system

Abstract

The system according to this embodiment aims to propose an optimal plan for the installation and improvement of base stations. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and an implementation unit. The data collection unit collects data related to the installation and improvement of base stations. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal installation location and improvement method. The implementation unit installs or improves base stations based on the plan proposed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 it is difficult to efficiently propose an optimal plan regarding the installation and improvement of a base station.

[0005] The system according to the embodiment aims to propose an optimal plan regarding the installation and improvement of a base station.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an implementation unit. The collection unit collects data regarding the installation and improvement of a base station. The analysis unit analyzes the data collected by the collection unit and proposes an optimal installation location and improvement method. The implementation unit implements the installation and improvement of the base station based on the plan proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal plan for the installation and improvement of base stations. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The base station installation optimization system according to an embodiment of the present invention is a system that proposes an optimal plan for the installation and improvement of base stations by utilizing generative AI. The base station installation optimization system collects data related to the installation and improvement of base stations, the generative AI analyzes it, and proposes the optimal installation location and improvement method. This proposal enables efficient operation of base stations and contributes to improved communication quality and cost reduction. First, data related to the installation and improvement of base stations is collected. This data includes geographic information, population density, communication traffic, and the placement of existing base stations. For example, in urban areas, the population density is high and communication traffic is also high, so it is necessary to carefully select the installation location of base stations. By collecting such data, basic information regarding the installation and improvement of base stations can be obtained. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI proposes the optimal installation location and improvement method. For example, it selects the installation location of a base station by considering geographic information and population density. In addition, by analyzing communication traffic data and optimizing the placement of existing base stations, it is possible to improve communication quality. The generative AI comprehensively analyzes this data and proposes an optimal plan. Furthermore, the installation and improvement of base stations are carried out based on the plan proposed by the generative AI. For example, a new base station can be installed at a location suggested by the generating AI. Furthermore, the placement of existing base stations can be reviewed to distribute communication traffic and improve communication quality. In this way, utilizing the generating AI enables efficient operation of base stations. This system allows for the proposal of optimal plans for base station installation and improvement. This results in improved communication quality, cost reduction, and efficient base station operation. For instance, in urban areas, where population density is high and communication traffic is heavy, careful selection of base station installation locations is necessary. By collecting and analyzing such data, the generating AI can propose optimal installation locations and improvement methods. This results in improved communication quality, cost reduction, and efficient base station operation. Thus, the base station installation optimization system can propose optimal plans for base station installation and improvement, achieving improved communication quality and cost reduction.

[0029] The base station installation optimization system according to the embodiment comprises a data collection unit, an analysis unit, and an implementation unit. The data collection unit collects data related to the installation and improvement of base stations. The data collection unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. For example, the data collection unit can collect geographic information using GPS data. The data collection unit can also collect population density using census data. Furthermore, the data collection unit can collect communication traffic using network logs. For example, the data collection unit can collect communication traffic using real-time monitoring. The data collection unit can collect location information and coverage area of ​​base stations. The analysis unit analyzes the data collected by the data collection unit and proposes optimal installation locations and improvement methods. The analysis unit analyzes the collected data using, for example, generative AI. For example, the analysis unit can select a base station installation location considering geographic information and population density. The analysis unit can also analyze communication traffic data and optimize the placement of existing base stations. For example, the analysis unit can distribute communication traffic using generative AI. The implementation unit installs or improves base stations based on the plan proposed by the analysis unit. For example, the implementation unit can install new base stations at locations proposed by the generation AI. The implementation unit can also review the placement of existing base stations to distribute communication traffic. For example, the implementation unit can implement improvement methods proposed by the generation AI. As a result, the base station installation optimization system according to the embodiment can propose an optimal plan for base station installation and improvement, thereby improving communication quality and reducing costs.

[0030] The data collection unit collects data related to the installation and improvement of base stations. For example, the unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. Specifically, the unit can collect geographic information using GPS data. GPS data provides detailed geographic information such as terrain topography, road layout, and building height, making it crucial data for identifying suitable locations for base station installation. The unit can also collect population density using census data. Census data provides information such as regional population distribution, age groups, and household numbers, and is used to identify areas with high communication demand. Furthermore, the unit can collect communication traffic using network logs. Network logs record user communication volume, peak times, and data usage patterns in detail, helping to identify areas with concentrated communication traffic. For example, the unit can collect communication traffic using real-time monitoring. Real-time monitoring allows for immediate understanding of current communication conditions and the detection of sudden traffic increases or abnormal communication patterns. Existing base station placement data can collect base station location information and coverage areas. This allows the data collection unit to understand the coverage area of ​​existing base stations and provide foundational data for optimizing the placement of new base stations. By centrally managing this diverse data and making it accessible to the analysis and implementation units, the data collection unit can improve the overall efficiency of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit and proposes optimal installation locations and improvement methods. For example, the analysis unit uses generative AI to analyze the collected data. Generative AI is a powerful tool for quickly and accurately analyzing large amounts of data and proposing optimal installation locations and improvement methods. For example, the analysis unit can select base station installation locations considering geographical information and population density. Generative AI can analyze geographical information and simulate radio wave propagation characteristics considering terrain topography and building layouts. It can also analyze population density data to identify areas with high communication demand and propose optimal base station installation locations for those areas. Furthermore, the analysis unit can analyze communication traffic data and optimize the placement of existing base stations. Generative AI can analyze the distribution of communication traffic, identify areas with traffic concentration, and distribute communication traffic by installing additional base stations in those areas. For example, the analysis unit can use generative AI to distribute communication traffic. Based on past communication traffic data, generative AI can predict future traffic increases and propose optimal base station placement. This allows the analysis unit to propose optimal base station locations and improvement methods based on the collected data, thereby improving communication quality and reducing costs.

[0032] The implementation department installs or improves base stations based on the plan proposed by the analysis department. For example, the implementation department can install new base stations at locations proposed by the generation AI. Specifically, the implementation department conducts on-site surveys of the proposed locations to confirm detailed conditions such as terrain, building layout, and securing power and communication lines. This allows them to evaluate whether the proposed locations are actually suitable and make adjustments as needed. The implementation department can also review the placement of existing base stations to distribute communication traffic. For example, the implementation department can implement improvement methods proposed by the generation AI. Specifically, they can adjust the direction and output of existing base station antennas to optimize coverage areas and distribute communication traffic. Furthermore, the implementation department manages the construction and installation work associated with the installation of new base stations and the improvement of existing base stations, and adjusts it to proceed according to schedule. This allows the implementation department to quickly and reliably execute the plan proposed by the analysis department, achieving improved communication quality and cost reduction. In addition, even after installation or improvement is completed, the implementation department can maintain the performance of the base stations by performing regular maintenance and monitoring. As a result, the base station installation optimization system according to the embodiment can propose an optimal plan for the installation and improvement of base stations, thereby achieving improved communication quality and cost reduction.

[0033] The data collection unit can collect data such as geographic information, population density, communication traffic, and the location of existing base stations. For example, the data collection unit uses GPS data to collect geographic information. For example, the data collection unit can also collect geographic information using map information. The data collection unit also uses census data to collect population density. For example, the data collection unit can also collect population density using real-time data. Furthermore, the data collection unit uses network logs to collect communication traffic. For example, the data collection unit can also collect communication traffic using real-time monitoring. Data on the location of existing base stations can collect base station location information and coverage area. For example, the data collection unit can collect base station location information and identify coverage areas. This allows for the efficient collection of data necessary for the installation and improvement of base stations.

[0034] The analysis unit can propose optimal installation locations and improvement methods based on the collected data. For example, the analysis unit selects base station installation locations considering collected geographic information and population density. For example, the analysis unit can use generative AI to analyze geographic information and population density and propose optimal installation locations. The analysis unit can also analyze communication traffic data and optimize the placement of existing base stations. For example, the analysis unit can use generative AI to distribute communication traffic. Furthermore, the analysis unit can comprehensively analyze the collected data and propose an optimal plan. For example, the analysis unit can use generative AI to comprehensively analyze geographic information, population density, communication traffic, and existing base station placement data and propose optimal installation locations and improvement methods. This allows the analysis unit to propose optimal installation locations and improvement methods based on the collected data.

[0035] The implementation unit can install a new base station at the location proposed by the generating AI. For example, the implementation unit can install a new base station at the location proposed by the generating AI. For example, the implementation unit can execute the procedure for installing a new base station based on the location proposed by the generating AI. This improves communication quality by installing a new base station at the location proposed by the generating AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use an AI model to execute the procedure for installing a new base station based on the location proposed by the generating AI.

[0036] The implementation unit can review the placement of existing base stations and distribute communication traffic. For example, the implementation unit can review the placement of existing base stations and distribute communication traffic based on improvement methods proposed by the generating AI. This improves communication quality by reviewing the placement of existing base stations and distributing communication traffic. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use an AI model to execute the procedure for reviewing the placement of existing base stations and distributing communication traffic based on improvement methods proposed by the generating AI.

[0037] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. For example, the data collection unit can optimize the collection frequency based on past data collection history. The data collection unit can also analyze past data collection history and adjust the collection range. For example, the data collection unit can analyze past data collection history and dynamically adjust the collection range. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can adjust the collection range based on specific events or seasonal variations during data collection. For example, the collection unit can enhance data collection around a specific event if one occurs. For example, the collection unit can adjust the data collection range in response to seasonal variations. The collection unit can also dynamically change the collection range based on specific events or seasonal variations. For example, the collection unit can optimize the collection range based on specific events or seasonal variations. This enables efficient data collection by adjusting the collection range based on specific events or seasonal variations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generating AI execute a procedure for adjusting the collection range based on specific events or seasonal variations.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. For example, the data collection unit can optimize the collection range by considering geographical location information. The data collection unit can also determine the priority of collected data based on geographical location information. For example, the data collection unit can dynamically adjust the priority of collected data based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0040] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze social media activity and prioritize the collection of relevant data. For example, the data collection unit can adjust the collection scope based on social media activity. The data collection unit can also analyze social media activity and determine the priority of the data to be collected. For example, the data collection unit can dynamically adjust the priority of the data to be collected based on social media activity. This enables efficient data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a geographic information-specific analysis algorithm to geographic information data. For example, the analysis unit can apply a communication traffic-specific analysis algorithm to communication traffic data. Furthermore, the analysis unit can apply a population density-specific analysis algorithm to population density data. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may lower the priority of analysis for older data. The analysis unit can also dynamically adjust the analysis priority based on the data collection period. For example, the analysis unit can optimize the analysis priority based on the data collection period. This enables efficient analysis by determining the analysis priority based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0045] The implementation unit can analyze past implementation history and select the optimal implementation method at the time of implementation. For example, the implementation unit can select the most efficient implementation method from past implementation history. For example, the implementation unit can optimize the implementation procedure based on past implementation history. The implementation unit can also analyze past implementation history and adjust the implementation method. For example, the implementation unit can analyze past implementation history and dynamically adjust the implementation method. This allows the optimal implementation method to be selected by analyzing past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0046] The implementation unit can customize the implementation means based on specific environmental conditions during implementation. For example, the implementation unit can customize the implementation means according to specific environmental conditions. For example, the implementation unit can adjust the implementation procedure based on environmental conditions. The implementation unit can also optimize the implementation method based on specific environmental conditions. For example, the implementation unit can dynamically adjust the implementation means based on specific environmental conditions. This enables efficient implementation by customizing the implementation means based on specific environmental conditions. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input specific environmental conditions into a generating AI and have the generating AI perform the customization of the implementation means.

[0047] The implementation unit can select the optimal implementation method while considering geographical location information. For example, the implementation unit can select the optimal implementation method based on geographical location information. For example, the implementation unit can optimize the implementation procedure while considering geographical location information. The implementation unit can also adjust the implementation method based on geographical location information. For example, the implementation unit can dynamically adjust the implementation method based on geographical location information. This enables efficient implementation by selecting the optimal implementation method while considering geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input geographical location information into a generating AI and have the generating AI select the optimal implementation method.

[0048] The implementation unit can analyze social media activity and propose implementation methods during implementation. For example, the implementation unit can analyze social media activity and propose the optimal implementation method. For example, the implementation unit can adjust the implementation procedure based on social media activity. The implementation unit can also analyze social media activity and optimize the implementation method. For example, the implementation unit can dynamically adjust the implementation method based on social media activity. This enables efficient implementation by analyzing social media activity and proposing implementation methods. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input social media activity into a generating AI and have the generating AI execute the proposal of implementation methods.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. For example, the data collection unit can optimize the collection frequency based on past data collection history. The data collection unit can also analyze past data collection history and adjust the collection range. For example, the data collection unit can analyze past data collection history and dynamically adjust the collection range. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0051] The data collection unit can adjust the collection range based on specific events or seasonal variations during data collection. For example, the collection unit can enhance data collection around a specific event if one occurs. For example, the collection unit can adjust the data collection range in response to seasonal variations. The collection unit can also dynamically change the collection range based on specific events or seasonal variations. For example, the collection unit can optimize the collection range based on specific events or seasonal variations. This enables efficient data collection by adjusting the collection range based on specific events or seasonal variations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generating AI execute a procedure for adjusting the collection range based on specific events or seasonal variations.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0053] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a geographic information-specific analysis algorithm to geographic information data. For example, the analysis unit can apply a communication traffic-specific analysis algorithm to communication traffic data. Furthermore, the analysis unit can apply a population density-specific analysis algorithm to population density data. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0054] The implementation unit can analyze past implementation history and select the optimal implementation method at the time of implementation. For example, the implementation unit can select the most efficient implementation method from past implementation history. For example, the implementation unit can optimize the implementation procedure based on past implementation history. The implementation unit can also analyze past implementation history and adjust the implementation method. For example, the implementation unit can analyze past implementation history and dynamically adjust the implementation method. This allows the optimal implementation method to be selected by analyzing past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects data related to the installation and improvement of base stations. The data collection unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. For example, geographic information is collected using GPS data, and population density is collected using census data. Communication traffic can also be collected using network logs and real-time monitoring. Data on the placement of existing base stations collects the location information and coverage area of ​​the base stations. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes optimal installation locations and improvement methods. The analysis unit uses generative AI to analyze the collected data and selects base station installation locations considering geographical information and population density. It also analyzes communication traffic data and optimizes the placement of existing base stations. Communication traffic can be distributed using generative AI. Step 3: The implementation team installs or improves base stations based on the plan proposed by the analysis team. The implementation team installs new base stations at the locations proposed by the generation AI and reviews the placement of existing base stations to distribute communication traffic. The improvement methods proposed by the generation AI can be implemented.

[0057] (Example of form 2) The base station installation optimization system according to an embodiment of the present invention is a system that proposes an optimal plan for the installation and improvement of base stations by utilizing generative AI. The base station installation optimization system collects data related to the installation and improvement of base stations, the generative AI analyzes it, and proposes the optimal installation location and improvement method. This proposal enables efficient operation of base stations and contributes to improved communication quality and cost reduction. First, data related to the installation and improvement of base stations is collected. This data includes geographic information, population density, communication traffic, and the placement of existing base stations. For example, in urban areas, the population density is high and communication traffic is also high, so it is necessary to carefully select the installation location of base stations. By collecting such data, basic information regarding the installation and improvement of base stations can be obtained. Next, the generative AI analyzes the collected data. Based on the collected data, the generative AI proposes the optimal installation location and improvement method. For example, it selects the installation location of a base station by considering geographic information and population density. In addition, by analyzing communication traffic data and optimizing the placement of existing base stations, it is possible to improve communication quality. The generative AI comprehensively analyzes this data and proposes an optimal plan. Furthermore, the installation and improvement of base stations are carried out based on the plan proposed by the generative AI. For example, a new base station can be installed at a location suggested by the generating AI. Furthermore, the placement of existing base stations can be reviewed to distribute communication traffic and improve communication quality. In this way, utilizing the generating AI enables efficient operation of base stations. This system allows for the proposal of optimal plans for base station installation and improvement. This results in improved communication quality, cost reduction, and efficient base station operation. For instance, in urban areas, where population density is high and communication traffic is heavy, careful selection of base station installation locations is necessary. By collecting and analyzing such data, the generating AI can propose optimal installation locations and improvement methods. This results in improved communication quality, cost reduction, and efficient base station operation. Thus, the base station installation optimization system can propose optimal plans for base station installation and improvement, achieving improved communication quality and cost reduction.

[0058] The base station installation optimization system according to the embodiment comprises a data collection unit, an analysis unit, and an implementation unit. The data collection unit collects data related to the installation and improvement of base stations. The data collection unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. For example, the data collection unit can collect geographic information using GPS data. The data collection unit can also collect population density using census data. Furthermore, the data collection unit can collect communication traffic using network logs. For example, the data collection unit can collect communication traffic using real-time monitoring. The data collection unit can collect location information and coverage area of ​​base stations. The analysis unit analyzes the data collected by the data collection unit and proposes optimal installation locations and improvement methods. The analysis unit analyzes the collected data using, for example, generative AI. For example, the analysis unit can select a base station installation location considering geographic information and population density. The analysis unit can also analyze communication traffic data and optimize the placement of existing base stations. For example, the analysis unit can distribute communication traffic using generative AI. The implementation unit installs or improves base stations based on the plan proposed by the analysis unit. For example, the implementation unit can install new base stations at locations proposed by the generation AI. The implementation unit can also review the placement of existing base stations to distribute communication traffic. For example, the implementation unit can implement improvement methods proposed by the generation AI. As a result, the base station installation optimization system according to the embodiment can propose an optimal plan for base station installation and improvement, thereby improving communication quality and reducing costs.

[0059] The data collection unit collects data related to the installation and improvement of base stations. For example, the unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. Specifically, the unit can collect geographic information using GPS data. GPS data provides detailed geographic information such as terrain topography, road layout, and building height, making it crucial data for identifying suitable locations for base station installation. The unit can also collect population density using census data. Census data provides information such as regional population distribution, age groups, and household numbers, and is used to identify areas with high communication demand. Furthermore, the unit can collect communication traffic using network logs. Network logs record user communication volume, peak times, and data usage patterns in detail, helping to identify areas with concentrated communication traffic. For example, the unit can collect communication traffic using real-time monitoring. Real-time monitoring allows for immediate understanding of current communication conditions and the detection of sudden traffic increases or abnormal communication patterns. Existing base station placement data can collect base station location information and coverage areas. This allows the data collection unit to understand the coverage area of ​​existing base stations and provide foundational data for optimizing the placement of new base stations. By centrally managing this diverse data and making it accessible to the analysis and implementation units, the data collection unit can improve the overall efficiency of the system.

[0060] The analysis unit analyzes the data collected by the data collection unit and proposes optimal installation locations and improvement methods. For example, the analysis unit uses generative AI to analyze the collected data. Generative AI is a powerful tool for quickly and accurately analyzing large amounts of data and proposing optimal installation locations and improvement methods. For example, the analysis unit can select base station installation locations considering geographical information and population density. Generative AI can analyze geographical information and simulate radio wave propagation characteristics considering terrain topography and building layouts. It can also analyze population density data to identify areas with high communication demand and propose optimal base station installation locations for those areas. Furthermore, the analysis unit can analyze communication traffic data and optimize the placement of existing base stations. Generative AI can analyze the distribution of communication traffic, identify areas with traffic concentration, and distribute communication traffic by installing additional base stations in those areas. For example, the analysis unit can use generative AI to distribute communication traffic. Based on past communication traffic data, generative AI can predict future traffic increases and propose optimal base station placement. This allows the analysis unit to propose optimal base station locations and improvement methods based on the collected data, thereby improving communication quality and reducing costs.

[0061] The implementation department installs or improves base stations based on the plan proposed by the analysis department. For example, the implementation department can install new base stations at locations proposed by the generation AI. Specifically, the implementation department conducts on-site surveys of the proposed locations to confirm detailed conditions such as terrain, building layout, and securing power and communication lines. This allows them to evaluate whether the proposed locations are actually suitable and make adjustments as needed. The implementation department can also review the placement of existing base stations to distribute communication traffic. For example, the implementation department can implement improvement methods proposed by the generation AI. Specifically, they can adjust the direction and output of existing base station antennas to optimize coverage areas and distribute communication traffic. Furthermore, the implementation department manages the construction and installation work associated with the installation of new base stations and the improvement of existing base stations, and adjusts it to proceed according to schedule. This allows the implementation department to quickly and reliably execute the plan proposed by the analysis department, achieving improved communication quality and cost reduction. In addition, even after installation or improvement is completed, the implementation department can maintain the performance of the base stations by performing regular maintenance and monitoring. As a result, the base station installation optimization system according to the embodiment can propose an optimal plan for the installation and improvement of base stations, thereby achieving improved communication quality and cost reduction.

[0062] The data collection unit can collect data such as geographic information, population density, communication traffic, and the location of existing base stations. For example, the data collection unit uses GPS data to collect geographic information. For example, the data collection unit can also collect geographic information using map information. The data collection unit also uses census data to collect population density. For example, the data collection unit can also collect population density using real-time data. Furthermore, the data collection unit uses network logs to collect communication traffic. For example, the data collection unit can also collect communication traffic using real-time monitoring. Data on the location of existing base stations can collect base station location information and coverage area. For example, the data collection unit can collect base station location information and identify coverage areas. This allows for the efficient collection of data necessary for the installation and improvement of base stations.

[0063] The analysis unit can propose optimal installation locations and improvement methods based on the collected data. For example, the analysis unit selects base station installation locations considering collected geographic information and population density. For example, the analysis unit can use generative AI to analyze geographic information and population density and propose optimal installation locations. The analysis unit can also analyze communication traffic data and optimize the placement of existing base stations. For example, the analysis unit can use generative AI to distribute communication traffic. Furthermore, the analysis unit can comprehensively analyze the collected data and propose an optimal plan. For example, the analysis unit can use generative AI to comprehensively analyze geographic information, population density, communication traffic, and existing base station placement data and propose optimal installation locations and improvement methods. This allows the analysis unit to propose optimal installation locations and improvement methods based on the collected data.

[0064] The implementation unit can install a new base station at the location proposed by the generating AI. For example, the implementation unit can install a new base station at the location proposed by the generating AI. For example, the implementation unit can execute the procedure for installing a new base station based on the location proposed by the generating AI. This improves communication quality by installing a new base station at the location proposed by the generating AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use an AI model to execute the procedure for installing a new base station based on the location proposed by the generating AI.

[0065] The implementation unit can review the placement of existing base stations and distribute communication traffic. For example, the implementation unit can review the placement of existing base stations and distribute communication traffic based on improvement methods proposed by the generating AI. This improves communication quality by reviewing the placement of existing base stations and distributing communication traffic. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can use an AI model to execute the procedure for reviewing the placement of existing base stations and distributing communication traffic based on improvement methods proposed by the generating AI.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. Also, if the user is in a hurry, the data collection unit can quickly collect the necessary data, prioritizing efficiency. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0067] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. For example, the data collection unit can optimize the collection frequency based on past data collection history. The data collection unit can also analyze past data collection history and adjust the collection range. For example, the data collection unit can analyze past data collection history and dynamically adjust the collection range. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0068] The data collection unit can adjust the collection range based on specific events or seasonal variations during data collection. For example, the collection unit can enhance data collection around a specific event if one occurs. For example, the collection unit can adjust the data collection range in response to seasonal variations. The collection unit can also dynamically change the collection range based on specific events or seasonal variations. For example, the collection unit can optimize the collection range based on specific events or seasonal variations. This enables efficient data collection by adjusting the collection range based on specific events or seasonal variations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generating AI execute a procedure for adjusting the collection range based on specific events or seasonal variations.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This enables efficient data collection by determining the priority of 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. For example, the data collection unit can optimize the collection range by considering geographical location information. The data collection unit can also determine the priority of collected data based on geographical location information. For example, the data collection unit can dynamically adjust the priority of collected data based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0071] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze social media activity and prioritize the collection of relevant data. For example, the data collection unit can adjust the collection scope based on social media activity. The data collection unit can also analyze social media activity and determine the priority of the data to be collected. For example, the data collection unit can dynamically adjust the priority of the data to be collected based on social media activity. This enables efficient data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0072] 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for efficient analysis by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a geographic information-specific analysis algorithm to geographic information data. For example, the analysis unit can apply a communication traffic-specific analysis algorithm to communication traffic data. Furthermore, the analysis unit can apply a population density-specific analysis algorithm to population density data. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for efficient analysis by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may lower the priority of analysis for older data. The analysis unit can also dynamically adjust the analysis priority based on the data collection period. For example, the analysis unit can optimize the analysis priority based on the data collection period. This enables efficient analysis by determining the analysis priority based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0078] The implementation unit can estimate the user's emotions and adjust the implementation method based on the estimated user emotions. For example, if the user is relaxed, the implementation unit can provide detailed instructions. For example, if the user is in a hurry, the implementation unit can provide concise instructions. Furthermore, if the user is excited, the implementation unit can provide visually stimulating instructions. This allows for efficient implementation by adjusting the implementation method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can input user emotion data into a generative AI and have the generative AI adjust the implementation method.

[0079] The implementation unit can analyze past implementation history and select the optimal implementation method at the time of implementation. For example, the implementation unit can select the most efficient implementation method from past implementation history. For example, the implementation unit can optimize the implementation procedure based on past implementation history. The implementation unit can also analyze past implementation history and adjust the implementation method. For example, the implementation unit can analyze past implementation history and dynamically adjust the implementation method. This allows the optimal implementation method to be selected by analyzing past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0080] The implementation unit can customize the implementation means based on specific environmental conditions during implementation. For example, the implementation unit can customize the implementation means according to specific environmental conditions. For example, the implementation unit can adjust the implementation procedure based on environmental conditions. The implementation unit can also optimize the implementation method based on specific environmental conditions. For example, the implementation unit can dynamically adjust the implementation means based on specific environmental conditions. This enables efficient implementation by customizing the implementation means based on specific environmental conditions. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input specific environmental conditions into a generating AI and have the generating AI perform the customization of the implementation means.

[0081] The implementation unit can estimate the user's emotions and determine the priority of the implementation based on the estimated emotions. For example, if the user is stressed, the implementation unit will prioritize important implementation items. For example, if the user is relaxed, the implementation unit may prioritize detailed implementation items. Also, if the user is in a hurry, the implementation unit may prioritize items that can be completed quickly. This enables efficient implementation by determining the priority of the implementation 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI or not using AI. For example, the implementation unit can input user emotion data into a generative AI and have the generative AI determine the priority of the implementation.

[0082] The implementation unit can select the optimal implementation method while considering geographical location information. For example, the implementation unit can select the optimal implementation method based on geographical location information. For example, the implementation unit can optimize the implementation procedure while considering geographical location information. The implementation unit can also adjust the implementation method based on geographical location information. For example, the implementation unit can dynamically adjust the implementation method based on geographical location information. This enables efficient implementation by selecting the optimal implementation method while considering geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input geographical location information into a generating AI and have the generating AI select the optimal implementation method.

[0083] The implementation unit can analyze social media activity and propose implementation methods during implementation. For example, the implementation unit can analyze social media activity and propose the optimal implementation method. For example, the implementation unit can adjust the implementation procedure based on social media activity. The implementation unit can also analyze social media activity and optimize the implementation method. For example, the implementation unit can dynamically adjust the implementation method based on social media activity. This enables efficient implementation by analyzing social media activity and proposing implementation methods. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input social media activity into a generating AI and have the generating AI execute the proposal of implementation methods.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. Also, if the user is in a hurry, the data collection unit can quickly collect the necessary data, prioritizing efficiency. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0086] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. For example, the data collection unit can optimize the collection frequency based on past data collection history. The data collection unit can also analyze past data collection history and adjust the collection range. For example, the data collection unit can analyze past data collection history and dynamically adjust the collection range. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0087] The data collection unit can adjust the collection range based on specific events or seasonal variations during data collection. For example, the collection unit can enhance data collection around a specific event if one occurs. For example, the collection unit can adjust the data collection range in response to seasonal variations. The collection unit can also dynamically change the collection range based on specific events or seasonal variations. For example, the collection unit can optimize the collection range based on specific events or seasonal variations. This enables efficient data collection by adjusting the collection range based on specific events or seasonal variations. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generating AI execute a procedure for adjusting the collection range based on specific events or seasonal variations.

[0088] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This enables efficient data collection by determining the priority of 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0089] 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for efficient analysis by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a concise analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can optimize the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a geographic information-specific analysis algorithm to geographic information data. For example, the analysis unit can apply a communication traffic-specific analysis algorithm to communication traffic data. Furthermore, the analysis unit can apply a population density-specific analysis algorithm to population density data. For example, the analysis unit can select and apply the optimal analysis algorithm depending on the data category. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for efficient analysis by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0093] The implementation unit can estimate the user's emotions and adjust the implementation method based on the estimated user emotions. For example, if the user is relaxed, the implementation unit can provide detailed instructions. For example, if the user is in a hurry, the implementation unit can provide concise instructions. Furthermore, if the user is excited, the implementation unit can provide visually stimulating instructions. This allows for efficient implementation by adjusting the implementation method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can input user emotion data into a generative AI and have the generative AI adjust the implementation method.

[0094] The implementation unit can analyze past implementation history and select the optimal implementation method at the time of implementation. For example, the implementation unit can select the most efficient implementation method from past implementation history. For example, the implementation unit can optimize the implementation procedure based on past implementation history. The implementation unit can also analyze past implementation history and adjust the implementation method. For example, the implementation unit can analyze past implementation history and dynamically adjust the implementation method. This allows the optimal implementation method to be selected by analyzing past implementation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without using AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects data related to the installation and improvement of base stations. The data collection unit collects data such as geographic information, population density, communication traffic, and the placement of existing base stations. For example, geographic information is collected using GPS data, and population density is collected using census data. Communication traffic can also be collected using network logs and real-time monitoring. Data on the placement of existing base stations collects the location information and coverage area of ​​the base stations. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes optimal installation locations and improvement methods. The analysis unit uses generative AI to analyze the collected data and selects base station installation locations considering geographical information and population density. It also analyzes communication traffic data and optimizes the placement of existing base stations. Communication traffic can be distributed using generative AI. Step 3: The implementation team installs or improves base stations based on the plan proposed by the analysis team. The implementation team installs new base stations at the locations proposed by the generation AI and reviews the placement of existing base stations to distribute communication traffic. The improvement methods proposed by the generation AI can be implemented.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and implementation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects geographic information using the camera 42 and GPS data of the smart device 14, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses a generating AI to analyze the collected data and propose the optimal installation location and improvement method. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14, and installs a new base station at the installation location proposed by the generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and implementation 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 geographic information using the camera 42 and GPS data of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using a generating AI to propose the optimal installation location and improvement method. The implementation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and installs a new base station at the installation location proposed by the generating AI. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and implementation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects geographic information using the camera 42 and GPS data of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the collected data using a generating AI to propose the optimal installation location and improvement method. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314, and installs a new base station at the installation location proposed by the generating AI. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0144] In 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.

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0148] The data processing system 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and implementation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects geographic information using the camera 42 and GPS data of the robot 414, and this information is analyzed by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and uses a generating AI to analyze the collected data and propose the optimal installation location and improvement method. The implementation unit is implemented by, for example, the control unit 46A of the robot 414, and installs a new base station at the installation location proposed by the generating AI. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A data collection unit that collects data related to the installation and improvement of base stations, The data collected by the aforementioned collection unit is analyzed by an analysis unit, which then proposes the optimal installation location and improvement methods. The system includes an implementation unit that installs or improves base stations based on the plan proposed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as geographic information, population density, communication traffic, and the location of existing base stations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we propose the optimal installation location and improvement methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned implementation unit is A new base station will be installed at the location suggested by the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned implementation unit is Review the placement of existing base stations to distribute communication traffic. 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 When collecting data, adjust the collection range based on specific events and seasonal variations. 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 When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. 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 how the analysis results are displayed based on the estimated 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 aforementioned implementation unit is We estimate the user's emotions and adjust the implementation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned implementation unit is During implementation, past implementation history will be analyzed to select the optimal implementation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned implementation unit is During implementation, the implementation methods are customized based on specific environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned implementation unit is We estimate user sentiment and determine implementation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned implementation unit is During implementation, the optimal implementation method will be selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned implementation unit is During implementation, we will analyze social media activity and propose implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 related to the installation and improvement of base stations, The data collected by the aforementioned collection unit is analyzed by an analysis unit, which then proposes the optimal installation location and improvement methods. The system includes an implementation unit that installs or improves base stations based on the plan proposed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as geographic information, population density, communication traffic, and the location of existing base stations. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we propose the optimal installation location and improvement methods. The system according to feature 1.

4. The aforementioned implementation unit is A new base station will be installed at the location suggested by the generating AI. The system according to feature 1.

5. The aforementioned implementation unit is Review the placement of existing base stations to distribute communication traffic. 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 When collecting data, adjust the collection range based on specific events and seasonal variations. The system according to feature 1.

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 according to feature 1.

10. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

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