system

Abstract

We provide the system. [Solution] A means of acquiring information to collect user preference information, A recommendation means for generating candidate exercise activities based on the aforementioned preference information, An information provision means that provides local event information related to the aforementioned candidate exercise activity, A matching means that identifies other users with similar interests based on the aforementioned preference information and presents that information to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in 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 society, it is not easy to find sports activities that suit individual needs and preferences. Many people wish to pursue a healthy lifestyle, but finding appropriate means and partners for this has become an issue. In particular, it is difficult to select sports activities suitable for personal preferences, obtain event information in the area, and match with friends and communities.

Means for Solving the Problems

[0005] This invention provides a means for collecting user preference information and, based on this information, presents a list of recommended exercise activities to meet individual needs. It also provides information on local events related to exercise activities, creating an environment where users can easily participate. Furthermore, it identifies other users with similar interests and presents information to facilitate communication among users and assist in finding suitable partners.

[0006] "User preference information" refers to data about a user's individual preferences, interests, and activity tendencies.

[0007] "Information acquisition means" refers to the means of collecting user preference information, and includes various system interfaces and input tools.

[0008] "Recommendation methods" refer to algorithms and processes that suggest exercise activities suitable for a user based on collected user preference information.

[0009] "Potential exercise activities" refer to the various types of exercises and sports suggested to the user, or the content of those activities.

[0010] "Information provision means" refers to methods or devices for providing users with information about local sports events and activities.

[0011] "Local event information" refers to data about exercise-related events that users can participate in in their place of residence or selected region.

[0012] A "matching method" is a process or technology that evaluates the similarity between a user and other users based on their preferences and identifies appropriate communication partners. [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the 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, the 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, the 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, the labeled communication I / F (Interface) is an interface including 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] The system for implementing the present invention consists of a user terminal, a central server, and a database. Users input profile information via their terminal and register their exercise activity preferences. This information includes the user's interests, activity frequency, goals, and place of residence.

[0035] The terminal transmits the entered information to a central server. The server receives and analyzes the user's preferences. Based on the analyzed data, the server generates a list of suitable exercise activities for the user. These suggestions include a variety of sports and activities and are optimized according to the user's activity level and interests.

[0036] The server also takes the user's location data into consideration and collects information on relevant events and activities held in the area. This information, including the date, time, location, and participation requirements of the event, is notified to the user. This makes it easy for users to access events that interest them.

[0037] Furthermore, the server uses a matching algorithm to identify other users with similar interests. This process makes it easier for users to connect with each other and provides opportunities to participate in activities with others who share a common interest in sports. The terminal presents this recommendation and matching information to the user and guides them through communication options according to their choices.

[0038] For example, if a user enters information such as "I'm interested in tennis, but I don't have anyone to play with," the server will suggest nearby tennis tournaments and other users who are also interested in tennis. The user receives this information through their device and can participate in events with like-minded individuals.

[0039] This invention is a form that expands the user's options for physical activity and provides an appropriate sports experience.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users access the registration interface using their own devices and enter profile information and information about their exercise preferences. This includes sports they are interested in, activity frequency, health goals, and place of residence.

[0043] Step 2:

[0044] The terminal formats the registered user information and sends it to the server. The transmitted data is organized along with the user's individual ID and converted into a format suitable for analysis.

[0045] Step 3:

[0046] The server receives user information and stores it in a database. Based on the stored information, an algorithm is executed to analyze user preferences and identify patterns in interests and activity levels.

[0047] Step 4:

[0048] The server uses the analysis results to select suitable exercise activities for the user. It then compares these with activity information in the database to list activities that match the user's profile.

[0049] Step 5:

[0050] The server collects information about local exercise events based on the user's location. This information includes the event date, time, venue, and participation requirements. This information is then organized for user recommendations.

[0051] Step 6:

[0052] The server uses a matching algorithm to filter other users with similar interests. It analyzes data from users with similar activity patterns and calculates a matching score.

[0053] Step 7:

[0054] The device receives suggested exercise activities, local event information, and information about other matched users from the server. This information is then notified to and displayed to the user.

[0055] Step 8:

[0056] Users review the information displayed on their devices and select exercise activities and events that interest them. If necessary, they take action to contact matched users.

[0057] Step 9:

[0058] The terminal feeds user selections back to the server, providing data to improve future recommendation accuracy. The server uses this information to improve the overall system performance.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, finding exercise activities that suit one's preferences can be complex and time-consuming, and finding companions to participate with can also be difficult. Furthermore, there are insufficient means to effectively obtain information about local sports events and to securely transmit data while protecting privacy. As a result, users face challenges in promoting their health and opportunities for social interaction.

[0062] 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.

[0063] In this invention, the server includes data acquisition means for acquiring user preference data, list generation means for creating a list of recommended exercise activities based on the preference data, and event information acquisition means for providing local event information related to the exercise activities. This enables users to efficiently find the exercise activities best suited to them and to easily obtain information about relevant local events. Furthermore, it enables secure data exchange while protecting privacy.

[0064] "Data acquisition means" refers to equipment or a system for collecting data related to user preferences.

[0065] A "list generation means" is a device or mechanism for generating a list of optimal exercise activity candidates based on collected user preference data.

[0066] "Event information acquisition means" refers to equipment or a system for collecting and providing local event information related to exercise activities.

[0067] "Method for identifying similar users" refers to a device or mechanism that analyzes the preference data of multiple users to identify and present other users who share common interests.

[0068] "Data transmission means" refers to equipment or a system for encrypting collected data and securely transmitting it to a server or other terminal.

[0069] This invention is a system that suggests exercise activities based on user preferences, provides information on local events related to those activities, and matches users with each other. The system mainly consists of a user's terminal, a server, and a database.

[0070] Users access a dedicated application or website using their own devices and enter their profile information. This information includes their interests in exercise, place of residence, activity frequency, and goals. This data is then transmitted from the device to the server. To ensure security, encrypted communication such as HTTPS is used for data transmission.

[0071] The server utilizes analysis software such as Python and R to analyze the received data. Based on the analysis results, it generates a list of suggested exercise activities tailored to the user. This list reflects information extracted from an existing database and is customized according to the user's exercise level and interests.

[0072] The server also searches a database of local events based on the user's location and collects event information for that area. This allows users to obtain detailed information about sports events held in their vicinity (date, time, location, participation requirements, etc.).

[0073] Furthermore, the server compares the preferences of other users and matches them with other users who share similar interests. This allows users to find like-minded individuals and engage in activities together. This matching function provides opportunities to promote interaction among users.

[0074] For example, if a user enters "I'm interested in cycling, but I want to find someone to go with," the server will search for nearby cycling events and suggest other users who are also interested in cycling. The user can then check this information through their device and enjoy cycling with new companions.

[0075] Example prompts for generative AI models:

[0076] "Please enter the type of exercise you are interested in. Examples: cycling, running, Pilates, etc."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users access a dedicated application or website using their device and enter their profile information. This includes their interests in exercise, location, activity frequency, and goals. The entered data is temporarily stored in the device's local storage. Next, to securely transmit this data to the server, the data is encrypted using an encryption protocol such as HTTPS and prepared for transmission.

[0080] Step 2:

[0081] The terminal sends encrypted user data to a central server. The server decrypts this received data and stores it in a database for analysis. The data received as input includes the user's interests, location, and activity schedule. The server loads necessary reference data from the database in preparation for processing the data.

[0082] Step 3:

[0083] The server uses data analysis software such as Python and R to analyze user preference data. Specifically, it applies machine learning algorithms to analyze user interest trends and activity frequency. The input for the analysis is user preference data, and the output is a list of exercise activity candidates optimized for the user. This list is personalized based on the type and level of exercise.

[0084] Step 4:

[0085] The server searches a database of sports events and activities in the user's region based on their location information. The user's location data and the event database are used as input for this process. The output extracts event information for the region (date, time, location, and participation requirements). The server then formats this event information for the user to provide.

[0086] Step 5:

[0087] The server collects preference data from other users and runs a clustering algorithm to identify users with similar interests. The input is preference data from multiple users, and the output is a list of matching candidates. This matches users who share common interests.

[0088] Step 6:

[0089] The server sends a list of generated exercise activity candidates, event information, and matching candidate information to the terminal. The terminal presents this information to the user. The user can browse the information through an intuitive UI and plan to participate in exercises and events that interest them. During communication, data is encrypted again as needed and securely transmitted to the user's terminal.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern society, it is difficult for individual users to easily find activities that match their exercise preferences, and opportunities to connect with others who share similar interests are limited. As a result, even when visiting physical stores, there is a problem in that they cannot immediately access the products or activities that are best suited to them.

[0093] 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.

[0094] In this invention, the server includes a processing unit for acquiring user preference information, a computing unit for generating recommended physical activity options based on the preference information, and a visual device for providing information on activity equipment and group activities based on the user's interests in real-world stores. This allows users to visually check product and event information that matches their exercise preferences in real time, and also facilitates communication with other users who share similar interests.

[0095] A "processing device" is a device used to receive, collect, and manage user preference information.

[0096] A "computational device" is a device that generates a selection of physical activity options based on the user's preference information.

[0097] A "device that provides event information in a geographical area" is a device that provides information about events held in a specific region.

[0098] A "matching device" is a device that identifies other users with similar preferences and presents that information to the user.

[0099] A "visual device" is a device that provides information about activity equipment and group activities in a real-world store, based on the user's interests, in a visual way.

[0100] In the system for implementing this invention, the user inputs exercise preference information using a smart device and transmits this information to a server. The server analyzes the received data using Python and the Django framework and generates physical activity and geographical event information suitable for the user. As smart devices, smart glasses and smartphones are often used, as the system is intended for use in stores.

[0101] The server sends analysis results back to the smart device via a RESTful API. It can also integrate with external databases to provide geographical event information and recommended exercise equipment.

[0102] Users can view information displayed on their smart devices in real time as they move around the store. For example, if a user enters "I'm interested in running," their smart glasses will display information about running equipment in the store and nearby running events. They will also be guided on how to connect with other users who share similar interests.

[0103] To facilitate communication between users in the real world, visual devices play a role in presenting users with recommended event information and visualizing related products. In this way, it is a system that enriches the real-world store experience and encourages participation in physical activities.

[0104] An example of a prompt message might be: "Please display a list of sports events the user is interested in, sorted by the events taking place today. The user is interested in running."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] Users input preference information such as their interest in exercise, current activity level, and goals using a smart device. This input information is collected through the smart device's application and transmitted to a server. The input data constitutes the user's unique exercise profile.

[0108] Step 2:

[0109] The server analyzes the received preference information. Using Python and the Django framework, the server interacts with the database to generate the most suitable physical activity options for the user. Based on the input data, data calculations are performed to recommend exercise activities and related events that match the user's interests.

[0110] Step 3:

[0111] The server collects and provides information on events held locally based on the analysis results. It retrieves information such as the date, time, and location of sports events corresponding to geographical areas from an external database and generates a list of events that match the user's interests. This information is sent to the client device via a RESTful API.

[0112] Step 4:

[0113] Based on the information received, the device displays optimized exercise activity options and event information for the user. Users can access this information in real time via smart glasses or a smartphone's user interface (UI). Furthermore, it guides users to the nearest events and recommended product sections based on their location.

[0114] Step 5:

[0115] The server uses a matching device to identify other users with similar interests. Leveraging machine learning algorithms, it detects users with common interests from the submitted dataset, creating opportunities for mutual interaction. This result is notified to the user, facilitating communication.

[0116] Step 6:

[0117] Users utilize the information provided by their devices to participate in exercise activities and events that interest them. They also interact with other users who share similar interests. This entire process enriches users' daily activities and encourages their participation in exercise.

[0118] 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.

[0119] This invention provides a system for suggesting exercise activities that take into account the user's preferences and emotional state. This system consists of the user's terminal, a central server, and a database including an emotion engine.

[0120] Users input profile information and exercise preferences via their devices. This includes sports of interest, activity frequency, health goals, and place of residence. The device then sends this information, along with the emotion engine, to the server.

[0121] The server recommends exercise activities based on user information received from the terminal and emotional data analyzed by the emotion engine. The emotion engine estimates emotions from facial recognition technology and user input, and determines the user's current emotional state in real time. For example, if the user is feeling stressed, it recommends activities with relaxing effects such as yoga or walking.

[0122] The server selects appropriate exercise activity candidates based on the user's preferences and emotional state. This process adjusts the intensity and type of exercise to list activities that the user can participate in most effectively. Furthermore, it collects information on relevant local events and notifies the user at the appropriate time based on their emotional state.

[0123] The matching algorithm also utilizes data from the emotion engine to identify other users with similar emotional states and preferences. It then presents users with communication partners and activity partners that match their mood. This feature enables appropriate communication and collaborative activities based on emotions.

[0124] For example, if a user feels "interested in running, but lacks motivation today," the emotion engine reads this emotional information and transmits it to the server. The server then provides event information, including suggestions for running groups and encouraging messages to boost the user's motivation. This information is delivered to the user through their device, allowing them to choose activities that match their emotional state.

[0125] This system allows users to enjoy flexible exercise plans based on their emotions and lifestyle, and receive support to achieve a healthy lifestyle tailored to their individual needs.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users use their devices to enter profile information and information about their exercise preferences. For example, users provide information such as "I want to play tennis three times a week" or "I do it for stress relief."

[0129] Step 2:

[0130] The device transmits user input information to the emotion engine. Furthermore, the device collects real-time data from the user's facial expressions and input data so that the emotion engine can analyze their emotional state.

[0131] Step 3:

[0132] The emotion engine analyzes the user's facial expressions and past input data to determine their current emotional state. For example, if it detects that the user is feeling stressed, it returns that information to the server.

[0133] Step 4:

[0134] Based on the user's preferences and emotional state, the server generates activity suggestions. The server adjusts the intensity and type of exercise to provide suggestions that are appropriate for the user's current emotional state.

[0135] Step 5:

[0136] The server retrieves local event information from the database and filters it to include only events that match the user's interests and emotional state. This selects the event information that is of the highest interest to the user.

[0137] Step 6:

[0138] The server uses a matching algorithm to identify other users with similar emotional states and motor preferences. It then uses emotional feedback to list potential optimal communication partners.

[0139] Step 7:

[0140] The device receives a list of activities provided by the server, filtered events, and information about other matched users.

[0141] Step 8:

[0142] The device displays received information to the user. This includes detailed information about the exercise, a list of available events, and contact information for matching users. The user then decides on their next action based on this information.

[0143] Step 9:

[0144] The device records user-selected activities and events and sends them to the server as feedback. This allows the system to better adjust recommendations for the user in the future.

[0145] (Example 2)

[0146] 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".

[0147] Conventional physical activity suggestion systems failed to adequately consider the user's feelings when suggesting exercise activities, nor did they achieve appropriate matching with other users who shared similar feelings. As a result, it was difficult for users to create activity plans that suited their own feelings and circumstances, and support for achieving a healthy lifestyle was insufficient.

[0148] 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.

[0149] In this invention, the server includes data acquisition means for collecting user attribute information and emotional state, recommendation means for generating candidate physical activities recommended based on the attribute information and emotional state, and information provision means for providing local event information related to the candidate physical activities. This makes it possible to plan exercise activities optimized for the user's emotional state and circumstances.

[0150] "User attribute information" refers to characteristic data provided by users, such as their personal interests, activity frequency, health goals, and place of residence.

[0151] "Emotional state" refers to information that indicates the user's current emotions and mental condition, and is estimated from facial recognition technology and user input.

[0152] "Data acquisition means" refers to technical components for collecting attribute information and emotional state from users.

[0153] "Recommendation methods" refer to technical components that generate suitable physical activity options for the user based on acquired attribute information and emotional state.

[0154] "Information provision means" refers to a technical component that provides users with information about local events related to potential physical activities.

[0155] "Connection means" refers to a technical component for identifying other users with similar interests and sentiments and presenting that information to the user.

[0156] This invention proposes a system that optimizes physical activity based on the user's emotional state and attribute information. The system mainly consists of a user terminal, a central server, and a database containing an emotion analysis engine.

[0157] Device usage:

[0158] Users input attribute information using their own devices. This attribute information includes their interests in physical activities, activity frequency, health goals, and place of residence. Based on this information, the device also collects emotional states using its camera and sensors. Images captured by the user's camera are sent to the server as emotional states by an emotion analysis engine using facial recognition technology.

[0159] Server usage:

[0160] After receiving attribute information and analyzed emotional information transmitted from the terminal, the server uses a generative AI model to suggest exercise activities based on this information. The generative AI model analyzes multiple data points and extracts physical activities suitable for the user. For example, if the user is feeling stressed, it recommends relaxation activities such as yoga or walking.

[0161] The server also automatically collects information on relevant events happening in the region and suggests them to the user at a suitable time. This suggestion is made by sending a prompt message generated by a generative AI model as a text notification to the user's device.

[0162] Examples of specific cases and prompt statements:

[0163] For example, if a user expresses interest in "finding physical activities they can participate in on the weekend," the system can take into account the user's current mood and suggest yoga classes or walking events.

[0164] An example of a prompt message would be, "Based on the user's interest in sports activities and emotional data, what kind of exercise would you recommend?" Based on such prompt messages, the system generates appropriate activity information and informs the user.

[0165] In this way, the present invention can propose optimal physical activities based on the user's feelings and personal interests, and support a healthy lifestyle.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users fill out profile information in an input form using their device. This includes their areas of interest in physical activities, activity frequency, health goals, and place of residence. The data entered by the user is sent to the server via the device. This data is processed as user attribute information and stored on the server for further analysis.

[0169] Step 2:

[0170] The device uses a camera to understand the user's emotional state and analyzes image data obtained through facial recognition technology. This image data is processed by an emotion analysis engine, which estimates the user's emotional state in real time. The analysis results are sent to the server as emotional data.

[0171] Step 3:

[0172] The server inputs a prompt message into the generating AI model based on the received user attribute information and emotional data. The prompt message is "Please suggest the most suitable exercise activity based on the user's attribute information and emotional data." The generating AI model analyzes the data according to this prompt message and outputs a recommendation for physical activity suitable for the user. This recommendation includes the type of exercise, intensity, and time of day.

[0173] Step 4:

[0174] The server uses recommended physical activities and user attribute information to search a database of local events and retrieve relevant event information. The retrieved event information is then filtered to best suit the user's schedule and mood.

[0175] Step 5:

[0176] The server compiles recommendations and event information and sends it to the device. The device displays this information on its screen and notifies the user. The user can then check the notification on their device and select the activities and events they wish to participate in to plan their physical activity.

[0177] Step 6:

[0178] Users register for suggested physical activities and events through their devices. This registration information is sent to the server and saved as an updated schedule. This allows users to select activities that suit their mood and lifestyle, and create a feasible exercise plan.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] In today's world, it is crucial to provide fitness experiences that take into account the emotional state and exercise preferences of individual users. In particular, there is a need to offer real-time suggestions for exercise activities tailored to each person's unique emotional needs and provide an optimal fitness plan. The lack of such a system can lead to decreased motivation and inefficient fitness activities among users.

[0182] 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.

[0183] In this invention, the server includes information acquisition means for collecting user preference information and emotional state, recommendation means for generating candidate exercise activities based on the preference information and emotional state, and information provision means for providing local event information related to the candidate exercise activities. This makes it possible to suggest optimal exercise activities according to the user's emotional state and to suggest collaborative exercise plans among simultaneous users.

[0184] "User preference information" refers to information that indicates an individual user's preferences and interests regarding exercise.

[0185] "Emotional state" refers to information that indicates the user's current psychological or emotional state.

[0186] "Information acquisition methods" refer to means of collecting information about user preferences and emotional states.

[0187] "Recommendation methods" refer to methods for suggesting exercise activities suitable for the user based on acquired preference information and emotional state.

[0188] "Information provision means" refers to means of providing users with information about local events related to potential exercise activities.

[0189] A "matching method" is a means of identifying other users with similar interests or emotional states and presenting that information to the user.

[0190] "Means to promote cooperative activities" refers to providing means for users to enjoy exercise activities together.

[0191] The system that implements this application consists of a user's smart device, a server, and an emotion engine. The user's smart device collects the user's preference information and emotional state, and transmits this information to a central server. The emotion engine analyzes the user's emotional state using facial recognition technology such as OpenCV. The server generates candidate exercise activities suitable for the user based on the collected preference information and emotional state. In this process, the server uses recommendation tools to select the most effective exercise activity according to the user's emotions. It also collects and provides relevant event information through information provision tools for local events. Furthermore, the server uses matching tools to identify other users with similar preferences and emotional states and provides that information. As a specific example, if a user visiting a fitness club is feeling "anxious," a yoga class to help them relax might be suggested. This function utilizes prompt statements generated by a generative AI model. For example, a prompt statement such as "The user is feeling tense at the fitness club. What exercise would you suggest to help them calm down?" can be generated, and an exercise plan can be created based on this.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] Users input their preferences (such as preferred exercises, frequency, and health goals) using a smart device. This information is sent from the user's device to the server. The input preferences are stored on the server as string data.

[0195] Step 2:

[0196] The user's device uses its camera function to capture a picture of the user's face and sends this image data to the emotion engine. The emotion engine uses facial recognition software (e.g., OpenCV) to analyze the image and sends the user's emotional state as numerical data to the server. In this step, the input is image data, and the output is numerically represented emotion data.

[0197] Step 3:

[0198] The server uses a pre-configured algorithm to recommend optimal exercise activities based on the user's preferences and acquired emotional data. This algorithm determines the appropriate exercise intensity according to the user's health goals and emotional state. The recommended exercise activities are output as text data, including a description of the activity and a list of necessary equipment.

[0199] Step 4:

[0200] The server retrieves relevant event information from a database of local fitness events. The user's place of residence is also considered during this process. The retrieved local event information is provided to the user's terminal as a text message. The input is the search criteria from the database, and the output is the event information.

[0201] Step 5:

[0202] The server runs a matching algorithm to identify other users with similar tastes and emotional states. This facilitates connections between users and suggests partners for collaborative activities. The input is the taste information and emotional states of all users, and the output is a list of potential partners.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The system for implementing the present invention consists of a user terminal, a central server, and a database. Users input profile information via their terminal and register their exercise activity preferences. This information includes the user's interests, activity frequency, goals, and place of residence.

[0220] The terminal transmits the entered information to a central server. The server receives and analyzes the user's preferences. Based on the analyzed data, the server generates a list of suitable exercise activities for the user. These suggestions include a variety of sports and activities and are optimized according to the user's activity level and interests.

[0221] The server also takes the user's location data into consideration and collects information on relevant events and activities held in the area. This information, including the date, time, location, and participation requirements of the event, is notified to the user. This makes it easy for users to access events that interest them.

[0222] Furthermore, the server uses a matching algorithm to identify other users with similar interests. This process makes it easier for users to connect with each other and provides opportunities to participate in activities with others who share a common interest in sports. The terminal presents this recommendation and matching information to the user and guides them through communication options according to their choices.

[0223] For example, if a user enters information such as "I'm interested in tennis, but I don't have anyone to play with," the server will suggest nearby tennis tournaments and other users who are also interested in tennis. The user receives this information through their device and can participate in events with like-minded individuals.

[0224] This invention is a form that expands the user's options for physical activity and provides an appropriate sports experience.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users access the registration interface using their own devices and enter their profile information and preferences regarding exercise activities. This includes their favorite sports, activity frequency, health goals, and place of residence.

[0228] Step 2:

[0229] The terminal formats the registered user information and sends it to the server. The transmitted data is organized along with the user's individual ID and converted into a format suitable for analysis.

[0230] Step 3:

[0231] The server receives user information and stores it in a database. Based on the stored information, an algorithm is executed to analyze user preferences and identify patterns in interests and activity levels.

[0232] Step 4:

[0233] The server uses the analysis results to select suitable exercise activities for the user. It then compares these with activity information in the database to list activities that match the user's profile.

[0234] Step 5:

[0235] The server collects information about local exercise events based on the user's location. This information includes the event date, time, venue, and participation requirements. This information is then organized for user recommendations.

[0236] Step 6:

[0237] The server uses a matching algorithm to filter other users with similar interests. It analyzes data from users with similar activity patterns and calculates a matching score.

[0238] Step 7:

[0239] The device receives suggested exercise activities, local event information, and information about other matched users from the server. This information is then notified to and displayed to the user.

[0240] Step 8:

[0241] Users review the information displayed on their devices and select exercise activities and events that interest them. If necessary, they take action to contact matched users.

[0242] Step 9:

[0243] The terminal feeds user selections back to the server, providing data to improve future recommendation accuracy. The server uses this information to improve the overall system performance.

[0244] (Example 1)

[0245] 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."

[0246] In modern society, finding exercise activities that suit one's preferences can be complex and time-consuming, and finding companions to participate with can also be difficult. Furthermore, there are insufficient means to effectively obtain information about local sports events and to securely transmit data while protecting privacy. As a result, users face challenges in promoting their health and opportunities for social interaction.

[0247] 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.

[0248] In this invention, the server includes data acquisition means for acquiring user preference data, list generation means for creating a list of recommended exercise activities based on the preference data, and event information acquisition means for providing local event information related to the exercise activities. This enables users to efficiently find the exercise activities best suited to them and to easily obtain information about relevant local events. Furthermore, it enables secure data exchange while protecting privacy.

[0249] "Data acquisition means" refers to equipment or a system for collecting data related to user preferences.

[0250] A "list generation means" is a device or mechanism for generating a list of optimal exercise activity candidates based on collected user preference data.

[0251] "Event information acquisition means" refers to equipment or a system for collecting and providing local event information related to exercise activities.

[0252] "Method for identifying similar users" refers to a device or system that analyzes the preference data of multiple users to identify and present other users who share common interests.

[0253] "Data transmission means" refers to equipment or a system for encrypting collected data and securely transmitting it to a server or other terminal.

[0254] This invention is a system that suggests exercise activities based on user preferences, provides information on local events related to those activities, and matches users with each other. The system mainly consists of a user's terminal, a server, and a database.

[0255] Users access a dedicated application or website using their own devices and enter their profile information. This information includes their interests in exercise, place of residence, activity frequency, and goals. This data is then transmitted from the device to the server. To ensure security, encrypted communication such as HTTPS is used for data transmission.

[0256] The server utilizes analysis software such as Python and R to analyze the received data. Based on the analysis results, it generates a list of suggested exercise activities tailored to the user. This list reflects information extracted from an existing database and is customized according to the user's exercise level and interests.

[0257] The server also searches a database of local events based on the user's location and collects event information for that area. This allows users to obtain detailed information about sports events held in their vicinity (date, time, location, participation requirements, etc.).

[0258] Furthermore, the server compares the preferences of other users and matches them with other users who share similar interests. This allows users to find like-minded individuals and engage in activities together. This matching function provides opportunities to promote interaction among users.

[0259] For example, if a user enters "I'm interested in cycling, but I want to find someone to go with," the server will search for nearby cycling events and suggest other users who are also interested in cycling. The user can then check this information through their device and enjoy cycling with new companions.

[0260] Example prompts for generative AI models:

[0261] "Please enter the type of exercise you are interested in. Examples: cycling, running, Pilates, etc."

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] Users access a dedicated application or website using their device and enter their profile information. This includes their interests in exercise, location, activity frequency, and goals. The entered data is temporarily stored in the device's local storage. Next, to securely transmit this data to the server, the data is encrypted using an encryption protocol such as HTTPS and prepared for transmission.

[0265] Step 2:

[0266] The terminal sends encrypted user data to a central server. The server decrypts this received data and stores it in a database for analysis. The data received as input includes the user's interests, location, and activity schedule. The server loads necessary reference data from the database in preparation for processing the data.

[0267] Step 3:

[0268] The server uses data analysis software such as Python and R to analyze user preference data. Specifically, it applies machine learning algorithms to analyze user interest trends and activity frequency. The input for the analysis is user preference data, and the output is a list of exercise activity candidates optimized for the user. This list is personalized based on the type and level of exercise.

[0269] Step 4:

[0270] The server searches a database of sports events and activities in the user's region based on their location information. The user's location data and the event database are used as input for this process. The output extracts event information for the region (date, time, location, and participation requirements). The server then formats this event information for the user to provide.

[0271] Step 5:

[0272] The server collects preference data from other users and runs a clustering algorithm to identify users with similar interests. The input is preference data from multiple users, and the output is a list of matching candidates. This matches users who share common interests.

[0273] Step 6:

[0274] The server sends a list of generated exercise activity candidates, event information, and matching candidate information to the terminal. The terminal presents this information to the user. The user can browse the information through an intuitive UI and plan to participate in exercises and events that interest them. During communication, data is encrypted again as needed and securely transmitted to the user's terminal.

[0275] (Application Example 1)

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

[0277] In modern society, it is difficult for individual users to easily find activities that match their exercise preferences, and opportunities to connect with others who share similar interests are limited. As a result, even when visiting physical stores, there is a problem in that they cannot immediately access the products or activities that are best suited to them.

[0278] 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.

[0279] In this invention, the server includes a processing unit for acquiring user preference information, a computing unit for generating recommended physical activity options based on the preference information, and a visual device for providing information on activity equipment and group activities based on the user's interests in real-world stores. This allows users to visually check product and event information that matches their exercise preferences in real time, and also facilitates communication with other users who share similar interests.

[0280] A "processing device" is a device used to receive, collect, and manage user preference information.

[0281] A "computational device" is a device that generates a selection of physical activity options based on the user's preference information.

[0282] The "device for supplying event information in a geographical area" is a device for providing event information held in a specific area.

[0283] The "matching device" is a device for identifying other users with similar preferences and presenting that information to the user.

[0284] The "visual device" is a device for visually providing information on activity equipment and group activities based on the interests of the user in real-world stores.

[0285] In the system for implementing this invention, the user inputs preference information regarding exercise using a smart device and transmits that information to the server. The server analyzes the received data using Python and the Django framework and generates physical activity and event information within the geographical area suitable for the user. As smart devices, smart glasses and smartphones are often used assuming use within a store.

[0286] The server sends back the analysis results to the smart device via a RESTful API. At this time, in order to supply event information in the geographical area and information on recommended exercise equipment, it is also possible to cooperate with an external database.

[0287] The user can check the information displayed on the smart device in real time while moving within the store. For example, if the user inputs that they "are interested in running", running supplies information within the store and nearby running events are displayed on the smart glasses. Also, a method for meeting other users with similar interests is guided.

[0288] In order to promote communication between users in the real world, the visual device plays a role in presenting recommended event information to the user or visualizing related products. In this way, it is a mechanism for enriching the real store experience and promoting participation in exercise activities.

[0289] An example of a prompt message might be: "Please display a list of sports events the user is interested in, sorted by the events taking place today. The user is interested in running."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] Users input preference information such as their interest in exercise, current activity level, and goals using a smart device. This input information is collected through the smart device application and transmitted to a server. The entered data constitutes the user's unique exercise profile.

[0293] Step 2:

[0294] The server analyzes the received preference information. Using Python and the Django framework, the server interacts with the database to generate the most suitable physical activity options for the user. Based on the input data, data calculations are performed to recommend exercise activities and related events that match the user's interests.

[0295] Step 3:

[0296] The server collects and provides information on events held locally based on the analysis results. It retrieves information such as the date, time, and location of sports events corresponding to geographical areas from an external database and generates a list of events that match the user's interests. This information is sent to the client device via a RESTful API.

[0297] Step 4:

[0298] Based on the information received, the device displays optimized exercise activity options and event information for the user. Users can access this information in real time via smart glasses or a smartphone's user interface (UI). Furthermore, it guides users to the nearest events and recommended product sections based on their location.

[0299] Step 5:

[0300] The server uses a matching device to identify other users with similar interests. Leveraging machine learning algorithms, it detects users with common interests from the submitted dataset, creating opportunities for mutual interaction. This result is notified to the user, facilitating communication.

[0301] Step 6:

[0302] Users utilize the information provided by their devices to participate in exercise activities and events that interest them. They also interact with other users who share similar interests. This entire process enriches users' daily activities and encourages their participation in exercise.

[0303] 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.

[0304] This invention provides a system for suggesting exercise activities that take into account the user's preferences and emotional state. This system consists of the user's terminal, a central server, and a database including an emotion engine.

[0305] Users input profile information and exercise preferences via their devices. This includes sports of interest, activity frequency, health goals, and place of residence. The device then sends this information, along with the emotion engine, to the server.

[0306] The server recommends exercise activities based on the user information received from the terminal and the emotion data analyzed by the emotion engine. The emotion engine estimates emotions from face recognition technology and user input, and judges the user's current emotional state in real time. For example, when the user is feeling stressed, it recommends yoga or walking that has a relaxing effect.

[0307] The server selects appropriate exercise activity candidates based on the user's preference information and emotional state. In this process, it adjusts the intensity and type of exercise and lists up the activities that the user can participate in most effectively. Furthermore, it collects information on related events held in the area and notifies the user at an appropriate timing according to the user's emotional state.

[0308] The matching algorithm also utilizes the data of the emotion engine to identify other users with similar emotional states and preferences. It presents to the user communication partners or activity partners that suit their mood. This function enables appropriate communication and collaborative activities according to emotions.

[0309] As a specific example, when the user feels that "they are interested in running but lack motivation today", the emotion engine reads this emotion information and transmits it to the server. The server provides event information including a proposal for a running group to boost the user's motivation and encouraging messages. This information is delivered to the user through the terminal, and the user can select an activity that suits their emotional state.

[0310] With this system, the user can enjoy a flexible exercise plan based on their emotions and life situation and receive support for realizing a healthy lifestyle according to their individual needs.

[0311] The following explains the processing flow.

[0312] Step 1:

[0313] Users use their devices to enter profile information and exercise preferences. For example, users might provide information such as "I want to play tennis three times a week" or "I do it for stress relief."

[0314] Step 2:

[0315] The device transmits user input information to the emotion engine. Furthermore, the device collects real-time data from the user's facial expressions and input data so that the emotion engine can analyze their emotional state.

[0316] Step 3:

[0317] The emotion engine analyzes the user's facial expressions and past input data to determine their current emotional state. For example, if it detects that the user is feeling stressed, it returns that information to the server.

[0318] Step 4:

[0319] Based on the user's preferences and emotional state, the server generates activity suggestions. The server adjusts the intensity and type of exercise to provide suggestions that are appropriate for the user's current emotional state.

[0320] Step 5:

[0321] The server retrieves local event information from the database and filters it to include only events that match the user's interests and emotional state. This selects the event information that is of the highest interest to the user.

[0322] Step 6:

[0323] The server uses a matching algorithm to identify other users with similar emotional states and motor preferences. It then uses emotional feedback to list potential optimal communication partners.

[0324] Step 7:

[0325] The device receives a list of activities provided by the server, filtered events, and information about other matched users.

[0326] Step 8:

[0327] The device displays received information to the user. This includes detailed information about the exercise, a list of available events, and contact information for matching users. The user then decides on their next action based on this information.

[0328] Step 9:

[0329] The device records user-selected activities and events and sends them to the server as feedback. This allows the system to better adjust recommendations for the user in the future.

[0330] (Example 2)

[0331] 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".

[0332] Conventional physical activity suggestion systems failed to adequately consider the user's feelings when suggesting exercise activities, nor did they achieve appropriate matching with other users who shared similar feelings. As a result, it was difficult for users to create activity plans that suited their own feelings and circumstances, and support for achieving a healthy lifestyle was insufficient.

[0333] 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.

[0334] In this invention, the server includes data acquisition means for collecting user attribute information and emotional state, recommendation means for generating candidate physical activities recommended based on the attribute information and emotional state, and information provision means for providing local event information related to the candidate physical activities. This makes it possible to plan exercise activities optimized for the user's emotional state and circumstances.

[0335] "User attribute information" refers to characteristic data provided by users, such as their personal interests, activity frequency, health goals, and place of residence.

[0336] "Emotional state" refers to information that indicates the user's current emotions and mental condition, and is estimated from facial recognition technology and user input.

[0337] "Data acquisition means" refers to technical components for collecting attribute information and emotional state from users.

[0338] "Recommendation method" refers to a technical component that generates suitable physical activity options for the user based on acquired attribute information and emotional state.

[0339] "Information provision means" refers to a technical component that provides users with information about local events related to potential physical activities.

[0340] "Connection means" refers to a technical component for identifying other users with similar interests and sentiments and presenting that information to the user.

[0341] This invention proposes a system that optimizes physical activity based on the user's emotional state and attribute information. The system mainly consists of a user terminal, a central server, and a database containing an emotion analysis engine.

[0342] Device usage:

[0343] Users input attribute information using their own devices. This attribute information includes their interests in physical activities, activity frequency, health goals, and place of residence. Based on this information, the device also collects emotional states using its camera and sensors. Images captured by the user's camera are sent to the server as emotional states by an emotion analysis engine using facial recognition technology.

[0344] Server usage:

[0345] After receiving attribute information and analyzed emotional information transmitted from the terminal, the server uses a generative AI model to suggest exercise activities based on this information. The generative AI model analyzes multiple data points and extracts physical activities suitable for the user. For example, if the user is feeling stressed, it recommends relaxation activities such as yoga or walking.

[0346] The server also automatically collects information on relevant events taking place in the region and suggests them to the user at a suitable time. This suggestion is made by sending a prompt message generated by a generative AI model as a text notification to the user's device.

[0347] Examples of specific cases and prompt statements:

[0348] For example, if a user expresses interest in "finding physical activities they can participate in on the weekend," the system can take into account the user's current mood and suggest yoga classes or walking events.

[0349] An example of a prompt message is, "Based on the user's interest in sports activities and emotional data, what kind of exercise would you recommend?" Based on such prompt messages, the system generates appropriate activity information and informs the user.

[0350] In this way, the present invention can propose optimal physical activities based on the user's feelings and personal interests, and support a healthy lifestyle.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users fill out profile information in an input form using their device. This includes their areas of interest in physical activities, activity frequency, health goals, and place of residence. The data entered by the user is sent to the server via the device. This data is processed as user attribute information and stored on the server for further analysis.

[0354] Step 2:

[0355] The device uses a camera to understand the user's emotional state and analyzes image data obtained through facial recognition technology. This image data is processed by an emotion analysis engine, which estimates the user's emotional state in real time. The analysis results are sent to the server as emotional data.

[0356] Step 3:

[0357] The server inputs a prompt message into the generating AI model based on the received user attribute information and emotional data. The prompt message is "Please suggest the most suitable exercise activity based on the user's attribute information and emotional data." The generating AI model analyzes the data according to this prompt message and outputs a recommendation for physical activity suitable for the user. This recommendation includes the type of exercise, intensity, and time of day.

[0358] Step 4:

[0359] The server uses recommended physical activities and user attribute information to search a database of local events and retrieve relevant event information. The retrieved event information is then filtered to best suit the user's schedule and mood.

[0360] Step 5:

[0361] The server compiles recommendations and event information and sends it to the device. The device displays this information on its screen and notifies the user. The user can then check the notification on their device and select the activities and events they wish to participate in to plan their physical activity.

[0362] Step 6:

[0363] Users register for suggested physical activities and events through their devices. This registration information is sent to the server and saved as an updated schedule. This allows users to select activities that suit their mood and lifestyle, and create a feasible exercise plan.

[0364] (Application Example 2)

[0365] 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."

[0366] In today's world, it is crucial to provide fitness experiences that take into account the emotional state and exercise preferences of individual users. In particular, there is a need to offer real-time suggestions for exercise activities tailored to each person's unique emotional needs and provide an optimal fitness plan. The lack of such a system can lead to decreased motivation and inefficient fitness activities among users.

[0367] 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.

[0368] In this invention, the server includes information acquisition means for collecting user preference information and emotional state, recommendation means for generating candidate exercise activities based on the preference information and emotional state, and information provision means for providing local event information related to the candidate exercise activities. This makes it possible to suggest optimal exercise activities according to the user's emotional state and to suggest collaborative exercise plans among simultaneous users.

[0369] "User preference information" refers to information that indicates an individual user's preferences and interests regarding exercise.

[0370] "Emotional state" refers to information that indicates the user's current psychological or emotional state.

[0371] "Information acquisition methods" refer to means of collecting information about user preferences and emotional states.

[0372] "Recommendation methods" refer to methods for suggesting exercise activities suitable for the user based on acquired preference information and emotional state.

[0373] "Information provision means" refers to means of providing users with information about local events related to potential exercise activities.

[0374] A "matching method" is a means of identifying other users with similar interests or emotional states and presenting that information to the user.

[0375] "Means to promote cooperative activities" refers to providing means for users to enjoy exercise activities together.

[0376] The system that implements this application consists of a user's smart device, a server, and an emotion engine. The user's smart device collects the user's preference information and emotional state, and transmits this information to a central server. The emotion engine analyzes the user's emotional state using facial recognition technology such as OpenCV. The server generates candidate exercise activities suitable for the user based on the collected preference information and emotional state. In this process, the server uses recommendation tools to select the most effective exercise activity according to the user's emotions. It also collects and provides relevant event information through information provision tools for local events. Furthermore, the server uses matching tools to identify other users with similar preferences and emotional states and provides that information. As a specific example, if a user visiting a fitness club is feeling "anxious," a yoga class to help them relax might be suggested. This function utilizes prompt statements generated by a generative AI model. For example, a prompt statement such as "The user is feeling tense at the fitness club. What exercise would you suggest to help them calm down?" can be generated, and an exercise plan can be created based on this.

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] Users input their preferences (such as preferred exercises, frequency, and health goals) using a smart device. This information is sent from the user's device to the server. The input preferences are stored on the server as string data.

[0380] Step 2:

[0381] The user's device uses its camera function to capture a picture of the user's face and sends this image data to the emotion engine. The emotion engine uses facial recognition software (e.g., OpenCV) to analyze the image and sends the user's emotional state as numerical data to the server. In this step, the input is image data, and the output is numerically represented emotion data.

[0382] Step 3:

[0383] The server uses a pre-configured algorithm to recommend optimal exercise activities based on the user's preferences and acquired emotional data. This algorithm determines the appropriate exercise intensity according to the user's health goals and emotional state. The recommended exercise activities are output as text data, including a description of the activity and a list of necessary equipment.

[0384] Step 4:

[0385] The server retrieves relevant event information from a database of local fitness events. The user's place of residence is also considered during this process. The retrieved local event information is provided to the user's terminal as a text message. The input is the search criteria from the database, and the output is the event information.

[0386] Step 5:

[0387] The server runs a matching algorithm to identify other users with similar tastes and emotional states. This facilitates connections between users and suggests partners for collaborative activities. The input is the taste information and emotional states of all users, and the output is a list of potential partners.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] The system for implementing the present invention consists of a user terminal, a central server, and a database. Users input profile information via their terminal and register their exercise activity preferences. This information includes the user's interests, activity frequency, goals, and place of residence.

[0405] The terminal transmits the entered information to a central server. The server receives and analyzes the user's preferences. Based on the analyzed data, the server generates a list of suitable exercise activities for the user. These suggestions include a variety of sports and activities and are optimized according to the user's activity level and interests.

[0406] The server also takes the user's location data into consideration and collects information on relevant events and activities held in the area. This information, including the date, time, location, and participation requirements of the event, is notified to the user. This makes it easy for users to access events that interest them.

[0407] Furthermore, the server uses a matching algorithm to identify other users with similar interests. This process makes it easier for users to connect with each other and provides opportunities to participate in activities with others who share a common interest in sports. The terminal presents this recommendation and matching information to the user and guides them through communication options according to their choices.

[0408] For example, if a user enters information such as "I'm interested in tennis, but I don't have anyone to play with," the server will suggest nearby tennis tournaments and other users who are also interested in tennis. The user receives this information through their device and can participate in events with like-minded individuals.

[0409] This invention is a form that expands the user's options for physical activity and provides an appropriate sports experience.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] Users access the registration interface using their own devices and enter their profile information and preferences regarding exercise activities. This includes their favorite sports, activity frequency, health goals, and place of residence.

[0413] Step 2:

[0414] The terminal formats the registered user information and sends it to the server. The transmitted data is organized along with the user's individual ID and converted into a format suitable for analysis.

[0415] Step 3:

[0416] The server receives user information and stores it in a database. Based on the stored information, an algorithm is executed to analyze user preferences and identify patterns in interests and activity levels.

[0417] Step 4:

[0418] The server uses the analysis results to select suitable exercise activities for the user. It then compares these with activity information in the database to list activities that match the user's profile.

[0419] Step 5:

[0420] The server collects information about local exercise events based on the user's location. This information includes the event date, time, venue, and participation requirements. This information is then organized for user recommendations.

[0421] Step 6:

[0422] The server uses a matching algorithm to filter other users with similar interests. It analyzes data from users with similar activity patterns and calculates a matching score.

[0423] Step 7:

[0424] The device receives suggested exercise activities, local event information, and information about other matched users from the server. This information is then notified to and displayed to the user.

[0425] Step 8:

[0426] Users review the information displayed on their devices and select exercise activities and events that interest them. If necessary, they take action to contact matched users.

[0427] Step 9:

[0428] The terminal feeds user selections back to the server, providing data to improve future recommendation accuracy. The server uses this information to improve the overall system performance.

[0429] (Example 1)

[0430] 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."

[0431] In modern society, finding exercise activities that suit one's preferences can be complex and time-consuming, and finding companions to participate with can also be difficult. Furthermore, there are insufficient means to effectively obtain information about local sports events and to securely transmit data while protecting privacy. As a result, users face challenges in promoting their health and opportunities for social interaction.

[0432] 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.

[0433] In this invention, the server includes data acquisition means for acquiring user preference data, list generation means for creating a list of recommended exercise activities based on the preference data, and event information acquisition means for providing local event information related to the exercise activities. This enables users to efficiently find the exercise activities best suited to them and to easily obtain information about relevant local events. Furthermore, it enables secure data exchange while protecting privacy.

[0434] "Data acquisition means" refers to equipment or a system for collecting data related to user preferences.

[0435] A "list generation means" is a device or mechanism for generating a list of optimal exercise activity candidates based on collected user preference data.

[0436] "Event information acquisition means" refers to equipment or a system for collecting and providing local event information related to exercise activities.

[0437] "Method for identifying similar users" refers to a device or system that analyzes the preference data of multiple users to identify and present other users who share common interests.

[0438] "Data transmission means" refers to equipment or a system for encrypting collected data and securely transmitting it to a server or other terminal.

[0439] This invention is a system that suggests exercise activities based on user preferences, provides information on local events related to those activities, and matches users with each other. The system mainly consists of a user's terminal, a server, and a database.

[0440] Users access a dedicated application or website using their own devices and enter their profile information. This information includes their interests in exercise, place of residence, activity frequency, and goals. This data is then transmitted from the device to the server. To ensure security, encrypted communication such as HTTPS is used for data transmission.

[0441] The server utilizes analysis software such as Python and R to analyze the received data. Based on the analysis results, it generates a list of suggested exercise activities tailored to the user. This list reflects information extracted from an existing database and is customized according to the user's exercise level and interests.

[0442] The server also searches a database of local events based on the user's location and collects event information for that area. This allows users to obtain detailed information about sports events held in their vicinity (date, time, location, participation requirements, etc.).

[0443] Furthermore, the server compares the preferences of other users and matches them with other users who share similar interests. This allows users to find like-minded individuals and engage in activities together. This matching function provides opportunities to promote interaction among users.

[0444] For example, if a user enters "I'm interested in cycling, but I want to find someone to go with," the server will search for nearby cycling events and suggest other users who are also interested in cycling. The user can then check this information through their device and enjoy cycling with new companions.

[0445] Example prompts for generative AI models:

[0446] "Please enter the type of exercise you are interested in. Examples: cycling, running, Pilates, etc."

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] Users access a dedicated application or website using their device and enter their profile information. This includes their interests in exercise, location, activity frequency, and goals. The entered data is temporarily stored in the device's local storage. Next, to securely transmit this data to the server, the data is encrypted using an encryption protocol such as HTTPS and prepared for transmission.

[0450] Step 2:

[0451] The terminal sends encrypted user data to a central server. The server decrypts this received data and stores it in a database for analysis. The data received as input includes the user's interests, location, and activity schedule. The server loads necessary reference data from the database in preparation for processing the data.

[0452] Step 3:

[0453] The server uses data analysis software such as Python and R to analyze user preference data. Specifically, it applies machine learning algorithms to analyze user interest trends and activity frequency. The input for the analysis is user preference data, and the output is a list of exercise activity candidates optimized for the user. This list is personalized based on the type and level of exercise.

[0454] Step 4:

[0455] The server searches a database of sports events and activities in the user's region based on their location information. The user's location data and the event database are used as input for this process. The output extracts event information for the region (date, time, location, and participation requirements). The server then formats this event information for the user to provide.

[0456] Step 5:

[0457] The server collects preference data from other users and runs a clustering algorithm to identify users with similar interests. The input is preference data from multiple users, and the output is a list of matching candidates. This matches users who share common interests.

[0458] Step 6:

[0459] The server sends a list of generated exercise activity candidates, event information, and matching candidate information to the terminal. The terminal presents this information to the user. The user can browse the information through an intuitive UI and plan to participate in exercises and events that interest them. During communication, data is encrypted again as needed and securely transmitted to the user's terminal.

[0460] (Application Example 1)

[0461] 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."

[0462] In modern society, it is difficult for individual users to easily find activities that match their exercise preferences, and opportunities to connect with others who share similar interests are limited. As a result, even when visiting physical stores, there is a problem in that they cannot immediately access the products or activities that are best suited to them.

[0463] 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.

[0464] In this invention, the server includes a processing unit for acquiring user preference information, a computing unit for generating recommended physical activity options based on the preference information, and a visual device for providing information on activity equipment and group activities based on the user's interests in real-world stores. This allows users to visually check product and event information that matches their exercise preferences in real time, and also facilitates communication with other users who share similar interests.

[0465] A "processing device" is a device used to receive, collect, and manage user preference information.

[0466] A "computational device" is a device that generates a selection of physical activity options based on the user's preference information.

[0467] A "device that provides event information in a geographical area" is a device that provides information about events held in a specific region.

[0468] A "matching device" is a device that identifies other users with similar preferences and presents that information to the user.

[0469] A "visual device" is a device that provides information about activity equipment and group activities in a real-world store, based on the user's interests, in a visual way.

[0470] In the system for implementing this invention, the user inputs exercise preference information using a smart device and transmits this information to a server. The server analyzes the received data using Python and the Django framework and generates physical activity and geographical event information suitable for the user. As smart devices, smart glasses and smartphones are often used, as the system is intended for use in stores.

[0471] The server sends analysis results back to the smart device via a RESTful API. It can also integrate with external databases to provide geographical event information and recommended exercise equipment.

[0472] Users can view information displayed on their smart devices in real time as they move around the store. For example, if a user enters "I'm interested in running," their smart glasses will display information about running equipment in the store and nearby running events. They will also be guided on how to connect with other users who share similar interests.

[0473] To facilitate communication between users in the real world, visual devices play a role in presenting users with recommended event information and visualizing related products. In this way, it is a system that enriches the real-world store experience and encourages participation in physical activities.

[0474] An example of a prompt message might be: "Please display a list of sports events the user is interested in, sorted by the events taking place today. The user is interested in running."

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] Users input preference information such as their interest in exercise, current activity level, and goals using a smart device. This input information is collected through the smart device application and transmitted to a server. The entered data constitutes the user's unique exercise profile.

[0478] Step 2:

[0479] The server analyzes the received preference information. Using Python and the Django framework, the server interacts with the database to generate the most suitable physical activity options for the user. Based on the input data, data calculations are performed to recommend exercise activities and related events that match the user's interests.

[0480] Step 3:

[0481] The server collects and provides information on events held locally based on the analysis results. It retrieves information such as the date, time, and location of sports events corresponding to geographical areas from an external database and generates a list of events that match the user's interests. This information is sent to the client device via a RESTful API.

[0482] Step 4:

[0483] Based on the information received, the device displays optimized exercise activity options and event information for the user. Users can access this information in real time via smart glasses or a smartphone's user interface (UI). Furthermore, it guides users to the nearest events and recommended product sections based on their location.

[0484] Step 5:

[0485] The server uses a matching device to identify other users with similar interests. Leveraging machine learning algorithms, it detects users with common interests from the submitted dataset, creating opportunities for mutual interaction. This result is notified to the user, facilitating communication.

[0486] Step 6:

[0487] Users utilize the information provided by their devices to participate in exercise activities and events that interest them. They also interact with other users who share similar interests. This entire process enriches users' daily activities and encourages their participation in exercise.

[0488] 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.

[0489] This invention provides a system for suggesting exercise activities that take into account the user's preferences and emotional state. This system consists of the user's terminal, a central server, and a database including an emotion engine.

[0490] Users input profile information and exercise preferences via their devices. This includes sports of interest, activity frequency, health goals, and place of residence. The device then sends this information, along with the emotion engine, to the server.

[0491] The server recommends exercise activities based on user information received from the terminal and emotional data analyzed by the emotion engine. The emotion engine estimates emotions from facial recognition technology and user input, and determines the user's current emotional state in real time. For example, if the user is feeling stressed, it recommends activities with relaxing effects such as yoga or walking.

[0492] The server selects appropriate exercise activity candidates based on the user's preferences and emotional state. This process adjusts the intensity and type of exercise to list activities that the user can participate in most effectively. Furthermore, it collects information on relevant local events and notifies the user at the appropriate time based on their emotional state.

[0493] The matching algorithm also utilizes data from the emotion engine to identify other users with similar emotional states and preferences. It then presents users with communication partners and activity partners that match their mood. This feature enables appropriate communication and collaborative activities based on emotions.

[0494] For example, if a user feels "interested in running, but lacks motivation today," the emotion engine reads this emotional information and transmits it to the server. The server then provides event information, including suggestions for running groups and encouraging messages to boost the user's motivation. This information is delivered to the user through their device, allowing them to choose activities that match their emotional state.

[0495] This system allows users to enjoy flexible exercise plans based on their emotions and lifestyle, and receive support to achieve a healthy lifestyle tailored to their individual needs.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] Users use their devices to enter profile information and exercise preferences. For example, users might provide information such as "I want to play tennis three times a week" or "I do it for stress relief."

[0499] Step 2:

[0500] The device transmits user input information to the emotion engine. Furthermore, the device collects real-time data from the user's facial expressions and input data so that the emotion engine can analyze their emotional state.

[0501] Step 3:

[0502] The emotion engine analyzes the user's facial expressions and past input data to determine their current emotional state. For example, if it detects that the user is feeling stressed, it returns that information to the server.

[0503] Step 4:

[0504] Based on the user's preferences and emotional state, the server generates activity suggestions. The server adjusts the intensity and type of exercise to provide suggestions that are appropriate for the user's current emotional state.

[0505] Step 5:

[0506] The server retrieves local event information from the database and filters it to include only events that match the user's interests and emotional state. This selects the event information that is of the highest interest to the user.

[0507] Step 6:

[0508] The server uses a matching algorithm to identify other users with similar emotional states and motor preferences. It then uses emotional feedback to list potential optimal communication partners.

[0509] Step 7:

[0510] The device receives a list of activities provided by the server, filtered events, and information about other matched users.

[0511] Step 8:

[0512] The device displays received information to the user. This includes detailed information about the exercise, a list of available events, and contact information for matching users. The user then decides on their next action based on this information.

[0513] Step 9:

[0514] The device records user-selected activities and events and sends them to the server as feedback. This allows the system to better adjust recommendations for the user in the future.

[0515] (Example 2)

[0516] 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."

[0517] Conventional physical activity suggestion systems failed to adequately consider the user's feelings when suggesting exercise activities, nor did they achieve appropriate matching with other users who shared similar feelings. As a result, it was difficult for users to create activity plans that suited their own feelings and circumstances, and support for achieving a healthy lifestyle was insufficient.

[0518] 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.

[0519] In this invention, the server includes data acquisition means for collecting user attribute information and emotional state, recommendation means for generating candidate physical activities recommended based on the attribute information and emotional state, and information provision means for providing local event information related to the candidate physical activities. This makes it possible to plan exercise activities optimized for the user's emotional state and circumstances.

[0520] "User attribute information" refers to characteristic data provided by users, such as their personal interests, activity frequency, health goals, and place of residence.

[0521] "Emotional state" refers to information that indicates the user's current emotions and mental condition, and is estimated from facial recognition technology and user input.

[0522] "Data acquisition means" refers to technical components for collecting attribute information and emotional state from users.

[0523] "Recommendation method" refers to a technical component that generates suitable physical activity options for the user based on acquired attribute information and emotional state.

[0524] "Information provision means" refers to a technical component that provides users with information about local events related to potential physical activities.

[0525] "Connection means" refers to a technical component for identifying other users with similar interests and sentiments and presenting that information to the user.

[0526] This invention proposes a system that optimizes physical activity based on the user's emotional state and attribute information. The system mainly consists of a user terminal, a central server, and a database containing an emotion analysis engine.

[0527] Device usage:

[0528] Users input attribute information using their own devices. This attribute information includes their interests in physical activities, activity frequency, health goals, and place of residence. Based on this information, the device also collects emotional states using its camera and sensors. Images captured by the user's camera are sent to the server as emotional states by an emotion analysis engine using facial recognition technology.

[0529] Server usage:

[0530] After receiving attribute information and analyzed emotional information transmitted from the terminal, the server uses a generative AI model to suggest exercise activities based on this information. The generative AI model analyzes multiple data points and extracts physical activities suitable for the user. For example, if the user is feeling stressed, it recommends relaxation activities such as yoga or walking.

[0531] The server also automatically collects information on relevant events taking place in the region and suggests them to the user at a suitable time. This suggestion is made by sending a prompt message generated by a generative AI model as a text notification to the user's device.

[0532] Examples of specific cases and prompt statements:

[0533] For example, if a user expresses interest in "finding physical activities they can participate in on the weekend," the system can take into account the user's current mood and suggest yoga classes or walking events.

[0534] An example of a prompt message is, "Based on the user's interest in sports activities and emotional data, what kind of exercise would you recommend?" Based on such prompt messages, the system generates appropriate activity information and informs the user.

[0535] In this way, the present invention can propose optimal physical activities based on the user's feelings and personal interests, and support a healthy lifestyle.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] Users fill out profile information in an input form using their device. This includes their areas of interest in physical activities, activity frequency, health goals, and place of residence. The data entered by the user is sent to the server via the device. This data is processed as user attribute information and stored on the server for further analysis.

[0539] Step 2:

[0540] The device uses a camera to understand the user's emotional state and analyzes image data obtained through facial recognition technology. This image data is processed by an emotion analysis engine, which estimates the user's emotional state in real time. The analysis results are sent to the server as emotional data.

[0541] Step 3:

[0542] The server inputs a prompt message into the generating AI model based on the received user attribute information and emotional data. The prompt message is "Please suggest the most suitable exercise activity based on the user's attribute information and emotional data." The generating AI model analyzes the data according to this prompt message and outputs a recommendation for physical activity suitable for the user. This recommendation includes the type of exercise, intensity, and time of day.

[0543] Step 4:

[0544] The server uses recommended physical activities and user attribute information to search a database of local events and retrieve relevant event information. The retrieved event information is then filtered to best suit the user's schedule and mood.

[0545] Step 5:

[0546] The server compiles recommendations and event information and sends it to the device. The device displays this information on its screen and notifies the user. The user can then check the notification on their device and select the activities and events they wish to participate in to plan their physical activity.

[0547] Step 6:

[0548] Users register for suggested physical activities and events through their devices. This registration information is sent to the server and saved as an updated schedule. This allows users to select activities that suit their mood and lifestyle, and create a feasible exercise plan.

[0549] (Application Example 2)

[0550] 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."

[0551] In today's world, it is crucial to provide fitness experiences that take into account the emotional state and exercise preferences of individual users. In particular, there is a need to offer real-time suggestions for exercise activities tailored to each person's unique emotional needs and provide an optimal fitness plan. The lack of such a system can lead to decreased motivation and inefficient fitness activities among users.

[0552] 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.

[0553] In this invention, the server includes information acquisition means for collecting user preference information and emotional state, recommendation means for generating candidate exercise activities based on the preference information and emotional state, and information provision means for providing local event information related to the candidate exercise activities. This makes it possible to suggest optimal exercise activities according to the user's emotional state and to suggest collaborative exercise plans among simultaneous users.

[0554] "User preference information" refers to information that indicates an individual user's preferences and interests regarding exercise.

[0555] "Emotional state" refers to information that indicates the user's current psychological or emotional state.

[0556] "Information acquisition methods" refer to means of collecting information about user preferences and emotional states.

[0557] "Recommendation methods" refer to methods for suggesting exercise activities suitable for the user based on acquired preference information and emotional state.

[0558] "Information provision means" refers to means of providing users with information about local events related to potential exercise activities.

[0559] A "matching method" is a means of identifying other users with similar interests or emotional states and presenting that information to the user.

[0560] "Means to promote cooperative activities" refers to providing means for users to enjoy exercise activities together.

[0561] The system that implements this application consists of a user's smart device, a server, and an emotion engine. The user's smart device collects the user's preference information and emotional state, and transmits this information to a central server. The emotion engine analyzes the user's emotional state using facial recognition technology such as OpenCV. The server generates candidate exercise activities suitable for the user based on the collected preference information and emotional state. In this process, the server uses recommendation tools to select the most effective exercise activity according to the user's emotions. It also collects and provides relevant event information through information provision tools for local events. Furthermore, the server uses matching tools to identify other users with similar preferences and emotional states and provides that information. As a specific example, if a user visiting a fitness club is feeling "anxious," a yoga class to help them relax might be suggested. This function utilizes prompt statements generated by a generative AI model. For example, a prompt statement such as "The user is feeling tense at the fitness club. What exercise would you suggest to help them calm down?" can be generated, and an exercise plan can be created based on this.

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] Users input their preferences (such as preferred exercises, frequency, and health goals) using a smart device. This information is sent from the user's device to the server. The input preferences are stored on the server as string data.

[0565] Step 2:

[0566] The user's device uses its camera function to capture a picture of the user's face and sends this image data to the emotion engine. The emotion engine uses facial recognition software (e.g., OpenCV) to analyze the image and sends the user's emotional state as numerical data to the server. In this step, the input is image data, and the output is numerically represented emotion data.

[0567] Step 3:

[0568] The server uses a pre-configured algorithm to recommend optimal exercise activities based on the user's preferences and acquired emotional data. This algorithm determines the appropriate exercise intensity according to the user's health goals and emotional state. The recommended exercise activities are output as text data, including a description of the activity and a list of necessary equipment.

[0569] Step 4:

[0570] The server retrieves relevant event information from a database of local fitness events. The user's place of residence is also considered during this process. The retrieved local event information is provided to the user's terminal as a text message. The input is the search criteria from the database, and the output is the event information.

[0571] Step 5:

[0572] The server runs a matching algorithm to identify other users with similar tastes and emotional states. This facilitates connections between users and suggests partners for collaborative activities. The input is the taste information and emotional states of all users, and the output is a list of potential partners.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] [Fourth Embodiment]

[0577] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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".

[0590] The system for implementing the present invention consists of a user terminal, a central server, and a database. Users input profile information via their terminal and register their exercise activity preferences. This information includes the user's interests, activity frequency, goals, and place of residence.

[0591] The terminal transmits the entered information to a central server. The server receives and analyzes the user's preferences. Based on the analyzed data, the server generates a list of suitable exercise activities for the user. These suggestions include a variety of sports and activities and are optimized according to the user's activity level and interests.

[0592] The server also takes the user's location data into consideration and collects information on relevant events and activities held in the area. This information, including the date, time, location, and participation requirements of the event, is notified to the user. This makes it easy for users to access events that interest them.

[0593] Furthermore, the server uses a matching algorithm to identify other users with similar interests. This process makes it easier for users to connect with each other and provides opportunities to participate in activities with others who share a common interest in sports. The terminal presents this recommendation and matching information to the user and guides them through communication options according to their choices.

[0594] For example, if a user enters information such as "I'm interested in tennis, but I don't have anyone to play with," the server will suggest nearby tennis tournaments and other users who are also interested in tennis. The user receives this information through their device and can participate in events with like-minded individuals.

[0595] This invention is a form that expands the user's options for physical activity and provides an appropriate sports experience.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users access the registration interface using their own devices and enter their profile information and preferences regarding exercise activities. This includes their favorite sports, activity frequency, health goals, and place of residence.

[0599] Step 2:

[0600] The terminal formats the registered user information and sends it to the server. The transmitted data is organized along with the user's individual ID and converted into a format suitable for analysis.

[0601] Step 3:

[0602] The server receives user information and stores it in a database. Based on the stored information, an algorithm is executed to analyze user preferences and identify patterns in interests and activity levels.

[0603] Step 4:

[0604] The server uses the analysis results to select suitable exercise activities for the user. It then compares these with activity information in the database to list activities that match the user's profile.

[0605] Step 5:

[0606] The server collects information about local exercise events based on the user's location. This information includes the event date, time, venue, and participation requirements. This information is then organized for user recommendations.

[0607] Step 6:

[0608] The server uses a matching algorithm to filter other users with similar interests. It analyzes data from users with similar activity patterns and calculates a matching score.

[0609] Step 7:

[0610] The device receives suggested exercise activities, local event information, and information about other matched users from the server. This information is then notified to and displayed to the user.

[0611] Step 8:

[0612] Users review the information displayed on their devices and select exercise activities and events that interest them. If necessary, they take action to contact matched users.

[0613] Step 9:

[0614] The terminal feeds user selections back to the server, providing data to improve future recommendation accuracy. The server uses this information to improve the overall system performance.

[0615] (Example 1)

[0616] 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".

[0617] In modern society, finding exercise activities that suit one's preferences can be complex and time-consuming, and finding companions to participate with can also be difficult. Furthermore, there are insufficient means to effectively obtain information about local sports events and to securely transmit data while protecting privacy. As a result, users face challenges in promoting their health and opportunities for social interaction.

[0618] 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.

[0619] In this invention, the server includes data acquisition means for acquiring user preference data, list generation means for creating a list of recommended exercise activities based on the preference data, and event information acquisition means for providing local event information related to the exercise activities. This enables users to efficiently find the exercise activities best suited to them and to easily obtain information about relevant local events. Furthermore, it enables secure data exchange while protecting privacy.

[0620] "Data acquisition means" refers to equipment or a system for collecting data related to user preferences.

[0621] A "list generation means" is a device or mechanism for generating a list of optimal exercise activity candidates based on collected user preference data.

[0622] "Event information acquisition means" refers to equipment or a system for collecting and providing local event information related to exercise activities.

[0623] "Method for identifying similar users" refers to a device or system that analyzes the preference data of multiple users to identify and present other users who share common interests.

[0624] "Data transmission means" refers to equipment or a system for encrypting collected data and securely transmitting it to a server or other terminal.

[0625] This invention is a system that suggests exercise activities based on user preferences, provides information on local events related to those activities, and matches users with each other. The system mainly consists of a user's terminal, a server, and a database.

[0626] Users access a dedicated application or website using their own devices and enter their profile information. This information includes their interests in exercise, place of residence, activity frequency, and goals. This data is then transmitted from the device to the server. To ensure security, encrypted communication such as HTTPS is used for data transmission.

[0627] The server utilizes analysis software such as Python and R to analyze the received data. Based on the analysis results, it generates a list of suggested exercise activities tailored to the user. This list reflects information extracted from an existing database and is customized according to the user's exercise level and interests.

[0628] The server also searches a database of local events based on the user's location and collects event information for that area. This allows users to obtain detailed information about sports events held in their vicinity (date, time, location, participation requirements, etc.).

[0629] Furthermore, the server compares the preferences of other users and matches them with other users who share similar interests. This allows users to find like-minded individuals and engage in activities together. This matching function provides opportunities to promote interaction among users.

[0630] For example, if a user enters "I'm interested in cycling, but I want to find someone to go with," the server will search for nearby cycling events and suggest other users who are also interested in cycling. The user can then check this information through their device and enjoy cycling with new companions.

[0631] Example prompts for generative AI models:

[0632] "Please enter the type of exercise you are interested in. Examples: cycling, running, Pilates, etc."

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] Users access a dedicated application or website using their device and enter their profile information. This includes their interests in exercise, location, activity frequency, and goals. The entered data is temporarily stored in the device's local storage. Next, to securely transmit this data to the server, the data is encrypted using an encryption protocol such as HTTPS and prepared for transmission.

[0636] Step 2:

[0637] The terminal sends encrypted user data to a central server. The server decrypts this received data and stores it in a database for analysis. The data received as input includes the user's interests, location, and activity schedule. The server loads necessary reference data from the database in preparation for processing the data.

[0638] Step 3:

[0639] The server uses data analysis software such as Python and R to analyze user preference data. Specifically, it applies machine learning algorithms to analyze user interest trends and activity frequency. The input for the analysis is user preference data, and the output is a list of exercise activity candidates optimized for the user. This list is personalized based on the type and level of exercise.

[0640] Step 4:

[0641] The server searches a database of sports events and activities in the user's region based on their location information. The user's location data and the event database are used as input for this process. The output extracts event information for the region (date, time, location, and participation requirements). The server then formats this event information for the user to provide.

[0642] Step 5:

[0643] The server collects preference data from other users and runs a clustering algorithm to identify users with similar interests. The input is preference data from multiple users, and the output is a list of matching candidates. This matches users who share common interests.

[0644] Step 6:

[0645] The server sends a list of generated exercise activity candidates, event information, and matching candidate information to the terminal. The terminal presents this information to the user. The user can browse the information through an intuitive UI and plan to participate in exercises and events that interest them. During communication, data is encrypted again as needed and securely transmitted to the user's terminal.

[0646] (Application Example 1)

[0647] 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".

[0648] In modern society, it is difficult for individual users to easily find activities that match their exercise preferences, and opportunities to connect with others who share similar interests are limited. As a result, even when visiting physical stores, there is a problem in that they cannot immediately access the products or activities that are best suited to them.

[0649] 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.

[0650] In this invention, the server includes a processing unit for acquiring user preference information, a computing unit for generating recommended physical activity options based on the preference information, and a visual device for providing information on activity equipment and group activities based on the user's interests in real-world stores. This allows users to visually check product and event information that matches their exercise preferences in real time, and also facilitates communication with other users who share similar interests.

[0651] A "processing device" is a device used to receive, collect, and manage user preference information.

[0652] A "computational device" is a device that generates a selection of physical activity options based on the user's preference information.

[0653] A "device that provides event information in a geographical area" is a device that provides information about events held in a specific region.

[0654] A "matching device" is a device that identifies other users with similar preferences and presents that information to the user.

[0655] A "visual device" is a device that provides information about activity equipment and group activities in a real-world store, based on the user's interests, in a visual way.

[0656] In the system for implementing this invention, the user inputs exercise preference information using a smart device and transmits this information to a server. The server analyzes the received data using Python and the Django framework and generates physical activity and geographical event information suitable for the user. As smart devices, smart glasses and smartphones are often used, as the system is intended for use in stores.

[0657] The server sends analysis results back to the smart device via a RESTful API. It can also integrate with external databases to provide geographical event information and recommended exercise equipment.

[0658] Users can view information displayed on their smart devices in real time as they move around the store. For example, if a user enters "I'm interested in running," their smart glasses will display information about running equipment in the store and nearby running events. They will also be guided on how to connect with other users who share similar interests.

[0659] To facilitate communication between users in the real world, visual devices play a role in presenting users with recommended event information and visualizing related products. In this way, it is a system that enriches the real-world store experience and encourages participation in physical activities.

[0660] An example of a prompt message might be: "Please display a list of sports events the user is interested in, sorted by the events taking place today. The user is interested in running."

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] Users input preference information such as their interest in exercise, current activity level, and goals using a smart device. This input information is collected through the smart device application and transmitted to a server. The entered data constitutes the user's unique exercise profile.

[0664] Step 2:

[0665] The server analyzes the received preference information. Using Python and the Django framework, the server interacts with the database to generate the most suitable physical activity options for the user. Based on the input data, data calculations are performed to recommend exercise activities and related events that match the user's interests.

[0666] Step 3:

[0667] The server collects and provides information on events held locally based on the analysis results. It retrieves information such as the date, time, and location of sports events corresponding to geographical areas from an external database and generates a list of events that match the user's interests. This information is sent to the client device via a RESTful API.

[0668] Step 4:

[0669] Based on the information received, the device displays optimized exercise activity options and event information for the user. Users can access this information in real time via smart glasses or a smartphone's user interface (UI). Furthermore, it guides users to the nearest events and recommended product sections based on their location.

[0670] Step 5:

[0671] The server uses a matching device to identify other users with similar interests. Leveraging machine learning algorithms, it detects users with common interests from the submitted dataset, creating opportunities for mutual interaction. This result is notified to the user, facilitating communication.

[0672] Step 6:

[0673] Users utilize the information provided by their devices to participate in exercise activities and events that interest them. They also interact with other users who share similar interests. This entire process enriches users' daily activities and encourages their participation in exercise.

[0674] 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.

[0675] This invention provides a system for suggesting exercise activities that take into account the user's preferences and emotional state. This system consists of the user's terminal, a central server, and a database including an emotion engine.

[0676] Users input profile information and exercise preferences via their devices. This includes sports of interest, activity frequency, health goals, and place of residence. The device then sends this information, along with the emotion engine, to the server.

[0677] The server recommends exercise activities based on user information received from the terminal and emotional data analyzed by the emotion engine. The emotion engine estimates emotions from facial recognition technology and user input, and determines the user's current emotional state in real time. For example, if the user is feeling stressed, it recommends activities with relaxing effects such as yoga or walking.

[0678] The server selects appropriate exercise activity candidates based on the user's preferences and emotional state. This process adjusts the intensity and type of exercise to list activities that the user can participate in most effectively. Furthermore, it collects information on relevant local events and notifies the user at the appropriate time based on their emotional state.

[0679] The matching algorithm also utilizes data from the emotion engine to identify other users with similar emotional states and preferences. It then presents users with communication partners and activity partners that match their mood. This feature enables appropriate communication and collaborative activities based on emotions.

[0680] For example, if a user feels "interested in running, but lacks motivation today," the emotion engine reads this emotional information and transmits it to the server. The server then provides event information, including suggestions for running groups and encouraging messages to boost the user's motivation. This information is delivered to the user through their device, allowing them to choose activities that match their emotional state.

[0681] This system allows users to enjoy flexible exercise plans based on their emotions and lifestyle, and receive support to achieve a healthy lifestyle tailored to their individual needs.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] Users use their devices to enter profile information and exercise preferences. For example, users might provide information such as "I want to play tennis three times a week" or "I do it for stress relief."

[0685] Step 2:

[0686] The device transmits user input information to the emotion engine. Furthermore, the device collects real-time data from the user's facial expressions and input data so that the emotion engine can analyze their emotional state.

[0687] Step 3:

[0688] The emotion engine analyzes the user's facial expressions and past input data to determine their current emotional state. For example, if it detects that the user is feeling stressed, it returns that information to the server.

[0689] Step 4:

[0690] Based on the user's preferences and emotional state, the server generates activity suggestions. The server adjusts the intensity and type of exercise to provide suggestions that are appropriate for the user's current emotional state.

[0691] Step 5:

[0692] The server retrieves local event information from the database and filters it to include only events that match the user's interests and emotional state. This selects the event information that is of the highest interest to the user.

[0693] Step 6:

[0694] The server uses a matching algorithm to identify other users with similar emotional states and motor preferences. It then uses emotional feedback to list potential optimal communication partners.

[0695] Step 7:

[0696] The device receives a list of activities provided by the server, filtered events, and information about other matched users.

[0697] Step 8:

[0698] The device displays received information to the user. This includes detailed information about the exercise, a list of available events, and contact information for matching users. The user then decides on their next action based on this information.

[0699] Step 9:

[0700] The device records user-selected activities and events and sends them to the server as feedback. This allows the system to better adjust recommendations for the user in the future.

[0701] (Example 2)

[0702] 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".

[0703] Conventional physical activity suggestion systems failed to adequately consider the user's feelings when suggesting exercise activities, nor did they achieve appropriate matching with other users who shared similar feelings. As a result, it was difficult for users to create activity plans that suited their own feelings and circumstances, and support for achieving a healthy lifestyle was insufficient.

[0704] 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.

[0705] In this invention, the server includes data acquisition means for collecting user attribute information and emotional state, recommendation means for generating candidate physical activities recommended based on the attribute information and emotional state, and information provision means for providing local event information related to the candidate physical activities. This makes it possible to plan exercise activities optimized for the user's emotional state and circumstances.

[0706] "User attribute information" refers to characteristic data provided by users, such as their personal interests, activity frequency, health goals, and place of residence.

[0707] "Emotional state" refers to information that indicates the user's current emotions and mental condition, and is estimated from facial recognition technology and user input.

[0708] "Data acquisition means" refers to technical components for collecting attribute information and emotional state from users.

[0709] "Recommendation method" refers to a technical component that generates suitable physical activity options for the user based on acquired attribute information and emotional state.

[0710] "Information provision means" refers to a technical component that provides users with information about local events related to potential physical activities.

[0711] "Connection means" refers to a technical component for identifying other users with similar interests and sentiments and presenting that information to the user.

[0712] This invention proposes a system that optimizes physical activity based on the user's emotional state and attribute information. The system mainly consists of a user terminal, a central server, and a database containing an emotion analysis engine.

[0713] Device usage:

[0714] Users input attribute information using their own devices. This attribute information includes their interests in physical activities, activity frequency, health goals, and place of residence. Based on this information, the device also collects emotional states using its camera and sensors. Images captured by the user's camera are sent to the server as emotional states by an emotion analysis engine using facial recognition technology.

[0715] Server usage:

[0716] After receiving attribute information and analyzed emotional information transmitted from the terminal, the server uses a generative AI model to suggest exercise activities based on this information. The generative AI model analyzes multiple data points and extracts physical activities suitable for the user. For example, if the user is feeling stressed, it recommends relaxation activities such as yoga or walking.

[0717] The server also automatically collects information on relevant events taking place in the region and suggests them to the user at a suitable time. This suggestion is made by sending a prompt message generated by a generative AI model as a text notification to the user's device.

[0718] Examples of specific cases and prompt statements:

[0719] For example, if a user expresses interest in "finding physical activities they can participate in on the weekend," the system can take into account the user's current mood and suggest yoga classes or walking events.

[0720] An example of a prompt message is, "Based on the user's interest in sports activities and emotional data, what kind of exercise would you recommend?" Based on such prompt messages, the system generates appropriate activity information and informs the user.

[0721] In this way, the present invention can propose optimal physical activities based on the user's feelings and personal interests, and support a healthy lifestyle.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] Users fill out profile information in an input form using their device. This includes their areas of interest in physical activities, activity frequency, health goals, and place of residence. The data entered by the user is sent to the server via the device. This data is processed as user attribute information and stored on the server for further analysis.

[0725] Step 2:

[0726] The device uses a camera to understand the user's emotional state and analyzes image data obtained through facial recognition technology. This image data is processed by an emotion analysis engine, which estimates the user's emotional state in real time. The analysis results are sent to the server as emotional data.

[0727] Step 3:

[0728] The server inputs a prompt message into the generating AI model based on the received user attribute information and emotional data. The prompt message is "Please suggest the most suitable exercise activity based on the user's attribute information and emotional data." The generating AI model analyzes the data according to this prompt message and outputs a recommendation for physical activity suitable for the user. This recommendation includes the type of exercise, intensity, and time of day.

[0729] Step 4:

[0730] The server uses recommended physical activities and user attribute information to search a database of local events and retrieve relevant event information. The retrieved event information is then filtered to best suit the user's schedule and mood.

[0731] Step 5:

[0732] The server compiles recommendations and event information and sends it to the device. The device displays this information on its screen and notifies the user. The user can then check the notification on their device and select the activities and events they wish to participate in to plan their physical activity.

[0733] Step 6:

[0734] Users register for suggested physical activities and events through their devices. This registration information is sent to the server and saved as an updated schedule. This allows users to select activities that suit their mood and lifestyle, and create a feasible exercise plan.

[0735] (Application Example 2)

[0736] 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".

[0737] In today's world, it is crucial to provide fitness experiences that take into account the emotional state and exercise preferences of individual users. In particular, there is a need to offer real-time suggestions for exercise activities tailored to each person's unique emotional needs and provide an optimal fitness plan. The lack of such a system can lead to decreased motivation and inefficient fitness activities among users.

[0738] 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.

[0739] In this invention, the server includes information acquisition means for collecting user preference information and emotional state, recommendation means for generating candidate exercise activities based on the preference information and emotional state, and information provision means for providing local event information related to the candidate exercise activities. This makes it possible to suggest optimal exercise activities according to the user's emotional state and to suggest collaborative exercise plans among simultaneous users.

[0740] "User preference information" refers to information that indicates an individual user's preferences and interests regarding exercise.

[0741] "Emotional state" refers to information that indicates the user's current psychological or emotional state.

[0742] "Information acquisition methods" refer to means of collecting information about user preferences and emotional states.

[0743] "Recommendation methods" refer to methods for suggesting exercise activities suitable for the user based on acquired preference information and emotional state.

[0744] "Information provision means" refers to means of providing users with information about local events related to potential exercise activities.

[0745] A "matching method" is a means of identifying other users with similar interests or emotional states and presenting that information to the user.

[0746] "Means to promote cooperative activities" refers to providing means for users to enjoy exercise activities together.

[0747] The system that implements this application consists of a user's smart device, a server, and an emotion engine. The user's smart device collects the user's preference information and emotional state, and transmits this information to a central server. The emotion engine analyzes the user's emotional state using facial recognition technology such as OpenCV. The server generates candidate exercise activities suitable for the user based on the collected preference information and emotional state. In this process, the server uses recommendation tools to select the most effective exercise activity according to the user's emotions. It also collects and provides relevant event information through information provision tools for local events. Furthermore, the server uses matching tools to identify other users with similar preferences and emotional states and provides that information. As a specific example, if a user visiting a fitness club is feeling "anxious," a yoga class to help them relax might be suggested. This function utilizes prompt statements generated by a generative AI model. For example, a prompt statement such as "The user is feeling tense at the fitness club. What exercise would you suggest to help them calm down?" can be generated, and an exercise plan can be created based on this.

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] Users input their preferences (such as preferred exercises, frequency, and health goals) using a smart device. This information is sent from the user's device to the server. The input preferences are stored on the server as string data.

[0751] Step 2:

[0752] The user's device uses its camera function to capture a picture of the user's face and sends this image data to the emotion engine. The emotion engine uses facial recognition software (e.g., OpenCV) to analyze the image and sends the user's emotional state as numerical data to the server. In this step, the input is image data, and the output is numerically represented emotion data.

[0753] Step 3:

[0754] The server uses a pre-configured algorithm to recommend optimal exercise activities based on the user's preferences and acquired emotional data. This algorithm determines the appropriate exercise intensity according to the user's health goals and emotional state. The recommended exercise activities are output as text data, including a description of the activity and a list of necessary equipment.

[0755] Step 4:

[0756] The server retrieves relevant event information from a database of local fitness events. The user's place of residence is also considered during this process. The retrieved local event information is provided to the user's terminal as a text message. The input is the search criteria from the database, and the output is the event information.

[0757] Step 5:

[0758] The server runs a matching algorithm to identify other users with similar tastes and emotional states. This facilitates connections between users and suggests partners for collaborative activities. The input is the taste information and emotional states of all users, and the output is a list of potential partners.

[0759] 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.

[0760] 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.

[0761] 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 robot 414.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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."

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] A means of acquiring information to collect user preference information,

[0783] A recommendation means for generating candidate exercise activities based on the aforementioned preference information,

[0784] An information provision means that provides local event information related to the aforementioned candidate exercise activity,

[0785] A matching means that identifies other users with similar interests based on the aforementioned preference information and presents that information to the user,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, characterized in that the recommendation means generates an exercise activity schedule optimized for the user based on the results of the analysis of the preference information.

[0789] (Claim 3)

[0790] The system according to claim 1, characterized in that the matching means includes means for facilitating communication between users.

[0791] "Example 1"

[0792] (Claim 1)

[0793] A data acquisition method for obtaining user preference data,

[0794] A list generation means for creating a list of recommended exercise activities based on the aforementioned preference data,

[0795] An event information acquisition means that provides local event information related to the aforementioned sports activity,

[0796] A similar user identification means that analyzes the aforementioned preference data to identify other users with similar preferences and presents that information to the user,

[0797] Data transmission means that ensures the aforementioned data is encrypted and transmitted securely,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, characterized in that the list generation means generates a schedule of exercise activities optimized for the user based on the results of the analysis of the preference data.

[0801] (Claim 3)

[0802] The system according to claim 1, characterized in that the similar user identification means includes means for promoting interaction between users.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] A processing device for acquiring user preference information,

[0806] A computing device that generates a selection of recommended physical activities based on the aforementioned preference information,

[0807] A device that provides event information in geographical areas related to the aforementioned physical activity options,

[0808] A matching device that identifies other users with similar preferences based on the aforementioned preference information and presents that information,

[0809] A visual device that provides information on activity equipment and group activities based on the user's interests in a real-world store,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, characterized in that the recommendation device generates a plan of physical activity adapted to the user based on the results of the analysis of the preference information.

[0813] (Claim 3)

[0814] The system according to claim 1, characterized in that the matching device includes means for facilitating dialogue between users.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] A data acquisition means for collecting user attribute information and emotional state,

[0818] A recommendation means for generating candidate physical activities based on the aforementioned attribute information and emotional state,

[0819] An information provision means that provides information on local events related to the aforementioned candidate physical activity,

[0820] A connection means that identifies other users with similar interests and feelings based on the aforementioned attribute information and emotional state, and presents that information to the user.

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, characterized in that the recommendation means generates a schedule of physical activities optimized for the user based on the attribute information and the results of the analysis of the emotional state.

[0824] (Claim 3)

[0825] The system according to claim 1, characterized in that the connection means includes means for facilitating communication between users.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] Information acquisition means for collecting user preference information and emotional state,

[0829] A recommendation means for generating candidate exercise activities based on the aforementioned preference information and emotional state,

[0830] An information provision means that provides information on local events related to the aforementioned candidate exercise activities,

[0831] A matching means that identifies other users with similar interests and emotional states based on the aforementioned preference information and emotional state, and presents that information to the user.

[0832] A means for promoting cooperative activities among users based on the matching means,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, characterized in that the recommendation means generates an exercise activity schedule optimized for the user based on the analysis results of the preference information and emotional state.

[0836] (Claim 3)

[0837] The system according to claim 1, characterized in that the matching means includes means for proposing a joint exercise plan among simultaneous users based on their emotions and preferences at that time. [Explanation of Symbols]

[0838] 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 acquiring information to collect user preference information, A recommendation means for generating candidate exercise activities based on the aforementioned preference information, An information provision means that provides local event information related to the aforementioned candidate exercise activity, A matching means that identifies other users with similar interests based on the aforementioned preference information and presents that information to the user, A system that includes this.

2. The system according to claim 1, characterized in that the recommendation means generates an exercise activity schedule optimized for the user based on the results of the analysis of the preference information.

3. The system according to claim 1, characterized in that the matching means includes means for facilitating communication between users.

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