system

Abstract

We provide the system. [Solution] A means for collecting data including geographical information of user terminals from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions, A means of delivering real-time weather and snow depth information to users based on the analyzed results, Means for providing the above information to the user using visual and audio output, A system that includes a means for users to share information about ski resorts via social media.

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, the method 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] For users who enjoy skiing and snowboarding, it is time-consuming and cumbersome to utilize multiple information sources to select an optimal ski slope. In addition, conventional weather forecasts and snow accumulation information lack precision and often do not match the actual local conditions. Therefore, it is difficult for users to accurately grasp the skiing conditions, which becomes an obstacle to obtaining the best experience.

Means for Solving the Problems

[0005] This invention provides a means for collecting data, including geographical information from the user's terminal, from external sources and analyzing weather forecasts and skiing conditions using machine learning algorithms, thereby delivering real-time and highly accurate slope information to users. Furthermore, this information can be output visually and audibly, providing it in a format easily understood by users. In addition, by including a means for sharing slope information via social networking services (SNS), the invention promotes information exchange among users and aims to improve the overall skiing experience.

[0006] A "user terminal" is an electronic device used to receive and display information, and includes mobile devices and computers.

[0007] "Geographic information" refers to data that indicates a specific location or region, including GPS coordinates and address information.

[0008] "External information sources" refer to third-party databases and APIs that provide weather data and snow cover information.

[0009] A "machine learning algorithm" is a computational method used to analyze large amounts of data and identify patterns or make predictions from it.

[0010] A "weather forecast" is data and information based on that data used to predict future weather.

[0011] "Skiing conditions" refer to information indicating environmental factors such as snow quality, snow depth, and weather conditions necessary for skiing or snowboarding.

[0012] "Means of analysis" refers to the technical procedures for collecting and processing data and transforming it into valuable information.

[0013] "Real-time" refers to processing or providing information in an immediate manner, close to the present time.

[0014] "Visual output" refers to a method of conveying information to a user visually, and includes text and images on a display.

[0015] "Voice output" is a method for aurally transmitting information to a user, including voice guidance and notification sounds.

[0016] "SNS" is an abbreviation for Social Network Service, which is an online platform for users to share and communicate information.

[0017] "Information exchange" is an act of mutually providing data and knowledge among multiple users.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system that provides real-time, accurate slope information for winter sports enthusiasts such as skiers and snowboarders. The system consists of a user terminal, a server, and an external information source.

[0040] First, the user's device uses location services to identify its geographical location. This information is then sent to a server to identify ski resorts the user might access and to collect relevant data.

[0041] The server acquires weather forecast data and snow depth information from external sources and analyzes this data using machine learning algorithms. This analysis allows for predictions of future weather changes and skiing conditions. For example, by predicting snow quality and quantity, the server can recommend appropriate skiing times to users.

[0042] The analyzed data is distributed from the server to the user's terminal, which receives it and communicates it to the user visually and audibly. For example, snow conditions on the ski slopes are displayed on the screen, along with voice notifications. This allows users to obtain quick and accurate information.

[0043] Furthermore, users can utilize the app's features to share information obtained through social media in real time. For example, they can exchange information with other users by posting photos they took on the slopes or weather information they obtained. This enhances the skiing experience for the entire community.

[0044] Thus, the present invention revolutionizes the winter sports experience by integrating multiple data sources, utilizing machine learning to make highly accurate predictions, and providing users with comprehensive slope information.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server collects the latest weather and snowfall information from external sources via APIs. This data includes temperature, wind speed, and snowfall amount.

[0048] Step 2:

[0049] The server stores the collected data in a database and performs necessary data cleaning before applying machine learning algorithms. This includes imputing missing values ​​and correcting outliers.

[0050] Step 3:

[0051] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on the cleaned data. The model is trained on historical data and predicts future conditions based on new input data.

[0052] Step 4:

[0053] The server analyzes the prediction results and generates information important to the user (e.g., optimal skiing time and slope conditions). The generated information is then prepared for output as visual and audio content.

[0054] Step 5:

[0055] The server sends the generated information to the user's terminal. The transmitted information is designed to be delivered quickly while maintaining real-time accuracy.

[0056] Step 6:

[0057] The terminal visually displays information received from the server and provides voice navigation as needed. This allows users to check current slope conditions and weather forecasts.

[0058] Step 7:

[0059] Users can optimize their slope selection and skiing plans based on information received through their devices. They can also utilize a dedicated sharing function to share the information they obtain on social media.

[0060] Step 8:

[0061] The device supports users posting to social media and sharing information with other users. This allows the entire community to exchange information in real time and have a better skiing experience.

[0062] (Example 1)

[0063] 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."

[0064] There is a challenge in that winter sports enthusiasts lack the information necessary to accurately understand local weather and snow conditions in real time, enabling them to enjoy sports comfortably and safely. Furthermore, users often cannot obtain the necessary information when planning their next steps or movements. Existing information provision systems have been criticized for their insufficient data accuracy and the provision of personalized information.

[0065] 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.

[0066] In this invention, the server includes means for collecting weather data and snow conditions from external information sources using data including location information of the user device, and analyzing this information using a data processing model for analysis; means for providing the user with immediate weather and snow conditions based on the analysis results; and means for presenting the above information to the user using a display device and an audio output device. As a result, the user can always obtain the latest and most accurate information on local weather and slope conditions, and can obtain real-time information to make informed decisions about enjoying winter sports with peace of mind.

[0067] A "user device" refers to an electronic device that a user can hold in their hand and operate to generate or use location information or other data.

[0068] "Location information" refers to the geographical coordinate data of the user's device's current location, usually expressed as latitude and longitude.

[0069] "External information sources" refer to information services accessible via the internet that provide weather data and snow depth data.

[0070] "Weather data" refers to data that shows the current or expected weather conditions in a particular area, and includes temperature, precipitation, wind speed, etc.

[0071] "Snow cover conditions" refers to information indicating the density, height, and quality of snow in a specific region.

[0072] A "data processing model" refers to a computational method that uses a specific algorithm or process to analyze collected data and transform it into meaningful information.

[0073] "Analyzed results" refers to a set of information generated by a data processing model that provides useful insights to the user.

[0074] "Real-time weather and snow information" refers to information that provides users with detailed and accurate data on real-time weather and snow conditions immediately.

[0075] A "display device" refers to a hardware device used to visually display information in the form of text, images, or video.

[0076] An "acoustic output device" refers to a hardware device that generates voice or sound and transmits auditory information to the user.

[0077] This invention provides a system for winter sports enthusiasts to obtain real-time and accurate information about ski slopes. The system mainly consists of a user terminal, a server, and an external information source.

[0078] First, the user's device is an electronic device such as a smartphone or tablet, which obtains the user's location information via GPS. This location information is transmitted to the server via Wi-Fi or a mobile network. When the user launches an application, the device quickly determines the current latitude and longitude and plays a role in transmitting the necessary data to the server.

[0079] Based on the received location information, the server collects the latest weather and snow depth data from multiple external sources (e.g., weather forecasting services and snow depth information services). This process retrieves data in JSON or XML format and uses services such as OpenWeatherMap and weather APIs.

[0080] The collected data is analyzed using machine learning algorithms. Specifically, libraries such as TENSORFLOW® and PyTorch are used to process the data and predict future weather changes and slope conditions. This analysis allows users to obtain information that predicts the optimal skiing time and slope conditions for the day and the following day.

[0081] Based on the analysis results, the server sends information to the user's terminal. The terminal then displays the information on its screen in text and graphic format, and provides alerts and notifications to the user using an audio output device. For example, if a sudden weather change is predicted, the user will be alerted through visual and audio notifications.

[0082] Furthermore, users can utilize social networking within the application to share their skiing experiences and photos taken on the slopes with other users. This promotes communication among users and enriches the skiing experience as a community.

[0083] An example of a prompt is: "Please describe in detail how the server retrieves data from an external API, parses it, and generates a notification message after receiving the user's location information." This prompt can be used to test the system's operation and verify the accuracy of predictions and notifications.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The device obtains the user's current location using GPS functionality. This input includes latitude and longitude data. The device transmits this geographical data to the server via Wi-Fi or a mobile network. In this step, location information is obtained immediately when the user launches the app, and data transfer from the device to the server begins.

[0087] Step 2:

[0088] The server collects weather and snow cover information from external sources based on location information received from the terminal. Specifically, the server sends requests to external sources via APIs and receives weather forecasts and snow cover information in JSON format as responses. Through this data collection process, the server maintains up-to-date weather information related to the user's current location.

[0089] Step 3:

[0090] The server analyzes the acquired weather data and snow cover information using a machine learning model. In this step, the data processing model uses libraries such as TensorFlow to receive data as input and generate weather variability forecasts and skiing condition forecasts. As a result of the analysis, the server outputs future weather conditions to provide to the user.

[0091] Step 4:

[0092] The server organizes the analyzed data and generates notification messages to send to the user's terminal. These messages may include specific weather advice, such as "Snowfall is expected between 2 and 3 PM." This output allows users to receive real-time information.

[0093] Step 5:

[0094] The terminal receives notification messages from the server, displays them on its screen, and provides voice alerts to the user using an audio output device. In this process, the terminal visualizes the received data, and speech synthesis software communicates the information to the user.

[0095] Step 6:

[0096] Users operate their devices to share acquired weather and snow depth information on a social networking platform. In this step, users add comments based on the provided information and post content including photos and other media. This output allows users to share slope information with other users, stimulating information exchange within the community.

[0097] (Application Example 1)

[0098] 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."

[0099] When enjoying winter sports, traditional methods make it difficult for users to determine the optimal skiing time because they cannot obtain accurate information about changing weather conditions and snow depth in real time. Furthermore, the lack of visual information on the slopes makes quick and efficient decision-making difficult. In addition, there are limited means to effectively share the information obtained with other users. A system is needed to solve these problems and improve the user experience.

[0100] 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.

[0101] In this invention, the server includes means for collecting data including geographical information of the user terminal from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions; means for delivering real-time weather and snow depth information to the user based on the analysis results; and means for providing the above information to the user using visual and audio output. This enables the user to determine the optimal skiing time on the slopes and respond quickly while visually confirming the information.

[0102] A "user terminal" is a device that acquires geographical information, receives the analyzed data, and provides the user with information visually and audibly.

[0103] "External information sources" refer to the data supply infrastructure that servers access to provide data such as weather forecasts and snowfall information.

[0104] A "machine learning algorithm" is a technical method for analyzing collected weather data and snow cover information to predict skiing conditions.

[0105] "Real-time weather and snow information" refers to detailed data about the immediate environmental conditions at the user's current location.

[0106] "Visual and audio output" refers to methods that utilize displays and audio guidance to provide information to users.

[0107] "The ability to overlay information onto the field of view" refers to a technology that displays information directly within the user's field of view, allowing them to view it in conjunction with the real world.

[0108] "Sharing ski resort information via SNS" refers to a function that allows users to exchange and share information they have obtained with other users through social networking services.

[0109] "Recommending an appropriate skiing time" means suggesting the most desirable start and end times for the user.

[0110] A "generative AI model" is an artificial intelligence technology that generates or analyzes diverse information based on given prompts.

[0111] A "prompt statement" is an instruction given to a system when generating specific information.

[0112] This embodiment relates to a system for providing winter sports enthusiasts with real-time, accurate slope information. This system is built utilizing user terminals, servers, and external information sources.

[0113] The user's device uses GPS functionality to determine its geographical location. Once the device obtains location information, it sends it to the server, prompting the server to retrieve relevant ski slope information. The server collects weather forecast data and snow depth data from external sources. This utilizes open weather APIs and existing databases that provide snow depth information.

[0114] The collected data is analyzed on the server using machine learning algorithms. The purpose of the analysis is to predict changes in skiing conditions and weather, and to recommend the optimal skiing time for the user.

[0115] The analysis results are delivered to the user's terminal in real time. The terminal visually presents the information through a display device (e.g., smart glasses or a smartphone display). It is also possible to alert the user using an audio output function. This allows the user to quickly obtain information on-site and appropriately adjust their plan.

[0116] Users can use social media to share some of the information they obtain with other users. This process helps to spread users' own experiences and improve the winter sports experience for the entire community.

[0117] For example, if a user checks snow conditions at a ski resort using smart glasses, and it predicts snowfall in the morning and improved weather in the afternoon, the device will notify the user that the afternoon is the recommended time for skiing. Based on this, the user can change their skiing plan to start in the afternoon.

[0118] An example of a prompt using a generative AI model is: "I'm planning a ski trip for next weekend, so please tell me the optimal time to ski based on the current weather forecast and snow conditions for the ski areas in Nagano Prefecture."

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user's device obtains its current location using GPS functionality. This location information becomes input data to the server. The user's device transmits this geographical information to the server.

[0122] Step 2:

[0123] The server collects weather forecast and snow depth data from relevant external sources based on the received geographical information. Here, it calls an open weather API to obtain the necessary data. This data will serve as input for subsequent analysis.

[0124] Step 3:

[0125] The server uses the collected data to run machine learning algorithms. This calculation predicts weather changes and skiing conditions. The output of the analysis includes information such as the best time to ski and changes in snow cover.

[0126] Step 4:

[0127] Based on the analysis results, the server delivers information to user terminals in real time. This information includes predicted weather conditions and snow cover. This information is provided as visual and audio output on the user terminal.

[0128] Step 5:

[0129] Based on the information received, users adjust their skiing plans. For example, if the recommended time for skiing is the afternoon based on snowfall in the morning and improved weather in the afternoon, the user will change their schedule.

[0130] Step 6:

[0131] Users can share the information they obtain with other users via social media. This helps spread information about ski resorts and improves the overall experience for the community. When sharing, a generative AI model is used to appropriately summarize the information using prompt sentences.

[0132] 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.

[0133] This invention provides a system that offers highly accurate slope information to users who enjoy skiing and snowboarding, and in particular, enables the personalization of information through user emotion recognition. The system mainly consists of a user terminal, a server, an emotion engine, and external information sources.

[0134] The user terminal acquires the user's geographical information and sends it to the server. Furthermore, the emotion engine analyzes the user's voice input and facial expressions to identify the user's emotional state. This information is sent to the server and used to optimize how information is delivered.

[0135] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on weather data and snow depth information collected from external sources. The results of this analysis are transmitted to the user's terminal in real time and displayed visually on the terminal's screen. In addition, the server adjusts the content and format of the information presented based on the user's emotions obtained by the emotion engine. For example, if the user indicates excitement, new slope information will be presented along with positive wording.

[0136] The device visually and audibly conveys information provided by the server to the user. Based on this information, users can choose a ski resort or plan their skiing trip. Furthermore, they can share environmental information and comments based on their own feelings on social media. By utilizing this sharing function, other users can leverage real-time information to enhance the overall skiing experience for the community.

[0137] This system allows users to receive slope information optimized for their geographical location and personal emotions, thereby improving the accuracy and satisfaction of their skiing experience. By combining emotion recognition technology and weather information processing technology, this system offers a new way to enjoy winter sports.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users enter their geographical information into the app using their device. This information may also be automatically retrieved by the device, for example, by using GPS to determine the user's current location.

[0141] Step 2:

[0142] The terminal transmits the acquired geographical information to the server. This provides the basic data needed to determine the range of ski slope information that the user can access.

[0143] Step 3:

[0144] The device uses voice input and facial expression analysis to recognize the user's emotional state through an emotion engine. This information is accumulated as the user interacts with the app.

[0145] Step 4:

[0146] The server collects relevant weather and snow depth information from external sources based on the user's geographical information. It uses an API to retrieve the latest weather conditions.

[0147] Step 5:

[0148] The server cleans the collected data, performs data transformations as needed, and then applies machine learning algorithms to predict future weather forecasts and skiing conditions.

[0149] Step 6:

[0150] The server selects the most relevant information to provide to the user based on predictive data. It then personalizes the presentation format and content of the information, taking into account the user's emotional state as determined by the emotion engine.

[0151] Step 7:

[0152] The server transmits the selected information to the user's terminal in real time. This can include emotionally-based customized messages and visual effects.

[0153] Step 8:

[0154] The terminal receives information from the server, displays it visually on the screen, and plays audio guides as needed to provide information to the user. As a result, users can easily understand the slope conditions and weather forecast.

[0155] Step 9:

[0156] Based on the information they receive, users can plan their skiing trips and choose recommended slopes. They can also share comments, including emotional expressions, through social media, sharing their experiences with other users.

[0157] This series of processes allows users to enjoy a more personalized skiing experience.

[0158] (Example 2)

[0159] 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".

[0160] When enjoying winter sports, weather and snow conditions are crucial factors, but obtaining this information in real time and in an individualized manner is difficult. Furthermore, information is not provided in a way that takes into account the user's emotional state, resulting in a problem where user satisfaction is not sufficiently high.

[0161] 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.

[0162] In this invention, the server includes means for acquiring spatial information of the user terminal and combining it with environmental data obtained from external information sources to evaluate weather forecasts and skiing conditions using a predictive analysis model; means for providing personalized weather and snow depth information in real time based on the analyzed results and the user's emotional state; and means for communicating the above information to the user through visual and auditory outputs. This makes it possible to appropriately provide information optimized for each user's situation in real time.

[0163] A "user terminal" is an electronic device used by a user that has functions such as acquiring location information and analyzing emotional states.

[0164] "Spatial information" refers to location data acquired by the user's terminal, primarily geographical coordinate information such as latitude and longitude.

[0165] "External information sources" refer to external data providers and databases that the server accesses, primarily providing information on weather and snowfall.

[0166] A "predictive analysis model" refers to an algorithm or mathematical model used to predict future weather and snow conditions based on past data.

[0167] "Emotional state" refers to a psychological state identified by analyzing the user's voice and facial expressions, and includes states such as "excitement," "reassurance," and "anxiety."

[0168] A "communication network" refers to a digital communication infrastructure that allows users to share information with other users via the internet or other means.

[0169] "Data quality" refers to a measure of how to evaluate the accuracy, reliability, and temporal precision of information, and is important for improving the accuracy of collected data.

[0170] This invention is a system that provides personalized ski resort information based on the user's geographical and emotional information, aimed at winter sports enthusiasts. The system mainly consists of a user terminal, a server, an external data source, and an emotional analysis engine.

[0171] The user's device acquires its current location using a GPS module. It also collects voice and facial expressions as input data using a microphone and camera, and sends this data to an emotion analysis engine. This allows the user's emotional state to be identified.

[0172] The server receives location and sentiment information transmitted from the terminal. The server uses APIs to collect weather and snow depth data from external data sources. For example, it uses APIs from common weather data providers for weather information. The server uses predictive analytics models built with machine learning libraries to predict weather and skiing conditions and sends these results to the terminal.

[0173] The device visually displays data and also conveys information to the user audibly through a voice assistant. Based on the information obtained, the user can plan their skiing trips or share information with other users via social media. A simple interface via a communication network is used for this purpose.

[0174] As a concrete example, if a user asks the terminal, "What are the skiing conditions today?", the system uses location and sentiment information to provide optimized weather information in real time. One example of a prompt message is, "Show detailed information about the ski resort based on the user's geographical information and sentiment." By processing this prompt on the server, the system can provide the user with completely personalized information.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user terminal first uses a GPS module to obtain the user's current location. The input is latitude and longitude information provided by the GPS, and the output is the user's current location data. The terminal transmits this location data to the server in real time.

[0178] Step 2:

[0179] Next, the user terminal uses a microphone and camera to capture the user's voice and facial expressions. At this time, the voice data and image data become input and are passed to voice analysis software and facial expression analysis algorithms, respectively. This identifies the user's emotional state (e.g., "excited," "calm"), and the results are sent to the server as output.

[0180] Step 3:

[0181] The server accesses external data sources and collects weather and snow depth data via APIs. This external environmental data serves as input, and is analyzed using machine learning libraries. The output is an evaluation of future weather and skiing conditions, which is then sent to the user's terminal.

[0182] Step 4:

[0183] The server creates optimized information based on location and sentiment information received from the user. Geographic and sentiment information are used as input, and the analyzed data is adjusted to generate personalized weather information as output.

[0184] Step 5:

[0185] The user terminal receives information from the server and provides guidance through a visual display and voice assistant. Here, the analysis results from the server serve as input, and based on this, speech synthesis and screen displays are output. Operations are then performed to provide the user with specific action guidelines.

[0186] Step 6:

[0187] Users share information obtained through their devices with other users using social networking services (SNS). Here, the input consists of comments based on information and emotions selected by the user, which are then posted to an online platform as output. This process enables real-time information sharing.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] Existing information systems for winter sports only provide general weather and slope information, and do not optimize information based on the individual user's condition or feelings. Therefore, there is a problem in that the usefulness and satisfaction of the information provided to users are not sufficiently improved.

[0191] 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.

[0192] In this invention, the server includes means for aggregating and analyzing data, including the user's geographical information; means for recognizing the user's emotional state and adjusting the presented information accordingly; and means for improving the accuracy of the collected data, evaluating its reliability, and optimizing the information. This makes it possible to provide real-time weather and recommendation information optimized to each user's individual emotions and state.

[0193] A "user terminal" is a portable or stationary electronic device used by a user to input and output information.

[0194] "Geographic information" refers to data related to location information and the user's activity locations.

[0195] An "external information source" is a data source outside the system that provides weather data, snow cover information, and other similar information.

[0196] A "machine learning algorithm" is a programming technique that automatically extracts regularities and patterns from large amounts of data to perform predictions and classifications.

[0197] "Emotion recognition means" refers to technology for analyzing and identifying a user's emotional state from their voice and facial expressions.

[0198] "Visual and auditory output" refers to two methods of information presentation: image information via a display and auditory information via speakers.

[0199] "Means of sharing ski resort information via SNS" refers to methods of exchanging personal experiences and information with other users through social networking services.

[0200] "Methods for evaluating data reliability" refer to technologies that establish criteria for judging the accuracy and validity of collected information and for utilizing it.

[0201] The system that implements this application first obtains geographical information from the user's device, such as a smartphone or tablet, and sends it to a server. This allows the system to determine the user's location and prepares to collect relevant weather and snow depth information from external sources.

[0202] The server analyzes collected weather data and snow cover information using machine learning algorithms. This analysis predicts real-time weather forecasts and skiing conditions. Furthermore, the server uses an emotion recognition engine to recognize the user's emotional state from their voice input and facial expressions, and appropriately adjusts the information presented.

[0203] The user terminal provides users with analysis results from the server, both visually and audibly. For example, it can display the latest slope information as an image on the screen and transmit information audibly through a speaker. In addition, users can share their experiences and real-time information with other users via social networking services (SNS).

[0204] As a concrete example, to further enhance the ski resort experience, the app will present information about newly opened ski slopes along with a positive message when it detects that the user's emotional state is "excited." This method allows for the catering to individual user needs and helps them enjoy skiing and snowboarding to the fullest.

[0205] An example of prompt text to input into a generative AI model is: "If the user's emotional state is recognized as 'excited,' generate information about a newly opened ski resort in Sendai and a positive message."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user's terminal uses GPS functionality to obtain the user's geographical information. This geographical information is sent to the server as input data to identify the user's current location and activity area. Based on this location information, the server selects the necessary external information sources.

[0209] Step 2:

[0210] The server uses machine learning algorithms to perform predictive analysis based on weather data and snow depth information collected from external sources. This process generates analyzed weather forecasts and skiing conditions. The output results serve as the basic data provided to users.

[0211] Step 3:

[0212] The user terminal collects the user's emotional state through voice input and a facial camera. This data is sent to the server as input to identify the user's emotional state. An emotion recognition engine analyzes this data to determine the user's emotional state.

[0213] Step 4:

[0214] The server adjusts the information it presents based on the user's identified emotional state. For example, if the user is "excited," the server will select information about newly opened ski slopes along with positive wording. This adjusted information is then output and provided to the user.

[0215] Step 5:

[0216] The user terminal provides the user with optimized information received from the server, both visually (information displayed on the screen) and audibly (audio output from the speaker). Based on this output, the user can make decisions based on real-time information and create a skiing plan.

[0217] Step 6:

[0218] Users share the information provided and their own experiences with other users via social media. This sharing allows users to utilize the outputted information and promotes information exchange and improvement of the quality of experiences throughout the community.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system that provides real-time, accurate slope information for winter sports enthusiasts such as skiers and snowboarders. The system consists of a user terminal, a server, and an external information source.

[0236] First, the user's device uses location services to identify its geographical location. This information is then sent to a server to identify ski resorts the user might access and to collect relevant data.

[0237] The server acquires weather forecast data and snow depth information from external sources and analyzes this data using machine learning algorithms. This analysis allows for predictions of future weather changes and skiing conditions. For example, by predicting snow quality and quantity, the server can recommend appropriate skiing times to users.

[0238] The analyzed data is distributed from the server to the user's terminal, which receives it and communicates it to the user visually and audibly. For example, snow conditions on the ski slopes are displayed on the screen, along with voice notifications. This allows users to obtain quick and accurate information.

[0239] Furthermore, users can utilize the app's features to share information obtained through social media in real time. For example, they can exchange information with other users by posting photos they took on the slopes or weather information they obtained. This enhances the skiing experience for the entire community.

[0240] Thus, the present invention revolutionizes the winter sports experience by integrating multiple data sources, utilizing machine learning to make highly accurate predictions, and providing users with comprehensive slope information.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server collects the latest weather and snowfall information from external sources via APIs. This data includes temperature, wind speed, and snowfall amount.

[0244] Step 2:

[0245] The server stores the collected data in a database and performs necessary data cleaning before applying machine learning algorithms. This includes imputing missing values ​​and correcting outliers.

[0246] Step 3:

[0247] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on the cleaned data. The model is trained on historical data and predicts future conditions based on new input data.

[0248] Step 4:

[0249] The server analyzes the prediction results and generates information important to the user (e.g., optimal skiing time and slope conditions). The generated information is then prepared for output as visual and audio content.

[0250] Step 5:

[0251] The server sends the generated information to the user's terminal. The transmitted information is designed to be delivered quickly while maintaining real-time accuracy.

[0252] Step 6:

[0253] The terminal visually displays information received from the server and provides voice navigation as needed. This allows users to check current slope conditions and weather forecasts.

[0254] Step 7:

[0255] Users can optimize their slope selection and skiing plans based on information received through their devices. They can also utilize a dedicated sharing function to share the information they obtain on social media.

[0256] Step 8:

[0257] The device supports users posting to social media and sharing information with other users. This allows the entire community to exchange information in real time and have a better skiing experience.

[0258] (Example 1)

[0259] 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".

[0260] There is a challenge in that winter sports enthusiasts lack the information necessary to accurately understand local weather and snow conditions in real time, enabling them to enjoy sports comfortably and safely. Furthermore, users often cannot obtain the necessary information when planning their next steps or movements. Existing information provision systems have been criticized for their insufficient data accuracy and the provision of personalized information.

[0261] 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.

[0262] In this invention, the server includes means for collecting weather data and snow conditions from external information sources using data including location information of the user device, and analyzing this information using a data processing model for analysis; means for providing the user with immediate weather and snow conditions based on the analysis results; and means for presenting the above information to the user using a display device and an audio output device. As a result, the user can always obtain the latest and most accurate information on local weather and slope conditions, and can obtain real-time information to make informed decisions about enjoying winter sports with peace of mind.

[0263] A "user device" refers to an electronic device that a user can hold in their hand and operate to generate or use location information or other data.

[0264] "Location information" refers to the geographical coordinate data of the user's device's current location, usually expressed as latitude and longitude.

[0265] "External information sources" refer to information services accessible via the internet that provide weather data and snow depth data.

[0266] "Weather data" refers to data that shows the current or expected weather conditions in a particular area, and includes temperature, precipitation, wind speed, etc.

[0267] "Snow cover conditions" refers to information indicating the density, height, and quality of snow in a specific region.

[0268] A "data processing model" refers to a computational method that uses a specific algorithm or process to analyze collected data and transform it into meaningful information.

[0269] "Analyzed results" refers to a set of information generated by a data processing model that provides useful insights to the user.

[0270] "Real-time weather and snow information" refers to information that provides users with detailed and accurate data on real-time weather and snow conditions immediately.

[0271] A "display device" refers to a hardware device used to visually display information in the form of text, images, or video.

[0272] An "acoustic output device" refers to a hardware device that generates voice or sound and transmits auditory information to the user.

[0273] This invention provides a system for winter sports enthusiasts to obtain real-time and accurate information about ski slopes. The system mainly consists of a user terminal, a server, and an external information source.

[0274] First, the user's device is an electronic device such as a smartphone or tablet, which obtains the user's location information via GPS. This location information is transmitted to the server via Wi-Fi or a mobile network. When the user launches an application, the device quickly determines the current latitude and longitude and plays a role in transmitting the necessary data to the server.

[0275] Based on the received location information, the server collects the latest weather and snow depth data from multiple external sources (e.g., weather forecasting services and snow depth information services). This process retrieves data in JSON or XML format and uses services such as OpenWeatherMap and weather APIs.

[0276] The collected data is analyzed using machine learning algorithms. Specifically, libraries such as TensorFlow and PyTorch are used to process the data and predict future weather changes and slope conditions. This analysis allows users to obtain information that predicts the optimal skiing time and slope conditions for the day and the following day.

[0277] Based on the analysis results, the server sends information to the user's terminal. The terminal then displays the information on its screen in text and graphic format, and provides alerts and notifications to the user using an audio output device. For example, if a sudden weather change is predicted, the user will be alerted through visual and audio notifications.

[0278] Furthermore, users can utilize social networking within the application to share their skiing experiences and photos taken on the slopes with other users. This promotes communication among users and enriches the skiing experience as a community.

[0279] Examples of prompt texts include the following. "After the server receives the user's location information, please explain in detail how to obtain data from an external API, perform analysis, and generate a notification message." By using this prompt, it is possible to test the operation of the system and verify the prediction accuracy and notification accuracy.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The terminal obtains the user's current location using the GPS function. This input includes latitude and longitude data. The terminal transmits this geographical data to the server via Wi-Fi or a mobile network. In this step, when the user launches the app, the location information is immediately obtained and the data transfer from the terminal to the server is started.

[0283] Step 2:

[0284] Based on the location information received from the terminal, the server collects weather data and snow accumulation information from external information sources. Specifically, the server sends a request to the external information source via an API and obtains the weather forecast and snow accumulation status in JSON format as a response. Through this data collection process, the server holds the latest weather information related to the user's current location.

[0285] Step 3:

[0286] The server analyzes the obtained weather data and snow accumulation information using a machine learning model. In this step, the data processing model uses a library such as TensorFlow, receives the data as input, and generates predictions of weather fluctuations and skidding conditions. As a result of the analysis, the server outputs future weather conditions for providing to the user.

[0287] Step 4:

[0288] The server organizes the analyzed data and generates notification messages to send to the user's terminal. These messages may include specific weather advice, such as "Snowfall is expected between 2 and 3 PM." This output allows users to receive real-time information.

[0289] Step 5:

[0290] The terminal receives notification messages from the server, displays them on its screen, and provides voice alerts to the user using an audio output device. In this process, the terminal visualizes the received data, and speech synthesis software communicates the information to the user.

[0291] Step 6:

[0292] Users operate their devices to share acquired weather and snow depth information on a social networking platform. In this step, users add comments based on the provided information and post content including photos and other media. This output allows users to share slope information with other users, stimulating information exchange within the community.

[0293] (Application Example 1)

[0294] 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."

[0295] When enjoying winter sports, traditional methods make it difficult for users to determine the optimal skiing time because they cannot obtain accurate information about changing weather conditions and snow depth in real time. Furthermore, the lack of visual information on the slopes makes quick and efficient decision-making difficult. In addition, there are limited means to effectively share the information obtained with other users. A system is needed to solve these problems and improve the user experience.

[0296] 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.

[0297] In this invention, the server includes means for collecting data including geographical information of the user terminal from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions; means for delivering real-time weather and snow depth information to the user based on the analysis results; and means for providing the above information to the user using visual and audio output. This enables the user to determine the optimal skiing time on the slopes and respond quickly while visually confirming the information.

[0298] A "user terminal" is a device that acquires geographical information, receives the analyzed data, and provides the user with information visually and audibly.

[0299] "External information sources" refer to the data supply infrastructure that servers access to provide data such as weather forecasts and snowfall information.

[0300] A "machine learning algorithm" is a technical method for analyzing collected weather data and snow cover information to predict skiing conditions.

[0301] "Real-time weather and snow information" refers to detailed data about the immediate environmental conditions at the user's current location.

[0302] "Visual and audio output" refers to methods that utilize displays and audio guidance to provide information to users.

[0303] "The ability to overlay information onto the field of view" refers to a technology that displays information directly within the user's field of view, allowing them to view it in conjunction with the real world.

[0304] "Sharing ski resort information via SNS" refers to a function that allows users to exchange and share information they have obtained with other users through social networking services.

[0305] "Recommending suitable skiing time" means suggesting the most desirable start and end times of skiing for the user.

[0306] "Generative AI model" refers to an artificial intelligence technology that generates or analyzes various information based on a given prompt.

[0307] "Prompt text" is an instruction text provided to the system when generating specific information. <<

[0308] This embodiment relates to a system for providing real-time and accurate ski resort information to winter sports enthusiasts. This system is constructed by utilizing user terminals, servers, and external information sources.

[0309] The user terminal uses the GPS function to identify geographical information. The terminal that has obtained the location information sends it to the server to prompt the acquisition of relevant ski resort information. The server collects weather forecast data and snow accumulation data from external information sources. For this, open weather APIs and existing databases that provide snow accumulation information are utilized.

[0310] The collected data is analyzed on the server using machine learning algorithms. The purpose of the analysis is to predict skiing conditions and weather changes, and to recommend the optimal skiing time for the user.

[0311] The analysis results are delivered to the user terminal in real time. The terminal visually presents the information through a display device (such as the display of smart glasses or a smartphone). It is also possible to use the voice output function to prompt the user's attention. As a result, the user can quickly obtain information on-site and appropriately adjust their plan.

[0312] Users can use social media to share some of the information they obtain with other users. This process helps to spread users' own experiences and improve the winter sports experience for the entire community.

[0313] For example, if a user checks snow conditions at a ski resort using smart glasses, and it predicts snowfall in the morning and improved weather in the afternoon, the device will notify the user that the afternoon is the recommended time for skiing. Based on this, the user can change their skiing plan to start in the afternoon.

[0314] An example of a prompt using a generative AI model is: "I'm planning a ski trip for next weekend, so please tell me the optimal time to ski based on the current weather forecast and snow conditions for the ski areas in Nagano Prefecture."

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The user's device obtains its current location using GPS functionality. This location information becomes input data to the server. The user's device transmits this geographical information to the server.

[0318] Step 2:

[0319] The server collects weather forecast and snow depth data from relevant external sources based on the received geographical information. Here, it calls an open weather API to obtain the necessary data. This data will serve as input for subsequent analysis.

[0320] Step 3:

[0321] The server uses the collected data to run machine learning algorithms. This calculation predicts weather changes and skiing conditions. The output of the analysis includes information such as the best time to ski and changes in snow cover.

[0322] Step 4:

[0323] Based on the analysis results, the server delivers information to user terminals in real time. This information includes predicted weather conditions and snow cover. This information is provided as visual and audio output on the user terminal.

[0324] Step 5:

[0325] Based on the information received, users adjust their skiing plans. For example, if the recommended time for skiing is the afternoon based on snowfall in the morning and improved weather in the afternoon, the user will change their schedule.

[0326] Step 6:

[0327] Users can share the information they obtain with other users via social media. This helps spread information about ski resorts and improves the overall experience for the community. When sharing, a generative AI model is used to appropriately summarize the information using prompt sentences.

[0328] 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.

[0329] This invention provides a system that offers highly accurate slope information to users who enjoy skiing and snowboarding, and in particular, enables the personalization of information through user emotion recognition. The system mainly consists of a user terminal, a server, an emotion engine, and external information sources.

[0330] The user terminal acquires the user's geographical information and sends it to the server. Furthermore, the emotion engine analyzes the user's voice input and facial expressions to identify the user's emotional state. This information is sent to the server and used to optimize how information is delivered.

[0331] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on weather data and snow depth information collected from external sources. The results of this analysis are transmitted to the user's terminal in real time and displayed visually on the terminal's screen. In addition, the server adjusts the content and format of the information presented based on the user's emotions obtained by the emotion engine. For example, if the user indicates excitement, new slope information will be presented along with positive wording.

[0332] The device visually and audibly conveys information provided by the server to the user. Based on this information, users can choose a ski resort or plan their skiing trip. Furthermore, they can share environmental information and comments based on their own feelings on social media. By utilizing this sharing function, other users can leverage real-time information to enhance the overall skiing experience for the community.

[0333] This system allows users to receive slope information optimized for their geographical location and personal emotions, thereby improving the accuracy and satisfaction of their skiing experience. By combining emotion recognition technology and weather information processing technology, this system offers a new way to enjoy winter sports.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users enter their geographical information into the app using their device. This information may also be automatically retrieved by the device, for example, by using GPS to determine the user's current location.

[0337] Step 2:

[0338] The terminal transmits the acquired geographical information to the server. This provides the basic data needed to determine the range of ski slope information that the user can access.

[0339] Step 3:

[0340] The device uses voice input and facial expression analysis to recognize the user's emotional state through an emotion engine. This information is accumulated as the user interacts with the app.

[0341] Step 4:

[0342] The server collects relevant weather and snow depth information from external sources based on the user's geographical information. It uses an API to retrieve the latest weather conditions.

[0343] Step 5:

[0344] The server cleans the collected data, performs data transformations as needed, and then applies machine learning algorithms to predict future weather forecasts and skiing conditions.

[0345] Step 6:

[0346] The server selects the most relevant information to provide to the user based on predictive data. It then personalizes the presentation format and content of the information, taking into account the user's emotional state as determined by the emotion engine.

[0347] Step 7:

[0348] The server transmits the selected information to the user's terminal in real time. This can include emotionally-based customized messages and visual effects.

[0349] Step 8:

[0350] The terminal receives information from the server, displays it visually on the screen, and plays audio guides as needed to provide information to the user. As a result, users can easily understand the slope conditions and weather forecast.

[0351] Step 9:

[0352] Based on the information they receive, users can plan their skiing trips and choose recommended slopes. They can also share comments, including emotional expressions, through social media, sharing their experiences with other users.

[0353] This series of processes allows users to enjoy a more personalized skiing experience.

[0354] (Example 2)

[0355] 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".

[0356] When enjoying winter sports, weather and snow conditions are crucial factors, but obtaining this information in real time and in an individualized manner is difficult. Furthermore, information is not provided in a way that takes into account the user's emotional state, resulting in a problem where user satisfaction is not sufficiently high.

[0357] 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.

[0358] In this invention, the server includes means for acquiring spatial information of the user terminal and combining it with environmental data obtained from external information sources to evaluate weather forecasts and skiing conditions using a predictive analysis model; means for providing personalized weather and snow depth information in real time based on the analyzed results and the user's emotional state; and means for communicating the above information to the user through visual and auditory outputs. This makes it possible to appropriately provide information optimized for each user's situation in real time.

[0359] A "user terminal" is an electronic device used by a user that has functions such as acquiring location information and analyzing emotional states.

[0360] "Spatial information" refers to location data acquired by the user's terminal, primarily geographical coordinate information such as latitude and longitude.

[0361] "External information sources" refer to external data providers and databases that the server accesses, primarily providing information on weather and snowfall.

[0362] A "predictive analysis model" refers to an algorithm or mathematical model used to predict future weather and snow conditions based on past data.

[0363] "Emotional state" refers to a psychological state identified by analyzing the user's voice and facial expressions, and includes states such as "excitement," "reassurance," and "anxiety."

[0364] A "communication network" refers to a digital communication infrastructure that allows users to share information with other users via the internet or other means.

[0365] "Data quality" refers to a measure of how to evaluate the accuracy, reliability, and temporal precision of information, and is important for improving the accuracy of collected data.

[0366] This invention is a system that provides personalized ski resort information based on the user's geographical and emotional information, aimed at winter sports enthusiasts. The system mainly consists of a user terminal, a server, an external data source, and an emotional analysis engine.

[0367] The user's device acquires its current location using a GPS module. It also collects voice and facial expressions as input data using a microphone and camera, and sends this data to an emotion analysis engine. This allows the user's emotional state to be identified.

[0368] The server receives location and sentiment information transmitted from the terminal. The server uses APIs to collect weather and snow depth data from external data sources. For example, it uses APIs from common weather data providers for weather information. The server uses predictive analytics models built with machine learning libraries to predict weather and skiing conditions and sends these results to the terminal.

[0369] The device visually displays data and also conveys information to the user audibly through a voice assistant. Based on the information obtained, the user can plan their skiing trips or share information with other users via social media. A simple interface via a communication network is used for this purpose.

[0370] As a concrete example, if a user asks the terminal, "What are the skiing conditions today?", the system uses location and sentiment information to provide optimized weather information in real time. One example of a prompt message is, "Show detailed information about the ski resort based on the user's geographical information and sentiment." By processing this prompt on the server, the system can provide the user with completely personalized information.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user terminal first uses a GPS module to obtain the user's current location. The input is latitude and longitude information provided by the GPS, and the output is the user's current location data. The terminal transmits this location data to the server in real time.

[0374] Step 2:

[0375] Next, the user terminal uses a microphone and camera to capture the user's voice and facial expressions. At this time, the voice data and image data become input and are passed to voice analysis software and facial expression analysis algorithms, respectively. This identifies the user's emotional state (e.g., "excited," "calm"), and the results are sent to the server as output.

[0376] Step 3:

[0377] The server accesses external data sources and collects weather and snow depth data via APIs. This external environmental data serves as input, and is analyzed using machine learning libraries. The output is an evaluation of future weather and skiing conditions, which is then sent to the user's terminal.

[0378] Step 4:

[0379] The server creates optimized information based on location and sentiment information received from the user. Geographic and sentiment information are used as input, and the analyzed data is adjusted to generate personalized weather information as output.

[0380] Step 5:

[0381] The user terminal receives information from the server and provides guidance through a visual display and voice assistant. Here, the analysis results from the server serve as input, and based on this, speech synthesis and screen displays are output. Operations are then performed to provide the user with specific action guidelines.

[0382] Step 6:

[0383] Users share information obtained through their devices with other users using social networking services (SNS). Here, the input consists of comments based on information and emotions selected by the user, which are then posted to an online platform as output. This process enables real-time information sharing.

[0384] (Application Example 2)

[0385] 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."

[0386] Existing information systems for winter sports only provide general weather and slope information, and do not optimize information based on the individual user's condition or feelings. Therefore, there is a problem in that the usefulness and satisfaction of the information provided to users are not sufficiently improved.

[0387] 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.

[0388] In this invention, the server includes means for aggregating and analyzing data, including the user's geographical information; means for recognizing the user's emotional state and adjusting the presented information accordingly; and means for improving the accuracy of the collected data, evaluating its reliability, and optimizing the information. This makes it possible to provide real-time weather and recommendation information optimized to each user's individual emotions and state.

[0389] A "user terminal" is a portable or stationary electronic device used by a user to input and output information.

[0390] "Geographic information" refers to data related to location information and the user's activity locations.

[0391] An "external information source" is a data source outside the system that provides weather data, snow cover information, and other similar information.

[0392] A "machine learning algorithm" is a programming technique that automatically extracts regularities and patterns from large amounts of data to perform predictions and classifications.

[0393] "Emotion recognition means" refers to technology for analyzing and identifying a user's emotional state from their voice and facial expressions.

[0394] "Visual and auditory output" refers to two methods of information presentation: image information via a display and auditory information via speakers.

[0395] "Means of sharing ski resort information via SNS" refers to methods of exchanging personal experiences and information with other users through social networking services.

[0396] "Methods for evaluating data reliability" refer to technologies that establish criteria for judging the accuracy and validity of collected information and for utilizing it.

[0397] The system that implements this application first obtains geographical information from the user's device, such as a smartphone or tablet, and sends it to a server. This allows the system to determine the user's location and prepares to collect relevant weather and snow depth information from external sources.

[0398] The server analyzes collected weather data and snow cover information using machine learning algorithms. This analysis predicts real-time weather forecasts and skiing conditions. Furthermore, the server uses an emotion recognition engine to recognize the user's emotional state from their voice input and facial expressions, and appropriately adjusts the information presented.

[0399] The user terminal provides users with analysis results from the server, both visually and audibly. For example, it can display the latest slope information as an image on the screen and transmit information audibly through a speaker. In addition, users can share their experiences and real-time information with other users via social networking services (SNS).

[0400] As a concrete example, to further enhance the ski resort experience, the app will present information about newly opened ski slopes along with a positive message when it detects that the user's emotional state is "excited." This method allows for the catering to individual user needs and helps them enjoy skiing and snowboarding to the fullest.

[0401] An example of prompt text to input into a generative AI model is: "If the user's emotional state is recognized as 'excited,' generate information about a newly opened ski resort in Sendai and a positive message."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user's terminal uses GPS functionality to obtain the user's geographical information. This geographical information is sent to the server as input data to identify the user's current location and activity area. Based on this location information, the server selects the necessary external information sources.

[0405] Step 2:

[0406] The server uses machine learning algorithms to perform predictive analysis based on weather data and snow depth information collected from external sources. This process generates analyzed weather forecasts and skiing conditions. The output results serve as the basic data provided to users.

[0407] Step 3:

[0408] The user terminal collects the user's emotional state through voice input and a facial camera. This data is sent to the server as input to identify the user's emotional state. An emotion recognition engine analyzes this data to determine the user's emotional state.

[0409] Step 4:

[0410] The server adjusts the information it presents based on the user's identified emotional state. For example, if the user is "excited," the server will select information about newly opened ski slopes along with positive wording. This adjusted information is then output and provided to the user.

[0411] Step 5:

[0412] The user terminal provides the user with optimized information received from the server, both visually (information displayed on the screen) and audibly (audio output from the speaker). Based on this output, the user can make decisions based on real-time information and create a skiing plan.

[0413] Step 6:

[0414] Users share the information provided and their own experiences with other users via social media. This sharing allows users to utilize the outputted information and promotes information exchange and improvement of the quality of experiences throughout the community.

[0415] 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.

[0416] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is a system that provides real-time, accurate slope information for winter sports enthusiasts such as skiers and snowboarders. The system consists of a user terminal, a server, and an external information source.

[0432] First, the user's device uses location services to identify its geographical location. This information is then sent to a server to identify ski resorts the user might access and to collect relevant data.

[0433] The server acquires weather forecast data and snow depth information from external sources and analyzes this data using machine learning algorithms. This analysis allows for predictions of future weather changes and skiing conditions. For example, by predicting snow quality and quantity, the server can recommend appropriate skiing times to users.

[0434] The analyzed data is distributed from the server to the user's terminal, which receives it and communicates it to the user visually and audibly. For example, snow conditions on the ski slopes are displayed on the screen, along with voice notifications. This allows users to obtain quick and accurate information.

[0435] Furthermore, users can utilize the app's features to share information obtained through social media in real time. For example, they can exchange information with other users by posting photos they took on the slopes or weather information they obtained. This enhances the skiing experience for the entire community.

[0436] Thus, the present invention revolutionizes the winter sports experience by integrating multiple data sources, utilizing machine learning to make highly accurate predictions, and providing users with comprehensive slope information.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The server collects the latest weather and snowfall information from external sources via APIs. This data includes temperature, wind speed, and snowfall amount.

[0440] Step 2:

[0441] The server stores the collected data in a database and performs necessary data cleaning before applying machine learning algorithms. This includes imputing missing values ​​and correcting outliers.

[0442] Step 3:

[0443] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on the cleaned data. The model is trained on historical data and predicts future conditions based on new input data.

[0444] Step 4:

[0445] The server analyzes the prediction results and generates information important to the user (e.g., optimal skiing time and slope conditions). The generated information is then prepared for output as visual and audio content.

[0446] Step 5:

[0447] The server sends the generated information to the user's terminal. The transmitted information is designed to be delivered quickly while maintaining real-time accuracy.

[0448] Step 6:

[0449] The terminal visually displays information received from the server and provides voice navigation as needed. This allows users to check current slope conditions and weather forecasts.

[0450] Step 7:

[0451] Users can optimize their slope selection and skiing plans based on information received through their devices. They can also utilize a dedicated sharing function to share the information they obtain on social media.

[0452] Step 8:

[0453] The device supports users posting to social media and sharing information with other users. This allows the entire community to exchange information in real time and have a better skiing experience.

[0454] (Example 1)

[0455] 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."

[0456] There is a challenge in that winter sports enthusiasts lack the information necessary to accurately understand local weather and snow conditions in real time, enabling them to enjoy sports comfortably and safely. Furthermore, users often cannot obtain the necessary information when planning their next steps or movements. Existing information provision systems have been criticized for their insufficient data accuracy and the provision of personalized information.

[0457] 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.

[0458] In this invention, the server includes means for collecting weather data and snow conditions from external information sources using data including location information of the user device, and analyzing this information using a data processing model for analysis; means for providing the user with immediate weather and snow conditions based on the analysis results; and means for presenting the above information to the user using a display device and an audio output device. As a result, the user can always obtain the latest and most accurate information on local weather and slope conditions, and can obtain real-time information to make informed decisions about enjoying winter sports with peace of mind.

[0459] A "user device" refers to an electronic device that a user can hold in their hand and operate to generate or use location information or other data.

[0460] "Location information" refers to the geographical coordinate data of the user's device's current location, usually expressed as latitude and longitude.

[0461] "External information sources" refer to information services accessible via the internet that provide weather data and snow depth data.

[0462] "Weather data" refers to data that shows the current or expected weather conditions in a particular area, and includes temperature, precipitation, wind speed, etc.

[0463] "Snow cover conditions" refers to information indicating the density, height, and quality of snow in a specific region.

[0464] A "data processing model" refers to a computational method that uses a specific algorithm or process to analyze collected data and transform it into meaningful information.

[0465] "Analyzed results" refers to a set of information generated by a data processing model that provides useful insights to the user.

[0466] "Real-time weather and snow information" refers to information that provides users with detailed and accurate data on real-time weather and snow conditions immediately.

[0467] A "display device" refers to a hardware device used to visually display information in the form of text, images, or video.

[0468] An "acoustic output device" refers to a hardware device that generates voice or sound and transmits auditory information to the user.

[0469] This invention provides a system for winter sports enthusiasts to obtain real-time and accurate information about ski slopes. The system mainly consists of a user terminal, a server, and an external information source.

[0470] First, the user's device is an electronic device such as a smartphone or tablet, which obtains the user's location information via GPS. This location information is transmitted to the server via Wi-Fi or a mobile network. When the user launches an application, the device quickly determines the current latitude and longitude and plays a role in transmitting the necessary data to the server.

[0471] Based on the received location information, the server collects the latest weather and snow depth data from multiple external sources (e.g., weather forecasting services and snow depth information services). This process retrieves data in JSON or XML format and uses services such as OpenWeatherMap and weather APIs.

[0472] The collected data is analyzed using machine learning algorithms. Specifically, libraries such as TensorFlow and PyTorch are used to process the data and predict future weather changes and slope conditions. This analysis allows users to obtain information that predicts the optimal skiing time and slope conditions for the day and the following day.

[0473] Based on the analysis results, the server sends information to the user's terminal. The terminal then displays the information on its screen in text and graphic format, and provides alerts and notifications to the user using an audio output device. For example, if a sudden weather change is predicted, the user will be alerted through visual and audio notifications.

[0474] Furthermore, users can utilize social networking within the application to share their skiing experiences and photos taken on the slopes with other users. This promotes communication among users and enriches the skiing experience as a community.

[0475] An example of a prompt is: "Please describe in detail how the server retrieves data from an external API, parses it, and generates a notification message after receiving the user's location information." This prompt can be used to test the system's operation and verify the accuracy of predictions and notifications.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device obtains the user's current location using GPS functionality. This input includes latitude and longitude data. The device transmits this geographical data to the server via Wi-Fi or a mobile network. In this step, location information is obtained immediately when the user launches the app, and data transfer from the device to the server begins.

[0479] Step 2:

[0480] The server collects weather and snow cover information from external sources based on location information received from the terminal. Specifically, the server sends requests to external sources via APIs and receives weather forecasts and snow cover information in JSON format as responses. Through this data collection process, the server maintains up-to-date weather information related to the user's current location.

[0481] Step 3:

[0482] The server analyzes the acquired weather data and snow cover information using a machine learning model. In this step, the data processing model uses libraries such as TensorFlow to receive data as input and generate weather variability forecasts and skiing condition forecasts. As a result of the analysis, the server outputs future weather conditions to provide to the user.

[0483] Step 4:

[0484] The server organizes the analyzed data and generates notification messages to send to the user's terminal. These messages may include specific weather advice, such as "Snowfall is expected between 2 and 3 PM." This output allows users to receive real-time information.

[0485] Step 5:

[0486] The terminal receives notification messages from the server, displays them on its screen, and provides voice alerts to the user using an audio output device. In this process, the terminal visualizes the received data, and speech synthesis software communicates the information to the user.

[0487] Step 6:

[0488] Users operate their devices to share acquired weather and snow depth information on a social networking platform. In this step, users add comments based on the provided information and post content including photos and other media. This output allows users to share slope information with other users, stimulating information exchange within the community.

[0489] (Application Example 1)

[0490] 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."

[0491] When enjoying winter sports, traditional methods make it difficult for users to determine the optimal skiing time because they cannot obtain accurate information about changing weather conditions and snow depth in real time. Furthermore, the lack of visual information on the slopes makes quick and efficient decision-making difficult. In addition, there are limited means to effectively share the information obtained with other users. A system is needed to solve these problems and improve the user experience.

[0492] 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.

[0493] In this invention, the server includes means for collecting data including geographical information of the user terminal from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions; means for delivering real-time weather and snow depth information to the user based on the analysis results; and means for providing the above information to the user using visual and audio output. This enables the user to determine the optimal skiing time on the slopes and respond quickly while visually confirming the information.

[0494] A "user terminal" is a device that acquires geographical information, receives the analyzed data, and provides the user with information visually and audibly.

[0495] "External information sources" refer to the data supply infrastructure that servers access to provide data such as weather forecasts and snowfall information.

[0496] A "machine learning algorithm" is a technical method for analyzing collected weather data and snow cover information to predict skiing conditions.

[0497] "Real-time weather and snow information" refers to detailed data about the immediate environmental conditions at the user's current location.

[0498] "Visual and audio output" refers to methods that utilize displays and audio guidance to provide information to users.

[0499] "The ability to overlay information onto the field of view" refers to a technology that displays information directly within the user's field of view, allowing them to view it in conjunction with the real world.

[0500] "Sharing ski resort information via SNS" refers to a function that allows users to exchange and share information they have obtained with other users through social networking services.

[0501] "Recommending an appropriate skiing time" means suggesting the most desirable start and end times for the user.

[0502] A "generative AI model" is an artificial intelligence technology that generates or analyzes diverse information based on given prompts.

[0503] A "prompt statement" is an instruction given to a system when generating specific information.

[0504] This embodiment relates to a system for providing winter sports enthusiasts with real-time, accurate slope information. This system is built utilizing user terminals, servers, and external information sources.

[0505] The user's device uses GPS functionality to determine its geographical location. Once the device obtains location information, it sends it to the server, prompting the server to retrieve relevant ski slope information. The server collects weather forecast data and snow depth data from external sources. This utilizes open weather APIs and existing databases that provide snow depth information.

[0506] The collected data is analyzed on the server using machine learning algorithms. The purpose of the analysis is to predict changes in skiing conditions and weather, and to recommend the optimal skiing time for the user.

[0507] The analysis results are delivered to the user's terminal in real time. The terminal visually presents the information through a display device (e.g., smart glasses or a smartphone display). It is also possible to alert the user using an audio output function. This allows the user to quickly obtain information on-site and appropriately adjust their plan.

[0508] Users can use social media to share some of the information they obtain with other users. This process helps to spread users' own experiences and improve the winter sports experience for the entire community.

[0509] For example, if a user checks snow conditions at a ski resort using smart glasses, and it predicts snowfall in the morning and improved weather in the afternoon, the device will notify the user that the afternoon is the recommended time for skiing. Based on this, the user can change their skiing plan to start in the afternoon.

[0510] An example of a prompt using a generative AI model is: "I'm planning a ski trip for next weekend, so please tell me the optimal time to ski based on the current weather forecast and snow conditions for the ski areas in Nagano Prefecture."

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user's device obtains its current location using GPS functionality. This location information becomes input data to the server. The user's device transmits this geographical information to the server.

[0514] Step 2:

[0515] The server collects weather forecast and snow depth data from relevant external sources based on the received geographical information. Here, it calls an open weather API to obtain the necessary data. This data will serve as input for subsequent analysis.

[0516] Step 3:

[0517] The server uses the collected data to run machine learning algorithms. This calculation predicts weather changes and skiing conditions. The output of the analysis includes information such as the best time to ski and changes in snow cover.

[0518] Step 4:

[0519] Based on the analysis results, the server delivers information to user terminals in real time. This information includes predicted weather conditions and snow cover. This information is provided as visual and audio output on the user terminal.

[0520] Step 5:

[0521] Based on the information received, users adjust their skiing plans. For example, if the recommended time for skiing is the afternoon based on snowfall in the morning and improved weather in the afternoon, the user will change their schedule.

[0522] Step 6:

[0523] Users can share the information they obtain with other users via social media. This helps spread information about ski resorts and improves the overall experience for the community. When sharing, a generative AI model is used to appropriately summarize the information using prompt sentences.

[0524] 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.

[0525] This invention provides a system that offers highly accurate slope information to users who enjoy skiing and snowboarding, and in particular, enables the personalization of information through user emotion recognition. The system mainly consists of a user terminal, a server, an emotion engine, and external information sources.

[0526] The user terminal acquires the user's geographical information and sends it to the server. Furthermore, the emotion engine analyzes the user's voice input and facial expressions to identify the user's emotional state. This information is sent to the server and used to optimize how information is delivered.

[0527] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on weather data and snow depth information collected from external sources. The results of this analysis are transmitted to the user's terminal in real time and displayed visually on the terminal's screen. In addition, the server adjusts the content and format of the information presented based on the user's emotions obtained by the emotion engine. For example, if the user indicates excitement, new slope information will be presented along with positive wording.

[0528] The device visually and audibly conveys information provided by the server to the user. Based on this information, users can choose a ski resort or plan their skiing trip. Furthermore, they can share environmental information and comments based on their own feelings on social media. By utilizing this sharing function, other users can leverage real-time information to enhance the overall skiing experience for the community.

[0529] This system allows users to receive slope information optimized for their geographical location and personal emotions, thereby improving the accuracy and satisfaction of their skiing experience. By combining emotion recognition technology and weather information processing technology, this system offers a new way to enjoy winter sports.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users enter their geographical information into the app using their device. This information may also be automatically retrieved by the device, for example, by using GPS to determine the user's current location.

[0533] Step 2:

[0534] The terminal transmits the acquired geographical information to the server. This provides the basic data needed to determine the range of ski slope information that the user can access.

[0535] Step 3:

[0536] The device uses voice input and facial expression analysis to recognize the user's emotional state through an emotion engine. This information is accumulated as the user interacts with the app.

[0537] Step 4:

[0538] The server collects relevant weather and snow depth information from external sources based on the user's geographical information. It uses an API to retrieve the latest weather conditions.

[0539] Step 5:

[0540] The server cleans the collected data, performs data transformations as needed, and then applies machine learning algorithms to predict future weather forecasts and skiing conditions.

[0541] Step 6:

[0542] The server selects the most relevant information to provide to the user based on predictive data. It then personalizes the presentation format and content of the information, taking into account the user's emotional state as determined by the emotion engine.

[0543] Step 7:

[0544] The server transmits the selected information to the user's terminal in real time. This can include emotionally-based customized messages and visual effects.

[0545] Step 8:

[0546] The terminal receives information from the server, displays it visually on the screen, and plays audio guides as needed to provide information to the user. As a result, users can easily understand the slope conditions and weather forecast.

[0547] Step 9:

[0548] Based on the information they receive, users can plan their skiing trips and choose recommended slopes. They can also share comments, including emotional expressions, through social media, sharing their experiences with other users.

[0549] This series of processes allows users to enjoy a more personalized skiing experience.

[0550] (Example 2)

[0551] 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."

[0552] When enjoying winter sports, weather and snow conditions are crucial factors, but obtaining this information in real time and in an individualized manner is difficult. Furthermore, information is not provided in a way that takes into account the user's emotional state, resulting in a problem where user satisfaction is not sufficiently high.

[0553] 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.

[0554] In this invention, the server includes means for acquiring spatial information of the user terminal and combining it with environmental data obtained from external information sources to evaluate weather forecasts and skiing conditions using a predictive analysis model; means for providing personalized weather and snow depth information in real time based on the analyzed results and the user's emotional state; and means for communicating the above information to the user through visual and auditory outputs. This makes it possible to appropriately provide information optimized for each user's situation in real time.

[0555] A "user terminal" is an electronic device used by a user that has functions such as acquiring location information and analyzing emotional states.

[0556] "Spatial information" refers to location data acquired by the user's terminal, primarily geographical coordinate information such as latitude and longitude.

[0557] "External information sources" refer to external data providers and databases that the server accesses, primarily providing information on weather and snowfall.

[0558] A "predictive analysis model" refers to an algorithm or mathematical model used to predict future weather and snow conditions based on past data.

[0559] "Emotional state" refers to a psychological state identified by analyzing the user's voice and facial expressions, and includes states such as "excitement," "reassurance," and "anxiety."

[0560] A "communication network" refers to a digital communication infrastructure that allows users to share information with other users via the internet or other means.

[0561] "Data quality" refers to a measure of how to evaluate the accuracy, reliability, and temporal precision of information, and is important for improving the accuracy of collected data.

[0562] This invention is a system that provides personalized ski resort information based on the user's geographical and emotional information, aimed at winter sports enthusiasts. The system mainly consists of a user terminal, a server, an external data source, and an emotional analysis engine.

[0563] The user's device acquires its current location using a GPS module. It also collects voice and facial expressions as input data using a microphone and camera, and sends this data to an emotion analysis engine. This allows the user's emotional state to be identified.

[0564] The server receives location and sentiment information transmitted from the terminal. The server uses APIs to collect weather and snow depth data from external data sources. For example, it uses APIs from common weather data providers for weather information. The server uses predictive analytics models built with machine learning libraries to predict weather and skiing conditions and sends these results to the terminal.

[0565] The device visually displays data and also conveys information to the user audibly through a voice assistant. Based on the information obtained, the user can plan their skiing trips or share information with other users via social media. A simple interface via a communication network is used for this purpose.

[0566] As a concrete example, if a user asks the terminal, "What are the skiing conditions today?", the system uses location and sentiment information to provide optimized weather information in real time. One example of a prompt message is, "Show detailed information about the ski resort based on the user's geographical information and sentiment." By processing this prompt on the server, the system can provide the user with completely personalized information.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The user terminal first uses a GPS module to obtain the user's current location. The input is latitude and longitude information provided by the GPS, and the output is the user's current location data. The terminal transmits this location data to the server in real time.

[0570] Step 2:

[0571] Next, the user terminal uses a microphone and camera to capture the user's voice and facial expressions. At this time, the voice data and image data become input and are passed to voice analysis software and facial expression analysis algorithms, respectively. This identifies the user's emotional state (e.g., "excited," "calm"), and the results are sent to the server as output.

[0572] Step 3:

[0573] The server accesses external data sources and collects weather and snow depth data via APIs. This external environmental data serves as input, and is analyzed using machine learning libraries. The output is an evaluation of future weather and skiing conditions, which is then sent to the user's terminal.

[0574] Step 4:

[0575] The server creates optimized information based on location and sentiment information received from the user. Geographic and sentiment information are used as input, and the analyzed data is adjusted to generate personalized weather information as output.

[0576] Step 5:

[0577] The user terminal receives information from the server and provides guidance through a visual display and voice assistant. Here, the analysis results from the server serve as input, and based on this, speech synthesis and screen displays are output. Operations are then performed to provide the user with specific action guidelines.

[0578] Step 6:

[0579] Users share information obtained through their devices with other users using social networking services (SNS). Here, the input consists of comments based on information and emotions selected by the user, which are then posted to an online platform as output. This process enables real-time information sharing.

[0580] (Application Example 2)

[0581] 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."

[0582] Existing information systems for winter sports only provide general weather and slope information, and do not optimize information based on the individual user's condition or feelings. Therefore, there is a problem in that the usefulness and satisfaction of the information provided to users are not sufficiently improved.

[0583] 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.

[0584] In this invention, the server includes means for aggregating and analyzing data, including the user's geographical information; means for recognizing the user's emotional state and adjusting the presented information accordingly; and means for improving the accuracy of the collected data, evaluating its reliability, and optimizing the information. This makes it possible to provide real-time weather and recommendation information optimized to each user's individual emotions and state.

[0585] A "user terminal" is a portable or stationary electronic device used by a user to input and output information.

[0586] "Geographic information" refers to data related to location information and the user's activity locations.

[0587] An "external information source" is a data source outside the system that provides weather data, snow cover information, and other similar information.

[0588] A "machine learning algorithm" is a programming technique that automatically extracts regularities and patterns from large amounts of data to perform predictions and classifications.

[0589] "Emotion recognition means" refers to technology for analyzing and identifying a user's emotional state from their voice and facial expressions.

[0590] "Visual and auditory output" refers to two methods of information presentation: image information via a display and auditory information via speakers.

[0591] "Means of sharing ski resort information via SNS" refers to methods of exchanging personal experiences and information with other users through social networking services.

[0592] "Methods for evaluating data reliability" refer to technologies that establish criteria for judging the accuracy and validity of collected information and for utilizing it.

[0593] The system that implements this application first obtains geographical information from the user's device, such as a smartphone or tablet, and sends it to a server. This allows the system to determine the user's location and prepares to collect relevant weather and snow depth information from external sources.

[0594] The server analyzes collected weather data and snow cover information using machine learning algorithms. This analysis predicts real-time weather forecasts and skiing conditions. Furthermore, the server uses an emotion recognition engine to recognize the user's emotional state from their voice input and facial expressions, and appropriately adjusts the information presented.

[0595] The user terminal provides users with analysis results from the server, both visually and audibly. For example, it can display the latest slope information as an image on the screen and transmit information audibly through a speaker. In addition, users can share their experiences and real-time information with other users via social networking services (SNS).

[0596] As a concrete example, to further enhance the ski resort experience, the app will present information about newly opened ski slopes along with a positive message when it detects that the user's emotional state is "excited." This method allows for the catering to individual user needs and helps them enjoy skiing and snowboarding to the fullest.

[0597] An example of prompt text to input into a generative AI model is: "If the user's emotional state is recognized as 'excited,' generate information about a newly opened ski resort in Sendai and a positive message."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user's terminal uses GPS functionality to obtain the user's geographical information. This geographical information is sent to the server as input data to identify the user's current location and activity area. Based on this location information, the server selects the necessary external information sources.

[0601] Step 2:

[0602] The server uses machine learning algorithms to perform predictive analysis based on weather data and snow depth information collected from external sources. This process generates analyzed weather forecasts and skiing conditions. The output results serve as the basic data provided to users.

[0603] Step 3:

[0604] The user terminal collects the user's emotional state through voice input and a facial camera. This data is sent to the server as input to identify the user's emotional state. An emotion recognition engine analyzes this data to determine the user's emotional state.

[0605] Step 4:

[0606] The server adjusts the information it presents based on the user's identified emotional state. For example, if the user is "excited," the server will select information about newly opened ski slopes along with positive wording. This adjusted information is then output and provided to the user.

[0607] Step 5:

[0608] The user terminal provides the user with optimized information received from the server, both visually (information displayed on the screen) and audibly (audio output from the speaker). Based on this output, the user can make decisions based on real-time information and create a skiing plan.

[0609] Step 6:

[0610] Users share the information provided and their own experiences with other users via social media. This sharing allows users to utilize the outputted information and promotes information exchange and improvement of the quality of experiences throughout the community.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention is a system that provides real-time, accurate slope information for winter sports enthusiasts such as skiers and snowboarders. The system consists of a user terminal, a server, and an external information source.

[0629] First, the user's device uses location services to identify its geographical location. This information is then sent to a server to identify ski resorts the user might access and to collect relevant data.

[0630] The server acquires weather forecast data and snow depth information from external sources and analyzes this data using machine learning algorithms. This analysis allows for predictions of future weather changes and skiing conditions. For example, by predicting snow quality and quantity, the server can recommend appropriate skiing times to users.

[0631] The analyzed data is distributed from the server to the user's terminal, which receives it and communicates it to the user visually and audibly. For example, snow conditions on the ski slopes are displayed on the screen, along with voice notifications. This allows users to obtain quick and accurate information.

[0632] Furthermore, users can utilize the app's features to share information obtained through social media in real time. For example, they can exchange information with other users by posting photos they took on the slopes or weather information they obtained. This enhances the skiing experience for the entire community.

[0633] Thus, the present invention revolutionizes the winter sports experience by integrating multiple data sources, utilizing machine learning to make highly accurate predictions, and providing users with comprehensive slope information.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] The server collects the latest weather and snowfall information from external sources via APIs. This data includes temperature, wind speed, and snowfall amount.

[0637] Step 2:

[0638] The server stores the collected data in a database and performs necessary data cleaning before applying machine learning algorithms. This includes imputing missing values ​​and correcting outliers.

[0639] Step 3:

[0640] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on the cleaned data. The model is trained on historical data and predicts future conditions based on new input data.

[0641] Step 4:

[0642] The server analyzes the prediction results and generates information important to the user (e.g., optimal skiing time and slope conditions). The generated information is then prepared for output as visual and audio content.

[0643] Step 5:

[0644] The server sends the generated information to the user's terminal. The transmitted information is designed to be delivered quickly while maintaining real-time accuracy.

[0645] Step 6:

[0646] The terminal visually displays information received from the server and provides voice navigation as needed. This allows users to check current slope conditions and weather forecasts.

[0647] Step 7:

[0648] Users can optimize their slope selection and skiing plans based on information received through their devices. They can also utilize a dedicated sharing function to share the information they obtain on social media.

[0649] Step 8:

[0650] The device supports users posting to social media and sharing information with other users. This allows the entire community to exchange information in real time and have a better skiing experience.

[0651] (Example 1)

[0652] 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".

[0653] There is a challenge in that winter sports enthusiasts lack the information necessary to accurately understand local weather and snow conditions in real time, enabling them to enjoy sports comfortably and safely. Furthermore, users often cannot obtain the necessary information when planning their next steps or movements. Existing information provision systems have been criticized for their insufficient data accuracy and the provision of personalized information.

[0654] 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.

[0655] In this invention, the server includes means for collecting weather data and snow conditions from external information sources using data including location information of the user device, and analyzing this information using a data processing model for analysis; means for providing the user with immediate weather and snow conditions based on the analysis results; and means for presenting the above information to the user using a display device and an audio output device. As a result, the user can always obtain the latest and most accurate information on local weather and slope conditions, and can obtain real-time information to make informed decisions about enjoying winter sports with peace of mind.

[0656] A "user device" refers to an electronic device that a user can hold in their hand and operate to generate or use location information or other data.

[0657] "Location information" refers to the geographical coordinate data of the user's device's current location, usually expressed as latitude and longitude.

[0658] "External information sources" refer to information services accessible via the internet that provide weather data and snow depth data.

[0659] "Weather data" refers to data that shows the current or expected weather conditions in a particular area, and includes temperature, precipitation, wind speed, etc.

[0660] "Snow cover conditions" refers to information indicating the density, height, and quality of snow in a specific region.

[0661] A "data processing model" refers to a computational method that uses a specific algorithm or process to analyze collected data and transform it into meaningful information.

[0662] "Analyzed results" refers to a set of information generated by a data processing model that provides useful insights to the user.

[0663] "Real-time weather and snow information" refers to information that provides users with detailed and accurate data on real-time weather and snow conditions immediately.

[0664] A "display device" refers to a hardware device used to visually display information in the form of text, images, or video.

[0665] An "acoustic output device" refers to a hardware device that generates voice or sound and transmits auditory information to the user.

[0666] This invention provides a system for winter sports enthusiasts to obtain real-time and accurate information about ski slopes. The system mainly consists of a user terminal, a server, and an external information source.

[0667] First, the user's device is an electronic device such as a smartphone or tablet, which obtains the user's location information via GPS. This location information is transmitted to the server via Wi-Fi or a mobile network. When the user launches an application, the device quickly determines the current latitude and longitude and plays a role in transmitting the necessary data to the server.

[0668] Based on the received location information, the server collects the latest weather and snow depth data from multiple external sources (e.g., weather forecasting services and snow depth information services). This process retrieves data in JSON or XML format and uses services such as OpenWeatherMap and weather APIs.

[0669] The collected data is analyzed using machine learning algorithms. Specifically, libraries such as TensorFlow and PyTorch are used to process the data and predict future weather changes and slope conditions. This analysis allows users to obtain information that predicts the optimal skiing time and slope conditions for the day and the following day.

[0670] Based on the analysis results, the server sends information to the user's terminal. The terminal then displays the information on its screen in text and graphic format, and provides alerts and notifications to the user using an audio output device. For example, if a sudden weather change is predicted, the user will be alerted through visual and audio notifications.

[0671] Furthermore, users can utilize social networking within the application to share their skiing experiences and photos taken on the slopes with other users. This promotes communication among users and enriches the skiing experience as a community.

[0672] An example of a prompt is: "Please describe in detail how the server retrieves data from an external API, parses it, and generates a notification message after receiving the user's location information." This prompt can be used to test the system's operation and verify the accuracy of predictions and notifications.

[0673] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0674] Step 1:

[0675] The device obtains the user's current location using GPS functionality. This input includes latitude and longitude data. The device transmits this geographical data to the server via Wi-Fi or a mobile network. In this step, location information is obtained immediately when the user launches the app, and data transfer from the device to the server begins.

[0676] Step 2:

[0677] The server collects weather and snow cover information from external sources based on location information received from the terminal. Specifically, the server sends requests to external sources via APIs and receives weather forecasts and snow cover information in JSON format as responses. Through this data collection process, the server maintains up-to-date weather information related to the user's current location.

[0678] Step 3:

[0679] The server analyzes the acquired weather data and snow cover information using a machine learning model. In this step, the data processing model uses libraries such as TensorFlow to receive data as input and generate weather variability forecasts and skiing condition forecasts. As a result of the analysis, the server outputs future weather conditions to provide to the user.

[0680] Step 4:

[0681] The server organizes the analyzed data and generates notification messages to send to the user's terminal. These messages may include specific weather advice, such as "Snowfall is expected between 2 and 3 PM." This output allows users to receive real-time information.

[0682] Step 5:

[0683] The terminal receives notification messages from the server, displays them on its screen, and provides voice alerts to the user using an audio output device. In this process, the terminal visualizes the received data, and speech synthesis software communicates the information to the user.

[0684] Step 6:

[0685] Users operate their devices to share acquired weather and snow depth information on a social networking platform. In this step, users add comments based on the provided information and post content including photos and other media. This output allows users to share slope information with other users, stimulating information exchange within the community.

[0686] (Application Example 1)

[0687] 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".

[0688] When enjoying winter sports, traditional methods make it difficult for users to determine the optimal skiing time because they cannot obtain accurate information about changing weather conditions and snow depth in real time. Furthermore, the lack of visual information on the slopes makes quick and efficient decision-making difficult. In addition, there are limited means to effectively share the information obtained with other users. A system is needed to solve these problems and improve the user experience.

[0689] 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.

[0690] In this invention, the server includes means for collecting data including geographical information of the user terminal from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions; means for delivering real-time weather and snow depth information to the user based on the analysis results; and means for providing the above information to the user using visual and audio output. This enables the user to determine the optimal skiing time on the slopes and respond quickly while visually confirming the information.

[0691] A "user terminal" is a device that acquires geographical information, receives the analyzed data, and provides the user with information visually and audibly.

[0692] "External information sources" refer to the data supply infrastructure that servers access to provide data such as weather forecasts and snowfall information.

[0693] A "machine learning algorithm" is a technical method for analyzing collected weather data and snow cover information to predict skiing conditions.

[0694] "Real-time weather and snow information" refers to detailed data about the immediate environmental conditions at the user's current location.

[0695] "Visual and audio output" refers to methods that utilize displays and audio guidance to provide information to users.

[0696] "The ability to overlay information onto the field of view" refers to a technology that displays information directly within the user's field of view, allowing them to view it in conjunction with the real world.

[0697] "Sharing ski resort information via SNS" refers to a function that allows users to exchange and share information they have obtained with other users through social networking services.

[0698] "Recommending an appropriate skiing time" means suggesting the most desirable start and end times for the user.

[0699] A "generative AI model" is an artificial intelligence technology that generates or analyzes diverse information based on given prompts.

[0700] A "prompt statement" is an instruction given to a system when generating specific information.

[0701] This embodiment relates to a system for providing winter sports enthusiasts with real-time, accurate slope information. This system is built utilizing user terminals, servers, and external information sources.

[0702] The user's device uses GPS functionality to determine its geographical location. Once the device obtains location information, it sends it to the server, prompting the server to retrieve relevant ski slope information. The server collects weather forecast data and snow depth data from external sources. This utilizes open weather APIs and existing databases that provide snow depth information.

[0703] The collected data is analyzed on the server using machine learning algorithms. The purpose of the analysis is to predict changes in skiing conditions and weather, and to recommend the optimal skiing time for the user.

[0704] The analysis results are delivered to the user's terminal in real time. The terminal visually presents the information through a display device (e.g., smart glasses or a smartphone display). It is also possible to alert the user using an audio output function. This allows the user to quickly obtain information on-site and appropriately adjust their plan.

[0705] Users can use social media to share some of the information they obtain with other users. This process helps to spread users' own experiences and improve the winter sports experience for the entire community.

[0706] For example, if a user checks snow conditions at a ski resort using smart glasses, and it predicts snowfall in the morning and improved weather in the afternoon, the device will notify the user that the afternoon is the recommended time for skiing. Based on this, the user can change their skiing plan to start in the afternoon.

[0707] An example of a prompt using a generative AI model is: "I'm planning a ski trip for next weekend, so please tell me the optimal time to ski based on the current weather forecast and snow conditions for the ski areas in Nagano Prefecture."

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The user's device obtains its current location using GPS functionality. This location information becomes input data to the server. The user's device transmits this geographical information to the server.

[0711] Step 2:

[0712] The server collects weather forecast and snow depth data from relevant external sources based on the received geographical information. Here, it calls an open weather API to obtain the necessary data. This data will serve as input for subsequent analysis.

[0713] Step 3:

[0714] The server uses the collected data to run machine learning algorithms. This calculation predicts weather changes and skiing conditions. The output of the analysis includes information such as the best time to ski and changes in snow cover.

[0715] Step 4:

[0716] Based on the analysis results, the server delivers information to user terminals in real time. This information includes predicted weather conditions and snow cover. This information is provided as visual and audio output on the user terminal.

[0717] Step 5:

[0718] Based on the information received, users adjust their skiing plans. For example, if the recommended time for skiing is the afternoon based on snowfall in the morning and improved weather in the afternoon, the user will change their schedule.

[0719] Step 6:

[0720] Users can share the information they obtain with other users via social media. This helps spread information about ski resorts and improves the overall experience for the community. When sharing, a generative AI model is used to appropriately summarize the information using prompt sentences.

[0721] 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.

[0722] This invention provides a system that offers highly accurate slope information to users who enjoy skiing and snowboarding, and in particular, enables the personalization of information through user emotion recognition. The system mainly consists of a user terminal, a server, an emotion engine, and external information sources.

[0723] The user terminal acquires the user's geographical information and sends it to the server. Furthermore, the emotion engine analyzes the user's voice input and facial expressions to identify the user's emotional state. This information is sent to the server and used to optimize how information is delivered.

[0724] The server uses machine learning algorithms to predict weather forecasts and skiing conditions based on weather data and snow depth information collected from external sources. The results of this analysis are transmitted to the user's terminal in real time and displayed visually on the terminal's screen. In addition, the server adjusts the content and format of the information presented based on the user's emotions obtained by the emotion engine. For example, if the user indicates excitement, new slope information will be presented along with positive wording.

[0725] The device visually and audibly conveys information provided by the server to the user. Based on this information, users can choose a ski resort or plan their skiing trip. Furthermore, they can share environmental information and comments based on their own feelings on social media. By utilizing this sharing function, other users can leverage real-time information to enhance the overall skiing experience for the community.

[0726] This system allows users to receive slope information optimized for their geographical location and personal emotions, thereby improving the accuracy and satisfaction of their skiing experience. By combining emotion recognition technology and weather information processing technology, this system offers a new way to enjoy winter sports.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] Users enter their geographical information into the app using their device. This information may also be automatically retrieved by the device, for example, by using GPS to determine the user's current location.

[0730] Step 2:

[0731] The terminal transmits the acquired geographical information to the server. This provides the basic data needed to determine the range of ski slope information that the user can access.

[0732] Step 3:

[0733] The device uses voice input and facial expression analysis to recognize the user's emotional state through an emotion engine. This information is accumulated as the user interacts with the app.

[0734] Step 4:

[0735] The server collects relevant weather and snow depth information from external sources based on the user's geographical information. It uses an API to retrieve the latest weather conditions.

[0736] Step 5:

[0737] The server cleans the collected data, performs data transformations as needed, and then applies machine learning algorithms to predict future weather forecasts and skiing conditions.

[0738] Step 6:

[0739] The server selects the most relevant information to provide to the user based on predictive data. It then personalizes the presentation format and content of the information, taking into account the user's emotional state as determined by the emotion engine.

[0740] Step 7:

[0741] The server transmits the selected information to the user's terminal in real time. This can include emotionally-based customized messages and visual effects.

[0742] Step 8:

[0743] The terminal receives information from the server, displays it visually on the screen, and plays audio guides as needed to provide information to the user. As a result, users can easily understand the slope conditions and weather forecast.

[0744] Step 9:

[0745] Based on the information they receive, users can plan their skiing trips and choose recommended slopes. They can also share comments, including emotional expressions, through social media, sharing their experiences with other users.

[0746] This series of processes allows users to enjoy a more personalized skiing experience.

[0747] (Example 2)

[0748] 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".

[0749] When enjoying winter sports, weather and snow conditions are crucial factors, but obtaining this information in real time and in an individualized manner is difficult. Furthermore, information is not provided in a way that takes into account the user's emotional state, resulting in a problem where user satisfaction is not sufficiently high.

[0750] 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.

[0751] In this invention, the server includes means for acquiring spatial information of the user terminal and combining it with environmental data obtained from external information sources to evaluate weather forecasts and skiing conditions using a predictive analysis model; means for providing personalized weather and snow depth information in real time based on the analyzed results and the user's emotional state; and means for communicating the above information to the user through visual and auditory outputs. This makes it possible to appropriately provide information optimized for each user's situation in real time.

[0752] A "user terminal" is an electronic device used by a user that has functions such as acquiring location information and analyzing emotional states.

[0753] "Spatial information" refers to location data acquired by the user's terminal, primarily geographical coordinate information such as latitude and longitude.

[0754] "External information sources" refer to external data providers and databases that the server accesses, primarily providing information on weather and snowfall.

[0755] A "predictive analysis model" refers to an algorithm or mathematical model used to predict future weather and snow conditions based on past data.

[0756] "Emotional state" refers to a psychological state identified by analyzing the user's voice and facial expressions, and includes states such as "excitement," "reassurance," and "anxiety."

[0757] A "communication network" refers to a digital communication infrastructure that allows users to share information with other users via the internet or other means.

[0758] "Data quality" refers to a measure of how to evaluate the accuracy, reliability, and temporal precision of information, and is important for improving the accuracy of collected data.

[0759] This invention is a system that provides personalized ski resort information based on the user's geographical and emotional information, aimed at winter sports enthusiasts. The system mainly consists of a user terminal, a server, an external data source, and an emotional analysis engine.

[0760] The user's device acquires its current location using a GPS module. It also collects voice and facial expressions as input data using a microphone and camera, and sends this data to an emotion analysis engine. This allows the user's emotional state to be identified.

[0761] The server receives location and sentiment information transmitted from the terminal. The server uses APIs to collect weather and snow depth data from external data sources. For example, it uses APIs from common weather data providers for weather information. The server uses predictive analytics models built with machine learning libraries to predict weather and skiing conditions and sends these results to the terminal.

[0762] The device visually displays data and also conveys information to the user audibly through a voice assistant. Based on the information obtained, the user can plan their skiing trips or share information with other users via social media. A simple interface via a communication network is used for this purpose.

[0763] As a concrete example, if a user asks the terminal, "What are the skiing conditions today?", the system uses location and sentiment information to provide optimized weather information in real time. One example of a prompt message is, "Show detailed information about the ski resort based on the user's geographical information and sentiment." By processing this prompt on the server, the system can provide the user with completely personalized information.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] The user terminal first uses a GPS module to obtain the user's current location. The input is latitude and longitude information provided by the GPS, and the output is the user's current location data. The terminal transmits this location data to the server in real time.

[0767] Step 2:

[0768] Next, the user terminal uses a microphone and camera to capture the user's voice and facial expressions. At this time, the voice data and image data become input and are passed to voice analysis software and facial expression analysis algorithms, respectively. This identifies the user's emotional state (e.g., "excited," "calm"), and the results are sent to the server as output.

[0769] Step 3:

[0770] The server accesses external data sources and collects weather and snow depth data via APIs. This external environmental data serves as input, and is analyzed using machine learning libraries. The output is an evaluation of future weather and skiing conditions, which is then sent to the user's terminal.

[0771] Step 4:

[0772] The server creates optimized information based on location and sentiment information received from the user. Geographic and sentiment information are used as input, and the analyzed data is adjusted to generate personalized weather information as output.

[0773] Step 5:

[0774] The user terminal receives information from the server and provides guidance through a visual display and voice assistant. Here, the analysis results from the server serve as input, and based on this, speech synthesis and screen displays are output. Operations are then performed to provide the user with specific action guidelines.

[0775] Step 6:

[0776] Users share information obtained through their devices with other users using social networking services (SNS). Here, the input consists of comments based on information and emotions selected by the user, which are then posted to an online platform as output. This process enables real-time information sharing.

[0777] (Application Example 2)

[0778] 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".

[0779] Existing information systems for winter sports only provide general weather and slope information, and do not optimize information based on the individual user's condition or feelings. Therefore, there is a problem in that the usefulness and satisfaction of the information provided to users are not sufficiently improved.

[0780] 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.

[0781] In this invention, the server includes means for aggregating and analyzing data, including the user's geographical information; means for recognizing the user's emotional state and adjusting the presented information accordingly; and means for improving the accuracy of the collected data, evaluating its reliability, and optimizing the information. This makes it possible to provide real-time weather and recommendation information optimized to each user's individual emotions and state.

[0782] A "user terminal" is a portable or stationary electronic device used by a user to input and output information.

[0783] "Geographic information" refers to data related to location information and the user's activity locations.

[0784] An "external information source" is a data source outside the system that provides weather data, snow cover information, and other similar information.

[0785] A "machine learning algorithm" is a programming technique that automatically extracts regularities and patterns from large amounts of data to perform predictions and classifications.

[0786] "Emotion recognition means" refers to technology for analyzing and identifying a user's emotional state from their voice and facial expressions.

[0787] "Visual and auditory output" refers to two methods of information presentation: image information via a display and auditory information via speakers.

[0788] "Means of sharing ski resort information via SNS" refers to methods of exchanging personal experiences and information with other users through social networking services.

[0789] "Methods for evaluating data reliability" refer to technologies that establish criteria for judging the accuracy and validity of collected information and for utilizing it.

[0790] The system that implements this application first obtains geographical information from the user's device, such as a smartphone or tablet, and sends it to a server. This allows the system to determine the user's location and prepares to collect relevant weather and snow depth information from external sources.

[0791] The server analyzes collected weather data and snow cover information using machine learning algorithms. This analysis predicts real-time weather forecasts and skiing conditions. Furthermore, the server uses an emotion recognition engine to recognize the user's emotional state from their voice input and facial expressions, and appropriately adjusts the information presented.

[0792] The user terminal provides users with analysis results from the server, both visually and audibly. For example, it can display the latest slope information as an image on the screen and transmit information audibly through a speaker. In addition, users can share their experiences and real-time information with other users via social networking services (SNS).

[0793] As a concrete example, to further enhance the ski resort experience, the app will present information about newly opened ski slopes along with a positive message when it detects that the user's emotional state is "excited." This method allows for the catering to individual user needs and helps them enjoy skiing and snowboarding to the fullest.

[0794] An example of prompt text to input into a generative AI model is: "If the user's emotional state is recognized as 'excited,' generate information about a newly opened ski resort in Sendai and a positive message."

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The user's terminal uses GPS functionality to obtain the user's geographical information. This geographical information is sent to the server as input data to identify the user's current location and activity area. Based on this location information, the server selects the necessary external information sources.

[0798] Step 2:

[0799] The server uses machine learning algorithms to perform predictive analysis based on weather data and snow depth information collected from external sources. This process generates analyzed weather forecasts and skiing conditions. The output results serve as the basic data provided to users.

[0800] Step 3:

[0801] The user terminal collects the user's emotional state through voice input and a facial camera. This data is sent to the server as input to identify the user's emotional state. An emotion recognition engine analyzes this data to determine the user's emotional state.

[0802] Step 4:

[0803] The server adjusts the information it presents based on the user's identified emotional state. For example, if the user is "excited," the server will select information about newly opened ski slopes along with positive wording. This adjusted information is then output and provided to the user.

[0804] Step 5:

[0805] The user terminal provides the user with optimized information received from the server, both visually (information displayed on the screen) and audibly (audio output from the speaker). Based on this output, the user can make decisions based on real-time information and create a skiing plan.

[0806] Step 6:

[0807] Users share the information provided and their own experiences with other users via social media. This sharing allows users to utilize the outputted information and promotes information exchange and improvement of the quality of experiences throughout the community.

[0808] 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.

[0809] 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.

[0810] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A means for collecting data including geographical information of user terminals from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions,

[0832] A means of delivering real-time weather and snow depth information to users based on the analyzed results,

[0833] Means for providing the above information to the user using visual and audio output,

[0834] A system that includes a means for users to share information about ski resorts via social media.

[0835] (Claim 2)

[0836] The system according to claim 1, which provides personalized weather information optimized for the user.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for evaluating the reliability of data from external sources in order to improve the accuracy of the collected data.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A means for collecting weather data and snow conditions from external sources using data including the location information of the user device, and analyzing this information using a data processing model for analysis,

[0842] A means of providing users with immediate weather and snow depth information based on the analyzed results,

[0843] A means for presenting the above information to the user using a display device and an audio output device,

[0844] A means for users to transmit information about the ski slopes to other users via communication media,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which presents personalized weather data optimized based on user information.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for determining the credibility of data from external sources in order to improve the accuracy of the collected data.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A means for collecting data including geographical information of user terminals from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions,

[0853] A means of delivering real-time weather and snow depth information to users based on the analyzed results,

[0854] Means for providing the above information to the user using visual and audio output,

[0855] A means to enable a user to overlay a display onto their field of view via an information terminal,

[0856] A system that includes a means for users to share information about ski resorts via social media.

[0857] (Claim 2)

[0858] The system according to claim 1, which provides personalized weather information optimized for the user and recommends a suitable skiing time.

[0859] (Claim 3)

[0860] The system according to claim 1, which includes means for evaluating the reliability of data from external sources in order to improve the accuracy of the collected data, and provides information in response to input to a generating AI model.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A means for acquiring spatial information from a user terminal, combining it with environmental data obtained from external sources, and evaluating weather forecasts and skiing conditions using a predictive analysis model,

[0864] A means for providing real-time, personalized weather and snowfall information based on analyzed results and the user's emotional state,

[0865] Means for conveying the above information to the user through visual and auditory output,

[0866] A system that includes means for users to share activity location information via a communication network.

[0867] (Claim 2)

[0868] The system according to claim 1, comprising means for adjusting the content and format of information presented based on the results of user sentiment analysis.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for evaluating the accuracy of acquired external information and improving data quality.

[0871] "Application example 2 when combining with an emotional engine"

[0872] (Claim 1)

[0873] A means for collecting data including geographical information of user terminals from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions,

[0874] A means of delivering real-time weather and snow depth information to users based on the analyzed results,

[0875] Means for providing the above information to the user using visual and audio output,

[0876] A means for users to share information about ski resorts via social media,

[0877] An emotion recognition means for recognizing the user's emotional state and adjusting the content of information presented based on that state,

[0878] A means of providing relevant recommendation information when the user's emotional state is in a specific state,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which provides user-optimized weather information and personalized information based on emotions.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means for evaluating the reliability of data from external sources and optimizing the information in relation to the user's emotional state in order to improve the accuracy of the collected data. [Explanation of Symbols]

[0884] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting data including geographical information of user terminals from external sources and analyzing it using machine learning algorithms for analyzing weather forecasts and skiing conditions, A means of delivering real-time weather and snow depth information to users based on the analyzed results, Means for providing the above information to the user using visual and audio output, A system that includes a means for users to share information about ski resorts via social media.

2. The system according to claim 1, which provides personalized weather information optimized for the user.

3. The system according to claim 1, comprising means for evaluating the reliability of data from external sources in order to improve the accuracy of the collected data.

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