System

Abstract

A system is provided.SOLUTION: The system of claim 1, further comprising: means for generating feedback including improvements and optimizations based on the results of the evaluation; and means for displaying the generated feedback and the results of the evaluation, wherein the generated feedback and the results of the evaluation are provided to a user of the system, and wherein the generated feedback and the results of the evaluation are provided to the user of the system, and wherein the generated feedback and the results of the evaluation are provided to the user of the system via the AI model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When it comes to evaluating the performance and quality of base stations, engineers and quality control personnel at mobile phone companies currently find it difficult to obtain quantitative and objective indicators. Furthermore, performance evaluations often lack consistency and accuracy, often requiring a great deal of time and effort to find optimal improvement plans. Rapid market expansion demands high-quality operation of base stations, and efficient methods for achieving this are highly desirable. This invention aims to solve these issues by objectively evaluating the performance and quality of base stations and providing appropriate feedback and improvement plans. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. First, a means for inputting base station type, specifications, and performance data is provided. Next, a means for preprocessing the input data is provided. Preprocessing includes complementing incomplete data and detecting and correcting outliers. Furthermore, a means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station is provided. Based on this evaluation, a means for generating feedback including improvement points and optimization suggestions is provided. Finally, a means for displaying the generated feedback and evaluation results is provided. Combining these means makes it possible to consistently evaluate the performance and quality of base stations and efficiently provide improvement suggestions.

[0006] A "base station" is a fixed communications facility in a communications network that serves as a connection for mobile terminals.

[0007] "Type" refers to the basic classification or type of base station, including different scales and uses, such as macrocell, microcell, picocell, etc.

[0008] "Specifications" refers to information that indicates the technical characteristics, components, operating conditions, etc. of a base station.

[0009] "Performance data" refers to numerical information such as transmission success rate, communication delay, and data transfer volume that is collected when a base station is in operation.

[0010] "Input means" is an interface or method by which a user provides base station type, specifications, and performance data to the system.

[0011] "Preprocessing means" refers to the process of analyzing input data, complementing incomplete data, correcting outliers, normalizing data, and so on.

[0012] A "generative AI model" is an artificial intelligence model trained based on collected data and used to evaluate the performance and quality of base stations.

[0013] The "means of evaluation" is the process of quantitatively analyzing the performance and quality of base stations using a generative AI model and outputting evaluation results.

[0014] "Improvements and optimization proposals" is information that provides specific proposals and guidelines for improving the performance and quality of base stations based on the evaluation results.

[0015] "Means of generating feedback" is the process of creating reports or messages based on the evaluation results that detail areas for improvement and optimization.

[0016] The "display means" refers to an interface that includes, for example, a display or graph generation function, which allows the user to visually confirm the evaluation results and feedback. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for evaluating the performance and quality of a base station, and mainly includes the following steps: An embodiment of the invention will be described through a specific use example.

[0039] Entering base station data

[0040] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data input is done through a dedicated app or web interface.

[0041] Data Preprocessing

[0042] The server receives the data sent by the user and begins preprocessing the data. During this stage, it fills in gaps in the data, detects and corrects outliers, and normalizes the data into a unified format. For example, all numerical data is converted to the same units, which makes subsequent processing easier.

[0043] Evaluation by generative AI model

[0044] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on the evaluation results.

[0045] Generate feedback

[0046] The server receives the evaluation results and generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, specific instructions such as "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0047] Notification and display of results

[0048] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0049] Specific examples

[0050] For example, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0051] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0055] Step 2:

[0056] The server receives the data sent from the device, performs basic validation of the data to ensure it does not contain incomplete or invalid data, and generates a warning if there are missing values.

[0057] Step 3:

[0058] The server preprocesses the received data. This includes data formatting (e.g., standardizing units), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it will be corrected by referring to past data.

[0059] Step 4:

[0060] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0061] Step 5:

[0062] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0063] Step 6:

[0064] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0065] Step 7:

[0066] The server creates graphs and charts to display the generated feedback and evaluation results, making it easier for users to visually understand the results.

[0067] Step 8:

[0068] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0069] Step 9:

[0070] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0071] Example 1

[0072] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0073] Systems that accurately evaluate the performance and quality of base stations often lack accuracy due to insufficient handling of missing data and outliers. Furthermore, there are limited means to visually display the evaluation results in a way that is easy for users to understand. As a result, there is a challenge in accurately and efficiently optimizing base station performance.

[0074] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0075] In this invention, the server includes means for inputting base station type, specifications, and performance data, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation data, means for displaying the generated feedback and evaluation data, means for filling in missing data and detecting and correcting outliers, and means for normalizing all numerical data to the same units, thereby enabling accurate evaluation and rapid optimization of base station data.

[0076] "Base station data" refers to information about the type, specifications, and performance of a base station, and specifically includes the base station ID, antenna height, output power, number of users, data usage, etc.

[0077] "Preprocessing" refers to the process of preparing input data in a format suitable for subsequent processing by performing operations such as filling in missing parts, detecting and correcting outliers, and normalizing numerical data.

[0078] A "generative AI model" refers to an artificial intelligence model for evaluating base station performance based on large amounts of training data, specifically calculating performance indicators such as transmission success rate, communication delay, and downlink throughput.

[0079] "Feedback" refers to information that specifically indicates areas for improvement in base station performance and optimization suggestions based on the evaluation results of the generative AI model.

[0080] "Normalization" refers to the process of aligning all numerical data to the same units, making it easier to compare and integrate data.

[0081] An "outlier" is a value that is clearly abnormal compared to other data in the input data and that deviates from the normal range.

[0082] The present invention provides a system for evaluating and optimizing the performance and quality of a base station. This system mainly includes the following functions:

[0083] First, the user collects the base station type, specifications, and performance data and inputs it into the server from their device using a dedicated app or web interface. This data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc.

[0084] The server then performs preprocessing on the received data, including filling in gaps in the data and detecting and correcting outliers. It also performs normalization to align all numerical data to the same units. For example, if the antenna altitude is measured in both meters and feet, it will be normalized to meters.

[0085] The preprocessed data is input into a generative AI model by the server. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance. Based on these evaluation results, the server generates feedback including improvements and optimization proposals for the base station. The feedback includes specific instructions (e.g., "Resource allocation needs to be reviewed to reduce communication delays").

[0086] The generated feedback and evaluation results are sent from the server to the terminal, where the user can check them. The feedback and evaluation results are displayed visually in graphs and charts, making them easy for the user to understand and adjust or improve the base station as needed.

[0087] As a specific use case, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0088] Here are some examples of prompts:

[0089] "Base station ID: A123, Antenna height: 20m, Output power: 50W, Number of users: 100, Data usage: 50GB / day. Please rate the performance and quality of this base station and let us know what needs to be improved."

[0090] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] The user inputs base station data, including base station ID, antenna altitude, output power, number of current users, data usage, etc. The user then sends this information from their device to the server using a dedicated app or web interface.

[0094] Input: Base station ID, antenna altitude, output power, number of current users, data usage.

[0095] Output: Base station data sent to the server.

[0096] Step 2:

[0097] The server receives the data sent by the user and performs preprocessing on the data. This preprocessing involves filling in missing data and detecting and correcting outliers. It also normalizes all numerical data to use the same units. For example, if altitude units are mixed, such as meters and feet, it will unify all data into meters.

[0098] Input: Base station data sent by the user.

[0099] Output: Preprocessed data.

[0100] Step 3:

[0101] The server inputs the preprocessed data into a generative AI model, which calculates base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance.

[0102] Input: Preprocessed data.

[0103] Output: Performance indicators of the base station.

[0104] Step 4:

[0105] The server generates feedback based on the performance indicators obtained from the generative AI model. Specifically, it analyzes the evaluation data and derives improvements and optimization proposals for the base station. For example, it generates feedback such as "resource allocation needs to be reviewed to reduce communication latency."

[0106] Input: Performance metrics obtained from the generative AI model.

[0107] Output: Feedback with improvements and optimization suggestions.

[0108] Step 5:

[0109] The server sends the generated feedback and evaluation results to the device, which receives them and displays them for the user to review. The results are presented visually in an easy-to-understand manner using graphs and charts.

[0110] Input: Feedback including improvements and optimization suggestions.

[0111] Output: A device that displays the feedback and evaluation results.

[0112] Through this process, users will be able to efficiently and accurately evaluate the performance and quality of base stations and quickly implement improvement proposals.

[0113] (Application example 1)

[0114] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] In conventional industrial robot systems, the wireless communication environment between robots is not adequately evaluated or optimized, resulting in problems such as communication delays and reduced data throughput. This can lead to reduced factory production efficiency and make it difficult to ensure quality. Therefore, it is necessary to monitor and evaluate the wireless communication environment of robots in real time and provide optimal improvement proposals.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0117] In this invention, the server includes means for inputting the type, specifications, and performance data of the base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation results, means for displaying the generated feedback and evaluation results, means for inputting communication environment data of the robot and evaluating its performance, and means for generating proposals for optimizing the communication environment based on the evaluation results. This makes it possible to improve the communication quality between robots in a factory and increase production efficiency.

[0118] A "base station" is a communication facility installed to provide wireless communication, and is a device that relays and manages communications within a specific area.

[0119] "Performance data" refers to information that measures the operation and capabilities of base stations and robots, and refers to specific numerical data such as transmission success rate, communication delay, and data throughput.

[0120] "Preprocessing" is the process of organizing input raw data and processing or shaping the data to make it easier to analyze and evaluate.

[0121] A "generative AI model" is an artificial intelligence system that has been trained in advance using large amounts of data. It is used to evaluate performance and quality based on the input data and propose optimal solutions.

[0122] "Feedback" refers to information such as guidance, advice, and specific improvement proposals that are generated based on the results of evaluation and analysis.

[0123] "Communication environment" refers to the physical and electronic conditions and circumstances under which wireless communications occur, including signal strength, interference, data throughput, etc.

[0124] "Areas for improvement" are points that are identified based on the evaluation results and require specific corrections or changes to improve performance or quality.

[0125] "Optimization suggestions" are specific methods or procedures proposed to maximize performance or quality.

[0126] "Evaluation results" are the results of data analyzed by the generative AI model, and show the numerical values ​​and trends of specific performance and quality indicators.

[0127] "Display" refers to the act of visually presenting evaluation results, feedback, and improvement proposals to users, often using graphs, charts, and the like.

[0128] This paper describes a system for evaluating and optimizing the wireless communication environment of robots in a factory. This system collects various data, evaluates the communication environment using a generative AI model, and proposes improvements.

[0129] Entering base station data

[0130] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device. Specific data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is input via a dedicated app or web interface.

[0131] Data Preprocessing

[0132] The server receives the data sent by the user and begins preprocessing the data. At this stage, missing parts of the data are filled in, and outliers are detected and corrected. The data is also normalized to form a unified format. For example, all numerical data is aligned to the same units to facilitate subsequent processing. Specifically, the numerical data is scaled using StandardScaler.

[0133] Evaluation by generative AI model

[0134] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators of the base station (transmission success rate, communication delay, downlink throughput, etc.) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on these evaluation results.

[0135] Generate feedback

[0136] The server then generates detailed feedback on the base station's performance and quality, including specific improvements and optimization suggestions. For example, it might provide specific instructions such as, "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0137] Notification and display of results

[0138] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0139] Specific examples of wireless communication environment evaluation and optimization

[0140] For example, when multiple robots communicate simultaneously in a factory, a specific robot may experience communication delays. The application collects the robot's communication data and evaluates it using an AI model. As a result of this evaluation, it is determined that the communication delay is high, and feedback is provided stating that "routing needs to be revised to reduce communication delays."

[0141] Prompt Sentence Examples

[0142] Evaluate the wireless communication environment for robots in your factory and provide performance metrics for communication latency and data throughput. Data collected includes robot ID, signal strength, signal interference, data throughput, etc. Also generate improvement suggestions based on the evaluation results.

[0143] In this way, the present invention provides a system that improves the quality of communication between robots in a factory, thereby contributing to improved production efficiency and quality.

[0144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0145] Step 1:

[0146] Users collect base station type, specifications, and performance data and input it into the server from their devices. Specific data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is sent to the server via a dedicated app or web interface.

[0147] Step 2:

[0148] The server receives data sent by users. The received data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. The server first fills in missing data and detects and corrects outliers. This preprocessing step also scales the data using StandardScaler to align all numerical data to the same units.

[0149] Step 3:

[0150] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (transmission success rate, communication delay, downlink throughput, etc.) based on a large amount of training data collected in advance. This model quickly evaluates performance based on the input data and outputs the results.

[0151] Step 4:

[0152] The server that receives the evaluation results generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, if the transmission success rate is low, the feedback may include specific suggestions such as "increase the base station's output power," or if communication latency is high, "review of resource allocation is necessary."

[0153] Step 5:

[0154] The server automatically sends the generated feedback and evaluation results to the terminal. This feedback, including graphs and charts, is provided to the user in a visually easy-to-understand format, allowing the user to review it and make adjustments or improvements to the base station.

[0155] Step 6:

[0156] Based on the evaluation results and feedback displayed on the device, the user can make necessary adjustments, such as changing the angle of the base station antenna or adjusting the output power, according to the specific instructions provided in the feedback.

[0157] In this way, the system of the present invention optimizes the quality of communication between robots in a factory through a series of steps, starting with the collection of basic data, followed by data preprocessing, evaluation using a generative AI model, feedback generation, notification of results, and actual adjustment work.

[0158] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] The present invention is a system for evaluating the performance and quality of a base station and providing feedback that takes into account user sentiment, and includes the following steps: The embodiments of the invention will be described through specific use examples.

[0160] Entering base station data

[0161] The user collects base station type, specifications, and performance data and inputs it to the server from the terminal, such as base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then sends this information to the server.

[0162] Data Preprocessing

[0163] The server receives the data sent by the user, performs basic validation of the data to ensure it is not incomplete or invalid, completes any incomplete data, corrects any outliers, and normalizes the data to ensure accurate evaluation.

[0164] Evaluation by generative AI model

[0165] The server inputs the preprocessed data into a generative AI model, which evaluates the performance and quality of the base station based on a pre-trained algorithm, calculating indicators such as transmission success rate, communication delay, and downlink throughput.

[0166] Generate feedback

[0167] The server receives the results from the generative AI model and generates detailed feedback. Based on the evaluation results, it provides specific improvements and optimization suggestions. For example, it creates a report with specific instructions such as, "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0168] Recognizing user emotions with an emotion engine

[0169] Based on the generated feedback and evaluation results, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, typing speed, and specific keywords to determine the user's emotional state.

[0170] Customized Feedback

[0171] After recognizing the user's emotions, the server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, the server gently explains what needs to be improved and provides specific support methods. For a satisfied user, the server presents a positive evaluation result and offers further improvement suggestions.

[0172] Notification and display of results

[0173] The customized feedback and evaluation results are automatically sent to the device. The user can review the evaluation results and feedback displayed on the device and make adjustments or improvements to the base station as needed. Graphs and charts are used for visual display to present the results in an easy-to-understand manner.

[0174] Specific examples

[0175] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0176] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0177] The processing flow will be explained below.

[0178] Step 1:

[0179] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0180] Step 2:

[0181] The server receives the data sent from the device. After receiving it, it performs basic validation of the data to check whether it contains incomplete or invalid data. If there is any incomplete data, it sends a notification to the user to complete it.

[0182] Step 3:

[0183] The server preprocesses the received data. This includes data shaping (e.g., standardizing all data to a common unit), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it can be corrected by looking at past data.

[0184] Step 4:

[0185] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0186] Step 5:

[0187] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0188] Step 6:

[0189] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0190] Step 7:

[0191] The server recognizes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, input speed, and specific keywords to determine the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0192] Step 8:

[0193] The server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, it gently explains what needs to be improved and provides specific support methods. For a satisfied user, it presents the evaluation results in a positive light and offers further suggestions for improvement.

[0194] Step 9:

[0195] The server generates graphs and charts to display customized feedback and assessment results in a visually easy-to-understand format.

[0196] Step 10:

[0197] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0198] Step 11:

[0199] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0200] Specific examples

[0201] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0202] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0203] Example 2

[0204] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] As communication infrastructure continues to become more complex and sophisticated, it will be necessary to accurately and quickly evaluate the performance and quality of base stations and provide appropriate feedback. Conventional systems face technical limitations due to the lack of data preprocessing, performance evaluation, and methods for providing feedback that takes user emotions into account. Taking appropriate measures based on the user's emotional state can facilitate the acceptance and implementation of improvements, potentially improving the overall user experience. Against this background, the objective of this invention is to provide a base station performance evaluation system that can take user emotions into account and provide feedback that is appropriate to the user's emotions.

[0206] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the type, specifications, and performance data of the base station, a means for preprocessing the input data, a means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, a means for generating feedback including improvements and optimization suggestions based on the evaluation results, and a means for recognizing the user's emotions and customizing the feedback according to the user's emotional state. This makes it possible to accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0207] A "base station" is a device that communicates with mobile terminals in a communication network.

[0208] "Performance data" refers to information related to the communication quality and performance of a base station, and specifically includes transmission success rate, communication delay, data throughput, etc.

[0209] "Preprocessing" refers to the process of validating, imputing, and normalizing data to improve its accuracy.

[0210] A "generative AI model" is a model trained using machine learning algorithms to evaluate the performance and quality of base stations based on input data.

[0211] "Feedback" refers to reports and instructions, including suggestions for improvement and optimization, provided based on the results of the evaluation.

[0212] "Emotion engine" refers to an algorithm or module for analyzing and recognizing a user's emotional state.

[0213] "Customization" refers to the adjustment or modification of content to meet specific conditions or requirements.

[0214] "Evaluation Results" refers to the measurement results regarding the performance and quality of base stations obtained by the generative AI model.

[0215] "Visually displayed" refers to displaying information using visual means such as graphs or charts.

[0216] The present invention provides a system for evaluating the performance and quality of a base station and providing feedback that takes into account the user's feelings. An embodiment of the program for this system will be described in detail below.

[0217] Entering base station data

[0218] The user uses the device's input interface (e.g., a web form or mobile app) to collect and input base station type, specifications, and performance data to the server. This data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. Once the user completes the input, the device sends the data to the server using an HTTP POST request.

[0219] Data Preprocessing

[0220] The server receives the data submitted by the user and stores it in a database (e.g., MySQL or PostgreSQL). The server then performs basic validation of the data to ensure it does not contain incomplete or invalid data. The server then executes logic to impute incomplete data and correct outliers. Finally, the data is normalized and converted into a consistent format.

[0221] Evaluation by generative AI model

[0222] The server calls a generative AI model built in a programming language such as Python or R. The server inputs the preprocessed data into the generative AI model and performs performance evaluation. This evaluation process generates results such as a 92% transmission success rate, a 35ms communication delay, and a 120Mbps downlink throughput.

[0223] Generate feedback

[0224] The server receives the evaluation results from the generated AI model and automatically generates a detailed feedback report. The feedback includes specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." This feedback report is generated in PDF or HTML format and converted into a format that users can easily view.

[0225] Recognizing user emotions with an emotion engine

[0226] The server recognizes the user's emotions using an emotion engine based on the generated feedback and evaluation results. This emotion engine uses modules (e.g., OpenCV and TensorFlow) for facial expression recognition and voice analysis. The server determines the user's emotional state and reflects the results in the feedback generation process.

[0227] Customized Feedback

[0228] The server customizes the feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will explain areas for improvement in a gentle manner and provide specific support methods. If the user is satisfied, the server will present the evaluation results in a positive light and suggest further improvements.

[0229] Notification and display of results

[0230] The customized feedback and evaluation results are automatically sent to the device, which implements an interface to display these feedback reports to the user. Visual displays include graphs and charts to present the results in an easy-to-understand manner.

[0231] Specific examples

[0232] For example, consider the evaluation of a suburban base station. A user enters information into a form on their device, such as the base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." This data is then sent to the server, which preprocesses it and performs an evaluation using a generative AI model. The evaluation results are a 92% transmission success rate, a 35ms communication latency, and a 120Mbps downlink throughput. Based on this, the server generates feedback suggesting that resource allocation be revised to reduce communication latency. If the emotion engine detects that the user is stressed, it offers suggestions for improvement in a gentle manner. Finally, this feedback is sent to the device, where the user can review it and make appropriate adjustments, thereby improving the performance and quality of the base station.

[0233] Prompt Sentence Examples

[0234] "Please evaluate the following: base station ID: 12345, antenna altitude: 15m, output power: 30W, current number of users: 50, data usage: 20GB / day. Calculate the current transmission success rate, communication delay, and downlink throughput, and provide feedback on the optimal resource allocation."

[0235] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0236] Step 1: Enter base station data

[0237] The user uses the device's input interface (web form or mobile app) to input base station type, specifications, and performance data. This data includes base station ID, antenna altitude, output power, current number of users, data usage, etc. Specifically, the user inputs base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." The device temporarily stores the input data, and once all the data is collected, it sends it to the server using an HTTP POST request. The input triggers data transfer from the device to the server, and the output is the data set received by the server.

[0238] Step 2: Data Preprocessing

[0239] The server receives data sent by users and stores it in a database (for example, MySQL or PostgreSQL). The input is the raw data received from the base station, and the output is normalized data that has been verified and stored in the database. First, the server validates the data. Specifically, it checks whether numeric fields contain character data or abnormal values ​​(for example, negative values ​​or very large values). If there is incomplete data, it is complemented using a predetermined method (for example, complementing with the average value), and if an abnormal value is found, it is corrected to fall within a specific threshold range. Finally, the data is normalized. For example, data usage is standardized to units such as MB or GB.

[0240] Step 3: Evaluation with a generative AI model

[0241] The server inputs the preprocessed data into the generative AI model. The input is a normalized dataset, and the output is the performance evaluation results. The server calls the generative AI model, which is built using a programming language such as Python or R. Specifically, the server sends the input data to the generative AI model and starts the evaluation process. The generative AI model evaluates the performance of the base station based on a pre-trained algorithm. For example, the generated evaluation results might be "transmission success rate 92%, communication latency 35 ms, and downlink throughput 120 Mbps." These results are collected by the server and used to proceed to the next step.

[0242] Step 4: Generate feedback

[0243] The server receives the evaluation results obtained from the generative AI model and generates a detailed feedback report. The input is the evaluation results and the output is the feedback report. The server runs a script that automatically generates a feedback report based on the evaluation results. For example, the server provides specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." The feedback report is generated in PDF or HTML format and is converted into a format that can be easily viewed by users. Specific operations include analyzing the evaluation results, identifying areas for improvement, and generating a report.

[0244] Step 5: Recognizing user emotions with the emotion engine

[0245] The server uses an emotion engine to recognize the user's emotional state based on the generated feedback and evaluation results. The inputs are the evaluation results, feedback content, and the user's reaction data, and the output is the analyzed emotional state. The emotion engine uses modules for facial expression recognition (e.g., OpenCV) and voice analysis (e.g., TensorFlow). The server uses these tools to analyze the user's facial expressions, voice, text input speed, and specific keywords. This determines the user's emotional state. For example, if the user has a stiff expression or inputs slowly, it is determined that the user is feeling stressed.

[0246] Step 6: Customize your feedback

[0247] The server customizes the feedback according to the user's emotional state. The input is the analyzed emotional state and a feedback report, and the output is a customized feedback report. For example, if the user is feeling stressed, logic is executed to explain areas for improvement in gentle terms and provide specific support methods. Wording such as "Communication latency has improved, but further optimization would be beneficial" is used. If the user is satisfied, the evaluation results are presented positively and the content is changed to suggest further improvements.

[0248] Step 7: Notification and display of results

[0249] Customized feedback and evaluation results are automatically sent to the device. The input is the customized feedback report, and the output is the feedback report displayed on the user's device. The server sends an HTTP request to an API endpoint to send the feedback report to the device. The device implements an interface to display the received feedback report to the user. Specifically, it generates visually easy-to-understand graphs and charts and displays the results in a format that the user can easily understand.

[0250] Through the above processing steps, the system can accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0251] (Application example 2)

[0252] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] Autonomous vehicles are required to perform self-evaluation to improve safety and efficiency. It is also necessary to provide feedback that takes into account the emotions of the driver or operator, thereby reducing the mental burden and providing a better driving experience. However, conventional systems have not adequately evaluated the performance of autonomous vehicles or provided feedback based on the user's emotions, limiting the application of evaluation results and the effectiveness of feedback.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0255] In this invention, the server includes means for inputting the type, specifications, and performance data of a base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station, means for generating feedback including improvements and optimization suggestions based on the evaluation results, means for using an emotion engine that recognizes the emotional state of a user based on the evaluation results and feedback, means for customizing the feedback based on the emotional state of the user, and means for displaying the generated feedback and evaluation results, thereby enabling performance evaluation of an autonomous vehicle and providing feedback according to the user's emotions.

[0256] A "base station" is a hardware device in a communication system that transmits or receives radio waves and communicates with user devices.

[0257] A "type" is a group or category classified according to specific criteria.

[0258] A "specification" is a detailed description or characterization of the design and functionality of a system or device.

[0259] "Performance Data" means any measurement or record relating to the operation or functionality of a device or system.

[0260] "Preprocessing" refers to the initial processing and shaping of raw data to convert it into a form suitable for analysis and evaluation.

[0261] A "generative AI model" is an artificial intelligence system that is trained using machine learning algorithms on large amounts of data.

[0262] "Performance" refers to the degree or quality of the functions or operations that a device or system can perform.

[0263] "Quality" is the degree to which a product or service meets specific requirements or standards.

[0264] "Evaluation" means judging the value or performance of an object based on specified criteria.

[0265] An "improvement" is a change or modification that needs to be made to improve a system or process.

[0266] An "optimization proposal" is a specific strategy or measure to optimize a system or process.

[0267] "Feedback" means providing a response or evaluation of an action or behavior.

[0268] An "emotion engine" is a system that analyzes a user's emotional state and adjusts feedback based on that.

[0269] A "user's emotional state" refers to the sensations and emotions a user experiences in a particular situation.

[0270] "Customization" means modifying or adapting a system or service to meet the specific needs and requirements of a user.

[0271] This invention is a system that evaluates the performance of an autonomous vehicle and provides feedback according to the driver's emotions. Below, we will explain the outline of the program for realizing this system and its processing.

[0272] Generating a Program

[0273] The server first receives various performance data (such as location, speed, acceleration, and road conditions) collected from the autonomous vehicle as input data. This data is collected through sensors and cameras. The server then performs preprocessing on the data, which includes missing value imputation, incorrect data detection and correction, and normalization.

[0274] Next, the preprocessed data is input into a generative AI model for evaluation. This generative AI model evaluates performance based on indicators such as transmission success rate, communication delay, and downlink throughput, and obtains the results.

[0275] Based on the evaluation results, the server generates feedback including improvement points and optimization suggestions. This feedback indicates areas that need improvement and specific optimization suggestions. For example, a report may be created with specific instructions such as, "The obstacle avoidance success rate is 95%, but the lane-keeping accuracy is low, so the sensors need to be adjusted."

[0276] Based on the evaluation results and the generated feedback, the server uses an emotion engine to recognize the driver's emotional state. The emotion engine analyzes facial expressions and tone of voice data obtained from input devices such as cameras and microphones to determine the user's emotional state. For example, if the driver is feeling stressed, the content of the feedback will be softened.

[0277] Finally, the generated feedback and evaluation results are displayed on a device such as a head-mounted display (HMD) in the driver's seat or a smartphone, and the results are presented visually using graphs and charts.

[0278] Examples of specific examples and prompts

[0279] Specific examples

[0280] Simulation data for an autonomous vehicle traveling at 60 km / h on dry roads is input. Evaluation using a generative AI model yields results such as a 95% obstacle avoidance success rate, 20 ms communication latency, and 150 Mbps downlink throughput. Based on these results, the emotion engine determines the driver is in a mild stress state and provides polite feedback, such as "Consider recalibrating your sensors to improve lane-keeping accuracy."

[0281] Prompt Sentence Examples

[0282] Please rate the performance of the self-driving vehicle based on the following data.

[0283] Vehicle ID: 12345

[0284] Speed: 60km / h

[0285] Acceleration: 3 m / s²

[0286] Road Condition: Dry

[0287] Based on the evaluation results, feedback should also be generated for users when they are experiencing emotional stress.

[0288] This system makes it possible to accurately assess vehicle performance and provide feedback to the driver based on their emotions.

[0289] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0290] Step 1:

[0291] The user inputs performance data of the autonomous vehicle and sends it to the server.

[0292] Input data includes location information, speed, acceleration, road conditions, etc. Users collect this data from various sensors and cameras on their vehicle and send it from their device to the server, where the basic data required for evaluation is accumulated.

[0293] Step 2:

[0294] The server pre-processes the received data.

[0295] The server first verifies the received data and detects incomplete or anomalous data. It then complements or corrects the incomplete data and eliminates any anomalous data. It also normalizes the various data and formats them into a unified format. This preprocessing ensures that the generative AI model can accurately and consistently evaluate the data.

[0296] Step 3:

[0297] The preprocessed data is input into a generative AI model to evaluate vehicle performance.

[0298] The server inputs the normalized data into the generative AI model and evaluates it based on various performance indicators (e.g., obstacle avoidance success rate, communication latency, downlink throughput). The generative AI model is trained in advance using a large amount of learning data, enabling it to obtain highly accurate evaluation results. The evaluation results for each indicator are generated as output.

[0299] Step 4:

[0300] Generate feedback based on the evaluation results.

[0301] The server analyzes the evaluation results obtained from the generative AI model and generates specific improvement and optimization suggestions. For example, if the obstacle avoidance success rate is high and communication latency is low, feedback is generated that indicates good overall performance, but that the accuracy of specific sensors should be further improved. The output is a detailed feedback report with improvement suggestions.

[0302] Step 5:

[0303] The user's emotional state is recognized based on the evaluation results and feedback.

[0304] The server uses an emotion engine to analyze the evaluation results and feedback. It determines the user's emotional state based on facial expressions, tone of voice, and typing speed. This is done based on input data from the camera and microphone. The output is the user's recognized emotional state.

[0305] Step 6:

[0306] Customize feedback based on the user's emotional state.

[0307] The server adapts the feedback depending on the user's perceived emotional state. For example, if the user is stressed, it softens the feedback and suggests specific ways to support them. Conversely, if the user is satisfied, it provides positive suggestions for improvement. The output is customized feedback.

[0308] Step 7:

[0309] The generated feedback and evaluation results are displayed on the terminal.

[0310] The server sends the feedback and evaluation results to the device, which then visually displays the information to the user, specifically using graphs and charts to present the results in an easy-to-understand manner, allowing the user to make appropriate adjustments and improvements.

[0311] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0312] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0313] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0314] [Second embodiment]

[0315] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0316] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0317] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0318] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0319] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0320] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0321] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0322] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0323] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0324] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0325] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0326] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0327] The present invention is a system for evaluating the performance and quality of a base station, and mainly includes the following steps: An embodiment of the invention will be described through a specific use example.

[0328] Entering base station data

[0329] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data input is done through a dedicated app or web interface.

[0330] Data Preprocessing

[0331] The server receives the data sent by the user and begins preprocessing the data. During this stage, it fills in gaps in the data, detects and corrects outliers, and normalizes the data into a unified format. For example, all numerical data is converted to the same units, which makes subsequent processing easier.

[0332] Evaluation by generative AI model

[0333] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on the evaluation results.

[0334] Generate feedback

[0335] The server receives the evaluation results and generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, specific instructions such as "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0336] Notification and display of results

[0337] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0338] Specific examples

[0339] For example, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0340] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0341] The processing flow will be explained below.

[0342] Step 1:

[0343] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0344] Step 2:

[0345] The server receives the data sent from the device, performs basic validation of the data to ensure it does not contain incomplete or invalid data, and generates a warning if there are missing values.

[0346] Step 3:

[0347] The server preprocesses the received data. This includes data formatting (e.g., standardizing units), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it will be corrected by referring to past data.

[0348] Step 4:

[0349] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0350] Step 5:

[0351] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0352] Step 6:

[0353] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0354] Step 7:

[0355] The server creates graphs and charts to display the generated feedback and evaluation results, making it easier for users to visually understand the results.

[0356] Step 8:

[0357] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0358] Step 9:

[0359] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0360] Example 1

[0361] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0362] Systems that accurately evaluate the performance and quality of base stations often lack accuracy due to insufficient handling of missing data and outliers. Furthermore, there are limited means to visually display the evaluation results in a way that is easy for users to understand. As a result, there is a challenge in accurately and efficiently optimizing base station performance.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0364] In this invention, the server includes means for inputting base station type, specifications, and performance data, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation data, means for displaying the generated feedback and evaluation data, means for filling in missing data and detecting and correcting outliers, and means for normalizing all numerical data to the same units, thereby enabling accurate evaluation and rapid optimization of base station data.

[0365] "Base station data" refers to information about the type, specifications, and performance of a base station, and specifically includes the base station ID, antenna height, output power, number of users, data usage, etc.

[0366] "Preprocessing" refers to the process of preparing input data in a format suitable for subsequent processing by performing operations such as filling in missing parts, detecting and correcting outliers, and normalizing numerical data.

[0367] A "generative AI model" refers to an artificial intelligence model for evaluating base station performance based on large amounts of training data, specifically calculating performance indicators such as transmission success rate, communication delay, and downlink throughput.

[0368] "Feedback" refers to information that specifically indicates areas for improvement in base station performance and optimization suggestions based on the evaluation results of the generative AI model.

[0369] "Normalization" refers to the process of aligning all numerical data to the same units, making it easier to compare and integrate data.

[0370] An "outlier" is a value that is clearly abnormal compared to other data in the input data and that deviates from the normal range.

[0371] The present invention provides a system for evaluating and optimizing the performance and quality of a base station. This system mainly includes the following functions:

[0372] First, the user collects the base station type, specifications, and performance data and inputs it into the server from their device using a dedicated app or web interface. This data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc.

[0373] The server then performs preprocessing on the received data, including filling in gaps in the data and detecting and correcting outliers. It also performs normalization to align all numerical data to the same units. For example, if the antenna altitude is measured in both meters and feet, it will be normalized to meters.

[0374] The preprocessed data is input into a generative AI model by the server. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance. Based on these evaluation results, the server generates feedback including improvements and optimization proposals for the base station. The feedback includes specific instructions (e.g., "Resource allocation needs to be reviewed to reduce communication delays").

[0375] The generated feedback and evaluation results are sent from the server to the terminal, where the user can check them. The feedback and evaluation results are displayed visually in graphs and charts, making them easy for the user to understand and adjust or improve the base station as needed.

[0376] As a specific use case, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0377] Here are some examples of prompts:

[0378] "Base station ID: A123, Antenna height: 20m, Output power: 50W, Number of users: 100, Data usage: 50GB / day. Please rate the performance and quality of this base station and let us know what needs to be improved."

[0379] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] The user inputs base station data, including base station ID, antenna altitude, output power, number of current users, data usage, etc. The user then sends this information from their device to the server using a dedicated app or web interface.

[0383] Input: Base station ID, antenna altitude, output power, number of current users, data usage.

[0384] Output: Base station data sent to the server.

[0385] Step 2:

[0386] The server receives the data sent by the user and performs preprocessing on the data. This preprocessing involves filling in missing data and detecting and correcting outliers. It also normalizes all numerical data to use the same units. For example, if altitude units are mixed, such as meters and feet, it will unify all data into meters.

[0387] Input: Base station data sent by the user.

[0388] Output: Preprocessed data.

[0389] Step 3:

[0390] The server inputs the preprocessed data into a generative AI model, which calculates base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance.

[0391] Input: Preprocessed data.

[0392] Output: Performance indicators of the base station.

[0393] Step 4:

[0394] The server generates feedback based on the performance indicators obtained from the generative AI model. Specifically, it analyzes the evaluation data and derives improvements and optimization proposals for the base station. For example, it generates feedback such as "resource allocation needs to be reviewed to reduce communication latency."

[0395] Input: Performance metrics obtained from the generative AI model.

[0396] Output: Feedback with improvements and optimization suggestions.

[0397] Step 5:

[0398] The server sends the generated feedback and evaluation results to the device, which receives them and displays them for the user to review. The results are presented visually in an easy-to-understand manner using graphs and charts.

[0399] Input: Feedback including improvements and optimization suggestions.

[0400] Output: A device that displays the feedback and evaluation results.

[0401] Through this process, users will be able to efficiently and accurately evaluate the performance and quality of base stations and quickly implement improvement proposals.

[0402] (Application example 1)

[0403] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0404] In conventional industrial robot systems, the wireless communication environment between robots is not adequately evaluated or optimized, resulting in problems such as communication delays and reduced data throughput. This can lead to reduced factory production efficiency and make it difficult to ensure quality. Therefore, it is necessary to monitor and evaluate the wireless communication environment of robots in real time and provide optimal improvement proposals.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0406] In this invention, the server includes means for inputting the type, specifications, and performance data of the base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation results, means for displaying the generated feedback and evaluation results, means for inputting communication environment data of the robot and evaluating its performance, and means for generating proposals for optimizing the communication environment based on the evaluation results. This makes it possible to improve the communication quality between robots in a factory and increase production efficiency.

[0407] A "base station" is a communication facility installed to provide wireless communication, and is a device that relays and manages communications within a specific area.

[0408] "Performance data" refers to information that measures the operation and capabilities of base stations and robots, and refers to specific numerical data such as transmission success rate, communication delay, and data throughput.

[0409] "Preprocessing" is the process of organizing input raw data and processing or shaping the data to make it easier to analyze and evaluate.

[0410] A "generative AI model" is an artificial intelligence system that has been trained in advance using large amounts of data. It is used to evaluate performance and quality based on the input data and propose optimal solutions.

[0411] "Feedback" refers to information such as guidance, advice, and specific improvement proposals that are generated based on the results of evaluation and analysis.

[0412] "Communication environment" refers to the physical and electronic conditions and circumstances under which wireless communications occur, including signal strength, interference, data throughput, etc.

[0413] "Areas for improvement" are points that are identified based on the evaluation results and require specific corrections or changes to improve performance or quality.

[0414] "Optimization suggestions" are specific methods or procedures proposed to maximize performance or quality.

[0415] "Evaluation results" are the results of data analyzed by the generative AI model, and show the numerical values ​​and trends of specific performance and quality indicators.

[0416] "Display" refers to the act of visually presenting evaluation results, feedback, and improvement proposals to users, often using graphs, charts, and the like.

[0417] This paper describes a system for evaluating and optimizing the wireless communication environment of robots in a factory. This system collects various data, evaluates the communication environment using a generative AI model, and proposes improvements.

[0418] Entering base station data

[0419] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device. Specific data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is input via a dedicated app or web interface.

[0420] Data Preprocessing

[0421] The server receives the data sent by the user and begins preprocessing the data. At this stage, missing parts of the data are filled in, and outliers are detected and corrected. The data is also normalized to form a unified format. For example, all numerical data is aligned to the same units to facilitate subsequent processing. Specifically, the numerical data is scaled using StandardScaler.

[0422] Evaluation by generative AI model

[0423] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators of the base station (transmission success rate, communication delay, downlink throughput, etc.) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on these evaluation results.

[0424] Generate feedback

[0425] The server then generates detailed feedback on the base station's performance and quality, including specific improvements and optimization suggestions. For example, it might provide specific instructions such as, "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0426] Notification and display of results

[0427] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0428] Specific examples of wireless communication environment evaluation and optimization

[0429] For example, when multiple robots communicate simultaneously in a factory, a specific robot may experience communication delays. The application collects the robot's communication data and evaluates it using an AI model. As a result of this evaluation, it is determined that the communication delay is high, and feedback is provided stating that "routing needs to be revised to reduce communication delays."

[0430] Prompt Sentence Examples

[0431] Evaluate the wireless communication environment for robots in your factory and provide performance metrics for communication latency and data throughput. Data collected includes robot ID, signal strength, signal interference, data throughput, etc. Also generate improvement suggestions based on the evaluation results.

[0432] In this way, the present invention provides a system that improves the quality of communication between robots in a factory, thereby contributing to improved production efficiency and quality.

[0433] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0434] Step 1:

[0435] Users collect base station type, specifications, and performance data and input it into the server from their devices. Specific data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is sent to the server via a dedicated app or web interface.

[0436] Step 2:

[0437] The server receives data sent by users. The received data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. The server first fills in missing data and detects and corrects outliers. This preprocessing step also scales the data using StandardScaler to align all numerical data to the same units.

[0438] Step 3:

[0439] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (transmission success rate, communication delay, downlink throughput, etc.) based on a large amount of training data collected in advance. This model quickly evaluates performance based on the input data and outputs the results.

[0440] Step 4:

[0441] The server that receives the evaluation results generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, if the transmission success rate is low, the feedback may include specific suggestions such as "increase the base station's output power," or if communication latency is high, "review of resource allocation is necessary."

[0442] Step 5:

[0443] The server automatically sends the generated feedback and evaluation results to the terminal. This feedback, including graphs and charts, is provided to the user in a visually easy-to-understand format, allowing the user to review it and make adjustments or improvements to the base station.

[0444] Step 6:

[0445] Based on the evaluation results and feedback displayed on the device, the user can make necessary adjustments, such as changing the angle of the base station antenna or adjusting the output power, according to the specific instructions provided in the feedback.

[0446] In this way, the system of the present invention optimizes the quality of communication between robots in a factory through a series of steps, starting with the collection of basic data, followed by data preprocessing, evaluation using a generative AI model, feedback generation, notification of results, and actual adjustment work.

[0447] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0448] The present invention is a system for evaluating the performance and quality of a base station and providing feedback that takes into account user sentiment, and includes the following steps: The embodiments of the invention will be described through specific use examples.

[0449] Entering base station data

[0450] The user collects base station type, specifications, and performance data and inputs it to the server from the terminal, such as base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then sends this information to the server.

[0451] Data Preprocessing

[0452] The server receives the data sent by the user, performs basic validation of the data to ensure it is not incomplete or invalid, completes any incomplete data, corrects any outliers, and normalizes the data to ensure accurate evaluation.

[0453] Evaluation by generative AI model

[0454] The server inputs the preprocessed data into a generative AI model, which evaluates the performance and quality of the base station based on a pre-trained algorithm, calculating indicators such as transmission success rate, communication delay, and downlink throughput.

[0455] Generate feedback

[0456] The server receives the results from the generative AI model and generates detailed feedback. Based on the evaluation results, it provides specific improvements and optimization suggestions. For example, it creates a report with specific instructions such as, "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0457] Recognizing user emotions with an emotion engine

[0458] Based on the generated feedback and evaluation results, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, typing speed, and specific keywords to determine the user's emotional state.

[0459] Customized Feedback

[0460] After recognizing the user's emotions, the server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, the server gently explains what needs to be improved and provides specific support methods. For a satisfied user, the server presents a positive evaluation result and offers further improvement suggestions.

[0461] Notification and display of results

[0462] The customized feedback and evaluation results are automatically sent to the device. The user can review the evaluation results and feedback displayed on the device and make adjustments or improvements to the base station as needed. Graphs and charts are used for visual display to present the results in an easy-to-understand manner.

[0463] Specific examples

[0464] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0465] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0469] Step 2:

[0470] The server receives the data sent from the device. After receiving it, it performs basic validation of the data to check whether it contains incomplete or invalid data. If there is any incomplete data, it sends a notification to the user to complete it.

[0471] Step 3:

[0472] The server preprocesses the received data. This includes data shaping (e.g., standardizing all data to a common unit), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it can be corrected by looking at past data.

[0473] Step 4:

[0474] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0475] Step 5:

[0476] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0477] Step 6:

[0478] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0479] Step 7:

[0480] The server recognizes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, input speed, and specific keywords to determine the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0481] Step 8:

[0482] The server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, it gently explains what needs to be improved and provides specific support methods. For a satisfied user, it presents the evaluation results in a positive light and offers further suggestions for improvement.

[0483] Step 9:

[0484] The server generates graphs and charts to display customized feedback and assessment results in a visually easy-to-understand format.

[0485] Step 10:

[0486] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0487] Step 11:

[0488] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0489] Specific examples

[0490] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0491] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0492] Example 2

[0493] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] As communication infrastructure continues to become more complex and sophisticated, it will be necessary to accurately and quickly evaluate the performance and quality of base stations and provide appropriate feedback. Conventional systems face technical limitations due to the lack of data preprocessing, performance evaluation, and methods for providing feedback that takes user emotions into account. Taking appropriate measures based on the user's emotional state can facilitate the acceptance and implementation of improvements, potentially improving the overall user experience. Against this background, the objective of this invention is to provide a base station performance evaluation system that can take user emotions into account and provide feedback that is appropriate to the user's emotions.

[0495] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the type, specifications, and performance data of the base station, a means for preprocessing the input data, a means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, a means for generating feedback including improvements and optimization suggestions based on the evaluation results, and a means for recognizing the user's emotions and customizing the feedback according to the user's emotional state. This makes it possible to accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0496] A "base station" is a device that communicates with mobile terminals in a communication network.

[0497] "Performance data" refers to information related to the communication quality and performance of a base station, and specifically includes transmission success rate, communication delay, data throughput, etc.

[0498] "Preprocessing" refers to the process of validating, imputing, and normalizing data to improve its accuracy.

[0499] A "generative AI model" is a model trained using machine learning algorithms to evaluate the performance and quality of base stations based on input data.

[0500] "Feedback" refers to reports and instructions, including suggestions for improvement and optimization, provided based on the results of the evaluation.

[0501] "Emotion engine" refers to an algorithm or module for analyzing and recognizing a user's emotional state.

[0502] "Customization" refers to the adjustment or modification of content to meet specific conditions or requirements.

[0503] "Evaluation Results" refers to the measurement results regarding the performance and quality of base stations obtained by the generative AI model.

[0504] "Visually displayed" refers to displaying information using visual means such as graphs or charts.

[0505] The present invention provides a system for evaluating the performance and quality of a base station and providing feedback that takes into account the user's feelings. An embodiment of the program for this system will be described in detail below.

[0506] Entering base station data

[0507] The user uses the device's input interface (e.g., a web form or mobile app) to collect and input base station type, specifications, and performance data to the server. This data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. Once the user completes the input, the device sends the data to the server using an HTTP POST request.

[0508] Data Preprocessing

[0509] The server receives the data submitted by the user and stores it in a database (e.g., MySQL or PostgreSQL). The server then performs basic validation of the data to ensure it does not contain incomplete or invalid data. The server then executes logic to impute incomplete data and correct outliers. Finally, the data is normalized and converted into a consistent format.

[0510] Evaluation by generative AI model

[0511] The server calls a generative AI model built in a programming language such as Python or R. The server inputs the preprocessed data into the generative AI model and performs performance evaluation. This evaluation process generates results such as a 92% transmission success rate, a 35ms communication delay, and a 120Mbps downlink throughput.

[0512] Generate feedback

[0513] The server receives the evaluation results from the generated AI model and automatically generates a detailed feedback report. The feedback includes specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." This feedback report is generated in PDF or HTML format and converted into a format that users can easily view.

[0514] Recognizing user emotions with an emotion engine

[0515] The server recognizes the user's emotions using an emotion engine based on the generated feedback and evaluation results. This emotion engine uses modules (e.g., OpenCV and TensorFlow) for facial expression recognition and voice analysis. The server determines the user's emotional state and reflects the results in the feedback generation process.

[0516] Customized Feedback

[0517] The server customizes the feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will explain areas for improvement in a gentle manner and provide specific support methods. If the user is satisfied, the server will present the evaluation results in a positive light and suggest further improvements.

[0518] Notification and display of results

[0519] The customized feedback and evaluation results are automatically sent to the device, which implements an interface to display these feedback reports to the user. Visual displays include graphs and charts to present the results in an easy-to-understand manner.

[0520] Specific examples

[0521] For example, consider the evaluation of a suburban base station. A user enters information into a form on their device, such as the base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." This data is then sent to the server, which preprocesses it and performs an evaluation using a generative AI model. The evaluation results are a 92% transmission success rate, a 35ms communication latency, and a 120Mbps downlink throughput. Based on this, the server generates feedback suggesting that resource allocation be revised to reduce communication latency. If the emotion engine detects that the user is stressed, it offers suggestions for improvement in a gentle manner. Finally, this feedback is sent to the device, where the user can review it and make appropriate adjustments, thereby improving the performance and quality of the base station.

[0522] Prompt Sentence Examples

[0523] "Please evaluate the following: base station ID: 12345, antenna altitude: 15m, output power: 30W, current number of users: 50, data usage: 20GB / day. Calculate the current transmission success rate, communication delay, and downlink throughput, and provide feedback on the optimal resource allocation."

[0524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0525] Step 1: Enter base station data

[0526] The user uses the device's input interface (web form or mobile app) to input base station type, specifications, and performance data. This data includes base station ID, antenna altitude, output power, current number of users, data usage, etc. Specifically, the user inputs base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." The device temporarily stores the input data, and once all the data is collected, it sends it to the server using an HTTP POST request. The input triggers data transfer from the device to the server, and the output is the data set received by the server.

[0527] Step 2: Data Preprocessing

[0528] The server receives data sent by users and stores it in a database (for example, MySQL or PostgreSQL). The input is the raw data received from the base station, and the output is normalized data that has been verified and stored in the database. First, the server validates the data. Specifically, it checks whether numeric fields contain character data or abnormal values ​​(for example, negative values ​​or very large values). If there is incomplete data, it is complemented using a predetermined method (for example, complementing with the average value), and if an abnormal value is found, it is corrected to fall within a specific threshold range. Finally, the data is normalized. For example, data usage is standardized to units such as MB or GB.

[0529] Step 3: Evaluation with a generative AI model

[0530] The server inputs the preprocessed data into the generative AI model. The input is a normalized dataset, and the output is the performance evaluation results. The server calls the generative AI model, which is built using a programming language such as Python or R. Specifically, the server sends the input data to the generative AI model and starts the evaluation process. The generative AI model evaluates the performance of the base station based on a pre-trained algorithm. For example, the generated evaluation results might be "transmission success rate 92%, communication latency 35 ms, and downlink throughput 120 Mbps." These results are collected by the server and used to proceed to the next step.

[0531] Step 4: Generate feedback

[0532] The server receives the evaluation results obtained from the generative AI model and generates a detailed feedback report. The input is the evaluation results and the output is the feedback report. The server runs a script that automatically generates a feedback report based on the evaluation results. For example, the server provides specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." The feedback report is generated in PDF or HTML format and is converted into a format that can be easily viewed by users. Specific operations include analyzing the evaluation results, identifying areas for improvement, and generating a report.

[0533] Step 5: Recognizing user emotions with the emotion engine

[0534] The server uses an emotion engine to recognize the user's emotional state based on the generated feedback and evaluation results. The inputs are the evaluation results, feedback content, and the user's reaction data, and the output is the analyzed emotional state. The emotion engine uses modules for facial expression recognition (e.g., OpenCV) and voice analysis (e.g., TensorFlow). The server uses these tools to analyze the user's facial expressions, voice, text input speed, and specific keywords. This determines the user's emotional state. For example, if the user has a stiff expression or inputs slowly, it is determined that the user is feeling stressed.

[0535] Step 6: Customize your feedback

[0536] The server customizes the feedback according to the user's emotional state. The input is the analyzed emotional state and a feedback report, and the output is a customized feedback report. For example, if the user is feeling stressed, logic is executed to explain areas for improvement in gentle terms and provide specific support methods. Wording such as "Communication latency has improved, but further optimization would be beneficial" is used. If the user is satisfied, the evaluation results are presented positively and the content is changed to suggest further improvements.

[0537] Step 7: Notification and display of results

[0538] Customized feedback and evaluation results are automatically sent to the device. The input is the customized feedback report, and the output is the feedback report displayed on the user's device. The server sends an HTTP request to an API endpoint to send the feedback report to the device. The device implements an interface to display the received feedback report to the user. Specifically, it generates visually easy-to-understand graphs and charts and displays the results in a format that the user can easily understand.

[0539] Through the above processing steps, the system can accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0540] (Application example 2)

[0541] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0542] Autonomous vehicles are required to perform self-evaluation to improve safety and efficiency. It is also necessary to provide feedback that takes into account the emotions of the driver or operator, thereby reducing the mental burden and providing a better driving experience. However, conventional systems have not adequately evaluated the performance of autonomous vehicles or provided feedback based on the user's emotions, limiting the application of evaluation results and the effectiveness of feedback.

[0543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0544] In this invention, the server includes means for inputting the type, specifications, and performance data of a base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station, means for generating feedback including improvements and optimization suggestions based on the evaluation results, means for using an emotion engine that recognizes the emotional state of a user based on the evaluation results and feedback, means for customizing the feedback based on the emotional state of the user, and means for displaying the generated feedback and evaluation results, thereby enabling performance evaluation of an autonomous vehicle and providing feedback according to the user's emotions.

[0545] A "base station" is a hardware device in a communication system that transmits or receives radio waves and communicates with user devices.

[0546] A "type" is a group or category classified according to specific criteria.

[0547] A "specification" is a detailed description or characterization of the design and functionality of a system or device.

[0548] "Performance Data" means any measurement or record relating to the operation or functionality of a device or system.

[0549] "Preprocessing" refers to the initial processing and shaping of raw data to convert it into a form suitable for analysis and evaluation.

[0550] A "generative AI model" is an artificial intelligence system that is trained using machine learning algorithms on large amounts of data.

[0551] "Performance" refers to the degree or quality of the functions or operations that a device or system can perform.

[0552] "Quality" is the degree to which a product or service meets specific requirements or standards.

[0553] "Evaluation" means judging the value or performance of an object based on specified criteria.

[0554] An "improvement" is a change or modification that needs to be made to improve a system or process.

[0555] An "optimization proposal" is a specific strategy or measure to optimize a system or process.

[0556] "Feedback" means providing a response or evaluation of an action or behavior.

[0557] An "emotion engine" is a system that analyzes a user's emotional state and adjusts feedback based on that.

[0558] A "user's emotional state" refers to the sensations and emotions a user experiences in a particular situation.

[0559] "Customization" means modifying or adapting a system or service to meet the specific needs and requirements of a user.

[0560] This invention is a system that evaluates the performance of an autonomous vehicle and provides feedback according to the driver's emotions. Below, we will explain the outline of the program for realizing this system and its processing.

[0561] Generating a Program

[0562] The server first receives various performance data (such as location, speed, acceleration, and road conditions) collected from the autonomous vehicle as input data. This data is collected through sensors and cameras. The server then performs preprocessing on the data, which includes missing value imputation, incorrect data detection and correction, and normalization.

[0563] Next, the preprocessed data is input into a generative AI model for evaluation. This generative AI model evaluates performance based on indicators such as transmission success rate, communication delay, and downlink throughput, and obtains the results.

[0564] Based on the evaluation results, the server generates feedback including improvement points and optimization suggestions. This feedback indicates areas that need improvement and specific optimization suggestions. For example, a report may be created with specific instructions such as, "The obstacle avoidance success rate is 95%, but the lane-keeping accuracy is low, so the sensors need to be adjusted."

[0565] Based on the evaluation results and the generated feedback, the server uses an emotion engine to recognize the driver's emotional state. The emotion engine analyzes facial expressions and tone of voice data obtained from input devices such as cameras and microphones to determine the user's emotional state. For example, if the driver is feeling stressed, the content of the feedback will be softened.

[0566] Finally, the generated feedback and evaluation results are displayed on a device such as a head-mounted display (HMD) in the driver's seat or a smartphone, and the results are presented visually using graphs and charts.

[0567] Examples of specific examples and prompts

[0568] Specific examples

[0569] Simulation data for an autonomous vehicle traveling at 60 km / h on dry roads is input. Evaluation using a generative AI model yields results such as a 95% obstacle avoidance success rate, 20 ms communication latency, and 150 Mbps downlink throughput. Based on these results, the emotion engine determines the driver is in a mild stress state and provides polite feedback, such as "Consider recalibrating your sensors to improve lane-keeping accuracy."

[0570] Prompt Sentence Examples

[0571] Please rate the performance of the self-driving vehicle based on the following data.

[0572] Vehicle ID: 12345

[0573] Speed: 60km / h

[0574] Acceleration: 3 m / s²

[0575] Road Condition: Dry

[0576] Based on the evaluation results, feedback should also be generated for users when they are experiencing emotional stress.

[0577] This system makes it possible to accurately assess vehicle performance and provide feedback to the driver based on their emotions.

[0578] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0579] Step 1:

[0580] The user inputs performance data of the autonomous vehicle and sends it to the server.

[0581] Input data includes location information, speed, acceleration, road conditions, etc. Users collect this data from various sensors and cameras on their vehicle and send it from their device to the server, where the basic data required for evaluation is accumulated.

[0582] Step 2:

[0583] The server pre-processes the received data.

[0584] The server first verifies the received data and detects incomplete or anomalous data. It then complements or corrects the incomplete data and eliminates any anomalous data. It also normalizes the various data and formats them into a unified format. This preprocessing ensures that the generative AI model can accurately and consistently evaluate the data.

[0585] Step 3:

[0586] The preprocessed data is input into a generative AI model to evaluate vehicle performance.

[0587] The server inputs the normalized data into the generative AI model and evaluates it based on various performance indicators (e.g., obstacle avoidance success rate, communication latency, downlink throughput). The generative AI model is trained in advance using a large amount of learning data, enabling it to obtain highly accurate evaluation results. The evaluation results for each indicator are generated as output.

[0588] Step 4:

[0589] Generate feedback based on the evaluation results.

[0590] The server analyzes the evaluation results obtained from the generative AI model and generates specific improvement and optimization suggestions. For example, if the obstacle avoidance success rate is high and communication latency is low, feedback is generated that indicates good overall performance, but that the accuracy of specific sensors should be further improved. The output is a detailed feedback report with improvement suggestions.

[0591] Step 5:

[0592] The user's emotional state is recognized based on the evaluation results and feedback.

[0593] The server uses an emotion engine to analyze the evaluation results and feedback. It determines the user's emotional state based on facial expressions, tone of voice, and typing speed. This is done based on input data from the camera and microphone. The output is the user's recognized emotional state.

[0594] Step 6:

[0595] Customize feedback based on the user's emotional state.

[0596] The server adapts the feedback depending on the user's perceived emotional state. For example, if the user is stressed, it softens the feedback and suggests specific ways to support them. Conversely, if the user is satisfied, it provides positive suggestions for improvement. The output is customized feedback.

[0597] Step 7:

[0598] The generated feedback and evaluation results are displayed on the terminal.

[0599] The server sends the feedback and evaluation results to the device, which then visually displays the information to the user, specifically using graphs and charts to present the results in an easy-to-understand manner, allowing the user to make appropriate adjustments and improvements.

[0600] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0601] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0603] [Third embodiment]

[0604] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0605] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0606] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0607] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0608] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0609] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0610] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0611] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0612] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0613] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0614] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0615] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0616] The present invention is a system for evaluating the performance and quality of a base station, and mainly includes the following steps: An embodiment of the invention will be described through a specific use example.

[0617] Entering base station data

[0618] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data input is done through a dedicated app or web interface.

[0619] Data Preprocessing

[0620] The server receives the data sent by the user and begins preprocessing the data. During this stage, it fills in gaps in the data, detects and corrects outliers, and normalizes the data into a unified format. For example, all numerical data is converted to the same units, which makes subsequent processing easier.

[0621] Evaluation by generative AI model

[0622] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on the evaluation results.

[0623] Generate feedback

[0624] The server receives the evaluation results and generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, specific instructions such as "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0625] Notification and display of results

[0626] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0627] Specific examples

[0628] For example, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0629] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0630] The processing flow will be explained below.

[0631] Step 1:

[0632] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0633] Step 2:

[0634] The server receives the data sent from the device, performs basic validation of the data to ensure it does not contain incomplete or invalid data, and generates a warning if there are missing values.

[0635] Step 3:

[0636] The server preprocesses the received data. This includes data formatting (e.g., standardizing units), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it will be corrected by referring to past data.

[0637] Step 4:

[0638] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0639] Step 5:

[0640] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0641] Step 6:

[0642] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0643] Step 7:

[0644] The server creates graphs and charts to display the generated feedback and evaluation results, making it easier for users to visually understand the results.

[0645] Step 8:

[0646] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0647] Step 9:

[0648] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0649] Example 1

[0650] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0651] Systems that accurately evaluate the performance and quality of base stations often lack accuracy due to insufficient handling of missing data and outliers. Furthermore, there are limited means to visually display the evaluation results in a way that is easy for users to understand. As a result, there is a challenge in accurately and efficiently optimizing base station performance.

[0652] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0653] In this invention, the server includes means for inputting base station type, specifications, and performance data, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation data, means for displaying the generated feedback and evaluation data, means for filling in missing data and detecting and correcting outliers, and means for normalizing all numerical data to the same units, thereby enabling accurate evaluation and rapid optimization of base station data.

[0654] "Base station data" refers to information about the type, specifications, and performance of a base station, and specifically includes the base station ID, antenna height, output power, number of users, data usage, etc.

[0655] "Preprocessing" refers to the process of preparing input data in a format suitable for subsequent processing by performing operations such as filling in missing parts, detecting and correcting outliers, and normalizing numerical data.

[0656] A "generative AI model" refers to an artificial intelligence model for evaluating base station performance based on large amounts of training data, specifically calculating performance indicators such as transmission success rate, communication delay, and downlink throughput.

[0657] "Feedback" refers to information that specifically indicates areas for improvement in base station performance and optimization suggestions based on the evaluation results of the generative AI model.

[0658] "Normalization" refers to the process of aligning all numerical data to the same units, making it easier to compare and integrate data.

[0659] An "outlier" is a value that is clearly abnormal compared to other data in the input data and that deviates from the normal range.

[0660] The present invention provides a system for evaluating and optimizing the performance and quality of a base station. This system mainly includes the following functions:

[0661] First, the user collects the base station type, specifications, and performance data and inputs it into the server from their device using a dedicated app or web interface. This data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc.

[0662] The server then performs preprocessing on the received data, including filling in gaps in the data and detecting and correcting outliers. It also performs normalization to align all numerical data to the same units. For example, if the antenna altitude is measured in both meters and feet, it will be normalized to meters.

[0663] The preprocessed data is input into a generative AI model by the server. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance. Based on these evaluation results, the server generates feedback including improvements and optimization proposals for the base station. The feedback includes specific instructions (e.g., "Resource allocation needs to be reviewed to reduce communication delays").

[0664] The generated feedback and evaluation results are sent from the server to the terminal, where the user can check them. The feedback and evaluation results are displayed visually in graphs and charts, making them easy for the user to understand and adjust or improve the base station as needed.

[0665] As a specific use case, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0666] Here are some examples of prompts:

[0667] "Base station ID: A123, Antenna height: 20m, Output power: 50W, Number of users: 100, Data usage: 50GB / day. Please rate the performance and quality of this base station and let us know what needs to be improved."

[0668] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0669] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] The user inputs base station data, including base station ID, antenna altitude, output power, number of current users, data usage, etc. The user then sends this information from their device to the server using a dedicated app or web interface.

[0672] Input: Base station ID, antenna altitude, output power, number of current users, data usage.

[0673] Output: Base station data sent to the server.

[0674] Step 2:

[0675] The server receives the data sent by the user and performs preprocessing on the data. This preprocessing involves filling in missing data and detecting and correcting outliers. It also normalizes all numerical data to use the same units. For example, if altitude units are mixed, such as meters and feet, it will unify all data into meters.

[0676] Input: Base station data sent by the user.

[0677] Output: Preprocessed data.

[0678] Step 3:

[0679] The server inputs the preprocessed data into a generative AI model, which calculates base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance.

[0680] Input: Preprocessed data.

[0681] Output: Performance indicators of the base station.

[0682] Step 4:

[0683] The server generates feedback based on the performance indicators obtained from the generative AI model. Specifically, it analyzes the evaluation data and derives improvements and optimization proposals for the base station. For example, it generates feedback such as "resource allocation needs to be reviewed to reduce communication latency."

[0684] Input: Performance metrics obtained from the generative AI model.

[0685] Output: Feedback with improvements and optimization suggestions.

[0686] Step 5:

[0687] The server sends the generated feedback and evaluation results to the device, which receives them and displays them for the user to review. The results are presented visually in an easy-to-understand manner using graphs and charts.

[0688] Input: Feedback including improvements and optimization suggestions.

[0689] Output: A device that displays the feedback and evaluation results.

[0690] Through this process, users will be able to efficiently and accurately evaluate the performance and quality of base stations and quickly implement improvement proposals.

[0691] (Application example 1)

[0692] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0693] In conventional industrial robot systems, the wireless communication environment between robots is not adequately evaluated or optimized, resulting in problems such as communication delays and reduced data throughput. This can lead to reduced factory production efficiency and make it difficult to ensure quality. Therefore, it is necessary to monitor and evaluate the wireless communication environment of robots in real time and provide optimal improvement proposals.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0695] In this invention, the server includes means for inputting the type, specifications, and performance data of the base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation results, means for displaying the generated feedback and evaluation results, means for inputting communication environment data of the robot and evaluating its performance, and means for generating proposals for optimizing the communication environment based on the evaluation results. This makes it possible to improve the communication quality between robots in a factory and increase production efficiency.

[0696] A "base station" is a communication facility installed to provide wireless communication, and is a device that relays and manages communications within a specific area.

[0697] "Performance data" refers to information that measures the operation and capabilities of base stations and robots, and refers to specific numerical data such as transmission success rate, communication delay, and data throughput.

[0698] "Preprocessing" is the process of organizing input raw data and processing or shaping the data to make it easier to analyze and evaluate.

[0699] A "generative AI model" is an artificial intelligence system that has been trained in advance using large amounts of data. It is used to evaluate performance and quality based on the input data and propose optimal solutions.

[0700] "Feedback" refers to information such as guidance, advice, and specific improvement proposals that are generated based on the results of evaluation and analysis.

[0701] "Communication environment" refers to the physical and electronic conditions and circumstances under which wireless communications occur, including signal strength, interference, data throughput, etc.

[0702] "Areas for improvement" are points that are identified based on the evaluation results and require specific corrections or changes to improve performance or quality.

[0703] "Optimization suggestions" are specific methods or procedures proposed to maximize performance or quality.

[0704] "Evaluation results" are the results of data analyzed by the generative AI model, and show the numerical values ​​and trends of specific performance and quality indicators.

[0705] "Display" refers to the act of visually presenting evaluation results, feedback, and improvement proposals to users, often using graphs, charts, and the like.

[0706] This paper describes a system for evaluating and optimizing the wireless communication environment of robots in a factory. This system collects various data, evaluates the communication environment using a generative AI model, and proposes improvements.

[0707] Entering base station data

[0708] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device. Specific data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is input via a dedicated app or web interface.

[0709] Data Preprocessing

[0710] The server receives the data sent by the user and begins preprocessing the data. At this stage, missing parts of the data are filled in, and outliers are detected and corrected. The data is also normalized to form a unified format. For example, all numerical data is aligned to the same units to facilitate subsequent processing. Specifically, the numerical data is scaled using StandardScaler.

[0711] Evaluation by generative AI model

[0712] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators of the base station (transmission success rate, communication delay, downlink throughput, etc.) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on these evaluation results.

[0713] Generate feedback

[0714] The server then generates detailed feedback on the base station's performance and quality, including specific improvements and optimization suggestions. For example, it might provide specific instructions such as, "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0715] Notification and display of results

[0716] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0717] Specific examples of wireless communication environment evaluation and optimization

[0718] For example, when multiple robots communicate simultaneously in a factory, a specific robot may experience communication delays. The application collects the robot's communication data and evaluates it using an AI model. As a result of this evaluation, it is determined that the communication delay is high, and feedback is provided stating that "routing needs to be revised to reduce communication delays."

[0719] Prompt Sentence Examples

[0720] Evaluate the wireless communication environment for robots in your factory and provide performance metrics for communication latency and data throughput. Data collected includes robot ID, signal strength, signal interference, data throughput, etc. Also generate improvement suggestions based on the evaluation results.

[0721] In this way, the present invention provides a system that improves the quality of communication between robots in a factory, thereby contributing to improved production efficiency and quality.

[0722] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0723] Step 1:

[0724] Users collect base station type, specifications, and performance data and input it into the server from their devices. Specific data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is sent to the server via a dedicated app or web interface.

[0725] Step 2:

[0726] The server receives data sent by users. The received data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. The server first fills in missing data and detects and corrects outliers. This preprocessing step also scales the data using StandardScaler to align all numerical data to the same units.

[0727] Step 3:

[0728] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (transmission success rate, communication delay, downlink throughput, etc.) based on a large amount of training data collected in advance. This model quickly evaluates performance based on the input data and outputs the results.

[0729] Step 4:

[0730] The server that receives the evaluation results generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, if the transmission success rate is low, the feedback may include specific suggestions such as "increase the base station's output power," or if communication latency is high, "review of resource allocation is necessary."

[0731] Step 5:

[0732] The server automatically sends the generated feedback and evaluation results to the terminal. This feedback, including graphs and charts, is provided to the user in a visually easy-to-understand format, allowing the user to review it and make adjustments or improvements to the base station.

[0733] Step 6:

[0734] Based on the evaluation results and feedback displayed on the device, the user can make necessary adjustments, such as changing the angle of the base station antenna or adjusting the output power, according to the specific instructions provided in the feedback.

[0735] In this way, the system of the present invention optimizes the quality of communication between robots in a factory through a series of steps, starting with the collection of basic data, followed by data preprocessing, evaluation using a generative AI model, feedback generation, notification of results, and actual adjustment work.

[0736] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0737] The present invention is a system for evaluating the performance and quality of a base station and providing feedback that takes into account user sentiment, and includes the following steps: The embodiments of the invention will be described through specific use examples.

[0738] Entering base station data

[0739] The user collects base station type, specifications, and performance data and inputs it to the server from the terminal, such as base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then sends this information to the server.

[0740] Data Preprocessing

[0741] The server receives the data sent by the user, performs basic validation of the data to ensure it is not incomplete or invalid, completes any incomplete data, corrects any outliers, and normalizes the data to ensure accurate evaluation.

[0742] Evaluation by generative AI model

[0743] The server inputs the preprocessed data into a generative AI model, which evaluates the performance and quality of the base station based on a pre-trained algorithm, calculating indicators such as transmission success rate, communication delay, and downlink throughput.

[0744] Generate feedback

[0745] The server receives the results from the generative AI model and generates detailed feedback. Based on the evaluation results, it provides specific improvements and optimization suggestions. For example, it creates a report with specific instructions such as, "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0746] Recognizing user emotions with an emotion engine

[0747] Based on the generated feedback and evaluation results, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, typing speed, and specific keywords to determine the user's emotional state.

[0748] Customized Feedback

[0749] After recognizing the user's emotions, the server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, the server gently explains what needs to be improved and provides specific support methods. For a satisfied user, the server presents a positive evaluation result and offers further improvement suggestions.

[0750] Notification and display of results

[0751] The customized feedback and evaluation results are automatically sent to the device. The user can review the evaluation results and feedback displayed on the device and make adjustments or improvements to the base station as needed. Graphs and charts are used for visual display to present the results in an easy-to-understand manner.

[0752] Specific examples

[0753] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0754] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0755] The processing flow will be explained below.

[0756] Step 1:

[0757] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0758] Step 2:

[0759] The server receives the data sent from the device. After receiving it, it performs basic validation of the data to check whether it contains incomplete or invalid data. If there is any incomplete data, it sends a notification to the user to complete it.

[0760] Step 3:

[0761] The server preprocesses the received data. This includes data shaping (e.g., standardizing all data to a common unit), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it can be corrected by looking at past data.

[0762] Step 4:

[0763] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0764] Step 5:

[0765] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0766] Step 6:

[0767] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0768] Step 7:

[0769] The server recognizes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, input speed, and specific keywords to determine the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[0770] Step 8:

[0771] The server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, it gently explains what needs to be improved and provides specific support methods. For a satisfied user, it presents the evaluation results in a positive light and offers further suggestions for improvement.

[0772] Step 9:

[0773] The server generates graphs and charts to display customized feedback and assessment results in a visually easy-to-understand format.

[0774] Step 10:

[0775] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0776] Step 11:

[0777] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0778] Specific examples

[0779] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[0780] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[0781] Example 2

[0782] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0783] As communication infrastructure continues to become more complex and sophisticated, it will be necessary to accurately and quickly evaluate the performance and quality of base stations and provide appropriate feedback. Conventional systems face technical limitations due to the lack of data preprocessing, performance evaluation, and methods for providing feedback that takes user emotions into account. Taking appropriate measures based on the user's emotional state can facilitate the acceptance and implementation of improvements, potentially improving the overall user experience. Against this background, the objective of this invention is to provide a base station performance evaluation system that can take user emotions into account and provide feedback that is appropriate to the user's emotions.

[0784] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the type, specifications, and performance data of the base station, a means for preprocessing the input data, a means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, a means for generating feedback including improvements and optimization suggestions based on the evaluation results, and a means for recognizing the user's emotions and customizing the feedback according to the user's emotional state. This makes it possible to accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0785] A "base station" is a device that communicates with mobile terminals in a communication network.

[0786] "Performance data" refers to information related to the communication quality and performance of a base station, and specifically includes transmission success rate, communication delay, data throughput, etc.

[0787] "Preprocessing" refers to the process of validating, imputing, and normalizing data to improve its accuracy.

[0788] A "generative AI model" is a model trained using machine learning algorithms to evaluate the performance and quality of base stations based on input data.

[0789] "Feedback" refers to reports and instructions, including suggestions for improvement and optimization, provided based on the results of the evaluation.

[0790] "Emotion engine" refers to an algorithm or module for analyzing and recognizing a user's emotional state.

[0791] "Customization" refers to the adjustment or modification of content to meet specific conditions or requirements.

[0792] "Evaluation Results" refers to the measurement results regarding the performance and quality of base stations obtained by the generative AI model.

[0793] "Visually displayed" refers to displaying information using visual means such as graphs or charts.

[0794] The present invention provides a system for evaluating the performance and quality of a base station and providing feedback that takes into account the user's feelings. An embodiment of the program for this system will be described in detail below.

[0795] Entering base station data

[0796] The user uses the device's input interface (e.g., a web form or mobile app) to collect and input base station type, specifications, and performance data to the server. This data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. Once the user completes the input, the device sends the data to the server using an HTTP POST request.

[0797] Data Preprocessing

[0798] The server receives the data submitted by the user and stores it in a database (e.g., MySQL or PostgreSQL). The server then performs basic validation of the data to ensure it does not contain incomplete or invalid data. The server then executes logic to impute incomplete data and correct outliers. Finally, the data is normalized and converted into a consistent format.

[0799] Evaluation by generative AI model

[0800] The server calls a generative AI model built in a programming language such as Python or R. The server inputs the preprocessed data into the generative AI model and performs performance evaluation. This evaluation process generates results such as a 92% transmission success rate, a 35ms communication delay, and a 120Mbps downlink throughput.

[0801] Generate feedback

[0802] The server receives the evaluation results from the generated AI model and automatically generates a detailed feedback report. The feedback includes specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." This feedback report is generated in PDF or HTML format and converted into a format that users can easily view.

[0803] Recognizing user emotions with an emotion engine

[0804] The server recognizes the user's emotions using an emotion engine based on the generated feedback and evaluation results. This emotion engine uses modules (e.g., OpenCV and TensorFlow) for facial expression recognition and voice analysis. The server determines the user's emotional state and reflects the results in the feedback generation process.

[0805] Customized Feedback

[0806] The server customizes the feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will explain areas for improvement in a gentle manner and provide specific support methods. If the user is satisfied, the server will present the evaluation results in a positive light and suggest further improvements.

[0807] Notification and display of results

[0808] The customized feedback and evaluation results are automatically sent to the device, which implements an interface to display these feedback reports to the user. Visual displays include graphs and charts to present the results in an easy-to-understand manner.

[0809] Specific examples

[0810] For example, consider the evaluation of a suburban base station. A user enters information into a form on their device, such as the base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." This data is then sent to the server, which preprocesses it and performs an evaluation using a generative AI model. The evaluation results are a 92% transmission success rate, a 35ms communication latency, and a 120Mbps downlink throughput. Based on this, the server generates feedback suggesting that resource allocation be revised to reduce communication latency. If the emotion engine detects that the user is stressed, it offers suggestions for improvement in a gentle manner. Finally, this feedback is sent to the device, where the user can review it and make appropriate adjustments, thereby improving the performance and quality of the base station.

[0811] Prompt Sentence Examples

[0812] "Please evaluate the following: base station ID: 12345, antenna altitude: 15m, output power: 30W, current number of users: 50, data usage: 20GB / day. Calculate the current transmission success rate, communication delay, and downlink throughput, and provide feedback on the optimal resource allocation."

[0813] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0814] Step 1: Enter base station data

[0815] The user uses the device's input interface (web form or mobile app) to input base station type, specifications, and performance data. This data includes base station ID, antenna altitude, output power, current number of users, data usage, etc. Specifically, the user inputs base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." The device temporarily stores the input data, and once all the data is collected, it sends it to the server using an HTTP POST request. The input triggers data transfer from the device to the server, and the output is the data set received by the server.

[0816] Step 2: Data Preprocessing

[0817] The server receives data sent by users and stores it in a database (for example, MySQL or PostgreSQL). The input is the raw data received from the base station, and the output is normalized data that has been verified and stored in the database. First, the server validates the data. Specifically, it checks whether numeric fields contain character data or abnormal values ​​(for example, negative values ​​or very large values). If there is incomplete data, it is complemented using a predetermined method (for example, complementing with the average value), and if an abnormal value is found, it is corrected to fall within a specific threshold range. Finally, the data is normalized. For example, data usage is standardized to units such as MB or GB.

[0818] Step 3: Evaluation with a generative AI model

[0819] The server inputs the preprocessed data into the generative AI model. The input is a normalized dataset, and the output is the performance evaluation results. The server calls the generative AI model, which is built using a programming language such as Python or R. Specifically, the server sends the input data to the generative AI model and starts the evaluation process. The generative AI model evaluates the performance of the base station based on a pre-trained algorithm. For example, the generated evaluation results might be "transmission success rate 92%, communication latency 35 ms, and downlink throughput 120 Mbps." These results are collected by the server and used to proceed to the next step.

[0820] Step 4: Generate feedback

[0821] The server receives the evaluation results obtained from the generative AI model and generates a detailed feedback report. The input is the evaluation results and the output is the feedback report. The server runs a script that automatically generates a feedback report based on the evaluation results. For example, the server provides specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." The feedback report is generated in PDF or HTML format and is converted into a format that can be easily viewed by users. Specific operations include analyzing the evaluation results, identifying areas for improvement, and generating a report.

[0822] Step 5: Recognizing user emotions with the emotion engine

[0823] The server uses an emotion engine to recognize the user's emotional state based on the generated feedback and evaluation results. The inputs are the evaluation results, feedback content, and the user's reaction data, and the output is the analyzed emotional state. The emotion engine uses modules for facial expression recognition (e.g., OpenCV) and voice analysis (e.g., TensorFlow). The server uses these tools to analyze the user's facial expressions, voice, text input speed, and specific keywords. This determines the user's emotional state. For example, if the user has a stiff expression or inputs slowly, it is determined that the user is feeling stressed.

[0824] Step 6: Customize your feedback

[0825] The server customizes the feedback according to the user's emotional state. The input is the analyzed emotional state and a feedback report, and the output is a customized feedback report. For example, if the user is feeling stressed, logic is executed to explain areas for improvement in gentle terms and provide specific support methods. Wording such as "Communication latency has improved, but further optimization would be beneficial" is used. If the user is satisfied, the evaluation results are presented positively and the content is changed to suggest further improvements.

[0826] Step 7: Notification and display of results

[0827] Customized feedback and evaluation results are automatically sent to the device. The input is the customized feedback report, and the output is the feedback report displayed on the user's device. The server sends an HTTP request to an API endpoint to send the feedback report to the device. The device implements an interface to display the received feedback report to the user. Specifically, it generates visually easy-to-understand graphs and charts and displays the results in a format that the user can easily understand.

[0828] Through the above processing steps, the system can accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[0829] (Application example 2)

[0830] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0831] Autonomous vehicles are required to perform self-evaluation to improve safety and efficiency. It is also necessary to provide feedback that takes into account the emotions of the driver or operator, thereby reducing the mental burden and providing a better driving experience. However, conventional systems have not adequately evaluated the performance of autonomous vehicles or provided feedback based on the user's emotions, limiting the application of evaluation results and the effectiveness of feedback.

[0832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0833] In this invention, the server includes means for inputting the type, specifications, and performance data of a base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station, means for generating feedback including improvements and optimization suggestions based on the evaluation results, means for using an emotion engine that recognizes the emotional state of a user based on the evaluation results and feedback, means for customizing the feedback based on the emotional state of the user, and means for displaying the generated feedback and evaluation results, thereby enabling performance evaluation of an autonomous vehicle and providing feedback according to the user's emotions.

[0834] A "base station" is a hardware device in a communication system that transmits or receives radio waves and communicates with user devices.

[0835] A "type" is a group or category classified according to specific criteria.

[0836] A "specification" is a detailed description or characterization of the design and functionality of a system or device.

[0837] "Performance Data" means any measurement or record relating to the operation or functionality of a device or system.

[0838] "Preprocessing" refers to the initial processing and shaping of raw data to convert it into a form suitable for analysis and evaluation.

[0839] A "generative AI model" is an artificial intelligence system that is trained using machine learning algorithms on large amounts of data.

[0840] "Performance" refers to the degree or quality of the functions or operations that a device or system can perform.

[0841] "Quality" is the degree to which a product or service meets specific requirements or standards.

[0842] "Evaluation" means judging the value or performance of an object based on specified criteria.

[0843] An "improvement" is a change or modification that needs to be made to improve a system or process.

[0844] An "optimization proposal" is a specific strategy or measure to optimize a system or process.

[0845] "Feedback" means providing a response or evaluation of an action or behavior.

[0846] An "emotion engine" is a system that analyzes a user's emotional state and adjusts feedback based on that.

[0847] A "user's emotional state" refers to the sensations and emotions a user experiences in a particular situation.

[0848] "Customization" means modifying or adapting a system or service to meet the specific needs and requirements of a user.

[0849] This invention is a system that evaluates the performance of an autonomous vehicle and provides feedback according to the driver's emotions. Below, we will explain the outline of the program for realizing this system and its processing.

[0850] Generating a Program

[0851] The server first receives various performance data (such as location, speed, acceleration, and road conditions) collected from the autonomous vehicle as input data. This data is collected through sensors and cameras. The server then performs preprocessing on the data, which includes missing value imputation, incorrect data detection and correction, and normalization.

[0852] Next, the preprocessed data is input into a generative AI model for evaluation. This generative AI model evaluates performance based on indicators such as transmission success rate, communication delay, and downlink throughput, and obtains the results.

[0853] Based on the evaluation results, the server generates feedback including improvement points and optimization suggestions. This feedback indicates areas that need improvement and specific optimization suggestions. For example, a report may be created with specific instructions such as, "The obstacle avoidance success rate is 95%, but the lane-keeping accuracy is low, so the sensors need to be adjusted."

[0854] Based on the evaluation results and the generated feedback, the server uses an emotion engine to recognize the driver's emotional state. The emotion engine analyzes facial expressions and tone of voice data obtained from input devices such as cameras and microphones to determine the user's emotional state. For example, if the driver is feeling stressed, the content of the feedback will be softened.

[0855] Finally, the generated feedback and evaluation results are displayed on a device such as a head-mounted display (HMD) in the driver's seat or a smartphone, and the results are presented visually using graphs and charts.

[0856] Examples of specific examples and prompts

[0857] Specific examples

[0858] Simulation data for an autonomous vehicle traveling at 60 km / h on dry roads is input. Evaluation using a generative AI model yields results such as a 95% obstacle avoidance success rate, 20 ms communication latency, and 150 Mbps downlink throughput. Based on these results, the emotion engine determines the driver is in a mild stress state and provides polite feedback, such as "Consider recalibrating your sensors to improve lane-keeping accuracy."

[0859] Prompt Sentence Examples

[0860] Please rate the performance of the self-driving vehicle based on the following data.

[0861] Vehicle ID: 12345

[0862] Speed: 60km / h

[0863] Acceleration: 3 m / s²

[0864] Road Condition: Dry

[0865] Based on the evaluation results, feedback should also be generated for users when they are experiencing emotional stress.

[0866] This system makes it possible to accurately assess vehicle performance and provide feedback to the driver based on their emotions.

[0867] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0868] Step 1:

[0869] The user inputs performance data of the autonomous vehicle and sends it to the server.

[0870] Input data includes location information, speed, acceleration, road conditions, etc. Users collect this data from various sensors and cameras on their vehicle and send it from their device to the server, where the basic data required for evaluation is accumulated.

[0871] Step 2:

[0872] The server pre-processes the received data.

[0873] The server first verifies the received data and detects incomplete or anomalous data. It then complements or corrects the incomplete data and eliminates any anomalous data. It also normalizes the various data and formats them into a unified format. This preprocessing ensures that the generative AI model can accurately and consistently evaluate the data.

[0874] Step 3:

[0875] The preprocessed data is input into a generative AI model to evaluate vehicle performance.

[0876] The server inputs the normalized data into the generative AI model and evaluates it based on various performance indicators (e.g., obstacle avoidance success rate, communication latency, downlink throughput). The generative AI model is trained in advance using a large amount of learning data, enabling it to obtain highly accurate evaluation results. The evaluation results for each indicator are generated as output.

[0877] Step 4:

[0878] Generate feedback based on the evaluation results.

[0879] The server analyzes the evaluation results obtained from the generative AI model and generates specific improvement and optimization suggestions. For example, if the obstacle avoidance success rate is high and communication latency is low, feedback is generated that indicates good overall performance, but that the accuracy of specific sensors should be further improved. The output is a detailed feedback report with improvement suggestions.

[0880] Step 5:

[0881] The user's emotional state is recognized based on the evaluation results and feedback.

[0882] The server uses an emotion engine to analyze the evaluation results and feedback. It determines the user's emotional state based on facial expressions, tone of voice, and typing speed. This is done based on input data from the camera and microphone. The output is the user's recognized emotional state.

[0883] Step 6:

[0884] Customize feedback based on the user's emotional state.

[0885] The server adapts the feedback depending on the user's perceived emotional state. For example, if the user is stressed, it softens the feedback and suggests specific ways to support them. Conversely, if the user is satisfied, it provides positive suggestions for improvement. The output is customized feedback.

[0886] Step 7:

[0887] The generated feedback and evaluation results are displayed on the terminal.

[0888] The server sends the feedback and evaluation results to the device, which then visually displays the information to the user, specifically using graphs and charts to present the results in an easy-to-understand manner, allowing the user to make appropriate adjustments and improvements.

[0889] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0890] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0891] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0892] [Fourth embodiment]

[0893] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0894] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0895] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0896] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0897] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0898] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0899] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0900] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0901] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0902] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0903] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0904] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0905] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0906] The present invention is a system for evaluating the performance and quality of a base station, and mainly includes the following steps: An embodiment of the invention will be described through a specific use example.

[0907] Entering base station data

[0908] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data input is done through a dedicated app or web interface.

[0909] Data Preprocessing

[0910] The server receives the data sent by the user and begins preprocessing the data. During this stage, it fills in gaps in the data, detects and corrects outliers, and normalizes the data into a unified format. For example, all numerical data is converted to the same units, which makes subsequent processing easier.

[0911] Evaluation by generative AI model

[0912] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on the evaluation results.

[0913] Generate feedback

[0914] The server receives the evaluation results and generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, specific instructions such as "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[0915] Notification and display of results

[0916] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[0917] Specific examples

[0918] For example, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0919] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[0923] Step 2:

[0924] The server receives the data sent from the device, performs basic validation of the data to ensure it does not contain incomplete or invalid data, and generates a warning if there are missing values.

[0925] Step 3:

[0926] The server preprocesses the received data. This includes data formatting (e.g., standardizing units), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it will be corrected by referring to past data.

[0927] Step 4:

[0928] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[0929] Step 5:

[0930] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[0931] Step 6:

[0932] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[0933] Step 7:

[0934] The server creates graphs and charts to display the generated feedback and evaluation results, making it easier for users to visually understand the results.

[0935] Step 8:

[0936] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[0937] Step 9:

[0938] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[0939] Example 1

[0940] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0941] Systems that accurately evaluate the performance and quality of base stations often lack accuracy due to insufficient handling of missing data and outliers. Furthermore, there are limited means to visually display the evaluation results in a way that is easy for users to understand. As a result, there is a challenge in accurately and efficiently optimizing base station performance.

[0942] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0943] In this invention, the server includes means for inputting base station type, specifications, and performance data, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation data, means for displaying the generated feedback and evaluation data, means for filling in missing data and detecting and correcting outliers, and means for normalizing all numerical data to the same units, thereby enabling accurate evaluation and rapid optimization of base station data.

[0944] "Base station data" refers to information about the type, specifications, and performance of a base station, and specifically includes the base station ID, antenna height, output power, number of users, data usage, etc.

[0945] "Preprocessing" refers to the process of preparing input data in a format suitable for subsequent processing by performing operations such as filling in missing parts, detecting and correcting outliers, and normalizing numerical data.

[0946] A "generative AI model" refers to an artificial intelligence model for evaluating base station performance based on large amounts of training data, specifically calculating performance indicators such as transmission success rate, communication delay, and downlink throughput.

[0947] "Feedback" refers to information that specifically indicates areas for improvement in base station performance and optimization suggestions based on the evaluation results of the generative AI model.

[0948] "Normalization" refers to the process of aligning all numerical data to the same units, making it easier to compare and integrate data.

[0949] An "outlier" is a value that is clearly abnormal compared to other data in the input data and that deviates from the normal range.

[0950] The present invention provides a system for evaluating and optimizing the performance and quality of a base station. This system mainly includes the following functions:

[0951] First, the user collects the base station type, specifications, and performance data and inputs it into the server from their device using a dedicated app or web interface. This data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc.

[0952] The server then performs preprocessing on the received data, including filling in gaps in the data and detecting and correcting outliers. It also performs normalization to align all numerical data to the same units. For example, if the antenna altitude is measured in both meters and feet, it will be normalized to meters.

[0953] The preprocessed data is input into a generative AI model by the server. The generative AI model calculates various performance indicators for the base station (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance. Based on these evaluation results, the server generates feedback including improvements and optimization proposals for the base station. The feedback includes specific instructions (e.g., "Resource allocation needs to be reviewed to reduce communication delays").

[0954] The generated feedback and evaluation results are sent from the server to the terminal, where the user can check them. The feedback and evaluation results are displayed visually in graphs and charts, making them easy for the user to understand and adjust or improve the base station as needed.

[0955] As a specific use case, consider the evaluation of a base station installed in an urban area. The user inputs information such as the base station ID, antenna altitude (20m), output power (50W), number of users (100), and data usage (50GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 98%, communication delay 40ms, and downlink throughput 150Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication delay." This feedback is then sent to the device, where the user can review it and make appropriate adjustments.

[0956] Here are some examples of prompts:

[0957] "Base station ID: A123, Antenna height: 20m, Output power: 50W, Number of users: 100, Data usage: 50GB / day. Please rate the performance and quality of this base station and let us know what needs to be improved."

[0958] In this way, the present invention realizes a system that efficiently and accurately evaluates the performance and quality of a base station and provides suggestions for improvement.

[0959] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0960] Step 1:

[0961] The user inputs base station data, including base station ID, antenna altitude, output power, number of current users, data usage, etc. The user then sends this information from their device to the server using a dedicated app or web interface.

[0962] Input: Base station ID, antenna altitude, output power, number of current users, data usage.

[0963] Output: Base station data sent to the server.

[0964] Step 2:

[0965] The server receives the data sent by the user and performs preprocessing on the data. This preprocessing involves filling in missing data and detecting and correcting outliers. It also normalizes all numerical data to use the same units. For example, if altitude units are mixed, such as meters and feet, it will unify all data into meters.

[0966] Input: Base station data sent by the user.

[0967] Output: Preprocessed data.

[0968] Step 3:

[0969] The server inputs the preprocessed data into a generative AI model, which calculates base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a large amount of training data collected in advance.

[0970] Input: Preprocessed data.

[0971] Output: Performance indicators of the base station.

[0972] Step 4:

[0973] The server generates feedback based on the performance indicators obtained from the generative AI model. Specifically, it analyzes the evaluation data and derives improvements and optimization proposals for the base station. For example, it generates feedback such as "resource allocation needs to be reviewed to reduce communication latency."

[0974] Input: Performance metrics obtained from the generative AI model.

[0975] Output: Feedback with improvements and optimization suggestions.

[0976] Step 5:

[0977] The server sends the generated feedback and evaluation results to the device, which receives them and displays them for the user to review. The results are presented visually in an easy-to-understand manner using graphs and charts.

[0978] Input: Feedback including improvements and optimization suggestions.

[0979] Output: A device that displays the feedback and evaluation results.

[0980] Through this process, users will be able to efficiently and accurately evaluate the performance and quality of base stations and quickly implement improvement proposals.

[0981] (Application example 1)

[0982] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0983] In conventional industrial robot systems, the wireless communication environment between robots is not adequately evaluated or optimized, resulting in problems such as communication delays and reduced data throughput. This can lead to reduced factory production efficiency and make it difficult to ensure quality. Therefore, it is necessary to monitor and evaluate the wireless communication environment of robots in real time and provide optimal improvement proposals.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0985] In this invention, the server includes means for inputting the type, specifications, and performance data of the base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, means for generating feedback including improvements and optimization proposals based on the evaluation results, means for displaying the generated feedback and evaluation results, means for inputting communication environment data of the robot and evaluating its performance, and means for generating proposals for optimizing the communication environment based on the evaluation results. This makes it possible to improve the communication quality between robots in a factory and increase production efficiency.

[0986] A "base station" is a communication facility installed to provide wireless communication, and is a device that relays and manages communications within a specific area.

[0987] "Performance data" refers to information that measures the operation and capabilities of base stations and robots, and refers to specific numerical data such as transmission success rate, communication delay, and data throughput.

[0988] "Preprocessing" is the process of organizing input raw data and processing or shaping the data to make it easier to analyze and evaluate.

[0989] A "generative AI model" is an artificial intelligence system that has been trained in advance using large amounts of data. It is used to evaluate performance and quality based on the input data and propose optimal solutions.

[0990] "Feedback" refers to information such as guidance, advice, and specific improvement proposals that are generated based on the results of evaluation and analysis.

[0991] "Communication environment" refers to the physical and electronic conditions and circumstances under which wireless communications occur, including signal strength, interference, data throughput, etc.

[0992] "Areas for improvement" are points that are identified based on the evaluation results and require specific corrections or changes to improve performance or quality.

[0993] "Optimization suggestions" are specific methods or procedures proposed to maximize performance or quality.

[0994] "Evaluation results" are the results of data analyzed by the generative AI model, and show the numerical values ​​and trends of specific performance and quality indicators.

[0995] "Display" refers to the act of visually presenting evaluation results, feedback, and improvement proposals to users, often using graphs, charts, and the like.

[0996] This paper describes a system for evaluating and optimizing the wireless communication environment of robots in a factory. This system collects various data, evaluates the communication environment using a generative AI model, and proposes improvements.

[0997] Entering base station data

[0998] First, the user collects the type, specifications, and performance data of the base station and inputs it into the server from their device. Specific data includes the base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is input via a dedicated app or web interface.

[0999] Data Preprocessing

[1000] The server receives the data sent by the user and begins preprocessing the data. At this stage, missing parts of the data are filled in, and outliers are detected and corrected. The data is also normalized to form a unified format. For example, all numerical data is aligned to the same units to facilitate subsequent processing. Specifically, the numerical data is scaled using StandardScaler.

[1001] Evaluation by generative AI model

[1002] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators of the base station (transmission success rate, communication delay, downlink throughput, etc.) based on the large amount of training data collected in advance. It also automatically creates improvement and optimization proposals based on these evaluation results.

[1003] Generate feedback

[1004] The server then generates detailed feedback on the base station's performance and quality, including specific improvements and optimization suggestions. For example, it might provide specific instructions such as, "The transmission success rate is 95% and communication latency is slightly longer than normal, so resource allocation needs to be optimized."

[1005] Notification and display of results

[1006] The generated feedback and evaluation results are automatically sent to the device. The user can check the evaluation results displayed on the device and make adjustments or improvements to the base station as necessary. Graphs and charts are used to display the results in an easy-to-understand visual format.

[1007] Specific examples of wireless communication environment evaluation and optimization

[1008] For example, when multiple robots communicate simultaneously in a factory, a specific robot may experience communication delays. The application collects the robot's communication data and evaluates it using an AI model. As a result of this evaluation, it is determined that the communication delay is high, and feedback is provided stating that "routing needs to be revised to reduce communication delays."

[1009] Prompt Sentence Examples

[1010] Evaluate the wireless communication environment for robots in your factory and provide performance metrics for communication latency and data throughput. Data collected includes robot ID, signal strength, signal interference, data throughput, etc. Also generate improvement suggestions based on the evaluation results.

[1011] In this way, the present invention provides a system that improves the quality of communication between robots in a factory, thereby contributing to improved production efficiency and quality.

[1012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1013] Step 1:

[1014] Users collect base station type, specifications, and performance data and input it into the server from their devices. Specific data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. This data is sent to the server via a dedicated app or web interface.

[1015] Step 2:

[1016] The server receives data sent by users. The received data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. The server first fills in missing data and detects and corrects outliers. This preprocessing step also scales the data using StandardScaler to align all numerical data to the same units.

[1017] Step 3:

[1018] The server inputs the preprocessed data into a generative AI model to evaluate the performance and quality of the base station. The generative AI model calculates various performance indicators for the base station (transmission success rate, communication delay, downlink throughput, etc.) based on a large amount of training data collected in advance. This model quickly evaluates performance based on the input data and outputs the results.

[1019] Step 4:

[1020] The server that receives the evaluation results generates detailed feedback on the base station's performance and quality. The feedback includes specific improvements and optimization suggestions. For example, if the transmission success rate is low, the feedback may include specific suggestions such as "increase the base station's output power," or if communication latency is high, "review of resource allocation is necessary."

[1021] Step 5:

[1022] The server automatically sends the generated feedback and evaluation results to the terminal. This feedback, including graphs and charts, is provided to the user in a visually easy-to-understand format, allowing the user to review it and make adjustments or improvements to the base station.

[1023] Step 6:

[1024] Based on the evaluation results and feedback displayed on the device, the user can make necessary adjustments, such as changing the angle of the base station antenna or adjusting the output power, according to the specific instructions provided in the feedback.

[1025] In this way, the system of the present invention optimizes the quality of communication between robots in a factory through a series of steps, starting with the collection of basic data, followed by data preprocessing, evaluation using a generative AI model, feedback generation, notification of results, and actual adjustment work.

[1026] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1027] The present invention is a system for evaluating the performance and quality of a base station and providing feedback that takes into account user sentiment, and includes the following steps: The embodiments of the invention will be described through specific use examples.

[1028] Entering base station data

[1029] The user collects base station type, specifications, and performance data and inputs it to the server from the terminal, such as base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then sends this information to the server.

[1030] Data Preprocessing

[1031] The server receives the data sent by the user, performs basic validation of the data to ensure it is not incomplete or invalid, completes any incomplete data, corrects any outliers, and normalizes the data to ensure accurate evaluation.

[1032] Evaluation by generative AI model

[1033] The server inputs the preprocessed data into a generative AI model, which evaluates the performance and quality of the base station based on a pre-trained algorithm, calculating indicators such as transmission success rate, communication delay, and downlink throughput.

[1034] Generate feedback

[1035] The server receives the results from the generative AI model and generates detailed feedback. Based on the evaluation results, it provides specific improvements and optimization suggestions. For example, it creates a report with specific instructions such as, "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[1036] Recognizing user emotions with an emotion engine

[1037] Based on the generated feedback and evaluation results, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, typing speed, and specific keywords to determine the user's emotional state.

[1038] Customized Feedback

[1039] After recognizing the user's emotions, the server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, the server gently explains what needs to be improved and provides specific support methods. For a satisfied user, the server presents a positive evaluation result and offers further improvement suggestions.

[1040] Notification and display of results

[1041] The customized feedback and evaluation results are automatically sent to the device. The user can review the evaluation results and feedback displayed on the device and make adjustments or improvements to the base station as needed. Graphs and charts are used for visual display to present the results in an easy-to-understand manner.

[1042] Specific examples

[1043] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[1044] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[1045] The processing flow will be explained below.

[1046] Step 1:

[1047] The user inputs the type, specifications, and performance data of the base station using the terminal, such as the base station ID, antenna altitude, output power, number of current users, data usage, etc. The terminal then transmits this data to the server.

[1048] Step 2:

[1049] The server receives the data sent from the device. After receiving it, it performs basic validation of the data to check whether it contains incomplete or invalid data. If there is any incomplete data, it sends a notification to the user to complete it.

[1050] Step 3:

[1051] The server preprocesses the received data. This includes data shaping (e.g., standardizing all data to a common unit), filling in missing data, and detecting and correcting outliers. For example, if the number of users is abnormally high, it can be corrected by looking at past data.

[1052] Step 4:

[1053] The server inputs the preprocessed data into a generative AI model, which analyzes the data and calculates various base station performance indicators (e.g., transmission success rate, communication delay, downlink throughput) based on a pre-trained algorithm.

[1054] Step 5:

[1055] The server receives the results from the generative AI model and evaluates the performance and quality of the base station. The evaluation results include numerical values ​​for each performance indicator, as well as improvements and optimization suggestions proposed by the AI ​​model.

[1056] Step 6:

[1057] The server generates detailed feedback based on the evaluation results, creating a report that includes specific improvement suggestions, such as "The transmission success rate is 95% and the communication latency is 40 ms, so resource reallocation is necessary."

[1058] Step 7:

[1059] The server recognizes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial expressions, tone of voice, input speed, and specific keywords to determine the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time.

[1060] Step 8:

[1061] The server customizes the feedback according to the user's emotional state. For example, for a user who is feeling stressed, it gently explains what needs to be improved and provides specific support methods. For a satisfied user, it presents the evaluation results in a positive light and offers further suggestions for improvement.

[1062] Step 9:

[1063] The server generates graphs and charts to display customized feedback and assessment results in a visually easy-to-understand format.

[1064] Step 10:

[1065] The server sends the completed feedback report and evaluation results to the user's device, which includes specific numerical data, improvement suggestions, and visual graphs.

[1066] Step 11:

[1067] The user checks the evaluation results and feedback report using the device. Based on the report, the user can change the base station settings or adjust the hardware. If necessary, the user can also prepare additional data for the next evaluation.

[1068] Specific examples

[1069] For example, consider the evaluation of a base station installed in the suburbs. The user inputs information such as the base station ID, antenna altitude (15m), output power (30W), number of users (50), and data usage (20GB / day). The server receives this information, performs preprocessing, and then evaluates it using a generative AI model. The evaluation results are "transmission success rate 92%, communication latency 35ms, and downlink throughput 120Mbps." The server analyzes this and generates feedback stating that "resource allocation needs to be reviewed to reduce communication latency." The server's emotion engine recognizes the user's emotions, and if it determines that the user is feeling stressed, it will suggest improvements in kind words. A feedback report is sent to the device, where the user can review it and make appropriate adjustments.

[1070] In this way, the present invention realizes a system that accurately evaluates the performance and quality of a base station and provides feedback according to the user's emotions.

[1071] Example 2

[1072] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1073] As communication infrastructure continues to become more complex and sophisticated, it will be necessary to accurately and quickly evaluate the performance and quality of base stations and provide appropriate feedback. Conventional systems face technical limitations due to the lack of data preprocessing, performance evaluation, and methods for providing feedback that takes user emotions into account. Taking appropriate measures based on the user's emotional state can facilitate the acceptance and implementation of improvements, potentially improving the overall user experience. Against this background, the objective of this invention is to provide a base station performance evaluation system that can take user emotions into account and provide feedback that is appropriate to the user's emotions.

[1074] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the type, specifications, and performance data of the base station, a means for preprocessing the input data, a means for inputting the preprocessed data into a generative AI model and evaluating the performance and quality of the base station, a means for generating feedback including improvements and optimization suggestions based on the evaluation results, and a means for recognizing the user's emotions and customizing the feedback according to the user's emotional state. This makes it possible to accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[1075] A "base station" is a device that communicates with mobile terminals in a communication network.

[1076] "Performance data" refers to information related to the communication quality and performance of a base station, and specifically includes transmission success rate, communication delay, data throughput, etc.

[1077] "Preprocessing" refers to the process of validating, imputing, and normalizing data to improve its accuracy.

[1078] A "generative AI model" is a model trained using machine learning algorithms to evaluate the performance and quality of base stations based on input data.

[1079] "Feedback" refers to reports and instructions, including suggestions for improvement and optimization, provided based on the results of the evaluation.

[1080] "Emotion engine" refers to an algorithm or module for analyzing and recognizing a user's emotional state.

[1081] "Customization" refers to the adjustment or modification of content to meet specific conditions or requirements.

[1082] "Evaluation Results" refers to the measurement results regarding the performance and quality of base stations obtained by the generative AI model.

[1083] "Visually displayed" refers to displaying information using visual means such as graphs or charts.

[1084] The present invention provides a system for evaluating the performance and quality of a base station and providing feedback that takes into account the user's feelings. An embodiment of the program for this system will be described in detail below.

[1085] Entering base station data

[1086] The user uses the device's input interface (e.g., a web form or mobile app) to collect and input base station type, specifications, and performance data to the server. This data includes base station ID, antenna altitude, output power, number of current users, data usage, etc. Once the user completes the input, the device sends the data to the server using an HTTP POST request.

[1087] Data Preprocessing

[1088] The server receives the data submitted by the user and stores it in a database (e.g., MySQL or PostgreSQL). The server then performs basic validation of the data to ensure it does not contain incomplete or invalid data. The server then executes logic to impute incomplete data and correct outliers. Finally, the data is normalized and converted into a consistent format.

[1089] Evaluation by generative AI model

[1090] The server calls a generative AI model built in a programming language such as Python or R. The server inputs the preprocessed data into the generative AI model and performs performance evaluation. This evaluation process generates results such as a 92% transmission success rate, a 35ms communication delay, and a 120Mbps downlink throughput.

[1091] Generate feedback

[1092] The server receives the evaluation results from the generated AI model and automatically generates a detailed feedback report. The feedback includes specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." This feedback report is generated in PDF or HTML format and converted into a format that users can easily view.

[1093] Recognizing user emotions with an emotion engine

[1094] The server recognizes the user's emotions using an emotion engine based on the generated feedback and evaluation results. This emotion engine uses modules (e.g., OpenCV and TensorFlow) for facial expression recognition and voice analysis. The server determines the user's emotional state and reflects the results in the feedback generation process.

[1095] Customized Feedback

[1096] The server customizes the feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will explain areas for improvement in a gentle manner and provide specific support methods. If the user is satisfied, the server will present the evaluation results in a positive light and suggest further improvements.

[1097] Notification and display of results

[1098] The customized feedback and evaluation results are automatically sent to the device, which implements an interface to display these feedback reports to the user. Visual displays include graphs and charts to present the results in an easy-to-understand manner.

[1099] Specific examples

[1100] For example, consider the evaluation of a suburban base station. A user enters information into a form on their device, such as the base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." This data is then sent to the server, which preprocesses it and performs an evaluation using a generative AI model. The evaluation results are a 92% transmission success rate, a 35ms communication latency, and a 120Mbps downlink throughput. Based on this, the server generates feedback suggesting that resource allocation be revised to reduce communication latency. If the emotion engine detects that the user is stressed, it offers suggestions for improvement in a gentle manner. Finally, this feedback is sent to the device, where the user can review it and make appropriate adjustments, thereby improving the performance and quality of the base station.

[1101] Prompt Sentence Examples

[1102] "Please evaluate the following: base station ID: 12345, antenna altitude: 15m, output power: 30W, current number of users: 50, data usage: 20GB / day. Calculate the current transmission success rate, communication delay, and downlink throughput, and provide feedback on the optimal resource allocation."

[1103] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1104] Step 1: Enter base station data

[1105] The user uses the device's input interface (web form or mobile app) to input base station type, specifications, and performance data. This data includes base station ID, antenna altitude, output power, current number of users, data usage, etc. Specifically, the user inputs base station ID "12345," antenna altitude "15m," output power "30W," number of users "50," and data usage "20GB / day." The device temporarily stores the input data, and once all the data is collected, it sends it to the server using an HTTP POST request. The input triggers data transfer from the device to the server, and the output is the data set received by the server.

[1106] Step 2: Data Preprocessing

[1107] The server receives data sent by users and stores it in a database (for example, MySQL or PostgreSQL). The input is the raw data received from the base station, and the output is normalized data that has been verified and stored in the database. First, the server validates the data. Specifically, it checks whether numeric fields contain character data or abnormal values ​​(for example, negative values ​​or very large values). If there is incomplete data, it is complemented using a predetermined method (for example, complementing with the average value), and if an abnormal value is found, it is corrected to fall within a specific threshold range. Finally, the data is normalized. For example, data usage is standardized to units such as MB or GB.

[1108] Step 3: Evaluation with a generative AI model

[1109] The server inputs the preprocessed data into the generative AI model. The input is a normalized dataset, and the output is the performance evaluation results. The server calls the generative AI model, which is built using a programming language such as Python or R. Specifically, the server sends the input data to the generative AI model and starts the evaluation process. The generative AI model evaluates the performance of the base station based on a pre-trained algorithm. For example, the generated evaluation results might be "transmission success rate 92%, communication latency 35 ms, and downlink throughput 120 Mbps." These results are collected by the server and used to proceed to the next step.

[1110] Step 4: Generate feedback

[1111] The server receives the evaluation results obtained from the generative AI model and generates a detailed feedback report. The input is the evaluation results and the output is the feedback report. The server runs a script that automatically generates a feedback report based on the evaluation results. For example, the server provides specific improvements and optimization suggestions, such as "resource allocation needs to be reviewed to reduce communication latency." The feedback report is generated in PDF or HTML format and is converted into a format that can be easily viewed by users. Specific operations include analyzing the evaluation results, identifying areas for improvement, and generating a report.

[1112] Step 5: Recognizing user emotions with the emotion engine

[1113] The server uses an emotion engine to recognize the user's emotional state based on the generated feedback and evaluation results. The inputs are the evaluation results, feedback content, and the user's reaction data, and the output is the analyzed emotional state. The emotion engine uses modules for facial expression recognition (e.g., OpenCV) and voice analysis (e.g., TensorFlow). The server uses these tools to analyze the user's facial expressions, voice, text input speed, and specific keywords. This determines the user's emotional state. For example, if the user has a stiff expression or inputs slowly, it is determined that the user is feeling stressed.

[1114] Step 6: Customize your feedback

[1115] The server customizes the feedback according to the user's emotional state. The input is the analyzed emotional state and a feedback report, and the output is a customized feedback report. For example, if the user is feeling stressed, logic is executed to explain areas for improvement in gentle terms and provide specific support methods. Wording such as "Communication latency has improved, but further optimization would be beneficial" is used. If the user is satisfied, the evaluation results are presented positively and the content is changed to suggest further improvements.

[1116] Step 7: Notification and display of results

[1117] Customized feedback and evaluation results are automatically sent to the device. The input is the customized feedback report, and the output is the feedback report displayed on the user's device. The server sends an HTTP request to an API endpoint to send the feedback report to the device. The device implements an interface to display the received feedback report to the user. Specifically, it generates visually easy-to-understand graphs and charts and displays the results in a format that the user can easily understand.

[1118] Through the above processing steps, the system can accurately evaluate the performance and quality of the base station and provide appropriate feedback according to the user's emotions.

[1119] (Application example 2)

[1120] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1121] Autonomous vehicles are required to perform self-evaluation to improve safety and efficiency. It is also necessary to provide feedback that takes into account the emotions of the driver or operator, thereby reducing the mental burden and providing a better driving experience. However, conventional systems have not adequately evaluated the performance of autonomous vehicles or provided feedback based on the user's emotions, limiting the application of evaluation results and the effectiveness of feedback.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1123] In this invention, the server includes means for inputting the type, specifications, and performance data of a base station, means for preprocessing the input data, means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station, means for generating feedback including improvements and optimization suggestions based on the evaluation results, means for using an emotion engine that recognizes the emotional state of a user based on the evaluation results and feedback, means for customizing the feedback based on the emotional state of the user, and means for displaying the generated feedback and evaluation results, thereby enabling performance evaluation of an autonomous vehicle and providing feedback according to the user's emotions.

[1124] A "base station" is a hardware device in a communication system that transmits or receives radio waves and communicates with user devices.

[1125] A "type" is a group or category classified according to specific criteria.

[1126] A "specification" is a detailed description or characterization of the design and functionality of a system or device.

[1127] "Performance Data" means any measurement or record relating to the operation or functionality of a device or system.

[1128] "Preprocessing" refers to the initial processing and shaping of raw data to convert it into a form suitable for analysis and evaluation.

[1129] A "generative AI model" is an artificial intelligence system that is trained using machine learning algorithms on large amounts of data.

[1130] "Performance" refers to the degree or quality of the functions or operations that a device or system can perform.

[1131] "Quality" is the degree to which a product or service meets specific requirements or standards.

[1132] "Evaluation" means judging the value or performance of an object based on specified criteria.

[1133] An "improvement" is a change or modification that needs to be made to improve a system or process.

[1134] An "optimization proposal" is a specific strategy or measure to optimize a system or process.

[1135] "Feedback" means providing a response or evaluation of an action or behavior.

[1136] An "emotion engine" is a system that analyzes a user's emotional state and adjusts feedback based on that.

[1137] A "user's emotional state" refers to the sensations and emotions a user experiences in a particular situation.

[1138] "Customization" means modifying or adapting a system or service to meet the specific needs and requirements of a user.

[1139] This invention is a system that evaluates the performance of an autonomous vehicle and provides feedback according to the driver's emotions. Below, we will explain the outline of the program for realizing this system and its processing.

[1140] Generating a Program

[1141] The server first receives various performance data (such as location, speed, acceleration, and road conditions) collected from the autonomous vehicle as input data. This data is collected through sensors and cameras. The server then performs preprocessing on the data, which includes missing value imputation, incorrect data detection and correction, and normalization.

[1142] Next, the preprocessed data is input into a generative AI model for evaluation. This generative AI model evaluates performance based on indicators such as transmission success rate, communication delay, and downlink throughput, and obtains the results.

[1143] Based on the evaluation results, the server generates feedback including improvement points and optimization suggestions. This feedback indicates areas that need improvement and specific optimization suggestions. For example, a report may be created with specific instructions such as, "The obstacle avoidance success rate is 95%, but the lane-keeping accuracy is low, so the sensors need to be adjusted."

[1144] Based on the evaluation results and the generated feedback, the server uses an emotion engine to recognize the driver's emotional state. The emotion engine analyzes facial expressions and tone of voice data obtained from input devices such as cameras and microphones to determine the user's emotional state. For example, if the driver is feeling stressed, the content of the feedback will be softened.

[1145] Finally, the generated feedback and evaluation results are displayed on a device such as a head-mounted display (HMD) in the driver's seat or a smartphone, and the results are presented visually using graphs and charts.

[1146] Examples of specific examples and prompts

[1147] Specific examples

[1148] Simulation data for an autonomous vehicle traveling at 60 km / h on dry roads is input. Evaluation using a generative AI model yields results such as a 95% obstacle avoidance success rate, 20 ms communication latency, and 150 Mbps downlink throughput. Based on these results, the emotion engine determines the driver is in a mild stress state and provides polite feedback, such as "Consider recalibrating your sensors to improve lane-keeping accuracy."

[1149] Prompt Sentence Examples

[1150] Please rate the performance of the self-driving vehicle based on the following data.

[1151] Vehicle ID: 12345

[1152] Speed: 60km / h

[1153] Acceleration: 3 m / s²

[1154] Road Condition: Dry

[1155] Based on the evaluation results, feedback should also be generated for users when they are experiencing emotional stress.

[1156] This system makes it possible to accurately assess vehicle performance and provide feedback to the driver based on their emotions.

[1157] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1158] Step 1:

[1159] The user inputs performance data of the autonomous vehicle and sends it to the server.

[1160] Input data includes location information, speed, acceleration, road conditions, etc. Users collect this data from various sensors and cameras on their vehicle and send it from their device to the server, where the basic data required for evaluation is accumulated.

[1161] Step 2:

[1162] The server pre-processes the received data.

[1163] The server first verifies the received data and detects incomplete or anomalous data. It then complements or corrects the incomplete data and eliminates any anomalous data. It also normalizes the various data and formats them into a unified format. This preprocessing ensures that the generative AI model can accurately and consistently evaluate the data.

[1164] Step 3:

[1165] The preprocessed data is input into a generative AI model to evaluate vehicle performance.

[1166] The server inputs the normalized data into the generative AI model and evaluates it based on various performance indicators (e.g., obstacle avoidance success rate, communication latency, downlink throughput). The generative AI model is trained in advance using a large amount of learning data, enabling it to obtain highly accurate evaluation results. The evaluation results for each indicator are generated as output.

[1167] Step 4:

[1168] Generate feedback based on the evaluation results.

[1169] The server analyzes the evaluation results obtained from the generative AI model and generates specific improvement and optimization suggestions. For example, if the obstacle avoidance success rate is high and communication latency is low, feedback is generated that indicates good overall performance, but that the accuracy of specific sensors should be further improved. The output is a detailed feedback report with improvement suggestions.

[1170] Step 5:

[1171] The user's emotional state is recognized based on the evaluation results and feedback.

[1172] The server uses an emotion engine to analyze the evaluation results and feedback. It determines the user's emotional state based on facial expressions, tone of voice, and typing speed. This is done based on input data from the camera and microphone. The output is the user's recognized emotional state.

[1173] Step 6:

[1174] Customize feedback based on the user's emotional state.

[1175] The server adapts the feedback depending on the user's perceived emotional state. For example, if the user is stressed, it softens the feedback and suggests specific ways to support them. Conversely, if the user is satisfied, it provides positive suggestions for improvement. The output is customized feedback.

[1176] Step 7:

[1177] The generated feedback and evaluation results are displayed on the terminal.

[1178] The server sends the feedback and evaluation results to the device, which then visually displays the information to the user, specifically using graphs and charts to present the results in an easy-to-understand manner, allowing the user to make appropriate adjustments and improvements.

[1179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1181] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1183] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1189] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1190] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1192] 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.

[1193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1194] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1195] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1200] The following is further disclosed regarding the above embodiment.

[1201] (Claim 1)

[1202] Enter base station type, specifications, and performance data

[1203] Means and

[1204] Preprocessing the input data

[1205] Means and

[1206] Preprocessed data is fed into a generative AI model to evaluate base station performance and quality.

[1207] Means and

[1208] Generate feedback based on the evaluation results, including suggestions for improvement and optimization

[1209] Means and

[1210] View generated feedback and assessment results

[1211] Means and

[1212] A system including:

[1213] (Claim 2)

[1214] 10. The system of claim 1, wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

[1215] (Claim 3)

[1216] 10. The system of claim 1, wherein a graph or chart is generated to visually display the evaluation results.

[1217] "Example 1"

[1218] (Claim 1)

[1219] means for inputting base station type, specifications, and performance data;

[1220] means for preprocessing input data;

[1221] A means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station;

[1222] means for generating feedback including improvement and optimization suggestions based on the evaluation data;

[1223] means for displaying the generated feedback and evaluation data;

[1224] A means to fill in gaps in the data and detect and correct outliers;

[1225] A means of normalizing all numerical data to the same units;

[1226] A system including:

[1227] (Claim 2)

[1228] 10. The system of claim 1, wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

[1229] (Claim 3)

[1230] 10. The system of claim 1, wherein a graph or chart is generated to visually display the evaluation results.

[1231] "Application Example 1"

[1232] (Claim 1)

[1233] Enter base station type, specifications, and performance data

[1234] Means and

[1235] Preprocessing the input data

[1236] Means and

[1237] Preprocessed data is fed into a generative AI model to evaluate base station performance and quality.

[1238] Means and

[1239] Generate feedback based on the evaluation results, including suggestions for improvement and optimization

[1240] Means and

[1241] View generated feedback and assessment results

[1242] Means and

[1243] Input the robot's communication environment data and evaluate its performance

[1244] Means and

[1245] Generate proposals for optimizing the communication environment based on the evaluation results

[1246] Means and

[1247] A system including:

[1248] (Claim 2)

[1249] 10. The system of claim 1, wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

[1250] (Claim 3)

[1251] 10. The system of claim 1, wherein a graph or chart is generated to visually display the evaluation results.

[1252] "Example 2: Combining Emotion Engines"

[1253] (Claim 1)

[1254] means for inputting base station type, specifications, and performance data;

[1255] means for preprocessing input data;

[1256] A means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station;

[1257] means for generating feedback including improvement and optimization suggestions based on the evaluation results;

[1258] a means for displaying the generated feedback and evaluation results;

[1259] means for recognizing a user's emotions and customizing feedback according to the user's emotional state;

[1260] A system including:

[1261] (Claim 2)

[1262] 10. The system of claim 1, wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

[1263] (Claim 3)

[1264] 10. The system of claim 1, wherein a graph or chart is generated to visually display the evaluation results.

[1265] "Application example 2 when combining emotion engines"

[1266] (Claim 1)

[1267] Enter base station type, specifications, and performance data

[1268] Means and

[1269] Preprocessing the input data

[1270] Means and

[1271] Preprocessed data is fed into a generative AI model to evaluate base station performance and quality.

[1272] Means and

[1273] Generate feedback based on the evaluation results, including suggestions for improvement and optimization

[1274] Means and

[1275] Use an emotion engine that recognizes the user's emotional state based on the evaluation results and feedback

[1276] Means and

[1277] Customize feedback based on the user's emotional state

[1278] Means and

[1279] View generated feedback and assessment results

[1280] Means and

[1281] A system including:

[1282] (Claim 2)

[1283] 10. The system of claim 1, wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

[1284] (Claim 3)

[1285] 2. The system according to claim 1, wherein a graph or chart for visually displaying the evaluation results is generated and displayed on a user terminal. [Explanation of symbols]

[1286] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for inputting base station type, specifications, and performance data; means for preprocessing input data; A means for inputting the preprocessed data into a generative AI model to evaluate the performance and quality of the base station; means for generating feedback including improvement and optimization suggestions based on the evaluation results; a means for displaying the generated feedback and evaluation results; A system including:

2. The system of claim 1 , wherein the preprocessed data is evaluated using uniform criteria and personalized feedback is provided.

3. The system of claim 1 , wherein a graph or chart is generated to visually display the evaluation results.

Images