System

Abstract

An object of a system according to an embodiment is to improve the quality and efficiency of a network.SOLUTION: A system includes a data collection unit, an analysis unit, a proposal unit, and a setting change unit. The data collection unit collects network data. The analysis unit analyzes the network data collected by the data collection unit. The proposal unit proposes optimization of the network on the basis of a result analyzed by the analysis unit. The setting change unit changes the network setting based on the optimization proposal proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not been sufficiently optimized to improve network quality and efficiency, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the quality and efficiency of the network. [Means for solving the problem]

[0006] A system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a setting change unit. The data collection unit collects network data. The analysis unit analyzes the network data collected by the data collection unit. The proposal unit makes network optimization proposals based on the results of the analysis by the analysis unit. The setting change unit changes network settings based on the optimization proposals proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the quality and efficiency of the network. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A network optimization system according to an embodiment of the present invention is a system that utilizes AI to optimize a telecommunications carrier's network and realizes improvement in quality and efficiency. As a result, the network optimization system enables telecommunications carriers to efficiently manage their networks and provide high-quality services.

[0029] A network optimization system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a configuration change unit. The data collection unit collects network data. For example, the data collection unit collects data such as communication traffic, connection status, and error logs. The data collection unit can also collect data from IoT devices. For example, the data collection unit collects data from smart home devices and wearable devices. The analysis unit analyzes the collected network data. For example, the analysis unit detects anomalies using data mining techniques. The analysis unit can also predict traffic increases using machine learning algorithms. The analysis unit can also predict long-term trends by comparing data with past data. The proposal unit makes network optimization proposals based on the analysis results. For example, if communication traffic is concentrated in a specific area, the proposal unit can propose allocating additional resources to that area. The proposal unit can also propose an operation method to minimize energy consumption. If communication delays occur, the proposal unit can identify the cause and propose measures to minimize the delays. The configuration change unit changes network settings based on the optimization proposals proposed by the proposal unit. For example, when an increase in traffic is detected, the setting change unit automatically allocates additional bandwidth. The setting change unit can also monitor network security risks in real time and automatically take countermeasures when a risk occurs. The setting change unit can also continuously monitor network performance and make adjustments to maintain optimal performance. This allows the network optimization system according to the embodiment to enable telecommunications carriers to efficiently manage their networks and provide high-quality services. For example, it is possible to analyze network traffic data to efficiently manage traffic during peak periods and analyze error logs to quickly resolve problems. Furthermore, automated network management can reduce manual management work. This is expected to enable telecommunications carriers to provide high-quality network services and improve operational efficiency.

[0030] The analysis unit analyzes network traffic data and can identify times of day and areas where communication volume increases. The analysis unit, for example, monitors network traffic data in real time and detects abnormal patterns. For example, it detects sudden increases or decreases that exceed normal traffic volume and issues an alert as an anomaly. The analysis unit also analyzes past traffic data and identifies seasonal trends. For example, it predicts a tendency for traffic to increase during the New Year holidays or specific event periods. The analysis unit also identifies increases in communication volume in specific times of day and areas based on the traffic data. For example, it identifies increases in communication volume in specific areas during peak hours. This makes it possible to identify increases in communication volume and take appropriate measures.

[0031] The suggestion unit can suggest allocating additional resources to a specific area when communication traffic is concentrated in that area. For example, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating additional bandwidth. Furthermore, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional server resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating an additional server. Furthermore, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional storage resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating additional storage. This makes it possible to alleviate communication traffic concentration and improve network efficiency.

[0032] The setting change unit can perform a setting to automatically allocate additional bandwidth when it detects an increase in traffic. The setting change unit, for example, performs a setting to automatically allocate additional bandwidth when it detects an increase in traffic. For example, a setting to allocate additional bandwidth when traffic increases. Furthermore, the setting change unit performs a setting to automatically allocate additional server resources when it detects an increase in traffic. For example, a setting to allocate an additional server when traffic increases. Furthermore, the setting change unit performs a setting to automatically allocate additional storage resources when it detects an increase in traffic. For example, a setting to allocate additional storage when traffic increases. This makes it possible to quickly respond to increases in traffic and maintain network performance.

[0033] The proposal unit can identify the cause of a communication delay when it occurs and propose measures to minimize the delay. For example, when a communication delay occurs, the proposal unit identifies the cause and proposes measures to minimize the delay. For example, when a delay is caused by network congestion, the proposal unit proposes distributing traffic. Furthermore, when a delay is caused by a hardware failure, the proposal unit proposes replacing the failed hardware. For example, when a delay is caused by a router failure, the proposal unit proposes replacing the router. Furthermore, when a delay is caused by a configuration error, the proposal unit proposes correcting the settings. For example, when a delay is caused by a routing configuration error, the proposal unit proposes correcting the routing settings. This makes it possible to minimize communication delays and improve network quality.

[0034] The proposal unit can propose an operation method for minimizing energy consumption and an optimization of resource allocation for cost reduction. The proposal unit, for example, proposes an operation method for minimizing energy consumption. For example, it proposes the use of a sleep mode. The proposal unit also proposes load balancing. For example, it proposes traffic distribution. The proposal unit also proposes the use of energy-efficient equipment. For example, it proposes the use of an energy-efficient router. The proposal unit also proposes optimization of resource allocation for cost reduction. For example, it proposes dynamic resource allocation. The proposal unit also proposes load balancing. For example, it proposes traffic distribution. The proposal unit also proposes priority setting. For example, it proposes setting priority to important traffic. This makes it possible to suppress energy consumption and reduce costs.

[0035] The data collection unit can collect a wider range of data by including data from IoT devices. For example, the data collection unit collects data from IoT devices and uses it for network traffic analysis. For example, it collects data from smart home devices and wearable devices. The data collection unit also collects data from IoT devices and uses it for network anomaly detection. For example, it collects data from sensors and detects abnormal patterns. The data collection unit also collects data from IoT devices and uses it for network demand forecasting. For example, it collects data from smart meters and predicts energy consumption trends. This makes it possible to collect a wider range of data by including data from IoT devices.

[0036] The data collection unit can share data between different telecommunications carriers and build a cooperative system for mutual network optimization. The data collection unit, for example, shares traffic data between different telecommunications carriers and makes mutual optimization proposals. For example, traffic data from adjacent areas can be shared to optimize resource allocation. The data collection unit also shares error logs between different telecommunications carriers and mutually solves problems. For example, if there is a problem with a specific device or software, that information can be shared and quickly corrected. The data collection unit also shares connection status data between different telecommunications carriers and mutually improves network quality. For example, connection status data can be shared to improve connection stability. This allows different telecommunications carriers to share data and cooperate in network optimization.

[0037] In analyzing network data, the analysis unit compares it with past data and predicts long-term trends, thereby making it possible to predict future network demand. The analysis unit, for example, analyzes traffic data from the past few years to identify seasonal trends. For example, it predicts a tendency for traffic to increase during the New Year holidays or specific event periods. The analysis unit also compares it with past traffic data to predict long-term trends. For example, it predicts future traffic increases based on past data. The analysis unit also predicts future network demand based on past data. For example, it predicts future traffic increases based on past traffic data. This makes it possible to predict future network demand and take appropriate measures.

[0038] The proposal unit makes proposals to maximize energy efficiency and reduce the environmental load. The proposal unit makes proposals to maximize energy efficiency, for example. For example, it proposes putting devices into sleep mode during low traffic hours at night. The proposal unit also proposes the use of highly energy-efficient devices. For example, it proposes the use of highly energy-efficient routers. The proposal unit also proposes operating methods that consume less energy. For example, it proposes load balancing to reduce energy consumption. This makes it possible to maximize energy efficiency and reduce the environmental load.

[0039] The proposal unit can propose optimal resource allocation taking into account the physical layout and geographical conditions of the network. The proposal unit, for example, analyzes the physical layout of the network and proposes optimal resource allocation taking into account geographical conditions. For example, it proposes resource allocation according to geographical conditions such as mountainous areas or urban areas. The proposal unit also proposes optimizing the layout of network equipment. For example, it proposes optimizing the layout of a data center. The proposal unit also proposes optimizing cable wiring. For example, it proposes efficient cable wiring. This makes it possible to achieve optimal resource allocation taking into account the physical layout and geographical conditions of the network.

[0040] The proposal unit can be applied to different industries and make optimization proposals specialized for each industry. For example, the proposal unit makes network optimization proposals specialized for the medical industry. For example, it analyzes network traffic within a hospital and optimizes data communication for medical equipment. The proposal unit also makes network optimization proposals specialized for the education industry. For example, it analyzes network traffic within a school and optimizes communication for online classes. The proposal unit also makes network optimization proposals specialized for the manufacturing industry. For example, it analyzes network traffic within a factory and optimizes data communication for manufacturing equipment. This makes it possible to make optimization proposals specialized for different industries.

[0041] The proposal unit works in conjunction with other AI systems, allowing multiple AIs to work together to perform optimization. For example, the proposal unit may involve a traffic analysis AI and an error log analysis AI working together to propose optimal resource allocation. For example, the traffic analysis AI detects an increase in traffic, and the error log analysis AI analyzes the error log to identify the problem and propose optimal resource allocation. The proposal unit may also involve a machine learning system and a natural language processing system working together to make optimization proposals. For example, the machine learning system analyzes data, and the natural language processing system generates optimization proposals based on the analysis results. The proposal unit may also involve an image recognition system and a voice recognition system working together to make optimization proposals. For example, the image recognition system monitors the status of network equipment, and the voice recognition system analyzes the administrator's instructions and makes optimization proposals. This allows multiple AI systems to work together to perform optimization.

[0042] The configuration change unit can monitor network security risks in real time and automatically take countermeasures when a risk occurs. The configuration change unit, for example, monitors network traffic in real time and automatically takes security countermeasures when an abnormal pattern is detected. For example, it automatically changes firewall settings when unauthorized access is detected. The configuration change unit also uses threat intelligence to monitor security risks in real time and automatically take countermeasures when a risk occurs. For example, it performs vulnerability scans and automatically applies patches when vulnerabilities are discovered. The configuration change unit also uses real-time alerts to monitor security risks and automatically take countermeasures when a risk occurs. For example, it issues an alert when abnormal traffic is detected and takes countermeasures. This makes it possible to monitor network security risks in real time and take countermeasures quickly.

[0043] The setting change unit can continuously monitor network performance and make adjustments to maintain optimal performance. The setting change unit, for example, continuously monitors network performance data and automatically adjusts resources according to increases or decreases in traffic. For example, it allocates additional bandwidth when traffic increases. The setting change unit also monitors network performance and automatically takes measures when performance deteriorates. For example, it changes routing settings when performance deteriorates. The setting change unit also monitors network performance and makes adjustments to maintain optimal performance. For example, it distributes traffic to maintain performance. In this way, it is possible to continuously monitor network performance and maintain optimal performance.

[0044] The setting change unit can work in cooperation with other automated systems to perform comprehensive optimization. The setting change unit, for example, works in cooperation with an autonomous vehicle management system to optimize the network. For example, it can optimize data communication for autonomous vehicles to alleviate traffic congestion. The setting change unit can also work in cooperation with a building management system to optimize the network. For example, it can optimize network traffic within a building to reduce energy consumption. The setting change unit can also work in cooperation with an energy management system to optimize the network. For example, it can optimize energy consumption traffic to improve energy efficiency. This allows the setting change unit to work in cooperation with other automated systems to perform comprehensive optimization.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The network optimization system may further include a behavior analysis unit that analyzes user behavior patterns. The behavior analysis unit may, for example, analyze the amount of time a user spends using the Internet and the types of websites accessed to identify the user's behavior patterns. For example, it may analyze the behavior of a user who frequently accesses a specific website during a specific time period. The behavior analysis unit may also analyze the user's device usage status and identify usage patterns for each device. For example, it may analyze the difference in usage time between smartphones and PCs. The behavior analysis unit may also analyze the user's location information and identify network usage status in specific locations. For example, it may analyze network usage patterns at the office or at home. This allows the network to be optimized based on the user's behavior patterns.

[0047] The analysis unit can also predict network security risks. For example, it can analyze past security incident data and identify specific patterns. For example, it can predict security risks that occur during specific time periods or with specific traffic volumes. The analysis unit can also monitor network traffic data in real time and detect abnormal patterns. For example, it can detect sudden increases or decreases in traffic volume that exceed normal levels and issue an alert as a security risk. The analysis unit can also predict future security risks using machine learning algorithms. For example, it can predict the probability of future security risks occurring based on past data. This makes it possible to predict network security risks and take appropriate measures.

[0048] The setting change unit can further perform settings to minimize energy consumption in the network. For example, when an increase in traffic is detected, the setting is made to give priority to the use of energy-efficient devices. For example, the setting is made to use a highly energy-efficient router. Furthermore, when a decrease in traffic is detected, the setting change unit performs settings to put unnecessary devices into sleep mode. For example, the setting is made to put devices into sleep mode during times of low traffic, such as at night. Furthermore, the setting change unit performs settings to automatically select an operating method that consumes less energy. For example, the setting is made to distribute the load and reduce energy consumption. This makes it possible to minimize energy consumption in the network.

[0049] The network optimization system may further include an anomaly detection unit. The anomaly detection unit, for example, monitors network traffic data in real time and detects abnormal patterns. For example, it detects a sudden increase or decrease in traffic volume that exceeds the normal amount and issues an alert as an anomaly. The anomaly detection unit also analyzes past traffic data and identifies abnormal patterns. For example, it identifies abnormal traffic patterns that occur during specific time periods or in specific areas. The anomaly detection unit can also predict future anomalies using machine learning algorithms. For example, it predicts the probability of future anomalies occurring based on past data. This allows network anomalies to be detected early and appropriate measures to be taken.

[0050] The network optimization system may further include a prediction unit. The prediction unit, for example, analyzes past traffic data and predicts future traffic increases. For example, it predicts traffic increases during specific time periods or specific areas. The prediction unit may also identify seasonal trends and predict future traffic increases. For example, it may predict a tendency for traffic to increase during the New Year holidays or specific event periods. The prediction unit may also predict future traffic increases using a machine learning algorithm. For example, it may predict future traffic increases based on past data. This makes it possible to predict future traffic increases and take appropriate measures.

[0051] The network optimization system may further include a feedback collection unit that collects user feedback. The feedback collection unit, for example, collects feedback from users when they use the network and uses it to optimize the network. For example, it collects problems and areas for improvement that users have noticed when using the network. The feedback collection unit also investigates user satisfaction and uses it to improve the quality of the network. For example, it investigates user satisfaction through a questionnaire. The feedback collection unit also proposes new functions and services based on user opinions. For example, it proposes new network functions in response to user requests. This makes it possible to optimize the network based on user feedback.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects network data, such as communication traffic, connection status, error logs, and data from smart home devices and wearable devices. Step 2: The analysis unit analyzes the collected network data, for example, using data mining techniques to detect anomalies, machine learning algorithms to predict traffic increases, and comparing it with past data to forecast long-term trends. Step 3: The proposal unit makes network optimization proposals based on the analysis results, such as allocating additional resources when communication traffic is concentrated in a specific area, operating methods to minimize energy consumption, and measures to minimize communication delays. Step 4: The configuration change unit changes the network settings based on the optimization proposals made by the proposal unit. For example, the configuration can be changed to automatically allocate additional bandwidth when an increase in traffic is detected, or to monitor network security risks in real time and automatically take countermeasures when a risk occurs.

[0054] (Example 2) A network optimization system according to an embodiment of the present invention is a system that utilizes AI to optimize a telecommunications carrier's network and realizes improvement in quality and efficiency. As a result, the network optimization system enables telecommunications carriers to efficiently manage their networks and provide high-quality services.

[0055] A network optimization system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, and a configuration change unit. The data collection unit collects network data. For example, the data collection unit collects data such as communication traffic, connection status, and error logs. The data collection unit can also collect data from IoT devices. For example, the data collection unit collects data from smart home devices and wearable devices. The analysis unit analyzes the collected network data. For example, the analysis unit detects anomalies using data mining techniques. The analysis unit can also predict traffic increases using machine learning algorithms. The analysis unit can also predict long-term trends by comparing data with past data. The proposal unit makes network optimization proposals based on the analysis results. For example, if communication traffic is concentrated in a specific area, the proposal unit can propose allocating additional resources to that area. The proposal unit can also propose an operation method to minimize energy consumption. If communication delays occur, the proposal unit can identify the cause and propose measures to minimize the delays. The configuration change unit changes network settings based on the optimization proposals proposed by the proposal unit. For example, when an increase in traffic is detected, the setting change unit automatically allocates additional bandwidth. The setting change unit can also monitor network security risks in real time and automatically take countermeasures when a risk occurs. The setting change unit can also continuously monitor network performance and make adjustments to maintain optimal performance. This allows the network optimization system according to the embodiment to enable telecommunications carriers to efficiently manage their networks and provide high-quality services. For example, it is possible to analyze network traffic data to efficiently manage traffic during peak periods and analyze error logs to quickly resolve problems. Furthermore, automated network management can reduce manual management work. This is expected to enable telecommunications carriers to provide high-quality network services and improve operational efficiency.

[0056] The analysis unit analyzes network traffic data and can identify times of day and areas where communication volume increases. The analysis unit, for example, monitors network traffic data in real time and detects abnormal patterns. For example, it detects sudden increases or decreases that exceed normal traffic volume and issues an alert as an anomaly. The analysis unit also analyzes past traffic data and identifies seasonal trends. For example, it predicts a tendency for traffic to increase during the New Year holidays or specific event periods. The analysis unit also identifies increases in communication volume in specific times of day and areas based on the traffic data. For example, it identifies increases in communication volume in specific areas during peak hours. This makes it possible to identify increases in communication volume and take appropriate measures.

[0057] The suggestion unit can suggest allocating additional resources to a specific area when communication traffic is concentrated in that area. For example, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating additional bandwidth. Furthermore, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional server resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating an additional server. Furthermore, when communication traffic is concentrated in a specific area, the suggestion unit suggests allocating additional storage resources to that area. For example, when traffic is increasing in a specific area, the suggestion unit suggests allocating additional storage. This makes it possible to alleviate communication traffic concentration and improve network efficiency.

[0058] The setting change unit can perform a setting to automatically allocate additional bandwidth when it detects an increase in traffic. The setting change unit, for example, performs a setting to automatically allocate additional bandwidth when it detects an increase in traffic. For example, a setting to allocate additional bandwidth when traffic increases. Furthermore, the setting change unit performs a setting to automatically allocate additional server resources when it detects an increase in traffic. For example, a setting to allocate an additional server when traffic increases. Furthermore, the setting change unit performs a setting to automatically allocate additional storage resources when it detects an increase in traffic. For example, a setting to allocate additional storage when traffic increases. This makes it possible to quickly respond to increases in traffic and maintain network performance.

[0059] The proposal unit can identify the cause of a communication delay when it occurs and propose measures to minimize the delay. For example, when a communication delay occurs, the proposal unit identifies the cause and proposes measures to minimize the delay. For example, when a delay is caused by network congestion, the proposal unit proposes distributing traffic. Furthermore, when a delay is caused by a hardware failure, the proposal unit proposes replacing the failed hardware. For example, when a delay is caused by a router failure, the proposal unit proposes replacing the router. Furthermore, when a delay is caused by a configuration error, the proposal unit proposes correcting the settings. For example, when a delay is caused by a routing configuration error, the proposal unit proposes correcting the routing settings. This makes it possible to minimize communication delays and improve network quality.

[0060] The proposal unit can propose an operation method for minimizing energy consumption and an optimization of resource allocation for cost reduction. The proposal unit, for example, proposes an operation method for minimizing energy consumption. For example, it proposes the use of a sleep mode. The proposal unit also proposes load balancing. For example, it proposes traffic distribution. The proposal unit also proposes the use of energy-efficient equipment. For example, it proposes the use of an energy-efficient router. The proposal unit also proposes optimization of resource allocation for cost reduction. For example, it proposes dynamic resource allocation. The proposal unit also proposes load balancing. For example, it proposes traffic distribution. The proposal unit also proposes priority setting. For example, it proposes setting priority to important traffic. This makes it possible to suppress energy consumption and reduce costs.

[0061] The data collection unit can collect a wider range of data by including data from IoT devices. For example, the data collection unit collects data from IoT devices and uses it for network traffic analysis. For example, it collects data from smart home devices and wearable devices. The data collection unit also collects data from IoT devices and uses it for network anomaly detection. For example, it collects data from sensors and detects abnormal patterns. The data collection unit also collects data from IoT devices and uses it for network demand forecasting. For example, it collects data from smart meters and predicts energy consumption trends. This makes it possible to collect a wider range of data by including data from IoT devices.

[0062] The data collection unit can share data between different telecommunications carriers and build a cooperative system for mutual network optimization. The data collection unit, for example, shares traffic data between different telecommunications carriers and makes mutual optimization proposals. For example, traffic data from adjacent areas can be shared to optimize resource allocation. The data collection unit also shares error logs between different telecommunications carriers and mutually solves problems. For example, if there is a problem with a specific device or software, that information can be shared and quickly corrected. The data collection unit also shares connection status data between different telecommunications carriers and mutually improves network quality. For example, connection status data can be shared to improve connection stability. This allows different telecommunications carriers to share data and cooperate in network optimization.

[0063] In analyzing network data, the analysis unit compares it with past data and predicts long-term trends, thereby making it possible to predict future network demand. The analysis unit, for example, analyzes traffic data from the past few years to identify seasonal trends. For example, it predicts a tendency for traffic to increase during the New Year holidays or specific event periods. The analysis unit also compares it with past traffic data to predict long-term trends. For example, it predicts future traffic increases based on past data. The analysis unit also predicts future network demand based on past data. For example, it predicts future traffic increases based on past traffic data. This makes it possible to predict future network demand and take appropriate measures.

[0064] The proposal unit makes proposals to maximize energy efficiency and reduce the environmental load. The proposal unit makes proposals to maximize energy efficiency, for example. For example, it proposes putting devices into sleep mode during low traffic hours at night. The proposal unit also proposes the use of highly energy-efficient devices. For example, it proposes the use of highly energy-efficient routers. The proposal unit also proposes operating methods that consume less energy. For example, it proposes load balancing to reduce energy consumption. This makes it possible to maximize energy efficiency and reduce the environmental load.

[0065] The proposal unit can propose optimal resource allocation taking into account the physical layout and geographical conditions of the network. The proposal unit, for example, analyzes the physical layout of the network and proposes optimal resource allocation taking into account geographical conditions. For example, it proposes resource allocation according to geographical conditions such as mountainous areas or urban areas. The proposal unit also proposes optimizing the layout of network equipment. For example, it proposes optimizing the layout of a data center. The proposal unit also proposes optimizing cable wiring. For example, it proposes efficient cable wiring. This makes it possible to achieve optimal resource allocation taking into account the physical layout and geographical conditions of the network.

[0066] The suggestion unit uses the emotion estimation function to analyze the user's emotions when using the network, identify points of dissatisfaction, and make improvement suggestions. For example, the suggestion unit uses the emotion estimation function to analyze the user's emotions when using the network, identify points of dissatisfaction, and make improvement suggestions. For example, the suggestion unit captures the user's facial expression when using the network with a camera and analyzes the emotions using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression and makes improvement suggestions. The suggestion unit also records the user's voice when using the network and estimates the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and makes improvement suggestions. The suggestion unit also collects biometric data (heart rate and electrodermal activity) of the user when using the network with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on fluctuations in heart rate and makes improvement suggestions. This makes it possible to identify the user's dissatisfaction and make appropriate improvement suggestions.

[0067] The suggestion unit can use the emotion estimation function to analyze the stress level of the network administrator and make suggestions to reduce the administrator's burden. The suggestion unit, for example, uses the emotion estimation function to analyze the stress level of the network administrator and make suggestions to reduce the administrator's burden. For example, the suggestion unit captures the network administrator's facial expression with a camera and analyzes the stress level using an emotion estimation algorithm. For example, the suggestion unit calculates a stress score based on changes in facial expression and makes suggestions to reduce the burden. The suggestion unit also records the network administrator's voice and estimates the stress level using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates a stress score, and makes suggestions to reduce the burden. The suggestion unit also collects the network administrator's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the stress level using an emotion estimation algorithm. For example, the suggestion unit calculates a stress score based on fluctuations in heart rate and makes suggestions to reduce the burden. In this way, the network administrator's stress level can be analyzed and suggestions to reduce the burden can be made.

[0068] The proposal unit can be applied to different industries and make optimization proposals specialized for each industry. For example, the proposal unit makes network optimization proposals specialized for the medical industry. For example, it analyzes network traffic within a hospital and optimizes data communication for medical equipment. The proposal unit also makes network optimization proposals specialized for the education industry. For example, it analyzes network traffic within a school and optimizes communication for online classes. The proposal unit also makes network optimization proposals specialized for the manufacturing industry. For example, it analyzes network traffic within a factory and optimizes data communication for manufacturing equipment. This makes it possible to make optimization proposals specialized for different industries.

[0069] The proposal unit works in conjunction with other AI systems, allowing multiple AIs to work together to perform optimization. For example, the proposal unit may involve a traffic analysis AI and an error log analysis AI working together to propose optimal resource allocation. For example, the traffic analysis AI detects an increase in traffic, and the error log analysis AI analyzes the error log to identify the problem and propose optimal resource allocation. The proposal unit may also involve a machine learning system and a natural language processing system working together to make optimization proposals. For example, the machine learning system analyzes data, and the natural language processing system generates optimization proposals based on the analysis results. The proposal unit may also involve an image recognition system and a voice recognition system working together to make optimization proposals. For example, the image recognition system monitors the status of network equipment, and the voice recognition system analyzes the administrator's instructions and makes optimization proposals. This allows multiple AI systems to work together to perform optimization.

[0070] The suggestion unit can use the emotion estimation function to analyze the emotions of the network administrator and suggest an environment in which the administrator can work most efficiently. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of the network administrator and suggest an environment in which the administrator can work most efficiently. For example, the suggestion unit captures the network administrator's facial expression with a camera, analyzes the emotion using an emotion estimation algorithm, and suggests an efficient work environment. The suggestion unit also records the network administrator's voice, estimates the emotion using voice analysis technology, and suggests an efficient work environment. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and suggests an efficient work environment. The suggestion unit also collects the network administrator's biometric data (heart rate and electrodermal activity) with a sensor, analyzes the emotion using an emotion estimation algorithm, and suggests an efficient work environment. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations and suggests an efficient work environment. In this way, the network administrator's emotions can be analyzed and an efficient work environment can be suggested.

[0071] The configuration change unit can monitor network security risks in real time and automatically take countermeasures when a risk occurs. The configuration change unit, for example, monitors network traffic in real time and automatically takes security countermeasures when an abnormal pattern is detected. For example, it automatically changes firewall settings when unauthorized access is detected. The configuration change unit also uses threat intelligence to monitor security risks in real time and automatically take countermeasures when a risk occurs. For example, it performs vulnerability scans and automatically applies patches when vulnerabilities are discovered. The configuration change unit also uses real-time alerts to monitor security risks and automatically take countermeasures when a risk occurs. For example, it issues an alert when abnormal traffic is detected and takes countermeasures. This makes it possible to monitor network security risks in real time and take countermeasures quickly.

[0072] The setting change unit can continuously monitor network performance and make adjustments to maintain optimal performance. The setting change unit, for example, continuously monitors network performance data and automatically adjusts resources according to increases or decreases in traffic. For example, it allocates additional bandwidth when traffic increases. The setting change unit also monitors network performance and automatically takes measures when performance deteriorates. For example, it changes routing settings when performance deteriorates. The setting change unit also monitors network performance and makes adjustments to maintain optimal performance. For example, it distributes traffic to maintain performance. In this way, it is possible to continuously monitor network performance and maintain optimal performance.

[0073] The setting change unit uses the emotion estimation function to analyze the user's emotion when using the network in real time, and can automatically take measures before the user becomes dissatisfied. The setting change unit, for example, uses the emotion estimation function to analyze the user's emotion when using the network in real time, and can automatically take measures before the user becomes dissatisfied. For example, the setting change unit adds bandwidth when it detects a delay in a streaming service. The setting change unit also uses the emotion estimation function to analyze the user's emotion when using the network in real time, and can automatically take measures before the user becomes dissatisfied. For example, the setting change unit adds bandwidth when it detects a delay in an online game. The setting change unit also uses the emotion estimation function to analyze the user's emotion when using the network in real time, and can automatically take measures before the user becomes dissatisfied. For example, the setting change unit adds bandwidth when it detects a delay in a video conference. In this way, the user's emotion can be analyzed in real time, and measures can be taken before the user becomes dissatisfied.

[0074] The setting change unit can work in cooperation with other automated systems to perform comprehensive optimization. The setting change unit, for example, works in cooperation with an autonomous vehicle management system to optimize the network. For example, it can optimize data communication for autonomous vehicles to alleviate traffic congestion. The setting change unit can also work in cooperation with a building management system to optimize the network. For example, it can optimize network traffic within a building to reduce energy consumption. The setting change unit can also work in cooperation with an energy management system to optimize the network. For example, it can optimize energy consumption traffic to improve energy efficiency. This allows the setting change unit to work in cooperation with other automated systems to perform comprehensive optimization.

[0075] The setting change unit can use the emotion estimation function to analyze the emotions of the network administrator and automatically adjust the environment so that the administrator can work most efficiently. The setting change unit, for example, uses the emotion estimation function to analyze the emotions of the network administrator and automatically adjust the efficient work environment. For example, the setting change unit captures the network administrator's facial expression with a camera, analyzes the emotions using an emotion estimation algorithm, and automatically adjusts the efficient work environment. The setting change unit also records the network administrator's voice, estimates the emotions using voice analysis technology, and automatically adjusts the efficient work environment. For example, the setting change unit analyzes the tone and speed of the voice, calculates an emotion score, and automatically adjusts the efficient work environment. The setting change unit also collects the network administrator's biometric data (heart rate and electrodermal activity) with a sensor, analyzes the emotions using an emotion estimation algorithm, and automatically adjusts the efficient work environment. For example, the setting change unit calculates an emotion score based on heart rate fluctuations, and automatically adjusts the efficient work environment. In this way, the network administrator's emotions can be analyzed and the efficient work environment can be automatically adjusted.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The network optimization system may further include a behavior analysis unit that analyzes user behavior patterns. The behavior analysis unit may, for example, analyze the amount of time a user spends using the Internet and the types of websites accessed to identify the user's behavior patterns. For example, it may analyze the behavior of a user who frequently accesses a specific website during a specific time period. The behavior analysis unit may also analyze the user's device usage status and identify usage patterns for each device. For example, it may analyze the difference in usage time between smartphones and PCs. The behavior analysis unit may also analyze the user's location information and identify network usage status in specific locations. For example, it may analyze network usage patterns at the office or at home. This allows the network to be optimized based on the user's behavior patterns.

[0078] The analysis unit can also predict network security risks. For example, it can analyze past security incident data and identify specific patterns. For example, it can predict security risks that occur during specific time periods or with specific traffic volumes. The analysis unit can also monitor network traffic data in real time and detect abnormal patterns. For example, it can detect sudden increases or decreases in traffic volume that exceed normal levels and issue an alert as a security risk. The analysis unit can also predict future security risks using machine learning algorithms. For example, it can predict the probability of future security risks occurring based on past data. This makes it possible to predict network security risks and take appropriate measures.

[0079] The suggestion unit can use the emotion estimation function to make network optimization suggestions based on the user's emotions. For example, if a user feels stressed while using the network, the suggestion unit identifies the cause and makes suggestions to reduce the stress. For example, if the user feels stressed due to network delays, the suggestion unit makes suggestions to minimize the delays. Furthermore, if the user is satisfied, the suggestion unit makes suggestions to maintain that state. For example, if the current network settings are optimal for the user, the suggestion unit makes suggestions to maintain those settings. Furthermore, the suggestion unit can predict future network demands based on the user's emotion data and take appropriate measures. For example, if a user is satisfied with a particular time period, the suggestion unit makes suggestions to optimize the network settings for that time period. In this way, network optimization suggestions can be made based on the user's emotions.

[0080] The setting change unit can further perform settings to minimize energy consumption in the network. For example, when an increase in traffic is detected, the setting is made to give priority to the use of energy-efficient devices. For example, the setting is made to use a highly energy-efficient router. Furthermore, when a decrease in traffic is detected, the setting change unit performs settings to put unnecessary devices into sleep mode. For example, the setting is made to put devices into sleep mode during times of low traffic, such as at night. Furthermore, the setting change unit performs settings to automatically select an operating method that consumes less energy. For example, the setting is made to distribute the load and reduce energy consumption. This makes it possible to minimize energy consumption in the network.

[0081] The suggestion unit can use the emotion estimation function to analyze the emotions of the network administrator and suggest an environment in which the administrator can work most efficiently. For example, the network administrator's facial expression is captured with a camera, and the emotion is analyzed using an emotion estimation algorithm to suggest an efficient work environment. The suggestion unit also records the network administrator's voice, estimates the emotion using voice analysis technology, and suggests an efficient work environment. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and suggests an efficient work environment. The suggestion unit also collects the network administrator's biometric data (heart rate and electrodermal activity) with a sensor, analyzes the emotion using an emotion estimation algorithm, and suggests an efficient work environment. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations and suggests an efficient work environment. In this way, the network administrator's emotions can be analyzed and an efficient work environment can be suggested.

[0082] The network optimization system may further include an anomaly detection unit. The anomaly detection unit, for example, monitors network traffic data in real time and detects abnormal patterns. For example, it detects a sudden increase or decrease in traffic volume that exceeds the normal amount and issues an alert as an anomaly. The anomaly detection unit also analyzes past traffic data and identifies abnormal patterns. For example, it identifies abnormal traffic patterns that occur during specific time periods or in specific areas. The anomaly detection unit can also predict future anomalies using machine learning algorithms. For example, it predicts the probability of future anomalies occurring based on past data. This allows network anomalies to be detected early and appropriate measures to be taken.

[0083] The suggestion unit uses the emotion estimation function to analyze the user's emotions when using the network, identify points of dissatisfaction, and make improvement suggestions. For example, the suggestion unit captures the user's facial expression while using the network with a camera and analyzes the emotions using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression and makes improvement suggestions. The suggestion unit also records the user's voice while using the network and estimates the emotion using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and makes improvement suggestions. The suggestion unit also collects biometric data (heart rate and electrodermal activity) of the user while using the network with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on fluctuations in heart rate and makes improvement suggestions. This makes it possible to identify the user's dissatisfaction and make appropriate improvement suggestions.

[0084] The network optimization system may further include a prediction unit. The prediction unit, for example, analyzes past traffic data and predicts future traffic increases. For example, it predicts traffic increases during specific time periods or specific areas. The prediction unit may also identify seasonal trends and predict future traffic increases. For example, it may predict a tendency for traffic to increase during the New Year holidays or specific event periods. The prediction unit may also predict future traffic increases using a machine learning algorithm. For example, it may predict future traffic increases based on past data. This makes it possible to predict future traffic increases and take appropriate measures.

[0085] The suggestion unit can use the emotion estimation function to analyze the stress level of the network administrator and make suggestions to reduce the administrator's workload. For example, the suggestion unit captures the network administrator's facial expression with a camera and analyzes the stress level using an emotion estimation algorithm. For example, the suggestion unit calculates a stress score based on changes in facial expression and makes suggestions to reduce the workload. The suggestion unit also records the network administrator's voice and estimates the stress level using voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates a stress score, and makes suggestions to reduce the workload. The suggestion unit also collects the network administrator's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the stress level using an emotion estimation algorithm. For example, the suggestion unit calculates a stress score based on fluctuations in heart rate and makes suggestions to reduce the workload. This makes it possible to analyze the network administrator's stress level and make suggestions to reduce the workload.

[0086] The network optimization system may further include a feedback collection unit that collects user feedback. The feedback collection unit, for example, collects feedback from users when they use the network and uses it to optimize the network. For example, it collects problems and areas for improvement that users have noticed when using the network. The feedback collection unit also investigates user satisfaction and uses it to improve the quality of the network. For example, it investigates user satisfaction through a questionnaire. The feedback collection unit also proposes new functions and services based on user opinions. For example, it proposes new network functions in response to user requests. This makes it possible to optimize the network based on user feedback.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The data collection unit collects network data, such as communication traffic, connection status, error logs, and data from smart home devices and wearable devices. Step 2: The analysis unit analyzes the collected network data, for example, using data mining techniques to detect anomalies, machine learning algorithms to predict traffic increases, and comparing it with past data to forecast long-term trends. Step 3: The proposal unit makes network optimization proposals based on the analysis results, such as allocating additional resources when communication traffic is concentrated in a specific area, operating methods to minimize energy consumption, and measures to minimize communication delays. Step 4: The configuration change unit changes the network settings based on the optimization proposals made by the proposal unit. For example, the configuration can be changed to automatically allocate additional bandwidth when an increase in traffic is detected, or to monitor network security risks in real time and automatically take countermeasures when a risk occurs.

[0089] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, a 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects network data; an analysis unit that analyzes the network data collected by the data collection unit; a proposal unit that proposes network optimization based on the results of the analysis by the analysis unit; a setting change unit that changes network settings based on the optimization proposal proposed by the proposal unit. A system characterized by:

2. The analysis unit Analyzing the traffic data of the network data, and identifying the time zone and area where the traffic volume will increase 2. The system of claim 1.

3. The data collection unit Broader data collection by including data from IoT devices 2. The system of claim 1.

4. The proposal unit Providing suggestions to maximize energy efficiency and reduce environmental impact 2. The system of claim 1.

5. The setting change unit Monitor the network security risks in real time and automatically take measures when a risk occurs.

2. The system of claim 1.

6. The proposal unit Analyze users' feelings when using the network, identify points of dissatisfaction, and propose improvements 2. The system of claim 1.

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