system

Abstract

The system according to this embodiment aims to streamline base station maintenance operations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a request unit. The collection unit collects data such as alarm information, technical specifications, and past response history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a response method based on the results of the analysis by the analysis unit. The request unit makes a request based on the response method generated by the generation unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that manual response is required in the maintenance support work of base stations, resulting in low efficiency.

[0005] The system according to the embodiment aims to improve the efficiency of the maintenance support work of base stations. [[ID=:41]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a request unit. The collection unit collects data such as alarm information, technical specifications, and past response history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates response methods based on the results of the analysis by the analysis unit. The request unit makes requests based on the response methods generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline base station maintenance operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The maintenance response system according to an embodiment of the present invention is a system that uses a generating AI to replace base station maintenance response tasks. Currently, maintenance response systems involve humans devising response methods based on alarm information, technical specifications, and past response history, and then making requests to external vendors. By replacing these tasks with a generating AI, the workload of base station maintenance operations can be significantly reduced, and fault response that would be impossible for humans can be achieved. For example, the maintenance response system collects data such as alarm information, technical specifications, and past response history. Next, the maintenance response system uses a generating AI to analyze this data and generate the optimal response method. Based on the generated response method, the maintenance response system makes a request to an external vendor. This mechanism improves the efficiency of base station maintenance operations and enables fault response that would be impossible for humans. For example, if an alarm occurs at a base station, the generating AI analyzes the alarm information and generates the optimal response method by referring to past response history and technical specifications. Based on the generated response method, the maintenance response system makes a request to an external vendor, enabling a rapid response. This system not only significantly reduces the workload of base station maintenance operations but also enables fault response that would be impossible for humans. For example, even in the event of a complex failure, the maintenance response system can quickly generate the optimal response method using AI and take action. This ensures the stable operation of base stations and is expected to improve service quality. As a result, the maintenance response system can significantly reduce the man-hours required for base station maintenance and achieve failure response that would be impossible for humans.

[0029] The maintenance response system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a request unit. The collection unit collects data such as alarm information, technical specifications, and past response history. The collection unit can, for example, collect alarm information in real time. The collection unit can also collect technical specifications in digital format. Furthermore, the collection unit can obtain past response history from a database. For example, the collection unit directly acquires alarm information from sensors and stores it in the database in real time. Technical specifications are collected in PDF or text format and stored in the database. Past response history is obtained from the database using queries and provided to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generation AI and generates the optimal response method. The analysis unit can also analyze data patterns using data mining techniques. Furthermore, the analysis unit can analyze data trends using statistical analysis techniques. For example, the analysis unit inputs alarm information, technical specifications, and past response history into the generation AI and generates the optimal response method. Data mining techniques extract useful patterns from data and use them to generate response methods. Statistical analysis techniques analyze data trends and improve the accuracy of response methods. The generation unit generates response methods based on the results analyzed by the analysis unit. The generation unit generates response methods using, for example, a generation AI. The generation unit can also generate response methods based on procedures and guidelines. Furthermore, the generation unit can generate response methods by referring to past response history. For example, the generation unit inputs the analysis results into a generation AI to generate the optimal response method. Procedures and guidelines are used as references in generating response methods. Past response history provides response methods for similar problems. The requesting unit makes requests based on the response methods generated by the generation unit. The requesting unit makes requests to, for example, external vendors. The requesting unit can also automatically generate request forms and send them to external vendors. Furthermore, the requesting unit can review the request content and make corrections as needed. For example, the requesting unit automatically generates a request form based on the generated response method and sends it to an external vendor. The request form details the procedures to be followed and provides specific instructions to the external contractor.The request details are reviewed by the requesting department and modified as necessary. As a result, the maintenance response system according to this embodiment can significantly reduce the man-hours required for base station maintenance work and enable fault response that would be impossible for humans to perform.

[0030] The data collection unit collects data such as alarm information, technical specifications, and past response history. Specifically, alarm information is acquired from sensors in real time and immediately stored in the database. This ensures that the system always has the latest alarm information, enabling rapid response. Technical specifications are digitized in PDF or text format and stored in the database. This allows engineers to quickly search and refer to the necessary information. Past response history is retrieved from the database using queries and provided to the analysis unit. This enables analysis based on past response history, and is expected to generate more accurate response methods. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit implements verification processes to ensure data integrity and reliability, maintaining data quality. For example, data accuracy can be improved by using algorithms that detect and automatically correct data duplication and missing data. This allows the data collection unit to provide highly reliable data, improving the overall reliability of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generative AI to analyze the data and generate the optimal response method. Specifically, it inputs alarm information, technical specifications, and past response history into the generative AI to generate the optimal response method. The generative AI uses natural language processing technology to analyze the technical specifications and past response history and proposes a response method based on the alarm information. Data mining technology extracts useful patterns from the data and helps generate response methods. For example, it finds common patterns from past response history and identifies the optimal response method for similar failures. Statistical analysis technology analyzes data trends and improves the accuracy of response methods. For example, it can predict the likelihood of a specific failure occurring based on the frequency of alarm information and the content of technical specifications, and take countermeasures in advance. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past failure data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates response methods based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate response methods. Specifically, it inputs the analysis results into the generation AI to generate the optimal response method. The generation AI proposes the optimal response procedure, referencing past response history and technical specifications. Procedure manuals and guidelines are referenced in generating response methods. For example, a specific response method can be customized based on standard response procedures for a particular failure. Past response history provides response methods for similar failures. For example, the optimal response procedure can be generated by referring to response methods used when similar failures occurred in the past. The generation unit can integrate this information and quickly generate the optimal response method. Furthermore, the generation unit can evaluate the accuracy and effectiveness of the generated response method and make corrections as needed. For example, it can simulate the generated response method to confirm whether it fits the actual situation. The generation unit can also collect feedback from users and use it to improve the response method. This allows the generation unit to always provide highly accurate response methods based on the latest information, supporting quick and appropriate responses. Furthermore, the generation unit can automatically document the generated response method and share it with other departments and systems. This ensures consistency and transparency in response methods, improving the overall efficiency of the system.

[0033] The requesting department makes requests based on the response methods generated by the generation department. Specifically, it makes requests to external vendors. The requesting department can also automatically generate request forms and send them to external vendors. For example, it can automatically generate request forms based on the generated response methods and send them to external vendors. The request form will describe the details of the response methods and provide specific instructions to the external vendors. The request content will be reviewed by the requesting department and revised as necessary. For example, the department will review the content of the request form and correct any errors or unclear points. The requesting department can also monitor the progress of requests and follow up as necessary. For example, it will receive reports from external vendors and check the progress of the response. In addition, the requesting department can record the request content and progress in a database and use it as reference material for the future. This allows the requesting department to make requests efficiently and effectively, improving the reliability and efficiency of the entire system. Furthermore, the requesting department can share the request content and progress with other departments and systems to strengthen overall collaboration. For example, it can notify other departments of the request content and request necessary support. In addition, the requesting department can evaluate the results of requests and use them to improve in the future. This allows the requesting department to always provide the optimal response and improve the overall system performance.

[0034] The data collection unit can collect data such as alarm information, technical specifications, and past response history. For example, the data collection unit can collect alarm information in real time. The data collection unit can also collect technical specifications in digital format. The data collection unit can also retrieve past response history from a database. For example, the data collection unit can directly acquire alarm information from sensors and save it to the database in real time. Technical specifications are collected in PDF or text format and saved to the database. Past response history is retrieved from the database using queries and provided to the analysis unit. This allows for the efficient collection of necessary data.

[0035] The analysis unit can analyze collected data and generate the optimal response method. For example, the analysis unit can use a generation AI to analyze data and generate the optimal response method. The analysis unit can also use data mining techniques to analyze data patterns. Furthermore, the analysis unit can use statistical analysis techniques to analyze data trends. For example, the analysis unit inputs alarm information, technical specifications, and past response history into a generation AI to generate the optimal response method. Data mining techniques extract useful patterns from the data and utilize them to generate response methods. Statistical analysis techniques analyze data trends and improve the accuracy of response methods. This allows the system to analyze collected data and generate the optimal response method.

[0036] The generation unit can request external vendors to perform the generated response methods. For example, the generation unit can generate response methods using a generation AI. The generation unit can also generate response methods based on procedures and guidelines. Furthermore, the generation unit can generate response methods by referring to past response history. For example, the generation unit inputs analysis results into a generation AI to generate the optimal response method. Procedures and guidelines are used as reference in generating response methods. Past response history provides response methods for similar incidents. This allows for requests to be made to external vendors based on the generated response methods.

[0037] The requesting department can make requests based on the generated response methods. For example, the requesting department can make requests to external vendors. The requesting department can also automatically generate a request form and send it to the external vendor. The requesting department can also review the request content and revise it as needed. For example, the requesting department can automatically generate a request form based on the generated response methods and send it to the external vendor. The request form will describe the details of the response methods and provide specific instructions to the external vendor. The request content will be reviewed by the requesting department and revised as needed. This allows requests to be made based on the generated response methods.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and apply it to future data collection. The data collection unit can also select the optimal collection method for specific time periods or conditions based on past data collection history. The data collection unit can also analyze past data collection history, identify areas for improvement in collection methods, and optimize them. For example, the data collection unit can retrieve past data collection history from a database and analyze it using data mining techniques. The data collection unit can select a data collection method for specific time periods or conditions to achieve efficient data collection. By identifying areas for improvement in collection methods and optimizing them, the data collection unit can improve the accuracy and efficiency of data collection. This allows for the analysis of past data collection history and the selection of the optimal collection method.

[0039] The data collection unit can filter data based on the base station's current operational status and environmental conditions. For example, the data collection unit can monitor the base station's operational status in real time and collect data only when an anomaly occurs. The data collection unit can also prioritize data collection under specific conditions, taking environmental conditions (weather, temperature, etc.) into consideration. The data collection unit can also adjust the type and amount of data collected based on the base station's operational status and environmental conditions. For example, the data collection unit can monitor the base station's operational status in real time and collect data only when an anomaly occurs. The data collection unit can prioritize data collection under specific conditions, taking environmental conditions (weather, temperature, etc.) into consideration. The data collection unit can adjust the type and amount of data collected based on the base station's operational status and environmental conditions. This allows for filtering of data collection based on the base station's operational status and environmental conditions.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the base station during data collection. For example, the data collection unit can prioritize the collection of surrounding environmental data based on the geographical location information of the base station. The data collection unit can also prioritize data collection in a specific area by considering the geographical location information of the base station. The data collection unit can also filter and collect highly relevant data based on the geographical location information of the base station. For example, the data collection unit can prioritize the collection of surrounding environmental data based on the geographical location information of the base station. The data collection unit can prioritize data collection in a specific area by considering the geographical location information of the base station. The data collection unit can filter and collect highly relevant data based on the geographical location information of the base station. This allows for the priority collection of highly relevant data by considering the geographical location information of the base station.

[0041] The data collection unit can analyze the social media activity of base stations and collect relevant data during data collection. For example, the data collection unit monitors the social media activity of base stations and collects relevant data. The data collection unit can also analyze trends on social media and prioritize the collection of data relevant to base stations. Furthermore, the data collection unit can collect data relevant to base stations based on user feedback on social media. This allows for the analysis of social media activity of base stations and the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit determines the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to data based on technical specifications. The analysis unit can also apply a different analysis algorithm to data based on alarm information. The analysis unit can also apply yet another analysis algorithm to data based on past response history. For example, the analysis unit applies a specific analysis algorithm to data based on technical specifications. The analysis unit applies a different analysis algorithm to data based on alarm information. The analysis unit applies yet another analysis algorithm to data based on past response history. This allows different analysis algorithms to be applied depending on the data category.

[0044] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of data from a specific period based on past data. The analysis unit can also adjust the order of analysis according to the data collection period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may prioritize the analysis of data from a specific period based on past data. The analysis unit can adjust the order of analysis according to the data collection period. This allows the analysis priority to be determined based on the data collection period.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data.

[0046] The generation unit can adjust the level of detail generated based on the importance of the data when generating response methods. For example, the generation unit generates detailed response methods based on high-importance data. The generation unit can also generate simplified response methods based on low-importance data. The generation unit can also determine the priority of the response methods to be generated according to the importance of the data. For example, the generation unit generates detailed response methods based on high-importance data. The generation unit generates simplified response methods based on low-importance data. The generation unit determines the priority of the response methods to be generated according to the importance of the data. This allows the level of detail generated to be adjusted based on the importance of the data.

[0047] The generation unit can apply different generation algorithms depending on the data category when generating response methods. For example, the generation unit applies a specific generation algorithm to response methods based on technical specifications. The generation unit can also apply a different generation algorithm to response methods based on alarm information. The generation unit can also apply yet another generation algorithm to response methods based on past response history. For example, the generation unit applies a specific generation algorithm to response methods based on technical specifications. The generation unit applies a different generation algorithm to response methods based on alarm information. The generation unit applies yet another generation algorithm to response methods based on past response history. This allows different generation algorithms to be applied depending on the data category.

[0048] The generation unit can determine the priority of response methods based on the data collection timing when generating them. For example, the generation unit may prioritize generating response methods based on the latest data. The generation unit may also postpone generating response methods based on past data. The generation unit can also adjust the order of the response methods generated according to the data collection timing. For example, the generation unit may prioritize generating response methods based on the latest data. The generation unit may postpone generating response methods based on past data. The generation unit adjusts the order of the response methods generated according to the data collection timing. This allows the generation priority to be determined based on the data collection timing.

[0049] The generation unit can adjust the generation order based on the relevance of the data when generating response methods. For example, the generation unit can prioritize generating response methods based on highly relevant data. The generation unit can also postpone generating response methods based on less relevant data. The generation unit can also dynamically adjust the order of the response methods to be generated according to the relevance of the data. For example, the generation unit can prioritize generating response methods based on highly relevant data. The generation unit can postpone generating response methods based on less relevant data. The generation unit can dynamically adjust the order of the response methods to be generated according to the relevance of the data. This allows the generation order to be adjusted based on the relevance of the data.

[0050] The requesting department can adjust the level of detail in a request based on the importance of the proposed solution. For example, the requesting department will provide detailed instructions for highly important solutions, while simplifying instructions for less important solutions. The requesting department can also determine the priority of requests based on the importance of the solutions. For example, the requesting department will provide detailed instructions for highly important solutions, while simplifying instructions for less important solutions. The requesting department will determine the priority of requests based on the importance of the solutions. This allows the level of detail in requests to be adjusted based on the importance of the solutions.

[0051] The requesting department can apply different request algorithms depending on the category of the response method when a request is made. For example, the requesting department can apply a specific request algorithm to response methods based on technical specifications. The requesting department can also apply a different request algorithm to response methods based on alarm information. The requesting department can also apply yet another request algorithm to response methods based on past response history. For example, the requesting department can apply a specific request algorithm to response methods based on technical specifications. The requesting department can apply a different request algorithm to response methods based on alarm information. The requesting department can also apply yet another request algorithm to response methods based on past response history. This allows different request algorithms to be applied depending on the category of the response method.

[0052] The requesting department can determine the priority of requests based on when the response methods were created. For example, the requesting department will prioritize requests based on the latest response methods. The requesting department can also postpone requests based on older response methods. The requesting department can also adjust the order of requests according to when the response methods were created. For example, the requesting department will prioritize requests based on the latest response methods. The requesting department will postpone requests based on older response methods. The requesting department will adjust the order of requests according to when the response methods were created. This allows the priority of requests to be determined based on when the response methods were created.

[0053] The requesting department can adjust the order of requests based on the relevance of the response methods. For example, the requesting department will prioritize requests based on highly relevant response methods. The requesting department can also postpone requests based on less relevant response methods. The requesting department can also dynamically adjust the order of requests according to the relevance of the response methods. For example, the requesting department will prioritize requests based on highly relevant response methods. The requesting department will postpone requests based on less relevant response methods. The requesting department dynamically adjusts the order of requests according to the relevance of the response methods. This allows the order of requests to be adjusted based on the relevance of the response methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The maintenance response system can also be equipped with a prediction unit. The prediction unit can predict future failures based on collected data. For example, the prediction unit can analyze past failure patterns to predict the likelihood of future failures. The prediction unit can also use machine learning algorithms to learn data trends and predict future failures. Furthermore, the prediction unit can analyze data in real time and detect signs of impending failures. This allows the maintenance response system to take preventative measures before failures occur, further ensuring the stable operation of base stations.

[0056] The maintenance response system can also include a notification unit. This unit can notify relevant parties of generated response methods and predicted fault information. For example, the notification unit can send notifications to relevant parties via email or SMS. The notification unit can also send notifications in real time through a dedicated application. The notification unit can also customize notification content and provide information tailored to the roles of the relevant parties. This allows relevant parties to quickly understand response methods and fault information and take appropriate action.

[0057] The maintenance response system may further include an evaluation unit. The evaluation unit can assess the effectiveness of the generated response methods and identify areas for improvement. For example, the evaluation unit can collect results after the implementation of the response methods and evaluate their effectiveness. The evaluation unit can also quantitatively evaluate the effectiveness of the response methods using data analysis techniques. Furthermore, the evaluation unit can collect feedback from stakeholders and identify areas for improvement in the response methods. This allows the maintenance response system to continuously improve its response methods and streamline base station maintenance operations.

[0058] The maintenance response system can also be equipped with a learning unit. The learning unit can train the generating AI based on collected data and evaluation results. For example, the learning unit can improve the generating AI's algorithm using past response history and evaluation results. The learning unit can also improve the accuracy of the generating AI using machine learning techniques. The learning unit can also periodically update data to maintain the latest state of the generating AI. This allows the maintenance response system to constantly generate the optimal response method, thereby improving the efficiency of base station maintenance operations.

[0059] The maintenance response system can also include a reporting function. This function can compile the generated response methods and implementation results into a report. For example, the reporting function can automatically compile details of the response methods and implementation results into a report. The reporting function can also generate reports in PDF or text format and provide them to relevant parties. The reporting function can also customize the content of the reports, providing information tailored to the needs of the parties involved. This allows parties to gain a detailed understanding of the response methods and implementation results, which can then be used to inform future responses.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data such as alarm information, technical specifications, and past response history. For example, the data collection unit acquires alarm information directly from sensors in real time and stores it in a database. Technical specifications are collected in PDF or text format and stored in the database. Past response history is retrieved from the database using queries. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the data and generate the optimal response method. It can also use data mining techniques to analyze data patterns and statistical analysis techniques to analyze data trends. Step 3: The generation unit generates a response method based on the results analyzed by the analysis unit. For example, the generation unit can generate a response method using generation AI, or it can generate a response method based on procedure manuals or guidelines. It can also generate a response method by referring to past response history. Step 4: The requesting unit makes a request based on the response method generated by the generation unit. For example, the requesting unit makes a request to an external vendor, automatically generates a request form, and sends it to the external vendor. The requesting unit can also review the request details and make corrections as needed.

[0062] (Example of form 2) The maintenance response system according to an embodiment of the present invention is a system that uses a generating AI to replace base station maintenance response tasks. Currently, maintenance response systems involve humans devising response methods based on alarm information, technical specifications, and past response history, and then making requests to external vendors. By replacing these tasks with a generating AI, the workload of base station maintenance operations can be significantly reduced, and fault response that would be impossible for humans can be achieved. For example, the maintenance response system collects data such as alarm information, technical specifications, and past response history. Next, the maintenance response system uses a generating AI to analyze this data and generate the optimal response method. Based on the generated response method, the maintenance response system makes a request to an external vendor. This mechanism improves the efficiency of base station maintenance operations and enables fault response that would be impossible for humans. For example, if an alarm occurs at a base station, the generating AI analyzes the alarm information and generates the optimal response method by referring to past response history and technical specifications. Based on the generated response method, the maintenance response system makes a request to an external vendor, enabling a rapid response. This system not only significantly reduces the workload of base station maintenance operations but also enables fault response that would be impossible for humans. For example, even in the event of a complex failure, the maintenance response system can quickly generate the optimal response method using AI and take action. This ensures the stable operation of base stations and is expected to improve service quality. As a result, the maintenance response system can significantly reduce the man-hours required for base station maintenance and achieve failure response that would be impossible for humans.

[0063] The maintenance response system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a request unit. The collection unit collects data such as alarm information, technical specifications, and past response history. The collection unit can, for example, collect alarm information in real time. The collection unit can also collect technical specifications in digital format. Furthermore, the collection unit can obtain past response history from a database. For example, the collection unit directly acquires alarm information from sensors and stores it in the database in real time. Technical specifications are collected in PDF or text format and stored in the database. Past response history is obtained from the database using queries and provided to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generation AI and generates the optimal response method. The analysis unit can also analyze data patterns using data mining techniques. Furthermore, the analysis unit can analyze data trends using statistical analysis techniques. For example, the analysis unit inputs alarm information, technical specifications, and past response history into the generation AI and generates the optimal response method. Data mining techniques extract useful patterns from data and use them to generate response methods. Statistical analysis techniques analyze data trends and improve the accuracy of response methods. The generation unit generates response methods based on the results analyzed by the analysis unit. The generation unit generates response methods using, for example, a generation AI. The generation unit can also generate response methods based on procedures and guidelines. Furthermore, the generation unit can generate response methods by referring to past response history. For example, the generation unit inputs the analysis results into a generation AI to generate the optimal response method. Procedures and guidelines are used as references in generating response methods. Past response history provides response methods for similar problems. The requesting unit makes requests based on the response methods generated by the generation unit. The requesting unit makes requests to, for example, external vendors. The requesting unit can also automatically generate request forms and send them to external vendors. Furthermore, the requesting unit can review the request content and make corrections as needed. For example, the requesting unit automatically generates a request form based on the generated response method and sends it to an external vendor. The request form details the procedures to be followed and provides specific instructions to the external contractor.The request details are reviewed by the requesting department and modified as necessary. As a result, the maintenance response system according to this embodiment can significantly reduce the man-hours required for base station maintenance work and enable fault response that would be impossible for humans to perform.

[0064] The data collection unit collects data such as alarm information, technical specifications, and past response history. Specifically, alarm information is acquired from sensors in real time and immediately stored in the database. This ensures that the system always has the latest alarm information, enabling rapid response. Technical specifications are digitized in PDF or text format and stored in the database. This allows engineers to quickly search and refer to the necessary information. Past response history is retrieved from the database using queries and provided to the analysis unit. This enables analysis based on past response history, and is expected to generate more accurate response methods. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit implements verification processes to ensure data integrity and reliability, maintaining data quality. For example, data accuracy can be improved by using algorithms that detect and automatically correct data duplication and missing data. This allows the data collection unit to provide highly reliable data, improving the overall reliability of the system.

[0065] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses a generative AI to analyze the data and generate the optimal response method. Specifically, it inputs alarm information, technical specifications, and past response history into the generative AI to generate the optimal response method. The generative AI uses natural language processing technology to analyze the technical specifications and past response history and proposes a response method based on the alarm information. Data mining technology extracts useful patterns from the data and helps generate response methods. For example, it finds common patterns from past response history and identifies the optimal response method for similar failures. Statistical analysis technology analyzes data trends and improves the accuracy of response methods. For example, it can predict the likelihood of a specific failure occurring based on the frequency of alarm information and the content of technical specifications, and take countermeasures in advance. Furthermore, the analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past failure data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The generation unit generates response methods based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate response methods. Specifically, it inputs the analysis results into the generation AI to generate the optimal response method. The generation AI proposes the optimal response procedure, referencing past response history and technical specifications. Procedure manuals and guidelines are referenced in generating response methods. For example, a specific response method can be customized based on standard response procedures for a particular failure. Past response history provides response methods for similar failures. For example, the optimal response procedure can be generated by referring to response methods used when similar failures occurred in the past. The generation unit can integrate this information and quickly generate the optimal response method. Furthermore, the generation unit can evaluate the accuracy and effectiveness of the generated response method and make corrections as needed. For example, it can simulate the generated response method to confirm whether it fits the actual situation. The generation unit can also collect feedback from users and use it to improve the response method. This allows the generation unit to always provide highly accurate response methods based on the latest information, supporting quick and appropriate responses. Furthermore, the generation unit can automatically document the generated response method and share it with other departments and systems. This ensures consistency and transparency in response methods, improving the overall efficiency of the system.

[0067] The requesting department makes requests based on the response methods generated by the generation department. Specifically, it makes requests to external vendors. The requesting department can also automatically generate request forms and send them to external vendors. For example, it can automatically generate request forms based on the generated response methods and send them to external vendors. The request form will describe the details of the response methods and provide specific instructions to the external vendors. The request content will be reviewed by the requesting department and revised as necessary. For example, the department will review the content of the request form and correct any errors or unclear points. The requesting department can also monitor the progress of requests and follow up as necessary. For example, it will receive reports from external vendors and check the progress of the response. In addition, the requesting department can record the request content and progress in a database and use it as reference material for the future. This allows the requesting department to make requests efficiently and effectively, improving the reliability and efficiency of the entire system. Furthermore, the requesting department can share the request content and progress with other departments and systems to strengthen overall collaboration. For example, it can notify other departments of the request content and request necessary support. In addition, the requesting department can evaluate the results of requests and use them to improve in the future. This allows the requesting department to always provide the optimal response and improve the overall system performance.

[0068] The data collection unit can collect data such as alarm information, technical specifications, and past response history. For example, the data collection unit can collect alarm information in real time. The data collection unit can also collect technical specifications in digital format. The data collection unit can also retrieve past response history from a database. For example, the data collection unit can directly acquire alarm information from sensors and save it to the database in real time. Technical specifications are collected in PDF or text format and saved to the database. Past response history is retrieved from the database using queries and provided to the analysis unit. This allows for the efficient collection of necessary data.

[0069] The analysis unit can analyze collected data and generate the optimal response method. For example, the analysis unit can use a generation AI to analyze data and generate the optimal response method. The analysis unit can also use data mining techniques to analyze data patterns. Furthermore, the analysis unit can use statistical analysis techniques to analyze data trends. For example, the analysis unit inputs alarm information, technical specifications, and past response history into a generation AI to generate the optimal response method. Data mining techniques extract useful patterns from the data and utilize them to generate response methods. Statistical analysis techniques analyze data trends and improve the accuracy of response methods. This allows the system to analyze collected data and generate the optimal response method.

[0070] The generation unit can request external vendors to perform the generated response methods. For example, the generation unit can generate response methods using a generation AI. The generation unit can also generate response methods based on procedures and guidelines. Furthermore, the generation unit can generate response methods by referring to past response history. For example, the generation unit inputs analysis results into a generation AI to generate the optimal response method. Procedures and guidelines are used as reference in generating response methods. Past response history provides response methods for similar incidents. This allows for requests to be made to external vendors based on the generated response methods.

[0071] The requesting department can make requests based on the generated response methods. For example, the requesting department can make requests to external vendors. The requesting department can also automatically generate a request form and send it to the external vendor. The requesting department can also review the request content and revise it as needed. For example, the requesting department can automatically generate a request form based on the generated response methods and send it to the external vendor. The request form will describe the details of the response methods and provide specific instructions to the external vendor. The request content will be reviewed by the requesting department and revised as needed. This allows requests to be made based on the generated response methods.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can speed up the timing of data collection and collect necessary data quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on heart rate fluctuations. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0073] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and apply it to future data collection. The data collection unit can also select the optimal collection method for specific time periods or conditions based on past data collection history. The data collection unit can also analyze past data collection history, identify areas for improvement in collection methods, and optimize them. For example, the data collection unit can retrieve past data collection history from a database and analyze it using data mining techniques. The data collection unit can select a data collection method for specific time periods or conditions to achieve efficient data collection. By identifying areas for improvement in collection methods and optimizing them, the data collection unit can improve the accuracy and efficiency of data collection. This allows for the analysis of past data collection history and the selection of the optimal collection method.

[0074] The data collection unit can filter data based on the base station's current operational status and environmental conditions. For example, the data collection unit can monitor the base station's operational status in real time and collect data only when an anomaly occurs. The data collection unit can also prioritize data collection under specific conditions, taking environmental conditions (weather, temperature, etc.) into consideration. The data collection unit can also adjust the type and amount of data collected based on the base station's operational status and environmental conditions. For example, the data collection unit can monitor the base station's operational status in real time and collect data only when an anomaly occurs. The data collection unit can prioritize data collection under specific conditions, taking environmental conditions (weather, temperature, etc.) into consideration. The data collection unit can adjust the type and amount of data collected based on the base station's operational status and environmental conditions. This allows for filtering of data collection based on the base station's operational status and environmental conditions.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on heart rate variability. This allows the data collection unit to prioritize the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the base station during data collection. For example, the data collection unit can prioritize the collection of surrounding environmental data based on the geographical location information of the base station. The data collection unit can also prioritize data collection in a specific area by considering the geographical location information of the base station. The data collection unit can also filter and collect highly relevant data based on the geographical location information of the base station. For example, the data collection unit can prioritize the collection of surrounding environmental data based on the geographical location information of the base station. The data collection unit can prioritize data collection in a specific area by considering the geographical location information of the base station. The data collection unit can filter and collect highly relevant data based on the geographical location information of the base station. This allows for the priority collection of highly relevant data by considering the geographical location information of the base station.

[0077] The data collection unit can analyze the social media activity of base stations and collect relevant data during data collection. For example, the data collection unit monitors the social media activity of base stations and collects relevant data. The data collection unit can also analyze trends on social media and prioritize the collection of data relevant to base stations. Furthermore, the data collection unit can collect data relevant to base stations based on user feedback on social media. This allows for the analysis of social media activity of base stations and the collection of relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit determines the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted based on the importance of the data.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to data based on technical specifications. The analysis unit can also apply a different analysis algorithm to data based on alarm information. The analysis unit can also apply yet another analysis algorithm to data based on past response history. For example, the analysis unit applies a specific analysis algorithm to data based on technical specifications. The analysis unit applies a different analysis algorithm to data based on alarm information. The analysis unit applies yet another analysis algorithm to data based on past response history. This allows different analysis algorithms to be applied depending on the data category.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of data from a specific period based on past data. The analysis unit can also adjust the order of analysis according to the data collection period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may prioritize the analysis of data from a specific period based on past data. The analysis unit can adjust the order of analysis according to the data collection period. This allows the analysis priority to be determined based on the data collection period.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data.

[0084] The generation unit can estimate the user's emotions and adjust the expression of the response it generates based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a simple and easily recognizable response. If the user is relaxed, the generation unit can also generate a detailed response. If the user is in a hurry, the generation unit can generate a concise response. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the expression of the response generated to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The generation unit can adjust the level of detail generated based on the importance of the data when generating response methods. For example, the generation unit generates detailed response methods based on high-importance data. The generation unit can also generate simplified response methods based on low-importance data. The generation unit can also determine the priority of the response methods to be generated according to the importance of the data. For example, the generation unit generates detailed response methods based on high-importance data. The generation unit generates simplified response methods based on low-importance data. The generation unit determines the priority of the response methods to be generated according to the importance of the data. This allows the level of detail generated to be adjusted based on the importance of the data.

[0086] The generation unit can apply different generation algorithms depending on the data category when generating response methods. For example, the generation unit applies a specific generation algorithm to response methods based on technical specifications. The generation unit can also apply a different generation algorithm to response methods based on alarm information. The generation unit can also apply yet another generation algorithm to response methods based on past response history. For example, the generation unit applies a specific generation algorithm to response methods based on technical specifications. The generation unit applies a different generation algorithm to response methods based on alarm information. The generation unit applies yet another generation algorithm to response methods based on past response history. This allows different generation algorithms to be applied depending on the data category.

[0087] The generation unit can estimate the user's emotions and determine the priority of responses to generate based on the estimated emotions. For example, if the user is nervous, the generation unit will prioritize generating responses of high importance. If the user is relaxed, the generation unit may also prioritize generating detailed responses. If the user is in a hurry, the generation unit may also prioritize generating responses that can be generated quickly. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the generation unit to determine the priority of responses to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The generation unit can determine the priority of response methods based on the data collection timing when generating them. For example, the generation unit may prioritize generating response methods based on the latest data. The generation unit may also postpone generating response methods based on past data. The generation unit can also adjust the order of the response methods generated according to the data collection timing. For example, the generation unit may prioritize generating response methods based on the latest data. The generation unit may postpone generating response methods based on past data. The generation unit adjusts the order of the response methods generated according to the data collection timing. This allows the generation priority to be determined based on the data collection timing.

[0089] The generation unit can adjust the generation order based on the relevance of the data when generating response methods. For example, the generation unit can prioritize generating response methods based on highly relevant data. The generation unit can also postpone generating response methods based on less relevant data. The generation unit can also dynamically adjust the order of the response methods to be generated according to the relevance of the data. For example, the generation unit can prioritize generating response methods based on highly relevant data. The generation unit can postpone generating response methods based on less relevant data. The generation unit can dynamically adjust the order of the response methods to be generated according to the relevance of the data. This allows the generation order to be adjusted based on the relevance of the data.

[0090] The request unit can estimate the user's emotions and adjust the way the request is expressed based on the estimated emotions. For example, if the user is nervous, the request unit can provide a simple and easily understandable request. If the user is relaxed, the request unit can also provide a detailed request. If the user is in a hurry, the request unit can provide a concise request. For example, the request unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The request unit can also record the user's voice and estimate their emotions using voice analysis technology. The request unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the request unit can calculate an emotion score based on heart rate fluctuations. This allows the request to be expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The requesting department can adjust the level of detail in a request based on the importance of the proposed solution. For example, the requesting department will provide detailed instructions for highly important solutions, while simplifying instructions for less important solutions. The requesting department can also determine the priority of requests based on the importance of the solutions. For example, the requesting department will provide detailed instructions for highly important solutions, while simplifying instructions for less important solutions. The requesting department will determine the priority of requests based on the importance of the solutions. This allows the level of detail in requests to be adjusted based on the importance of the solutions.

[0092] The requesting department can apply different request algorithms depending on the category of the response method when a request is made. For example, the requesting department can apply a specific request algorithm to response methods based on technical specifications. The requesting department can also apply a different request algorithm to response methods based on alarm information. The requesting department can also apply yet another request algorithm to response methods based on past response history. For example, the requesting department can apply a specific request algorithm to response methods based on technical specifications. The requesting department can apply a different request algorithm to response methods based on alarm information. The requesting department can also apply yet another request algorithm to response methods based on past response history. This allows different request algorithms to be applied depending on the category of the response method.

[0093] The requesting unit can estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is nervous, the requesting unit will prioritize high-priority requests. If the user is relaxed, the requesting unit may also prioritize detailed requests. If the user is in a hurry, the requesting unit may also prioritize methods that allow for quick requests. For example, the requesting unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The requesting unit can also record the user's voice and estimate their emotions using voice analysis technology. The requesting unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the requesting unit can calculate an emotion score based on heart rate fluctuations. This allows the requesting unit to prioritize requests according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The requesting department can determine the priority of requests based on when the response methods were created. For example, the requesting department will prioritize requests based on the latest response methods. The requesting department can also postpone requests based on older response methods. The requesting department can also adjust the order of requests according to when the response methods were created. For example, the requesting department will prioritize requests based on the latest response methods. The requesting department will postpone requests based on older response methods. The requesting department will adjust the order of requests according to when the response methods were created. This allows the priority of requests to be determined based on when the response methods were created.

[0095] The requesting department can adjust the order of requests based on the relevance of the response methods. For example, the requesting department will prioritize requests based on highly relevant response methods. The requesting department can also postpone requests based on less relevant response methods. The requesting department can also dynamically adjust the order of requests according to the relevance of the response methods. For example, the requesting department will prioritize requests based on highly relevant response methods. The requesting department will postpone requests based on less relevant response methods. The requesting department dynamically adjusts the order of requests according to the relevance of the response methods. This allows the order of requests to be adjusted based on the relevance of the response methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The maintenance response system can also be equipped with a prediction unit. The prediction unit can predict future failures based on collected data. For example, the prediction unit can analyze past failure patterns to predict the likelihood of future failures. The prediction unit can also use machine learning algorithms to learn data trends and predict future failures. Furthermore, the prediction unit can analyze data in real time and detect signs of impending failures. This allows the maintenance response system to take preventative measures before failures occur, further ensuring the stable operation of base stations.

[0098] The maintenance response system can also include a notification unit. This unit can notify relevant parties of generated response methods and predicted fault information. For example, the notification unit can send notifications to relevant parties via email or SMS. The notification unit can also send notifications in real time through a dedicated application. The notification unit can also customize notification content and provide information tailored to the roles of the relevant parties. This allows relevant parties to quickly understand response methods and fault information and take appropriate action.

[0099] The maintenance response system may further include an evaluation unit. The evaluation unit can assess the effectiveness of the generated response methods and identify areas for improvement. For example, the evaluation unit can collect results after the implementation of the response methods and evaluate their effectiveness. The evaluation unit can also quantitatively evaluate the effectiveness of the response methods using data analysis techniques. Furthermore, the evaluation unit can collect feedback from stakeholders and identify areas for improvement in the response methods. This allows the maintenance response system to continuously improve its response methods and streamline base station maintenance operations.

[0100] The maintenance response system can also be equipped with a learning unit. The learning unit can train the generating AI based on collected data and evaluation results. For example, the learning unit can improve the generating AI's algorithm using past response history and evaluation results. The learning unit can also improve the accuracy of the generating AI using machine learning techniques. The learning unit can also periodically update data to maintain the latest state of the generating AI. This allows the maintenance response system to constantly generate the optimal response method, thereby improving the efficiency of base station maintenance operations.

[0101] The maintenance response system can also include a reporting function. This function can compile the generated response methods and implementation results into a report. For example, the reporting function can automatically compile details of the response methods and implementation results into a report. The reporting function can also generate reports in PDF or text format and provide them to relevant parties. The reporting function can also customize the content of the reports, providing information tailored to the needs of the parties involved. This allows parties to gain a detailed understanding of the response methods and implementation results, which can then be used to inform future responses.

[0102] The maintenance response system can estimate the user's emotions and adjust the notification content based on those emotions. For example, if the user is stressed, the notification content can be made concise and only essential information can be provided. If the user is relaxed, detailed information can be provided. If the user is in a hurry, information requiring immediate attention can be prioritized. This allows the system to adjust notification content according to the user's emotions and provide appropriate information.

[0103] The maintenance response system can estimate the user's emotions and adjust the presentation of evaluation results based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand evaluation results. If the user is relaxed, it can provide detailed evaluation results. If the user is in a hurry, it can provide concise evaluation results. This allows the system to adjust the presentation of evaluation results according to the user's emotions and provide appropriate information.

[0104] The maintenance response system can estimate the user's emotions and select training data based on those emotions. For example, if the user is stressed, the amount of training data can be reduced to lessen the burden. If the user is relaxed, more detailed training data can be selected. If the user is in a hurry, data that allows for rapid learning can be selected. This allows for efficient learning by selecting training data according to the user's emotions.

[0105] The maintenance response system can estimate the user's emotions and adjust the report content based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read report. If the user is relaxed, it can provide a detailed report. If the user is in a hurry, it can provide a concise report. This allows the system to adjust the report content according to the user's emotions and provide appropriate information.

[0106] The maintenance response system can estimate the user's emotions and adjust the presentation of prediction results based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand prediction results. If the user is relaxed, it can provide detailed prediction results. If the user is in a hurry, it can provide concise prediction results. This allows the system to adjust the presentation of prediction results according to the user's emotions and provide appropriate information.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data such as alarm information, technical specifications, and past response history. For example, the data collection unit acquires alarm information directly from sensors in real time and stores it in a database. Technical specifications are collected in PDF or text format and stored in the database. Past response history is retrieved from the database using queries. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the data and generate the optimal response method. It can also use data mining techniques to analyze data patterns and statistical analysis techniques to analyze data trends. Step 3: The generation unit generates a response method based on the results analyzed by the analysis unit. For example, the generation unit can generate a response method using generation AI, or it can generate a response method based on procedure manuals or guidelines. It can also generate a response method by referring to past response history. Step 4: The requesting unit makes a request based on the response method generated by the generation unit. For example, the requesting unit makes a request to an external vendor, automatically generates a request form, and sends it to the external vendor. The requesting unit can also review the request details and make corrections as needed.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and request unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects alarm information in real time using the camera 42 and sensors of the smart device 14, and the specific processing unit 290 of the data processing unit 12 retrieves technical specifications and past response history from the database 24. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12 and generates the optimal response method. The generation unit generates a response method based on the analysis results by the specific processing unit 290 of the data processing unit 12. The request unit makes a request to an external vendor based on the response method generated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and request unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects alarm information in real time using the camera 42 and sensors of the smart glasses 214, and the identification processing unit 290 of the data processing unit 12 retrieves technical specifications and past response history from the database 24. The analysis unit analyzes the data using generated AI, for example, by the identification processing unit 290 of the data processing unit 12, and generates the optimal response method. The generation unit generates a response method based on the analysis results, for example, by the identification processing unit 290 of the data processing unit 12. The request unit makes a request to an external vendor based on the response method generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and request unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects alarm information in real time using the camera 42 and sensors of the headset terminal 314, and the specific processing unit 290 of the data processing unit 12 retrieves technical specifications and past response history from the database 24. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12 and generates the optimal response method. The generation unit generates a response method based on the analysis results by the specific processing unit 290 of the data processing unit 12. The request unit makes a request to an external vendor based on the response method generated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and request unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects alarm information in real time using the camera 42 and sensors of the robot 414, and the specific processing unit 290 of the data processing unit 12 retrieves technical specifications and past response history from the database 24. The analysis unit analyzes the data using generated AI, for example, the specific processing unit 290 of the data processing unit 12, and generates the optimal response method. The generation unit generates a response method based on the analysis results, for example, the specific processing unit 290 of the data processing unit 12. The request unit makes a request to an external vendor based on the response method generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects data such as alarm information, technical specifications, and past response history, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a corresponding method based on the results of the analysis performed by the aforementioned analysis unit, The system includes a request unit that makes a request based on the corresponding method generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as alarm information, technical specifications, and past response history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to generate the optimal response method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We will request external vendors to handle the generated response plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned request unit, Request based on the generated response method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the current operating status and environmental conditions of the base station. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of base stations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the social media activity of base stations is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a response method, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a response method, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of responses to be generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating response methods, the generation priority is determined based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating response methods, the generation order is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned request unit, The system estimates the user's emotions and adjusts the way requests are phrased based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned request unit, When making a request, adjust the level of detail based on the importance of the desired solution. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned request unit, When a request is made, a different request algorithm is applied depending on the category of the response method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned request unit, It estimates the user's emotions and determines the priority of requests based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned request unit, When a request is made, the priority of the request is determined based on when the solution was generated. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned request unit, When making a request, adjust the order of requests based on the relevance of the required solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as alarm information, technical specifications, and past response history, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a corresponding method based on the results of the analysis performed by the aforementioned analysis unit, The system includes a request unit that makes a request based on the corresponding method generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as alarm information, technical specifications, and past response history. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to generate the optimal response method. The system according to feature 1.

4. The generating unit is We will request external vendors to handle the generated response plan. The system according to feature 1.

5. The aforementioned request unit, Request based on the generated response method. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the current operating status and environmental conditions of the base station. The system according to feature 1.

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