system

Abstract

The system according to this embodiment aims to quickly and accurately identify network quality problems and respond appropriately to them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects user feedback from an SNS platform. The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. The generation unit determines network tuning based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the network based on the tuning determined by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and accurately identify problems with network quality and appropriately respond to them.

[0005] The system according to the embodiment aims to quickly and accurately identify problems with network quality and appropriately respond to them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects user feedback from an SNS platform. The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. The generation unit determines network tuning based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the network based on the tuning determined by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately identify network quality problems and respond appropriately. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication among a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a network quality improvement system that automatically collects user feedback from an SNS platform and aims to improve network quality. The network quality improvement system automatically collects user feedback from the SNS platform. For example, it collects negative comments such as "the network is congested." In this case, the collected comments also include location information where the posts were made. Next, the network quality improvement system analyzes network quality data at the relevant location based on the collected negative comments and their location information. For example, it incorporates detailed network parameters such as data transfer speed, signal strength, and hardware settings to accurately identify areas and times where problems occur. The network quality improvement system inputs this collected information into a generating AI, which then determines network tuning in real time. Specifically, it adjusts the signal strength of base stations, reallocates bandwidth to specific areas, or adjusts network parameters. As a result, the network is automatically tuned, and the user experience at the location and time of the problem is significantly improved. In this way, the use of the generating AI immediately improves the user experience and achieves overall improvement in network quality. For example, the network quality improvement system automatically collects user feedback from an SNS platform. For example, it collects negative comments such as "the network is congested." In this process, the collected comments also include location information where they were posted. Next, the network quality improvement system analyzes network quality data at the relevant location based on the collected negative comments and their location information. For example, it incorporates detailed network parameters such as data transfer speed, signal strength, and hardware settings to accurately identify areas and times of trouble. The network quality improvement system inputs this collected information into a generating AI, which then determines network tuning in real time. Specifically, this might involve adjusting the signal strength of base stations, reallocating bandwidth to specific areas, or adjusting network parameters.As a result, the network is automatically tuned, significantly improving the user experience in the locations and times where problems occurred. Thus, the use of generative AI enables immediate improvements to the user experience and overall network quality. This allows the network quality improvement system to automatically improve network quality based on user feedback.

[0029] The network quality improvement system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects user feedback from an SNS platform. The collection unit collects, for example, negative comments. The collection unit can include location information where the comments were posted in the collected comments. The collection unit can automatically collect comments from an SNS platform and add location information. The collection unit can also automatically filter negative comments using AI. The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. The analysis unit analyzes network parameters such as data transfer rate, signal strength, and hardware settings. The analysis unit can also automatically analyze network parameters using AI. The analysis unit can, for example, analyze fluctuations in data transfer rate to identify problem areas in the network. The analysis unit can also detect a decrease in signal strength and identify troubled areas. The analysis unit can also analyze hardware settings to identify the cause of the problem. The generation unit determines network tuning based on the analysis results obtained by the analysis unit. The generation unit determines network tuning in real time using generation AI. The generation unit may, for example, decide to adjust the signal strength of a base station. The generation unit may also decide to reallocate bandwidth to a specific area. The generation unit may also decide to adjust network parameters. The generation unit may also select the optimal tuning method using generation AI. The adjustment unit adjusts the network based on the tuning determined by the generation unit. The adjustment unit may, for example, adjust the signal strength of a base station. The adjustment unit may also reallocate bandwidth to a specific area. The adjustment unit may also adjust network parameters. The adjustment unit may also automatically adjust the network using AI. The adjustment unit may, for example, adjust the signal strength of a base station in real time. The adjustment unit may also dynamically reallocate bandwidth to a specific area. The adjustment unit may also optimize network parameters.As a result, the network quality improvement system according to this embodiment can automatically improve network quality based on feedback from the SNS platform.

[0030] The data collection unit gathers user feedback from social networking services (SNS) platforms. Specifically, the unit utilizes APIs to automatically collect comments and reviews posted on SNS platforms. The unit focuses particularly on collecting negative comments, using AI to analyze the sentiment of comments and automatically filter out negative feedback. For example, it uses natural language processing techniques to analyze the content of comments and detect keywords and phrases that indicate negative sentiment. Furthermore, the unit can add location information to the collected comments. This makes it easier to identify network quality problems in specific regions or areas. The unit implements scripts and programs to automatically collect comments from SNS platforms and add location information. The unit centrally manages this data and stores it in a database. The database stores collected comments, location information, sentiment analysis results, etc., making it accessible to subsequent analysis and generation units. This allows the unit to efficiently collect feedback from SNS platforms and use it to improve network quality.

[0031] The analysis department analyzes network quality data based on feedback and location information collected by the data collection department. Specifically, the analysis department analyzes network parameters such as data transfer rate, signal strength, and hardware settings in detail. The analysis department uses AI to automatically analyze these network parameters and identify problem areas. For example, it can analyze fluctuations in data transfer rate to detect speed reductions in specific time periods or regions. It can also detect signal strength drops and identify problematic areas. Furthermore, it can analyze hardware settings to identify the cause of problems. The AI ​​uses machine learning algorithms to detect anomalies by comparing them with historical data. For example, it uses anomaly detection algorithms to identify data that deviates from normal patterns, enabling early problem detection. Based on these analysis results, the analysis department identifies the problem areas and causes of network issues and proposes improvement measures. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, based on historical data, it can predict fluctuations in network quality in specific regions or time periods and formulate future countermeasures. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, contributing to improved network quality.

[0032] The generation unit determines network tuning based on the analysis results obtained by the analysis unit. Specifically, the generation unit uses a generation AI to determine network tuning in real time. The generation AI uses a deep learning model to select the optimal tuning method. For example, it may decide to adjust the signal strength of base stations. Based on the collected data and analysis results, the generation AI can also decide to reallocate bandwidth to a specific area. Furthermore, the generation AI can also decide to adjust network parameters. Based on these decisions, the generation unit generates specific instructions for network optimization. For example, it generates detailed instructions such as how much to adjust the signal strength of base stations and how to reallocate bandwidth to a specific area. The generation unit transmits these instructions to the adjustment unit, providing a foundation for actual network adjustment. The generation unit utilizes historical data and simulation results to select the optimal tuning method using the generation AI. For example, it selects the most effective adjustment method based on the results of past network adjustments, contributing to future improvements in network quality. This enables the generation unit to efficiently perform real-time network tuning and achieve improvements in network quality.

[0033] The adjustment unit adjusts the network based on the tuning determined by the generation unit. Specifically, the adjustment unit adjusts the signal strength of base stations. The adjustment unit can also reallocate bandwidth to specific areas. The adjustment unit can also adjust network parameters. The adjustment unit can also automatically adjust the network using AI. For example, to adjust the signal strength of base stations in real time, the AI ​​calculates the optimal setting and applies it immediately. To dynamically reallocate bandwidth to specific areas, the AI ​​monitors network usage and adjusts the bandwidth as needed. To optimize network parameters, the adjustment unit uses AI to select the optimal settings based on historical and real-time data. This allows the adjustment unit to always maintain optimal network quality. Furthermore, the adjustment unit can monitor the adjustment results and readjust as needed. For example, it can monitor the network quality after adjustment and readjust if problems are not resolved or new problems arise. This allows the adjustment unit to continuously maintain and improve network quality. Based on instructions from the generation unit, the adjustment unit can adjust the network quickly and accurately, improving user satisfaction.

[0034] The collection unit can collect negative comments. For example, the collection unit can automatically collect negative comments from social networking platforms. The collection unit can also filter negative comments using AI. For example, the collection unit can identify negative comments using sentiment analysis algorithms. The collection unit can also extract negative comments using keyword matching. By collecting negative comments, the collection unit makes it easier to identify problem areas in the network. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input comments collected from social networking platforms into a generating AI and have the generating AI identify negative comments.

[0035] The analysis unit can analyze network parameters such as data transfer rate, signal strength, and hardware settings. For example, the analysis unit can analyze fluctuations in data transfer rate to identify network problems. The analysis unit can also detect drops in signal strength and identify problematic areas. The analysis unit can also analyze hardware settings to identify the cause of problems. The analysis unit can also use AI to automatically analyze network parameters. For example, the analysis unit can analyze fluctuations in data transfer rate in real time. The analysis unit can also instantly detect drops in signal strength. The analysis unit can also dynamically analyze hardware settings. This allows for an accurate understanding of network problems by analyzing detailed network parameters. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input network parameters collected by the collection unit into a generating AI and have the generating AI identify network problems.

[0036] The generation unit can determine network tuning in real time. For example, the generation unit can use a generation AI to determine network tuning in real time. The generation unit can determine whether to adjust the signal strength of base stations. The generation unit can also determine whether to reallocate bandwidth to a specific area. The generation unit can also determine whether to adjust network parameters. The generation unit can also use a generation AI to select the optimal tuning method. For example, the generation unit can use a generation AI to monitor the network status in real time and determine the optimal tuning method. The generation unit can also use a generation AI to analyze network performance data and propose the optimal tuning method. The generation unit can also use a generation AI to predict the future network status based on past data and determine the tuning method. This allows for immediate improvement of network quality by determining network tuning in real time. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input network parameters obtained by the analysis unit into the generation AI and have the generation AI perform the determination of the network tuning method.

[0037] The adjustment unit can adjust the signal strength of the base station. For example, the adjustment unit can adjust the signal strength of the base station in real time. The adjustment unit can also automatically adjust the signal strength of the base station using AI. For example, the adjustment unit can dynamically adjust the signal strength of the base station to improve network quality in a specific area. The adjustment unit can also optimize the signal strength of the base station to improve network performance. By adjusting the signal strength of the base station, the adjustment unit can improve network quality in a specific area. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the signal strength adjustment method determined by the generation unit to the generation AI and have the generation AI perform the signal strength adjustment.

[0038] The adjustment unit can reallocate bandwidth to a specific area. For example, the adjustment unit can reallocate bandwidth to a specific area in real time. The adjustment unit can also automatically reallocate bandwidth to a specific area using AI. For example, the adjustment unit can analyze the network congestion status of a specific area and dynamically reallocate bandwidth. The adjustment unit can also monitor the network usage status of a specific area and optimize bandwidth. By reallocating bandwidth to a specific area, the adjustment unit can alleviate network congestion. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the bandwidth reallocation method determined by the generation unit to the generation AI and have the generation AI perform the bandwidth reallocation.

[0039] The adjustment unit can adjust network parameters. For example, the adjustment unit can adjust network parameters in real time. The adjustment unit can also automatically adjust network parameters using AI. For example, the adjustment unit can dynamically adjust network parameters such as data transfer rate, signal strength, and delay. The adjustment unit can also optimize network parameters to improve overall network quality. By adjusting network parameters, the adjustment unit can improve overall network quality. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the method for adjusting network parameters determined by the generation unit to the generation AI, and have the generation AI perform the adjustment of network parameters.

[0040] The data collection unit can analyze a user's past posting history and select the optimal collection method when collecting feedback. For example, the data collection unit can analyze the time periods when a user frequently posted in the past and collect feedback during those times. The data collection unit can also prioritize suggesting feedback formats (text, images, videos, etc.) that the user has used in the past. The data collection unit can also analyze the content of a user's past posts and automatically generate relevant questions to collect feedback. This allows the optimal collection method to be selected by analyzing the user's past posting history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past posting history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter feedback based on the user's current areas of interest and activity. For example, the data collection unit can prioritize collecting feedback related to topics the user is currently interested in. The data collection unit can also suggest appropriate feedback formats based on the user's current activity (e.g., traveling, taking a break). The data collection unit can also analyze the user's social media activity and collect relevant feedback. This allows for the collection of highly relevant feedback by filtering based on the user's current areas of interest and activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest and activity into a generating AI and have the generating AI perform the feedback filtering.

[0042] The data collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location when collecting feedback. For example, if the user is in a specific region, the data collection unit will prioritize collecting feedback related to that region. If the user is on the move, the data collection unit can also collect feedback related to the destination region. If the user is participating in a specific event, the data collection unit can also prioritize collecting feedback related to that event. In this way, by considering the user's geographical location, highly relevant feedback can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the feedback.

[0043] The collection unit can analyze a user's social media activity and collect relevant feedback when gathering feedback. For example, if a user uses a specific hashtag, the collection unit can collect feedback related to that hashtag. If a user participates in a specific group or community, the collection unit can also collect feedback related to that group or community. If a user frequently posts about a specific topic, the collection unit can also collect feedback related to that topic. In this way, relevant feedback can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant feedback.

[0044] The analysis unit can predict current problems by referring to past network quality data during analysis. For example, the analysis unit can predict problems that may occur during a specific time period based on past data. The analysis unit can also predict problems that may occur in a specific region based on past data. The analysis unit can also predict problems related to specific network parameters based on past data. This makes it easier to predict current problems by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past network quality data into a generating AI and have the generating AI perform a prediction of current problems.

[0045] The analysis unit can apply different analysis methods to each category of network parameters during the analysis. For example, the analysis unit can apply a specific analysis method to identify problems related to data transfer speed. The analysis unit can also apply another analysis method to identify problems related to signal strength. The analysis unit can also apply yet another analysis method to identify problems related to hardware settings. This allows for a more detailed analysis by applying different analysis methods to each category of network parameters. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis methods for each category of network parameters into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can analyze changes in network quality based on the timing of feedback submissions during the analysis process. For example, the analysis unit can analyze problems that occur during specific time periods based on the timing of feedback submissions. The analysis unit can also analyze problems that occur on specific days of the week based on the timing of feedback submissions. The analysis unit can also analyze problems that occur during specific seasons based on the timing of feedback submissions. This makes it easier to identify problems by time of day or season by analyzing changes in network quality based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of feedback submissions into a generating AI and have the generating AI perform an analysis of changes in network quality.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant network literature during the analysis process. For example, the analysis unit can refer to relevant network literature and apply the latest analytical methods. The analysis unit can also refer to relevant network literature and perform analysis based on past cases. The analysis unit can also refer to relevant network literature and perform analysis based on other research results. In this way, the accuracy of the analysis can be improved by referring to relevant network literature. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant network literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0048] The generation unit can improve the accuracy of tuning by considering the interrelationships of the network during generation. For example, the generation unit can consider the interrelationships of the network and select the optimal tuning method. The generation unit can also consider the interrelationships of the network and perform tuning for a specific area. The generation unit can also consider the interrelationships of the network and improve the overall network quality. As a result, the accuracy of tuning can be improved by considering the interrelationships of the network. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the interrelationships of the network into a generation AI and have the generation AI perform the tuning accuracy improvement.

[0049] The generation unit can apply different tuning algorithms based on network usage during generation. For example, the generation unit can select the optimal tuning algorithm based on network usage. The generation unit can also apply a tuning algorithm suitable for a specific time period based on network usage. The generation unit can also apply a tuning algorithm suitable for a specific region based on network usage. By applying the optimal tuning algorithm based on network usage, network quality can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input network usage into a generation AI and have the generation AI execute the application of a tuning algorithm.

[0050] The generation unit can perform tuning while considering the geographical distribution of the network during generation. For example, the generation unit can perform tuning for a specific region based on the geographical distribution of the network. The generation unit can also improve the overall network quality based on the geographical distribution of the network. The generation unit can also prioritize tuning for a specific area based on the geographical distribution of the network. This allows for optimal tuning for a specific region by considering the geographical distribution of the network. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the geographical distribution of the network into a generation AI and have the generation AI perform the tuning.

[0051] The generation unit can improve the accuracy of tuning by referring to relevant network literature during generation. For example, the generation unit can refer to relevant network literature and apply the latest tuning techniques. The generation unit can also refer to relevant network literature and perform tuning based on past cases. The generation unit can also refer to relevant network literature and perform tuning based on other research results. In this way, the accuracy of tuning can be improved by referring to relevant network literature. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevant network literature into a generation AI and have the generation AI perform the tuning accuracy improvement.

[0052] The adjustment unit can select the optimal adjustment method by referring to past network adjustment data during adjustment. For example, the adjustment unit selects the optimal adjustment method based on past network adjustment data. The adjustment unit can also select an adjustment method for a specific area based on past network adjustment data. The adjustment unit can also select an adjustment method suitable for a specific time period based on past network adjustment data. In this way, the optimal adjustment method can be selected by referring to past network adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past network adjustment data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0053] The adjustment unit can customize the adjustment methods based on the current network usage during adjustment. For example, the adjustment unit can select the optimal adjustment method based on the current network usage. The adjustment unit can also customize the adjustment method for a specific area based on the current network usage. The adjustment unit can also customize the adjustment method suitable for a specific time period based on the current network usage. By customizing the adjustment method based on the current network usage, more effective network adjustment can be achieved. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the current network usage into a generating AI and have the generating AI perform the customization of the adjustment method.

[0054] The adjustment unit can select the optimal adjustment method during adjustment by considering the network's geographical location information. For example, the adjustment unit can select an adjustment method for a specific region based on the network's geographical location information. The adjustment unit can also improve the overall network quality based on the network's geographical location information. The adjustment unit can also prioritize adjustments for a specific area based on the network's geographical location information. This allows the adjustment unit to select the optimal adjustment method for a specific region by considering the network's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the network's geographical location information into a generating AI and have the generating AI perform the selection of an adjustment method.

[0055] The adjustment unit can analyze the network's social media activity during adjustment and propose adjustment methods. For example, the adjustment unit can analyze the network's social media activity and propose the optimal adjustment method. The adjustment unit can also analyze the network's social media activity and propose adjustment methods for a specific area. The adjustment unit can also analyze the network's social media activity and propose adjustment methods suitable for a specific time period. In this way, by analyzing the network's social media activity, the optimal adjustment method can be proposed. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the network's social media activity into a generating AI and have the generating AI execute the proposal of adjustment methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The network quality improvement system can further collect user device information and use it to improve network quality. For example, the collection unit can collect the user's device type, OS version, and connection method (Wi-Fi, mobile data, etc.). The analysis unit can identify network problems related to specific devices or connection methods based on this device information. The generation unit can determine the optimal network tuning for specific devices or connection methods based on the device information. The adjustment unit can perform network adjustments for specific devices or connection methods based on the judgment of the generation unit. This makes it possible to improve network quality with greater accuracy by utilizing user device information.

[0058] The network quality improvement system can further analyze user behavior patterns and use this information to improve network quality. For example, the data collection unit can collect user movement history and app usage history. The analysis unit can identify network problems at specific times and locations based on these behavior patterns. The generation unit can determine the optimal network tuning for specific times and locations based on these behavior patterns. The adjustment unit can perform network adjustments for specific times and locations based on the judgment of the generation unit. This makes it possible to improve network quality more effectively by utilizing user behavior patterns.

[0059] The network quality improvement system can not only improve network quality but also enhance the user experience based on user feedback. For example, the data collection unit can collect not only complaints about the network but also complaints about apps and services from user feedback. The analysis unit can identify problems not only with the network but also with apps and services based on this feedback. The generation unit can determine how to improve not only the network but also with apps and services based on the feedback. The adjustment unit can then implement improvements to not only the network but also with apps and services based on the judgment of the generation unit. In this way, by comprehensively utilizing user feedback, it becomes possible to improve the overall user experience.

[0060] The network quality improvement system can further enhance not only network quality but also user safety based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about safety from user feedback. The analysis unit can identify safety issues based on this feedback. The generation unit can determine how to improve safety based on the feedback. The adjustment unit can implement safety improvements based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve both network quality and user safety.

[0061] The network quality improvement system can improve not only network quality but also energy efficiency based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about energy consumption from user feedback. The analysis unit can identify problems related to energy consumption based on this feedback. The generation unit can determine how to improve energy efficiency based on the feedback. The adjustment unit can improve energy efficiency based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve both network quality and energy efficiency.

[0062] The network quality improvement system can not only improve network quality but also reduce costs based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about costs from user feedback. The analysis unit can identify cost-related problems based on this feedback. The generation unit can determine methods for cost reduction based on the feedback. The adjustment unit can implement cost reductions based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve network quality and reduce costs at the same time.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects user feedback from social media platforms. For example, it can collect negative comments and include location information where the comments were posted. The collection unit automatically collects comments from social media platforms and adds location information. Furthermore, it can automatically filter negative comments using AI. Step 2: The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. For example, it can analyze network parameters such as data transfer rate, signal strength, and hardware settings, and can also perform automatic analysis using AI. It can analyze fluctuations in data transfer rate to identify problem areas in the network. It can also detect drops in signal strength to identify troubled areas. It can also analyze hardware settings to identify the cause of problems. Step 3: The generation unit determines network tuning based on the analysis results obtained by the analysis unit. Using the generation AI, it determines network tuning in real time, for example, whether to adjust the signal strength of base stations or reallocate bandwidth to specific areas. It can also determine whether to adjust network parameters and select the optimal tuning method. Step 4: The adjustment unit adjusts the network based on the tuning determined by the generation unit. For example, it can adjust the signal strength of base stations and reallocate bandwidth to specific areas. It can also adjust network parameters and automatically adjust the network using AI. It can adjust the signal strength of base stations in real time and dynamically reallocate bandwidth to specific areas. It can also optimize network parameters.

[0065] (Example of form 2) An embodiment of the present invention provides a network quality improvement system that automatically collects user feedback from an SNS platform and aims to improve network quality. The network quality improvement system automatically collects user feedback from the SNS platform. For example, it collects negative comments such as "the network is congested." In this case, the collected comments also include location information where the posts were made. Next, the network quality improvement system analyzes network quality data at the relevant location based on the collected negative comments and their location information. For example, it incorporates detailed network parameters such as data transfer speed, signal strength, and hardware settings to accurately identify areas and times where problems occur. The network quality improvement system inputs this collected information into a generating AI, which then determines network tuning in real time. Specifically, it adjusts the signal strength of base stations, reallocates bandwidth to specific areas, or adjusts network parameters. As a result, the network is automatically tuned, and the user experience at the location and time of the problem is significantly improved. In this way, the use of the generating AI immediately improves the user experience and achieves overall improvement in network quality. For example, the network quality improvement system automatically collects user feedback from an SNS platform. For example, it collects negative comments such as "the network is congested." In this process, the collected comments also include location information where they were posted. Next, the network quality improvement system analyzes network quality data at the relevant location based on the collected negative comments and their location information. For example, it incorporates detailed network parameters such as data transfer speed, signal strength, and hardware settings to accurately identify areas and times of trouble. The network quality improvement system inputs this collected information into a generating AI, which then determines network tuning in real time. Specifically, this might involve adjusting the signal strength of base stations, reallocating bandwidth to specific areas, or adjusting network parameters.As a result, the network is automatically tuned, significantly improving the user experience in the locations and times where problems occurred. Thus, the use of generative AI enables immediate improvements to the user experience and overall network quality. This allows the network quality improvement system to automatically improve network quality based on user feedback.

[0066] The network quality improvement system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects user feedback from an SNS platform. The collection unit collects, for example, negative comments. The collection unit can include location information where the comments were posted in the collected comments. The collection unit can automatically collect comments from an SNS platform and add location information. The collection unit can also automatically filter negative comments using AI. The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. The analysis unit analyzes network parameters such as data transfer rate, signal strength, and hardware settings. The analysis unit can also automatically analyze network parameters using AI. The analysis unit can, for example, analyze fluctuations in data transfer rate to identify problem areas in the network. The analysis unit can also detect a decrease in signal strength and identify troubled areas. The analysis unit can also analyze hardware settings to identify the cause of the problem. The generation unit determines network tuning based on the analysis results obtained by the analysis unit. The generation unit determines network tuning in real time using generation AI. The generation unit may, for example, decide to adjust the signal strength of a base station. The generation unit may also decide to reallocate bandwidth to a specific area. The generation unit may also decide to adjust network parameters. The generation unit may also select the optimal tuning method using generation AI. The adjustment unit adjusts the network based on the tuning determined by the generation unit. The adjustment unit may, for example, adjust the signal strength of a base station. The adjustment unit may also reallocate bandwidth to a specific area. The adjustment unit may also adjust network parameters. The adjustment unit may also automatically adjust the network using AI. The adjustment unit may, for example, adjust the signal strength of a base station in real time. The adjustment unit may also dynamically reallocate bandwidth to a specific area. The adjustment unit may also optimize network parameters.As a result, the network quality improvement system according to this embodiment can automatically improve network quality based on feedback from the SNS platform.

[0067] The data collection unit gathers user feedback from social networking services (SNS) platforms. Specifically, the unit utilizes APIs to automatically collect comments and reviews posted on SNS platforms. The unit focuses particularly on collecting negative comments, using AI to analyze the sentiment of comments and automatically filter out negative feedback. For example, it uses natural language processing techniques to analyze the content of comments and detect keywords and phrases that indicate negative sentiment. Furthermore, the unit can add location information to the collected comments. This makes it easier to identify network quality problems in specific regions or areas. The unit implements scripts and programs to automatically collect comments from SNS platforms and add location information. The unit centrally manages this data and stores it in a database. The database stores collected comments, location information, sentiment analysis results, etc., making it accessible to subsequent analysis and generation units. This allows the unit to efficiently collect feedback from SNS platforms and use it to improve network quality.

[0068] The analysis department analyzes network quality data based on feedback and location information collected by the data collection department. Specifically, the analysis department analyzes network parameters such as data transfer rate, signal strength, and hardware settings in detail. The analysis department uses AI to automatically analyze these network parameters and identify problem areas. For example, it can analyze fluctuations in data transfer rate to detect speed reductions in specific time periods or regions. It can also detect signal strength drops and identify problematic areas. Furthermore, it can analyze hardware settings to identify the cause of problems. The AI ​​uses machine learning algorithms to detect anomalies by comparing them with historical data. For example, it uses anomaly detection algorithms to identify data that deviates from normal patterns, enabling early problem detection. Based on these analysis results, the analysis department identifies the problem areas and causes of network issues and proposes improvement measures. In addition, the analysis department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, based on historical data, it can predict fluctuations in network quality in specific regions or time periods and formulate future countermeasures. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, contributing to improved network quality.

[0069] The generation unit determines network tuning based on the analysis results obtained by the analysis unit. Specifically, the generation unit uses a generation AI to determine network tuning in real time. The generation AI uses a deep learning model to select the optimal tuning method. For example, it may decide to adjust the signal strength of base stations. Based on the collected data and analysis results, the generation AI can also decide to reallocate bandwidth to a specific area. Furthermore, the generation AI can also decide to adjust network parameters. Based on these decisions, the generation unit generates specific instructions for network optimization. For example, it generates detailed instructions such as how much to adjust the signal strength of base stations and how to reallocate bandwidth to a specific area. The generation unit transmits these instructions to the adjustment unit, providing a foundation for actual network adjustment. The generation unit utilizes historical data and simulation results to select the optimal tuning method using the generation AI. For example, it selects the most effective adjustment method based on the results of past network adjustments, contributing to future improvements in network quality. This enables the generation unit to efficiently perform real-time network tuning and achieve improvements in network quality.

[0070] The adjustment unit adjusts the network based on the tuning determined by the generation unit. Specifically, the adjustment unit adjusts the signal strength of base stations. The adjustment unit can also reallocate bandwidth to specific areas. The adjustment unit can also adjust network parameters. The adjustment unit can also automatically adjust the network using AI. For example, to adjust the signal strength of base stations in real time, the AI ​​calculates the optimal setting and applies it immediately. To dynamically reallocate bandwidth to specific areas, the AI ​​monitors network usage and adjusts the bandwidth as needed. To optimize network parameters, the adjustment unit uses AI to select the optimal settings based on historical and real-time data. This allows the adjustment unit to always maintain optimal network quality. Furthermore, the adjustment unit can monitor the adjustment results and readjust as needed. For example, it can monitor the network quality after adjustment and readjust if problems are not resolved or new problems arise. This allows the adjustment unit to continuously maintain and improve network quality. Based on instructions from the generation unit, the adjustment unit can adjust the network quickly and accurately, improving user satisfaction.

[0071] The collection unit can collect negative comments. For example, the collection unit can automatically collect negative comments from social networking platforms. The collection unit can also filter negative comments using AI. For example, the collection unit can identify negative comments using sentiment analysis algorithms. The collection unit can also extract negative comments using keyword matching. By collecting negative comments, the collection unit makes it easier to identify problem areas in the network. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input comments collected from social networking platforms into a generating AI and have the generating AI identify negative comments.

[0072] The analysis unit can analyze network parameters such as data transfer rate, signal strength, and hardware settings. For example, the analysis unit can analyze fluctuations in data transfer rate to identify network problems. The analysis unit can also detect drops in signal strength and identify problematic areas. The analysis unit can also analyze hardware settings to identify the cause of problems. The analysis unit can also use AI to automatically analyze network parameters. For example, the analysis unit can analyze fluctuations in data transfer rate in real time. The analysis unit can also instantly detect drops in signal strength. The analysis unit can also dynamically analyze hardware settings. This allows for an accurate understanding of network problems by analyzing detailed network parameters. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input network parameters collected by the collection unit into a generating AI and have the generating AI identify network problems.

[0073] The generation unit can determine network tuning in real time. For example, the generation unit can use a generation AI to determine network tuning in real time. The generation unit can determine whether to adjust the signal strength of base stations. The generation unit can also determine whether to reallocate bandwidth to a specific area. The generation unit can also determine whether to adjust network parameters. The generation unit can also use a generation AI to select the optimal tuning method. For example, the generation unit can use a generation AI to monitor the network status in real time and determine the optimal tuning method. The generation unit can also use a generation AI to analyze network performance data and propose the optimal tuning method. The generation unit can also use a generation AI to predict the future network status based on past data and determine the tuning method. This allows for immediate improvement of network quality by determining network tuning in real time. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input network parameters obtained by the analysis unit into the generation AI and have the generation AI perform the determination of the network tuning method.

[0074] The adjustment unit can adjust the signal strength of the base station. For example, the adjustment unit can adjust the signal strength of the base station in real time. The adjustment unit can also automatically adjust the signal strength of the base station using AI. For example, the adjustment unit can dynamically adjust the signal strength of the base station to improve network quality in a specific area. The adjustment unit can also optimize the signal strength of the base station to improve network performance. By adjusting the signal strength of the base station, the adjustment unit can improve network quality in a specific area. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the signal strength adjustment method determined by the generation unit to the generation AI and have the generation AI perform the signal strength adjustment.

[0075] The adjustment unit can reallocate bandwidth to a specific area. For example, the adjustment unit can reallocate bandwidth to a specific area in real time. The adjustment unit can also automatically reallocate bandwidth to a specific area using AI. For example, the adjustment unit can analyze the network congestion status of a specific area and dynamically reallocate bandwidth. The adjustment unit can also monitor the network usage status of a specific area and optimize bandwidth. By reallocating bandwidth to a specific area, the adjustment unit can alleviate network congestion. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the bandwidth reallocation method determined by the generation unit to the generation AI and have the generation AI perform the bandwidth reallocation.

[0076] The adjustment unit can adjust network parameters. For example, the adjustment unit can adjust network parameters in real time. The adjustment unit can also automatically adjust network parameters using AI. For example, the adjustment unit can dynamically adjust network parameters such as data transfer rate, signal strength, and delay. The adjustment unit can also optimize network parameters to improve overall network quality. By adjusting network parameters, the adjustment unit can improve overall network quality. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the method for adjusting network parameters determined by the generation unit to the generation AI, and have the generation AI perform the adjustment of network parameters.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect feedback when the user is relaxed. If the user is relaxed, the data collection unit can also collect feedback immediately to grasp the network status in real time. If the user is in a hurry, the data collection unit can also collect feedback quickly to rapidly identify network problems. This allows for the collection of more appropriate feedback by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of feedback collection.

[0078] The data collection unit can analyze a user's past posting history and select the optimal collection method when collecting feedback. For example, the data collection unit can analyze the time periods when a user frequently posted in the past and collect feedback during those times. The data collection unit can also prioritize suggesting feedback formats (text, images, videos, etc.) that the user has used in the past. The data collection unit can also analyze the content of a user's past posts and automatically generate relevant questions to collect feedback. This allows the optimal collection method to be selected by analyzing the user's past posting history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past posting history into a generating AI and have the generating AI select the optimal collection method.

[0079] The data collection unit can filter feedback based on the user's current areas of interest and activity. For example, the data collection unit can prioritize collecting feedback related to topics the user is currently interested in. The data collection unit can also suggest appropriate feedback formats based on the user's current activity (e.g., traveling, taking a break). The data collection unit can also analyze the user's social media activity and collect relevant feedback. This allows for the collection of highly relevant feedback by filtering based on the user's current areas of interest and activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current areas of interest and activity into a generating AI and have the generating AI perform the feedback filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of feedback to collect based on the estimated emotions. For example, if the user is dissatisfied, the data collection unit will prioritize collecting that feedback and respond quickly. If the user is satisfied, the data collection unit can also postpone that feedback and prioritize other important feedback. If the user has neutral emotions, the data collection unit can treat that feedback equally with other feedback. This allows for the priority collection of important feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0081] The data collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location when collecting feedback. For example, if the user is in a specific region, the data collection unit will prioritize collecting feedback related to that region. If the user is on the move, the data collection unit can also collect feedback related to the destination region. If the user is participating in a specific event, the data collection unit can also prioritize collecting feedback related to that event. In this way, by considering the user's geographical location, highly relevant feedback can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the feedback.

[0082] The collection unit can analyze a user's social media activity and collect relevant feedback when gathering feedback. For example, if a user uses a specific hashtag, the collection unit can collect feedback related to that hashtag. If a user participates in a specific group or community, the collection unit can also collect feedback related to that group or community. If a user frequently posts about a specific topic, the collection unit can also collect feedback related to that topic. In this way, relevant feedback can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis method of network quality data based on the estimated user emotions. For example, if the user is dissatisfied, the analysis unit can perform a detailed analysis to identify the root cause of the problem. If the user is satisfied, the analysis unit can also perform a simplified analysis to grasp the overall trend. If the user has neutral emotions, the analysis unit can also perform a standard analysis to identify common problems. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0084] The analysis unit can predict current problems by referring to past network quality data during analysis. For example, the analysis unit can predict problems that may occur during a specific time period based on past data. The analysis unit can also predict problems that may occur in a specific region based on past data. The analysis unit can also predict problems related to specific network parameters based on past data. This makes it easier to predict current problems by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past network quality data into a generating AI and have the generating AI perform a prediction of current problems.

[0085] The analysis unit can apply different analysis methods to each category of network parameters during the analysis. For example, the analysis unit can apply a specific analysis method to identify problems related to data transfer speed. The analysis unit can also apply another analysis method to identify problems related to signal strength. The analysis unit can also apply yet another analysis method to identify problems related to hardware settings. This allows for a more detailed analysis by applying different analysis methods to each category of network parameters. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis methods for each category of network parameters into a generating AI and have the generating AI perform the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is dissatisfied, the analysis unit can display detailed analysis results and explain the root cause of the problem. If the user is satisfied, the analysis unit can also display simplified analysis results and show the overall trend. If the user has neutral emotions, the analysis unit can also display standard analysis results and show general issues. This allows for a more easily understandable display by adjusting how the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0087] The analysis unit can analyze changes in network quality based on the timing of feedback submissions during the analysis process. For example, the analysis unit can analyze problems that occur during specific time periods based on the timing of feedback submissions. The analysis unit can also analyze problems that occur on specific days of the week based on the timing of feedback submissions. The analysis unit can also analyze problems that occur during specific seasons based on the timing of feedback submissions. This makes it easier to identify problems by time of day or season by analyzing changes in network quality based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of feedback submissions into a generating AI and have the generating AI perform an analysis of changes in network quality.

[0088] The analysis unit can improve the accuracy of its analysis by referring to relevant network literature during the analysis process. For example, the analysis unit can refer to relevant network literature and apply the latest analytical methods. The analysis unit can also refer to relevant network literature and perform analysis based on past cases. The analysis unit can also refer to relevant network literature and perform analysis based on other research results. In this way, the accuracy of the analysis can be improved by referring to relevant network literature. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant network literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0089] The generation unit can estimate the user's emotions and adjust the network tuning criteria based on the estimated emotions. For example, if the user is dissatisfied, the generation unit can perform rapid network tuning. If the user is satisfied, the generation unit can also perform normal network tuning. If the user has neutral emotions, the generation unit can also perform standard network tuning. This allows for more appropriate tuning by adjusting the network tuning criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the network tuning criteria.

[0090] The generation unit can improve the accuracy of tuning by considering the interrelationships of the network during generation. For example, the generation unit can consider the interrelationships of the network and select the optimal tuning method. The generation unit can also consider the interrelationships of the network and perform tuning for a specific area. The generation unit can also consider the interrelationships of the network and improve the overall network quality. As a result, the accuracy of tuning can be improved by considering the interrelationships of the network. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the interrelationships of the network into a generation AI and have the generation AI perform the tuning accuracy improvement.

[0091] The generation unit can apply different tuning algorithms based on network usage during generation. For example, the generation unit can select the optimal tuning algorithm based on network usage. The generation unit can also apply a tuning algorithm suitable for a specific time period based on network usage. The generation unit can also apply a tuning algorithm suitable for a specific region based on network usage. By applying the optimal tuning algorithm based on network usage, network quality can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input network usage into a generation AI and have the generation AI execute the application of a tuning algorithm.

[0092] The generation unit can estimate the user's emotions and determine tuning priorities based on the estimated emotions. For example, if the user is dissatisfied, the generation unit will prioritize tuning that area. If the user is satisfied, the generation unit can also prioritize tuning other areas. If the user has neutral emotions, the generation unit can perform overall tuning. This allows for prioritizing tuning of important areas by determining tuning priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the tuning priorities.

[0093] The generation unit can perform tuning while considering the geographical distribution of the network during generation. For example, the generation unit can perform tuning for a specific region based on the geographical distribution of the network. The generation unit can also improve the overall network quality based on the geographical distribution of the network. The generation unit can also prioritize tuning for a specific area based on the geographical distribution of the network. This allows for optimal tuning for a specific region by considering the geographical distribution of the network. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the geographical distribution of the network into a generation AI and have the generation AI perform the tuning.

[0094] The generation unit can improve the accuracy of tuning by referring to relevant network literature during generation. For example, the generation unit can refer to relevant network literature and apply the latest tuning techniques. The generation unit can also refer to relevant network literature and perform tuning based on past cases. The generation unit can also refer to relevant network literature and perform tuning based on other research results. In this way, the accuracy of tuning can be improved by referring to relevant network literature. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevant network literature into a generation AI and have the generation AI perform the tuning accuracy improvement.

[0095] The adjustment unit can estimate the user's emotions and adjust the network adjustment method based on the estimated user emotions. For example, if the user is dissatisfied, the adjustment unit can quickly adjust the network. If the user is satisfied, the adjustment unit can also perform normal network adjustment. If the user has neutral emotions, the adjustment unit can also perform standard network adjustment. This allows for more appropriate network adjustment by adjusting the network adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the network adjustment method.

[0096] The adjustment unit can select the optimal adjustment method by referring to past network adjustment data during adjustment. For example, the adjustment unit selects the optimal adjustment method based on past network adjustment data. The adjustment unit can also select an adjustment method for a specific area based on past network adjustment data. The adjustment unit can also select an adjustment method suitable for a specific time period based on past network adjustment data. In this way, the optimal adjustment method can be selected by referring to past network adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past network adjustment data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0097] The adjustment unit can customize the adjustment methods based on the current network usage during adjustment. For example, the adjustment unit can select the optimal adjustment method based on the current network usage. The adjustment unit can also customize the adjustment method for a specific area based on the current network usage. The adjustment unit can also customize the adjustment method suitable for a specific time period based on the current network usage. By customizing the adjustment method based on the current network usage, more effective network adjustment can be achieved. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the current network usage into a generating AI and have the generating AI perform the customization of the adjustment method.

[0098] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is dissatisfied, the adjustment unit will prioritize adjusting that area. If the user is satisfied, the adjustment unit may also prioritize adjusting other areas. If the user has neutral emotions, the adjustment unit may perform overall adjustments. This allows for prioritizing adjustments to important areas by determining the priority of adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0099] The adjustment unit can select the optimal adjustment method during adjustment by considering the network's geographical location information. For example, the adjustment unit can select an adjustment method for a specific region based on the network's geographical location information. The adjustment unit can also improve the overall network quality based on the network's geographical location information. The adjustment unit can also prioritize adjustments for a specific area based on the network's geographical location information. This allows the adjustment unit to select the optimal adjustment method for a specific region by considering the network's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the network's geographical location information into a generating AI and have the generating AI perform the selection of an adjustment method.

[0100] The adjustment unit can analyze the network's social media activity during adjustment and propose adjustment methods. For example, the adjustment unit can analyze the network's social media activity and propose the optimal adjustment method. The adjustment unit can also analyze the network's social media activity and propose adjustment methods for a specific area. The adjustment unit can also analyze the network's social media activity and propose adjustment methods suitable for a specific time period. In this way, by analyzing the network's social media activity, the optimal adjustment method can be proposed. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the network's social media activity into a generating AI and have the generating AI execute the proposal of adjustment methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The network quality improvement system can further collect user device information and use it to improve network quality. For example, the collection unit can collect the user's device type, OS version, and connection method (Wi-Fi, mobile data, etc.). The analysis unit can identify network problems related to specific devices or connection methods based on this device information. The generation unit can determine the optimal network tuning for specific devices or connection methods based on the device information. The adjustment unit can perform network adjustments for specific devices or connection methods based on the judgment of the generation unit. This makes it possible to improve network quality with greater accuracy by utilizing user device information.

[0103] The network quality improvement system can further analyze user behavior patterns and use this information to improve network quality. For example, the data collection unit can collect user movement history and app usage history. The analysis unit can identify network problems at specific times and locations based on these behavior patterns. The generation unit can determine the optimal network tuning for specific times and locations based on these behavior patterns. The adjustment unit can perform network adjustments for specific times and locations based on the judgment of the generation unit. This makes it possible to improve network quality more effectively by utilizing user behavior patterns.

[0104] The network quality improvement system can further estimate user emotions and improve network quality based on those estimated emotions. For example, the data collection unit can estimate emotions from users' social media posts and message content. The analysis unit can identify the locations and times when users are feeling dissatisfied based on the estimated emotions. The generation unit can determine the optimal network tuning for the locations and times when users are feeling dissatisfied, based on the emotions. The adjustment unit can adjust the network for the locations and times when users are feeling dissatisfied, based on the judgment of the generation unit. This makes it possible to improve network quality more quickly and accurately by utilizing user emotions.

[0105] The network quality improvement system can not only improve network quality but also enhance the user experience based on user feedback. For example, the data collection unit can collect not only complaints about the network but also complaints about apps and services from user feedback. The analysis unit can identify problems not only with the network but also with apps and services based on this feedback. The generation unit can determine how to improve not only the network but also with apps and services based on the feedback. The adjustment unit can then implement improvements to not only the network but also with apps and services based on the judgment of the generation unit. In this way, by comprehensively utilizing user feedback, it becomes possible to improve the overall user experience.

[0106] The network quality improvement system can further estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, the collection unit can refrain from collecting feedback if the user is feeling stressed. The analysis unit can collect detailed feedback if the user is relaxed. The generation unit can determine the optimal feedback collection method based on the user's emotions. The adjustment unit can collect feedback in accordance with the user's emotions based on the generation unit's judgment. This makes it possible to collect feedback at a more appropriate time by taking the user's emotions into consideration.

[0107] The network quality improvement system can further enhance not only network quality but also user safety based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about safety from user feedback. The analysis unit can identify safety issues based on this feedback. The generation unit can determine how to improve safety based on the feedback. The adjustment unit can implement safety improvements based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve both network quality and user safety.

[0108] The network quality improvement system can further estimate user emotions and troubleshoot network issues based on those emotions. For example, the data collection unit can estimate emotions from users' social media posts and messages. The analysis unit can identify the locations and times when users are experiencing dissatisfaction based on the estimated emotions. The generation unit can determine the most suitable troubleshooting method for the locations and times when users are experiencing dissatisfaction based on those emotions. The adjustment unit can then troubleshoot the locations and times when users are experiencing dissatisfaction based on the generation unit's judgment. This allows for faster and more accurate troubleshooting by utilizing user emotions.

[0109] The network quality improvement system can improve not only network quality but also energy efficiency based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about energy consumption from user feedback. The analysis unit can identify problems related to energy consumption based on this feedback. The generation unit can determine how to improve energy efficiency based on the feedback. The adjustment unit can improve energy efficiency based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve both network quality and energy efficiency.

[0110] The network quality improvement system can further estimate user emotions and adjust network maintenance schedules based on those estimated emotions. For example, the data collection unit can estimate emotions from users' social media posts and message content. The analysis unit can identify locations and times when users are feeling dissatisfied based on the estimated emotions. The generation unit can determine the optimal maintenance schedule for those locations and times when users are feeling dissatisfied, based on the emotions. The adjustment unit can perform maintenance for those locations and times when users are feeling dissatisfied, based on the generation unit's determination. This allows for more appropriate timing of maintenance by utilizing user emotions.

[0111] The network quality improvement system can not only improve network quality but also reduce costs based on user feedback. For example, the data collection unit can collect not only complaints about the network but also concerns about costs from user feedback. The analysis unit can identify cost-related problems based on this feedback. The generation unit can determine methods for cost reduction based on the feedback. The adjustment unit can implement cost reductions based on the generation unit's determination. In this way, by comprehensively utilizing user feedback, it becomes possible to improve network quality and reduce costs at the same time.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects user feedback from social media platforms. For example, it can collect negative comments and include location information where the comments were posted. The collection unit automatically collects comments from social media platforms and adds location information. Furthermore, it can automatically filter negative comments using AI. Step 2: The analysis unit analyzes network quality data based on the feedback and location information collected by the collection unit. For example, it can analyze network parameters such as data transfer rate, signal strength, and hardware settings, and can also perform automatic analysis using AI. It can analyze fluctuations in data transfer rate to identify problem areas in the network. It can also detect drops in signal strength to identify troubled areas. It can also analyze hardware settings to identify the cause of problems. Step 3: The generation unit determines network tuning based on the analysis results obtained by the analysis unit. Using the generation AI, it determines network tuning in real time, for example, whether to adjust the signal strength of base stations or reallocate bandwidth to specific areas. It can also determine whether to adjust network parameters and select the optimal tuning method. Step 4: The adjustment unit adjusts the network based on the tuning determined by the generation unit. For example, it can adjust the signal strength of base stations and reallocate bandwidth to specific areas. It can also adjust network parameters and automatically adjust the network using AI. It can adjust the signal strength of base stations in real time and dynamically reallocate bandwidth to specific areas. It can also optimize network parameters.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and automatically collects user feedback from the SNS platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes network quality data based on the collected feedback and location information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and determines network tuning based on the analysis results. The adjustment unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and adjusts the network based on the determined tuning. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and automatically collects user feedback from an SNS platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes network quality data based on the collected feedback and location information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and determines network tuning based on the analysis results. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and adjusts the network based on the determined tuning. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and automatically collects user feedback from the SNS platform. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes network quality data based on the collected feedback and location information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and determines network tuning based on the analysis results. The adjustment unit is implemented by, for example, the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and adjusts the network based on the determined tuning. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and automatically collects user feedback from the SNS platform. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes network quality data based on the collected feedback and location information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and determines network tuning based on the analysis results. The adjustment unit is implemented, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and adjusts the network based on the determined tuning. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that collects user feedback from SNS platforms, An analysis unit analyzes network quality data based on the feedback and location information collected by the aforementioned collection unit, A generation unit that determines network tuning based on the analysis results obtained by the analysis unit, The system includes an adjustment unit that adjusts the network based on the tuning determined by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect negative comments The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze network parameters such as data transfer speed, signal strength, and hardware settings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Real-time network tuning decisions The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Adjusting the signal strength of the base station The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Reallocate bandwidth to a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 7) The adjustment unit is, Adjust network parameters The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting feedback, we analyze the user's past posting history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting feedback, filter it based on the user's current areas of interest and activities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of feedback to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting feedback, we analyze users' social media activity and gather relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of network quality data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, historical network quality data is referenced to predict current problems. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical methods are applied to each category of network parameters. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, we analyze changes in network quality based on when feedback was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, we refer to relevant literature within the network to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates user sentiment and adjusts network tuning criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the accuracy of tuning is improved by considering the interrelationships of the network. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different tuning algorithms are applied based on network usage. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines tuning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, tuning is performed considering the geographical distribution of the network. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, we refer to relevant network literature to improve tuning accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It estimates the user's emotions and adjusts the network tuning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During adjustment, past network adjustment data is referenced to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, During adjustment, the adjustment method is customized based on the current network usage. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the network's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, During the adjustment process, we analyze the network's social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user feedback from SNS platforms, An analysis unit analyzes network quality data based on the feedback and location information collected by the aforementioned collection unit, A generation unit that determines network tuning based on the analysis results obtained by the analysis unit, The system includes an adjustment unit that adjusts the network based on the tuning determined by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect negative comments The system according to feature 1.

3. The aforementioned analysis unit is Analyze network parameters such as data transfer speed, signal strength, and hardware settings. The system according to feature 1.

4. The generating unit is Real-time network tuning decisions The system according to feature 1.

5. The adjustment unit is, Adjusting the signal strength of the base station The system according to feature 1.

6. The adjustment unit is, Reallocate bandwidth to a specific area. The system according to feature 1.

7. The adjustment unit is, Adjust network parameters The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting feedback, we analyze the user's past posting history to select the most suitable collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting feedback, filter it based on the user's current areas of interest and activities. The system according to feature 1.

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