system

Abstract

We provide the system. [Solution] A means for collecting communication speed data and location information from a mobile terminal, A means including a generative model that uses collected data to analyze communication speeds in each region, A means for transmitting the analysis results to a mobile terminal and indicating the area where high-speed communication is possible, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional mobile communication system, since a user cannot easily grasp in advance the communication speed at their current location, there may be a case where the user stays in a place with a slow communication speed and feels dissatisfied. This problem is a factor that hinders a comfortable communication experience, especially for users who frequently use data communication.

Means for Solving the Problems

[0005] The present invention provides a system that analyzes the communication speed for each region using communication speed data and location information collected from a mobile terminal, and presents a high-speed communication available area to the mobile terminal. Thereby, since a user can easily identify a high-speed communication available area from their current location and move, it is possible to reduce dissatisfaction with the communication speed.

[0006] A "mobile terminal" is a portable communication device that can determine location information and collect communication speed data.

[0007] "Communication speed data" refers to information about the speed of data transmission at a specific time and location.

[0008] "Location information" refers to data that indicates the geographical location of a specific mobile device.

[0009] A "generative model" is a learning model that includes algorithms for predicting and analyzing communication speed based on collected data.

[0010] "Analysis results" refer to evaluation and prediction data regarding communication speeds for each region, obtained through generative models.

[0011] A "high-speed communication area" is a geographical area where data communication speeds are expected to be above a certain standard.

[0012] "Visualization" is the process of displaying analysis results visually, enabling users to intuitively understand the information. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0016] 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention provides a system for a mobile communication system that reduces user dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and a user.

[0035] Server Role

[0036] The server first collects real-time communication speed and location data from multiple mobile terminals. This data is encrypted and transmitted securely while protecting privacy. The server then analyzes the collected data using a generative model. The generative model uses machine learning algorithms to analyze real-time communication speeds in each area and predict future communication speeds. As a result of the analysis, it identifies areas where high-speed communication is predicted to be possible and transmits this information to mobile terminals.

[0037] Terminal role

[0038] Meanwhile, the mobile terminal receives the analysis results transmitted from the server. The terminal visually processes the received data and displays the high-speed communication area to the user. This display is usually done using maps or a graphical user interface, making it easy for the user to understand. The terminal may also provide voice instructions or notifications to the user as needed.

[0039] User roles

[0040] Based on information provided by the device, users move to the optimal high-speed communication area from their current location. For example, a business user in a large city, if they feel their communication speed is slowing down, can refer to the map information provided by their device and move to a high-speed communication area. This movement allows the user to enjoy a stable communication speed.

[0041] As a concrete example, suppose a user is working in a city cafe. Feeling that their internet connection is slow, the user checks a 3D map displayed on their device and discovers a plaza about 100 meters away with a faster connection. The user then moves from the cafe to the plaza and can continue working with a faster connection.

[0042] Thus, the system based on the present invention supports users in effectively selecting a communication environment and provides a comfortable communication experience.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server periodically collects communication speed data and location information from each mobile terminal. Communication is encrypted and conducted through a secure channel.

[0046] Step 2:

[0047] The server structures and preprocesses the collected data. This includes deduplication and imputation of missing values.

[0048] Step 3:

[0049] The server inputs pre-processed data into a generative model to analyze communication speeds for each region. This analysis yields speed patterns and fluctuation predictions for each region.

[0050] Step 4:

[0051] The server organizes and classifies the analysis results obtained from the generative model, and identifies areas where high-speed communication is particularly expected.

[0052] Step 5:

[0053] The server sends the analysis results to each user's terminal. This includes customized information based on the user's current location.

[0054] Step 6:

[0055] The terminal analyzes the results received from the server and displays the high-speed communication area as a map or graphical interface.

[0056] Step 7:

[0057] Users check the display on their device and use it as a reference to identify areas where high-speed communication is available. They can then move to those areas if necessary.

[0058] Step 8:

[0059] The device maintains a periodic connection with the server when the user's location is updated, in preparation for the next data collection and updating of results.

[0060] (Example 1)

[0061] 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."

[0062] In mobile communication systems, a key challenge is to enable users to select and utilize an appropriate communication environment without being dissatisfied with the communication speed. In particular, it is necessary to optimize the communication environment by making real-time fluctuations in communication speed predictable and providing users with appropriate information.

[0063] 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.

[0064] In this invention, the server includes means for acquiring communication performance data and geographic information from a mobile information terminal, means including a generation algorithm for processing the acquired information and evaluating the communication performance of each area, and means for transferring the evaluation results to the mobile information terminal and presenting high-performance communication areas. This enables the user to obtain information about the optimal communication environment in real time and decide to move to an appropriate area.

[0065] A "mobile information terminal" is a portable electronic device held by a user for collecting and presenting location information and communication performance data.

[0066] "Communication performance data" refers to numerical or statistical data regarding the speed and quality of communication.

[0067] "Geographic information" refers to data that indicates location signal information within a specific area.

[0068] A "generative algorithm" is a computational method used to analyze and predict regional communication performance based on historical and real-time data.

[0069] "Evaluation results" refer to analytical conclusions or indicators regarding communication performance for each region, obtained through a generation algorithm.

[0070] A "high-performance communication area" is a geographical area where communication performance is high and users are expected to have a superior communication experience.

[0071] A description of embodiments for carrying out the present invention will be provided.

[0072] The server processes communication performance data and geographical information collected from mobile information terminals. To achieve this, the server uses a computer system with advanced computing capabilities. Specifically, the server utilizes a cloud-based computing infrastructure to collect, encrypt, and analyze data in real time. A generative AI model applies machine learning algorithms to this data and predicts communication speeds in each region as an analysis result. This analysis uses historical communication data, making it possible to predict future fluctuations in communication performance with high accuracy.

[0073] The terminal receives analysis results transferred from the server and visualizes the information in a user-friendly format. This often involves an easy-to-use graphical user interface (GUI), displaying the information as colored areas on a map. Terminals equipped with voice assistants can also inform the user of the analysis results verbally. The terminal works seamlessly with the high data processing capabilities required of mobile information terminals to provide users with the latest communication performance information at all times.

[0074] Users can use the information provided by their device to move to the optimal communication area. In everyday use, for example, if a user experiences a decrease in communication speed while working in a cafe in the city, they can check the map displayed on their device and easily access an area where faster communication is available. This allows users to enjoy a smooth and uninterrupted communication experience.

[0075] As a concrete example, a prompt such as "Identify areas with faster communication speeds within 100 meters of my current location" can be used. This prompt serves as a basic instruction for the generative AI model to quickly identify suitable communication areas.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server receives communication performance data and geographical information from mobile information terminals. Specifically, real-time communication speed and location information measured from each terminal are encrypted and sent to the server. The input data is stored in a database and used for subsequent analysis.

[0079] Step 2:

[0080] The server inputs received communication performance data and geographical information into a generating AI model. The generating AI model uses machine learning algorithms to analyze past and present data and evaluate communication performance for each region. Specifically, it performs data cleansing and feature extraction to generate a communication speed prediction model. This yields evaluation results regarding high-performance communication areas.

[0081] Step 3:

[0082] The server transmits the evaluation results to mobile information terminals. This information includes identifying areas where high-performance communication is predicted to be possible. The server packages the analysis results along with metadata using a protocol and transfers the data to each terminal in the required format.

[0083] Step 4:

[0084] The terminal analyzes the evaluation results received from the server and visualizes them for display to the user. The input here is the analysis results sent from the server, and the output displays the high-performance communication area on a graphical interface. The information is presented as color-coded areas on a map, allowing the user to easily understand the current communication status.

[0085] Step 5:

[0086] The user moves to the optimal communication area based on the information displayed on the device. The user receives visual or audio instructions from the device to select their destination. Especially when communication is poor, the user makes a decision based on this information and attempts to access a high-performance communication area.

[0087] (Application Example 1)

[0088] 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."

[0089] In recent years, there has been a growing need for effective means to ensure users enjoy stable communication speeds in mobile communication systems. However, conventional technologies have struggled to effectively compensate for unstable communication speeds, and communication interruptions have been a particular problem in moving vehicles. Furthermore, there is no established means to analyze the vast amount of data from multiple mobile terminals in real time and quickly optimize the communication environment. Therefore, there is a strong demand for new technologies that can prevent communication interruptions, especially in autonomous vehicles, and provide passengers with a comfortable communication environment.

[0090] 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.

[0091] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible, and means for a device mounted on the vehicle to identify and present the optimal communication route. This makes it possible to prevent communication interruptions even in autonomous vehicles in motion and to always provide an optimized communication route, thereby ensuring a stable communication environment.

[0092] A "mobile terminal" is a communication device that is geographically mobile and has the function of collecting communication speed data and location information.

[0093] "Communication speed data" refers to information about the speed of communication at a specific time and place, and is a numerical representation of the amount of data transferred per unit time.

[0094] "Location information" refers to data that indicates the geographical location where a mobile device is located, and includes geographic coordinate information such as latitude and longitude.

[0095] A "generative model" is a mathematical model that uses machine learning algorithms to analyze collected communication speed data and location information to predict communication speeds for each region.

[0096] A "high-speed communication area" refers to a geographical area where communication speeds are expected to meet a certain standard, and within which devices can communicate stably.

[0097] "Vehicle-mounted devices" refer to electronic devices installed inside autonomous vehicles and used to display the optimal communication route.

[0098] "Optimal communication route" refers to the path selected to ensure that a moving vehicle enjoys the best possible communication environment, taking into account the predicted communication speed of each area located along that route.

[0099] A system for implementing this invention consists of a server, a mobile terminal, and a device mounted on a vehicle.

[0100] The server has the capability to collect communication speed data and location information from mobile terminals in real time. This data is securely transmitted using encryption technology. Subsequently, a machine learning algorithm based on a generated AI model is executed on the server to analyze the received data. This analysis predicts communication speeds for each region and identifies areas where high-speed communication is possible.

[0101] The mobile terminal receives analysis results from the server and visually displays the communication status based on them. Specifically, it visualizes areas where high-speed communication is possible on a map and informs the user. If the user perceives a decrease in communication speed, they can use this information to move to an optimal area. The terminal also shares information with devices installed in the vehicle and calculates and presents the optimal communication route. This device is integrated with the vehicle's navigation system and plays a role in guiding the autonomous vehicle along its route while maintaining an optimal communication environment.

[0102] A concrete example is an autonomous taxi operating in an urban area. To ensure passengers can comfortably enjoy video streaming within the taxi, the system would incorporate a route that provides the optimal communication speed into the navigation system. This function is particularly effective for intercity travel and in areas with unstable communication environments.

[0103] An example of a prompt related to analysis using a generative AI model is, "Generate an algorithm to identify the optimal communication route based on mobile communication data collected in real time."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects communication speed data and location information from mobile terminals in real time. In this process, the terminal obtains the current communication speed and location information via sensors, encrypts it, and sends it to the server. The input is raw data from the terminal, and the output is an encrypted dataset on the server.

[0107] Step 2:

[0108] The server runs a generative AI model to analyze the received data. In this step, the server uses machine learning algorithms to analyze the collected communication speed and location information and predict future communication speeds. The input is the encrypted data obtained in step 1, and the output is the predicted communication speed information for each region.

[0109] Step 3:

[0110] The server identifies high-speed communication areas based on the prediction results and transmits them to mobile terminals. The identified area information is stored in a database and sent as update information to the necessary terminals. The input is the prediction result from step 2, and the output is information on the identified high-speed communication areas.

[0111] Step 4:

[0112] The terminal visually presents the user with high-speed communication area information received from the server. This operation involves updating the graphical user interface using a map display function, making it easy for the user to understand. The input is the area information sent in step 3, and the output is the information displayed on the map that the user can view.

[0113] Step 5:

[0114] The terminal calculates and presents the optimal communication route to the device installed in the vehicle. In this step, the terminal's processing power is used to calculate the communication route, and the route information is provided to the vehicle's navigation system. The input is the area information presented in step 4, and the output is the optimal route information suitable for the vehicle.

[0115] Step 6:

[0116] The user guides the vehicle to the optimal communication route based on the information displayed on the device. This allows the user to enjoy an uninterrupted communication environment while riding. The input is the route information from step 5, and the output is the comfortable communication experience the user receives without interruption.

[0117] 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.

[0118] This invention provides a system for mobile communication systems that takes into account the user's emotional state and reduces dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and the user, and in particular, an emotion engine installed in the mobile terminal analyzes the user's emotional state and prompts actions based on that analysis.

[0119] Server Role

[0120] The server continues to collect communication speed data and location information from multiple mobile terminals, as before. Based on this data, it analyzes communication speeds for each region using a generative model and transmits the analysis results to terminals in real time. Through this process, information on areas where high-speed communication is possible is provided to terminals.

[0121] Terminal role

[0122] Meanwhile, mobile terminals not only receive and display communication speed analysis results provided by the server, but also evaluate the user's emotional state through an emotion engine. The emotion engine uses cameras, microphones, and sensors to infer emotions from the user's facial expressions and tone of voice.

[0123] The function of the emotional engine

[0124] The emotion engine built into the device, if it determines that the user is dissatisfied with the communication speed, will quickly recommend moving to a high-speed communication area based on the analysis results. It also considers the user's past emotional history to provide more personalized advice.

[0125] User roles

[0126] Users can check for high-speed communication areas on their device's display, and then improve their communication speed by moving to those areas based on input from the emotion engine. In this way, users can consciously manage their emotional state and obtain an optimal communication experience.

[0127] For example, if a user is frustrated by slow internet speeds at a subway station, the device's emotion engine instantly analyzes their facial expression and the network environment, suggesting that going above ground would improve communication speed. The user follows the suggestion, moves above ground, and can resume a smooth internet connection. In this way, a system incorporating an emotion engine comprehensively considers the user's emotions and the network environment to support an optimal communication experience.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server periodically collects communication speed data and location information from mobile terminals. This updates the database with the latest communication environment information.

[0131] Step 2:

[0132] The server inputs the collected data into a generative model and analyzes communication speeds for each region. As a result of the analysis, it generates information that identifies areas where high-speed communication is possible.

[0133] Step 3:

[0134] The server transmits the analysis results to the mobile terminal. The information is provided in real time and includes data from high-speed communication areas available to the user.

[0135] Step 4:

[0136] The terminal checks the analysis results received from the server and displays the information to the user visually using maps and graphical interfaces.

[0137] Step 5:

[0138] The device's emotion engine monitors the user's emotional state. Using the camera, microphone, and other sensors, it analyzes facial expressions and voice tone to detect user stress and dissatisfaction.

[0139] Step 6:

[0140] If the emotion engine detects user dissatisfaction, the device will suggest specific actions to the user based on information about high-speed communication areas obtained from the server. For example, it might recommend moving in a direction where high-speed communication is available.

[0141] Step 7:

[0142] The user accepts the suggestion and moves to a high-speed communication area according to the device's instructions. This aims to improve communication speed.

[0143] Step 8:

[0144] After moving, the device reconnects to the server based on the new location information and obtains the latest communication speed data. This allows the user to continuously obtain an optimal communication environment.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In mobile communication networks, it is a problem that users frequently experience dissatisfaction with communication speeds. Furthermore, conventional systems fail to provide appropriate feedback based on emotions, which is a challenge in resolving dissatisfaction with communication speeds.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for collecting communication speed data and location information from a mobile communication device, means including a data processing model that uses the collected data to analyze communication speeds for each region, and means for transmitting the analysis results to the mobile communication device and presenting areas where high-speed communication is possible. This makes it possible to analyze the user's emotional state, evaluate dissatisfaction with communication speed in real time, and propose appropriate actions to the user based on that.

[0150] A "mobile communication device" is a device used for wireless communication in a mobile location, and typically has the function of acquiring communication speed data and location information.

[0151] "Communication speed data" refers to information about the speed at which data is sent and received over a specific communication network.

[0152] "Location information" refers to data indicating the current geographical location of a mobile communication device, which is usually obtained through GPS or similar means.

[0153] A "data processing model" is a collection of algorithms and programs used to analyze collected data, and in particular, it uses generative AI models.

[0154] "High-speed communication area" refers to the range within a communication network where high-speed data transmission and reception are possible.

[0155] An "emotion analysis device" is a set of equipment and software necessary to analyze a user's emotional state, and typically uses a combination of sensors, cameras, and microphones.

[0156] "Judging dissatisfaction" means evaluating the negative feelings a user has regarding communication speed and recognizing them as dissatisfaction according to specific criteria.

[0157] "Visualization" is the process of displaying data-based information in a way that is easy for users to understand, such as providing information through graphs or maps.

[0158] This invention is designed to improve the user's communication experience in mobile communication systems. The entire system operates in cooperation with three parties: a server, a mobile terminal, and the user.

[0159] The server collects communication speed data and location information from mobile terminals. Using this data, the server employs a generative AI model to analyze communication speeds in each region. For this analysis, the collected communication speed data is input, and prompts such as "Please analyze the current communication speed situation in a specific region" are given to the generative AI model. The results are calculated in real time as a speed distribution for each region, and the analysis results are sent to the terminal.

[0160] The device displays communication speed information provided by the server. Furthermore, it analyzes the user's emotional state using a built-in emotion analyzer. Specifically, the device uses a camera, microphone, and sensors to detect and analyze the user's facial expressions and voice tone. Based on this information, the device determines the user's dissatisfaction with the communication speed.

[0161] Users can check information about high-speed communication areas provided by their device. Based on suggestions from an emotion analysis device, they can also choose to move to that area. For example, if a user is frustrated with the communication speed, the device might suggest a specific action, such as "Moving to ground level may improve communication speed." By taking action based on this suggestion, users can obtain a better communication experience.

[0162] This allows for a comfortable communication environment that takes both emotions and communication conditions into consideration.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server collects communication speed data and location information from mobile terminals. It receives data sent from the terminals and records it in a database. The input to this database is the communication speed measured by each terminal and its current location information. The output is a collection of communication speed data for each location, which is used in subsequent analysis steps.

[0166] Step 2:

[0167] The server uses the collected data to analyze communication speeds for each region using a generated AI model. By inputting the prompt "Analyze the current communication speed situation in a specific region" into the model, the speed patterns for each region are analyzed. The input data consists of communication speed and location information obtained in step 1, and the output is the analysis result of the communication speed for that region.

[0168] Step 3:

[0169] The server transmits the analyzed communication speed results to each mobile terminal. When transmitting the results to the terminal, it sends real-time updated speed information as data packets. The input is the analysis results generated in step 2, and the output is the data transmission to the terminal.

[0170] Step 4:

[0171] The terminal displays communication speed information provided by the server. It also uses a built-in emotion analyzer to analyze the user's emotional state in real time. It analyzes facial expressions and voice tone through the camera and microphone to infer dissatisfaction with the communication speed. Inputs are communication speed information from the server and user emotion data, while outputs are visualized communication speed information and the results of the emotion analysis.

[0172] Step 5:

[0173] The device suggests specific actions to the user based on sentiment analysis. If dissatisfaction is detected from the determined sentiment data, it generates and displays a message such as, "Moving to ground level may improve communication speed." The input is the result of sentiment analysis, and the output is a suggestion for action to the user.

[0174] Step 6:

[0175] Users make decisions based on information about high-speed communication areas displayed on their devices and suggestions derived from sentiment analysis. They review the information and choose to improve their communication experience by moving to that area. The input is the action suggestion from the device, and the output is the action the user should take.

[0176] (Application Example 2)

[0177] 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".

[0178] In mobile communication systems, dissatisfaction with communication speed and delays can negatively impact users' emotional state, making it difficult to ensure a comfortable communication environment. To address this challenge, there is a need to provide a system that offers an optimal communication experience while considering users' feelings.

[0179] 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.

[0180] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, and means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible. This makes it possible for an emotion analysis device in the mobile terminal to determine the user's emotional state and prompt the user to move to the optimal communication area based on that state.

[0181] A "mobile terminal" refers to a device that has communication capabilities and is portable. This generally includes smartphones and in-car communication systems.

[0182] "Communication speed data" refers to information about the speed at which data is sent and received over a network.

[0183] "Location information" refers to data that indicates a physical location, and is usually obtained through systems such as GPS.

[0184] A "generative model" includes algorithms for analyzing and predicting communication speeds based on collected data.

[0185] An "emotion analysis device" refers to a device that uses sensors and analysis software to identify the emotional state of a user.

[0186] "High-speed communication area" is a term that refers to a region or location where communication speeds are relatively fast.

[0187] The "optimal communication area" refers to the region with the most suitable communication environment to provide users with a comfortable communication experience.

[0188] The system based on this invention is implemented through a mobile terminal, a server, and a suitable set of programs. Its configuration and operation are described in detail below.

[0189] The server is responsible for collecting communication speed data and location information transmitted from mobile terminals. This data is used with a generative AI model to analyze and predict communication speeds in specific areas. The server transmits these analysis results to mobile terminals in real time. This process allows users to instantly obtain information about high-speed communication areas.

[0190] The mobile terminal is equipped with an emotion analysis device that uses cameras, microphones, and other sensors to analyze the user's emotional state. Based on the analyzed emotional state, the terminal will, if necessary, suggest that the user move to a high-speed communication area. This suggestion is derived from the integrated analysis results of the emotion analysis device and communication speed information from the server. The aim is to shift the user's emotional state from dissatisfaction caused by communication delays to a comfortable state.

[0191] Furthermore, mobile terminals also take into account the user's past emotional history. Based on this information, it is possible to generate more personalized and optimal routes, improving the communication experience by being sensitive to the user's emotions. Real-time data communication between the server and mobile terminals is performed using communication modules and platforms (e.g., AWS® Lambda, Affectiva API).

[0192] For example, if a user of a mobile device wants to comfortably use a streaming service, the device can sense the user's stress level and suggest a route that appropriately utilizes high-speed communication areas.

[0193] An example of a prompt message is, "The children are bored and irritable. Please select a route with a faster connection speed and optimize the in-car entertainment." This instruction is generated according to the user's needs and situation.

[0194] In this way, we realize a system that provides users with an optimal communication environment that takes both emotions and communication speed into consideration.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server collects communication speed data and location information from the mobile terminal. This data is transmitted to the server in real time via the mobile terminal's network module. The input is communication speed and location data, and the output is that this data is stored in the server's data storage.

[0198] Step 2:

[0199] The server uses collected communication speed data and location information to analyze the communication speed in each region using a generative AI model. During this process, the data is clustered, and the model predicts the average communication speed and trends in specific regions. The input is raw communication speed data and location information, and the output is the analyzed predicted communication speed data.

[0200] Step 3:

[0201] The server transmits the analyzed communication speed results to the mobile terminal. The terminal receives this and presents it as information about areas where high-speed communication is possible. This allows the user to check which areas have high-speed communication. The input is the analyzed communication speed data, and the output is a list of presented high-speed communication areas.

[0202] Step 4:

[0203] Mobile device emotion analyzers use data from cameras, microphones, and sensors to determine the user's emotional state. This information is analyzed in real time and compiled into data on the user's stress and frustration. Input is data on facial expressions and voice tone, and output is the result of the determination of a specific emotional state.

[0204] Step 5:

[0205] The terminal integrates the user's emotional state and communication speed information received from the server to suggest a suitable high-speed communication area for the user. Past emotional history is also taken into consideration, making the suggestions more personalized. A generative AI model is used to generate prompt sentences. The input is the emotional state and communication speed data, and the output is a prompt for a specific movement suggestion.

[0206] Step 6:

[0207] Users can view travel suggestions on their device and then travel to areas with high-speed communication capabilities. This allows them to enjoy a more comfortable communication environment and reduce stress. The input is the suggested information presented by the device, and the output is the user's travel actions.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention provides a system for a mobile communication system that reduces user dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and a user.

[0225] Server Role

[0226] The server first collects real-time communication speed and location data from multiple mobile terminals. This data is encrypted and transmitted securely while protecting privacy. The server then analyzes the collected data using a generative model. The generative model uses machine learning algorithms to analyze real-time communication speeds in each area and predict future communication speeds. As a result of the analysis, it identifies areas where high-speed communication is predicted to be possible and transmits this information to mobile terminals.

[0227] Terminal role

[0228] Meanwhile, the mobile terminal receives the analysis results transmitted from the server. The terminal visually processes the received data and displays the high-speed communication area to the user. This display is usually done using maps or a graphical user interface, making it easy for the user to understand. The terminal may also provide voice instructions or notifications to the user as needed.

[0229] User roles

[0230] Based on information provided by the device, users move to the optimal high-speed communication area from their current location. For example, a business user in a large city, if they feel their communication speed is slowing down, can refer to the map information provided by their device and move to a high-speed communication area. This movement allows the user to enjoy a stable communication speed.

[0231] As a concrete example, suppose a user is working in a city cafe. Feeling that their internet connection is slow, the user checks a 3D map displayed on their device and discovers a plaza about 100 meters away with a faster connection. The user then moves from the cafe to the plaza and can continue working with a faster connection.

[0232] Thus, the system based on the present invention supports users in effectively selecting a communication environment and provides a comfortable communication experience.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server periodically collects communication speed data and location information from each mobile terminal. Communication is encrypted and conducted through a secure channel.

[0236] Step 2:

[0237] The server structures and preprocesses the collected data. This includes deduplication and imputation of missing values.

[0238] Step 3:

[0239] The server inputs pre-processed data into a generative model to analyze communication speeds for each region. This analysis yields speed patterns and fluctuation predictions for each region.

[0240] Step 4:

[0241] The server organizes and classifies the analysis results obtained from the generative model, and identifies areas where high-speed communication is particularly expected.

[0242] Step 5:

[0243] The server sends the analysis results to each user's terminal. This includes customized information based on the user's current location.

[0244] Step 6:

[0245] The terminal analyzes the results received from the server and displays the high-speed communication area as a map or graphical interface.

[0246] Step 7:

[0247] Users check the display on their device and use it as a reference to identify areas where high-speed communication is available. They can then move to those areas if necessary.

[0248] Step 8:

[0249] The device maintains a periodic connection with the server when the user's location is updated, in preparation for the next data collection and updating of results.

[0250] (Example 1)

[0251] 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."

[0252] In mobile communication systems, a key challenge is to enable users to select and utilize an appropriate communication environment without being dissatisfied with the communication speed. In particular, it is necessary to optimize the communication environment by making real-time fluctuations in communication speed predictable and providing users with appropriate information.

[0253] 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.

[0254] In this invention, the server includes means for acquiring communication performance data and geographic information from a mobile information terminal, means including a generation algorithm for processing the acquired information and evaluating the communication performance of each area, and means for transferring the evaluation results to the mobile information terminal and presenting high-performance communication areas. This enables the user to obtain information about the optimal communication environment in real time and decide to move to an appropriate area.

[0255] A "mobile information terminal" is a portable electronic device held by a user for collecting and presenting location information and communication performance data.

[0256] "Communication performance data" refers to numerical or statistical data regarding the speed and quality of communication.

[0257] "Geographic information" refers to data that indicates location signal information within a specific area.

[0258] A "generative algorithm" is a computational method used to analyze and predict regional communication performance based on historical and real-time data.

[0259] "Evaluation results" refer to analytical conclusions or indicators regarding communication performance for each region, obtained through a generation algorithm.

[0260] A "high-performance communication area" is a geographical area where communication performance is high and users are expected to have a superior communication experience.

[0261] A description of embodiments for carrying out the present invention will be provided.

[0262] The server processes communication performance data and geographical information collected from mobile information terminals. To achieve this, the server uses a computer system with advanced computing capabilities. Specifically, the server utilizes a cloud-based computing infrastructure to collect, encrypt, and analyze data in real time. A generative AI model applies machine learning algorithms to this data and predicts communication speeds in each region as an analysis result. This analysis uses historical communication data, making it possible to predict future fluctuations in communication performance with high accuracy.

[0263] The terminal receives analysis results transferred from the server and visualizes the information in a user-friendly format. This often involves an easy-to-use graphical user interface (GUI), displaying the information as colored areas on a map. Terminals equipped with voice assistants can also inform the user of the analysis results verbally. The terminal works seamlessly with the high data processing capabilities required of mobile information terminals to provide users with the latest communication performance information at all times.

[0264] Users can use the information provided by their device to move to the optimal communication area. In everyday use, for example, if a user experiences a decrease in communication speed while working in a cafe in the city, they can check the map displayed on their device and easily access an area where faster communication is available. This allows users to enjoy a smooth and uninterrupted communication experience.

[0265] As a concrete example, a prompt such as "Identify areas with faster communication speeds within 100 meters of my current location" can be used. This prompt serves as a basic instruction for the generative AI model to quickly identify suitable communication areas.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives communication performance data and geographical information from mobile information terminals. Specifically, real-time communication speed and location information measured from each terminal are encrypted and sent to the server. The input data is stored in a database and used for subsequent analysis.

[0269] Step 2:

[0270] The server inputs received communication performance data and geographical information into a generating AI model. The generating AI model uses machine learning algorithms to analyze past and present data and evaluate communication performance for each region. Specifically, it performs data cleansing and feature extraction to generate a communication speed prediction model. This yields evaluation results regarding high-performance communication areas.

[0271] Step 3:

[0272] The server transmits the evaluation results to mobile information terminals. This information includes identifying areas where high-performance communication is predicted to be possible. The server packages the analysis results along with metadata using a protocol and transfers the data to each terminal in the required format.

[0273] Step 4:

[0274] The terminal analyzes the evaluation results received from the server and visualizes them for display to the user. The input here is the analysis results sent from the server, and the output displays the high-performance communication area on a graphical interface. The information is presented as color-coded areas on a map, allowing the user to easily understand the current communication status.

[0275] Step 5:

[0276] The user moves to the optimal communication area based on the information displayed on the device. The user receives visual or audio instructions from the device to select their destination. Especially when communication is poor, the user makes a decision based on this information and attempts to access a high-performance communication area.

[0277] (Application Example 1)

[0278] 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."

[0279] In recent years, in mobile communication systems, effective means for users to enjoy stable communication speeds have been sought. However, with conventional technologies, it has been difficult to effectively correct unstable communication speed situations, and in particular, there has been a problem of communication interruptions occurring in vehicles during movement. Also, means for analyzing a vast amount of data from multiple mobile terminals in real time and quickly optimizing the communication environment have not been established. For this reason, there is a need for a new technology that can prevent communication interruptions, especially in autonomous vehicles, and provide a comfortable communication environment for passengers.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0281] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means for analyzing the communication speed for each region using the collected data, including a generation model, means for transmitting the analysis result to the mobile terminal and presenting a high-speed communication available area, and means for a device mounted on a vehicle to identify and present an optimal communication route. Thereby, it is possible to prevent communication interruptions even in an autonomous vehicle during movement and ensure a stable communication environment by always providing an optimized communication route.

[0282] A "mobile terminal" is a communication device that is geographically movable and has a function of collecting communication speed data and location information.

[0283] "Communication speed data" refers to information regarding the communication speed at a specific time and location, and is a quantification of the transfer amount per unit time of data capacity.

[0284] "Location information" is data indicating the geographical location where the mobile terminal exists and includes geographical coordinate information such as latitude and longitude.

[0285] A "generation model" is a mathematical model that uses a machine learning algorithm to analyze the collected communication speed data and location information and predict the communication speed for each region.

[0286] A "high-speed communication area" refers to a geographical area predicted to meet a certain communication speed standard or higher, which is the range where a terminal can perform stable communication.

[0287] A "device mounted on a vehicle" is an electronic device installed inside an autonomous vehicle and used to display the optimal communication route.

[0288] An "optimal communication route" refers to a route selected for a moving vehicle to enjoy an optimal communication environment, which takes into account the predicted communication speeds of each area located along that route.

[0289] The system for implementing this invention consists of a server, a mobile terminal, and a device mounted on a vehicle.

[0290] The server has a function of collecting communication speed data and location information in real time from the mobile terminal. This data is securely transferred using encryption technology. Then, a machine learning algorithm based on the generated AI model is executed on the server to analyze the received data. Through this analysis, the communication speed for each region is predicted and high-speed communication areas are identified.

[0291] The mobile terminal receives the analysis results from the server and visually presents the communication status based on them. Specifically, high-speed communication areas are visualized on a map and notified to the user. When the user feels a decrease in communication speed, they can move to an optimal area by referring to this information. The terminal also shares information with the device mounted on the vehicle and calculates and presents the optimal communication route. This device is integrated with the vehicle's navigation system and plays a role in guiding the route of the autonomous vehicle while maintaining an optimal communication environment.

[0292] A concrete example is an autonomous taxi operating in an urban area. To ensure passengers can comfortably enjoy video streaming within the taxi, the system would incorporate a route that provides the optimal communication speed into the navigation system. This function is particularly effective for intercity travel and in areas with unstable communication environments.

[0293] An example of a prompt related to analysis using a generative AI model is, "Generate an algorithm to identify the optimal communication route based on mobile communication data collected in real time."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects communication speed data and location information from mobile terminals in real time. In this process, the terminal obtains the current communication speed and location information via sensors, encrypts it, and sends it to the server. The input is raw data from the terminal, and the output is an encrypted dataset on the server.

[0297] Step 2:

[0298] The server runs a generative AI model to analyze the received data. In this step, the server uses machine learning algorithms to analyze the collected communication speed and location information and predict future communication speeds. The input is the encrypted data obtained in step 1, and the output is the predicted communication speed information for each region.

[0299] Step 3:

[0300] The server identifies high-speed communication areas based on the prediction results and transmits them to mobile terminals. The identified area information is stored in a database and sent as update information to the necessary terminals. The input is the prediction result from step 2, and the output is information on the identified high-speed communication areas.

[0301] Step 4:

[0302] The terminal visually presents the high-speed communication available area information received from the server to the user. This operation includes updating the graphical user interface using the map display function, and is presented so that the user can easily understand it. The input is the area information transmitted in Step 3, and the output is the display information on the map that the user can view.

[0303] Step 5:

[0304] The terminal calculates and presents the communication optimal route to the device mounted on the vehicle. In this step, the processing ability of the terminal is used to calculate the communication route, and the route information is provided to the navigation system of the vehicle. The input is the area information presented in Step 4, and the output is the optimal route information suitable for the vehicle.

[0305] Step 6:

[0306] Based on the information displayed by the terminal, the user guides the vehicle along the optimal communication route. As a result, the user in the vehicle can enjoy an uninterrupted communication environment. The input is the route information in Step 5, and the output is the comfortable communication experience that the user obtains without the communication being interrupted.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0308] The present invention provides a system for reducing dissatisfaction regarding communication speed by considering the emotional state of a user in a mobile communication system. This system operates with the cooperation of a server, a mobile terminal, and a user, and in particular, an emotion engine mounted on the mobile terminal analyzes the emotional state of the user and prompts actions based thereon.

[0309] Server Role

[0310] The server continues to collect communication speed data and location information from multiple mobile terminals, as before. Based on this data, it analyzes communication speeds for each region using a generative model and transmits the analysis results to terminals in real time. Through this process, information on areas where high-speed communication is possible is provided to terminals.

[0311] Terminal role

[0312] Meanwhile, mobile terminals not only receive and display communication speed analysis results provided by the server, but also evaluate the user's emotional state through an emotion engine. The emotion engine uses cameras, microphones, and sensors to infer emotions from the user's facial expressions and tone of voice.

[0313] The function of the emotional engine

[0314] The emotion engine built into the device, if it determines that the user is dissatisfied with the communication speed, will quickly recommend moving to a high-speed communication area based on the analysis results. It also considers the user's past emotional history to provide more personalized advice.

[0315] User roles

[0316] Users can check for high-speed communication areas on their device's display, and then improve their communication speed by moving to those areas based on input from the emotion engine. In this way, users can consciously manage their emotional state and obtain an optimal communication experience.

[0317] For example, if a user is frustrated by slow internet speeds at a subway station, the device's emotion engine instantly analyzes their facial expression and the network environment, suggesting that going above ground would improve communication speed. The user follows the suggestion, moves above ground, and can resume a smooth internet connection. In this way, a system incorporating an emotion engine comprehensively considers the user's emotions and the network environment to support an optimal communication experience.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The server periodically collects communication speed data and location information from mobile terminals. This updates the database with the latest communication environment information.

[0321] Step 2:

[0322] The server inputs the collected data into a generative model and analyzes communication speeds for each region. As a result of the analysis, it generates information that identifies areas where high-speed communication is possible.

[0323] Step 3:

[0324] The server transmits the analysis results to the mobile terminal. The information is provided in real time and includes data from high-speed communication areas available to the user.

[0325] Step 4:

[0326] The terminal checks the analysis results received from the server and displays the information to the user visually using maps and graphical interfaces.

[0327] Step 5:

[0328] The device's emotion engine monitors the user's emotional state. Using the camera, microphone, and other sensors, it analyzes facial expressions and voice tone to detect user stress and dissatisfaction.

[0329] Step 6:

[0330] If the emotion engine detects user dissatisfaction, the device will suggest specific actions to the user based on information about high-speed communication areas obtained from the server. For example, it might recommend moving in a direction where high-speed communication is available.

[0331] Step 7:

[0332] The user accepts the suggestion and moves to a high-speed communication area according to the device's instructions. This aims to improve communication speed.

[0333] Step 8:

[0334] After moving, the device reconnects to the server based on the new location information and obtains the latest communication speed data. This allows the user to continuously obtain an optimal communication environment.

[0335] (Example 2)

[0336] 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".

[0337] In mobile communication networks, it is a problem that users frequently experience dissatisfaction with communication speeds. Furthermore, conventional systems fail to provide appropriate feedback based on emotions, which is a challenge in resolving dissatisfaction with communication speeds.

[0338] 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.

[0339] In this invention, the server includes means for collecting communication speed data and location information from a mobile communication device, means including a data processing model that uses the collected data to analyze communication speeds for each region, and means for transmitting the analysis results to the mobile communication device and presenting areas where high-speed communication is possible. This makes it possible to analyze the user's emotional state, evaluate dissatisfaction with communication speed in real time, and propose appropriate actions to the user based on that.

[0340] A "mobile communication device" is a device used for wireless communication in a mobile location, and typically has the function of acquiring communication speed data and location information.

[0341] "Communication speed data" refers to information about the speed at which data is sent and received over a specific communication network.

[0342] "Location information" refers to data indicating the current geographical location of a mobile communication device, which is usually obtained through GPS or similar means.

[0343] A "data processing model" is a collection of algorithms and programs used to analyze collected data, and in particular, it uses generative AI models.

[0344] "High-speed communication area" refers to the range within a communication network where high-speed data transmission and reception are possible.

[0345] An "emotion analysis device" is a set of equipment and software necessary to analyze a user's emotional state, and typically uses a combination of sensors, cameras, and microphones.

[0346] "Judging dissatisfaction" means evaluating the negative feelings a user has regarding communication speed and recognizing them as dissatisfaction according to specific criteria.

[0347] "Visualization" is the process of displaying data-based information in a way that is easy for users to understand, such as providing information through graphs or maps.

[0348] This invention is designed to improve the user's communication experience in mobile communication systems. The entire system operates in cooperation with three parties: a server, a mobile terminal, and the user.

[0349] The server collects communication speed data and location information from mobile terminals. Using this data, the server employs a generative AI model to analyze communication speeds in each region. For this analysis, the collected communication speed data is input, and prompts such as "Please analyze the current communication speed situation in a specific region" are given to the generative AI model. The results are calculated in real time as a speed distribution for each region, and the analysis results are sent to the terminal.

[0350] The device displays communication speed information provided by the server. Furthermore, it analyzes the user's emotional state using a built-in emotion analyzer. Specifically, the device uses a camera, microphone, and sensors to detect and analyze the user's facial expressions and voice tone. Based on this information, the device determines the user's dissatisfaction with the communication speed.

[0351] Users can check information about high-speed communication areas provided by their device. Based on suggestions from an emotion analysis device, they can also choose to move to that area. For example, if a user is frustrated with the communication speed, the device might suggest a specific action, such as "Moving to ground level may improve communication speed." By taking action based on this suggestion, users can obtain a better communication experience.

[0352] This allows for a comfortable communication environment that takes both emotions and communication conditions into consideration.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The server collects communication speed data and location information from mobile terminals. It receives data sent from the terminals and records it in a database. The input to this database is the communication speed measured by each terminal and its current location information. The output is a collection of communication speed data for each location, which is used in subsequent analysis steps.

[0356] Step 2:

[0357] The server uses the collected data to analyze communication speeds for each region using a generated AI model. By inputting the prompt "Analyze the current communication speed situation in a specific region" into the model, the speed patterns for each region are analyzed. The input data consists of communication speed and location information obtained in step 1, and the output is the analysis result of the communication speed for that region.

[0358] Step 3:

[0359] The server transmits the analyzed communication speed results to each mobile terminal. When transmitting the results to the terminal, it sends real-time updated speed information as data packets. The input is the analysis results generated in step 2, and the output is the data transmission to the terminal.

[0360] Step 4:

[0361] The terminal displays communication speed information provided by the server. It also uses a built-in emotion analyzer to analyze the user's emotional state in real time. It analyzes facial expressions and voice tone through the camera and microphone to infer dissatisfaction with the communication speed. Inputs are communication speed information from the server and user emotion data, while outputs are visualized communication speed information and the results of the emotion analysis.

[0362] Step 5:

[0363] The device suggests specific actions to the user based on sentiment analysis. If dissatisfaction is detected from the determined sentiment data, it generates and displays a message such as, "Moving to ground level may improve communication speed." The input is the result of sentiment analysis, and the output is a suggestion for action to the user.

[0364] Step 6:

[0365] Users make decisions based on information about high-speed communication areas displayed on their devices and suggestions derived from sentiment analysis. They review the information and choose to improve their communication experience by moving to that area. The input is the action suggestion from the device, and the output is the action the user should take.

[0366] (Application Example 2)

[0367] 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."

[0368] In mobile communication systems, dissatisfaction with communication speed and delays can negatively impact users' emotional state, making it difficult to ensure a comfortable communication environment. To address this challenge, there is a need to provide a system that offers an optimal communication experience while considering users' feelings.

[0369] 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.

[0370] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, and means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible. This makes it possible for an emotion analysis device in the mobile terminal to determine the user's emotional state and prompt the user to move to the optimal communication area based on that state.

[0371] A "mobile terminal" refers to a device that has communication capabilities and is portable. This generally includes smartphones and in-car communication systems.

[0372] "Communication speed data" refers to information about the speed at which data is sent and received over a network.

[0373] "Location information" refers to data that indicates a physical location, and is usually obtained through systems such as GPS.

[0374] A "generative model" includes algorithms for analyzing and predicting communication speeds based on collected data.

[0375] An "emotion analysis device" refers to a device that uses sensors and analysis software to identify the emotional state of a user.

[0376] "High-speed communication area" is a term that refers to a region or location where communication speeds are relatively fast.

[0377] The "optimal communication area" refers to the region with the most suitable communication environment to provide users with a comfortable communication experience.

[0378] The system based on this invention is implemented through a mobile terminal, a server, and a suitable set of programs. Its configuration and operation are described in detail below.

[0379] The server is responsible for collecting communication speed data and location information transmitted from mobile terminals. This data is used with a generative AI model to analyze and predict communication speeds in specific areas. The server transmits these analysis results to mobile terminals in real time. This process allows users to instantly obtain information about high-speed communication areas.

[0380] The mobile terminal is equipped with an emotion analysis device that uses cameras, microphones, and other sensors to analyze the user's emotional state. Based on the analyzed emotional state, the terminal will, if necessary, suggest that the user move to a high-speed communication area. This suggestion is derived from the integrated analysis results of the emotion analysis device and communication speed information from the server. The aim is to shift the user's emotional state from dissatisfaction caused by communication delays to a comfortable state.

[0381] Furthermore, mobile terminals also take into account the user's past emotional history. Based on this information, it is possible to generate more personalized and optimal routes, improving the communication experience by being sensitive to the user's emotions. Real-time data communication between the server and mobile terminals is performed using communication modules and platforms (e.g., AWS Lambda, Affectiva API).

[0382] For example, if a user of a mobile device wants to comfortably use a streaming service, the device can sense the user's stress level and suggest a route that appropriately utilizes high-speed communication areas.

[0383] An example of a prompt message is, "The children are bored and irritable. Please select a route with a faster connection speed and optimize the in-car entertainment." This instruction is generated according to the user's needs and situation.

[0384] In this way, we realize a system that provides users with an optimal communication environment that takes both emotions and communication speed into consideration.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The server collects communication speed data and location information from the mobile terminal. This data is transmitted to the server in real time via the mobile terminal's network module. The input is communication speed and location data, and the output is that this data is stored in the server's data storage.

[0388] Step 2:

[0389] The server uses collected communication speed data and location information to analyze the communication speed in each region using a generative AI model. During this process, the data is clustered, and the model predicts the average communication speed and trends in specific regions. The input is raw communication speed data and location information, and the output is the analyzed predicted communication speed data.

[0390] Step 3:

[0391] The server transmits the analyzed communication speed results to the mobile terminal. The terminal receives this and presents it as information about areas where high-speed communication is possible. This allows the user to check which areas have high-speed communication. The input is the analyzed communication speed data, and the output is a list of presented high-speed communication areas.

[0392] Step 4:

[0393] Mobile device emotion analyzers use data from cameras, microphones, and sensors to determine the user's emotional state. This information is analyzed in real time and compiled into data on the user's stress and frustration. Input is data on facial expressions and voice tone, and output is the result of the determination of a specific emotional state.

[0394] Step 5:

[0395] The terminal integrates the user's emotional state and communication speed information received from the server to suggest a suitable high-speed communication area for the user. Past emotional history is also taken into consideration, making the suggestions more personalized. A generative AI model is used to generate prompt sentences. The input is the emotional state and communication speed data, and the output is a prompt for a specific movement suggestion.

[0396] Step 6:

[0397] Users can view travel suggestions on their device and then travel to areas with high-speed communication capabilities. This allows them to enjoy a more comfortable communication environment and reduce stress. The input is the suggested information presented by the device, and the output is the user's travel actions.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] This invention provides a system for a mobile communication system that reduces user dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and a user.

[0415] Server Role

[0416] The server first collects real-time communication speed and location data from multiple mobile terminals. This data is encrypted and transmitted securely while protecting privacy. The server then analyzes the collected data using a generative model. The generative model uses machine learning algorithms to analyze real-time communication speeds in each area and predict future communication speeds. As a result of the analysis, it identifies areas where high-speed communication is predicted to be possible and transmits this information to mobile terminals.

[0417] Terminal role

[0418] Meanwhile, the mobile terminal receives the analysis results transmitted from the server. The terminal visually processes the received data and displays the high-speed communication area to the user. This display is usually done using maps or a graphical user interface, making it easy for the user to understand. The terminal may also provide voice instructions or notifications to the user as needed.

[0419] User roles

[0420] Based on information provided by the device, users move to the optimal high-speed communication area from their current location. For example, a business user in a large city, if they feel their communication speed is slowing down, can refer to the map information provided by their device and move to a high-speed communication area. This movement allows the user to enjoy a stable communication speed.

[0421] As a concrete example, suppose a user is working in a city cafe. Feeling that their internet connection is slow, the user checks a 3D map displayed on their device and discovers a plaza about 100 meters away with a faster connection. The user then moves from the cafe to the plaza and can continue working with a faster connection.

[0422] Thus, the system based on the present invention supports users in effectively selecting a communication environment and provides a comfortable communication experience.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The server periodically collects communication speed data and location information from each mobile terminal. Communication is encrypted and conducted through a secure channel.

[0426] Step 2:

[0427] The server structures and preprocesses the collected data. This includes deduplication and imputation of missing values.

[0428] Step 3:

[0429] The server inputs pre-processed data into a generative model to analyze communication speeds for each region. This analysis yields speed patterns and fluctuation predictions for each region.

[0430] Step 4:

[0431] The server organizes and classifies the analysis results obtained from the generative model, and identifies areas where high-speed communication is particularly expected.

[0432] Step 5:

[0433] The server sends the analysis results to each user's terminal. This includes customized information based on the user's current location.

[0434] Step 6:

[0435] The terminal analyzes the results received from the server and displays the high-speed communication area as a map or graphical interface.

[0436] Step 7:

[0437] Users check the display on their device and use it as a reference to identify areas where high-speed communication is available. They can then move to those areas if necessary.

[0438] Step 8:

[0439] The device maintains a periodic connection with the server when the user's location is updated, in preparation for the next data collection and updating of results.

[0440] (Example 1)

[0441] 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."

[0442] In mobile communication systems, a key challenge is to enable users to select and utilize an appropriate communication environment without being dissatisfied with the communication speed. In particular, it is necessary to optimize the communication environment by making real-time fluctuations in communication speed predictable and providing users with appropriate information.

[0443] 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.

[0444] In this invention, the server includes means for acquiring communication performance data and geographic information from a mobile information terminal, means including a generation algorithm for processing the acquired information and evaluating the communication performance of each area, and means for transferring the evaluation results to the mobile information terminal and presenting high-performance communication areas. This enables the user to obtain information about the optimal communication environment in real time and decide to move to an appropriate area.

[0445] A "mobile information terminal" is a portable electronic device held by a user for collecting and presenting location information and communication performance data.

[0446] "Communication performance data" refers to numerical or statistical data regarding the speed and quality of communication.

[0447] "Geographic information" refers to data that indicates location signal information within a specific area.

[0448] A "generative algorithm" is a computational method used to analyze and predict regional communication performance based on historical and real-time data.

[0449] "Evaluation results" refer to analytical conclusions or indicators regarding communication performance for each region, obtained through a generation algorithm.

[0450] A "high-performance communication area" is a geographical area where communication performance is high and users are expected to have a superior communication experience.

[0451] A description of embodiments for carrying out the present invention will be provided.

[0452] The server processes communication performance data and geographical information collected from mobile information terminals. To achieve this, the server uses a computer system with advanced computing capabilities. Specifically, the server utilizes a cloud-based computing infrastructure to collect, encrypt, and analyze data in real time. A generative AI model applies machine learning algorithms to this data and predicts communication speeds in each region as an analysis result. This analysis uses historical communication data, making it possible to predict future fluctuations in communication performance with high accuracy.

[0453] The terminal receives analysis results transferred from the server and visualizes the information in a user-friendly format. This often involves an easy-to-use graphical user interface (GUI), displaying the information as colored areas on a map. Terminals equipped with voice assistants can also inform the user of the analysis results verbally. The terminal works seamlessly with the high data processing capabilities required of mobile information terminals to provide users with the latest communication performance information at all times.

[0454] Users can use the information provided by their device to move to the optimal communication area. In everyday use, for example, if a user experiences a decrease in communication speed while working in a cafe in the city, they can check the map displayed on their device and easily access an area where faster communication is available. This allows users to enjoy a smooth and uninterrupted communication experience.

[0455] As a concrete example, a prompt such as "Identify areas with faster communication speeds within 100 meters of my current location" can be used. This prompt serves as a basic instruction for the generative AI model to quickly identify suitable communication areas.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The server receives communication performance data and geographical information from mobile information terminals. Specifically, real-time communication speed and location information measured from each terminal are encrypted and sent to the server. The input data is stored in a database and used for subsequent analysis.

[0459] Step 2:

[0460] The server inputs received communication performance data and geographical information into a generating AI model. The generating AI model uses machine learning algorithms to analyze past and present data and evaluate communication performance for each region. Specifically, it performs data cleansing and feature extraction to generate a communication speed prediction model. This yields evaluation results regarding high-performance communication areas.

[0461] Step 3:

[0462] The server transmits the evaluation results to mobile information terminals. This information includes identifying areas where high-performance communication is predicted to be possible. The server packages the analysis results along with metadata using a protocol and transfers the data to each terminal in the required format.

[0463] Step 4:

[0464] The terminal analyzes the evaluation results received from the server and visualizes them for display to the user. The input here is the analysis results sent from the server, and the output displays the high-performance communication area on a graphical interface. The information is presented as color-coded areas on a map, allowing the user to easily understand the current communication status.

[0465] Step 5:

[0466] The user moves to the optimal communication area based on the information displayed on the device. The user receives visual or audio instructions from the device to select their destination. Especially when communication is poor, the user makes a decision based on this information and attempts to access a high-performance communication area.

[0467] (Application Example 1)

[0468] 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."

[0469] In recent years, there has been a growing need for effective means to ensure users enjoy stable communication speeds in mobile communication systems. However, conventional technologies have struggled to effectively compensate for unstable communication speeds, and communication interruptions have been a particular problem in moving vehicles. Furthermore, there is no established means to analyze the vast amount of data from multiple mobile terminals in real time and quickly optimize the communication environment. Therefore, there is a strong demand for new technologies that can prevent communication interruptions, especially in autonomous vehicles, and provide passengers with a comfortable communication environment.

[0470] 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.

[0471] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible, and means for a device mounted on the vehicle to identify and present the optimal communication route. This makes it possible to prevent communication interruptions even in autonomous vehicles in motion and to always provide an optimized communication route, thereby ensuring a stable communication environment.

[0472] A "mobile terminal" is a communication device that is geographically mobile and has the function of collecting communication speed data and location information.

[0473] "Communication speed data" refers to information about the speed of communication at a specific time and place, and is a numerical representation of the amount of data transferred per unit time.

[0474] "Location information" refers to data that indicates the geographical location where a mobile device is located, and includes geographic coordinate information such as latitude and longitude.

[0475] A "generative model" is a mathematical model that uses machine learning algorithms to analyze collected communication speed data and location information to predict communication speeds for each region.

[0476] A "high-speed communication area" refers to a geographical area where communication speeds are expected to meet a certain standard, and within which devices can communicate stably.

[0477] "Vehicle-mounted devices" refer to electronic devices installed inside autonomous vehicles and used to display the optimal communication route.

[0478] "Optimal communication route" refers to the path selected to ensure that a moving vehicle enjoys the best possible communication environment, taking into account the predicted communication speed of each area located along that route.

[0479] A system for implementing this invention consists of a server, a mobile terminal, and a device mounted on a vehicle.

[0480] The server has the capability to collect communication speed data and location information from mobile terminals in real time. This data is securely transmitted using encryption technology. Subsequently, a machine learning algorithm based on a generated AI model is executed on the server to analyze the received data. This analysis predicts communication speeds for each region and identifies areas where high-speed communication is possible.

[0481] The mobile terminal receives analysis results from the server and visually displays the communication status based on them. Specifically, it visualizes areas where high-speed communication is possible on a map and informs the user. If the user perceives a decrease in communication speed, they can use this information to move to an optimal area. The terminal also shares information with devices installed in the vehicle and calculates and presents the optimal communication route. This device is integrated with the vehicle's navigation system and plays a role in guiding the autonomous vehicle along its route while maintaining an optimal communication environment.

[0482] A concrete example is an autonomous taxi operating in an urban area. To ensure passengers can comfortably enjoy video streaming within the taxi, the system would incorporate a route that provides the optimal communication speed into the navigation system. This function is particularly effective for intercity travel and in areas with unstable communication environments.

[0483] An example of a prompt related to analysis using a generative AI model is, "Generate an algorithm to identify the optimal communication route based on mobile communication data collected in real time."

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The server collects communication speed data and location information from mobile terminals in real time. In this process, the terminal obtains the current communication speed and location information via sensors, encrypts it, and sends it to the server. The input is raw data from the terminal, and the output is an encrypted dataset on the server.

[0487] Step 2:

[0488] The server runs a generative AI model to analyze the received data. In this step, the server uses machine learning algorithms to analyze the collected communication speed and location information and predict future communication speeds. The input is the encrypted data obtained in step 1, and the output is the predicted communication speed information for each region.

[0489] Step 3:

[0490] The server identifies high-speed communication areas based on the prediction results and transmits them to mobile terminals. The identified area information is stored in a database and sent as update information to the necessary terminals. The input is the prediction result from step 2, and the output is information on the identified high-speed communication areas.

[0491] Step 4:

[0492] The terminal visually presents the user with high-speed communication area information received from the server. This operation involves updating the graphical user interface using a map display function, making it easy for the user to understand. The input is the area information sent in step 3, and the output is the information displayed on the map that the user can view.

[0493] Step 5:

[0494] The terminal calculates and presents the optimal communication route to the device installed in the vehicle. In this step, the terminal's processing power is used to calculate the communication route, and the route information is provided to the vehicle's navigation system. The input is the area information presented in step 4, and the output is the optimal route information suitable for the vehicle.

[0495] Step 6:

[0496] The user guides the vehicle to the optimal communication route based on the information displayed on the device. This allows the user to enjoy an uninterrupted communication environment while riding. The input is the route information from step 5, and the output is the comfortable communication experience the user receives without interruption.

[0497] 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.

[0498] This invention provides a system for mobile communication systems that takes into account the user's emotional state and reduces dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and the user, and in particular, an emotion engine installed in the mobile terminal analyzes the user's emotional state and prompts actions based on that analysis.

[0499] Server Role

[0500] The server continues to collect communication speed data and location information from multiple mobile terminals, as before. Based on this data, it analyzes communication speeds for each region using a generative model and transmits the analysis results to terminals in real time. Through this process, information on areas where high-speed communication is possible is provided to terminals.

[0501] Terminal role

[0502] Meanwhile, mobile terminals not only receive and display communication speed analysis results provided by the server, but also evaluate the user's emotional state through an emotion engine. The emotion engine uses cameras, microphones, and sensors to infer emotions from the user's facial expressions and tone of voice.

[0503] The function of the emotional engine

[0504] The emotion engine built into the device, if it determines that the user is dissatisfied with the communication speed, will quickly recommend moving to a high-speed communication area based on the analysis results. It also considers the user's past emotional history to provide more personalized advice.

[0505] User roles

[0506] Users can check for high-speed communication areas on their device's display, and then improve their communication speed by moving to those areas based on input from the emotion engine. In this way, users can consciously manage their emotional state and obtain an optimal communication experience.

[0507] For example, if a user is frustrated by slow internet speeds at a subway station, the device's emotion engine instantly analyzes their facial expression and the network environment, suggesting that going above ground would improve communication speed. The user follows the suggestion, moves above ground, and can resume a smooth internet connection. In this way, a system incorporating an emotion engine comprehensively considers the user's emotions and the network environment to support an optimal communication experience.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The server periodically collects communication speed data and location information from mobile terminals. This updates the database with the latest communication environment information.

[0511] Step 2:

[0512] The server inputs the collected data into a generative model and analyzes communication speeds for each region. As a result of the analysis, it generates information that identifies areas where high-speed communication is possible.

[0513] Step 3:

[0514] The server transmits the analysis results to the mobile terminal. The information is provided in real time and includes data from high-speed communication areas available to the user.

[0515] Step 4:

[0516] The terminal checks the analysis results received from the server and displays the information to the user visually using maps and graphical interfaces.

[0517] Step 5:

[0518] The device's emotion engine monitors the user's emotional state. Using the camera, microphone, and other sensors, it analyzes facial expressions and voice tone to detect user stress and dissatisfaction.

[0519] Step 6:

[0520] If the emotion engine detects user dissatisfaction, the device will suggest specific actions to the user based on information about high-speed communication areas obtained from the server. For example, it might recommend moving in a direction where high-speed communication is available.

[0521] Step 7:

[0522] The user accepts the suggestion and moves to a high-speed communication area according to the device's instructions. This aims to improve communication speed.

[0523] Step 8:

[0524] After moving, the device reconnects to the server based on the new location information and obtains the latest communication speed data. This allows the user to continuously obtain an optimal communication environment.

[0525] (Example 2)

[0526] 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."

[0527] In mobile communication networks, it is a problem that users frequently experience dissatisfaction with communication speeds. Furthermore, conventional systems fail to provide appropriate feedback based on emotions, which is a challenge in resolving dissatisfaction with communication speeds.

[0528] 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.

[0529] In this invention, the server includes means for collecting communication speed data and location information from a mobile communication device, means including a data processing model that uses the collected data to analyze communication speeds for each region, and means for transmitting the analysis results to the mobile communication device and presenting areas where high-speed communication is possible. This makes it possible to analyze the user's emotional state, evaluate dissatisfaction with communication speed in real time, and propose appropriate actions to the user based on that.

[0530] A "mobile communication device" is a device used for wireless communication in a mobile location, and typically has the function of acquiring communication speed data and location information.

[0531] "Communication speed data" refers to information about the speed at which data is sent and received over a specific communication network.

[0532] "Location information" refers to data indicating the current geographical location of a mobile communication device, which is usually obtained through GPS or similar means.

[0533] A "data processing model" is a collection of algorithms and programs used to analyze collected data, and in particular, it uses generative AI models.

[0534] "High-speed communication area" refers to the range within a communication network where high-speed data transmission and reception are possible.

[0535] An "emotion analysis device" is a set of equipment and software necessary to analyze a user's emotional state, and typically uses a combination of sensors, cameras, and microphones.

[0536] "Judging dissatisfaction" means evaluating the negative feelings a user has regarding communication speed and recognizing them as dissatisfaction according to specific criteria.

[0537] "Visualization" is the process of displaying data-based information in a way that is easy for users to understand, such as providing information through graphs or maps.

[0538] This invention is designed to improve the user's communication experience in mobile communication systems. The entire system operates in cooperation with three parties: a server, a mobile terminal, and the user.

[0539] The server collects communication speed data and location information from mobile terminals. Using this data, the server employs a generative AI model to analyze communication speeds in each region. For this analysis, the collected communication speed data is input, and prompts such as "Please analyze the current communication speed situation in a specific region" are given to the generative AI model. The results are calculated in real time as a speed distribution for each region, and the analysis results are sent to the terminal.

[0540] The device displays communication speed information provided by the server. Furthermore, it analyzes the user's emotional state using a built-in emotion analyzer. Specifically, the device uses a camera, microphone, and sensors to detect and analyze the user's facial expressions and voice tone. Based on this information, the device determines the user's dissatisfaction with the communication speed.

[0541] Users can check information about high-speed communication areas provided by their device. Based on suggestions from an emotion analysis device, they can also choose to move to that area. For example, if a user is frustrated with the communication speed, the device might suggest a specific action, such as "Moving to ground level may improve communication speed." By taking action based on this suggestion, users can obtain a better communication experience.

[0542] This allows for a comfortable communication environment that takes both emotions and communication conditions into consideration.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The server collects communication speed data and location information from mobile terminals. It receives data sent from the terminals and records it in a database. The input to this database is the communication speed measured by each terminal and its current location information. The output is a collection of communication speed data for each location, which is used in subsequent analysis steps.

[0546] Step 2:

[0547] The server uses the collected data to analyze communication speeds for each region using a generated AI model. By inputting the prompt "Analyze the current communication speed situation in a specific region" into the model, the speed patterns for each region are analyzed. The input data consists of communication speed and location information obtained in step 1, and the output is the analysis result of the communication speed for that region.

[0548] Step 3:

[0549] The server transmits the analyzed communication speed results to each mobile terminal. When transmitting the results to the terminal, it sends real-time updated speed information as data packets. The input is the analysis results generated in step 2, and the output is the data transmission to the terminal.

[0550] Step 4:

[0551] The terminal displays communication speed information provided by the server. It also uses a built-in emotion analyzer to analyze the user's emotional state in real time. It analyzes facial expressions and voice tone through the camera and microphone to infer dissatisfaction with the communication speed. Inputs are communication speed information from the server and user emotion data, while outputs are visualized communication speed information and the results of the emotion analysis.

[0552] Step 5:

[0553] The device suggests specific actions to the user based on sentiment analysis. If dissatisfaction is detected from the determined sentiment data, it generates and displays a message such as, "Moving to ground level may improve communication speed." The input is the result of sentiment analysis, and the output is a suggestion for action to the user.

[0554] Step 6:

[0555] Users make decisions based on information about high-speed communication areas displayed on their devices and suggestions derived from sentiment analysis. They review the information and choose to improve their communication experience by moving to that area. The input is the action suggestion from the device, and the output is the action the user should take.

[0556] (Application Example 2)

[0557] 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."

[0558] In mobile communication systems, dissatisfaction with communication speed and delays can negatively impact users' emotional state, making it difficult to ensure a comfortable communication environment. To address this challenge, there is a need to provide a system that offers an optimal communication experience while considering users' feelings.

[0559] 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.

[0560] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, and means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible. This makes it possible for an emotion analysis device in the mobile terminal to determine the user's emotional state and prompt the user to move to the optimal communication area based on that state.

[0561] A "mobile terminal" refers to a device that has communication capabilities and is portable. This generally includes smartphones and in-car communication systems.

[0562] "Communication speed data" refers to information about the speed at which data is sent and received over a network.

[0563] "Location information" refers to data that indicates a physical location, and is usually obtained through systems such as GPS.

[0564] A "generative model" includes algorithms for analyzing and predicting communication speeds based on collected data.

[0565] An "emotion analysis device" refers to a device that uses sensors and analysis software to identify the emotional state of a user.

[0566] "High-speed communication area" is a term that refers to a region or location where communication speeds are relatively fast.

[0567] The "optimal communication area" refers to the region with the most suitable communication environment to provide users with a comfortable communication experience.

[0568] The system based on this invention is implemented through a mobile terminal, a server, and a suitable set of programs. Its configuration and operation are described in detail below.

[0569] The server is responsible for collecting communication speed data and location information transmitted from mobile terminals. This data is used with a generative AI model to analyze and predict communication speeds in specific areas. The server transmits these analysis results to mobile terminals in real time. This process allows users to instantly obtain information about high-speed communication areas.

[0570] The mobile terminal is equipped with an emotion analysis device that uses cameras, microphones, and other sensors to analyze the user's emotional state. Based on the analyzed emotional state, the terminal will, if necessary, suggest that the user move to a high-speed communication area. This suggestion is derived from the integrated analysis results of the emotion analysis device and communication speed information from the server. The aim is to shift the user's emotional state from dissatisfaction caused by communication delays to a comfortable state.

[0571] Furthermore, mobile terminals also take into account the user's past emotional history. Based on this information, it is possible to generate more personalized and optimal routes, improving the communication experience by being sensitive to the user's emotions. Real-time data communication between the server and mobile terminals is performed using communication modules and platforms (e.g., AWS Lambda, Affectiva API).

[0572] For example, if a user of a mobile device wants to comfortably use a streaming service, the device can sense the user's stress level and suggest a route that appropriately utilizes high-speed communication areas.

[0573] An example of a prompt message is, "The children are bored and irritable. Please select a route with a faster connection speed and optimize the in-car entertainment." This instruction is generated according to the user's needs and situation.

[0574] In this way, we realize a system that provides users with an optimal communication environment that takes both emotions and communication speed into consideration.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server collects communication speed data and location information from the mobile terminal. This data is transmitted to the server in real time via the mobile terminal's network module. The input is communication speed and location data, and the output is that this data is stored in the server's data storage.

[0578] Step 2:

[0579] The server uses collected communication speed data and location information to analyze the communication speed in each region using a generative AI model. During this process, the data is clustered, and the model predicts the average communication speed and trends in specific regions. The input is raw communication speed data and location information, and the output is the analyzed predicted communication speed data.

[0580] Step 3:

[0581] The server transmits the analyzed communication speed results to the mobile terminal. The terminal receives this and presents it as information about areas where high-speed communication is possible. This allows the user to check which areas have high-speed communication. The input is the analyzed communication speed data, and the output is a list of presented high-speed communication areas.

[0582] Step 4:

[0583] Mobile device emotion analyzers use data from cameras, microphones, and sensors to determine the user's emotional state. This information is analyzed in real time and compiled into data on the user's stress and frustration. Input is data on facial expressions and voice tone, and output is the result of the determination of a specific emotional state.

[0584] Step 5:

[0585] The terminal integrates the user's emotional state and communication speed information received from the server to suggest a suitable high-speed communication area for the user. Past emotional history is also taken into consideration, making the suggestions more personalized. A generative AI model is used to generate prompt sentences. The input is the emotional state and communication speed data, and the output is a prompt for a specific movement suggestion.

[0586] Step 6:

[0587] Users can view travel suggestions on their device and then travel to areas with high-speed communication capabilities. This allows them to enjoy a more comfortable communication environment and reduce stress. The input is the suggested information presented by the device, and the output is the user's travel actions.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] This invention provides a system for a mobile communication system that reduces user dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and a user.

[0606] Server Role

[0607] The server first collects real-time communication speed and location data from multiple mobile terminals. This data is encrypted and transmitted securely while protecting privacy. The server then analyzes the collected data using a generative model. The generative model uses machine learning algorithms to analyze real-time communication speeds in each area and predict future communication speeds. As a result of the analysis, it identifies areas where high-speed communication is predicted to be possible and transmits this information to mobile terminals.

[0608] Terminal role

[0609] Meanwhile, the mobile terminal receives the analysis results transmitted from the server. The terminal visually processes the received data and displays the high-speed communication area to the user. This display is usually done using maps or a graphical user interface, making it easy for the user to understand. The terminal may also provide voice instructions or notifications to the user as needed.

[0610] User roles

[0611] Based on information provided by the device, users move to the optimal high-speed communication area from their current location. For example, a business user in a large city, if they feel their communication speed is slowing down, can refer to the map information provided by their device and move to a high-speed communication area. This movement allows the user to enjoy a stable communication speed.

[0612] As a concrete example, suppose a user is working in a city cafe. Feeling that their internet connection is slow, the user checks a 3D map displayed on their device and discovers a plaza about 100 meters away with a faster connection. The user then moves from the cafe to the plaza and can continue working with a faster connection.

[0613] Thus, the system based on the present invention supports users in effectively selecting a communication environment and provides a comfortable communication experience.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The server periodically collects communication speed data and location information from each mobile terminal. Communication is encrypted and conducted through a secure channel.

[0617] Step 2:

[0618] The server structures and preprocesses the collected data. This includes deduplication and imputation of missing values.

[0619] Step 3:

[0620] The server inputs pre-processed data into a generative model to analyze communication speeds for each region. This analysis yields speed patterns and fluctuation predictions for each region.

[0621] Step 4:

[0622] The server organizes and classifies the analysis results obtained from the generative model, and identifies areas where high-speed communication is particularly expected.

[0623] Step 5:

[0624] The server sends the analysis results to each user's terminal. This includes customized information based on the user's current location.

[0625] Step 6:

[0626] The terminal analyzes the results received from the server and displays the high-speed communication area as a map or graphical interface.

[0627] Step 7:

[0628] Users check the display on their device and use it as a reference to identify areas where high-speed communication is available. They can then move to those areas if necessary.

[0629] Step 8:

[0630] The device maintains a periodic connection with the server when the user's location is updated, in preparation for the next data collection and updating of results.

[0631] (Example 1)

[0632] 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".

[0633] In mobile communication systems, a key challenge is to enable users to select and utilize an appropriate communication environment without being dissatisfied with the communication speed. In particular, it is necessary to optimize the communication environment by making real-time fluctuations in communication speed predictable and providing users with appropriate information.

[0634] 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.

[0635] In this invention, the server includes means for acquiring communication performance data and geographic information from a mobile information terminal, means including a generation algorithm for processing the acquired information and evaluating the communication performance of each area, and means for transferring the evaluation results to the mobile information terminal and presenting high-performance communication areas. This enables the user to obtain information about the optimal communication environment in real time and decide to move to an appropriate area.

[0636] A "mobile information terminal" is a portable electronic device held by a user for collecting and presenting location information and communication performance data.

[0637] "Communication performance data" refers to numerical or statistical data regarding the speed and quality of communication.

[0638] "Geographic information" refers to data that indicates location signal information within a specific area.

[0639] A "generative algorithm" is a computational method used to analyze and predict regional communication performance based on historical and real-time data.

[0640] "Evaluation results" refer to analytical conclusions or indicators regarding communication performance for each region, obtained through a generation algorithm.

[0641] A "high-performance communication area" is a geographical area where communication performance is high and users are expected to have a superior communication experience.

[0642] A description of embodiments for carrying out the present invention will be provided.

[0643] The server processes communication performance data and geographical information collected from mobile information terminals. To achieve this, the server uses a computer system with advanced computing capabilities. Specifically, the server utilizes a cloud-based computing infrastructure to collect, encrypt, and analyze data in real time. A generative AI model applies machine learning algorithms to this data and predicts communication speeds in each region as an analysis result. This analysis uses historical communication data, making it possible to predict future fluctuations in communication performance with high accuracy.

[0644] The terminal receives analysis results transferred from the server and visualizes the information in a user-friendly format. This often involves an easy-to-use graphical user interface (GUI), displaying the information as colored areas on a map. Terminals equipped with voice assistants can also inform the user of the analysis results verbally. The terminal works seamlessly with the high data processing capabilities required of mobile information terminals to provide users with the latest communication performance information at all times.

[0645] Users can use the information provided by their device to move to the optimal communication area. In everyday use, for example, if a user experiences a decrease in communication speed while working in a cafe in the city, they can check the map displayed on their device and easily access an area where faster communication is available. This allows users to enjoy a smooth and uninterrupted communication experience.

[0646] As a concrete example, a prompt such as "Identify areas with faster communication speeds within 100 meters of my current location" can be used. This prompt serves as a basic instruction for the generative AI model to quickly identify suitable communication areas.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The server receives communication performance data and geographical information from mobile information terminals. Specifically, real-time communication speed and location information measured from each terminal are encrypted and sent to the server. The input data is stored in a database and used for subsequent analysis.

[0650] Step 2:

[0651] The server inputs received communication performance data and geographical information into a generating AI model. The generating AI model uses machine learning algorithms to analyze past and present data and evaluate communication performance for each region. Specifically, it performs data cleansing and feature extraction to generate a communication speed prediction model. This yields evaluation results regarding high-performance communication areas.

[0652] Step 3:

[0653] The server transmits the evaluation results to mobile information terminals. This information includes identifying areas where high-performance communication is predicted to be possible. The server packages the analysis results along with metadata using a protocol and transfers the data to each terminal in the required format.

[0654] Step 4:

[0655] The terminal analyzes the evaluation results received from the server and visualizes them for display to the user. The input here is the analysis results sent from the server, and the output displays the high-performance communication area on a graphical interface. The information is presented as color-coded areas on a map, allowing the user to easily understand the current communication status.

[0656] Step 5:

[0657] The user moves to the optimal communication area based on the information displayed on the device. The user receives visual or audio instructions from the device to select their destination. Especially when communication is poor, the user makes a decision based on this information and attempts to access a high-performance communication area.

[0658] (Application Example 1)

[0659] 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".

[0660] In recent years, there has been a growing need for effective means to ensure users enjoy stable communication speeds in mobile communication systems. However, conventional technologies have struggled to effectively compensate for unstable communication speeds, and communication interruptions have been a particular problem in moving vehicles. Furthermore, there is no established means to analyze the vast amount of data from multiple mobile terminals in real time and quickly optimize the communication environment. Therefore, there is a strong demand for new technologies that can prevent communication interruptions, especially in autonomous vehicles, and provide passengers with a comfortable communication environment.

[0661] 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.

[0662] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible, and means for a device mounted on the vehicle to identify and present the optimal communication route. This makes it possible to prevent communication interruptions even in autonomous vehicles in motion and to always provide an optimized communication route, thereby ensuring a stable communication environment.

[0663] A "mobile terminal" is a communication device that is geographically mobile and has the function of collecting communication speed data and location information.

[0664] "Communication speed data" refers to information about the speed of communication at a specific time and place, and is a numerical representation of the amount of data transferred per unit time.

[0665] "Location information" refers to data that indicates the geographical location where a mobile device is located, and includes geographic coordinate information such as latitude and longitude.

[0666] A "generative model" is a mathematical model that uses machine learning algorithms to analyze collected communication speed data and location information to predict communication speeds for each region.

[0667] A "high-speed communication area" refers to a geographical area where communication speeds are expected to meet a certain standard, and within which devices can communicate stably.

[0668] "Vehicle-mounted devices" refer to electronic devices installed inside autonomous vehicles and used to display the optimal communication route.

[0669] "Optimal communication route" refers to the path selected to ensure that a moving vehicle enjoys the best possible communication environment, taking into account the predicted communication speed of each area located along that route.

[0670] A system for implementing this invention consists of a server, a mobile terminal, and a device mounted on a vehicle.

[0671] The server has the capability to collect communication speed data and location information from mobile terminals in real time. This data is securely transmitted using encryption technology. Subsequently, a machine learning algorithm based on a generated AI model is executed on the server to analyze the received data. This analysis predicts communication speeds for each region and identifies areas where high-speed communication is possible.

[0672] The mobile terminal receives analysis results from the server and visually displays the communication status based on them. Specifically, it visualizes areas where high-speed communication is possible on a map and informs the user. If the user perceives a decrease in communication speed, they can use this information to move to an optimal area. The terminal also shares information with devices installed in the vehicle and calculates and presents the optimal communication route. This device is integrated with the vehicle's navigation system and plays a role in guiding the autonomous vehicle along its route while maintaining an optimal communication environment.

[0673] A concrete example is an autonomous taxi operating in an urban area. To ensure passengers can comfortably enjoy video streaming within the taxi, the system would incorporate a route that provides the optimal communication speed into the navigation system. This function is particularly effective for intercity travel and in areas with unstable communication environments.

[0674] An example of a prompt related to analysis using a generative AI model is, "Generate an algorithm to identify the optimal communication route based on mobile communication data collected in real time."

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The server collects communication speed data and location information from mobile terminals in real time. In this process, the terminal obtains the current communication speed and location information via sensors, encrypts it, and sends it to the server. The input is raw data from the terminal, and the output is an encrypted dataset on the server.

[0678] Step 2:

[0679] The server runs a generative AI model to analyze the received data. In this step, the server uses machine learning algorithms to analyze the collected communication speed and location information and predict future communication speeds. The input is the encrypted data obtained in step 1, and the output is the predicted communication speed information for each region.

[0680] Step 3:

[0681] The server identifies high-speed communication areas based on the prediction results and transmits them to mobile terminals. The identified area information is stored in a database and sent as update information to the necessary terminals. The input is the prediction result from step 2, and the output is information on the identified high-speed communication areas.

[0682] Step 4:

[0683] The terminal visually presents the user with high-speed communication area information received from the server. This operation involves updating the graphical user interface using a map display function, making it easy for the user to understand. The input is the area information sent in step 3, and the output is the information displayed on the map that the user can view.

[0684] Step 5:

[0685] The terminal calculates and presents the optimal communication route to the device installed in the vehicle. In this step, the terminal's processing power is used to calculate the communication route, and the route information is provided to the vehicle's navigation system. The input is the area information presented in step 4, and the output is the optimal route information suitable for the vehicle.

[0686] Step 6:

[0687] The user guides the vehicle to the optimal communication route based on the information displayed on the device. This allows the user to enjoy an uninterrupted communication environment while riding. The input is the route information from step 5, and the output is the comfortable communication experience the user receives without interruption.

[0688] 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.

[0689] This invention provides a system for mobile communication systems that takes into account the user's emotional state and reduces dissatisfaction with communication speed. This system operates in cooperation with a server, a mobile terminal, and the user, and in particular, an emotion engine installed in the mobile terminal analyzes the user's emotional state and prompts actions based on that analysis.

[0690] Server Role

[0691] The server continues to collect communication speed data and location information from multiple mobile terminals, as before. Based on this data, it analyzes communication speeds for each region using a generative model and transmits the analysis results to terminals in real time. Through this process, information on areas where high-speed communication is possible is provided to terminals.

[0692] Terminal role

[0693] Meanwhile, mobile terminals not only receive and display communication speed analysis results provided by the server, but also evaluate the user's emotional state through an emotion engine. The emotion engine uses cameras, microphones, and sensors to infer emotions from the user's facial expressions and tone of voice.

[0694] The function of the emotional engine

[0695] The emotion engine built into the device, if it determines that the user is dissatisfied with the communication speed, will quickly recommend moving to a high-speed communication area based on the analysis results. It also considers the user's past emotional history to provide more personalized advice.

[0696] User roles

[0697] Users can check for high-speed communication areas on their device's display, and then improve their communication speed by moving to those areas based on input from the emotion engine. In this way, users can consciously manage their emotional state and obtain an optimal communication experience.

[0698] For example, if a user is frustrated by slow internet speeds at a subway station, the device's emotion engine instantly analyzes their facial expression and the network environment, suggesting that going above ground would improve communication speed. The user follows the suggestion, moves above ground, and can resume a smooth internet connection. In this way, a system incorporating an emotion engine comprehensively considers the user's emotions and the network environment to support an optimal communication experience.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server periodically collects communication speed data and location information from mobile terminals. This updates the database with the latest communication environment information.

[0702] Step 2:

[0703] The server inputs the collected data into a generative model and analyzes communication speeds for each region. As a result of the analysis, it generates information that identifies areas where high-speed communication is possible.

[0704] Step 3:

[0705] The server transmits the analysis results to the mobile terminal. The information is provided in real time and includes data from high-speed communication areas available to the user.

[0706] Step 4:

[0707] The terminal checks the analysis results received from the server and displays the information to the user visually using maps and graphical interfaces.

[0708] Step 5:

[0709] The device's emotion engine monitors the user's emotional state. Using the camera, microphone, and other sensors, it analyzes facial expressions and voice tone to detect user stress and dissatisfaction.

[0710] Step 6:

[0711] If the emotion engine detects user dissatisfaction, the device will suggest specific actions to the user based on information about high-speed communication areas obtained from the server. For example, it might recommend moving in a direction where high-speed communication is available.

[0712] Step 7:

[0713] The user accepts the suggestion and moves to a high-speed communication area according to the device's instructions. This aims to improve communication speed.

[0714] Step 8:

[0715] After moving, the device reconnects to the server based on the new location information and obtains the latest communication speed data. This allows the user to continuously obtain an optimal communication environment.

[0716] (Example 2)

[0717] 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".

[0718] In mobile communication networks, it is a problem that users frequently experience dissatisfaction with communication speeds. Furthermore, conventional systems fail to provide appropriate feedback based on emotions, which is a challenge in resolving dissatisfaction with communication speeds.

[0719] 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.

[0720] In this invention, the server includes means for collecting communication speed data and location information from a mobile communication device, means including a data processing model that uses the collected data to analyze communication speeds for each region, and means for transmitting the analysis results to the mobile communication device and presenting areas where high-speed communication is possible. This makes it possible to analyze the user's emotional state, evaluate dissatisfaction with communication speed in real time, and propose appropriate actions to the user based on that.

[0721] A "mobile communication device" is a device used for wireless communication in a mobile location, and typically has the function of acquiring communication speed data and location information.

[0722] "Communication speed data" refers to information about the speed at which data is sent and received over a specific communication network.

[0723] "Location information" refers to data indicating the current geographical location of a mobile communication device, which is usually obtained through GPS or similar means.

[0724] A "data processing model" is a collection of algorithms and programs used to analyze collected data, and in particular, it uses generative AI models.

[0725] "High-speed communication area" refers to the range within a communication network where high-speed data transmission and reception are possible.

[0726] An "emotion analysis device" is a set of equipment and software necessary to analyze a user's emotional state, and typically uses a combination of sensors, cameras, and microphones.

[0727] "Judging dissatisfaction" means evaluating the negative feelings a user has regarding communication speed and recognizing them as dissatisfaction according to specific criteria.

[0728] "Visualization" is the process of displaying data-based information in a way that is easy for users to understand, such as providing information through graphs or maps.

[0729] This invention is designed to improve the user's communication experience in mobile communication systems. The entire system operates in cooperation with three parties: a server, a mobile terminal, and the user.

[0730] The server collects communication speed data and location information from mobile terminals. Using this data, the server employs a generative AI model to analyze communication speeds in each region. For this analysis, the collected communication speed data is input, and prompts such as "Please analyze the current communication speed situation in a specific region" are given to the generative AI model. The results are calculated in real time as a speed distribution for each region, and the analysis results are sent to the terminal.

[0731] The device displays communication speed information provided by the server. Furthermore, it analyzes the user's emotional state using a built-in emotion analyzer. Specifically, the device uses a camera, microphone, and sensors to detect and analyze the user's facial expressions and voice tone. Based on this information, the device determines the user's dissatisfaction with the communication speed.

[0732] Users can check information about high-speed communication areas provided by their device. Based on suggestions from an emotion analysis device, they can also choose to move to that area. For example, if a user is frustrated with the communication speed, the device might suggest a specific action, such as "Moving to ground level may improve communication speed." By taking action based on this suggestion, users can obtain a better communication experience.

[0733] This allows for a comfortable communication environment that takes both emotions and communication conditions into consideration.

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] The server collects communication speed data and location information from mobile terminals. It receives data sent from the terminals and records it in a database. The input to this database is the communication speed measured by each terminal and its current location information. The output is a collection of communication speed data for each location, which is used in subsequent analysis steps.

[0737] Step 2:

[0738] The server uses the collected data to analyze communication speeds for each region using a generated AI model. By inputting the prompt "Analyze the current communication speed situation in a specific region" into the model, the speed patterns for each region are analyzed. The input data consists of communication speed and location information obtained in step 1, and the output is the analysis result of the communication speed for that region.

[0739] Step 3:

[0740] The server transmits the analyzed communication speed results to each mobile terminal. When transmitting the results to the terminal, it sends real-time updated speed information as data packets. The input is the analysis results generated in step 2, and the output is the data transmission to the terminal.

[0741] Step 4:

[0742] The terminal displays communication speed information provided by the server. It also uses a built-in emotion analyzer to analyze the user's emotional state in real time. It analyzes facial expressions and voice tone through the camera and microphone to infer dissatisfaction with the communication speed. Inputs are communication speed information from the server and user emotion data, while outputs are visualized communication speed information and the results of the emotion analysis.

[0743] Step 5:

[0744] The device suggests specific actions to the user based on sentiment analysis. If dissatisfaction is detected from the determined sentiment data, it generates and displays a message such as, "Moving to ground level may improve communication speed." The input is the result of sentiment analysis, and the output is a suggestion for action to the user.

[0745] Step 6:

[0746] Users make decisions based on information about high-speed communication areas displayed on their devices and suggestions derived from sentiment analysis. They review the information and choose to improve their communication experience by moving to that area. The input is the action suggestion from the device, and the output is the action the user should take.

[0747] (Application Example 2)

[0748] 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".

[0749] In mobile communication systems, dissatisfaction with communication speed and delays can negatively impact users' emotional state, making it difficult to ensure a comfortable communication environment. To address this challenge, there is a need to provide a system that offers an optimal communication experience while considering users' feelings.

[0750] 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.

[0751] In this invention, the server includes means for collecting communication speed data and location information from a mobile terminal, means including a generative model for analyzing communication speeds in each region using the collected data, and means for transmitting the analysis results to the mobile terminal and presenting areas where high-speed communication is possible. This makes it possible for an emotion analysis device in the mobile terminal to determine the user's emotional state and prompt the user to move to the optimal communication area based on that state.

[0752] A "mobile terminal" refers to a device that has communication capabilities and is portable. This generally includes smartphones and in-car communication systems.

[0753] "Communication speed data" refers to information about the speed at which data is sent and received over a network.

[0754] "Location information" refers to data that indicates a physical location, and is usually obtained through systems such as GPS.

[0755] A "generative model" includes algorithms for analyzing and predicting communication speeds based on collected data.

[0756] An "emotion analysis device" refers to a device that uses sensors and analysis software to identify the emotional state of a user.

[0757] "High-speed communication area" is a term that refers to a region or location where communication speeds are relatively fast.

[0758] The "optimal communication area" refers to the region with the most suitable communication environment to provide users with a comfortable communication experience.

[0759] The system based on this invention is implemented through a mobile terminal, a server, and a suitable set of programs. Its configuration and operation are described in detail below.

[0760] The server is responsible for collecting communication speed data and location information transmitted from mobile terminals. This data is used with a generative AI model to analyze and predict communication speeds in specific areas. The server transmits these analysis results to mobile terminals in real time. This process allows users to instantly obtain information about high-speed communication areas.

[0761] The mobile terminal is equipped with an emotion analysis device that uses cameras, microphones, and other sensors to analyze the user's emotional state. Based on the analyzed emotional state, the terminal will, if necessary, suggest that the user move to a high-speed communication area. This suggestion is derived from the integrated analysis results of the emotion analysis device and communication speed information from the server. The aim is to shift the user's emotional state from dissatisfaction caused by communication delays to a comfortable state.

[0762] Furthermore, mobile terminals also take into account the user's past emotional history. Based on this information, it is possible to generate more personalized and optimal routes, improving the communication experience by being sensitive to the user's emotions. Real-time data communication between the server and mobile terminals is performed using communication modules and platforms (e.g., AWS Lambda, Affectiva API).

[0763] For example, if a user of a mobile device wants to comfortably use a streaming service, the device can sense the user's stress level and suggest a route that appropriately utilizes high-speed communication areas.

[0764] An example of a prompt message is, "The children are bored and irritable. Please select a route with a faster connection speed and optimize the in-car entertainment." This instruction is generated according to the user's needs and situation.

[0765] In this way, we realize a system that provides users with an optimal communication environment that takes both emotions and communication speed into consideration.

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The server collects communication speed data and location information from the mobile terminal. This data is transmitted to the server in real time via the mobile terminal's network module. The input is communication speed and location data, and the output is that this data is stored in the server's data storage.

[0769] Step 2:

[0770] The server uses collected communication speed data and location information to analyze the communication speed in each region using a generative AI model. During this process, the data is clustered, and the model predicts the average communication speed and trends in specific regions. The input is raw communication speed data and location information, and the output is the analyzed predicted communication speed data.

[0771] Step 3:

[0772] The server transmits the analyzed communication speed results to the mobile terminal. The terminal receives this and presents it as information about areas where high-speed communication is possible. This allows the user to check which areas have high-speed communication. The input is the analyzed communication speed data, and the output is a list of presented high-speed communication areas.

[0773] Step 4:

[0774] Mobile device emotion analyzers use data from cameras, microphones, and sensors to determine the user's emotional state. This information is analyzed in real time and compiled into data on the user's stress and frustration. Input is data on facial expressions and voice tone, and output is the result of the determination of a specific emotional state.

[0775] Step 5:

[0776] The terminal integrates the user's emotional state and communication speed information received from the server to suggest a suitable high-speed communication area for the user. Past emotional history is also taken into consideration, making the suggestions more personalized. A generative AI model is used to generate prompt sentences. The input is the emotional state and communication speed data, and the output is a prompt for a specific movement suggestion.

[0777] Step 6:

[0778] Users can view travel suggestions on their device and then travel to areas with high-speed communication capabilities. This allows them to enjoy a more comfortable communication environment and reduce stress. The input is the suggested information presented by the device, and the output is the user's travel actions.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means for collecting communication speed data and location information from a mobile terminal,

[0803] A means including a generative model that uses collected data to analyze communication speeds in each region,

[0804] A means for transmitting the analysis results to a mobile terminal and indicating the area where high-speed communication is possible,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein a generative model analyzes data in real time and predicts communication speeds for each region.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein a mobile terminal visualizes the high-speed communication area based on the received analysis results and notifies the user.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means of acquiring communication performance data and geographic information from a mobile information terminal,

[0813] A means including a generation algorithm that processes acquired information and evaluates the communication performance in each domain,

[0814] A means for transferring evaluation results to a mobile information terminal and indicating the high-performance communication area,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, in which a generation algorithm dynamically processes information and predicts the communication performance of each domain.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein a mobile information terminal visualizes the high-performance communication area based on the received evaluation results and notifies the user.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] A means for collecting communication speed data and location information from a mobile terminal,

[0823] A means including a generative model that uses collected data to analyze communication speeds in each region,

[0824] A means for transmitting the analysis results to a mobile terminal and indicating the area where high-speed communication is possible,

[0825] A device mounted on the vehicle provides a means for identifying and presenting the optimal communication route,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein a generative model analyzes data in real time, predicts communication speeds for each region, and determines the optimal communication route.

[0829] (Claim 3)

[0830] The system according to claim 1, in which a mobile terminal visualizes the high-speed communication area based on the received analysis results, notifies the user, and displays information on the optimal communication route.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means for collecting communication speed data and location information from a mobile communication device,

[0834] A means including a data processing model that uses collected data to analyze communication speeds for each region,

[0835] A means for transmitting the analysis results to a mobile communication device and indicating the high-speed communication area,

[0836] A means for collecting emotional data using an emotion analysis device to analyze the user's emotional state and to determine dissatisfaction with communication speed,

[0837] A means of suggesting actions to improve communication speed to users based on emotional data,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the data processing model analyzes data in real time and predicts communication speeds for each region.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the mobile communication device visualizes the high-speed communication area based on the analysis results, and further uses an emotion analysis device to personalize the notification content and present it to the user.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for collecting communication speed data and location information from a mobile terminal,

[0846] A means including a generative model that uses collected data to analyze communication speeds in each region,

[0847] A means for transmitting the analysis results to a mobile terminal and indicating the area where high-speed communication is possible,

[0848] A means by which an emotion analysis device within a mobile terminal determines the user's emotional state and prompts them to move to the optimal communication area based on that state,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein a generative model analyzes data in real time and predicts communication speeds for each region.

[0852] (Claim 3)

[0853] The system according to claim 1, in which a mobile terminal visualizes the high-speed communication area based on the received analysis results and notifies the user, and generates an optimal route based on the emotional state. [Explanation of Symbols]

[0854] 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 communication speed data and location information from a mobile terminal, A means including a generative model that uses collected data to analyze communication speeds in each region, A means for transmitting the analysis results to a mobile terminal and indicating the area where high-speed communication is possible, A system that includes this.

2. The system according to claim 1, wherein the generative model analyzes data in real time and predicts communication speeds for each region.

3. The system according to claim 1, wherein a mobile terminal visualizes the high-speed communication area based on the received analysis results and notifies the user.

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