system

Abstract

The system according to this embodiment aims to appropriately determine the necessity and priority of snow removal based on snowfall and snow removal conditions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, and a ranking unit. The collection unit collects snowfall conditions and snow removal conditions. The analysis unit analyzes the data collected by the collection unit. The determination unit determines the necessity of snow removal based on the data analyzed by the analysis unit. The ranking unit ranks the priority of snow removal based on the results determined by the determination unit.

Description

Technical Field

[0006] ,

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to appropriately determine the necessity and priority of snow removal based on the snowfall and snow removal situations, and there is a problem that efficient snow removal work has not been carried out.

[0005] The system according to the embodiment aims to appropriately determine the necessity and priority of snow removal based on the snowfall and snow removal situations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a ranking unit. The data collection unit collects snowfall conditions and snow removal conditions. The analysis unit analyzes the data collected by the data collection unit. The determination unit determines the necessity of snow removal based on the data analyzed by the analysis unit. The ranking unit ranks the priority of snow removal based on the results determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately determine the necessity and priority of snow removal based on snowfall and snow removal conditions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The snow removal management system according to an embodiment of the present invention is a system for solving snow removal problems in heavy snowfall areas. This snow removal management system takes in snowfall conditions, snow removal status, weather data, pedestrian flow data, etc., and considers the necessity and priority of snow removal. The target audience is citizens of snowy regions and snow removal companies. In heavy snowfall areas, snow removal has become a social problem due to labor shortages and abnormal weather. By using this data to investigate and prioritize the necessity of snow removal, the snow removal management system is expected to prevent economic losses due to the paralysis of urban functions, reduce the time and budget required for snow removal, decrease the workload of companies, and alleviate dissatisfaction among citizens and snow removal companies. For example, the snow removal management system takes in snowfall conditions and snow removal status in real time. This involves collecting data using devices such as sensors and cameras. Next, the snow removal management system takes in weather data and pedestrian flow data and analyzes this data. For example, weather data includes temperature, snowfall amount, wind speed, etc., and pedestrian flow data includes people's movement patterns and congestion levels. Based on this data, the snow removal management system determines the necessity of snow removal. For example, it determines whether snow removal around major roads and public facilities should be prioritized. Furthermore, the snow removal management system prioritizes snow removal. For example, roads with heavy traffic and areas that affect public transportation should be prioritized. This system is expected to prevent economic losses due to urban paralysis. For instance, if major roads are not cleared of snow, traffic congestion and accidents can occur, potentially having a significant impact on economic activity. It is also expected to reduce the time and budget required for snow removal. By efficiently determining the need for snow removal and prioritizing it, the snow removal management system can reduce unnecessary snow removal work. In addition, it is expected to reduce the workload of contractors and alleviate dissatisfaction among citizens and snow removal companies. By accurately determining the need for snow removal and prioritizing it, contractors can work efficiently, and citizens will be relieved of their dissatisfaction as snow removal is carried out quickly. In this way, the snow removal management system can prevent economic losses due to urban paralysis, reduce the time and budget required for snow removal, reduce the workload of contractors, and alleviate dissatisfaction among citizens and snow removal companies.

[0029] The snow removal management system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a ranking unit. The collection unit collects snowfall conditions and snow removal conditions. The collection unit collects data using devices such as sensors and cameras. For example, the collection unit can measure the temperature using a temperature sensor. The collection unit can also measure the humidity using a humidity sensor. Furthermore, the collection unit can measure the amount of snowfall using a snowfall sensor. For example, the collection unit can monitor the snow removal situation using a surveillance camera. The collection unit can also photograph the snow removal situation over a wide area using a drone camera. Furthermore, the collection unit can record the snow removal situation while moving using a vehicle-mounted camera. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes weather data and pedestrian flow data. For example, the analysis unit can analyze weather data such as temperature, snowfall amount, and wind speed. Furthermore, the analysis unit can analyze pedestrian flow data such as people's movement patterns and congestion levels. Furthermore, the analysis unit can optimize the analysis algorithm by referring to past weather data and pedestrian flow data. For example, the analysis unit can select the optimal analysis algorithm based on past temperature data. The analysis unit can also improve the accuracy of the analysis by referring to past precipitation data. Furthermore, the analysis unit can analyze past wind speed data and adjust the parameters of the analysis algorithm. The judgment unit determines the need for snow removal based on the data analyzed by the analysis unit. The judgment unit determines, for example, whether snow removal around major roads and public facilities should be prioritized. For example, if traffic volume is high, the judgment unit can enhance the judgment algorithm to improve accuracy. The judgment unit can also adjust the judgment parameters based on the importance of public facilities. Furthermore, the judgment unit can improve the accuracy of the judgment based on specific conditions. For example, if extreme weather occurs, the judgment unit can enhance the judgment algorithm to improve accuracy. The judgment unit can also make a quick judgment and enable immediate response in the event of an emergency. The ranking unit prioritizes snow removal based on the results determined by the judgment unit.The ranking unit prioritizes, for example, locations that affect roads with heavy traffic or the operation of public transportation. The ranking unit can prioritize data around major transportation routes. It can also prioritize data around public facilities. Furthermore, it can prioritize data in areas with high pedestrian traffic. As a result, the snow removal management system according to this embodiment can efficiently collect, analyze, judge, and rank snowfall and snow removal conditions.

[0030] The data collection unit collects information on snowfall and snow removal conditions. The unit uses devices such as sensors and cameras to collect data. Specifically, it can measure temperature using temperature sensors and humidity using humidity sensors. This allows for a detailed understanding of snowfall conditions and snow accumulation. It can also measure snowfall amounts using snowfall sensors. Snowfall sensors detect snow depth and snowfall intensity in real time and collect data. Furthermore, surveillance cameras can be used to monitor snow removal conditions. Surveillance cameras capture wide-area snow removal situations from fixed positions, helping to understand the progress of snow removal work. Drone cameras can also be used to capture wide-area snow removal situations. Drone cameras are effective for checking snow removal conditions in areas that are difficult to see from the ground, as they capture wide areas from the air. Additionally, vehicle-mounted cameras can be used to record snow removal conditions while moving. Vehicle-mounted cameras are installed on snow removal vehicles and record road conditions and snow removal progress while moving. This allows the data collection unit to collect a wide range of data using various devices and understand the situation in real time. The collected data is sent to a central database, making it accessible to the analysis and judgment units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes weather data and human flow data. Specifically, it analyzes weather data such as temperature, snowfall amount, and wind speed to understand snowfall patterns and the progress of snow accumulation. It also analyzes human flow data such as people's movement patterns and congestion levels to provide information for determining snow removal priorities. The analysis unit can also optimize its analysis algorithm by referring to past weather data and human flow data. For example, it can select the optimal analysis algorithm based on past temperature data to improve the accuracy of the analysis. It can also improve the accuracy of the analysis by referring to past precipitation data. Furthermore, it can analyze past wind speed data and adjust the parameters of the analysis algorithm. As a result, the analysis unit can quickly and accurately analyze the collected data and provide information for determining the necessity and priority of snow removal. In addition, the analysis unit uses AI to process data in real time and understand the surrounding situation. The AI ​​uses image recognition technology to analyze camera footage and identify the progress of snow removal and the state of snow accumulation. It also analyzes weather data and human flow data to formulate an optimal snow removal plan. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The determination unit determines the need for snow removal based on the data analyzed by the analysis unit. For example, the determination unit determines whether snow removal should be prioritized around major roads and public facilities. Specifically, it determines whether roads with heavy traffic or locations affecting public transport operations should be prioritized. The determination unit can enhance its determination algorithm and improve accuracy in cases of heavy traffic. It can also adjust the parameters of the determination based on the importance of public facilities. For example, it can determine whether snow removal around important public facilities such as hospitals and fire stations should be prioritized. Furthermore, the determination unit can improve the accuracy of its determinations based on specific conditions. For example, it can enhance its determination algorithm and improve accuracy in the event of extreme weather. It can also make rapid determinations and enable immediate responses in the event of an emergency. This allows the determination unit to accurately determine the need for snow removal based on the analyzed data and take appropriate action. In addition, the determination unit uses AI to analyze data and determine the need for snow removal in real time. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the judgment unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The ranking unit prioritizes snow removal based on the results determined by the judgment unit. For example, the ranking unit prioritizes areas that may affect roads with heavy traffic or public transportation operations. Specifically, it prioritizes data around major transportation routes to maximize the efficiency of snow removal work. It also prioritizes data around public facilities to ensure that the operation of important facilities is not disrupted. Furthermore, it prioritizes data in areas with high pedestrian traffic to ensure the safety of residents. The ranking unit uses AI to analyze the data and formulate an optimal snow removal plan. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessments and trend analysis. This allows the ranking unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the ranking unit can continuously revise the ranking results based on data that is updated in real time, so as to respond to the latest situation. For example, if the amount of snowfall or traffic volume changes rapidly, the ranking unit immediately takes in the new data and updates the ranking results. Furthermore, by considering the characteristics of each region and past disaster history, more accurate rankings can be achieved. As a result, the ranking unit can always provide highly accurate rankings based on the latest information, supporting a swift and appropriate response.

[0034] The data collection unit can collect data using devices such as sensors and cameras. For example, the data collection unit can measure the temperature using a temperature sensor. For example, the data collection unit can measure the humidity using a humidity sensor. For example, the data collection unit can measure the amount of snowfall using a snowfall sensor. For example, the data collection unit can monitor the snow removal situation using a surveillance camera. For example, the data collection unit can photograph the snow removal situation over a wide area using a drone camera. For example, the data collection unit can record the snow removal situation while moving using a vehicle-mounted camera. This improves the accuracy of data collection by using sensors and cameras. 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 data acquired by sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze weather data and human flow data. For example, the analysis unit can analyze weather data such as temperature, snowfall, and wind speed. For example, the analysis unit can analyze human flow data such as people's movement patterns and congestion levels. For example, the analysis unit can optimize the analysis algorithm by referring to past weather data and human flow data. For example, the analysis unit can select the optimal analysis algorithm based on past temperature data. For example, the analysis unit can improve the accuracy of the analysis by referring to past precipitation data. For example, the analysis unit can analyze past wind speed data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis by analyzing weather data and human flow data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input weather data and human flow data into a generating AI and have the generating AI perform the data analysis.

[0036] The determination unit can determine whether snow removal around major roads and public facilities should be prioritized. The determination unit can enhance its determination algorithm and improve accuracy, for example, when traffic volume is high. The determination unit can adjust the parameters of the determination based on the importance of public facilities, for example. The determination unit can improve the accuracy of the determination based on specific conditions, for example. The determination unit can enhance its determination algorithm and improve accuracy, for example, when extreme weather occurs. The determination unit can make a quick determination and enable immediate response, for example, when an emergency occurs. This improves the efficiency of snow removal work by determining the priority of snow removal around major roads and public facilities. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input data about major roads and public facilities into a generating AI and have the generating AI perform the determination of snow removal priority.

[0037] The ranking unit can prioritize locations that affect the operation of busy roads and public transport. For example, the ranking unit can prioritize data around major transportation routes. For example, the ranking unit can prioritize data around public facilities. For example, the ranking unit can prioritize data in areas with high pedestrian traffic. This improves the maintenance of urban functions by prioritizing snow removal in locations that affect the operation of busy roads and public transport. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data on locations that affect the operation of busy roads and public transport into a generating AI and have the generating AI perform the snow removal priority ranking.

[0038] The analysis unit can analyze weather data such as temperature, snowfall, and wind speed. For example, the analysis unit can select the optimal analysis algorithm based on temperature data. For example, the analysis unit can improve the accuracy of the analysis by referring to snowfall data. For example, the analysis unit can analyze wind speed data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis by analyzing weather data such as temperature, snowfall, and wind speed. 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 weather data such as temperature, snowfall, and wind speed into a generating AI and have the generating AI perform the data analysis.

[0039] The data collection unit can select the optimal data collection method by referring to past data when collecting snowfall and snow removal data. For example, the data collection unit can determine the most effective sensor placement based on past snowfall data. For example, the data collection unit can set an efficient data collection route by referring to past snow removal data. For example, the data collection unit can analyze past weather patterns to optimize the timing of data collection. This allows the optimal data collection method to be selected by referring to past data. 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 into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can adjust the frequency of data collection based on specific events. For example, if a traffic accident occurs, the data collection unit can frequently collect data on surrounding snowfall conditions. For example, if a large-scale event is held, the data collection unit can focus on collecting data on snow removal conditions around the venue. For example, if an emergency occurs, the data collection unit can collect data in real time, enabling a rapid response. This allows for a rapid response by adjusting the frequency of data collection based on specific events. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a specific event into a generating AI and have the generating AI adjust the frequency of data collection.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information. For example, the data collection unit can prioritize the collection of data around major transportation routes. For example, the data collection unit can focus on collecting data around public facilities. For example, the data collection unit can prioritize the collection of data in areas with high pedestrian traffic. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above-described 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 collection of highly relevant data.

[0042] The data collection unit can analyze information from social media and collect relevant data. For example, the data collection unit can analyze posts on social media and collect information about snowfall conditions. For example, the data collection unit can collect real-time information about snow removal conditions from user posts. For example, the data collection unit can analyze trends on social media and prioritize the collection of relevant data. This allows for the collection of relevant data by analyzing information from social media. Some or all of the above-described processes 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 data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can optimize the analysis algorithm by referring to past weather data and human flow data. For example, the analysis unit can select the optimal analysis algorithm based on past weather data. For example, the analysis unit can improve the accuracy of the analysis by referring to past human flow data. For example, the analysis unit can analyze past data and adjust the parameters of the analysis algorithm. This allows the analysis algorithm to be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past weather data and human flow data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis based on specific conditions. For example, in the event of extreme weather, the analysis unit can enhance the analysis algorithm to improve accuracy. For example, in the event of an emergency, the analysis unit can perform a rapid analysis to enable immediate response. For example, the analysis unit can adjust the analysis parameters based on specific conditions to improve accuracy. This allows for more accurate analysis by improving the accuracy of the analysis based on specific conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to specific conditions into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The analysis unit can improve the accuracy of the analysis by taking geographic location information into consideration. For example, the analysis unit can prioritize the analysis of data around major transportation routes. For example, the analysis unit can focus on analyzing data around public facilities. For example, the analysis unit can prioritize the analysis of data in areas with high pedestrian traffic. This improves the accuracy of the analysis by taking geographic location information into consideration. 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 geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, the analysis unit can improve its analysis algorithm by referring to the latest research papers. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant databases. For example, the analysis unit can enhance the reliability of its analysis results by incorporating expert opinions. This improves the accuracy of the analysis by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0047] The judgment unit can optimize the judgment algorithm by referring to past judgment results. For example, the judgment unit can select the optimal judgment algorithm based on past judgment results. For example, the judgment unit can improve the accuracy of judgments by referring to past data. For example, the judgment unit can analyze past judgment results and adjust the parameters of the algorithm. This allows the judgment algorithm to be optimized by referring to past judgment results. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input past judgment results into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0048] The judgment unit can improve the accuracy of its judgments based on specific conditions. For example, if there is heavy traffic, the judgment unit can enhance its judgment algorithm to improve accuracy. For example, the judgment unit can adjust the parameters of its judgments based on the importance of public facilities. The judgment unit can improve the accuracy of its judgments based on specific conditions. This makes it possible to make more accurate judgments by improving the accuracy of judgments based on specific conditions. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input data about specific conditions into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0049] The determination unit can improve the accuracy of its determination by taking geographic location information into consideration. For example, the determination unit can prioritize determining data around major transportation routes. For example, the determination unit can focus on determining data around public facilities. For example, the determination unit can prioritize determining data in areas with high pedestrian traffic. This improves the accuracy of the determination by taking geographic location information into consideration. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0050] The judgment unit can improve the accuracy of its judgments by referring to relevant literature and databases. For example, the judgment unit can improve its judgment algorithm by referring to the latest research papers. For example, the judgment unit can improve the accuracy of its judgments by referring to relevant databases. For example, the judgment unit can increase the reliability of its judgment results by incorporating expert opinions. As a result, the accuracy of the judgment is improved by referring to relevant literature and databases. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the improvement of the accuracy of the judgment.

[0051] The ranking unit can optimize its ranking algorithm by referring to past ranking results. For example, the ranking unit can select the optimal ranking algorithm based on past ranking results. For example, the ranking unit can improve the accuracy of ranking by referring to past data. For example, the ranking unit can analyze past ranking results and adjust the parameters of the algorithm. This allows the ranking algorithm to be optimized by referring to past ranking results. Some or all of the above processes in the ranking unit may be performed using AI, for example, or without using AI. For example, the ranking unit can input past ranking results into a generating AI and have the generating AI perform the optimization of the ranking algorithm.

[0052] The ranking unit can improve the accuracy of ranking based on specific conditions. For example, the ranking unit can enhance the ranking algorithm and improve accuracy when traffic volume is high. For example, the ranking unit can adjust the ranking parameters based on the operating status of public transport. For example, the ranking unit can improve the accuracy of ranking based on specific conditions. This makes it possible to perform more accurate rankings by improving the accuracy of ranking based on specific conditions. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data on specific conditions into a generating AI and have the generating AI perform the improvement of ranking accuracy.

[0053] The ranking unit can improve the accuracy of ranking by taking geographic location information into consideration. For example, the ranking unit can prioritize ranking data around major transportation routes. For example, the ranking unit can prioritize ranking data around public facilities. For example, the ranking unit can prioritize ranking data in areas with high pedestrian traffic. This improves the accuracy of ranking by taking geographic location information into consideration. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of ranking.

[0054] The ranking unit can improve the accuracy of its rankings by referring to relevant literature and databases. For example, the ranking unit can improve its ranking algorithm by referring to the latest research papers. For example, the ranking unit can improve the accuracy of its rankings by referring to relevant databases. For example, the ranking unit can increase the reliability of its ranking results by incorporating expert opinions. This improves the accuracy of rankings by referring to relevant literature and databases. Some or all of the above processes in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the task of improving the accuracy of its rankings.

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

[0056] The data collection unit can analyze information from social media and collect relevant data. For example, it can analyze posts on social media and collect information about snowfall conditions. It can also collect real-time information about snow removal conditions from user posts. Furthermore, it can analyze trends on social media and prioritize the collection of relevant data. In this way, relevant data can be collected by analyzing information from social media. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0057] The ranking unit can optimize its ranking algorithm by referring to past ranking results. For example, it can select the optimal ranking algorithm based on past ranking results. It can also improve the accuracy of ranking by referring to past data. Furthermore, it can analyze past ranking results and adjust the parameters of the algorithm. In this way, the ranking algorithm can be optimized by referring to past ranking results. Some or all of the above processes in the ranking unit may be performed using AI or not.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information. For example, it can prioritize the collection of data around major transportation routes. It can also prioritize the collection of data around public facilities. Furthermore, it can prioritize the collection of data in areas with high pedestrian traffic. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0059] The judgment unit can improve the accuracy of its judgments based on specific conditions. For example, if traffic volume is high, the judgment algorithm can be strengthened to improve accuracy. Furthermore, the judgment parameters can be adjusted based on the importance of the public facility. In addition, the accuracy of the judgment can be improved based on specific conditions. This allows for more accurate judgments by improving the accuracy of the judgment based on specific conditions. Some or all of the above-described processes in the judgment unit may be performed using AI, or they may not.

[0060] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, it can improve its analysis algorithm by referring to the latest research papers. It can also improve the accuracy of its analysis by referring to relevant databases. Furthermore, it can enhance the reliability of its analysis results by incorporating expert opinions. In this way, the accuracy of the analysis is improved by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0061] The ranking unit can improve the accuracy of ranking based on specific conditions. For example, if traffic volume is high, the ranking algorithm can be strengthened to improve accuracy. Furthermore, ranking parameters can be adjusted based on the operating status of public transport. In addition, the accuracy of ranking can be improved based on specific conditions. This allows for more accurate ranking by improving the accuracy of ranking based on specific conditions. Some or all of the above-described processes in the ranking unit may be performed using AI or not.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects data on snowfall and snow removal conditions. The collection unit collects data using devices such as sensors and cameras. For example, it measures the temperature using a temperature sensor, the humidity using a humidity sensor, and the amount of snowfall using a snowfall sensor. It can also monitor snow removal conditions using surveillance cameras, capture wide-area snow removal conditions using drone cameras, and record snow removal conditions while moving using vehicle-mounted cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes weather data and human flow data, including weather data such as temperature, snowfall, and wind speed, and human flow data such as people's movement patterns and congestion levels. Furthermore, it optimizes the analysis algorithm by referring to past weather data and human flow data to improve the accuracy of the analysis. Step 3: The determination unit determines the need for snow removal based on the data analyzed by the analysis unit. The determination unit determines whether snow removal should be prioritized around major roads and public facilities, and adjusts the determination parameters based on factors such as heavy traffic and the importance of the public facilities. Furthermore, in the event of extreme weather or an emergency, the determination algorithm is enhanced to provide a rapid determination. Step 4: The ranking unit prioritizes snow removal based on the results determined by the judgment unit. The ranking unit prioritizes areas that affect roads with heavy traffic or the operation of public transport, and prioritizes data around major transportation routes, data around public facilities, and data in areas with high pedestrian traffic.

[0064] (Example of form 2) The snow removal management system according to an embodiment of the present invention is a system for solving snow removal problems in heavy snowfall areas. This snow removal management system takes in snowfall conditions, snow removal status, weather data, pedestrian flow data, etc., and considers the necessity and priority of snow removal. The target audience is citizens of snowy regions and snow removal companies. In heavy snowfall areas, snow removal has become a social problem due to labor shortages and abnormal weather. By using this data to investigate and prioritize the necessity of snow removal, the snow removal management system is expected to prevent economic losses due to the paralysis of urban functions, reduce the time and budget required for snow removal, decrease the workload of companies, and alleviate dissatisfaction among citizens and snow removal companies. For example, the snow removal management system takes in snowfall conditions and snow removal status in real time. This involves collecting data using devices such as sensors and cameras. Next, the snow removal management system takes in weather data and pedestrian flow data and analyzes this data. For example, weather data includes temperature, snowfall amount, wind speed, etc., and pedestrian flow data includes people's movement patterns and congestion levels. Based on this data, the snow removal management system determines the necessity of snow removal. For example, it determines whether snow removal around major roads and public facilities should be prioritized. Furthermore, the snow removal management system prioritizes snow removal. For example, roads with heavy traffic and areas that affect public transportation should be prioritized. This system is expected to prevent economic losses due to urban paralysis. For instance, if major roads are not cleared of snow, traffic congestion and accidents can occur, potentially having a significant impact on economic activity. It is also expected to reduce the time and budget required for snow removal. By efficiently determining the need for snow removal and prioritizing it, the snow removal management system can reduce unnecessary snow removal work. In addition, it is expected to reduce the workload of contractors and alleviate dissatisfaction among citizens and snow removal companies. By accurately determining the need for snow removal and prioritizing it, contractors can work efficiently, and citizens will be relieved of their dissatisfaction as snow removal is carried out quickly. In this way, the snow removal management system can prevent economic losses due to urban paralysis, reduce the time and budget required for snow removal, reduce the workload of contractors, and alleviate dissatisfaction among citizens and snow removal companies.

[0065] The snow removal management system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a ranking unit. The collection unit collects snowfall conditions and snow removal conditions. The collection unit collects data using devices such as sensors and cameras. For example, the collection unit can measure the temperature using a temperature sensor. The collection unit can also measure the humidity using a humidity sensor. Furthermore, the collection unit can measure the amount of snowfall using a snowfall sensor. For example, the collection unit can monitor the snow removal situation using a surveillance camera. The collection unit can also photograph the snow removal situation over a wide area using a drone camera. Furthermore, the collection unit can record the snow removal situation while moving using a vehicle-mounted camera. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes weather data and pedestrian flow data. For example, the analysis unit can analyze weather data such as temperature, snowfall amount, and wind speed. Furthermore, the analysis unit can analyze pedestrian flow data such as people's movement patterns and congestion levels. Furthermore, the analysis unit can optimize the analysis algorithm by referring to past weather data and pedestrian flow data. For example, the analysis unit can select the optimal analysis algorithm based on past temperature data. The analysis unit can also improve the accuracy of the analysis by referring to past precipitation data. Furthermore, the analysis unit can analyze past wind speed data and adjust the parameters of the analysis algorithm. The judgment unit determines the need for snow removal based on the data analyzed by the analysis unit. The judgment unit determines, for example, whether snow removal around major roads and public facilities should be prioritized. For example, if traffic volume is high, the judgment unit can enhance the judgment algorithm to improve accuracy. The judgment unit can also adjust the judgment parameters based on the importance of public facilities. Furthermore, the judgment unit can improve the accuracy of the judgment based on specific conditions. For example, if extreme weather occurs, the judgment unit can enhance the judgment algorithm to improve accuracy. The judgment unit can also make a quick judgment and enable immediate response in the event of an emergency. The ranking unit prioritizes snow removal based on the results determined by the judgment unit.The ranking unit prioritizes, for example, locations that affect roads with heavy traffic or the operation of public transportation. The ranking unit can prioritize data around major transportation routes. It can also prioritize data around public facilities. Furthermore, it can prioritize data in areas with high pedestrian traffic. As a result, the snow removal management system according to this embodiment can efficiently collect, analyze, judge, and rank snowfall and snow removal conditions.

[0066] The data collection unit collects information on snowfall and snow removal conditions. The unit uses devices such as sensors and cameras to collect data. Specifically, it can measure temperature using temperature sensors and humidity using humidity sensors. This allows for a detailed understanding of snowfall conditions and snow accumulation. It can also measure snowfall amounts using snowfall sensors. Snowfall sensors detect snow depth and snowfall intensity in real time and collect data. Furthermore, surveillance cameras can be used to monitor snow removal conditions. Surveillance cameras capture wide-area snow removal situations from fixed positions, helping to understand the progress of snow removal work. Drone cameras can also be used to capture wide-area snow removal situations. Drone cameras are effective for checking snow removal conditions in areas that are difficult to see from the ground, as they capture wide areas from the air. Additionally, vehicle-mounted cameras can be used to record snow removal conditions while moving. Vehicle-mounted cameras are installed on snow removal vehicles and record road conditions and snow removal progress while moving. This allows the data collection unit to collect a wide range of data using various devices and understand the situation in real time. The collected data is sent to a central database, making it accessible to the analysis and judgment units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes weather data and human flow data. Specifically, it analyzes weather data such as temperature, snowfall amount, and wind speed to understand snowfall patterns and the progress of snow accumulation. It also analyzes human flow data such as people's movement patterns and congestion levels to provide information for determining snow removal priorities. The analysis unit can also optimize its analysis algorithm by referring to past weather data and human flow data. For example, it can select the optimal analysis algorithm based on past temperature data to improve the accuracy of the analysis. It can also improve the accuracy of the analysis by referring to past precipitation data. Furthermore, it can analyze past wind speed data and adjust the parameters of the analysis algorithm. As a result, the analysis unit can quickly and accurately analyze the collected data and provide information for determining the necessity and priority of snow removal. In addition, the analysis unit uses AI to process data in real time and understand the surrounding situation. The AI ​​uses image recognition technology to analyze camera footage and identify the progress of snow removal and the state of snow accumulation. It also analyzes weather data and human flow data to formulate an optimal snow removal plan. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The determination unit determines the need for snow removal based on the data analyzed by the analysis unit. For example, the determination unit determines whether snow removal should be prioritized around major roads and public facilities. Specifically, it determines whether roads with heavy traffic or locations affecting public transport operations should be prioritized. The determination unit can enhance its determination algorithm and improve accuracy in cases of heavy traffic. It can also adjust the parameters of the determination based on the importance of public facilities. For example, it can determine whether snow removal around important public facilities such as hospitals and fire stations should be prioritized. Furthermore, the determination unit can improve the accuracy of its determinations based on specific conditions. For example, it can enhance its determination algorithm and improve accuracy in the event of extreme weather. It can also make rapid determinations and enable immediate responses in the event of an emergency. This allows the determination unit to accurately determine the need for snow removal based on the analyzed data and take appropriate action. In addition, the determination unit uses AI to analyze data and determine the need for snow removal in real time. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. This allows the judgment unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The ranking unit prioritizes snow removal based on the results determined by the judgment unit. For example, the ranking unit prioritizes areas that may affect roads with heavy traffic or public transportation operations. Specifically, it prioritizes data around major transportation routes to maximize the efficiency of snow removal work. It also prioritizes data around public facilities to ensure that the operation of important facilities is not disrupted. Furthermore, it prioritizes data in areas with high pedestrian traffic to ensure the safety of residents. The ranking unit uses AI to analyze the data and formulate an optimal snow removal plan. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessments and trend analysis. This allows the ranking unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the ranking unit can continuously revise the ranking results based on data that is updated in real time, so as to respond to the latest situation. For example, if the amount of snowfall or traffic volume changes rapidly, the ranking unit immediately takes in the new data and updates the ranking results. Furthermore, by considering the characteristics of each region and past disaster history, more accurate rankings can be achieved. As a result, the ranking unit can always provide highly accurate rankings based on the latest information, supporting a swift and appropriate response.

[0070] The data collection unit can collect data using devices such as sensors and cameras. For example, the data collection unit can measure the temperature using a temperature sensor. For example, the data collection unit can measure the humidity using a humidity sensor. For example, the data collection unit can measure the amount of snowfall using a snowfall sensor. For example, the data collection unit can monitor the snow removal situation using a surveillance camera. For example, the data collection unit can photograph the snow removal situation over a wide area using a drone camera. For example, the data collection unit can record the snow removal situation while moving using a vehicle-mounted camera. This improves the accuracy of data collection by using sensors and cameras. 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 data acquired by sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0071] The analysis unit can analyze weather data and human flow data. For example, the analysis unit can analyze weather data such as temperature, snowfall, and wind speed. For example, the analysis unit can analyze human flow data such as people's movement patterns and congestion levels. For example, the analysis unit can optimize the analysis algorithm by referring to past weather data and human flow data. For example, the analysis unit can select the optimal analysis algorithm based on past temperature data. For example, the analysis unit can improve the accuracy of the analysis by referring to past precipitation data. For example, the analysis unit can analyze past wind speed data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis by analyzing weather data and human flow data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input weather data and human flow data into a generating AI and have the generating AI perform the data analysis.

[0072] The determination unit can determine whether snow removal around major roads and public facilities should be prioritized. The determination unit can enhance its determination algorithm and improve accuracy, for example, when traffic volume is high. The determination unit can adjust the parameters of the determination based on the importance of public facilities, for example. The determination unit can improve the accuracy of the determination based on specific conditions, for example. The determination unit can enhance its determination algorithm and improve accuracy, for example, when extreme weather occurs. The determination unit can make a quick determination and enable immediate response, for example, when an emergency occurs. This improves the efficiency of snow removal work by determining the priority of snow removal around major roads and public facilities. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input data about major roads and public facilities into a generating AI and have the generating AI perform the determination of snow removal priority.

[0073] The ranking unit can prioritize locations that affect the operation of busy roads and public transport. For example, the ranking unit can prioritize data around major transportation routes. For example, the ranking unit can prioritize data around public facilities. For example, the ranking unit can prioritize data in areas with high pedestrian traffic. This improves the maintenance of urban functions by prioritizing snow removal in locations that affect the operation of busy roads and public transport. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data on locations that affect the operation of busy roads and public transport into a generating AI and have the generating AI perform the snow removal priority ranking.

[0074] The analysis unit can analyze weather data such as temperature, snowfall, and wind speed. For example, the analysis unit can select the optimal analysis algorithm based on temperature data. For example, the analysis unit can improve the accuracy of the analysis by referring to snowfall data. For example, the analysis unit can analyze wind speed data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis by analyzing weather data such as temperature, snowfall, and wind speed. 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 weather data such as temperature, snowfall, and wind speed into a generating AI and have the generating AI perform the data analysis.

[0075] 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 increase the frequency of data collection to collect more detailed information. For example, if the user is in a hurry, the data collection unit can speed up the timing of data collection to enable immediate response. This allows for more appropriate 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 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. 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.

[0076] The data collection unit can select the optimal data collection method by referring to past data when collecting snowfall and snow removal data. For example, the data collection unit can determine the most effective sensor placement based on past snowfall data. For example, the data collection unit can set an efficient data collection route by referring to past snow removal data. For example, the data collection unit can analyze past weather patterns to optimize the timing of data collection. This allows the optimal data collection method to be selected by referring to past data. 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 into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can adjust the frequency of data collection based on specific events. For example, if a traffic accident occurs, the data collection unit can frequently collect data on surrounding snowfall conditions. For example, if a large-scale event is held, the data collection unit can focus on collecting data on snow removal conditions around the venue. For example, if an emergency occurs, the data collection unit can collect data in real time, enabling a rapid response. This allows for a rapid response by adjusting the frequency of data collection based on specific events. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a specific event into a generating AI and have the generating AI adjust the frequency of data collection.

[0078] 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 feeling anxious, the data collection unit can prioritize collecting data on the snow removal status of major roads. For example, if the user is feeling at ease, the data collection unit can collect detailed data to understand the overall situation. For example, if the user is in a hurry, the data collection unit can quickly collect important data to enable immediate action. This allows for more appropriate data collection by prioritizing the data to be collected 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, for example, 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 the data.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information. For example, the data collection unit can prioritize the collection of data around major transportation routes. For example, the data collection unit can focus on collecting data around public facilities. For example, the data collection unit can prioritize the collection of data in areas with high pedestrian traffic. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above-described 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 collection of highly relevant data.

[0080] The data collection unit can analyze information from social media and collect relevant data. For example, the data collection unit can analyze posts on social media and collect information about snowfall conditions. For example, the data collection unit can collect real-time information about snow removal conditions from user posts. For example, the data collection unit can analyze trends on social media and prioritize the collection of relevant data. This allows for the collection of relevant data by analyzing information from social media. Some or all of the above-described processes 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 data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method and provide results quickly. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis to enable immediate action. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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-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 analysis method.

[0082] The analysis unit can optimize the analysis algorithm by referring to past weather data and human flow data. For example, the analysis unit can select the optimal analysis algorithm based on past weather data. For example, the analysis unit can improve the accuracy of the analysis by referring to past human flow data. For example, the analysis unit can analyze past data and adjust the parameters of the analysis algorithm. This allows the analysis algorithm to be optimized by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past weather data and human flow data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis based on specific conditions. For example, in the event of extreme weather, the analysis unit can enhance the analysis algorithm to improve accuracy. For example, in the event of an emergency, the analysis unit can perform a rapid analysis to enable immediate response. For example, the analysis unit can adjust the analysis parameters based on specific conditions to improve accuracy. This allows for more accurate analysis by improving the accuracy of the analysis based on specific conditions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to specific conditions into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0084] 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. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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.

[0085] The analysis unit can improve the accuracy of the analysis by taking geographic location information into consideration. For example, the analysis unit can prioritize the analysis of data around major transportation routes. For example, the analysis unit can focus on analyzing data around public facilities. For example, the analysis unit can prioritize the analysis of data in areas with high pedestrian traffic. This improves the accuracy of the analysis by taking geographic location information into consideration. 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 geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, the analysis unit can improve its analysis algorithm by referring to the latest research papers. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant databases. For example, the analysis unit can enhance the reliability of its analysis results by incorporating expert opinions. This improves the accuracy of the analysis by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0087] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is stressed, the judgment unit can use simple judgment criteria and provide results quickly. For example, if the user is relaxed, the judgment unit can use detailed judgment criteria and provide comprehensive results. For example, if the user is in a hurry, the judgment unit can make a quick judgment and enable immediate action. This allows for more appropriate judgments by adjusting the judgment criteria 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 judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the judgment criteria.

[0088] The judgment unit can optimize the judgment algorithm by referring to past judgment results. For example, the judgment unit can select the optimal judgment algorithm based on past judgment results. For example, the judgment unit can improve the accuracy of judgments by referring to past data. For example, the judgment unit can analyze past judgment results and adjust the parameters of the algorithm. This allows the judgment algorithm to be optimized by referring to past judgment results. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input past judgment results into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0089] The judgment unit can improve the accuracy of its judgments based on specific conditions. For example, if there is heavy traffic, the judgment unit can enhance its judgment algorithm to improve accuracy. For example, the judgment unit can adjust the parameters of its judgments based on the importance of public facilities. The judgment unit can improve the accuracy of its judgments based on specific conditions. This makes it possible to make more accurate judgments by improving the accuracy of judgments based on specific conditions. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input data about specific conditions into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0090] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit can provide a simple and highly visible display method. For example, if the user is relaxed, the judgment unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. By adjusting the display method of the judgment result according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the judgment result.

[0091] The determination unit can improve the accuracy of its determination by taking geographic location information into consideration. For example, the determination unit can prioritize determining data around major transportation routes. For example, the determination unit can focus on determining data around public facilities. For example, the determination unit can prioritize determining data in areas with high pedestrian traffic. This improves the accuracy of the determination by taking geographic location information into consideration. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0092] The judgment unit can improve the accuracy of its judgments by referring to relevant literature and databases. For example, the judgment unit can improve its judgment algorithm by referring to the latest research papers. For example, the judgment unit can improve the accuracy of its judgments by referring to relevant databases. For example, the judgment unit can increase the reliability of its judgment results by incorporating expert opinions. As a result, the accuracy of the judgment is improved by referring to relevant literature and databases. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the improvement of the accuracy of the judgment.

[0093] The ranking unit can estimate the user's emotions and adjust the ranking criteria based on the estimated emotions. For example, if the user is stressed, the ranking unit can use simple ranking criteria and provide results quickly. For example, if the user is relaxed, the ranking unit can use detailed ranking criteria and provide comprehensive results. For example, if the user is in a hurry, the ranking unit can perform rapid ranking to enable immediate action. This allows for more appropriate ranking by adjusting the ranking criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ranking unit may be performed using AI or not. For example, the ranking unit can input user emotion data into the generative AI and have the generative AI adjust the ranking criteria.

[0094] The ranking unit can optimize its ranking algorithm by referring to past ranking results. For example, the ranking unit can select the optimal ranking algorithm based on past ranking results. For example, the ranking unit can improve the accuracy of ranking by referring to past data. For example, the ranking unit can analyze past ranking results and adjust the parameters of the algorithm. This allows the ranking algorithm to be optimized by referring to past ranking results. Some or all of the above processes in the ranking unit may be performed using AI, for example, or without using AI. For example, the ranking unit can input past ranking results into a generating AI and have the generating AI perform the optimization of the ranking algorithm.

[0095] The ranking unit can improve the accuracy of ranking based on specific conditions. For example, the ranking unit can enhance the ranking algorithm and improve accuracy when traffic volume is high. For example, the ranking unit can adjust the ranking parameters based on the operating status of public transport. For example, the ranking unit can improve the accuracy of ranking based on specific conditions. This makes it possible to perform more accurate rankings by improving the accuracy of ranking based on specific conditions. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data on specific conditions into a generating AI and have the generating AI perform the improvement of ranking accuracy.

[0096] The ranking unit can estimate the user's emotions and adjust the display method of the ranking results based on the estimated user emotions. For example, if the user is nervous, the ranking unit can provide a simple and highly visible display method. For example, if the user is relaxed, the ranking unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the ranking unit can provide a display method that gets straight to the point. By adjusting the display method of the ranking results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the ranking results.

[0097] The ranking unit can improve the accuracy of ranking by taking geographic location information into consideration. For example, the ranking unit can prioritize ranking data around major transportation routes. For example, the ranking unit can prioritize ranking data around public facilities. For example, the ranking unit can prioritize ranking data in areas with high pedestrian traffic. This improves the accuracy of ranking by taking geographic location information into consideration. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input geographic location information into a generating AI and have the generating AI perform the task of improving the accuracy of ranking.

[0098] The ranking unit can improve the accuracy of its rankings by referring to relevant literature and databases. For example, the ranking unit can improve its ranking algorithm by referring to the latest research papers. For example, the ranking unit can improve the accuracy of its rankings by referring to relevant databases. For example, the ranking unit can increase the reliability of its ranking results by incorporating expert opinions. This improves the accuracy of rankings by referring to relevant literature and databases. Some or all of the above processes in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input data from relevant literature and databases into a generating AI and have the generating AI perform the task of improving the accuracy of its rankings.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing data around major roads and public facilities. If the user is relaxed, the analysis unit can analyze detailed data and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis to enable immediate action. In this way, adjusting the analysis priority according to the user's emotions enables more appropriate analysis. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0101] The data collection unit can analyze information from social media and collect relevant data. For example, it can analyze posts on social media and collect information about snowfall conditions. It can also collect real-time information about snow removal conditions from user posts. Furthermore, it can analyze trends on social media and prioritize the collection of relevant data. In this way, relevant data can be collected by analyzing information from social media. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0102] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is stressed, a simple judgment criterion can be used to provide a quick result. If the user is relaxed, a detailed judgment criterion can be used to provide a comprehensive result. Furthermore, if the user is in a hurry, a quick judgment can be made to enable immediate action. In this way, adjusting the judgment criteria according to the user's emotions makes it possible to make more appropriate judgments. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0103] The ranking unit can optimize its ranking algorithm by referring to past ranking results. For example, it can select the optimal ranking algorithm based on past ranking results. It can also improve the accuracy of ranking by referring to past data. Furthermore, it can analyze past ranking results and adjust the parameters of the algorithm. In this way, the ranking algorithm can be optimized by referring to past ranking results. Some or all of the above processes in the ranking unit may be performed using AI or not.

[0104] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0105] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information. For example, it can prioritize the collection of data around major transportation routes. It can also prioritize the collection of data around public facilities. Furthermore, it can prioritize the collection of data in areas with high pedestrian traffic. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0106] The judgment unit can improve the accuracy of its judgments based on specific conditions. For example, if traffic volume is high, the judgment algorithm can be strengthened to improve accuracy. Furthermore, the judgment parameters can be adjusted based on the importance of the public facility. In addition, the accuracy of the judgment can be improved based on specific conditions. This allows for more accurate judgments by improving the accuracy of the judgment based on specific conditions. Some or all of the above-described processes in the judgment unit may be performed using AI, or they may not.

[0107] The ranking unit can estimate the user's emotions and adjust the ranking criteria based on those emotions. For example, if the user is stressed, a simple ranking criterion can be used to provide quick results. If the user is relaxed, a detailed ranking criterion can be used to provide comprehensive results. Furthermore, if the user is in a hurry, rapid ranking can be performed to enable immediate action. By adjusting the ranking criteria according to the user's emotions, more appropriate rankings can be achieved. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0108] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases. For example, it can improve its analysis algorithm by referring to the latest research papers. It can also improve the accuracy of its analysis by referring to relevant databases. Furthermore, it can enhance the reliability of its analysis results by incorporating expert opinions. In this way, the accuracy of the analysis is improved by referring to relevant literature and databases. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0109] The ranking unit can improve the accuracy of ranking based on specific conditions. For example, if traffic volume is high, the ranking algorithm can be strengthened to improve accuracy. Furthermore, ranking parameters can be adjusted based on the operating status of public transport. In addition, the accuracy of ranking can be improved based on specific conditions. This allows for more accurate ranking by improving the accuracy of ranking based on specific conditions. Some or all of the above-described processes in the ranking unit may be performed using AI or not.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects data on snowfall and snow removal conditions. The collection unit collects data using devices such as sensors and cameras. For example, it measures the temperature using a temperature sensor, the humidity using a humidity sensor, and the amount of snowfall using a snowfall sensor. It can also monitor snow removal conditions using surveillance cameras, capture wide-area snow removal conditions using drone cameras, and record snow removal conditions while moving using vehicle-mounted cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes weather data and human flow data, including weather data such as temperature, snowfall, and wind speed, and human flow data such as people's movement patterns and congestion levels. Furthermore, it optimizes the analysis algorithm by referring to past weather data and human flow data to improve the accuracy of the analysis. Step 3: The determination unit determines the need for snow removal based on the data analyzed by the analysis unit. The determination unit determines whether snow removal should be prioritized around major roads and public facilities, and adjusts the determination parameters based on factors such as heavy traffic and the importance of the public facilities. Furthermore, in the event of extreme weather or an emergency, the determination algorithm is enhanced to provide a rapid determination. Step 4: The ranking unit prioritizes snow removal based on the results determined by the judgment unit. The ranking unit prioritizes areas that affect roads with heavy traffic or the operation of public transport, and prioritizes data around major transportation routes, data around public facilities, and data in areas with high pedestrian traffic.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and ranking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects snowfall and snow removal conditions using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented in the specific processing unit 290 of the data processing unit 12 and determines the need for snow removal based on the analyzed data. The ranking unit is implemented in the specific processing unit 290 of the data processing unit 12 and ranks the priority of snow removal based on the determined results. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, determination unit, and ranking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects snowfall and snow removal conditions using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The determination unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and determines the need for snow removal based on the analyzed data. The ranking unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and ranks the priority of snow removal based on the determined results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and ranking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects snowfall and snow removal conditions using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented in the specific processing unit 290 of the data processing unit 12 and determines the necessity of snow removal based on the analyzed data. The ranking unit is implemented in the specific processing unit 290 of the data processing unit 12 and ranks the priority of snow removal based on the determined results. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and ranking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects snowfall and snow removal conditions using the camera 42 and sensors of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented in the specific processing unit 290 of the data processing unit 12 and determines the necessity of snow removal based on the analyzed data. The ranking unit is implemented in the specific processing unit 290 of the data processing unit 12 and ranks the priority of snow removal based on the determined results. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The collection department collects information on snowfall and snow removal conditions, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the necessity of snow removal based on the data analyzed by the aforementioned analysis unit, The system includes a ranking unit that prioritizes snow removal based on the results determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data using devices such as sensors and cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze weather data and human movement data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, Determine whether snow removal around major roads and public facilities should be prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 5) The ranking unit, Prioritize locations that have heavy traffic or affect public transport operations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze weather data such as temperature, snowfall, and wind speed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data on snowfall and snow removal conditions, the optimal collection method is selected by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Adjust the frequency of data collection based on specific events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is 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 12) The aforementioned collection unit is Analyze information from social media and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Optimize the analysis algorithm by referring to past weather data and human flow data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Improve the accuracy of analysis based on specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Improve the accuracy of the analysis by taking geographic location information into account. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, Optimize the judgment algorithm by referring to past judgment results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, Improve the accuracy of judgments based on specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, Improve the accuracy of the determination by taking geographical location information into account. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, Improve the accuracy of the assessment by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 25) The ranking unit, It estimates user sentiment and adjusts ranking criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The ranking unit, Optimize the ranking algorithm by referring to past ranking results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The ranking unit, Improve ranking accuracy based on specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The ranking unit, It estimates the user's sentiment and adjusts how ranking results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The ranking unit, Improve ranking accuracy by taking geographical location information into account. The system described in Appendix 1, characterized by the features described herein. (Note 30) The ranking unit, Improve ranking accuracy by referencing relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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. The collection department collects information on snowfall and snow removal conditions, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the necessity of snow removal based on the data analyzed by the aforementioned analysis unit, The system includes a ranking unit that prioritizes snow removal based on the results determined by the aforementioned determination unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data using devices such as sensors and cameras. The system according to feature 1.

3. The aforementioned analysis unit, Analyze weather data and human movement data. The system according to feature 1.

4. The determination unit, Determine whether snow removal around major roads and public facilities should be prioritized. The system according to feature 1.

5. The ranking unit, Prioritize locations that have heavy traffic or affect public transport operations. The system according to feature 1.

6. The aforementioned analysis unit, Analyze weather data such as temperature, snowfall, and wind speed. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting data on snowfall and snow removal conditions, the optimal collection method is selected by referring to past data. The system according to feature 1.

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