system

Abstract

The system according to this embodiment aims to evaluate the performance and quality of base stations and predict future trends. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a prediction unit. The data collection unit collects data from the base station. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. The prediction unit predicts future trends based on the evaluation results obtained by the evaluation 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 the 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 conventional technology, there is a problem that the performance and quality of a base station are not sufficiently evaluated efficiently and the future trend is not predicted sufficiently.

[0005] The system according to the embodiment aims to evaluate the performance and quality of a base station and predict the future trend.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a prediction unit. The data collection unit collects data from the base station. The analysis unit analyzes the data collected by the data collection unit. The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. The prediction unit predicts future trends based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the performance and quality of base stations and predict future trends. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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) The base station evaluation system according to an embodiment of the present invention is a system that evaluates the performance and quality of a base station using generative AI. The base station evaluation system collects data such as the signal strength, coverage area, and connection stability of the base station, and evaluates the performance and quality of the base station by analyzing this data with generative AI. This evaluation result can be used to improve the operation and maintenance of the base station. For example, if the signal strength of the base station decreases, the base station evaluation system can immediately detect the problem and propose appropriate countermeasures. Furthermore, the base station evaluation system can predict future trends based on past data and take appropriate countermeasures in advance. This improves the operational efficiency of the base station and improves communication quality. As a result, the base station evaluation system can efficiently evaluate the performance and quality of the base station and predict future trends.

[0029] The base station evaluation system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a prediction unit. The data collection unit collects data from the base station. The data collection unit can collect data such as the signal strength, coverage area, and connection stability of the base station. For example, the data collection unit measures the signal strength of the base station and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate the stability of the connection and record the connection duration and disconnection frequency. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. Furthermore, the analysis unit can analyze data trends and predict future increases in communication demand and base station degradation. The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. For example, the evaluation unit evaluates the base station using performance indicators and quality indicators. Furthermore, the evaluation unit can identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of the base station. In addition, if the signal strength of the base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. The prediction unit predicts future trends based on the evaluation results obtained by the evaluation unit. For example, the prediction unit predicts future increases in communication demand and base station degradation based on past data. The prediction unit can also predict future trends using time series analysis and prediction models and take appropriate countermeasures in advance. As a result, the base station evaluation system according to the embodiment can efficiently evaluate the performance and quality of base stations and predict future trends.

[0030] The data collection unit collects data from base stations. For example, it can collect data such as base station signal strength, coverage area, and connection stability. Specifically, it measures and records the base station signal strength in dBm units. Signal strength is measured by measuring the strength of radio waves transmitted from the base station's antenna, allowing for evaluation of communication quality within the base station's coverage area. The data collection unit can measure the coverage area as a geographical range and set measurement points. This allows for a detailed understanding of how far the base station's radio waves reach. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. Connection stability is evaluated by measuring the length of time a user is connected to the base station and the frequency of connection interruptions. This allows for evaluation of the base station's reliability and the quality of the user experience. The data collection unit collects this data in real time and transmits it to a central database. The database centrally manages the collected data and makes it accessible to the analysis and evaluation units. Furthermore, the data collection unit can adjust the data collection frequency and accuracy, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. Specifically, it generates statistical data on signal strength distribution, coverage area size, and connection stability, and evaluates the performance of the base station based on this data. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. For example, it can use machine learning models to identify the causes of signal strength degradation and connection instability and propose improvements. Furthermore, the analysis unit can analyze data trends and predict future increases in communication demand and base station degradation. This includes methods that use time series analysis and predictive models to predict future trends based on past data. For example, it can analyze past communication volume data to predict increases in communication demand during specific time periods or in specific areas. As a result, the analysis unit can quickly and accurately analyze the collected data, not only to evaluate the performance and quality of the base station but also to predict future communication demand and base station degradation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The evaluation unit evaluates the performance and quality of base stations based on data analyzed by the analysis unit. For example, the evaluation unit uses performance and quality indicators to evaluate base stations. Specifically, it evaluates the overall performance of base stations based on indicators such as signal strength, coverage area, and connection stability. Furthermore, the evaluation unit can identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of base stations. For example, it can identify areas with reduced signal strength and optimize the coverage area by adjusting antenna placement and output. If connection stability is low, it can propose hardware upgrades or software optimizations. In addition, the evaluation unit can immediately detect problems if the base station's signal strength decreases and propose appropriate countermeasures. This includes real-time monitoring and alert systems, enabling rapid response when problems occur. Based on these evaluation results, the evaluation unit can propose and implement specific measures to improve the operational efficiency of base stations. This allows the evaluation unit to evaluate the performance and quality of base stations with high accuracy, identify necessary improvements, and propose measures to improve operational efficiency.

[0033] The forecasting unit predicts future trends based on the evaluation results obtained by the evaluation unit. For example, the forecasting unit predicts future increases in communication demand and base station degradation based on historical data. Specifically, it can predict future trends using time series analysis and predictive models, enabling appropriate countermeasures to be taken in advance. For example, it can analyze historical communication volume data to predict increases in communication demand during specific time periods or in specific regions. This allows the forecasting unit to plan for the addition or upgrade of base stations to meet future communication demand. It can also predict base station degradation and plan maintenance or replacement in advance. Based on these prediction results, the forecasting unit can propose and implement specific countermeasures to address future communication demand and base station degradation. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if communication volume or base station performance changes rapidly, the forecasting unit immediately incorporates new data and updates the prediction results. The forecasting unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. As a result, the forecasting unit can always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0034] The data collection unit can collect data such as base station signal strength, coverage area, and connection stability. For example, the data collection unit measures the base station signal strength and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. By collecting data such as base station signal strength, coverage area, and connection stability, detailed evaluation becomes possible.

[0035] The analysis unit can analyze the collected data and evaluate the performance and quality of the base station. For example, the analysis unit can perform statistical analysis of the data to evaluate the performance and quality of the base station. Furthermore, the analysis unit can use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. In addition, the analysis unit can analyze data trends to predict future increases in communication demand and base station degradation. This allows for the evaluation of the base station's performance and quality by analyzing the collected data.

[0036] The evaluation unit can assess the performance and quality of base stations based on the analysis results and identify necessary improvements. For example, the evaluation unit evaluates base stations using performance and quality indicators. Furthermore, based on the analysis results, the evaluation unit can identify necessary improvements and propose measures to improve the operational efficiency of base stations. In addition, if the signal strength of a base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. As a result, by evaluating the performance and quality of base stations based on the analysis results and identifying necessary improvements, the operational efficiency of base stations is improved.

[0037] The forecasting unit can predict future increases in communication demand and base station degradation based on historical data. For example, it can predict future increases in communication demand based on historical traffic data and trends in the number of users. It can also predict base station degradation based on the lifespan and failure rate of the base station hardware. This allows for appropriate countermeasures to be taken in advance by predicting future increases in communication demand and base station degradation based on historical data.

[0038] The evaluation unit can immediately detect problems and propose appropriate countermeasures when the base station's signal strength decreases. For example, the evaluation unit can detect a decrease in signal strength by measuring the base station's signal strength in dBm units and setting a threshold. Furthermore, when the evaluation unit detects a decrease in signal strength, it can propose appropriate countermeasures such as antenna adjustment or hardware replacement. As a result, communication quality is improved by immediately detecting problems and proposing appropriate countermeasures when the base station's signal strength decreases.

[0039] The data collection unit can analyze the base station's past data collection history and select the optimal collection method. For example, the collection unit can select the most efficient collection method from past data collection history. Furthermore, the collection unit can optimize the collection frequency and timing based on past data collection history. In addition, the collection unit can analyze past data collection history and identify areas for improvement in the collection method. This allows for the selection of the optimal collection method and efficient data collection by analyzing past data collection history.

[0040] The data collection unit can filter data based on the current operating status and environmental conditions of the base station during data collection. For example, the data collection unit can select the type of data to collect based on the operating status of the base station. Furthermore, the data collection unit can filter the data to be collected based on environmental conditions (weather, time of day, etc.). In addition, the data collection unit can improve the accuracy of the collected data by considering the operating status and environmental conditions of the base station. Thus, by filtering the data based on the operating status and environmental conditions of the base station, the accuracy of the collected data is improved.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the base station during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of the base station. Furthermore, the data collection unit can improve the accuracy of the collected data by considering the geographical location information. In addition, the data collection unit can select the type of data to collect based on the geographical location information. As a result, by considering the geographical location information of the base station, highly relevant data can be prioritized for collection.

[0042] The data collection unit can analyze social media activity around the base station and collect relevant data during data collection. For example, the data collection unit can analyze social media activity around the base station and collect relevant data. Furthermore, the data collection unit can select the types of data to collect based on social media activity. In addition, the data collection unit can improve the accuracy of the collected data by considering social media activity. This allows for the collection of relevant data by analyzing social media activity around the base station.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a specific analysis algorithm to signal intensity data. It can also apply a different analysis algorithm to coverage area data. Furthermore, it can apply a dedicated analysis algorithm to connection stability data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved.

[0045] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit will prioritize the analysis of the most recent data. Furthermore, the analysis unit can determine the priority of analysis by referring to past data. In addition, the analysis unit can adjust the level of detail of the analysis based on the data collection period. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize analyzing highly relevant data. It can also postpone analyzing less relevant data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0047] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the analysis results during the evaluation process. For example, the evaluation unit can perform a detailed evaluation of analysis results with high importance. Conversely, the evaluation unit can perform a simplified evaluation of analysis results with low importance. Furthermore, the evaluation unit can determine the priority of the evaluation based on the importance of the analysis results. This allows for efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the analysis results.

[0048] The evaluation unit can apply different evaluation algorithms depending on the category of the analysis results during evaluation. For example, the evaluation unit can apply a specific evaluation algorithm to the analysis results of signal strength. Furthermore, the evaluation unit can apply a different evaluation algorithm to the analysis results of coverage area. In addition, the evaluation unit can apply a dedicated evaluation algorithm to the analysis results of connection stability. This improves the accuracy of the evaluation by applying different evaluation algorithms depending on the category of the analysis results.

[0049] The evaluation unit can determine the priority of evaluation based on the timing of analysis result collection during the evaluation process. For example, the evaluation unit will prioritize the evaluation of the most recent analysis results. Furthermore, the evaluation unit can determine the priority of evaluation by referring to past analysis results. In addition, the evaluation unit can adjust the level of detail of the evaluation based on the timing of analysis result collection. This allows for prioritizing the evaluation of the most recent analysis results by determining the priority of evaluation based on the timing of analysis result collection.

[0050] The evaluation unit can adjust the order of evaluation based on the relevance of the analysis results during the evaluation process. For example, the evaluation unit can prioritize evaluating highly relevant analysis results. It can also postpone evaluating less relevant analysis results. Furthermore, the evaluation unit can adjust the level of detail of the evaluation based on the relevance of the analysis results. This allows for efficient evaluation by adjusting the order of evaluation based on the relevance of the analysis results.

[0051] The prediction unit can adjust the level of detail of its predictions based on the importance of past data. For example, it can make detailed predictions based on high-importance past data, or simplified predictions based on low-importance past data. Furthermore, it can determine the priority of predictions based on the importance of past data. This allows for more efficient predictions by adjusting the level of detail based on the importance of past data.

[0052] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, it can apply a specific prediction algorithm to signal strength data. It can also apply a different prediction algorithm to coverage area data. Furthermore, it can apply a dedicated prediction algorithm to connection stability data. This improves prediction accuracy by applying different prediction algorithms depending on the data category.

[0053] The prediction unit can determine the priority of predictions based on the timing of historical data collection. For example, the prediction unit will prioritize the use of the most recent historical data in predictions. The prediction unit can also determine the priority of predictions by referring to historical data. Furthermore, the prediction unit can adjust the level of detail of predictions based on the timing of historical data collection. This allows the latest data to be used preferentially in predictions by determining the priority of predictions based on the timing of historical data collection.

[0054] The prediction unit can adjust the order of predictions based on the relevance of past data during the prediction process. For example, the prediction unit can prioritize the use of highly relevant past data in predictions. It can also postpone the use of less relevant past data. Furthermore, the prediction unit can adjust the level of detail of predictions based on the relevance of past data. This allows for more efficient predictions by adjusting the order of predictions based on the relevance of past data.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The base station evaluation system can further monitor changes in the surrounding environment of the base station in real time and adjust the timing of data collection. For example, the frequency of data collection can be adjusted according to changes in weather or increases or decreases in traffic volume. In addition, if a specific event occurs, data collection can be performed while taking its impact into consideration. Furthermore, the type of data to be collected can be selected based on changes in the surrounding environment of the base station. This enables data collection that responds to environmental changes in real time, allowing for more accurate evaluation.

[0057] The base station evaluation system further incorporates an anomaly detection algorithm in its analysis unit, enabling early detection of base station abnormalities. For example, it can detect sudden drops in signal strength or connection instability and immediately issue an alert. The anomaly detection algorithm can also identify anomalies by comparing them with past data and analyze their causes. Furthermore, if an anomaly occurs, it can predict the scope of its impact and propose appropriate countermeasures. This makes it possible to detect base station abnormalities early and respond quickly.

[0058] The base station evaluation system can further collect user feedback in its evaluation unit and incorporate it into the evaluation results. For example, it can analyze user feedback to improve the accuracy of the evaluation results. It can also adjust evaluation criteria based on user feedback to better meet user needs. Furthermore, collecting user feedback can increase the reliability of the evaluation results. This enables flexible evaluations that reflect user opinions, thereby improving the reliability of the evaluation results.

[0059] The base station evaluation system can further predict future trends by utilizing external data sources in its forecasting section. For example, it can incorporate weather data and demographic data to predict future fluctuations in communication demand. It can also utilize economic indicators and social event data to predict factors that will affect base station operations. Furthermore, by utilizing external data sources, more accurate forecasts become possible, allowing for appropriate countermeasures to be taken in advance. This enables advanced forecasting using external data, improving the operational efficiency of base stations.

[0060] The base station evaluation system can further collect energy consumption data from base stations and evaluate energy efficiency in its data collection unit. For example, it can monitor the power consumption of base stations and detect decreases in energy efficiency. It can also analyze energy consumption data and propose measures to improve energy efficiency. Furthermore, based on energy consumption data, it can predict future energy demand and implement appropriate energy management. This makes it possible to evaluate the energy efficiency of base stations and optimize energy consumption.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data from the base station. The data collection unit can collect data such as base station signal strength, coverage area, and connection stability. The data collection unit measures the base station signal strength and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. Furthermore, the analysis unit can analyze data trends to predict future increases in communication demand and base station degradation. Step 3: The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. The evaluation unit evaluates the base station using performance and quality indicators. The evaluation unit can also identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of the base station. Furthermore, if the signal strength of the base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. Step 4: The prediction unit predicts future trends based on the evaluation results obtained by the evaluation unit. The prediction unit predicts future increases in communication demand and base station degradation based on historical data. The prediction unit can also use time series analysis and predictive models to forecast future trends and take appropriate measures in advance.

[0063] (Example of form 2) The base station evaluation system according to an embodiment of the present invention is a system that evaluates the performance and quality of a base station using generative AI. The base station evaluation system collects data such as the signal strength, coverage area, and connection stability of the base station, and evaluates the performance and quality of the base station by analyzing this data with generative AI. This evaluation result can be used to improve the operation and maintenance of the base station. For example, if the signal strength of the base station decreases, the base station evaluation system can immediately detect the problem and propose appropriate countermeasures. Furthermore, the base station evaluation system can predict future trends based on past data and take appropriate countermeasures in advance. This improves the operational efficiency of the base station and improves communication quality. As a result, the base station evaluation system can efficiently evaluate the performance and quality of the base station and predict future trends.

[0064] The base station evaluation system according to the embodiment comprises a data collection unit, an analysis unit, an evaluation unit, and a prediction unit. The data collection unit collects data from the base station. The data collection unit can collect data such as the signal strength, coverage area, and connection stability of the base station. For example, the data collection unit measures the signal strength of the base station and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate the stability of the connection and record the connection duration and disconnection frequency. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. Furthermore, the analysis unit can analyze data trends and predict future increases in communication demand and base station degradation. The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. For example, the evaluation unit evaluates the base station using performance indicators and quality indicators. Furthermore, the evaluation unit can identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of the base station. In addition, if the signal strength of the base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. The prediction unit predicts future trends based on the evaluation results obtained by the evaluation unit. For example, the prediction unit predicts future increases in communication demand and base station degradation based on past data. The prediction unit can also predict future trends using time series analysis and prediction models and take appropriate countermeasures in advance. As a result, the base station evaluation system according to the embodiment can efficiently evaluate the performance and quality of base stations and predict future trends.

[0065] The data collection unit collects data from base stations. For example, it can collect data such as base station signal strength, coverage area, and connection stability. Specifically, it measures and records the base station signal strength in dBm units. Signal strength is measured by measuring the strength of radio waves transmitted from the base station's antenna, allowing for evaluation of communication quality within the base station's coverage area. The data collection unit can measure the coverage area as a geographical range and set measurement points. This allows for a detailed understanding of how far the base station's radio waves reach. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. Connection stability is evaluated by measuring the length of time a user is connected to the base station and the frequency of connection interruptions. This allows for evaluation of the base station's reliability and the quality of the user experience. The data collection unit collects this data in real time and transmits it to a central database. The database centrally manages the collected data and makes it accessible to the analysis and evaluation units. Furthermore, the data collection unit can adjust the data collection frequency and accuracy, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. Specifically, it generates statistical data on signal strength distribution, coverage area size, and connection stability, and evaluates the performance of the base station based on this data. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. For example, it can use machine learning models to identify the causes of signal strength degradation and connection instability and propose improvements. Furthermore, the analysis unit can analyze data trends and predict future increases in communication demand and base station degradation. This includes methods that use time series analysis and predictive models to predict future trends based on past data. For example, it can analyze past communication volume data to predict increases in communication demand during specific time periods or in specific areas. As a result, the analysis unit can quickly and accurately analyze the collected data, not only to evaluate the performance and quality of the base station but also to predict future communication demand and base station degradation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The evaluation unit evaluates the performance and quality of base stations based on data analyzed by the analysis unit. For example, the evaluation unit uses performance and quality indicators to evaluate base stations. Specifically, it evaluates the overall performance of base stations based on indicators such as signal strength, coverage area, and connection stability. Furthermore, the evaluation unit can identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of base stations. For example, it can identify areas with reduced signal strength and optimize the coverage area by adjusting antenna placement and output. If connection stability is low, it can propose hardware upgrades or software optimizations. In addition, the evaluation unit can immediately detect problems if the base station's signal strength decreases and propose appropriate countermeasures. This includes real-time monitoring and alert systems, enabling rapid response when problems occur. Based on these evaluation results, the evaluation unit can propose and implement specific measures to improve the operational efficiency of base stations. This allows the evaluation unit to evaluate the performance and quality of base stations with high accuracy, identify necessary improvements, and propose measures to improve operational efficiency.

[0068] The forecasting unit predicts future trends based on the evaluation results obtained by the evaluation unit. For example, the forecasting unit predicts future increases in communication demand and base station degradation based on historical data. Specifically, it can predict future trends using time series analysis and predictive models, enabling appropriate countermeasures to be taken in advance. For example, it can analyze historical communication volume data to predict increases in communication demand during specific time periods or in specific regions. This allows the forecasting unit to plan for the addition or upgrade of base stations to meet future communication demand. It can also predict base station degradation and plan maintenance or replacement in advance. Based on these prediction results, the forecasting unit can propose and implement specific countermeasures to address future communication demand and base station degradation. Furthermore, the forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if communication volume or base station performance changes rapidly, the forecasting unit immediately incorporates new data and updates the prediction results. The forecasting unit can also perform more accurate risk assessments by considering regional characteristics and past disaster history. As a result, the forecasting unit can always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0069] The data collection unit can collect data such as base station signal strength, coverage area, and connection stability. For example, the data collection unit measures the base station signal strength and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. By collecting data such as base station signal strength, coverage area, and connection stability, detailed evaluation becomes possible.

[0070] The analysis unit can analyze the collected data and evaluate the performance and quality of the base station. For example, the analysis unit can perform statistical analysis of the data to evaluate the performance and quality of the base station. Furthermore, the analysis unit can use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. In addition, the analysis unit can analyze data trends to predict future increases in communication demand and base station degradation. This allows for the evaluation of the base station's performance and quality by analyzing the collected data.

[0071] The evaluation unit can assess the performance and quality of base stations based on the analysis results and identify necessary improvements. For example, the evaluation unit evaluates base stations using performance and quality indicators. Furthermore, based on the analysis results, the evaluation unit can identify necessary improvements and propose measures to improve the operational efficiency of base stations. In addition, if the signal strength of a base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. As a result, by evaluating the performance and quality of base stations based on the analysis results and identifying necessary improvements, the operational efficiency of base stations is improved.

[0072] The forecasting unit can predict future increases in communication demand and base station degradation based on historical data. For example, it can predict future increases in communication demand based on historical traffic data and trends in the number of users. It can also predict base station degradation based on the lifespan and failure rate of the base station hardware. This allows for appropriate countermeasures to be taken in advance by predicting future increases in communication demand and base station degradation based on historical data.

[0073] The evaluation unit can immediately detect problems and propose appropriate countermeasures when the base station's signal strength decreases. For example, the evaluation unit can detect a decrease in signal strength by measuring the base station's signal strength in dBm units and setting a threshold. Furthermore, when the evaluation unit detects a decrease in signal strength, it can propose appropriate countermeasures such as antenna adjustment or hardware replacement. As a result, communication quality is improved by immediately detecting problems and proposing appropriate countermeasures when the base station's signal strength decreases.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can analyze the base station's past data collection history and select the optimal collection method. For example, the collection unit can select the most efficient collection method from past data collection history. Furthermore, the collection unit can optimize the collection frequency and timing based on past data collection history. In addition, the collection unit can analyze past data collection history and identify areas for improvement in the collection method. This allows for the selection of the optimal collection method and efficient data collection by analyzing past data collection history.

[0076] The data collection unit can filter data based on the current operating status and environmental conditions of the base station during data collection. For example, the data collection unit can select the type of data to collect based on the operating status of the base station. Furthermore, the data collection unit can filter the data to be collected based on environmental conditions (weather, time of day, etc.). In addition, the data collection unit can improve the accuracy of the collected data by considering the operating status and environmental conditions of the base station. Thus, by filtering the data based on the operating status and environmental conditions of the base station, the accuracy of the collected data is improved.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the base station during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of the base station. Furthermore, the data collection unit can improve the accuracy of the collected data by considering the geographical location information. In addition, the data collection unit can select the type of data to collect based on the geographical location information. As a result, by considering the geographical location information of the base station, highly relevant data can be prioritized for collection.

[0079] The data collection unit can analyze social media activity around the base station and collect relevant data during data collection. For example, the data collection unit can analyze social media activity around the base station and collect relevant data. Furthermore, the data collection unit can select the types of data to collect based on social media activity. In addition, the data collection unit can improve the accuracy of the collected data by considering social media activity. This allows for the collection of relevant data by analyzing social media activity around the base station.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a specific analysis algorithm to signal intensity data. It can also apply a different analysis algorithm to coverage area data. Furthermore, it can apply a dedicated analysis algorithm to connection stability data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide a quick analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit will prioritize the analysis of the most recent data. Furthermore, the analysis unit can determine the priority of analysis by referring to past data. In addition, the analysis unit can adjust the level of detail of the analysis based on the data collection period. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize analyzing highly relevant data. It can also postpone analyzing less relevant data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is stressed, the evaluation unit can provide a simple and easy-to-understand evaluation result. If the user is relaxed, the evaluation unit can provide a detailed evaluation result. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise evaluation result. In this way, by adjusting the evaluation method based on the user's emotions, it is possible to provide evaluation results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the analysis results during the evaluation process. For example, the evaluation unit can perform a detailed evaluation of analysis results with high importance. Conversely, the evaluation unit can perform a simplified evaluation of analysis results with low importance. Furthermore, the evaluation unit can determine the priority of the evaluation based on the importance of the analysis results. This allows for efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the analysis results.

[0088] The evaluation unit can apply different evaluation algorithms depending on the category of the analysis results during evaluation. For example, the evaluation unit can apply a specific evaluation algorithm to the analysis results of signal strength. Furthermore, the evaluation unit can apply a different evaluation algorithm to the analysis results of coverage area. In addition, the evaluation unit can apply a dedicated evaluation algorithm to the analysis results of connection stability. This improves the accuracy of the evaluation by applying different evaluation algorithms depending on the category of the analysis results.

[0089] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is stressed, the evaluation unit will prioritize high-importance evaluations. If the user is relaxed, the evaluation unit will prioritize detailed evaluations. Furthermore, if the user is in a hurry, the evaluation unit will perform evaluations quickly. In this way, by determining the priority of evaluations based on the user's emotions, important evaluations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The evaluation unit can determine the priority of evaluation based on the timing of analysis result collection during the evaluation process. For example, the evaluation unit will prioritize the evaluation of the most recent analysis results. Furthermore, the evaluation unit can determine the priority of evaluation by referring to past analysis results. In addition, the evaluation unit can adjust the level of detail of the evaluation based on the timing of analysis result collection. This allows for prioritizing the evaluation of the most recent analysis results by determining the priority of evaluation based on the timing of analysis result collection.

[0091] The evaluation unit can adjust the order of evaluation based on the relevance of the analysis results during the evaluation process. For example, the evaluation unit can prioritize evaluating highly relevant analysis results. It can also postpone evaluating less relevant analysis results. Furthermore, the evaluation unit can adjust the level of detail of the evaluation based on the relevance of the analysis results. This allows for efficient evaluation by adjusting the order of evaluation based on the relevance of the analysis results.

[0092] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is stressed, the prediction unit can provide a simple and easy-to-understand prediction result. If the user is relaxed, the prediction unit can provide a detailed prediction result. Furthermore, if the user is in a hurry, the prediction unit can provide a concise prediction result. In this way, by adjusting the prediction method based on the user's emotions, it is possible to provide prediction results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The prediction unit can adjust the level of detail of its predictions based on the importance of past data. For example, it can make detailed predictions based on high-importance past data, or simplified predictions based on low-importance past data. Furthermore, it can determine the priority of predictions based on the importance of past data. This allows for more efficient predictions by adjusting the level of detail based on the importance of past data.

[0094] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, it can apply a specific prediction algorithm to signal strength data. It can also apply a different prediction algorithm to coverage area data. Furthermore, it can apply a dedicated prediction algorithm to connection stability data. This improves prediction accuracy by applying different prediction algorithms depending on the data category.

[0095] The prediction unit can estimate the user's emotions and prioritize predictions based on those emotions. For example, if the user is stressed, the prediction unit will prioritize high-priority predictions. If the user is relaxed, the prediction unit will prioritize detailed predictions. Furthermore, if the user is in a hurry, the prediction unit will provide rapid predictions. This allows for prioritizing important predictions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The prediction unit can determine the priority of predictions based on the timing of historical data collection. For example, the prediction unit will prioritize the use of the most recent historical data in predictions. The prediction unit can also determine the priority of predictions by referring to historical data. Furthermore, the prediction unit can adjust the level of detail of predictions based on the timing of historical data collection. This allows the latest data to be used preferentially in predictions by determining the priority of predictions based on the timing of historical data collection.

[0097] The prediction unit can adjust the order of predictions based on the relevance of past data during the prediction process. For example, the prediction unit can prioritize the use of highly relevant past data in predictions. It can also postpone the use of less relevant past data. Furthermore, the prediction unit can adjust the level of detail of predictions based on the relevance of past data. This allows for more efficient predictions by adjusting the order of predictions based on the relevance of past data.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The base station evaluation system can further estimate the user's emotions and customize the evaluation results based on those emotions. For example, if the user is stressed, the evaluation results can be summarized concisely and presented in a visually easy-to-understand format. If the user is relaxed, detailed evaluation results can be provided for in-depth analysis. Furthermore, if the user is in a hurry, concise evaluation results can be provided quickly. This enables the provision of flexible evaluation results tailored to the user's emotions, thereby improving user satisfaction.

[0100] The base station evaluation system can further monitor changes in the surrounding environment of the base station in real time and adjust the timing of data collection. For example, the frequency of data collection can be adjusted according to changes in weather or increases or decreases in traffic volume. In addition, if a specific event occurs, data collection can be performed while taking its impact into consideration. Furthermore, the type of data to be collected can be selected based on changes in the surrounding environment of the base station. This enables data collection that responds to environmental changes in real time, allowing for more accurate evaluation.

[0101] The base station evaluation system further incorporates an anomaly detection algorithm in its analysis unit, enabling early detection of base station abnormalities. For example, it can detect sudden drops in signal strength or connection instability and immediately issue an alert. The anomaly detection algorithm can also identify anomalies by comparing them with past data and analyze their causes. Furthermore, if an anomaly occurs, it can predict the scope of its impact and propose appropriate countermeasures. This makes it possible to detect base station abnormalities early and respond quickly.

[0102] The base station evaluation system can further collect user feedback in its evaluation unit and incorporate it into the evaluation results. For example, it can analyze user feedback to improve the accuracy of the evaluation results. It can also adjust evaluation criteria based on user feedback to better meet user needs. Furthermore, collecting user feedback can increase the reliability of the evaluation results. This enables flexible evaluations that reflect user opinions, thereby improving the reliability of the evaluation results.

[0103] The base station evaluation system can further predict future trends by utilizing external data sources in its forecasting section. For example, it can incorporate weather data and demographic data to predict future fluctuations in communication demand. It can also utilize economic indicators and social event data to predict factors that will affect base station operations. Furthermore, by utilizing external data sources, more accurate forecasts become possible, allowing for appropriate countermeasures to be taken in advance. This enables advanced forecasting using external data, improving the operational efficiency of base stations.

[0104] The base station evaluation system can further estimate the user's emotions in the data collection unit and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, data can be collected quickly. In this way, by adjusting the data collection method based on the user's emotions, the user's burden is reduced and efficient data collection becomes possible.

[0105] The base station evaluation system can further estimate the user's emotions in its analysis unit and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis results can be summarized concisely and presented in a visually easy-to-understand format. If the user is relaxed, detailed analysis results can be provided for a deeper analysis. Furthermore, if the user is in a hurry, concise analysis results can be provided quickly. This enables the provision of flexible analysis results tailored to the user's emotions, thereby improving user satisfaction.

[0106] The base station evaluation system can further estimate the user's emotions in its evaluation unit and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is feeling stressed, the evaluation results can be summarized concisely and presented in a visually easy-to-understand format. If the user is relaxed, detailed evaluation results can be provided for in-depth analysis. Furthermore, if the user is in a hurry, concise evaluation results can be provided quickly. This enables the provision of flexible evaluation results that respond to the user's emotions, thereby improving user satisfaction.

[0107] The base station evaluation system can further estimate the user's emotions in its prediction unit and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is feeling stressed, the prediction results can be summarized concisely and presented in a visually easy-to-understand format. If the user is relaxed, detailed prediction results can be provided for in-depth analysis. Furthermore, if the user is in a hurry, concise prediction results can be provided quickly. This enables the provision of flexible prediction results that respond to the user's emotions, thereby improving user satisfaction.

[0108] The base station evaluation system can further collect energy consumption data from base stations and evaluate energy efficiency in its data collection unit. For example, it can monitor the power consumption of base stations and detect decreases in energy efficiency. It can also analyze energy consumption data and propose measures to improve energy efficiency. Furthermore, based on energy consumption data, it can predict future energy demand and implement appropriate energy management. This makes it possible to evaluate the energy efficiency of base stations and optimize energy consumption.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data from the base station. The data collection unit can collect data such as base station signal strength, coverage area, and connection stability. The data collection unit measures the base station signal strength and records it in dBm units. The data collection unit can also measure the coverage area as a geographical range and set measurement points. Furthermore, the data collection unit can evaluate connection stability and record connection duration and disconnection frequency. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit performs statistical analysis of the data to evaluate the performance and quality of the base station. The analysis unit can also use machine learning algorithms to analyze the data and evaluate the performance and quality of the base station. Furthermore, the analysis unit can analyze data trends to predict future increases in communication demand and base station degradation. Step 3: The evaluation unit evaluates the performance and quality of the base station based on the data analyzed by the analysis unit. The evaluation unit evaluates the base station using performance and quality indicators. The evaluation unit can also identify necessary improvements based on the analysis results and propose measures to improve the operational efficiency of the base station. Furthermore, if the signal strength of the base station decreases, the evaluation unit can immediately detect the problem and propose appropriate countermeasures. Step 4: The prediction unit predicts future trends based on the evaluation results obtained by the evaluation unit. The prediction unit predicts future increases in communication demand and base station degradation based on historical data. The prediction unit can also use time series analysis and predictive models to forecast future trends and take appropriate measures in advance.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects base station signal strength and coverage area data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the performance and quality of the base station based on the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future trends based on past data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects base station signal strength and coverage area data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using statistical analysis and machine learning algorithms. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and evaluates the performance and quality of the base station based on the analysis results. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts future trends based on past data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and prediction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects base station signal strength and coverage area data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the performance and quality of the base station based on the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future trends based on past data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, evaluation unit, and prediction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects base station signal strength and coverage area data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the performance and quality of the base station based on the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future trends based on past data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects data from base stations, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates the performance and quality of the base station based on the data analyzed by the aforementioned analysis unit, The system includes a prediction unit that predicts future trends based on the evaluation results obtained by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as base station signal strength, coverage area, and connection stability. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to evaluate the performance and quality of the base stations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Based on the analysis results, evaluate the performance and quality of the base station and identify areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, Based on past data, we predict future increases in communication demand and base station degradation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, If the base station signal strength drops, the system will immediately detect the problem and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the base station's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the current operating status and environmental conditions of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of base stations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes social media activity around the base station and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During evaluation, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, the priority of the evaluation is determined based on the timing of the collection of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, the order of evaluation will be adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, adjust the level of detail based on the importance of historical data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, different prediction algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the priority of predictions is determined based on when historical data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of past data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection unit that collects data from base stations, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates the performance and quality of the base station based on the data analyzed by the aforementioned analysis unit, The system includes a prediction unit that predicts future trends based on the evaluation results obtained by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as base station signal strength, coverage area, and connection stability. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to evaluate the performance and quality of the base stations. The system according to feature 1.

4. The evaluation unit described above, Based on the analysis results, evaluate the performance and quality of the base station and identify areas for improvement. The system according to feature 1.

5. The prediction unit, Based on past data, we predict future increases in communication demand and base station degradation. The system according to feature 1.

6. The evaluation unit described above, If the base station signal strength drops, the system will immediately detect the problem and propose appropriate countermeasures. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the base station's past data collection history and select the optimal collection method. The system according to feature 1.

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