During this process, a generation AI model operates, receiving the prompt message "User emotional data has been collected. Evaluate the current emotional state and generate the optimal food delivery suggestion based on it." As a result, optimized exercise route and facility candidates are output.

[0223] Step 4:

[0224] The terminal receives exercise route and facility information transmitted from the server and notifies the user. This notification includes detailed navigation information to the suggested route and facilities. Additionally, the advertising display mechanism is enabled, and advertisements are selected and displayed on the terminal based on the user's status. This advertisement selection involves data calculations using a pre-configured advertising database.

[0225] Step 5:

[0226] The user checks the notification on their device and performs the suggested exercise route. During the exercise, the activity tracking device measures the user's steps and distance traveled and sends this data back to the server. The server uses this data to calculate a reward (such as points) for the user, and the reward is notified on the device. This reward process involves data processing to determine the degree of goal achievement.

[0227] Step 6:

[0228] Finally, using data aggregation methods, the server analyzes all user behavior data and generates statistical information. This data is anonymized and then provided to external health management organizations, etc. The data processing here includes anonymization and aggregation processes to protect privacy.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention begins with a process in which the user registers basic information and preferences using a personal information input device. The device sends this information to a server, which stores it in a database. The user can measure their steps in real time during their daily activities using a step counting device. This measurement data is frequently sent to the server and recorded as the user's exercise pattern.

[0246] Next, the server uses a generation mechanism to create a personalized walking route based on the user's registration information and current location. This route is optimized to include scenic views and points of interest. The generated route is sent to the device, and a schedule is created based on the user's available time.

[0247] The scheduling mechanism notifies the user of the appropriate timing for exercise via the device. This allows the user to walk at the optimal time in their daily routine and continue exercising efficiently.

[0248] When a user achieves their set step goal using the reward system, points are awarded. These points can be easily checked on the device, and rewards are presented. This helps users maintain motivation.

[0249] Furthermore, the server's advertising display system will show information about specific stores along the user's walking route on their device. The advertisements will include promotional information about the stores, encouraging users to visit them.

[0250] In addition, the server uses data aggregation methods to analyze behavioral data collected from all users. This data is anonymized and provided to external parties in a lawful manner.

[0251] Specific example:

[0252] For example, user B sets a daily step goal of 7,000 steps in the app at 8 AM. The server generates a walking route that includes cafes and parks based on B's current location and their break time at 1 PM. At 1 PM, B receives a notification on their device saying, "Let's start walking." In addition to the 4,000 steps B took in the morning, B walks another 3,000 steps along this route. Once B achieves their goal, points are awarded, and promotional information for cafes is displayed on their device.

[0253] This invention enables users to maintain their health in their daily lives while finding and continuing to enjoy themselves. The aim is to provide new value in both health and business aspects.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user downloads the app to their device and enters basic information and preferences on a personal information input screen. The device then sends this information to the server.

[0257] Step 2:

[0258] The server receives user information and stores it in the database. Specifically, it creates an individual profile for each user.

[0259] Step 3:

[0260] The user sets a daily step goal on their device. The set goal is then sent from the device to the server.

[0261] Step 4:

[0262] The device uses a step counting device to measure the user's steps in real time. This data is transmitted to the server at regular intervals.

[0263] Step 5:

[0264] The server generates an appropriate walking route using a generation method based on the user's location information and profile. During this process, the server optimizes the route by taking into account the user's hobbies and preferred time of day.

[0265] Step 6:

[0266] The server generates a walking route and sends the recommended exercise time to the device. The device then notifies the user of this information.

[0267] Step 7:

[0268] The user receives a notification and starts walking at the designated time. The device continuously measures steps and reports progress to the server.

[0269] Step 8:

[0270] The server evaluates the user's step count progress and awards points as a reward when the goal is achieved. The awarded points can be viewed on the device.

[0271] Step 9:

[0272] The server uses the user's location information to access store data and generate advertisements. These advertisements are then sent to the user's device and displayed to them.

[0273] Step 10:

[0274] The server aggregates and analyzes behavioral data collected from all users. This data is anonymized and then provided to external parties.

[0275] (Example 1)

[0276] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In modern society, people are required to ensure they get an appropriate amount of exercise to maintain their health. However, the busyness of daily life makes it difficult to continue exercising. Furthermore, there is a lack of strategies to make exercise enjoyable while maintaining motivation. In addition, there is a need for efficient advertising display and data utilization methods that use location information.

[0278] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0279] In this invention, the server includes means for registering the user's basic information and hobbies through personal information input means, means for generating proposals based on the user's hobbies through generation means, and means for notifying the user's terminal of the optimal activity timing through notification means. As a result, the user can obtain an efficient and enjoyable method for maintaining an appropriate activity level in daily life. In addition, a reward system for promoting motivation maintenance and effective advertisement display are realized.

[0280] The "personal information input means" is a method or device for receiving and registering the user's basic information and hobbies.

[0281] The "step counting means" is a device or method for measuring the activity level in the user's daily activities and obtaining the data.

[0282] The "generation means" is a technical method for creating an optimal action route and proposals based on the user's registered information and location information.

[0283] The "scheduling means" is a method for proposing an appropriate activity time considering the user's inactive time.

[0284] The "reward providing means" is a system for giving an evaluation and presenting a reward when the user achieves the set goal.

[0285] The "advertisement display means" is a technology for displaying advertisement information about a specific facility to the user on the action route.

[0286] The "information aggregation means" is a mechanism for collecting and analyzing the action data obtained from multiple users into one.

[0287] The "notification means" is a method for notifying the user's terminal of the optimal activity timing and information.

[0288] This invention is a system aimed at promoting user health and maintaining motivation, achieving its effects by coordinating multiple means. The system mainly consists of three components: a server, a terminal, and a user.

[0289] The server receives users' personal information and location data and securely stores it in a database. Based on the registered information, the server uses a generative AI model to generate optimal routes and activity suggestions for the user. These generation processes employ profiling techniques based on each user's hobbies and preferences, providing personalized results. For example, by inputting a prompt such as, "Generate the optimal walking route based on the user's basic information and current location," the generative AI model will formulate a route suitable for the user.

[0290] The device functions as an easy-to-use interface for the user. Through the device, the user inputs personal information and target steps, and manages their daily activity plan. The device also uses built-in sensors to acquire the user's daily activity data, such as steps taken, in real time and transmits it to the server.

[0291] Based on the information provided on the device, users can choose actions to incorporate into their health management and daily lives. For example, the device's notification function allows them to know the right time to "start walking" and exercise accordingly. When goals are achieved, the device displays reward points and promotional information about nearby facilities, making daily exercise more enjoyable.

[0292] Throughout the entire system, the server aggregates and analyzes all data. This data is anonymized and securely provided externally, where it can be used for new health services and commercial strategies. This invention enables users to manage their health efficiently and enjoyably, and also offers new business opportunities for companies.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The user opens the application screen on their device and enters their basic information and interests. This information includes their name, age, and places of interest. The device transmits this information to the server via a secure protocol such as SSL. The data is encrypted and securely stored on the server.

[0296] Step 2:

[0297] Before saving received user information to the database, the server verifies the validity and integrity of the data. This checks for duplicate information and ensures the format is correct. Information that has been properly verified is saved to the database and becomes available for subsequent processing.

[0298] Step 3:

[0299] The user measures their steps during daily activities using sensors built into the device. The device acquires step count data in real time and periodically sends it to the server. The user's actual step count data is provided to the server as input, forming the basis for data processing on the server side.

[0300] Step 4:

[0301] The server uses a generative AI model to generate individual walking routes based on the user's basic information and current location. It designs the optimal route while considering location and points of interest. The generated route can be obtained by inputting the prompt message "Generate the optimal walking route based on the user's basic information and current location" into the generative AI model.

[0302] Step 5:

[0303] Based on the generated route information, the server schedules exercise times that match the user's inactive periods. Based on this schedule information, the terminal sends a notification to the user saying, "Let's start walking." When the user receives the notification on the terminal, actions are induced for the user to start moving.

[0304] Step 6:

[0305] The server monitors the user's progress towards achieving the exercise goal and awards points as a reward according to the achievement. The terminal displays the reward information in real time for the goals achieved by the user to maintain motivation. In this way, points are awarded and presented.

[0306] Step 7:

[0307] Based on the user's individual route, the server selects optimal advertising information and sends it to the terminal. The terminal displays this advertisement on the screen to promote specific stores or services, giving the user the opportunity to discover new interests.

[0308] Step 8:

[0309] The server collects the action data obtained from all users and analyzes it using information aggregation means. The data is anonymized, and the analysis results are utilized for improving the health promotion service and for business strategies. In this process, operations are performed to extract useful patterns from large amounts of data.

[0310] (Application Example 1)

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

[0312] In modern society, promoting health management and effective exercise habits among users is crucial. However, many individuals find it difficult to find appropriate exercise opportunities amidst their busy daily lives. Furthermore, a lack of motivation to continue exercising makes it difficult to improve health. In addition, there is a need to effectively utilize the time spent exercising and provide added value through advertising and promotions.

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

[0314] In this invention, the server includes means for registering the user's basic information and interests using personal information input means, means for measuring the amount of exercise based on the user's daily activities using exercise measurement means, and means for providing the user with information that helps with efficient exercise and health management based on the travel route. As a result, the user can easily manage their health by being offered individually optimized exercise routes and timings. Furthermore, the user's motivation can be improved by providing rewards according to the results of their exercise. In addition, value-added experiences can be provided to the user through specific advertising information, contributing to the promotion of health in society as a whole.

[0315] A "personal information input method" is a function that allows users to register their basic information and interests in the system.

[0316] "Exercise measurement means" refers to a function that measures the amount of exercise a user performs based on their daily activities.

[0317] The "generation means" refers to a function that uses collected user information and location information to create personalized travel routes for users.

[0318] A "scheduling method" is a function that suggests the optimal timing for exercise based on the user's available time.

[0319] A "reward provision mechanism" is a function that provides benefits or rewards to users when they achieve their exercise goals.

[0320] "Advertising display means" refers to a function that displays advertisements from specific businesses located along the user's travel route on the screen.

[0321] A "data aggregation method" is a function for integrating and analyzing behavioral information collected from users.

[0322] "A means of providing users with information that helps them exercise efficiently and manage their health based on their travel routes" refers to a function that provides useful information in real time to encourage users to exercise.

[0323] This system is designed as a comprehensive solution to provide users with healthy exercise habits. The server aggregates diverse user information and provides individually optimized exercise plans.

[0324] First, users input their basic information and interests using a smartphone or other device. This information is transmitted to the server via a personal information input device. Next, a movement measurement device with step counting and GPS functionality collects the user's daily activity level and location information in real time. This collected data is analyzed by a generation device to design travel routes that match each user's individual interests and free time.

[0325] The server then uses a scheduling mechanism to suggest the optimal exercise time to the user. This suggestion is notified on the device, and the user can start exercising at the recommended time. Upon achieving the goal, points are awarded through a reward system, and reward information is displayed on the user's device.

[0326] The advertising display system provides users with information about nearby businesses while they are on the move and presents promotional information that will encourage them to visit. Furthermore, the exercise data collected from users by the data aggregation system is anonymized, analyzed, and then provided to relevant businesses as pattern information.

[0327] As a concrete example, consider a scenario where a user, who works as a lunch delivery driver, sets a daily exercise goal. Before leaving, this user sets a goal of 7,000 steps using the app. Based on this information, the server creates an exercise route tailored to the user's current location and route, and suggests optimal stopping points. When a specific number of steps is achieved during the exercise, a notification appears on the user's smartphone saying, "Congratulations! You have a reward you can use on your next visit," providing additional motivation.

[0328] An example of a prompt using a generative AI model is: "As a feature for riders in a food delivery app, please suggest health promotion measures based on delivery routes. Riders should be able to track their steps and earn rewards for achieving specific goals."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] Users input their basic information and interests using a terminal. This input information is transmitted to the server via a personal information input system. This information is stored in the server's database and used as the design basis for individual plans generated by the system.

[0332] Step 2:

[0333] The device's exercise measurement system collects the user's exercise volume and location information in real time during their daily activities. This data is periodically transmitted to a server. The server analyzes the received exercise data and performs data processing to extract temporal trends and location patterns.

[0334] Step 3:

[0335] The server uses a generation mechanism to generate individual travel routes based on collected motion data and user interest information. This process uses an optimization algorithm to calculate how to incorporate user-interesting spots into the route.

[0336] Step 4:

[0337] The server uses a scheduling mechanism to suggest an exercise time that takes into account the user's available time based on the generated travel route. The suggested exercise time is sent as a notification to the user's device.

[0338] Step 5:

[0339] When a user actually starts exercising and reaches their step goal, the device sends that information back to the server. The server calculates points based on the user's achievement level via a reward system and sends them to the device. The user checks their points on the device and receives information about available rewards.

[0340] Step 6:

[0341] The advertising display method configured on the device provides advertisements from nearby businesses along the user's travel route. In this process, promotional information received from the server is displayed in a customized format based on the user's interests.

[0342] Step 7:

[0343] The server uses data aggregation to collect and analyze anonymized exercise data from all users. The analysis results are processed for presentation to vendors and provided as data to help users improve their exercise habits.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention is a system that utilizes emotion recognition means to evaluate a user's emotional state and provides an individually optimized exercise program based on that evaluation. The system works by having the user input personal information via a terminal and recording daily emotional fluctuations using an emotion engine. The emotion engine analyzes biometric data such as facial expressions, voice, and heart rate through sensors built into the device to detect the user's emotional state.

[0346] The device sends this data to the server, which uses a generation mechanism to create a walking route based on the user's current emotional state. In this route generation process, a more challenging route is suggested when the emotional state is positive, and a route aimed at relaxation is suggested when the emotional state is negative.

[0347] The scheduling system also considers emotional data and available time to recommend the optimal time for users to walk. Furthermore, a reward system awards points for achieving goals, which are provided as rewards that can be viewed on the device.

[0348] The advertising display method aims to attract customers to stores while soothing the user's emotions by showing store advertisements tailored to the user's emotional state and presenting useful promotions based on walking routes and location information.

[0349] Furthermore, the server's data aggregation method securely collects and analyzes user step counts and emotional data, anonymizes it, and provides it externally, thereby contributing to the health management of a wider range of people.

[0350] Specific example:

[0351] For example, user C opens the app at 9 AM using their device, and the emotion engine detects a state of "slight fatigue." Based on this information, the server suggests a short, scenic, and leisurely walking route, primarily around a nearby park. In the afternoon, the device notifies C with a walking guide including relaxing music, and as C begins walking this route, it provides a naturally refreshing experience. After completing the walk, C achieves their goal and earns points, and an advertisement for a discount promotion at a nearby cafe is displayed on the device.

[0352] This invention provides an exercise experience that takes into account not only physical health but also mental state, proposing a new value in fitness that meets individual needs.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The user launches the app using their device and enters personal and emotional information. The device collects this information and sends it to the server.

[0356] Step 2:

[0357] The server stores the user's basic information and emotional data in a database and analyzes the user's current emotional state using an emotion recognition system.

[0358] Step 3:

[0359] The emotion engine acquires biometric data from the device's sensors and analyzes facial expressions, voice, heart rate, etc., to evaluate the user's emotional state in real time.

[0360] Step 4:

[0361] The server uses a generation method to create individual walking routes based on the user's emotional state and location information. The route is optimized for relaxation or challenge depending on the emotional state.

[0362] Step 5:

[0363] The scheduling system calculates the optimal walking time, taking into account the generated walking route and the user's free time, and notifies the user from their device.

[0364] Step 6:

[0365] The user starts walking at a specified time, and the device measures the number of steps. The results are sent to a server, and the progress is monitored.

[0366] Step 7:

[0367] The server evaluates the user's goal achievement and awards points through reward programs. This information is reported to the user on their device, and reward details are also displayed.

[0368] Step 8:

[0369] Based on the user's emotional state, the server generates location-based store advertisements and displays them on the device at the appropriate time.

[0370] Step 9:

[0371] The server aggregates and analyzes user step count and sentiment data, anonymizes it, and then provides the data to external parties.

[0372] These steps enable the system to provide users with emotionally sensitive exercise plans and offer a valuable fitness experience that benefits both physical and mental well-being.

[0373] (Example 2)

[0374] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0375] In modern society, there is a growing demand for both physical health management and mental well-being. However, conventional health management systems tend to focus too much on the physical aspects of exercise and fail to provide optimal exercise programs that take into account the user's emotional state. As a result, exercise suggestions tailored to the mood and condition of individual users are insufficient, and there is a lack of motivation to continue exercising.

[0376] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0377] In this invention, the server includes means for registering the user's basic information and habitual information via personal information input means, means for collecting the user's facial expressions, voice, and heart rate data using sensors via emotion recognition means and analyzing their emotional state, and means for generating individual walking routes based on the user's registered information, location information, and emotional state via generation means. This makes it possible to provide exercise programs that take the user's emotional state into consideration.

[0378] A "means for inputting personal information" refers to a method for registering a user's basic information and information about their habits into the system.

[0379] A "step counting device" is a means of measuring the number of steps taken in order to record the amount of exercise a user takes on a daily basis.

[0380] "Emotion recognition means" refers to a method of collecting a user's biometric information using sensors, analyzing it, and identifying their emotional state.

[0381] "Generation method" refers to a method for creating personalized walking routes and other content based on the user's registration information, location information, and emotional state.

[0382] A "scheduling method" is a means of suggesting the optimal exercise time, taking into account the user's free time and emotional state.

[0383] A "reward provision method" is a means of awarding points and displaying rewards to users when they achieve their exercise goals.

[0384] "Advertising display means" refers to means of displaying appropriate store advertisements based on the user's emotional state and location information.

[0385] "Data aggregation means" refers to methods for collecting, analyzing, and aggregating user behavioral information and emotional data.

[0386] This invention is a system that evaluates a user's emotional state and proposes an optimal exercise program tailored to that state. This allows users to receive personalized health management and mental care simultaneously.

[0387] The user first enters personal information using the device. The device incorporates a means for entering personal information to record the user's basic information and daily habits. The device also uses built-in sensors such as a camera, microphone, and heart rate monitor to collect the user's facial expressions, voice, and heart rate data in real time. Based on this, the device's emotion recognition means analyzes the collected biometric data to identify the user's emotional state.

[0388] The analyzed emotional data is securely encrypted and sent to the server. The server uses a generative AI model to generate a personalized walking route based on the user's current emotional state, location, and registered personal information. For example, if the user is feeling "slightly tired," the server will suggest a relaxing route that passes through a quiet park.

[0389] Regarding exercise time, the server's scheduling mechanism considers the user's free time and emotional state to recommend the optimal exercise start time. If the user achieves their set goals as a result of exercising, points are awarded through the device's reward system. This motivates the user to continue exercising.

[0390] Furthermore, the device displays advertisements for relevant stores based on the user's emotional state, location information, and walking route. This advertising method aims to provide information that is sensitive to the user's emotions, thereby creating a sense of calm and relaxation.

[0391] Finally, the server's data aggregation system securely collects and analyzes user behavior and emotional data. This data is provided externally in an anonymized form and used to improve the health management of the entire community.

[0392] For example, if a user opens the app in the morning and the emotion engine determines they are "somewhat tired," the server will suggest a course that includes a nearby scenic park and then provide a notification with relaxing music in the afternoon, offering the user an exercise experience that helps relieve stress. An example of a prompt message would be, "Recognize the user's emotional state and suggest an exercise route that is best suited to their current mood."

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] Users enter basic personal information using their devices. This information includes data such as age, gender, and daily habits. The entered information is stored on the device and forms the user's basic profile. Based on this, the system obtains the basic data to provide a personalized experience.

[0396] Step 2:

[0397] The device uses built-in sensors to collect biometric data necessary to assess the user's current emotional state. This includes facial expression capture via camera, voice analysis via microphone, and heart rate measurement via heart rate monitor. For emotion recognition, this data is analyzed by an AI algorithm to output the user's emotional state (e.g., joy, stress, fatigue).

[0398] Step 3:

[0399] The device sends the analyzed emotional data to the server. This communication is encrypted, protecting the user's privacy. The server uses the received data to understand the emotional state and prepares it for use in the next step.

[0400] Step 4:

[0401] The server uses a generative AI model to generate an optimal walking route based on the user's emotional state, location information, and pre-entered basic information. By combining the input data, it creates an exercise plan tailored to the user's individual needs and condition. The output is detailed walking route information.

[0402] Step 5:

[0403] The generated walking route information is sent back to the device. The device displays this information to the user, providing details of the suggested route. The user confirms the suggested route on the device screen and begins exercising according to it.

[0404] Step 6:

[0405] The device provides relaxation guides and music to enrich the user experience while walking. This is to stabilize the user's emotional state and promote an enjoyable exercise experience. It also provides timely notifications and voice guidance according to the walking progress.

[0406] Step 7:

[0407] When a user finishes their walk, the device evaluates their achievement of the goal based on route information and step count. Points are then awarded through a reward system. The awarded points and reward information are displayed to the user on the device.

[0408] Step 8:

[0409] The server's data aggregation method collects and analyzes emotional and activity data after exercise. This provides insights to further improve suggestions for future users. The analyzed data may be anonymized and provided externally for use in health research.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0412] In modern society, providing exercise and dietary recommendations based on individual lifestyles and emotional states is a crucial health management issue. However, mechanisms for selecting optimized exercise routes and dining facilities that take into account individual user emotions and health conditions are not adequately provided. As a result, many individuals miss opportunities for exercise and healthy eating, and are not receiving appropriate services.

[0413] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0414] In this invention, the server includes means for evaluating the user's emotional state using emotion recognition means, means for providing an exercise route optimized for the user using generation means, and means for selecting the optimal meal provision facility based on location information and emotional data. This makes it possible to optimize exercise and meal provision according to the individual's emotional state and health condition.

[0415] A "personal information input method" is a function that allows users to register their basic information and preferences.

[0416] "Activity measurement means" refers to a function that measures the number of steps and amount of exercise based on the user's daily activities.

[0417] The "generation means" refers to a function that generates individually optimized exercise routes using the user's registration information and location information.

[0418] The "scheduling method" is a function that suggests appropriate exercise time based on the user's available time.

[0419] A "reward provision method" is a function that awards points to users for achieving their goals and then presents them with rewards as a result.

[0420] "Advertising display means" refers to a function that displays advertisements for specific facilities or services according to the exercise route or the user's emotional state.

[0421] An "emotion recognition method" is a function that evaluates a user's emotional state from facial expressions, voice, and biometric data.

[0422] A "data aggregation method" is a function for collecting user behavior data and obtaining analysis results from that data.

[0423] A system implementing this invention includes means for inputting personal information, means for measuring activity, means for generating data, means for scheduling, means for providing rewards, means for displaying advertisements, means for recognizing emotions, and means for aggregating data.

[0424] The server processes personal information and activity data received from the user to generate exercise routes and meal facilities optimized for that user. This takes into account the user's location and emotional state. The emotion recognition means is software that analyzes the user's facial expressions, voice, and heart rate using hardware such as the camera, microphone, and heart rate sensor of the smart glasses worn by the user. The emotional data obtained from this analysis is sent to the server and reflected in exercise route and facility suggestions tailored to the user's individual needs.

[0425] The user's device notifies them of suggested exercise routes and dining facilities, and the advertising display system selects and displays advertisements appropriate to the user's state. These advertisements take into account emotional state and location information, enabling effective customer acquisition.

[0426] For example, if the emotion engine determines that the user needs to relax, the server will suggest a route to a quiet cafe, input a prompt into the AI ​​model, and prepare suggestions tailored to the user's current needs. By using a prompt such as, "User's emotional data has been collected. Evaluate the user's current emotional state and generate the optimal food delivery suggestion based on it," it becomes possible to provide emotionally adaptive exercise and meal suggestions.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The user enters basic information and preferences into the terminal using a personal information input method. This entered data is sent to the server. The server receives the entered data and processes it by storing it in a database. As a result, a user profile is formed.

[0430] Step 2:

[0431] The user wears smart glasses, and the emotion recognition system collects biometric information such as facial expressions, voice, and heart rate. This data is transmitted in real time to a server via the device. The server uses an emotion recognition algorithm to evaluate the user's emotional state based on this biometric information. This evaluation result becomes the main input data for the next processing step.

[0432] Step 3:

[0433] The server uses a generation mechanism to generate the optimal exercise route and food delivery facilities based on user profile and emotional state data. During this process, a generation AI model operates, receiving the prompt message "User emotional data has been collected. Evaluate the current emotional state and generate the optimal food delivery suggestion based on it." As a result, optimized exercise route and facility candidates are output.

[0434] Step 4:

[0435] The terminal receives exercise route and facility information transmitted from the server and notifies the user. This notification includes detailed navigation information to the suggested route and facilities. Additionally, the advertising display mechanism is enabled, and advertisements are selected and displayed on the terminal based on the user's status. This advertisement selection involves data calculations using a pre-configured advertising database.

[0436] Step 5:

[0437] The user checks the notification on their device and performs the suggested exercise route. During the exercise, the activity tracking device measures the user's steps and distance traveled and sends this data back to the server. The server uses this data to calculate a reward (such as points) for the user, and the reward is notified on the device. This reward process involves data processing to determine the degree of goal achievement.

[0438] Step 6:

[0439] Finally, using data aggregation methods, the server analyzes all user behavior data and generates statistical information. This data is anonymized and then provided to external health management organizations, etc. The data processing here includes anonymization and aggregation processes to protect privacy.

[0440] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0441] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0442] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0443] [Third Embodiment]

[0444] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0445] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0446] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0447] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0448] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0449] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0450] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0451] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0452] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0453] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0454] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0455] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0456] This invention begins with a process in which the user registers basic information and preferences using a personal information input device. The device sends this information to a server, which stores it in a database. The user can measure their steps in real time during their daily activities using a step counting device. This measurement data is frequently sent to the server and recorded as the user's exercise pattern.

[0457] Next, the server uses a generation mechanism to create a personalized walking route based on the user's registration information and current location. This route is optimized to include scenic views and points of interest. The generated route is sent to the device, and a schedule is created based on the user's available time.

[0458] The scheduling mechanism notifies the user of the appropriate timing for exercise via the device. This allows the user to walk at the optimal time in their daily routine and continue exercising efficiently.

[0459] When a user achieves their set step goal using the reward system, points are awarded. These points can be easily checked on the device, and rewards are presented. This helps users maintain motivation.

[0460] Furthermore, the server's advertising display system will show information about specific stores along the user's walking route on their device. The advertisements will include promotional information about the stores, encouraging users to visit them.

[0461] In addition, the server uses data aggregation methods to analyze behavioral data collected from all users. This data is anonymized and provided to external parties in a lawful manner.

[0462] Specific example:

[0463] For example, user B sets a daily step goal of 7,000 steps in the app at 8 AM. The server generates a walking route that includes cafes and parks based on B's current location and their break time at 1 PM. At 1 PM, B receives a notification on their device saying, "Let's start walking." In addition to the 4,000 steps B took in the morning, B walks another 3,000 steps along this route. Once B achieves their goal, points are awarded, and promotional information for cafes is displayed on their device.

[0464] This invention enables users to maintain their health in their daily lives while finding and continuing to enjoy themselves. The aim is to provide new value in both health and business aspects.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The user downloads the app to their device and enters basic information and preferences on a personal information input screen. The device then sends this information to the server.

[0468] Step 2:

[0469] The server receives user information and stores it in the database. Specifically, it creates an individual profile for each user.

[0470] Step 3:

[0471] The user sets a daily step goal on their device. The set goal is then sent from the device to the server.

[0472] Step 4:

[0473] The device uses a step counting device to measure the user's steps in real time. This data is transmitted to the server at regular intervals.

[0474] Step 5:

[0475] The server generates an appropriate walking route using a generation method based on the user's location information and profile. During this process, the server optimizes the route by taking into account the user's hobbies and preferred time of day.

[0476] Step 6:

[0477] The server generates a walking route and sends the recommended exercise time to the device. The device then notifies the user of this information.

[0478] Step 7:

[0479] The user receives a notification and starts walking at the designated time. The device continuously measures steps and reports progress to the server.

[0480] Step 8:

[0481] The server evaluates the user's step count progress and awards points as a reward when the goal is achieved. The awarded points can be viewed on the device.

[0482] Step 9:

[0483] The server uses the user's location information to access store data and generate advertisements. These advertisements are then sent to the user's device and displayed to them.

[0484] Step 10:

[0485] The server aggregates and analyzes behavioral data collected from all users. This data is anonymized and then provided to external parties.

[0486] (Example 1)

[0487] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] In modern society, people are required to ensure they get an appropriate amount of exercise to maintain their health. However, the busyness of daily life makes it difficult to continue exercising. Furthermore, there is a lack of strategies to make exercise enjoyable while maintaining motivation. In addition, there is a need for efficient advertising display and data utilization methods that use location information.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0490] In this invention, the server includes means for registering the user's basic information and hobbies and preferences via personal information input means, means for generating suggestions based on the user's hobbies and preferences via generation means, and means for notifying the user's terminal of the optimal activity timing via notification means. This enables the user to obtain an efficient and enjoyable way to maintain an appropriate level of activity in their daily life. Furthermore, a reward system that promotes motivation and effective advertising display are realized.

[0491] "Personal information input means" refers to methods or devices for receiving and registering a user's basic information, hobbies, and preferences.

[0492] A "step counting device" refers to a device or method capable of measuring the amount of activity a user engages in during their daily activities and acquiring that data.

[0493] "Generation method" refers to a technical method for creating optimal routes and suggestions based on user registration information and location information.

[0494] A "scheduling method" is a method for suggesting appropriate activity times, taking into account the user's inactivity time.

[0495] A "reward provision system" is a system that provides evaluation and offers rewards when users achieve the goals they have set.

[0496] "Advertising display means" refers to technology that displays advertising information about specific facilities to users along their travel route.

[0497] An "information aggregation tool" is a system for combining and analyzing behavioral data obtained from multiple users.

[0498] A "notification method" is a way of informing a user's device of the optimal timing and information for their activities.

[0499] This invention is a system aimed at promoting user health and maintaining motivation, achieving its effects by coordinating multiple means. The system mainly consists of three components: a server, a terminal, and a user.

[0500] The server receives users' personal information and location data and securely stores it in a database. Based on the registered information, the server uses a generative AI model to generate optimal routes and activity suggestions for the user. These generation processes employ profiling techniques based on each user's hobbies and preferences, providing personalized results. For example, by inputting a prompt such as, "Generate the optimal walking route based on the user's basic information and current location," the generative AI model will formulate a route suitable for the user.

[0501] The device functions as an easy-to-use interface for the user. Through the device, the user inputs personal information and target steps, and manages their daily activity plan. The device also uses built-in sensors to acquire the user's daily activity data, such as steps taken, in real time and transmits it to the server.

[0502] Based on the information provided on the device, users can choose actions to incorporate into their health management and daily lives. For example, the device's notification function allows them to know the right time to "start walking" and exercise accordingly. When goals are achieved, the device displays reward points and promotional information about nearby facilities, making daily exercise more enjoyable.

[0503] Throughout the entire system, the server aggregates and analyzes all data. This data is anonymized and securely provided externally, where it can be used for new health services and commercial strategies. This invention enables users to manage their health efficiently and enjoyably, and also offers new business opportunities for companies.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The user opens the application screen on their device and enters their basic information and interests. This information includes their name, age, and places of interest. The device transmits this information to the server via a secure protocol such as SSL. The data is encrypted and securely stored on the server.

[0507] Step 2:

[0508] Before saving received user information to the database, the server verifies the validity and integrity of the data. This checks for duplicate information and ensures the format is correct. Information that has been properly verified is saved to the database and becomes available for subsequent processing.

[0509] Step 3:

[0510] The user measures their steps during daily activities using sensors built into the device. The device acquires step count data in real time and periodically sends it to the server. The user's actual step count data is provided to the server as input, forming the basis for data processing on the server side.

[0511] Step 4:

[0512] The server uses a generative AI model to generate individual walking routes based on the user's basic information and current location. It designs the optimal route while considering location and points of interest. The generated route can be obtained by inputting the prompt message "Generate the optimal walking route based on the user's basic information and current location" into the generative AI model.

[0513] Step 5:

[0514] The server uses the generated route information to schedule exercise time that matches the user's inactive period. The device then sends a notification to the user saying, "Let's start walking," based on this schedule information. Upon receiving the notification, the device prompts the user to take action to begin moving.

[0515] Step 6:

[0516] The server monitors the user's progress toward their exercise goals and awards points as rewards based on achievement. The terminal displays reward information in real time for goals achieved by the user to maintain motivation. Points are awarded and displayed in this manner.

[0517] Step 7:

[0518] The server selects the most relevant advertising information based on the user's individual browsing history and sends it to the device. The device then displays this advertisement on its screen, promoting specific stores or services. This gives the user an opportunity to discover new interests.

[0519] Step 8:

[0520] The server collects behavioral data from all users and analyzes it using data aggregation tools. The data is anonymized, and the analysis results are used to improve health promotion services and commercial strategies. This process involves calculations to extract useful patterns from large amounts of data.

[0521] (Application Example 1)

[0522] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In modern society, promoting health management and effective exercise habits among users is crucial. However, many individuals find it difficult to find appropriate exercise opportunities amidst their busy daily lives. Furthermore, a lack of motivation to continue exercising makes it difficult to improve health. In addition, there is a need to effectively utilize the time spent exercising and provide added value through advertising and promotions.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0525] In this invention, the server includes means for registering the user's basic information and interests using personal information input means, means for measuring the amount of exercise based on the user's daily activities using exercise measurement means, and means for providing the user with information that helps with efficient exercise and health management based on the travel route. As a result, the user can easily manage their health by being offered individually optimized exercise routes and timings. Furthermore, the user's motivation can be improved by providing rewards according to the results of their exercise. In addition, value-added experiences can be provided to the user through specific advertising information, contributing to the promotion of health in society as a whole.

[0526] A "personal information input method" is a function that allows users to register their basic information and interests in the system.

[0527] "Exercise measurement means" refers to a function that measures the amount of exercise a user performs based on their daily activities.

[0528] The "generation means" refers to a function that uses collected user information and location information to create personalized travel routes for users.

[0529] A "scheduling method" is a function that suggests the optimal timing for exercise based on the user's available time.

[0530] A "reward provision mechanism" is a function that provides benefits or rewards to users when they achieve their exercise goals.

[0531] "Advertising display means" refers to a function that displays advertisements from specific businesses located along the user's travel route on the screen.

[0532] A "data aggregation method" is a function for integrating and analyzing behavioral information collected from users.

[0533] "A means of providing users with information that helps them exercise efficiently and manage their health based on their travel routes" refers to a function that provides useful information in real time to promote users' exercise.

[0534] This system is designed as a comprehensive solution to provide users with healthy exercise habits. The server aggregates diverse user information and provides individually optimized exercise plans.

[0535] First, users input their basic information and interests using a smartphone or other device. This information is transmitted to the server via a personal information input device. Next, a movement measurement device with step counting and GPS functionality collects the user's daily activity level and location information in real time. This collected data is analyzed by a generation device to design travel routes that match each user's individual interests and free time.

[0536] The server then uses a scheduling mechanism to suggest the optimal exercise time to the user. This suggestion is notified on the device, and the user can start exercising at the recommended time. Upon achieving the goal, points are awarded through a reward system, and reward information is displayed on the user's device.

[0537] The advertising display system provides users with information about nearby businesses while they are on the move and presents promotional information that will encourage them to visit. Furthermore, the exercise data collected from users by the data aggregation system is anonymized, analyzed, and then provided to relevant businesses as pattern information.

[0538] As a concrete example, consider a scenario where a user, who works as a lunch delivery driver, sets a daily exercise goal. Before leaving, this user sets a goal of 7,000 steps using the app. Based on this information, the server creates an exercise route tailored to the user's current location and route, and suggests optimal stopping points. When a specific number of steps is achieved during the exercise, a notification appears on the user's smartphone saying, "Congratulations! You have a reward you can use on your next visit," providing additional motivation.

[0539] An example of a prompt using a generative AI model is: "As a feature for riders in a food delivery app, please suggest health promotion measures based on delivery routes. Riders should be able to track their steps and earn rewards for achieving specific goals."

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] Users input their basic information and interests using a terminal. This input information is transmitted to the server via a personal information input system. This information is stored in the server's database and used as the design basis for individual plans generated by the system.

[0543] Step 2:

[0544] The device's exercise measurement system collects the user's exercise volume and location information in real time during their daily activities. This data is periodically transmitted to a server. The server analyzes the received exercise data and performs data processing to extract temporal trends and location patterns.

[0545] Step 3:

[0546] The server uses a generation mechanism to generate individual travel routes based on collected motion data and user interest information. This process uses an optimization algorithm to calculate how to incorporate user-interesting spots into the route.

[0547] Step 4:

[0548] The server uses a scheduling mechanism to suggest an exercise time that takes into account the user's available time based on the generated travel route. The suggested exercise time is sent as a notification to the user's device.

[0549] Step 5:

[0550] When a user actually starts exercising and reaches their step goal, the device sends that information back to the server. The server calculates points based on the user's achievement level via a reward system and sends them to the device. The user checks their points on the device and receives information about available rewards.

[0551] Step 6:

[0552] The advertising display method configured on the device provides advertisements from nearby businesses along the user's travel route. In this process, promotional information received from the server is displayed in a customized format based on the user's interests.

[0553] Step 7:

[0554] The server uses data aggregation to collect and analyze anonymized exercise data from all users. The analysis results are processed for presentation to vendors and provided as data to help users improve their exercise habits.

[0555] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0556] This invention is a system that utilizes emotion recognition means to evaluate a user's emotional state and provides an individually optimized exercise program based on that evaluation. The system works by having the user input personal information via a terminal and recording daily emotional fluctuations using an emotion engine. The emotion engine analyzes biometric data such as facial expressions, voice, and heart rate through sensors built into the device to detect the user's emotional state.

[0557] The device sends this data to the server, which uses a generation mechanism to create a walking route based on the user's current emotional state. In this route generation process, a more challenging route is suggested when the emotional state is positive, and a route aimed at relaxation is suggested when the emotional state is negative.

[0558] The scheduling system also considers emotional data and available time to recommend the optimal time for users to walk. Furthermore, a reward system awards points for achieving goals, which are provided as rewards that can be viewed on the device.

[0559] The advertising display method aims to attract customers to stores while soothing the user's emotions by displaying store advertisements tailored to the user's emotional state and presenting useful promotions based on walking routes and location information.

[0560] Furthermore, the server's data aggregation method securely collects and analyzes user step counts and emotional data, anonymizes it, and provides it externally, thereby contributing to the health management of a wider range of people.

[0561] Specific example:

[0562] For example, user C opens the app at 9 AM using their device, and the emotion engine detects a state of "slight fatigue." Based on this information, the server suggests a short, scenic, and leisurely walking route, primarily around a nearby park. In the afternoon, the device notifies C with a walking guide including relaxing music, and as C begins walking this route, it provides a naturally refreshing experience. After completing the walk, C achieves their goal and earns points, and an advertisement for a discount promotion at a nearby cafe is displayed on the device.

[0563] This invention provides an exercise experience that takes into account not only physical health but also mental state, proposing a new value in fitness that meets individual needs.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The user launches the app using their device and enters personal and emotional information. The device collects this information and sends it to the server.

[0567] Step 2:

[0568] The server stores the user's basic information and emotional data in a database and analyzes the user's current emotional state using an emotion recognition system.

[0569] Step 3:

[0570] The emotion engine acquires biometric data from the device's sensors and analyzes facial expressions, voice, heart rate, etc., to evaluate the user's emotional state in real time.

[0571] Step 4:

[0572] The server uses a generation method to create individual walking routes based on the user's emotional state and location information. The route is optimized for relaxation or challenge depending on the emotional state.

[0573] Step 5:

[0574] The scheduling system calculates the optimal walking time, taking into account the generated walking route and the user's free time, and notifies the user from their device.

[0575] Step 6:

[0576] The user starts walking at a specified time, and the device measures the number of steps. The results are sent to a server, and the progress is monitored.

[0577] Step 7:

[0578] The server evaluates the user's goal achievement and awards points through reward programs. This information is reported to the user on their device, and reward information is also displayed.

[0579] Step 8:

[0580] Based on the user's emotional state, the server generates location-based store advertisements and displays them on the device at the appropriate time.

[0581] Step 9:

[0582] The server aggregates and analyzes user step count and sentiment data, anonymizes it, and then provides the data to external parties.

[0583] Through these steps, the system provides users with emotionally sensitive exercise plans, offering a valuable fitness experience that benefits both physical and mental well-being.

[0584] (Example 2)

[0585] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0586] In modern society, there is a growing demand for both physical health management and mental well-being. However, conventional health management systems tend to focus too much on the physical aspects of exercise and fail to provide optimal exercise programs that take into account the user's emotional state. As a result, exercise suggestions tailored to the mood and condition of individual users are insufficient, and there is a lack of motivation to continue exercising.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0588] In this invention, the server includes means for registering the user's basic information and habitual information via personal information input means, means for collecting the user's facial expressions, voice, and heart rate data using sensors via emotion recognition means and analyzing their emotional state, and means for generating individual walking routes based on the user's registered information, location information, and emotional state via generation means. This makes it possible to provide exercise programs that take the user's emotional state into consideration.

[0589] A "means for inputting personal information" refers to a method for registering a user's basic information and information about their habits into the system.

[0590] A "step counting device" is a means of measuring the number of steps taken in order to record the amount of exercise a user takes on a daily basis.

[0591] "Emotion recognition means" refers to a method of collecting a user's biometric information using sensors, analyzing it, and identifying their emotional state.

[0592] "Generation method" refers to a method for creating personalized walking routes and other content based on the user's registration information, location information, and emotional state.

[0593] A "scheduling method" is a means of suggesting the optimal exercise time, taking into account the user's free time and emotional state.

[0594] A "reward provision method" is a means of awarding points and displaying rewards to users when they achieve their exercise goals.

[0595] "Advertising display means" refers to means of displaying appropriate store advertisements based on the user's emotional state and location information.

[0596] "Data aggregation means" refers to methods for collecting, analyzing, and aggregating user behavioral information and emotional data.

[0597] This invention is a system that evaluates a user's emotional state and proposes an optimal exercise program tailored to that state. This allows users to receive personalized health management and mental care simultaneously.

[0598] The user first enters personal information using the device. The device incorporates a means for entering personal information to record the user's basic information and daily habits. The device also uses built-in sensors such as a camera, microphone, and heart rate monitor to collect the user's facial expressions, voice, and heart rate data in real time. Based on this, the device's emotion recognition means analyzes the collected biometric data to identify the user's emotional state.

[0599] The analyzed emotional data is securely encrypted and sent to the server. The server uses a generative AI model to generate a personalized walking route based on the user's current emotional state, location, and registered personal information. For example, if the user is feeling "slightly tired," the server will suggest a relaxing route that passes through a quiet park.

[0600] Regarding exercise time, the server's scheduling mechanism considers the user's free time and emotional state to recommend the optimal exercise start time. If the user achieves their set goals as a result of exercising, points are awarded through the device's reward system. This motivates the user to continue exercising.

[0601] Furthermore, the device displays advertisements for relevant stores based on the user's emotional state, location information, and walking route. This advertising method aims to provide information that is sensitive to the user's emotions, thereby creating a sense of calm and relaxation.

[0602] Finally, the server's data aggregation system securely collects and analyzes user behavior and emotional data. This data is provided externally in an anonymized form and used to improve the health management of the entire community.

[0603] For example, if a user opens the app in the morning and the emotion engine determines they are "somewhat tired," the server will suggest a course that includes a nearby scenic park and then provide a notification with relaxing music in the afternoon, offering the user an exercise experience that helps relieve stress. An example of a prompt message would be, "Recognize the user's emotional state and suggest an exercise route that is best suited to their current mood."

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] Users enter basic personal information using their devices. This information includes data such as age, gender, and daily habits. The entered information is stored on the device and forms the user's basic profile. Based on this, the system obtains the basic data to provide a personalized experience.

[0607] Step 2:

[0608] The device uses built-in sensors to collect biometric data necessary to assess the user's current emotional state. This includes facial expression capture via camera, voice analysis via microphone, and heart rate measurement via heart rate monitor. For emotion recognition, this data is analyzed by an AI algorithm to output the user's emotional state (e.g., joy, stress, fatigue).

[0609] Step 3:

[0610] The device sends the analyzed emotional data to the server. This communication is encrypted, protecting the user's privacy. The server uses the received data to understand the emotional state and prepares it for use in the next step.

[0611] Step 4:

[0612] The server uses a generative AI model to generate an optimal walking route based on the user's emotional state, location information, and pre-entered basic information. By combining the input data, it creates an exercise plan tailored to the user's individual needs and condition. The output is detailed walking route information.

[0613] Step 5:

[0614] The generated walking route information is sent back to the device. The device displays this information to the user, providing details of the suggested route. The user confirms the suggested route on the device screen and begins exercising according to it.

[0615] Step 6:

[0616] The device provides relaxation guides and music to enrich the user experience while walking. This is to stabilize the user's emotional state and promote an enjoyable exercise experience. It also provides timely notifications and voice guidance according to the walking progress.

[0617] Step 7:

[0618] When a user finishes their walk, the device evaluates their achievement of the goal based on route information and step count. Points are then awarded through a reward system. The awarded points and reward information are displayed to the user on the device.

[0619] Step 8:

[0620] The server's data aggregation method collects and analyzes emotional and activity data after exercise. This provides insights to further improve suggestions for future users. The analyzed data may be anonymized and provided externally for use in health research.

[0621] (Application Example 2)

[0622] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0623] In modern society, providing exercise and dietary recommendations based on individual lifestyles and emotional states is a crucial health management issue. However, mechanisms for selecting optimized exercise routes and dining facilities that take into account individual user emotions and health conditions are not adequately provided. As a result, many individuals miss opportunities for exercise and healthy eating, and are not receiving appropriate services.

[0624] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0625] In this invention, the server includes means for evaluating the user's emotional state using emotion recognition means, means for providing an exercise route optimized for the user using generation means, and means for selecting the optimal meal provision facility based on location information and emotional data. This makes it possible to optimize exercise and meal provision according to the individual's emotional state and health condition.

[0626] A "personal information input method" is a function that allows users to register their basic information and preferences.

[0627] "Activity measurement means" refers to a function that measures the number of steps and amount of exercise based on the user's daily activities.

[0628] The "generation means" refers to a function that generates individually optimized exercise routes using the user's registration information and location information.

[0629] The "scheduling method" is a function that suggests appropriate exercise time based on the user's available time.

[0630] A "reward provision method" is a function that awards points to users for achieving their goals and then presents them with rewards as a result.

[0631] "Advertising display means" refers to a function that displays advertisements for specific facilities or services according to the exercise route or the user's emotional state.

[0632] An "emotion recognition method" is a function that evaluates a user's emotional state from facial expressions, voice, and biometric data.

[0633] A "data aggregation method" is a function for collecting user behavior data and obtaining analysis results from that data.

[0634] A system implementing this invention includes means for inputting personal information, means for measuring activity, means for generating data, means for scheduling, means for providing rewards, means for displaying advertisements, means for recognizing emotions, and means for aggregating data.

[0635] The server processes personal information and activity data received from the user to generate exercise routes and meal facilities optimized for that user. This takes into account the user's location and emotional state. The emotion recognition means is software that analyzes the user's facial expressions, voice, and heart rate using hardware such as the camera, microphone, and heart rate sensor of the smart glasses worn by the user. The emotional data obtained from this analysis is sent to the server and reflected in exercise route and facility suggestions tailored to the user's individual needs.

[0636] The user's device notifies them of suggested exercise routes and dining facilities, and the advertising display system selects and displays advertisements appropriate to the user's state. These advertisements take into account emotional state and location information, enabling effective customer acquisition.

[0637] For example, if the emotion engine determines that the user needs to relax, the server will suggest a route to a quiet cafe, input a prompt into the AI ​​model, and prepare suggestions tailored to the user's current needs. By using a prompt such as, "User's emotional data has been collected. Evaluate the user's current emotional state and generate the optimal food delivery suggestion based on it," it becomes possible to provide emotionally adaptive exercise and meal suggestions.

[0638] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0639] Step 1:

[0640] The user enters basic information and preferences into the terminal using a personal information input method. This entered data is sent to the server. The server receives the entered data and processes it by storing it in a database. As a result, a user profile is formed.

[0641] Step 2:

[0642] The user wears smart glasses, and the emotion recognition system collects biometric information such as facial expressions, voice, and heart rate. This data is transmitted in real time to a server via the device. The server uses an emotion recognition algorithm to evaluate the user's emotional state based on this biometric information. This evaluation result becomes the main input data for the next processing step.

[0643] Step 3:

[0644] The server uses a generation mechanism to generate the optimal exercise route and food delivery facilities based on user profile and emotional state data. During this process, a generation AI model operates, receiving the prompt message "User emotional data has been collected. Evaluate the current emotional state and generate the optimal food delivery suggestion based on it." As a result, optimized exercise route and facility candidates are output.

[0645] Step 4:

[0646] The terminal receives exercise route and facility information transmitted from the server and notifies the user. This notification includes detailed navigation information to the suggested route and facilities. Additionally, the advertising display mechanism is enabled, and advertisements are selected and displayed on the terminal based on the user's status. This advertisement selection involves data calculations using a pre-configured advertising database.

[0647] Step 5:

[0648] The user checks the notification on their device and performs the suggested exercise route. During the exercise, the activity tracking device measures the user's steps and distance traveled and sends this data back to the server. The server uses this data to calculate a reward (such as points) for the user, and the reward is notified on the device. This reward process involves data processing to determine the degree of goal achievement.

[0649] Step 6:

[0650] Finally, using data aggregation methods, the server analyzes all user behavior data and generates statistical information. This data is anonymized and then provided to external health management organizations, etc. The data processing here includes anonymization and aggregation processes to protect privacy.

[0651] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0652] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0653] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0654] [Fourth Embodiment]

[0655] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0656] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0657] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0658] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0659] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0660] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0661] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0662] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0663] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0664] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0665] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0666] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0667] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] This invention begins with a process in which the user registers basic information and preferences using a personal information input device. The device sends this information to a server, which stores it in a database. The user can measure their steps in real time during their daily activities using a step counting device. This measurement data is frequently sent to the server and recorded as the user's exercise pattern.

[0669] Next, the server uses a generation mechanism to create a personalized walking route based on the user's registration information and current location. This route is optimized to include scenic views and points of interest. The generated route is sent to the device, and a schedule is created based on the user's available time.

[0670] The scheduling mechanism notifies the user of the appropriate timing for exercise via the device. This allows the user to walk at the optimal time in their daily routine and continue exercising efficiently.

[0671] When a user achieves their set step goal using the reward system, points are awarded. These points can be easily checked on the device, and rewards are presented. This helps users maintain motivation.

[0672] Furthermore, the server's advertising display system will show information about specific stores along the user's walking route on their device. The advertisements will include promotional information about the stores, encouraging users to visit them.

[0673] In addition, the server uses data aggregation methods to analyze behavioral data collected from all users. This data is anonymized and provided to external parties in a lawful manner.

[0674] Specific example:

[0675] For example, user B sets a daily step goal of 7,000 steps in the app at 8 AM. The server generates a walking route that includes cafes and parks based on B's current location and their break time at 1 PM. At 1 PM, B receives a notification on their device saying, "Let's start walking." In addition to the 4,000 steps B took in the morning, B walks another 3,000 steps along this route. Once B achieves their goal, points are awarded, and promotional information for cafes is displayed on their device.

[0676] This invention enables users to maintain their health in their daily lives while finding and continuing to enjoy themselves. The aim is to provide new value in both health and business aspects.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The user downloads the app to their device and enters basic information and preferences on a personal information input screen. The device then sends this information to the server.

[0680] Step 2:

[0681] The server receives user information and stores it in the database. Specifically, it creates an individual profile for each user.

[0682] Step 3:

[0683] The user sets a daily step goal on their device. The set goal is then sent from the device to the server.

[0684] Step 4:

[0685] The device uses a step counting device to measure the user's steps in real time. This data is transmitted to the server at regular intervals.

[0686] Step 5:

[0687] The server generates an appropriate walking route using a generation method based on the user's location information and profile. During this process, the server optimizes the route by taking into account the user's hobbies and preferred time of day.

[0688] Step 6:

[0689] The server generates a walking route and sends the recommended exercise time to the device. The device then notifies the user of this information.

[0690] Step 7:

[0691] The user receives a notification and starts walking at the designated time. The device continuously measures steps and reports progress to the server.

[0692] Step 8:

[0693] The server evaluates the user's step count progress and awards points as a reward when the goal is achieved. The awarded points can be viewed on the device.

[0694] Step 9:

[0695] The server uses the user's location information to access store data and generate advertisements. These advertisements are then sent to the user's device and displayed to them.

[0696] Step 10:

[0697] The server aggregates and analyzes behavioral data collected from all users. This data is anonymized and then provided to external parties.

[0698] (Example 1)

[0699] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In modern society, people are required to ensure they get an appropriate amount of exercise to maintain their health. However, the busyness of daily life makes it difficult to continue exercising. Furthermore, there is a lack of strategies to make exercise enjoyable while maintaining motivation. In addition, there is a need for efficient advertising display and data utilization methods that use location information.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0702] In this invention, the server includes means for registering the user's basic information and hobbies and preferences via personal information input means, means for generating suggestions based on the user's hobbies and preferences via generation means, and means for notifying the user's terminal of the optimal activity timing via notification means. This enables the user to obtain an efficient and enjoyable way to maintain an appropriate level of activity in their daily life. Furthermore, a reward system that promotes motivation and effective advertising display are realized.

[0703] "Personal information input means" refers to methods or devices for receiving and registering a user's basic information, hobbies, and preferences.

[0704] A "step counting device" refers to a device or method capable of measuring the amount of activity a user engages in during their daily activities and acquiring that data.

[0705] "Generation method" refers to a technical method for creating optimal routes and suggestions based on user registration information and location information.

[0706] A "scheduling method" is a method for suggesting appropriate activity times, taking into account the user's inactivity time.

[0707] A "reward provision system" is a system that provides evaluation and offers rewards when users achieve the goals they have set.

[0708] "Advertising display means" refers to technology that displays advertising information about specific facilities to users along their travel route.

[0709] An "information aggregation tool" is a system for combining and analyzing behavioral data obtained from multiple users.

[0710] A "notification method" is a way of informing a user's device of the optimal timing and information for their activities.

[0711] This invention is a system aimed at promoting user health and maintaining motivation, achieving its effects by coordinating multiple means. The system mainly consists of three components: a server, a terminal, and a user.

[0712] The server receives users' personal information and location data and securely stores it in a database. Based on the registered information, the server uses a generative AI model to generate optimal routes and activity suggestions for the user. These generation processes employ profiling techniques based on each user's hobbies and preferences, providing personalized results. For example, by inputting a prompt such as, "Generate the optimal walking route based on the user's basic information and current location," the generative AI model will formulate a route suitable for the user.

[0713] The device functions as an easy-to-use interface for the user. Through the device, the user inputs personal information and target steps, and manages their daily activity plan. The device also uses built-in sensors to acquire the user's daily activity data, such as steps taken, in real time and transmits it to the server.

[0714] Based on the information provided on the device, users can choose actions to incorporate into their health management and daily lives. For example, the device's notification function allows them to know the right time to "start walking" and exercise accordingly. When goals are achieved, the device displays reward points and promotional information about nearby facilities, making daily exercise more enjoyable.

[0715] Throughout the entire system, the server aggregates and analyzes all data. This data is anonymized and securely provided externally, where it can be used for new health services and commercial strategies. This invention enables users to manage their health efficiently and enjoyably, and also offers new business opportunities for companies.

[0716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0717] Step 1:

[0718] The user opens the application screen on their device and enters their basic information and interests. This information includes their name, age, and places of interest. The device transmits this information to the server via a secure protocol such as SSL. The data is encrypted and securely stored on the server.

[0719] Step 2:

[0720] Before saving received user information to the database, the server verifies the validity and integrity of the data. This checks for duplicate information and ensures the format is correct. Information that has been properly verified is saved to the database and becomes available for subsequent processing.

[0721] Step 3:

[0722] The user measures their steps during daily activities using sensors built into the device. The device acquires step count data in real time and periodically sends it to the server. The user's actual step count data is provided to the server as input, forming the basis for data processing on the server side.

[0723] Step 4:

[0724] The server uses a generative AI model to generate individual walking routes based on the user's basic information and current location. It designs the optimal route while considering location and points of interest. The generated route can be obtained by inputting the prompt message "Generate the optimal walking route based on the user's basic information and current location" into the generative AI model.

[0725] Step 5:

[0726] The server uses the generated route information to schedule exercise time that matches the user's inactive period. The device then sends a notification to the user saying, "Let's start walking," based on this schedule information. Upon receiving the notification, the device prompts the user to take action to begin moving.

[0727] Step 6:

[0728] The server monitors the user's progress toward their exercise goals and awards points as rewards based on achievement. The terminal displays reward information in real time for goals achieved by the user to maintain motivation. Points are awarded and displayed in this manner.

[0729] Step 7:

[0730] The server selects the most relevant advertising information based on the user's individual browsing history and sends it to the device. The device then displays this advertisement on its screen, promoting specific stores or services. This gives the user an opportunity to discover new interests.

[0731] Step 8:

[0732] The server collects behavioral data from all users and analyzes it using data aggregation tools. The data is anonymized, and the analysis results are used to improve health promotion services and commercial strategies. This process involves calculations to extract useful patterns from large amounts of data.

[0733] (Application Example 1)

[0734] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] In modern society, promoting health management and effective exercise habits among users is crucial. However, many individuals find it difficult to find appropriate exercise opportunities amidst their busy daily lives. Furthermore, a lack of motivation to continue exercising makes it difficult to improve health. In addition, there is a need to effectively utilize the time spent exercising and provide added value through advertising and promotions.

[0736] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0737] In this invention, the server includes means for registering the user's basic information and interests using personal information input means, means for measuring the amount of exercise based on the user's daily activities using exercise measurement means, and means for providing the user with information that helps with efficient exercise and health management based on the travel route. As a result, the user can easily manage their health by being offered individually optimized exercise routes and timings. Furthermore, the user's motivation can be improved by providing rewards according to the results of their exercise. In addition, value-added experiences can be provided to the user through specific advertising information, contributing to the promotion of health in society as a whole.

[0738] A "personal information input method" is a function that allows users to register their basic information and interests in the system.

[0739] "Exercise measurement means" refers to a function that measures the amount of exercise a user performs based on their daily activities.

[0740] The "generation means" refers to a function that uses collected user information and location information to create personalized travel routes for users.

[0741] A "scheduling method" is a function that suggests the optimal timing for exercise based on the user's available time.

[0742] A "reward provision mechanism" is a function that provides benefits or rewards to users when they achieve their exercise goals.

[0743] "Advertising display means" refers to a function that displays advertisements from specific businesses located along the user's travel route on the screen.

[0744] A "data aggregation method" is a function for integrating and analyzing behavioral information collected from users.

[0745] "A means of providing users with information that helps them exercise efficiently and manage their health based on their travel routes" refers to a function that provides useful information in real time to promote users' exercise.

[0746] This system is designed as a comprehensive solution to provide users with healthy exercise habits. The server aggregates diverse user information and provides individually optimized exercise plans.

[0747] First, users input their basic information and interests using a smartphone or other device. This information is transmitted to the server via a personal information input device. Next, a movement measurement device with step counting and GPS functionality collects the user's daily activity level and location information in real time. This collected data is analyzed by a generation device to design travel routes that match each user's individual interests and free time.

[0748] The server then uses a scheduling mechanism to suggest the optimal exercise time to the user. This suggestion is notified on the device, and the user can start exercising at the recommended time. Upon achieving the goal, points are awarded through a reward system, and reward information is displayed on the user's device.

[0749] The advertising display system provides users with information about nearby businesses while they are on the move and presents promotional information that will encourage them to visit. Furthermore, the exercise data collected from users by the data aggregation system is anonymized, analyzed, and then provided to relevant businesses as pattern information.

[0750] As a concrete example, consider a scenario where a user, who works as a lunch delivery driver, sets a daily exercise goal. Before leaving, this user sets a goal of 7,000 steps using the app. Based on this information, the server creates an exercise route tailored to the user's current location and route, and suggests optimal stopping points. When a specific number of steps is achieved during the exercise, a notification appears on the user's smartphone saying, "Congratulations! You have a reward you can use on your next visit," providing additional motivation.

[0751] An example of a prompt using a generative AI model is: "As a feature for riders in a food delivery app, please suggest health promotion measures based on delivery routes. Riders should be able to track their steps and earn rewards for achieving specific goals."

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] Users input their basic information and interests using a terminal. This input information is transmitted to the server via a personal information input system. This information is stored in the server's database and used as the design basis for individual plans generated by the system.

[0755] Step 2:

[0756] The device's exercise measurement system collects the user's exercise volume and location information in real time during their daily activities. This data is periodically transmitted to a server. The server analyzes the received exercise data and performs data processing to extract temporal trends and location patterns.

[0757] Step 3:

[0758] The server uses a generation mechanism to generate individual travel routes based on collected motion data and user interest information. This process uses an optimization algorithm to calculate how to incorporate user-interesting spots into the route.

[0759] Step 4:

[0760] The server uses a scheduling mechanism to suggest an exercise time that takes into account the user's available time based on the generated travel route. The suggested exercise time is sent as a notification to the user's device.

[0761] Step 5:

[0762] When a user actually starts exercising and reaches their step goal, the device sends that information back to the server. The server calculates points based on the user's achievement level via a reward system and sends them to the device. The user checks their points on the device and receives information about available rewards.

[0763] Step 6:

[0764] The advertising display method configured on the device provides advertisements from nearby businesses along the user's travel route. In this process, promotional information received from the server is displayed in a customized format based on the user's interests.

[0765] Step 7:

[0766] The server uses data aggregation to collect and analyze anonymized exercise data from all users. The analysis results are processed for presentation to vendors and provided as data to help users improve their exercise habits.

[0767] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0768] This invention is a system that utilizes emotion recognition means to evaluate a user's emotional state and provides an individually optimized exercise program based on that evaluation. The system works by having the user input personal information via a terminal and recording daily emotional fluctuations using an emotion engine. The emotion engine analyzes biometric data such as facial expressions, voice, and heart rate through sensors built into the device to detect the user's emotional state.

[0769] The device sends this data to the server, which uses a generation mechanism to create a walking route based on the user's current emotional state. In this route generation process, a more challenging route is suggested when the emotional state is positive, and a route aimed at relaxation is suggested when the emotional state is negative.

[0770] The scheduling system also considers emotional data and available time to recommend the optimal time for users to walk. Furthermore, a reward system awards points for achieving goals, which are provided as rewards that can be viewed on the device.

[0771] The advertising display method aims to attract customers to stores while soothing the user's emotions by displaying store advertisements tailored to the user's emotional state and presenting useful promotions based on walking routes and location information.

[0772] Furthermore, the server's data aggregation method securely collects and analyzes user step counts and emotional data, anonymizes it, and provides it externally, thereby contributing to the health management of a wider range of people.

[0773] Specific example:

[0774] For example, user C opens the app at 9 AM using their device, and the emotion engine detects a state of "slight fatigue." Based on this information, the server suggests a short, scenic, and leisurely walking route, primarily around a nearby park. In the afternoon, the device notifies C with a walking guide including relaxing music, and as C begins walking this route, it provides a naturally refreshing experience. After completing the walk, C achieves their goal and earns points, and an advertisement for a discount promotion at a nearby cafe is displayed on the device.

[0775] This invention provides an exercise experience that takes into account not only physical health but also mental state, proposing a new value in fitness that meets individual needs.

[0776] The following describes the processing flow.

[0777] Step 1:

[0778] The user launches the app using their device and enters personal and emotional information. The device collects this information and sends it to the server.

[0779] Step 2:

[0780] The server stores the user's basic information and emotional data in a database and analyzes the user's current emotional state using an emotion recognition system.

[0781] Step 3:

[0782] The emotion engine acquires biometric data from the device's sensors and analyzes facial expressions, voice, heart rate, etc., to evaluate the user's emotional state in real time.

[0783] Step 4:

[0784] The server uses a generation method to create individual walking routes based on the user's emotional state and location information. The route is optimized for relaxation or challenge depending on the emotional state.

[0785] Step 5:

[0786] The scheduling system calculates the optimal walking time, taking into account the generated walking route and the user's free time, and notifies the user from their device.

[0787] Step 6:

[0788] The user starts walking at a specified time, and the device measures the number of steps. The results are sent to a server, and the progress is monitored.

[0789] Step 7:

[0790] The server evaluates the user's goal achievement and awards points through reward programs. This information is reported to the user on their device, and reward information is also displayed.

[0791] Step 8:

[0792] Based on the user's emotional state, the server generates location-based store advertisements and displays them on the device at the appropriate time.

[0793] Step 9:

[0794] The server aggregates and analyzes user step count and sentiment data, anonymizes it, and then provides the data to external parties.

[0795] Through these steps, the system provides users with emotionally sensitive exercise plans, offering a valuable fitness experience that benefits both physical and mental well-being.

[0796] (Example 2)

[0797] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0798] In modern society, there is a growing demand for both physical health management and mental well-being. However, conventional health management systems tend to focus too much on the physical aspects of exercise and fail to provide optimal exercise programs that take into account the user's emotional state. As a result, exercise suggestions tailored to the mood and condition of individual users are insufficient, and there is a lack of motivation to continue exercising.

[0799] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0800] In this invention, the server includes means for registering the user's basic information and habitual information via personal information input means, means for collecting the user's facial expressions, voice, and heart rate data using sensors via emotion recognition means and analyzing their emotional state, and means for generating individual walking routes based on the user's registered information, location information, and emotional state via generation means. This makes it possible to provide exercise programs that take the user's emotional state into consideration.

[0801] A "means for inputting personal information" refers to a method for registering a user's basic information and information about their habits into the system.

[0802] A "step counting device" is a means of measuring the number of steps taken in order to record the amount of exercise a user takes on a daily basis.

[0803] "Emotion recognition means" refers to a method of collecting a user's biometric information using sensors, analyzing it, and identifying their emotional state.

[0804] "Generation method" refers to a method for creating personalized walking routes and other content based on the user's registration information, location information, and emotional state.

[0805] A "scheduling method" is a means of suggesting the optimal exercise time, taking into account the user's free time and emotional state.

[0806] A "reward provision method" is a means of awarding points and displaying rewards to users when they achieve their exercise goals.

[0807] "Advertising display means" refers to means of displaying appropriate store advertisements based on the user's emotional state and location information.

[0808] "Data aggregation means" refers to methods for collecting, analyzing, and aggregating user behavioral information and emotional data.

[0809] This invention is a system that evaluates a user's emotional state and proposes an optimal exercise program tailored to that state. This allows users to receive personalized health management and mental care simultaneously.

[0810] The user first enters personal information using the device. The device incorporates a means for entering personal information to record the user's basic information and daily habits. The device also uses built-in sensors such as a camera, microphone, and heart rate monitor to collect the user's facial expressions, voice, and heart rate data in real time. Based on this, the device's emotion recognition means analyzes the collected biometric data to identify the user's emotional state.

[0811] The analyzed emotional data is securely encrypted and sent to the server. The server uses a generative AI model to generate a personalized walking route based on the user's current emotional state, location, and registered personal information. For example, if the user is feeling "slightly tired," the server will suggest a relaxing route that passes through a quiet park.

[0812] Regarding exercise time, the server's scheduling mechanism considers the user's free time and emotional state to recommend the optimal exercise start time. If the user achieves their set goals as a result of exercising, points are awarded through the device's reward system. This motivates the user to continue exercising.

[0813] Furthermore, the device displays advertisements for relevant stores based on the user's emotional state, location information, and walking route. This advertising method aims to provide information that is sensitive to the user's emotions, thereby creating a sense of calm and relaxation.

[0814] Finally, the server's data aggregation system securely collects and analyzes user behavior and emotional data. This data is provided externally in an anonymized form and used to improve the health management of the entire community.

[0815] For example, if a user opens the app in the morning and the emotion engine determines they are "somewhat tired," the server will suggest a course that includes a nearby scenic park and then provide a notification with relaxing music in the afternoon, offering the user an exercise experience that helps relieve stress. An example of a prompt message would be, "Recognize the user's emotional state and suggest an exercise route that is best suited to their current mood."

[0816] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0817] Step 1:

[0818] Users enter basic personal information using their devices. This information includes data such as age, gender, and daily habits. The entered information is stored on the device and forms the user's basic profile. Based on this, the system obtains the basic data to provide a personalized experience.

[0819] Step 2:

[0820] The device uses built-in sensors to collect biometric data necessary to assess the user's current emotional state. This includes facial expression capture via camera, voice analysis via microphone, and heart rate measurement via heart rate monitor. For emotion recognition, this data is analyzed by an AI algorithm to output the user's emotional state (e.g., joy, stress, fatigue).

[0821] Step 3:

[0822] The device sends the analyzed emotional data to the server. This communication is encrypted, protecting the user's privacy. The server uses the received data to understand the emotional state and prepares it for use in the next step.

[0823] Step 4:

[0824] The server uses a generative AI model to generate an optimal walking route based on the user's emotional state, location information, and pre-entered basic information. By combining the input data, it creates an exercise plan tailored to the user's individual needs and condition. The output is detailed walking route information.

[0825] Step 5:

[0826] The generated walking route information is sent back to the device. The device displays this information to the user, providing details of the suggested route. The user confirms the suggested route on the device screen and begins exercising according to it.

[0827] Step 6:

[0828] The device provides relaxation guides and music to enrich the user experience while walking. This is to stabilize the user's emotional state and promote an enjoyable exercise experience. It also provides timely notifications and voice guidance according to the walking progress.

[0829] Step 7:

[0830] When a user finishes their walk, the device evaluates their achievement of the goal based on route information and step count. Points are then awarded through a reward system. The awarded points and reward information are displayed to the user on the device.

[0831] Step 8:

[0832] The server's data aggregation method collects and analyzes emotional and activity data after exercise. This provides insights to further improve suggestions for future users. The analyzed data may be anonymized and provided externally for use in health research.

[0833] (Application Example 2)

[0834] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0835] In modern society, providing exercise and dietary recommendations based on individual lifestyles and emotional states is a crucial health management issue. However, mechanisms for selecting optimized exercise routes and dining facilities that take into account individual user emotions and health conditions are not adequately provided. As a result, many individuals miss opportunities for exercise and healthy eating, and are not receiving appropriate services.

[0836] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0837] In this invention, the server includes means for evaluating the user's emotional state using emotion recognition means, means for providing an exercise route optimized for the user using generation means, and means for selecting the optimal meal provision facility based on location information and emotional data. This makes it possible to optimize exercise and meal provision according to the individual's emotional state and health condition.

[0838] A "personal information input method" is a function that allows users to register their basic information and preferences.

[0839] "Activity measurement means" refers to a function that measures the number of steps and amount of exercise based on the user's daily activities.

[0840] The "generation means" refers to a function that generates individually optimized exercise routes using the user's registration information and location information.

[0841] The "scheduling method" is a function that suggests appropriate exercise time based on the user's available time.

[0842] A "reward provision method" is a function that awards points to users for achieving their goals and then presents them with rewards as a result.

[0843] "Advertising display means" refers to a function that displays advertisements for specific facilities or services according to the exercise route or the user's emotional state.

[0844] An "emotion recognition method" is a function that evaluates a user's emotional state from facial expressions, voice, and biometric data.

[0845] A "data aggregation method" is a function for collecting user behavior data and obtaining analysis results from that data.

[0846] A system implementing this invention includes means for inputting personal information, means for measuring activity, means for generating data, means for scheduling, means for providing rewards, means for displaying advertisements, means for recognizing emotions, and means for aggregating data.

[0847] The server processes personal information and activity data received from the user to generate exercise routes and meal facilities optimized for that user. This takes into account the user's location and emotional state. The emotion recognition means is software that analyzes the user's facial expressions, voice, and heart rate using hardware such as the camera, microphone, and heart rate sensor of the smart glasses worn by the user. The emotional data obtained from this analysis is sent to the server and reflected in exercise route and facility suggestions tailored to the user's individual needs.

[0848] The user's device notifies them of suggested exercise routes and dining facilities, and the advertising display system selects and displays advertisements appropriate to the user's state. These advertisements take into account emotional state and location information, enabling effective customer acquisition.

[0849] For example, if the emotion engine determines that the user needs to relax, the server will suggest a route to a quiet cafe, input a prompt into the AI ​​model, and prepare suggestions tailored to the user's current needs. By using a prompt such as, "User's emotional data has been collected. Evaluate the user's current emotional state and generate the optimal food delivery suggestion based on it," it becomes possible to provide emotionally adaptive exercise and meal suggestions.

[0850] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0851] Step 1:

[0852] The user enters basic information and preferences into the terminal using a personal information input method. This entered data is sent to the server. The server receives the entered data and processes it by storing it in a database. As a result, a user profile is formed.

[0853] Step 2:

[0854] The user wears smart glasses, and the emotion recognition system collects biometric information such as facial expressions, voice, and heart rate. This data is transmitted in real time to a server via the device. The server uses an emotion recognition algorithm to evaluate the user's emotional state based on this biometric information. This evaluation result becomes the main input data for the next processing step.

[0855] Step 3:

[0856] The server uses a generation mechanism to generate the optimal exercise route and food delivery facilities based on user profile and emotional state data. During this process, a generation AI model operates, receiving the prompt message "User emotional data has been collected. Evaluate the current emotional state and generate the optimal food delivery suggestion based on it." As a result, optimized exercise route and facility candidates are output.

[0857] Step 4:

[0858] The terminal receives exercise route and facility information transmitted from the server and notifies the user. This notification includes detailed navigation information to the suggested route and facilities. Additionally, the advertising display mechanism is enabled, and advertisements are selected and displayed on the terminal based on the user's status. This advertisement selection involves data calculations using a pre-configured advertising database.

[0859] Step 5:

[0860] The user checks the notification on their device and performs the suggested exercise route. During the exercise, the activity tracking device measures the user's steps and distance traveled and sends this data back to the server. The server uses this data to calculate a reward (such as points) for the user, and the reward is notified on the device. This reward process involves data processing to determine the degree of goal achievement.

[0861] Step 6:

[0862] Finally, using data aggregation methods, the server analyzes all user behavior data and generates statistical information. This data is anonymized and then provided to external health management organizations, etc. The data processing here includes anonymization and aggregation processes to protect privacy.

[0863] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0864] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0865] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0866] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0867] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0868] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0869] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0870] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0871] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0872] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0873] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0874] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0875] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0876] 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.

[0877] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0878] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0879] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0880] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0881] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0882] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0883] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0884] The following is further disclosed regarding the embodiments described above.

[0885] (Claim 1)

[0886] A means of registering the user's basic information and hobbies / preferences through a personal information input method,

[0887] A step counting device is used to measure the number of steps taken by the user based on their daily activities.

[0888] The generation means includes a means for generating individual walking routes using the user's registration information and location information,

[0889] A scheduling mechanism that suggests exercise time based on the user's free time,

[0890] The reward system involves awarding points to users for achieving their goals and offering rewards accordingly.

[0891] An advertising display method that displays advertisements for specific stores to users along their walking route,

[0892] A means of aggregating and analyzing user behavior information through data aggregation means,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which selects the most suitable store based on location information and customizes the advertisement.

[0896] (Claim 3)

[0897] The system according to claim 1, which anonymizes the user's step count data and health information and provides it to an external party.

[0898] "Example 1"

[0899] (Claim 1)

[0900] A means of registering the user's basic information and hobbies / preferences through a personal information input method,

[0901] A means of measuring the amount of activity based on the user's daily activities using a step counting device,

[0902] The generation means includes a means for generating individual activity routes using the user's registration information and location information,

[0903] A scheduling mechanism that proposes activity times based on the user's inactivity time,

[0904] A means of providing rewards that evaluates and offers rewards for users' achievement of their goals,

[0905] An advertising display means that displays advertisements for specific facilities to users along their travel route,

[0906] Information aggregation means for collecting and analyzing user behavior information,

[0907] The generation means uses a generation model to generate suggestions based on the user's tastes and preferences,

[0908] The notification means provides a means to notify the user's device of the optimal timing for activities,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which selects the most suitable facility based on location information and customizes the advertisement.

[0912] (Claim 3)

[0913] The system according to claim 1, which anonymizes user activity data and health information and provides it to an external party.

[0914] "Application Example 1"

[0915] (Claim 1)

[0916] The means of inputting personal information allows users to register their basic information and interests.

[0917] A means of measuring the amount of exercise based on the user's daily activities using an exercise measurement device,

[0918] The generation means includes a means for generating individual travel routes using the user's registration information and location information,

[0919] A scheduling mechanism that suggests exercise time based on the user's available time,

[0920] The reward system provides rewards and benefits to users for achieving their goals, and the reward system offers incentives to users for achieving their goals.

[0921] An advertising display means that displays advertisements from specific businesses to users along their travel route,

[0922] A means of aggregating and analyzing user behavior information through data aggregation means,

[0923] A means of providing users with information that helps them exercise efficiently and manage their health based on their travel route,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, which selects the most suitable vendor and customizes advertisements based on location information.

[0927] (Claim 3)

[0928] The system according to claim 1, which anonymizes and provides to an external party user's exercise data and health information.

[0929] "Example 2 of combining an emotion engine"

[0930] (Claim 1)

[0931] The means of inputting personal information includes a means of registering the user's basic information and information about their habits,

[0932] A means of recording the number of steps taken by a user based on their daily activities,

[0933] The emotion recognition means collects the user's facial expressions, voice, and heart rate data using sensors and analyzes their emotional state.

[0934] The generation means generates individual walking routes based on the user's registration information, location information, and emotional state.

[0935] A scheduling mechanism that suggests exercise time based on the user's free time and emotional state,

[0936] The reward system includes a means of awarding points to users for achieving their goals and displaying the rewards,

[0937] The advertising display means includes a means for displaying store advertisements based on the user's emotional state and walking route,

[0938] A means of aggregating and analyzing user behavioral information and emotional data through data aggregation means,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, which selects the optimal store and customizes the advertisement based on location information and emotional state.

[0942] (Claim 3)

[0943] The system according to claim 1, which provides anonymized user step count data, health information, and emotional information to an external party.

[0944] "Application example 2 when combining with an emotional engine"

[0945] (Claim 1)

[0946] A means of registering the user's basic information and preferences through a personal information input method,

[0947] The activity measurement means measures the number of steps taken based on the user's daily activities,

[0948] The generation means includes a means for generating individual exercise routes using the user's registration information and location information,

[0949] A scheduling mechanism that suggests exercise time based on the user's free time,

[0950] The reward system involves awarding points to users for achieving their goals and offering rewards accordingly.

[0951] The advertising display means includes a means for displaying advertisements for specific facilities to users along their exercise route,

[0952] A means for evaluating the user's emotional state using emotion recognition means and optimizing the exercise route and meal provision facilities,

[0953] A means of aggregating and analyzing user behavior data through data aggregation means,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, which selects the most suitable service provider and customizes advertisements based on location information and sentiment data.

[0957] (Claim 3)

[0958] The system according to claim 1, which provides anonymized user activity data and health information to an external party. [Explanation of symbols]

[0959] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of registering the user's basic information and hobbies / preferences through a personal information input method, A step counting device is used to measure the number of steps taken by the user based on their daily activities. The generation means includes a means for generating individual walking routes using the user's registration information and location information, A scheduling mechanism that suggests exercise time based on the user's free time, The reward system involves awarding points to users for achieving their goals and offering rewards accordingly. An advertising display method that displays advertisements for specific stores to users along their walking route, A means of aggregating and analyzing user behavior information through data aggregation means, A system that includes this.

2. The system according to claim 1, which selects the most suitable store based on location information and customizes the advertisement.

3. The system according to claim 1, which anonymizes the user's step count data and health information and provides it to an external party.

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