system

Abstract

We provide the system. [Solution] A means for recording data collected from a communication device, A means for analyzing the aforementioned data in real time, A means for automatically adjusting the operation of the communication device based on the analysis results, A means for monitoring the communication status resulting from the adjustment and providing feedback, A means of analyzing urban traffic and event information to optimize communication infrastructure, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An increase in energy consumption in a communication device of a mobile phone network and a deterioration in communication quality due to rapid fluctuations in traffic are problems. In particular, in a situation where the traffic pattern is unpredictable, it is difficult to provide efficient energy management and stable communication services by conventional manual adjustment. Therefore, there is a need for a method to improve the operation efficiency of a communication device, optimize energy consumption, and consistently maintain a high level of communication quality.

Means for Solving the Problems

[0005] This invention is characterized by automatically optimizing the operation of a communication device by providing means for recording and analyzing data collected from the communication device in real time. This dynamically adjusts the operating parameters of the communication device based on the analysis results, thereby reducing energy consumption. Furthermore, by predicting traffic patterns, it prevents a decline in communication quality and provides a stable service. This automatic adjustment and prediction process is optimized through continuous monitoring and feedback, aiming to improve overall operational efficiency.

[0006] A "communication device" is a device that forms part of a communication network and has the function of transmitting, receiving, and processing data.

[0007] "Data" refers to a collection of numerical information and signals generated, collected, and analyzed by communication devices.

[0008] "Analysis" is the process of evaluating collected data to derive meaningful information.

[0009] "Automatic adjustment" refers to a function where the system changes the operating parameters of equipment without human intervention.

[0010] "Energy consumption" refers to the amount of electricity required for a communication device to operate.

[0011] A "traffic pattern" refers to the temporal and quantitative trends in the fluctuations of data flowing through a communication network.

[0012] "Monitoring" is the process of continuously monitoring the operational status of a system and recording any changes.

[0013] "Feedback" is the process of evaluating the output and results of a system and using that feedback to make further improvements or adjustments. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which 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

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] In the system of this invention, a program for efficiently operating communication devices runs on a server. This program aims to optimize the entire system by receiving and analyzing real-time data transmitted from the communication devices.

[0036] First, the terminal generates data based on the user's communication activity, and this data is transmitted to the server via the base station. The server stores the received data in a database and simultaneously analyzes the data using the generated AI. The analyzed data is used to detect traffic fluctuations and anomalies, and based on this, the AI ​​agent dynamically adjusts the operating parameters of the base station.

[0037] This adjustment allows, for example, the communication equipment's output to be increased in response to a sudden surge in traffic, and to switch to energy-saving mode when traffic decreases. The server continuously monitors this process and uses the obtained data as feedback for subsequent analysis. In this way, the system continues to learn on its own and autonomously builds the optimal operating environment.

[0038] As a concrete example, in areas where large-scale events are held, a temporary increase in user traffic is expected. By quickly detecting this situation and increasing the output of base stations in that area based on predictions, a decline in communication quality can be prevented. As a result, users can continue to comfortably use mobile services even during the event.

[0039] Thus, the system of the present invention stabilizes communication quality by optimizing the operation of communication devices in real time, while also contributing to improved energy efficiency.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device transmits data generated through the user's communication activities to the communication device. This data includes location information, data volume, and traffic patterns.

[0043] Step 2:

[0044] The server records the data received from the communication device in a database. This allows the server to build a comprehensive log of traffic conditions and system health.

[0045] Step 3:

[0046] The server passes the recorded data to a generating AI program for real-time analysis. The analysis results are used to identify fluctuations in communication load and signs of anomalies.

[0047] Step 4:

[0048] Based on the analysis results, the server activates an AI agent to automatically perform appropriate adjustments to the base station. These adjustments include optimizing the base station's transmission power and frequency, and modifying parameters for load balancing.

[0049] Step 5:

[0050] The server monitors the communication status after adjustment and incorporates that data back into the feedback loop. The information obtained is used to improve the accuracy of the next data analysis and adjustment process.

[0051] Step 6:

[0052] The system ensures users can comfortably utilize the service in an optimized communication environment. It operates without user awareness, maintaining a balance between communication quality and energy efficiency.

[0053] (Example 1)

[0054] 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."

[0055] Modern communication networks require handling rapid fluctuations in traffic volume and optimizing energy consumption. Furthermore, stabilizing communication quality and optimizing traffic management are crucial. Traditional systems have limitations in real-time dynamic adjustments and predictive accuracy, resulting in insufficient improvements to the user experience.

[0056] 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.

[0057] In this invention, the server includes means for recording data collected from communication devices, means for analyzing the received data in real time using a generative AI model, and means for dynamically and automatically adjusting the operating parameters of the communication infrastructure based on the analysis results. This makes it possible to efficiently operate the communication network and optimize communication quality in real time.

[0058] "Communication equipment" refers to hardware or software used to send and receive voice and data.

[0059] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to perform data analysis and predictions.

[0060] "Data analysis" refers to the process of processing collected information to derive useful insights and patterns.

[0061] "Real-time" refers to processing information almost instantly and reflecting it without delay.

[0062] "Communication infrastructure" refers to the collection of hardware and software that make up a communication network.

[0063] "Dynamic automatic adjustment" refers to the automatic and timely modification of various settings based on analyzed data.

[0064] "Analysis results" refer to the conclusions and insights obtained through data analysis.

[0065] "Operating parameters" refer to the operating conditions and settings of the communication infrastructure.

[0066] The following describes embodiments for carrying out the present invention. This system provides a method for optimizing a communication network.

[0067] The server first receives data transmitted from communication devices and stores it in a database. This data is generated based on the user's communication activity and includes voice data, internet usage data, location information, etc. The server analyzes the received data in real time using a generative AI model. The generative AI model used detects data trends and anomalies and predicts traffic patterns.

[0068] To utilize the generative AI model, the server sends prompt messages to the generative AI, such as, "Please suggest optimal base station settings based on current communication traffic." Based on the analysis results, the server dynamically adjusts the operating parameters of the communication infrastructure. This automatically optimizes the communication infrastructure settings according to the load on the communication network.

[0069] For example, around stadiums where large-scale sporting events are held, a temporary surge in user communication traffic is expected. In this situation, the server responds immediately through real-time analysis and adjusts the output of the communication infrastructure in the affected area to increase its capacity, thereby preventing a decline in communication quality.

[0070] Thus, the system of the present invention provides a dynamically operable infrastructure while maintaining the efficiency and quality of the communication network, and also contributes to improving energy efficiency.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server receives user communication data transmitted from communication devices through base stations. The data received as input includes call records, internet connection information, and location data. This data is stored in a database in preparation for later analysis.

[0074] Step 2:

[0075] The server analyzes the received data in real time using a generative AI model. In this step, the data analysis process takes place. The input is the data saved in step 1, and through the analysis, output is obtained that detects trends and anomalies in communication traffic. The generative AI model is given a prompt message that says, "Detect anomalies based on the current communication traffic."

[0076] Step 3:

[0077] The server adjusts the operating parameters of the communication infrastructure based on the analysis results. The input is the analysis results from step 2, and the output includes suggestions and applications of the adjusted operating parameters. Specific actions include increasing the base station's transmission power in response to a surge in traffic, or switching to energy-saving mode when traffic decreases.

[0078] Step 4:

[0079] The server continuously monitors the performance of the adjusted communication infrastructure. The input here is the actual operating state of the network, and the data obtained as output is used for subsequent analysis and feedback. Through this feedback loop, the system self-learns and improves the accuracy of subsequent communication parameter adjustments.

[0080] (Application Example 1)

[0081] 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."

[0082] In modern urban environments, efficient operation of communication networks is essential. In particular, communication traffic surges in densely populated areas and during events, raising concerns about a decline in communication quality. In such situations, the challenge lies in providing high-quality communication services while simultaneously adjusting the communication infrastructure in real time and suppressing energy consumption.

[0083] 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.

[0084] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, means for automatically adjusting the operation of the communication device based on the analysis results, and means for analyzing urban traffic information and event information to optimize the communication infrastructure. This makes it possible to improve communication quality and optimize energy efficiency in urban environments.

[0085] A "communication device" is a device that has the function of transmitting data and enables the exchange of information between systems and terminals.

[0086] "Means of recording" refers to functions or devices for storing collected data.

[0087] "Methods for real-time analysis" refer to the techniques and technologies used to analyze data instantaneously.

[0088] "Automatic adjustment mechanisms" refer to systems that change settings based on analysis results.

[0089] "Monitoring means" refer to devices or functions for continuously observing and confirming communication status and system conditions.

[0090] "Means of providing feedback" refer to methods and mechanisms for returning information based on monitoring results.

[0091] "Means for analyzing traffic and event information" refers to technologies for analyzing information about urban traffic conditions and special events.

[0092] "Means of optimizing communication infrastructure" refer to methods and technologies for efficiently adjusting network configurations and settings to create an optimal communication environment.

[0093] The system that implements this application consists of a program running on a server. The server receives real-time data transmitted from communication devices via base stations and stores this data in a database. Subsequently, it analyzes the data using a generative AI model. The main purpose of the analysis is to detect fluctuations in communication traffic and identify anomalies early.

[0094] Based on the analyzed data, the server's AI agent automatically adjusts the base station's operating parameters. For example, if traffic surges, it increases the base station's output, and if traffic decreases, it switches to energy-saving mode. This adjustment stabilizes communication quality while simultaneously improving energy efficiency.

[0095] For example, when a large-scale event is held within a smart city, the server quickly detects fluctuations in the area's communication traffic and optimizes the infrastructure settings according to the communication needs during the event. This allows citizens and visitors to continue using communication services comfortably.

[0096] An example of a prompt for a generated AI model is, "How can real-time data analysis be used to optimize the communication infrastructure of a smart city?"

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The terminal collects data generated by the user's communication activities. Inputs include communication logs and location information, and output is the transmission of this data to a server via a base station. Specifically, the terminal automatically formats the data into packets and transmits them when a communication event occurs.

[0100] Step 2:

[0101] The server stores real-time data received through the base station in a database. The input here is the communication data sent from the terminal in step 1, and the output is the accumulation of this data in the database. The server performs transaction processing to maintain data consistency and securely stores it in the database.

[0102] Step 3:

[0103] The server performs data analysis using a generative AI model. The input is the data saved in step 2, and the analysis yields output that detects traffic fluctuations and anomalies. Specifically, the generative AI model learns data patterns and predicts new anomalies and trends.

[0104] Step 4:

[0105] Based on the analysis results of the generated AI model, the server uses an AI agent to automatically adjust the operating parameters of the base station. The input is the analysis results, and the output is the adjusted base station configuration information. In this step, specific adjustments are made to the base station's output level and the assignment of communication channels.

[0106] Step 5:

[0107] The server continuously monitors the adjusted communication status. The input is the real-time communication status obtained as feedback from the base station, and based on this, an output is obtained that modifies or maintains the optimization process. Specifically, if an anomaly is detected, the server immediately performs a reanalysis and readjusts as necessary.

[0108] 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.

[0109] This invention is a system that combines a communication device with an emotion engine, recognizing the user's emotional state in real time and providing a corresponding communication environment. This system consists of a server-centered program.

[0110] First, the device collects data from the user's voice tone and sensing devices, and the emotion engine analyzes the user's emotional state. This analysis indicates the user's emotional state, such as whether they are relaxed or stressed. The analyzed emotional data is immediately sent to the server.

[0111] Based on this emotional data, the server works in conjunction with a generative AI to combine it with traffic information stored in a database and selects the most comfortable communication environment for the user. For example, if the server determines that the user is experiencing stress, it optimizes the base station settings to minimize communication delays and provide smoother communication.

[0112] For example, if a high stress level is detected from the user's voice tone during a video conference, the server immediately processes this information and takes measures to improve communication quality. As a result, the user can continue the meeting without experiencing stress.

[0113] This system enables the provision of a nuanced communication environment based on the user's emotional state, significantly improving usability. Furthermore, by combining emotional responses with automatic adjustment of communication quality, efficient network management and enhanced user experience are achieved simultaneously.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The device uses microphones and cameras to detect emotional indicators such as the user's voice and facial expressions. This data is analyzed in real time by a built-in emotion engine to infer the user's emotional state.

[0117] Step 2:

[0118] The server receives sentiment data sent from the terminal. Simultaneously, it stores this data in an existing traffic database, preparing it for comprehensive analysis.

[0119] Step 3:

[0120] The server uses generated AI based on the received emotion data to initiate processing to optimize the communication environment. The emotion data is analyzed in combination with traffic pattern data and used as a basis for determining the optimal communication environment for the current user's emotional state.

[0121] Step 4:

[0122] The server automatically adjusts the operating parameters of the communication device based on the analysis results. For example, if the analysis indicates that the user is experiencing stress, it will make the necessary adjustments to reduce communication delays.

[0123] Step 5:

[0124] Users will experience an optimized communication environment through this adjustment. They can enjoy high-quality communication with minimal stress, resulting in a comfortable user experience. Furthermore, user feedback will be collected again as sentiment data and used in the next optimization process.

[0125] (Example 2)

[0126] 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".

[0127] Conventional communication systems were unable to optimize the communication environment based on the user's emotional state, resulting in an insufficient user experience. Furthermore, the inability to perform real-time network adjustments in response to user emotions limited the potential for improving communication quality and energy efficiency.

[0128] 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.

[0129] In this invention, the server includes means for analyzing information indicating the user's emotional state collected from a terminal, means for selecting a communication environment in cooperation with a generating AI based on the analyzed information, and means for optimizing network settings based on the selected communication environment. This enables real-time optimization of the communication environment in accordance with the user's emotional state.

[0130] A "terminal" is an information processing device used to collect voice information and biometric data from users and transmit them externally.

[0131] "User emotional state" refers to psychological conditions such as relaxed or stressed states, which are analyzed from voice tone and biometric information.

[0132] "Means of analyzing information" refers to methods that include processes and technologies for analyzing and measuring a user's emotional state based on collected data.

[0133] A "server" is a central computing device that receives analyzed emotional information and processes it to provide the necessary communication environment.

[0134] "Generative AI" refers to artificial intelligence technology used to infer or select the optimal communication environment from received data.

[0135] "Communication environment" refers to the state of the system, including the network conditions and settings necessary for users to communicate properly.

[0136] "Methods for optimizing network settings" refer to technologies that adjust network parameters according to the user's emotional state to achieve better communication quality.

[0137] "Means of providing feedback" refers to methods for verifying whether the optimization of the communication environment was successful and informing the system or user of the results.

[0138] This invention relates to a system that recognizes a user's emotional state in real time and provides an optimal communication environment based on that state. Specifically, it consists of a terminal, a server, and a generative AI.

[0139] The device is designed to collect user voice information and biometric data. It uses microphones and sensors built into smartphones and smartwatches to acquire data such as voice tone, heart rate, and skin electrical activity. This data forms the basis for indicating the user's emotional state.

[0140] Data acquired from the device is analyzed using an emotion engine. This emotion engine includes software for natural language processing and speech recognition, such as speech analysis software and machine learning algorithms for analyzing voice tone. This analysis determines the user's emotional state, such as whether they are relaxed or stressed.

[0141] The analysis results are sent to the server. The server works with a generative AI model based on the analyzed emotional information to select a communication environment appropriate for the user's emotional state. For example, a cloud-based AI service is used to analyze the data and recommend optimal network conditions. This means that if the user is experiencing stress, the network settings are adjusted to minimize communication delays.

[0142] As a concrete example of its use, if a user experiences stress during a video conference, the server can instantly increase network bandwidth to improve communication quality. This allows the user to concentrate on the meeting without disruption.

[0143] An example of a prompt to input into the generating AI model is, "If the user's emotional state is stress, please tell me how to minimize communication delay and provide optimal network conditions."

[0144] This system provides a communication environment that responds to the user's emotional state, significantly improving usability.

[0145] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0146] Step 1:

[0147] The device collects user data such as voice tone and biometric data. This process involves recording voice using the smartphone's microphone and measuring heart rate and skin electrical activity using the smartwatch's sensors. The acquired data is used as input for analyzing the user's emotional state.

[0148] Step 2:

[0149] The device analyzes the collected data using an emotion engine. Voice data is converted to text using speech recognition software, and an emotion analysis algorithm determines the user's emotional state. This analysis outputs whether the user is relaxed or stressed. Specifically, it analyzes voice pitch and tone, as well as heart rate fluctuations.

[0150] Step 3:

[0151] The device sends the analyzed emotional state data to the server. Here, the data is transferred quickly and securely using a mobile communication protocol. The output of this step is data representing the user's emotional state.

[0152] Step 4:

[0153] The server integrates the received emotional state data with a generating AI model. Based on this data, the AI ​​infers and selects the optimal communication environment. By comparing it with traffic information and the current network state, it determines specific network settings. In this process, settings are output to minimize communication delays when the user is experiencing stress.

[0154] Step 5:

[0155] The server sends the selected network settings to the terminal. The terminal then follows the instructions, adjusting base station settings and routing to improve communication quality. For example, it might increase network bandwidth to reduce latency during video conferencing. This provides the user with an optimized communication environment.

[0156] Step 6:

[0157] The server monitors the effectiveness of the optimized communication environment and provides feedback as needed. Specifically, it monitors quality indicators such as communication delay and error rate, and makes further configuration adjustments based on these. The output consists of the improved communication state and data for further optimization.

[0158] (Application Example 2)

[0159] 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".

[0160] In today's communication environment, there is a need for dynamic adjustment of communication quality that responds to the user's emotional state. In particular, providing a communication environment that aligns with the user's emotions during online meetings and information sharing presents a significant challenge. This is because existing systems lack the ability to accurately capture user emotions and adjust communication settings in real time accordingly.

[0161] 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.

[0162] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, and means for analyzing the user's emotional state. This enables automatic and dynamic optimization of the communication environment based on the user's emotions.

[0163] A "communication device" is a device used for sending and receiving data, and functions as an interface with the user.

[0164] "Means of recording" refers to a device or program that has the function of storing acquired data and making it accessible as needed.

[0165] "Means of analysis" refers to a system that includes the process of interpreting collected data and identifying certain patterns or trends.

[0166] An "automatic adjustment mechanism" is a system that has the function of changing the settings of a communication device according to predefined rules or algorithms based on the analysis results.

[0167] A "means of monitoring and providing feedback" refers to a system that continuously observes the communication status and reports that information to users and system administrators.

[0168] "Means for analyzing emotional states" refer to algorithms and devices that identify a user's psychological state based on their voice and biometric information.

[0169] "Means for optimizing the communication environment" refers to methods or devices for dynamically improving communication quality in response to emotional states.

[0170] One embodiment of this invention is a program that constructs a system for optimizing the communication environment according to the user's emotional state. Details are provided below.

[0171] First, the device collects the user's voice data and biometric information. This involves using a microphone for voice analysis and sensors to acquire biometric information as needed. This information is sent to an analysis engine connected to the cloud, where the user's emotional state is analyzed in real time. Sentiment analysis utilizes a speech recognition engine (for example, cloud-based speech recognition that converts speech to text) and an emotion analysis algorithm.

[0172] The server receives the analyzed emotion data and processes it in conjunction with a generative AI model. Specifically, based on the emotion analysis results, it derives the communication settings best suited to the user's psychological state at that time. These communication settings include adjusting network traffic and optimizing audio and video quality. This reduces the user's stress and enables comfortable communication.

[0173] As an example of this system, when a user is participating in an online meeting, if stress is detected, settings are automatically changed to improve audio quality. This allows users to experience smoother conversations.

[0174] As an example of a prompt, the response can be adjusted using a generative AI model, such as "We will provide weather information in a calm voice that will help the user relax." In this way, it is possible to implement a system in which sentiment analysis and communication optimization are naturally integrated.

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] The device collects voice data and biometric information from the user. Inputs are the user's voice and sensor data, and output is raw data for emotion analysis. This includes recording voice and acquiring data such as heart rate and skin temperature through biosensors.

[0178] Step 2:

[0179] The device sends the collected data to the sentiment analysis engine. The input is the raw data obtained in step 1, and the output is the data passed to the cloud-based sentiment analysis system. This involves securely transmitting the data over the network.

[0180] Step 3:

[0181] The server analyzes the received data using a generating AI model. The input is user data necessary for emotion analysis, and the output is the analysis result indicating the user's emotional state. Here, the AI ​​model identifies emotions from the user's voice tone and biometric information.

[0182] Step 4:

[0183] The server optimizes communication settings based on the analysis results. The input is the emotional state data obtained in step 3, and the output is the optimal communication settings applied to the communication device. This includes actions such as adjusting network traffic and optimizing voice quality.

[0184] Step 5:

[0185] Users communicate comfortably in an optimized communication environment. The input is optimized communication settings, and the output is an improved user experience. This includes actions that allow users to participate in online meetings and obtain information without stress.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Second Embodiment]

[0190] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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".

[0202] In the system of this invention, a program for efficiently operating communication devices runs on a server. This program aims to optimize the entire system by receiving and analyzing real-time data transmitted from the communication devices.

[0203] First, the terminal generates data based on the user's communication activity, and this data is transmitted to the server via the base station. The server stores the received data in a database and simultaneously analyzes the data using the generated AI. The analyzed data is used to detect traffic fluctuations and anomalies, and based on this, the AI ​​agent dynamically adjusts the operating parameters of the base station.

[0204] This adjustment allows, for example, the communication equipment's output to be increased in response to a sudden surge in traffic, and to switch to energy-saving mode when traffic decreases. The server continuously monitors this process and uses the obtained data as feedback for subsequent analysis. In this way, the system continues to learn on its own and autonomously builds the optimal operating environment.

[0205] As a concrete example, in areas where large-scale events are held, a temporary increase in user traffic is expected. By quickly detecting this situation and increasing the output of base stations in that area based on predictions, a decline in communication quality can be prevented. As a result, users can continue to comfortably use mobile services even during the event.

[0206] Thus, the system of the present invention stabilizes communication quality by optimizing the operation of communication devices in real time, while also contributing to improved energy efficiency.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] The device transmits data generated through the user's communication activities to the communication device. This data includes location information, data volume, and traffic patterns.

[0210] Step 2:

[0211] The server records the data received from the communication device in a database. This allows the server to build a comprehensive log of traffic conditions and system health.

[0212] Step 3:

[0213] The server passes the recorded data to a generating AI program for real-time analysis. The analysis results are used to identify fluctuations in communication load and signs of anomalies.

[0214] Step 4:

[0215] Based on the analysis results, the server activates an AI agent to automatically perform appropriate adjustments to the base station. These adjustments include optimizing the base station's transmission power and frequency, and modifying parameters for load balancing.

[0216] Step 5:

[0217] The server monitors the communication status after adjustment and incorporates that data back into the feedback loop. The information obtained is used to improve the accuracy of the next data analysis and adjustment process.

[0218] Step 6:

[0219] The system ensures users can comfortably utilize the service in an optimized communication environment. It operates without user awareness, maintaining a balance between communication quality and energy efficiency.

[0220] (Example 1)

[0221] 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."

[0222] Modern communication networks require handling rapid fluctuations in traffic volume and optimizing energy consumption. Furthermore, stabilizing communication quality and optimizing traffic management are crucial. Traditional systems have limitations in real-time dynamic adjustments and predictive accuracy, resulting in insufficient improvements to the user experience.

[0223] 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.

[0224] In this invention, the server includes means for recording data collected from communication devices, means for analyzing the received data in real time using a generative AI model, and means for dynamically and automatically adjusting the operating parameters of the communication infrastructure based on the analysis results. This makes it possible to efficiently operate the communication network and optimize communication quality in real time.

[0225] "Communication equipment" refers to hardware or software used to send and receive voice and data.

[0226] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to perform data analysis and predictions.

[0227] "Data analysis" refers to the process of processing collected information to derive useful insights and patterns.

[0228] "Real-time" refers to processing information almost instantly and reflecting it without delay.

[0229] "Communication infrastructure" refers to the collection of hardware and software that make up a communication network.

[0230] "Dynamic automatic adjustment" refers to the automatic and timely modification of various settings based on analyzed data.

[0231] "Analysis results" refer to the conclusions and insights obtained through data analysis.

[0232] "Operating parameters" refer to the operating conditions and settings of the communication infrastructure.

[0233] The following describes embodiments for carrying out the present invention. This system provides a method for optimizing a communication network.

[0234] The server first receives data transmitted from communication devices and stores it in a database. This data is generated based on the user's communication activity and includes voice data, internet usage data, location information, etc. The server analyzes the received data in real time using a generative AI model. The generative AI model used detects data trends and anomalies and predicts traffic patterns.

[0235] To utilize the generative AI model, the server sends prompt messages to the generative AI, such as, "Please suggest optimal base station settings based on current communication traffic." Based on the analysis results, the server dynamically adjusts the operating parameters of the communication infrastructure. This automatically optimizes the communication infrastructure settings according to the load on the communication network.

[0236] For example, around stadiums where large-scale sporting events are held, a temporary surge in user communication traffic is expected. In this situation, the server responds immediately through real-time analysis and adjusts the output of the communication infrastructure in the affected area to increase its capacity, thereby preventing a decline in communication quality.

[0237] Thus, the system of the present invention provides a dynamically operable infrastructure while maintaining the efficiency and quality of the communication network, and also contributes to improving energy efficiency.

[0238] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0239] Step 1:

[0240] The server receives user communication data transmitted from communication devices through base stations. The data received as input includes call records, internet connection information, and location data. This data is stored in a database in preparation for later analysis.

[0241] Step 2:

[0242] The server analyzes the received data in real time using a generative AI model. In this step, the data analysis process takes place. The input is the data saved in step 1, and through the analysis, output is obtained that detects trends and anomalies in communication traffic. The generative AI model is given a prompt message that says, "Detect anomalies based on the current communication traffic."

[0243] Step 3:

[0244] The server adjusts the operating parameters of the communication infrastructure based on the analysis results. The input is the analysis results from step 2, and the output includes suggestions and applications of the adjusted operating parameters. Specific actions include increasing the base station's transmission power in response to a surge in traffic, or switching to energy-saving mode when traffic decreases.

[0245] Step 4:

[0246] The server continuously monitors the performance of the adjusted communication infrastructure. The input here is the actual operating state of the network, and the data obtained as output is used for subsequent analysis and feedback. Through this feedback loop, the system self-learns and improves the accuracy of subsequent communication parameter adjustments.

[0247] (Application Example 1)

[0248] 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."

[0249] In modern urban environments, efficient operation of communication networks is essential. In particular, communication traffic surges in densely populated areas and during events, raising concerns about a decline in communication quality. In such situations, the challenge lies in providing high-quality communication services while simultaneously adjusting the communication infrastructure in real time and suppressing energy consumption.

[0250] 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.

[0251] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, means for automatically adjusting the operation of the communication device based on the analysis results, and means for analyzing urban traffic information and event information to optimize the communication infrastructure. This makes it possible to improve communication quality and optimize energy efficiency in urban environments.

[0252] A "communication device" is a device that has the function of transmitting data and enables the exchange of information between systems and terminals.

[0253] "Means of recording" refers to functions or devices for storing collected data.

[0254] "Methods for real-time analysis" refer to the techniques and technologies used to analyze data instantaneously.

[0255] "Automatic adjustment mechanisms" refer to systems that change settings based on analysis results.

[0256] "Monitoring means" refer to devices or functions for continuously observing and confirming communication status and system conditions.

[0257] "Means of providing feedback" refer to methods and mechanisms for returning information based on monitoring results.

[0258] "Means for analyzing traffic and event information" refers to technologies for analyzing information about urban traffic conditions and special events.

[0259] "Means of optimizing communication infrastructure" refer to methods and technologies for efficiently adjusting network configurations and settings to create an optimal communication environment.

[0260] The system that implements this application consists of a program running on a server. The server receives real-time data transmitted from communication devices via base stations and stores this data in a database. Subsequently, it analyzes the data using a generative AI model. The main purpose of the analysis is to detect fluctuations in communication traffic and identify anomalies early.

[0261] Based on the analyzed data, the server's AI agent automatically adjusts the base station's operating parameters. For example, if traffic surges, it increases the base station's output, and if traffic decreases, it switches to energy-saving mode. This adjustment stabilizes communication quality while simultaneously improving energy efficiency.

[0262] For example, when a large-scale event is held within a smart city, the server quickly detects fluctuations in the area's communication traffic and optimizes the infrastructure settings according to the communication needs during the event. This allows citizens and visitors to continue using communication services comfortably.

[0263] An example of a prompt for a generated AI model is, "How can real-time data analysis be used to optimize the communication infrastructure of a smart city?"

[0264] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0265] Step 1:

[0266] The terminal collects data generated by the user's communication activities. Inputs include communication logs and location information, and output is the transmission of this data to a server via a base station. Specifically, the terminal automatically formats the data into packets and transmits them when a communication event occurs.

[0267] Step 2:

[0268] The server stores real-time data received through the base station in a database. The input here is the communication data sent from the terminal in step 1, and the output is the accumulation of this data in the database. The server performs transaction processing to maintain data consistency and securely stores it in the database.

[0269] Step 3:

[0270] The server performs data analysis using a generative AI model. The input is the data saved in step 2, and the analysis yields output that detects traffic fluctuations and anomalies. Specifically, the generative AI model learns data patterns and predicts new anomalies and trends.

[0271] Step 4:

[0272] Based on the analysis results of the generated AI model, the server uses an AI agent to automatically adjust the operating parameters of the base station. The input is the analysis results, and the output is the adjusted base station configuration information. In this step, specific adjustments are made to the base station's output level and the assignment of communication channels.

[0273] Step 5:

[0274] The server continuously monitors the adjusted communication status. The input is the real-time communication status obtained as feedback from the base station, and based on this, an output is obtained that modifies or maintains the optimization process. Specifically, if an anomaly is detected, the server immediately performs a reanalysis and readjusts as necessary.

[0275] 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.

[0276] This invention is a system that combines a communication device with an emotion engine, recognizing the user's emotional state in real time and providing a corresponding communication environment. This system consists of a server-centered program.

[0277] First, the device collects data from the user's voice tone and sensing devices, and the emotion engine analyzes the user's emotional state. This analysis indicates the user's emotional state, such as whether they are relaxed or stressed. The analyzed emotional data is immediately sent to the server.

[0278] Based on this emotional data, the server works in conjunction with a generative AI to combine it with traffic information stored in a database and selects the most comfortable communication environment for the user. For example, if the server determines that the user is experiencing stress, it optimizes the base station settings to minimize communication delays and provide smoother communication.

[0279] For example, if a high stress level is detected from the user's voice tone during a video conference, the server immediately processes this information and takes measures to improve communication quality. As a result, the user can continue the meeting without experiencing stress.

[0280] This system enables the provision of a fine-tuned communication environment based on the user's emotional state, significantly improving user experience. Also, by combining the automatic adjustment of emotions and communication quality, efficient network management and enhanced user experience can be achieved simultaneously.

[0281] The processing flow is described below.

[0282] Step 1:

[0283] The terminal uses a microphone and camera to sense emotional indicators such as the user's voice and expression. These data are analyzed in real time by the built-in emotion engine to infer the user's emotional state.

[0284] Step 2:

[0285] The server receives the emotional data sent from the terminal. At the same time, these data are saved in the existing traffic database to prepare for comprehensive analysis.

[0286] Step 3:

[0287] The server starts processing to optimize the communication situation using the generated AI based on the received emotional data. The emotional data is analyzed in combination with traffic pattern data and used as a basis for judgment to set the communication environment optimal for the current user's emotional state.​​​​​​​​​​​​​​​Users will experience an optimized communication environment through this adjustment. They can enjoy high-quality communication with minimal stress, resulting in a comfortable user experience. Furthermore, user feedback will be collected again as sentiment data and used in the next optimization process.

[0292] (Example 2)

[0293] 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".

[0294] Conventional communication systems were unable to optimize the communication environment based on the user's emotional state, resulting in an inadequate user experience. Furthermore, the inability to perform real-time network adjustments in response to user emotions limited the potential for improving communication quality and energy efficiency.

[0295] 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.

[0296] In this invention, the server includes means for analyzing information indicating the user's emotional state collected from a terminal, means for selecting a communication environment in cooperation with a generating AI based on the analyzed information, and means for optimizing network settings based on the selected communication environment. This enables real-time optimization of the communication environment in accordance with the user's emotional state.

[0297] A "terminal" is an information processing device used to collect voice information and biometric data from users and transmit them externally.

[0298] "User emotional state" refers to psychological conditions such as relaxed or stressed states, which are analyzed from voice tone and biometric information.

[0299] "Means of analyzing information" refers to methods that include processes and technologies for analyzing and measuring a user's emotional state based on collected data.

[0300] The "server" is a central computing device that receives the analyzed emotional information and performs processing to provide the required communication environment.

[0301] "Generative AI" refers to artificial intelligence technology for inferring or selecting an optimal communication environment from the received data.

[0302] The "communication environment" is the state of the system including the network conditions and settings necessary for the user to perform proper communication.

[0303] The "means for optimizing network settings" refers to the technology for adjusting network parameters according to the user's emotional state to achieve better communication quality.

[0304] The "means for providing feedback" is a method for checking whether the optimization of the communication environment has been successful and informing the system or the user of the result.

[0305] This invention relates to a system that recognizes the user's emotional state in real time and provides an optimal communication environment based on it. Specifically, it is composed of a combination of a terminal, a server, and generative AI.

[0306] The terminal is a device for collecting the user's voice information and biological data. Using the microphones and sensors built into smartphones and smartwatches, data such as voice tones, heart rates, and skin electrical activities are acquired. This data serves as the basis for indicating the user's emotional state.

[0307] The data obtained from the terminal is analyzed using an emotion engine. This emotion engine includes software for natural language processing and speech recognition, such as speech analysis software for analyzing voice tones and machine learning algorithms. Through this analysis, the emotional state of whether the user is relaxed or stressed is determined.

[0308] The analysis results are sent to the server. The server works with a generative AI model based on the analyzed emotional information to select a communication environment appropriate for the user's emotional state. For example, a cloud-based AI service is used to analyze the data and recommend optimal network conditions. This means that if the user is experiencing stress, the network settings are adjusted to minimize communication delays.

[0309] As a concrete example of its use, if a user experiences stress during a video conference, the server can instantly increase network bandwidth to improve communication quality. This allows the user to concentrate on the meeting without disruption.

[0310] An example of a prompt to input into the generating AI model is, "If the user's emotional state is stress, please tell me how to minimize communication delay and provide optimal network conditions."

[0311] This system provides a communication environment that responds to the user's emotional state, significantly improving usability.

[0312] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0313] Step 1:

[0314] The device collects user data such as voice tone and biometric data. This process involves recording voice using the smartphone's microphone and measuring heart rate and skin electrical activity using the smartwatch's sensors. The acquired data is used as input for analyzing the user's emotional state.

[0315] Step 2:

[0316] The device analyzes the collected data using an emotion engine. Voice data is converted to text using speech recognition software, and an emotion analysis algorithm determines the user's emotional state. This analysis outputs whether the user is relaxed or stressed. Specifically, it analyzes voice pitch and tone, as well as heart rate fluctuations.

[0317] Step 3:

[0318] The device sends the analyzed emotional state data to the server. Here, the data is transferred quickly and securely using a mobile communication protocol. The output of this step is data representing the user's emotional state.

[0319] Step 4:

[0320] The server integrates the received emotional state data with a generating AI model. Based on this data, the AI ​​infers and selects the optimal communication environment. By comparing it with traffic information and the current network state, it determines specific network settings. In this process, settings are output to minimize communication delays when the user is experiencing stress.

[0321] Step 5:

[0322] The server sends the selected network settings to the terminal. The terminal then follows the instructions, adjusting base station settings and routing to improve communication quality. For example, it might increase network bandwidth to reduce latency during video conferencing. This provides the user with an optimized communication environment.

[0323] Step 6:

[0324] The server monitors the effectiveness of the optimized communication environment and provides feedback as needed. Specifically, it monitors quality indicators such as communication delay and error rate, and makes further configuration adjustments based on these. The output consists of the improved communication state and data for further optimization.

[0325] (Application Example 2)

[0326] 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."

[0327] In today's communication environment, there is a need for dynamic adjustment of communication quality that responds to the user's emotional state. In particular, providing a communication environment that aligns with the user's emotions during online meetings and information sharing presents a significant challenge. This is because existing systems lack the ability to accurately capture user emotions and adjust communication settings in real time accordingly.

[0328] 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.

[0329] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, and means for analyzing the user's emotional state. This enables automatic and dynamic optimization of the communication environment based on the user's emotions.

[0330] A "communication device" is a device used for sending and receiving data, and functions as an interface with the user.

[0331] "Means of recording" refers to a device or program that has the function of storing acquired data and making it accessible as needed.

[0332] "Means of analysis" refers to a system that includes the process of interpreting collected data and identifying certain patterns or trends.

[0333] An "automatic adjustment mechanism" is a system that has the function of changing the settings of a communication device according to predefined rules or algorithms based on the analysis results.

[0334] A "means of monitoring and providing feedback" refers to a system that continuously observes the communication status and reports that information to users and system administrators.

[0335] "Means for analyzing emotional states" refer to algorithms and devices that identify a user's psychological state based on their voice and biometric information.

[0336] "Means for optimizing the communication environment" refers to methods or devices for dynamically improving communication quality in response to emotional states.

[0337] One embodiment of this invention is a program that constructs a system for optimizing the communication environment according to the user's emotional state. Details are provided below.

[0338] First, the device collects the user's voice data and biometric information. This involves using a microphone for voice analysis and sensors to acquire biometric information as needed. This information is sent to an analysis engine connected to the cloud, where the user's emotional state is analyzed in real time. Sentiment analysis utilizes a speech recognition engine (for example, cloud-based speech recognition that converts speech to text) and an emotion analysis algorithm.

[0339] The server receives the analyzed emotion data and processes it in conjunction with a generative AI model. Specifically, based on the emotion analysis results, it derives the communication settings best suited to the user's psychological state at that time. These communication settings include adjusting network traffic and optimizing audio and video quality. This reduces the user's stress and enables comfortable communication.

[0340] As an example of this system, when a user is participating in an online meeting, if stress is detected, settings are automatically changed to improve audio quality. This allows users to experience smoother conversations.

[0341] As an example of a prompt, the response can be adjusted using a generative AI model, such as "We will provide weather information in a calm voice that will help the user relax." In this way, it is possible to implement a system in which sentiment analysis and communication optimization are naturally integrated.

[0342] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0343] Step 1:

[0344] The device collects voice data and biometric information from the user. Inputs are the user's voice and sensor data, and output is raw data for emotion analysis. This includes recording voice and acquiring data such as heart rate and skin temperature through biosensors.

[0345] Step 2:

[0346] The device sends the collected data to the sentiment analysis engine. The input is the raw data obtained in step 1, and the output is the data passed to the cloud-based sentiment analysis system. This involves securely transmitting the data over the network.

[0347] Step 3:

[0348] The server analyzes the received data using a generating AI model. The input is user data necessary for emotion analysis, and the output is the analysis result indicating the user's emotional state. Here, the AI ​​model identifies emotions from the user's voice tone and biometric information.

[0349] Step 4:

[0350] The server optimizes communication settings based on the analysis results. The input is the emotional state data obtained in step 3, and the output is the optimal communication settings applied to the communication device. This includes actions such as adjusting network traffic and optimizing voice quality.

[0351] Step 5:

[0352] Users communicate comfortably in an optimized communication environment. The input is optimized communication settings, and the output is an improved user experience. This includes actions that allow users to participate in online meetings and obtain information without stress.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] [Third Embodiment]

[0357] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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".

[0369] In the system of this invention, a program for efficiently operating communication devices runs on a server. This program aims to optimize the entire system by receiving and analyzing real-time data transmitted from the communication devices.

[0370] First, the terminal generates data based on the user's communication activity, and this data is transmitted to the server via the base station. The server stores the received data in a database and simultaneously analyzes the data using the generated AI. The analyzed data is used to detect traffic fluctuations and anomalies, and based on this, the AI ​​agent dynamically adjusts the operating parameters of the base station.

[0371] This adjustment allows, for example, the communication equipment's output to be increased in response to a sudden surge in traffic, and to switch to energy-saving mode when traffic decreases. The server continuously monitors this process and uses the obtained data as feedback for subsequent analysis. In this way, the system continues to learn on its own and autonomously builds the optimal operating environment.

[0372] As a concrete example, in areas where large-scale events are held, a temporary increase in user traffic is expected. By quickly detecting this situation and increasing the output of base stations in that area based on predictions, a decline in communication quality can be prevented. As a result, users can continue to comfortably use mobile services even during the event.

[0373] Thus, the system of the present invention stabilizes communication quality by optimizing the operation of communication devices in real time, while also contributing to improved energy efficiency.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The device transmits data generated through the user's communication activities to the communication device. This data includes location information, data volume, and traffic patterns.

[0377] Step 2:

[0378] The server records the data received from the communication device in a database. This allows the server to build a comprehensive log of traffic conditions and system health.

[0379] Step 3:

[0380] The server passes the recorded data to a generating AI program for real-time analysis. The analysis results are used to identify fluctuations in communication load and signs of anomalies.

[0381] Step 4:

[0382] Based on the analysis results, the server activates an AI agent to automatically perform appropriate adjustments to the base station. These adjustments include optimizing the base station's transmission power and frequency, and modifying parameters for load balancing.

[0383] Step 5:

[0384] The server monitors the communication status after adjustment and incorporates that data back into the feedback loop. The information obtained is used to improve the accuracy of the next data analysis and adjustment process.

[0385] Step 6:

[0386] The system ensures users can comfortably utilize the service in an optimized communication environment. It operates without user awareness, maintaining a balance between communication quality and energy efficiency.

[0387] (Example 1)

[0388] 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."

[0389] Modern communication networks require handling rapid fluctuations in traffic volume and optimizing energy consumption. Furthermore, stabilizing communication quality and optimizing traffic management are crucial. Traditional systems have limitations in real-time dynamic adjustments and predictive accuracy, resulting in insufficient improvements to the user experience.

[0390] 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.

[0391] In this invention, the server includes means for recording data collected from communication devices, means for analyzing the received data in real time using a generative AI model, and means for dynamically and automatically adjusting the operating parameters of the communication infrastructure based on the analysis results. This makes it possible to efficiently operate the communication network and optimize communication quality in real time.

[0392] "Communication equipment" refers to hardware or software used to send and receive voice and data.

[0393] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to perform data analysis and predictions.

[0394] "Data analysis" refers to the process of processing collected information to derive useful insights and patterns.

[0395] "Real-time" refers to processing information almost instantly and reflecting it without delay.

[0396] "Communication infrastructure" refers to the collection of hardware and software that make up a communication network.

[0397] "Dynamic automatic adjustment" refers to the automatic and timely modification of various settings based on analyzed data.

[0398] "Analysis results" refer to the conclusions and insights obtained through data analysis.

[0399] "Operating parameters" refer to the operating conditions and settings of the communication infrastructure.

[0400] The following describes embodiments for carrying out the present invention. This system provides a method for optimizing a communication network.

[0401] The server first receives data transmitted from communication devices and stores it in a database. This data is generated based on the user's communication activity and includes voice data, internet usage data, location information, etc. The server analyzes the received data in real time using a generative AI model. The generative AI model used detects data trends and anomalies and predicts traffic patterns.

[0402] To utilize the generative AI model, the server sends prompt messages to the generative AI, such as, "Please suggest optimal base station settings based on current communication traffic." Based on the analysis results, the server dynamically adjusts the operating parameters of the communication infrastructure. This automatically optimizes the communication infrastructure settings according to the load on the communication network.

[0403] For example, around stadiums where large-scale sporting events are held, a temporary surge in user communication traffic is expected. In this situation, the server responds immediately through real-time analysis and adjusts the output of the communication infrastructure in the affected area to increase its capacity, thereby preventing a decline in communication quality.

[0404] Thus, the system of the present invention provides a dynamically operable infrastructure while maintaining the efficiency and quality of the communication network, and also contributes to improving energy efficiency.

[0405] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0406] Step 1:

[0407] The server receives user communication data transmitted from communication devices through base stations. The data received as input includes call records, internet connection information, and location data. This data is stored in a database in preparation for later analysis.

[0408] Step 2:

[0409] The server analyzes the received data in real time using a generative AI model. In this step, the data analysis process takes place. The input is the data saved in step 1, and through the analysis, output is obtained that detects trends and anomalies in communication traffic. The generative AI model is given a prompt message that says, "Detect anomalies based on the current communication traffic."

[0410] Step 3:

[0411] The server adjusts the operating parameters of the communication infrastructure based on the analysis results. The input is the analysis results from step 2, and the output includes suggestions and applications of the adjusted operating parameters. Specific actions include increasing the base station's transmission power in response to a surge in traffic, or switching to energy-saving mode when traffic decreases.

[0412] Step 4:

[0413] The server continuously monitors the performance of the adjusted communication infrastructure. The input here is the actual operating state of the network, and the data obtained as output is used for subsequent analysis and feedback. Through this feedback loop, the system self-learns and improves the accuracy of subsequent communication parameter adjustments.

[0414] (Application Example 1)

[0415] 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."

[0416] In modern urban environments, efficient operation of communication networks is essential. In particular, communication traffic surges in densely populated areas and during events, raising concerns about a decline in communication quality. In such situations, the challenge lies in providing high-quality communication services while simultaneously adjusting the communication infrastructure in real time and suppressing energy consumption.

[0417] 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.

[0418] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, means for automatically adjusting the operation of the communication device based on the analysis results, and means for analyzing urban traffic information and event information to optimize the communication infrastructure. This makes it possible to improve communication quality and optimize energy efficiency in urban environments.

[0419] A "communication device" is a device that has the function of transmitting data and enables the exchange of information between systems and terminals.

[0420] "Means of recording" refers to functions or devices for storing collected data.

[0421] "Methods for real-time analysis" refer to the techniques and technologies used to analyze data instantaneously.

[0422] "Automatic adjustment mechanisms" refer to systems that change settings based on analysis results.

[0423] "Monitoring means" refer to devices or functions for continuously observing and confirming communication status and system conditions.

[0424] "Means of providing feedback" refer to methods and mechanisms for returning information based on monitoring results.

[0425] "Means for analyzing traffic and event information" refers to technologies for analyzing information about urban traffic conditions and special events.

[0426] "Means of optimizing communication infrastructure" refer to methods and technologies for efficiently adjusting network configurations and settings to create an optimal communication environment.

[0427] The system that implements this application consists of a program running on a server. The server receives real-time data transmitted from communication devices via base stations and stores this data in a database. Subsequently, it analyzes the data using a generative AI model. The main purpose of the analysis is to detect fluctuations in communication traffic and identify anomalies early.

[0428] Based on the analyzed data, the server's AI agent automatically adjusts the base station's operating parameters. For example, if traffic surges, it increases the base station's output, and if traffic decreases, it switches to energy-saving mode. This adjustment stabilizes communication quality while simultaneously improving energy efficiency.

[0429] For example, when a large-scale event is held within a smart city, the server quickly detects fluctuations in the area's communication traffic and optimizes the infrastructure settings according to the communication needs during the event. This allows citizens and visitors to continue using communication services comfortably.

[0430] An example of a prompt for a generated AI model is, "How can real-time data analysis be used to optimize the communication infrastructure of a smart city?"

[0431] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0432] Step 1:

[0433] The terminal collects data generated by the user's communication activities. Inputs include communication logs and location information, and output is the transmission of this data to a server via a base station. Specifically, the terminal automatically formats the data into packets and transmits them when a communication event occurs.

[0434] Step 2:

[0435] The server stores real-time data received through the base station in a database. The input here is the communication data sent from the terminal in step 1, and the output is the accumulation of this data in the database. The server performs transaction processing to maintain data consistency and securely stores it in the database.

[0436] Step 3:

[0437] The server performs data analysis using a generative AI model. The input is the data saved in step 2, and the analysis yields output that detects traffic fluctuations and anomalies. Specifically, the generative AI model learns data patterns and predicts new anomalies and trends.

[0438] Step 4:

[0439] Based on the analysis results of the generated AI model, the server uses an AI agent to automatically adjust the operating parameters of the base station. The input is the analysis results, and the output is the adjusted base station configuration information. In this step, specific adjustments are made to the base station's output level and the assignment of communication channels.

[0440] Step 5:

[0441] The server continuously monitors the adjusted communication status. The input is the real-time communication status obtained as feedback from the base station, and based on this, an output is obtained that modifies or maintains the optimization process. Specifically, if an anomaly is detected, the server immediately performs a reanalysis and readjusts as necessary.

[0442] 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.

[0443] This invention is a system that combines a communication device with an emotion engine, recognizing the user's emotional state in real time and providing a corresponding communication environment. This system consists of a server-centered program.

[0444] First, the device collects data from the user's voice tone and sensing devices, and the emotion engine analyzes the user's emotional state. This analysis indicates the user's emotional state, such as whether they are relaxed or stressed. The analyzed emotional data is immediately sent to the server.

[0445] Based on this emotional data, the server works in conjunction with a generative AI to combine it with traffic information stored in a database and selects the most comfortable communication environment for the user. For example, if the server determines that the user is experiencing stress, it optimizes the base station settings to minimize communication delays and provide smoother communication.

[0446] For example, if a high stress level is detected from the user's voice tone during a video conference, the server immediately processes this information and takes measures to improve communication quality. As a result, the user can continue the meeting without experiencing stress.

[0447] This system enables the provision of a nuanced communication environment based on the user's emotional state, significantly improving usability. Furthermore, by combining emotional responses with automatic adjustment of communication quality, efficient network management and enhanced user experience are achieved simultaneously.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The device uses microphones and cameras to detect emotional indicators such as the user's voice and facial expressions. This data is analyzed in real time by a built-in emotion engine to infer the user's emotional state.

[0451] Step 2:

[0452] The server receives sentiment data sent from the terminal. Simultaneously, it stores this data in an existing traffic database, preparing it for comprehensive analysis.

[0453] Step 3:

[0454] The server uses generated AI based on the received emotion data to initiate processing to optimize the communication environment. The emotion data is analyzed in combination with traffic pattern data and used as a basis for determining the optimal communication environment for the current user's emotional state.

[0455] Step 4:

[0456] The server automatically adjusts the operating parameters of the communication device based on the analysis results. For example, if the analysis indicates that the user is experiencing stress, it will make the necessary adjustments to reduce communication delays.

[0457] Step 5:

[0458] Users will experience an optimized communication environment through this adjustment. They can enjoy high-quality communication with minimal stress, resulting in a comfortable user experience. Furthermore, user feedback will be collected again as sentiment data and used in the next optimization process.

[0459] (Example 2)

[0460] 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."

[0461] Conventional communication systems were unable to optimize the communication environment based on the user's emotional state, resulting in an inadequate user experience. Furthermore, the inability to perform real-time network adjustments in response to user emotions limited the potential for improving communication quality and energy efficiency.

[0462] 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.

[0463] In this invention, the server includes means for analyzing information indicating the user's emotional state collected from a terminal, means for selecting a communication environment in cooperation with a generating AI based on the analyzed information, and means for optimizing network settings based on the selected communication environment. This enables real-time optimization of the communication environment in accordance with the user's emotional state.

[0464] A "terminal" is an information processing device used to collect voice information and biometric data from users and transmit them externally.

[0465] "User emotional state" refers to psychological conditions such as relaxed or stressed states, which are analyzed from voice tone and biometric information.

[0466] "Means of analyzing information" refers to methods that include processes and technologies for analyzing and measuring a user's emotional state based on collected data.

[0467] A "server" is a central computing device that receives analyzed emotional information and processes it to provide the necessary communication environment.

[0468] "Generative AI" refers to artificial intelligence technology used to infer or select the optimal communication environment from received data.

[0469] "Communication environment" refers to the state of the system, including the network conditions and settings necessary for users to communicate properly.

[0470] "Methods for optimizing network settings" refer to technologies that adjust network parameters according to the user's emotional state to achieve better communication quality.

[0471] "Means of providing feedback" refers to methods for verifying whether the optimization of the communication environment was successful and informing the system or user of the results.

[0472] This invention relates to a system that recognizes a user's emotional state in real time and provides an optimal communication environment based on that state. Specifically, it consists of a terminal, a server, and a generative AI.

[0473] The device is designed to collect user voice information and biometric data. It uses microphones and sensors built into smartphones and smartwatches to acquire data such as voice tone, heart rate, and skin electrical activity. This data forms the basis for indicating the user's emotional state.

[0474] Data acquired from the device is analyzed using an emotion engine. This emotion engine includes software for natural language processing and speech recognition, such as speech analysis software and machine learning algorithms for analyzing voice tone. This analysis determines the user's emotional state, such as whether they are relaxed or stressed.

[0475] The analysis results are sent to the server. The server works with a generative AI model based on the analyzed emotional information to select a communication environment appropriate for the user's emotional state. For example, a cloud-based AI service is used to analyze the data and recommend optimal network conditions. This means that if the user is experiencing stress, the network settings are adjusted to minimize communication delays.

[0476] As a concrete example of its use, if a user experiences stress during a video conference, the server can instantly increase network bandwidth to improve communication quality. This allows the user to concentrate on the meeting without disruption.

[0477] An example of a prompt to input into the generating AI model is, "If the user's emotional state is stress, please tell me how to minimize communication delay and provide optimal network conditions."

[0478] This system provides a communication environment that responds to the user's emotional state, significantly improving usability.

[0479] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0480] Step 1:

[0481] The device collects user data such as voice tone and biometric data. This process involves recording voice using the smartphone's microphone and measuring heart rate and skin electrical activity using the smartwatch's sensors. The acquired data is used as input for analyzing the user's emotional state.

[0482] Step 2:

[0483] The device analyzes the collected data using an emotion engine. Voice data is converted to text using speech recognition software, and an emotion analysis algorithm determines the user's emotional state. This analysis outputs whether the user is relaxed or stressed. Specifically, it analyzes voice pitch and tone, as well as heart rate fluctuations.

[0484] Step 3:

[0485] The device sends the analyzed emotional state data to the server. Here, the data is transferred quickly and securely using a mobile communication protocol. The output of this step is data representing the user's emotional state.

[0486] Step 4:

[0487] The server integrates the received emotional state data with a generating AI model. Based on this data, the AI ​​infers and selects the optimal communication environment. By comparing it with traffic information and the current network state, it determines specific network settings. In this process, settings are output to minimize communication delays when the user is experiencing stress.

[0488] Step 5:

[0489] The server sends the selected network settings to the terminal. The terminal then follows the instructions, adjusting base station settings and routing to improve communication quality. For example, it might increase network bandwidth to reduce latency during video conferencing. This provides the user with an optimized communication environment.

[0490] Step 6:

[0491] The server monitors the effectiveness of the optimized communication environment and provides feedback as needed. Specifically, it monitors quality indicators such as communication delay and error rate, and makes further configuration adjustments based on these. The output consists of the improved communication state and data for further optimization.

[0492] (Application Example 2)

[0493] 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."

[0494] In today's communication environment, there is a need for dynamic adjustment of communication quality that responds to the user's emotional state. In particular, providing a communication environment that aligns with the user's emotions during online meetings and information sharing presents a significant challenge. This is because existing systems lack the ability to accurately capture user emotions and adjust communication settings in real time accordingly.

[0495] 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.

[0496] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, and means for analyzing the user's emotional state. This enables automatic and dynamic optimization of the communication environment based on the user's emotions.

[0497] A "communication device" is a device used for sending and receiving data, and functions as an interface with the user.

[0498] "Means of recording" refers to a device or program that has the function of storing acquired data and making it accessible as needed.

[0499] "Means of analysis" refers to a system that includes the process of interpreting collected data and identifying certain patterns or trends.

[0500] An "automatic adjustment mechanism" is a system that has the function of changing the settings of a communication device according to predefined rules or algorithms based on the analysis results.

[0501] A "means of monitoring and providing feedback" refers to a system that continuously observes the communication status and reports that information to users and system administrators.

[0502] "Means for analyzing emotional states" refer to algorithms and devices that identify a user's psychological state based on their voice and biometric information.

[0503] "Means for optimizing the communication environment" refers to methods or devices for dynamically improving communication quality in response to emotional states.

[0504] One embodiment of this invention is a program that constructs a system for optimizing the communication environment according to the user's emotional state. Details are provided below.

[0505] First, the device collects the user's voice data and biometric information. This involves using a microphone for voice analysis and sensors to acquire biometric information as needed. This information is sent to an analysis engine connected to the cloud, where the user's emotional state is analyzed in real time. Sentiment analysis utilizes a speech recognition engine (for example, cloud-based speech recognition that converts speech to text) and an emotion analysis algorithm.

[0506] The server receives the analyzed emotion data and processes it in conjunction with a generative AI model. Specifically, based on the emotion analysis results, it derives the communication settings best suited to the user's psychological state at that time. These communication settings include adjusting network traffic and optimizing audio and video quality. This reduces the user's stress and enables comfortable communication.

[0507] As an example of this system, when a user is participating in an online meeting, if stress is detected, settings are automatically changed to improve audio quality. This allows users to experience smoother conversations.

[0508] As an example of a prompt, the response can be adjusted using a generative AI model, such as "We will provide weather information in a calm voice that will help the user relax." In this way, it is possible to implement a system in which sentiment analysis and communication optimization are naturally integrated.

[0509] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0510] Step 1:

[0511] The device collects voice data and biometric information from the user. Inputs are the user's voice and sensor data, and output is raw data for emotion analysis. This includes recording voice and acquiring data such as heart rate and skin temperature through biosensors.

[0512] Step 2:

[0513] The device sends the collected data to the sentiment analysis engine. The input is the raw data obtained in step 1, and the output is the data passed to the cloud-based sentiment analysis system. This involves securely transmitting the data over the network.

[0514] Step 3:

[0515] The server analyzes the received data using a generating AI model. The input is user data necessary for emotion analysis, and the output is the analysis result indicating the user's emotional state. Here, the AI ​​model identifies emotions from the user's voice tone and biometric information.

[0516] Step 4:

[0517] The server optimizes communication settings based on the analysis results. The input is the emotional state data obtained in step 3, and the output is the optimal communication settings applied to the communication device. This includes actions such as adjusting network traffic and optimizing voice quality.

[0518] Step 5:

[0519] Users communicate comfortably in an optimized communication environment. The input is optimized communication settings, and the output is an improved user experience. This includes actions that allow users to participate in online meetings and obtain information without stress.

[0520] 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.

[0521] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0522] 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.

[0523] [Fourth Embodiment]

[0524] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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".

[0537] In the system of this invention, a program for efficiently operating communication devices runs on a server. This program aims to optimize the entire system by receiving and analyzing real-time data transmitted from the communication devices.

[0538] First, the terminal generates data based on the user's communication activity, and this data is transmitted to the server via the base station. The server stores the received data in a database and simultaneously analyzes the data using the generated AI. The analyzed data is used to detect traffic fluctuations and anomalies, and based on this, the AI ​​agent dynamically adjusts the operating parameters of the base station.

[0539] This adjustment allows, for example, the communication equipment's output to be increased in response to a sudden surge in traffic, and to switch to energy-saving mode when traffic decreases. The server continuously monitors this process and uses the obtained data as feedback for subsequent analysis. In this way, the system continues to learn on its own and autonomously builds the optimal operating environment.

[0540] As a concrete example, in areas where large-scale events are held, a temporary increase in user traffic is expected. By quickly detecting this situation and increasing the output of base stations in that area based on predictions, a decline in communication quality can be prevented. As a result, users can continue to comfortably use mobile services even during the event.

[0541] Thus, the system of the present invention stabilizes communication quality by optimizing the operation of communication devices in real time, while also contributing to improved energy efficiency.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The device transmits data generated through the user's communication activities to the communication device. This data includes location information, data volume, and traffic patterns.

[0545] Step 2:

[0546] The server records the data received from the communication device in a database. This allows the server to build a comprehensive log of traffic conditions and system health.

[0547] Step 3:

[0548] The server passes the recorded data to a generating AI program for real-time analysis. The analysis results are used to identify fluctuations in communication load and signs of anomalies.

[0549] Step 4:

[0550] Based on the analysis results, the server activates an AI agent to automatically perform appropriate adjustments to the base station. These adjustments include optimizing the base station's transmission power and frequency, and modifying parameters for load balancing.

[0551] Step 5:

[0552] The server monitors the communication status after adjustment and incorporates that data back into the feedback loop. The information obtained is used to improve the accuracy of the next data analysis and adjustment process.

[0553] Step 6:

[0554] The system ensures users can comfortably utilize the service in an optimized communication environment. It operates without user awareness, maintaining a balance between communication quality and energy efficiency.

[0555] (Example 1)

[0556] 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".

[0557] Modern communication networks require handling rapid fluctuations in traffic volume and optimizing energy consumption. Furthermore, stabilizing communication quality and optimizing traffic management are crucial. Traditional systems have limitations in real-time dynamic adjustments and predictive accuracy, resulting in insufficient improvements to the user experience.

[0558] 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.

[0559] In this invention, the server includes means for recording data collected from communication devices, means for analyzing the received data in real time using a generative AI model, and means for dynamically and automatically adjusting the operating parameters of the communication infrastructure based on the analysis results. This makes it possible to efficiently operate the communication network and optimize communication quality in real time.

[0560] "Communication equipment" refers to hardware or software used to send and receive voice and data.

[0561] A "generative AI model" refers to an artificial intelligence system that uses machine learning algorithms to perform data analysis and predictions.

[0562] "Data analysis" refers to the process of processing collected information to derive useful insights and patterns.

[0563] "Real-time" refers to processing information almost instantly and reflecting it without delay.

[0564] "Communication infrastructure" refers to the collection of hardware and software that make up a communication network.

[0565] "Dynamic automatic adjustment" refers to the automatic and timely modification of various settings based on analyzed data.

[0566] "Analysis results" refer to the conclusions and insights obtained through data analysis.

[0567] "Operating parameters" refer to the operating conditions and settings of the communication infrastructure.

[0568] The following describes embodiments for carrying out the present invention. This system provides a method for optimizing a communication network.

[0569] The server first receives data transmitted from communication devices and stores it in a database. This data is generated based on the user's communication activity and includes voice data, internet usage data, location information, etc. The server analyzes the received data in real time using a generative AI model. The generative AI model used detects data trends and anomalies and predicts traffic patterns.

[0570] To utilize the generative AI model, the server sends prompt messages to the generative AI, such as, "Please suggest optimal base station settings based on current communication traffic." Based on the analysis results, the server dynamically adjusts the operating parameters of the communication infrastructure. This automatically optimizes the communication infrastructure settings according to the load on the communication network.

[0571] For example, around stadiums where large-scale sporting events are held, a temporary surge in user communication traffic is expected. In this situation, the server responds immediately through real-time analysis and adjusts the output of the communication infrastructure in the affected area to increase its capacity, thereby preventing a decline in communication quality.

[0572] Thus, the system of the present invention provides a dynamically operable infrastructure while maintaining the efficiency and quality of the communication network, and also contributes to improving energy efficiency.

[0573] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0574] Step 1:

[0575] The server receives user communication data transmitted from communication devices through base stations. The data received as input includes call records, internet connection information, and location data. This data is stored in a database in preparation for later analysis.

[0576] Step 2:

[0577] The server analyzes the received data in real time using a generative AI model. In this step, the data analysis process takes place. The input is the data saved in step 1, and through the analysis, output is obtained that detects trends and anomalies in communication traffic. The generative AI model is given a prompt message that says, "Detect anomalies based on the current communication traffic."

[0578] Step 3:

[0579] The server adjusts the operating parameters of the communication infrastructure based on the analysis results. The input is the analysis results from step 2, and the output includes suggestions and applications of the adjusted operating parameters. Specific actions include increasing the base station's transmission power in response to a surge in traffic, or switching to energy-saving mode when traffic decreases.

[0580] Step 4:

[0581] The server continuously monitors the performance of the adjusted communication infrastructure. The input here is the actual operating state of the network, and the data obtained as output is used for subsequent analysis and feedback. Through this feedback loop, the system self-learns and improves the accuracy of subsequent communication parameter adjustments.

[0582] (Application Example 1)

[0583] 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".

[0584] In modern urban environments, efficient operation of communication networks is essential. In particular, communication traffic surges in densely populated areas and during events, raising concerns about a decline in communication quality. In such situations, the challenge lies in providing high-quality communication services while simultaneously adjusting the communication infrastructure in real time and suppressing energy consumption.

[0585] 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.

[0586] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, means for automatically adjusting the operation of the communication device based on the analysis results, and means for analyzing urban traffic information and event information to optimize the communication infrastructure. This makes it possible to improve communication quality and optimize energy efficiency in urban environments.

[0587] A "communication device" is a device that has the function of transmitting data and enables the exchange of information between systems and terminals.

[0588] "Means of recording" refers to functions or devices for storing collected data.

[0589] "Methods for real-time analysis" refer to the techniques and technologies used to analyze data instantaneously.

[0590] "Automatic adjustment mechanisms" refer to systems that change settings based on analysis results.

[0591] "Monitoring means" refer to devices or functions for continuously observing and confirming communication status and system conditions.

[0592] "Means of providing feedback" refer to methods and mechanisms for returning information based on monitoring results.

[0593] "Means for analyzing traffic and event information" refers to technologies for analyzing information about urban traffic conditions and special events.

[0594] "Means of optimizing communication infrastructure" refer to methods and technologies for efficiently adjusting network configurations and settings to create an optimal communication environment.

[0595] The system that implements this application consists of a program running on a server. The server receives real-time data transmitted from communication devices via base stations and stores this data in a database. Subsequently, it analyzes the data using a generative AI model. The main purpose of the analysis is to detect fluctuations in communication traffic and identify anomalies early.

[0596] Based on the analyzed data, the server's AI agent automatically adjusts the base station's operating parameters. For example, if traffic surges, it increases the base station's output, and if traffic decreases, it switches to energy-saving mode. This adjustment stabilizes communication quality while simultaneously improving energy efficiency.

[0597] For example, when a large-scale event is held within a smart city, the server quickly detects fluctuations in the area's communication traffic and optimizes the infrastructure settings according to the communication needs during the event. This allows citizens and visitors to continue using communication services comfortably.

[0598] An example of a prompt for a generated AI model is, "How can real-time data analysis be used to optimize the communication infrastructure of a smart city?"

[0599] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0600] Step 1:

[0601] The terminal collects data generated by the user's communication activities. Inputs include communication logs and location information, and output is the transmission of this data to a server via a base station. Specifically, the terminal automatically formats the data into packets and transmits them when a communication event occurs.

[0602] Step 2:

[0603] The server stores real-time data received through the base station in a database. The input here is the communication data sent from the terminal in step 1, and the output is the accumulation of this data in the database. The server performs transaction processing to maintain data consistency and securely stores it in the database.

[0604] Step 3:

[0605] The server performs data analysis using a generative AI model. The input is the data saved in step 2, and the analysis yields output that detects traffic fluctuations and anomalies. Specifically, the generative AI model learns data patterns and predicts new anomalies and trends.

[0606] Step 4:

[0607] Based on the analysis results of the generated AI model, the server uses an AI agent to automatically adjust the operating parameters of the base station. The input is the analysis results, and the output is the adjusted base station configuration information. In this step, specific adjustments are made to the base station's output level and the assignment of communication channels.

[0608] Step 5:

[0609] The server continuously monitors the adjusted communication status. The input is the real-time communication status obtained as feedback from the base station, and based on this, an output is obtained that modifies or maintains the optimization process. Specifically, if an anomaly is detected, the server immediately performs a reanalysis and readjusts as necessary.

[0610] 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.

[0611] This invention is a system that combines a communication device with an emotion engine, recognizing the user's emotional state in real time and providing a corresponding communication environment. This system consists of a server-centered program.

[0612] First, the device collects data from the user's voice tone and sensing devices, and the emotion engine analyzes the user's emotional state. This analysis indicates the user's emotional state, such as whether they are relaxed or stressed. The analyzed emotional data is immediately sent to the server.

[0613] Based on this emotional data, the server works in conjunction with a generative AI to combine it with traffic information stored in a database and selects the most comfortable communication environment for the user. For example, if the server determines that the user is experiencing stress, it optimizes the base station settings to minimize communication delays and provide smoother communication.

[0614] For example, if a high stress level is detected from the user's voice tone during a video conference, the server immediately processes this information and takes measures to improve communication quality. As a result, the user can continue the meeting without experiencing stress.

[0615] This system enables the provision of a nuanced communication environment based on the user's emotional state, significantly improving usability. Furthermore, by combining emotional responses with automatic adjustment of communication quality, efficient network management and enhanced user experience are achieved simultaneously.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The device uses microphones and cameras to detect emotional indicators such as the user's voice and facial expressions. This data is analyzed in real time by a built-in emotion engine to infer the user's emotional state.

[0619] Step 2:

[0620] The server receives sentiment data sent from the terminal. Simultaneously, it stores this data in an existing traffic database, preparing it for comprehensive analysis.

[0621] Step 3:

[0622] The server uses generated AI based on the received emotion data to initiate processing to optimize the communication environment. The emotion data is analyzed in combination with traffic pattern data and used as a basis for determining the optimal communication environment for the current user's emotional state.

[0623] Step 4:

[0624] The server automatically adjusts the operating parameters of the communication device based on the analysis results. For example, if the analysis indicates that the user is experiencing stress, it will make the necessary adjustments to reduce communication delays.

[0625] Step 5:

[0626] Users will experience an optimized communication environment through this adjustment. They can enjoy high-quality communication with minimal stress, resulting in a comfortable user experience. Furthermore, user feedback will be collected again as sentiment data and used in the next optimization process.

[0627] (Example 2)

[0628] 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".

[0629] Conventional communication systems were unable to optimize the communication environment based on the user's emotional state, resulting in an inadequate user experience. Furthermore, the inability to perform real-time network adjustments in response to user emotions limited the potential for improving communication quality and energy efficiency.

[0630] 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.

[0631] In this invention, the server includes means for analyzing information indicating the user's emotional state collected from a terminal, means for selecting a communication environment in cooperation with a generating AI based on the analyzed information, and means for optimizing network settings based on the selected communication environment. This enables real-time optimization of the communication environment in accordance with the user's emotional state.

[0632] A "terminal" is an information processing device used to collect voice information and biometric data from users and transmit them externally.

[0633] "User emotional state" refers to psychological conditions such as relaxed or stressed states, which are analyzed from voice tone and biometric information.

[0634] "Means of analyzing information" refers to methods that include processes and technologies for analyzing and measuring a user's emotional state based on collected data.

[0635] A "server" is a central computing device that receives analyzed emotional information and processes it to provide the necessary communication environment.

[0636] "Generative AI" refers to artificial intelligence technology used to infer or select the optimal communication environment from received data.

[0637] "Communication environment" refers to the state of the system, including the network conditions and settings necessary for users to communicate properly.

[0638] "Methods for optimizing network settings" refer to technologies that adjust network parameters according to the user's emotional state to achieve better communication quality.

[0639] "Means of providing feedback" refers to methods for verifying whether the optimization of the communication environment was successful and informing the system or user of the results.

[0640] This invention relates to a system that recognizes a user's emotional state in real time and provides an optimal communication environment based on that state. Specifically, it consists of a terminal, a server, and a generative AI.

[0641] The device is designed to collect user voice information and biometric data. It uses microphones and sensors built into smartphones and smartwatches to acquire data such as voice tone, heart rate, and skin electrical activity. This data forms the basis for indicating the user's emotional state.

[0642] Data acquired from the device is analyzed using an emotion engine. This emotion engine includes software for natural language processing and speech recognition, such as speech analysis software and machine learning algorithms for analyzing voice tone. This analysis determines the user's emotional state, such as whether they are relaxed or stressed.

[0643] The analysis results are sent to the server. The server works with a generative AI model based on the analyzed emotional information to select a communication environment appropriate for the user's emotional state. For example, a cloud-based AI service is used to analyze the data and recommend optimal network conditions. This means that if the user is experiencing stress, the network settings are adjusted to minimize communication delays.

[0644] As a concrete example of its use, if a user experiences stress during a video conference, the server can instantly increase network bandwidth to improve communication quality. This allows the user to concentrate on the meeting without disruption.

[0645] An example of a prompt to input into the generating AI model is, "If the user's emotional state is stress, please tell me how to minimize communication delay and provide optimal network conditions."

[0646] This system provides a communication environment that responds to the user's emotional state, significantly improving usability.

[0647] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0648] Step 1:

[0649] The device collects user data such as voice tone and biometric data. This process involves recording voice using the smartphone's microphone and measuring heart rate and skin electrical activity using the smartwatch's sensors. The acquired data is used as input for analyzing the user's emotional state.

[0650] Step 2:

[0651] The device analyzes the collected data using an emotion engine. Voice data is converted to text using speech recognition software, and an emotion analysis algorithm determines the user's emotional state. This analysis outputs whether the user is relaxed or stressed. Specifically, it analyzes voice pitch and tone, as well as heart rate fluctuations.

[0652] Step 3:

[0653] The device sends the analyzed emotional state data to the server. Here, the data is transferred quickly and securely using a mobile communication protocol. The output of this step is data representing the user's emotional state.

[0654] Step 4:

[0655] The server integrates the received emotional state data with a generating AI model. Based on this data, the AI ​​infers and selects the optimal communication environment. By comparing it with traffic information and the current network state, it determines specific network settings. In this process, settings are output to minimize communication delays when the user is experiencing stress.

[0656] Step 5:

[0657] The server sends the selected network settings to the terminal. The terminal then follows the instructions, adjusting base station settings and routing to improve communication quality. For example, it might increase network bandwidth to reduce latency during video conferencing. This provides the user with an optimized communication environment.

[0658] Step 6:

[0659] The server monitors the effectiveness of the optimized communication environment and provides feedback as needed. Specifically, it monitors quality indicators such as communication delay and error rate, and makes further configuration adjustments based on these. The output consists of the improved communication state and data for further optimization.

[0660] (Application Example 2)

[0661] 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".

[0662] In today's communication environment, there is a need for dynamic adjustment of communication quality that responds to the user's emotional state. In particular, providing a communication environment that aligns with the user's emotions during online meetings and information sharing presents a significant challenge. This is because existing systems lack the ability to accurately capture user emotions and adjust communication settings in real time accordingly.

[0663] 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.

[0664] In this invention, the server includes means for recording data collected from a communication device, means for analyzing the data in real time, and means for analyzing the user's emotional state. This enables automatic and dynamic optimization of the communication environment based on the user's emotions.

[0665] A "communication device" is a device used for sending and receiving data, and functions as an interface with the user.

[0666] "Means of recording" refers to a device or program that has the function of storing acquired data and making it accessible as needed.

[0667] "Means of analysis" refers to a system that includes the process of interpreting collected data and identifying certain patterns or trends.

[0668] An "automatic adjustment mechanism" is a system that has the function of changing the settings of a communication device according to predefined rules or algorithms based on the analysis results.

[0669] A "means of monitoring and providing feedback" refers to a system that continuously observes the communication status and reports that information to users and system administrators.

[0670] "Means for analyzing emotional states" refer to algorithms and devices that identify a user's psychological state based on their voice and biometric information.

[0671] "Means for optimizing the communication environment" refers to methods or devices for dynamically improving communication quality in response to emotional states.

[0672] One embodiment of this invention is a program that constructs a system for optimizing the communication environment according to the user's emotional state. Details are provided below.

[0673] First, the device collects the user's voice data and biometric information. This involves using a microphone for voice analysis and sensors to acquire biometric information as needed. This information is sent to an analysis engine connected to the cloud, where the user's emotional state is analyzed in real time. Sentiment analysis utilizes a speech recognition engine (for example, cloud-based speech recognition that converts speech to text) and an emotion analysis algorithm.

[0674] The server receives the analyzed emotion data and processes it in conjunction with a generative AI model. Specifically, based on the emotion analysis results, it derives the communication settings best suited to the user's psychological state at that time. These communication settings include adjusting network traffic and optimizing audio and video quality. This reduces the user's stress and enables comfortable communication.

[0675] As an example of this system, when a user is participating in an online meeting, if stress is detected, settings are automatically changed to improve audio quality. This allows users to experience smoother conversations.

[0676] As an example of a prompt, the response can be adjusted using a generative AI model, such as "We will provide weather information in a calm voice that will help the user relax." In this way, it is possible to implement a system in which sentiment analysis and communication optimization are naturally integrated.

[0677] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0678] Step 1:

[0679] The device collects voice data and biometric information from the user. Inputs are the user's voice and sensor data, and output is raw data for emotion analysis. This includes recording voice and acquiring data such as heart rate and skin temperature through biosensors.

[0680] Step 2:

[0681] The device sends the collected data to the sentiment analysis engine. The input is the raw data obtained in step 1, and the output is the data passed to the cloud-based sentiment analysis system. This involves securely transmitting the data over the network.

[0682] Step 3:

[0683] The server analyzes the received data using a generating AI model. The input is user data necessary for emotion analysis, and the output is the analysis result indicating the user's emotional state. Here, the AI ​​model identifies emotions from the user's voice tone and biometric information.

[0684] Step 4:

[0685] The server optimizes communication settings based on the analysis results. The input is the emotional state data obtained in step 3, and the output is the optimal communication settings applied to the communication device. This includes actions such as adjusting network traffic and optimizing voice quality.

[0686] Step 5:

[0687] Users communicate comfortably in an optimized communication environment. The input is optimized communication settings, and the output is an improved user experience. This includes actions that allow users to participate in online meetings and obtain information without stress.

[0688] 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.

[0689] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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."

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] The following is further disclosed regarding the embodiments described above.

[0710] (Claim 1)

[0711] A means for recording data collected from a communication device,

[0712] A means for analyzing the aforementioned data in real time,

[0713] A means for automatically adjusting the operation of the communication device based on the analysis results,

[0714] A means for monitoring the communication status resulting from the adjustment and providing feedback,

[0715] A system that includes this.

[0716] (Claim 2)

[0717] The system according to claim 1, further comprising means for data analysis to optimize the energy consumption of a communication device.

[0718] (Claim 3)

[0719] The system according to claim 1, comprising the means for data analysis to predict traffic patterns in order to improve communication quality.

[0720] "Example 1"

[0721] (Claim 1)

[0722] A means for recording data collected from a communication device,

[0723] A means for analyzing the aforementioned data in real time using a generating AI model,

[0724] A means for dynamically and automatically adjusting the operating parameters of the communication infrastructure based on the analysis results,

[0725] A means for monitoring the performance of the communication state obtained through the above adjustment and using it as feedback for the next analysis,

[0726] A system that includes this.

[0727] (Claim 2)

[0728] The system according to claim 1, comprising data analysis means using a generative AI model for improving the energy efficiency of communication infrastructure.

[0729] (Claim 3)

[0730] The system according to claim 1, comprising data analysis means for a generative AI model that predicts communication traffic patterns and provides optimized operational instructions in order to maintain or improve the quality of communication services.

[0731] "Application Example 1"

[0732] (Claim 1)

[0733] A means for recording data collected from a communication device,

[0734] A means for analyzing the aforementioned data in real time,

[0735] A means for automatically adjusting the operation of the communication device based on the analysis results,

[0736] A means for monitoring the communication status resulting from the adjustment and providing feedback,

[0737] A means of analyzing urban traffic and event information to optimize communication infrastructure,

[0738] A system that includes this.

[0739] (Claim 2)

[0740] The system according to claim 1, further comprising means for data analysis to optimize the energy consumption of a communication device.

[0741] (Claim 3)

[0742] The system according to claim 1, comprising the means for data analysis to predict traffic patterns in order to improve communication quality.

[0743] "Example 2 of combining an emotion engine"

[0744] (Claim 1)

[0745] A means for analyzing information indicating the user's emotional state collected from the device,

[0746] The means of transmitting the analyzed information to a server and collaborating with a generating AI to select a communication environment based on the user's emotional state,

[0747] Means for optimizing network settings to provide the selected communication environment to the user,

[0748] A means of monitoring the effectiveness of an optimized communication environment and providing feedback,

[0749] A system that includes this.

[0750] (Claim 2)

[0751] The system according to claim 1, further comprising means for analyzing information to optimize the energy consumption of a communication device according to the emotional state of the user.

[0752] (Claim 3)

[0753] The system according to claim 1, comprising means for information analysis to predict traffic patterns and adjust network settings in order to improve communication quality based on the emotional state of the user.

[0754] "Application example 2 when combining with an emotional engine"

[0755] (Claim 1)

[0756] A means for recording data collected from a communication device,

[0757] A means for analyzing the aforementioned data in real time,

[0758] A means for automatically adjusting the operation of the communication device based on the analysis results,

[0759] A means for monitoring the communication status resulting from the adjustment and providing feedback,

[0760] A means of analyzing the user's emotional state,

[0761] A means of optimizing the communication environment based on analyzed emotional data,

[0762] A system that includes this.

[0763] (Claim 2)

[0764] The system according to claim 1, comprising means for data analysis to optimize the energy consumption of a communication device and means for generating an adaptive response based on the user's emotional state.

[0765] (Claim 3)

[0766] The system according to claim 1, comprising means for data analysis to predict traffic patterns in order to improve communication quality, and means for generating prompt sentences according to emotional states. [Explanation of Symbols]

[0767] 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 recording data collected from a communication device, A means for analyzing the aforementioned data in real time, A means for automatically adjusting the operation of the communication device based on the analysis results, A means for monitoring the communication status resulting from the adjustment and providing feedback, A means of analyzing urban traffic and event information to optimize communication infrastructure, A system that includes this.

2. The system according to claim 1, further comprising means for data analysis to optimize the energy consumption of a communication device.

3. The system according to claim 1, further comprising the means for data analysis to predict traffic patterns in order to improve communication quality.

Images