system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining weather data in real time, A route optimization means that analyzes the information obtained by the data acquisition means and optimizes the removal path, Equipment control means that automatically performs removal work based on the generated optimal route, A feedback processing method that collects information from local residents and incorporates it into the next work, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 snowfall areas, snow removal work has mainly been carried out manually or by vehicles that require drivers, and shortages of personnel and the realization of efficient work are major issues. In particular, in order to quickly perform snow removal at appropriate times and places, efficient route selection based on real-time weather information and snow accumulation conditions is required, but it is difficult to achieve these with current methods.

Means for Solving the Problems

[0005] This invention provides a route optimization means that uses an information gathering means to acquire weather information in real time and analyzes the obtained data to determine the optimal snow removal route, thereby enabling efficient snow removal work at the appropriate time and place. Furthermore, by using a vehicle control means that performs snow removal by automatic driving based on the generated optimal route, it solves the problem of labor shortages and realizes an efficient and sustainable snow removal system.

[0006] "Information gathering means" refers to devices and systems used to obtain weather information in real time.

[0007] "Route optimization means" refers to algorithms and processes for analyzing collected data and generating the optimal snow removal route.

[0008] "Vehicle control means" refers to a control system that automatically operates snow removal vehicles based on the generated optimal route and performs snow removal work.

[0009] A "sensor" refers to a sensing device used to obtain information on snow removal conditions and vehicle location.

[0010] "Feedback" refers to information provided by users, including opinions and reports based on the quality and priority of snow removal work. [Brief explanation of the drawing]

[0011] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0023] 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.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] The 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.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to an automated snow removal system utilizing AI technology, and is implemented as follows: First, the server collects real-time weather information by integrating weather sensors and satellite data. This makes it possible to continuously monitor conditions such as snowfall and temperature.

[0033] Next, the server aggregates and analyzes the collected weather data and snow depth data from fixed sensors installed in the area. This analysis uses a predictive model based on machine learning, which can accurately predict future snow depth and weather changes. For example, it can predict the timing of the start and end of snowfall, making it possible to create an efficient snow removal schedule.

[0034] Based on the analysis results, the server optimizes the snow removal route by considering multiple parameters. It uses an algorithm that determines the most efficient route while prioritizing major roads and areas with heavy traffic. The server then transmits the optimized route information to the snowplow, which acts as a terminal.

[0035] The terminal performs snow removal in autonomous driving mode based on the received route information. Specifically, it uses the built-in GPS and various sensors to accurately determine its location as it moves. For example, it can sense the thickness of the snow on the road in real time and adjust the height and angle of the snow removal equipment as needed.

[0036] Furthermore, progress updates and vehicle status are transmitted to a server and used for further analysis and route adjustments. This enables flexible operations that can cope with unexpected weather changes and obstacles.

[0037] Users can use a dedicated smartphone app to check snow removal information and provide feedback. This allows local residents to understand the progress of snow removal work in real time and to communicate their requests. The feedback will be reflected in the next snow removal plan.

[0038] As described above, the present invention can realize efficient and sustainable snow removal operations and address the problem of labor shortages.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server obtains the latest weather data in real time via a weather API, including snowfall, temperature, and wind speed. It also periodically collects snow depth data from installed sensors.

[0042] Step 2:

[0043] The server stores the collected weather and snow depth data in a database and then performs preprocessing to remove outliers and noise. This improves the reliability of the data.

[0044] Step 3:

[0045] The server uses a trained machine learning model to predict future weather conditions and snow cover patterns. Based on the prediction results, it creates snow cover forecast maps for each region.

[0046] Step 4:

[0047] The server optimizes snow removal routes based on multiple factors (e.g., road importance, traffic volume), taking into account predictive maps and traffic information. The optimal route is calculated by an algorithm.

[0048] Step 5:

[0049] The server transmits optimized route information to the snowplow terminal, which includes specific geographical information and snow removal patterns.

[0050] Step 6:

[0051] The terminal (snowplow) automatically performs snow removal work while using its onboard GPS and sensors to precisely control its position according to the received route information.

[0052] Step 7:

[0053] The terminal transmits information such as the progress of the work and the presence of obstacles to the server in real time. This allows the server to dynamically adjust the route and work pattern as needed.

[0054] Step 8:

[0055] Users can check the snow removal status and provide feedback through a dedicated app. The server uses the collected feedback to optimize the next route.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Automated snow removal systems require the design of efficient snow removal routes that can quickly respond to changes in snowfall and traffic volume, as well as flexible system operation that reflects real-time feedback from users. However, existing systems often lack sufficient collection and analysis of weather data, resulting in lengthy route optimization processes. Furthermore, the lack of mechanisms to effectively utilize user information leads to a decrease in overall work efficiency.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes an information acquisition means for collecting weather data, a data analysis means for integrating the data obtained from the information acquisition means and analyzing it using a machine learning model, and a route communication means for designing high-priority routes based on the analysis results and transmitting route information to an autonomous vehicle via a communication means. This enables flexible response to real-time weather changes and efficient and safe snow removal operations that reflect user feedback.

[0061] "Meteorological data" refers to information about weather conditions such as temperature, precipitation, wind speed, and humidity, and is used to determine weather conditions.

[0062] "Information acquisition means" refers to devices or systems for collecting weather data and other necessary data, and this includes sensors and data provision services.

[0063] "Data analysis tools" refer to systems that integrate collected data, analyze it using machine learning models and statistical methods, and extract information for prediction and optimization.

[0064] A "machine learning model" is a type of algorithm used for data analysis that learns from past data to make predictions and classifications about unknown data.

[0065] "Route communication means" refers to a communication system or device for transmitting route data, determined based on analyzed information, to an autonomous vehicle.

[0066] An "autonomous vehicle" refers to a vehicle equipped with the ability to automatically operate according to received route information, and is used for snow removal work.

[0067] A "location tracking device" is a device used to measure the current position of a vehicle or object with high precision, and refers to devices that utilize technologies such as GPS.

[0068] A "surrounding object recognition device" refers to a sensor system that detects objects present around a vehicle and recognizes their position and shape.

[0069] This invention is an automated snow removal system utilizing AI technology, which achieves efficient snow removal by collecting and analyzing weather data. The system consists of three main components: a server, an automated vehicle (the terminal), and a user interface.

[0070] The server first collects weather data obtained from weather sensors and data provision services using information acquisition methods. In this process, comprehensive data including temperature, precipitation, and wind speed at each location is acquired, specifically via APIs.

[0071] Next, the server processes the collected weather data using data analysis tools. Specifically, it analyzes the data using programming languages ​​such as Python and machine learning frameworks such as Scikit-learn and TENSORFLOW®. This analysis predicts future snowfall patterns and temperature changes, and designs the optimal snow removal route.

[0072] The designed route is transmitted to the autonomous vehicle, which acts as the terminal, via a route communication system. This communication utilizes the latest high-speed communication technology, and the vehicle performs autonomous driving based on the received information. The terminal is equipped with a GPS module and LiDAR sensors, which are used to monitor the vehicle's current location and surrounding conditions in real time. For example, the LiDAR sensor can detect the thickness of snow during snow removal, and the device height can be automatically adjusted for efficient snow removal.

[0073] Users can use a dedicated application to operate this system to check the progress of snow removal and the current location of vehicles. Users can also provide feedback via their smartphones, using text input such as prompts like "Please tell me the progress of snow removal." This feedback is collected by the server and used to optimize future snow removal plans.

[0074] As described above, the system of the present invention enables real-time analysis of weather data and agile vehicle control, supporting efficient and flexible snow removal operations. This is expected to significantly improve the efficiency of snow damage countermeasures in local communities.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects weather data in real time from weather sensors and data provision services. It receives data such as temperature, precipitation, and wind speed at various locations via APIs as input. This data is stored in a database, and missing values ​​are imputed by cleaning the sensor data. This process outputs a clean and reliable weather dataset.

[0078] Step 2:

[0079] The server analyzes the collected weather data using a machine learning model. The weather data obtained in step 1 is supplied to the model as input. Specifically, it utilizes frameworks such as TensorFlow and Scikit-learn to predict snowfall patterns and temperature fluctuations through analysis. The output of this analysis is predictive information for optimizing future snow removal routes.

[0080] Step 3:

[0081] The server designs the optimal snow removal route based on the forecast information. Using the forecast information obtained in step 2 as input, it considers traffic volume and road priority using tools such as the Google® Maps API. This process calculates the shortest route using algorithms such as the A algorithm and outputs optimized route information.

[0082] Step 4:

[0083] The server transmits optimized route information to the autonomous vehicle, which is the terminal, using a communication method. As input, the route information generated in step 3 is prepared and transmitted to the vehicle via 5G communication. Through this communication, the terminal receives instructions to start snow removal work.

[0084] Step 5:

[0085] The terminal starts autonomous driving based on the received route information. The route information received in step 4 is used as input. The GPS module and LiDAR sensor mounted on the vehicle are activated to accurately determine its own position and surrounding environment as it proceeds. During snow removal, the snow thickness is detected by sensors, and the settings of the snow removal equipment are adjusted as needed. As output, data on the progress of the snow removal work and the status of the vehicle are generated in real time.

[0086] Step 6:

[0087] The terminal sends progress and vehicle status to the server. As input, it sends progress and vehicle data generated in step 5 to the server. The server analyzes this data and outputs information to issue new instructions as needed, and in emergencies, it issues immediate rerouting instructions.

[0088] Step 7:

[0089] Users can use a smartphone app to check the progress of snow removal and provide feedback. Input involves viewing real-time data displayed on the app. Feedback is also provided by sending prompt messages such as "Please tell me the progress of snow removal." This information is then sent to the server, and a response indicating that it will be reflected in the next snow removal plan is output.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] This invention aims to maintain the smooth flow of urban life by combining real-time collection and analysis of weather information, generation of optimized snow removal routes, automated snow removal operations, and user feedback, in order to carry out snow removal work efficiently and effectively. However, with previous technologies, it was difficult to respond to sudden changes in weather and to quickly reflect user feedback, which limited the realization of efficient snow removal plans.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes data acquisition means for acquiring weather data in real time, route optimization means for analyzing the information obtained by the data acquisition means and optimizing the removal route, equipment control means for automatically performing removal work based on the generated optimal route, and feedback processing means for collecting information from local residents and reflecting it in the next operation. This makes it possible to respond flexibly to rapid weather changes and to efficiently feed back the opinions of local residents into the snow removal plan.

[0095] A "data acquisition method for obtaining weather data in real time" refers to a function that collects the latest weather information moment by moment and uses it for analysis and decision-making within the system.

[0096] "Route optimization means that analyzes information obtained by the data acquisition means to optimize the removal route" refers to a method for processing collected weather data and calculating the optimal route for efficient removal work.

[0097] "Equipment control means that automatically performs removal work based on the generated optimal route" refers to a function that performs operations to mechanically and automatically carry out removal work based on optimized route information.

[0098] A "feedback processing method for collecting information from local residents and reflecting it in future work" is an information processing method that incorporates opinions and requests from residents and utilizes them in future planning.

[0099] The embodiments for carrying out this invention include real-time acquisition of weather data, optimization of removal routes based on data analysis, automated removal work, and incorporation of feedback from local residents.

[0100] The server utilizes cloud services such as AWS (registered trademark) to collect information from weather sensors and satellite data in real time. This data is analyzed by machine learning models written in Python (e.g., using TensorFlow or PyTorch) to generate the optimal snow removal route. Google Maps API and other tools can be used for route optimization.

[0101] The snowplows, which serve as terminals, are equipped with GPS devices and various sensors (such as LiDAR and camera sensors), and automatically perform snow removal work based on the optimal route generated by the server. This reduces the amount of manpower required on site and enables more efficient work. A smartphone app with real-time feedback functionality is developed using React Native, allowing users to send information, and this feedback is used to plan future operations.

[0102] As a concrete example, the administrators of a certain city can use an app to monitor the progress of snow removal and check for urgent requests from residents. This enables a more efficient response. An example of a prompt from the generated AI model would be: "Please tell me how to check the recent snow removal status in my area and report any problems. Also, please explain how this will be reflected in the next snow removal plan."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects weather information in real time from weather sensors and satellite data. Input data includes information such as temperature, snowfall, and wind speed received from various sensors. Output data includes integrated real-time data of this weather information. In this process, data is retrieved from various weather information providers via APIs to create an integrated dataset.

[0106] Step 2:

[0107] The server uses a machine learning model to predict future snowfall and weather changes based on collected real-time weather data. In this step, the input is the integrated weather data collected in step 1. For data processing, machine learning algorithms are used to make short-term and medium-term weather forecasts. The output is information such as predicted snowfall and snowfall timing. This information is used in subsequent steps.

[0108] Step 3:

[0109] The server generates the optimal snow removal route based on predicted weather data. The input data is the weather information predicted in step 2. Here, a route optimization algorithm is used to calculate the route, taking traffic data into consideration. The output is route information that allows for efficient and rapid snow removal. Specifically, the route is determined prioritizing major roads and areas with heavy traffic.

[0110] Step 4:

[0111] The snowplow, acting as the terminal, begins snow removal work automatically based on optimal route information transmitted from the server. The input is the optimal route information generated in step 3. As output, actual snow removal progress information is sent to the server. Throughout this process, the vehicle's position is constantly monitored using a GPS device, and the work proceeds while monitoring snow conditions in real time with sensors.

[0112] Step 5:

[0113] Users check the progress of snow removal and provide feedback through a smartphone app. The input information is the actual data accumulated on the server in step 4. User feedback is collected through the app and reflected in the next plan. The output is information on adjusting routes and work schedules based on the feedback. For example, a user can request areas where snow removal is insufficient through the app, and that information will be incorporated into the next snow removal plan.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention relates to an automated snow removal system that combines AI technology with emotion analysis. Specific embodiments are described below.

[0116] First, the server collects weather data and snow depth data in real time and stores it in a database. Next, it analyzes the collected data and uses a machine learning model to predict snow depth. The predicted data is used to optimize snow removal routes.

[0117] Next, the server also uses user feedback and emotional data for analysis. Users can provide feedback via voice or text through a smartphone app. This information is analyzed by the emotion engine, and emotional states (e.g., disillusionment, relief, dissatisfaction) are extracted. This allows for adjustments to priority snow removal areas based on the user's specific emotions.

[0118] The emotion engine extracts features from audio data and classifies emotions from user utterances. This classification result influences the prioritization and scheduling of snow removal.

[0119] Subsequently, the server transmits optimized snow removal route information to the snowplow, which acts as the terminal. The terminal performs snow removal in autonomous driving mode, sending progress and sensor information back to the server in real time. This allows the snow removal plan to be adjusted sequentially as needed.

[0120] Users can use the app to check the snow removal status and provide ongoing feedback. This feedback, along with sentiment analysis results, is used in the next route optimization process.

[0121] In this way, the present invention realizes efficient snow removal operations that take into account the actual needs of the region and the emotions of the users. By analyzing emotions, it is possible to improve user satisfaction and enhance the quality of snow removal services.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server retrieves real-time data from weather APIs and local sensors. This includes snowfall, temperature, and wind speed. The obtained data is stored in a database and prepared for analysis.

[0125] Step 2:

[0126] The server applies machine learning models to stored weather data to predict snowfall. This allows it to predict which areas will experience heavy snowfall in the future, forming the basis for snow removal planning.

[0127] Step 3:

[0128] Users send voice or text feedback through a smartphone app. The app provides an interface for extracting emotions from the user's speech and input.

[0129] Step 4:

[0130] The server analyzes the received user feedback using an emotion engine. In the case of audio data, features are extracted from the audio, and an emotion classification algorithm is used to identify the user's emotions. The resulting emotion data is used to inform snow removal plans.

[0131] Step 5:

[0132] The server integrates predictive data from machine learning models with user sentiment data from an emotion engine to calculate the optimal snow removal route. This takes into account factors such as road importance, traffic volume, and the user's emotional needs.

[0133] Step 6:

[0134] The server transmits the calculated route information to the snowplow, which acts as the terminal. The terminal then performs snow removal automatically according to the specified route.

[0135] Step 7:

[0136] The terminal transmits sensor information and location data collected during operation to the server in real time. This allows the server to dynamically adjust routes and work content according to the situation.

[0137] Step 8:

[0138] Users can check the current snow removal status through the app and provide additional feedback. This feedback will be used to inform future analysis and planning.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In autonomous snow removal operations, efficient and flexible route optimization is required, taking into account not only local snow conditions and weather, but also user emotions and feedback. However, conventional systems have difficulty effectively incorporating these elements, which may result in decreased user satisfaction. Furthermore, insufficient real-time monitoring and adjustment of work status can lead to problems in responding to sudden changes in conditions.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes information acquisition means for collecting weather information and snow depth information, data analysis means for analyzing the collected information and generating snow depth predictions using an AI model, and feedback processing means for analyzing sentiment data from users and adjusting priorities. This enables the setting of efficient snow removal routes that take into account snow depth conditions and user sentiment, as well as real-time monitoring and adjustment of work progress.

[0144] "Information acquisition means" refers to components for collecting weather information and snow depth information from sensors and web services and preparing them for the system.

[0145] "Data analysis methods" refer to the process of using a generative AI model to predict snowfall based on collected weather information and snow depth data.

[0146] "Route setting means" refers to a procedure for designing the optimal snow removal route based on the results of data analysis, thereby improving work efficiency.

[0147] A "feedback processing system" is a mechanism that analyzes emotional data and feedback provided by users and uses it to adjust the areas that should be prioritized for snow removal.

[0148] "Machine control means" refers to control technology for vehicles or machines that automatically perform snow removal work based on a set optimal route.

[0149] A "progress management system" is a function that monitors the progress of snow removal work and information from sensors in real time, and adjusts the plan as needed.

[0150] This invention is an advanced snow removal system that integrates autonomous driving technology with generative AI models. At the heart of this system is a server that collects weather information and snow depth information in real time and integrates it into a database. Specifically, it uses weather sensors and external weather APIs to accurately acquire the necessary information.

[0151] The server further analyzes the acquired information and uses generative AI models (e.g., machine learning algorithms such as LSTM and BERT) to predict snow conditions, and then designs the optimal snow removal route based on those predictions. This data analysis process makes snow removal operations more efficient.

[0152] Furthermore, users can provide feedback through a dedicated mobile application, sending emotional data via voice or text. This feedback is analyzed on a server and used to determine priority snow removal areas. Emotion recognition and natural language processing technologies are essential for feedback analysis. Specifically, multiple emotional states (e.g., reassurance, dissatisfaction) are automatically extracted and reflected in the system's route determination.

[0153] The optimized snow removal route information is transmitted to the snowplow, which acts as a terminal. The snowplow operates in autonomous driving mode based on this information, using LiDAR and GPS to recognize obstacles and conditions in real time as it works. During this process, the work status and data obtained from the sensors are continuously returned to the server, and the work plan is adjusted as needed.

[0154] For example, if a sudden increase in snowfall is predicted on a major road in a region, a user can send feedback such as, "I'm worried about the snow on the main road, so please clear it as soon as possible." The server analyzes this information using sentiment analysis and incorporates it into optimizing snow removal routes. In this way, rapid and flexible snow removal operations tailored to the specific circumstances of the region are realized.

[0155] An example of a prompt message might be: "Please explain how you will use user sentiment data to optimize the snow removal service."

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server collects weather information and snow depth information. Input is raw data obtained from weather sensors and weather APIs. This data undergoes consistency checks and preprocessing before being stored in an integrated database. Specific operations include sending information retrieval requests from weather APIs and periodic data polling from sensors. Output is weather and snow depth information converted into an analyzable format.

[0159] Step 2:

[0160] The server analyzes the collected data. The input is the data accumulated in Step 1. A generative AI model (such as BERT or LSTM) is used on this data to predict snow conditions. Specifically, it refers to past weather data and estimates future snowfall amounts using a machine learning algorithm. The output is the predicted snowfall data.

[0161] Step 3:

[0162] Users submit feedback using a smartphone app. Input is either voice or text feedback from the user. This feedback is sent to a server for sentiment analysis. Specifically, this involves converting speech to text using a speech recognition system and analyzing the sentiment using natural language processing. The output is the analyzed emotional state (e.g., reassured, dissatisfied).

[0163] Step 4:

[0164] The server optimizes snow removal routes based on sentiment analysis results. The inputs are the predicted data from step 2 and the sentiment state from step 3. This allows the server to determine priority snow removal areas and recalculate the route. Specifically, the server weights the sentiment analysis results and inputs them into the route optimization algorithm. The output is the optimized snow removal route.

[0165] Step 5:

[0166] The server sends optimized route information to the snowplow, which acts as the terminal. The input is the route information generated in step 4. Based on this, the snowplow starts operating in automatic driving mode. Specifically, it downloads route data using the vehicle's communication module and initializes the vehicle control system based on it. The output is the work command to be executed by the snowplow.

[0167] Step 6:

[0168] The terminal performs snow removal and sends progress and sensor information back to the server in real time. Input is various sensor data collected during snow removal. Specific operations include obstacle detection and position tracking using LiDAR and GPS devices, and recording of work progress. Output is progress and current route information stored in the database.

[0169] Step 7:

[0170] Users can check the snow removal status in real time through the app and provide additional feedback. The input is current work progress information provided by the server. Specifically, the user interface displays the current progress and a feedback input form. The output is the latest information on the snow removal status, which impacts user satisfaction.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] Conventional automated snow removal systems simply optimize routes and perform tasks based on weather data, and no system yet exists that can adjust to user emotions and feedback. Therefore, it has been difficult to contribute to improving resident satisfaction. Thus, the present invention aims to provide an efficient and flexible automated work system that reflects user emotions.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes data collection means for acquiring environmental information in real time, route setting means for analyzing the information obtained by the data collection means and optimizing the work route, machine control means for automatically performing work based on the generated optimal route, and emotion analysis means for analyzing user emotion information and dynamically adjusting the priority of snow removal work. This makes it possible to implement an optimal snow removal work plan that incorporates user emotions and feedback.

[0176] "Data collection methods" refer to technologies for acquiring environmental information and related data in real time.

[0177] "Route setting means" refers to a technology that optimizes and sets the work route based on acquired information.

[0178] "Machine control means" refers to technology for automatically performing tasks according to a set optimal path.

[0179] "Emotional analysis tools" are technologies that analyze users' emotional information and dynamically adjust task priorities and plans.

[0180] The system used to implement this application consists of a server, a terminal, and a user.

[0181] The server acquires weather data and related information through data collection mechanisms for collecting environmental information in real time. This data is efficiently processed using AWS Lambda, which operates on a cloud platform. Subsequently, a machine learning model using TensorFlow analyzes the acquired data and optimizes the workflow.

[0182] The terminal is equipped with machine control means for performing automated tasks. This allows the system to automatically continue tasks based on optimized path information. This control means utilizes sensing technology that can monitor progress and status in real time.

[0183] Furthermore, users can interact with the system through a smartphone app. This application uses Google Cloud Speech-to-Text to convert speech data into text and then supplies that data to an emotion analysis tool using NLTK. Users can check the progress of their work and easily provide emotion feedback through this application.

[0184] For example, if feedback is received in a certain area stating, "It's inconvenient because the street in front of my house hasn't been cleared of snow yet," this negative emotional data is read through an emotion analysis tool and reflected in the route planning tool. As a result, work in that area is prioritized for scheduling.

[0185] An example of a prompt for a generating AI model would be: "Three hours after the snowfall, dissatisfaction is increasing in the southern part of the city. Please suggest the optimal plan for which areas should be prioritized for snow removal." Such prompts allow the system to present an efficient work plan that takes people's emotions into account.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The server collects environmental information, particularly weather data and snow cover information, in real time using data collection methods. The input to this process is data provided by various sensors and the internet, and the output is a set of raw data for analysis.

[0189] Step 2:

[0190] The server utilizes a generative AI model based on the collected data and executes an optimization algorithm using TensorFlow. This optimizes the work path. The input is the set of raw data obtained in step 1, and the output is optimized work path information. Specifically, it recalculates the path priority using predictive modeling.

[0191] Step 3:

[0192] The terminal receives optimized work path information transmitted from the server and automatically performs the work via machine control means. The input is work path information provided by the server, and the output is actual snow removal work data. The specific operation of the terminal is to perform snow removal work according to the path.

[0193] Step 4:

[0194] Users use a smartphone app to monitor their work status in real time and provide feedback via voice or text. Inputs include work status and feedback data, while output is user emotion information. Specific actions include inputting emotion feedback within the app.

[0195] Step 5:

[0196] The server uses Google Cloud Speech-to-Text and NLTK to convert user voice data into text and analyze it using sentiment analysis tools. Input is user voice and text feedback, and output is analyzed sentiment data. Specifically, it extracts features from the audio to facilitate analysis.

[0197] Step 6:

[0198] The server re-evaluates which areas should be prioritized for snow removal using prompt messages, based on the sentiment analysis results and the generative AI model, and adjusts the route priorities. The input is the sentiment analysis results and prompt messages, and the output is the adjusted priority route information. Dynamic adjustment of priority settings is performed in this step.

[0199] 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.

[0200] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0206] 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.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0208] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] 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.

[0210] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The 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.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention relates to an automated snow removal system utilizing AI technology, and is implemented as follows: First, the server collects real-time weather information by integrating weather sensors and satellite data. This makes it possible to continuously monitor conditions such as snowfall and temperature.

[0216] Next, the server aggregates and analyzes the collected weather data and snow depth data from fixed sensors installed in the area. This analysis uses a predictive model based on machine learning, which can accurately predict future snow depth and weather changes. For example, it can predict the timing of the start and end of snowfall, making it possible to create an efficient snow removal schedule.

[0217] Based on the analysis results, the server optimizes the snow removal route by considering multiple parameters. It uses an algorithm that determines the most efficient route while prioritizing major roads and areas with heavy traffic. The server then transmits the optimized route information to the snowplow, which acts as a terminal.

[0218] The terminal performs snow removal in autonomous driving mode based on the received route information. Specifically, it uses the built-in GPS and various sensors to accurately determine its location as it moves. For example, it can sense the thickness of the snow on the road in real time and adjust the height and angle of the snow removal equipment as needed.

[0219] Furthermore, progress updates and vehicle status are transmitted to a server and used for further analysis and route adjustments. This enables flexible operations that can cope with unexpected weather changes and obstacles.

[0220] Users can use a dedicated smartphone app to check snow removal information and provide feedback. This allows local residents to understand the progress of snow removal work in real time and to communicate their requests. The feedback will be reflected in the next snow removal plan.

[0221] As described above, the present invention can realize efficient and sustainable snow removal operations and address the problem of labor shortages.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server obtains the latest weather data in real time via a weather API, including snowfall, temperature, and wind speed. It also periodically collects snow depth data from installed sensors.

[0225] Step 2:

[0226] The server stores the collected weather and snow depth data in a database and then performs preprocessing to remove outliers and noise. This improves the reliability of the data.

[0227] Step 3:

[0228] The server uses a trained machine learning model to predict future weather conditions and snow cover patterns. Based on the prediction results, it creates snow cover forecast maps for each region.

[0229] Step 4:

[0230] The server optimizes snow removal routes based on multiple factors (e.g., road importance, traffic volume), taking into account predictive maps and traffic information. The optimal route is calculated by an algorithm.

[0231] Step 5:

[0232] The server transmits optimized route information to the snowplow terminal, which includes specific geographical information and snow removal patterns.

[0233] Step 6:

[0234] The terminal (snowplow) automatically performs snow removal work while using its onboard GPS and sensors to precisely control its position according to the received route information.

[0235] Step 7:

[0236] The terminal transmits information such as the progress of the work and the presence of obstacles to the server in real time. This allows the server to dynamically adjust the route and work pattern as needed.

[0237] Step 8:

[0238] Users can check the snow removal status and provide feedback through a dedicated app. The server uses the collected feedback to optimize the next route.

[0239] (Example 1)

[0240] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0241] Automated snow removal systems require the design of efficient snow removal routes that can quickly respond to changes in snowfall and traffic volume, as well as flexible system operation that reflects real-time feedback from users. However, existing systems often lack sufficient collection and analysis of weather data, resulting in lengthy route optimization processes. Furthermore, the lack of mechanisms to effectively utilize user information leads to a decrease in overall work efficiency.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes an information acquisition means for collecting weather data, a data analysis means for integrating the data obtained from the information acquisition means and analyzing it using a machine learning model, and a route communication means for designing high-priority routes based on the analysis results and transmitting route information to an autonomous vehicle via a communication means. This enables flexible response to real-time weather changes and efficient and safe snow removal operations that reflect user feedback.

[0244] "Meteorological data" refers to information about weather conditions such as temperature, precipitation, wind speed, and humidity, and is used to determine weather conditions.

[0245] "Information acquisition means" refers to devices or systems for collecting weather data and other necessary data, and this includes sensors and data provision services.

[0246] "Data analysis tools" refer to systems that integrate collected data, analyze it using machine learning models and statistical methods, and extract information for prediction and optimization.

[0247] A "machine learning model" is a type of algorithm used for data analysis that learns from past data to make predictions and classifications about unknown data.

[0248] "Route communication means" refers to a communication system or device for transmitting route data, determined based on analyzed information, to an autonomous vehicle.

[0249] An "autonomous vehicle" refers to a vehicle equipped with the ability to automatically operate according to received route information, and is used for snow removal work.

[0250] A "location tracking device" is a device used to measure the current position of a vehicle or object with high precision, and refers to devices that utilize technologies such as GPS.

[0251] A "surrounding object recognition device" refers to a sensor system that detects objects present around a vehicle and recognizes their position and shape.

[0252] This invention is an automated snow removal system utilizing AI technology, which achieves efficient snow removal by collecting and analyzing weather data. The system consists of three main components: a server, an automated vehicle (the terminal), and a user interface.

[0253] The server first collects weather data obtained from weather sensors and data provision services using information acquisition methods. In this process, comprehensive data including temperature, precipitation, and wind speed at each location is acquired, specifically via APIs.

[0254] Next, the server processes the collected weather data using data analysis tools. Specifically, it analyzes the data using programming languages ​​such as Python and machine learning frameworks such as Scikit-learn and TensorFlow. This analysis predicts future snowfall patterns and temperature changes, and designs the optimal snow removal route.

[0255] The designed route is transmitted to the autonomous vehicle, which acts as the terminal, via a route communication system. This communication utilizes the latest high-speed communication technology, and the vehicle performs autonomous driving based on the received information. The terminal is equipped with a GPS module and LiDAR sensors, which are used to monitor the vehicle's current location and surrounding conditions in real time. For example, the LiDAR sensor can detect the thickness of snow during snow removal, and the device height can be automatically adjusted for efficient snow removal.

[0256] Users can use a dedicated application to operate this system to check the progress of snow removal and the current location of vehicles. Users can also provide feedback via their smartphones, using text input such as prompts like "Please tell me the progress of snow removal." This feedback is collected by the server and used to optimize future snow removal plans.

[0257] As described above, the system of the present invention enables real-time analysis of weather data and agile vehicle control, supporting efficient and flexible snow removal operations. This is expected to significantly improve the efficiency of snow damage countermeasures in local communities.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects weather data in real time from weather sensors and data provision services. It receives data such as temperature, precipitation, and wind speed at various locations via APIs as input. This data is stored in a database, and missing values ​​are imputed by cleaning the sensor data. This process outputs a clean and reliable weather dataset.

[0261] Step 2:

[0262] The server analyzes the collected weather data using a machine learning model. The weather data obtained in step 1 is supplied to the model as input. Specifically, it utilizes frameworks such as TensorFlow and Scikit-learn to predict snowfall patterns and temperature fluctuations through analysis. The output of this analysis is predictive information for optimizing future snow removal routes.

[0263] Step 3:

[0264] The server designs the optimal snow removal route based on the forecast information. Using the forecast information obtained in step 2 as input, it considers traffic volume and road priority using the Google Maps API, etc. This process calculates the shortest route using algorithms such as the A algorithm and outputs optimized route information.

[0265] Step 4:

[0266] The server transmits optimized route information to the autonomous vehicle, which is the terminal, using a communication method. As input, the route information generated in step 3 is prepared and transmitted to the vehicle via 5G communication. Through this communication, the terminal receives instructions to start snow removal work.

[0267] Step 5:

[0268] The terminal starts autonomous driving based on the received route information. The route information received in step 4 is used as input. The GPS module and LiDAR sensor mounted on the vehicle are activated to accurately determine its own position and surrounding environment as it proceeds. During snow removal, the snow thickness is detected by sensors, and the settings of the snow removal equipment are adjusted as needed. As output, data on the progress of the snow removal work and the status of the vehicle are generated in real time.

[0269] Step 6:

[0270] The terminal sends progress and vehicle status to the server. As input, it sends progress and vehicle data generated in step 5 to the server. The server analyzes this data and outputs information to issue new instructions as needed, and in emergencies, it issues immediate rerouting instructions.

[0271] Step 7:

[0272] Users can use a smartphone app to check the progress of snow removal and provide feedback. Input involves viewing real-time data displayed on the app. Feedback is also provided by sending prompt messages such as "Please tell me the progress of snow removal." This information is then sent to the server, and a response indicating that it will be reflected in the next snow removal plan is output.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] This invention aims to maintain the smooth flow of urban life by combining real-time collection and analysis of weather information, generation of optimized snow removal routes, automated snow removal operations, and user feedback, in order to carry out snow removal work efficiently and effectively. However, with previous technologies, it was difficult to respond to sudden changes in weather and to quickly reflect user feedback, which limited the realization of efficient snow removal plans.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes data acquisition means for acquiring weather data in real time, route optimization means for analyzing the information obtained by the data acquisition means and optimizing the removal route, equipment control means for automatically performing removal work based on the generated optimal route, and feedback processing means for collecting information from local residents and reflecting it in the next operation. This makes it possible to respond flexibly to rapid weather changes and to efficiently feed back the opinions of local residents into the snow removal plan.

[0278] A "data acquisition method for obtaining weather data in real time" refers to a function that collects the latest weather information moment by moment and uses it for analysis and decision-making within the system.

[0279] "Route optimization means that analyzes information obtained by the data acquisition means to optimize the removal route" refers to a method for processing collected weather data and calculating the optimal route for efficient removal work.

[0280] "Equipment control means that automatically performs removal work based on the generated optimal route" refers to a function that performs operations to mechanically and automatically carry out removal work based on optimized route information.

[0281] The "feedback processing means for collecting information from local residents and reflecting it in the next operation" is an information processing method that incorporates opinions and requests from residents and utilizes them in the next plan.

[0282] The embodiments for implementing this invention are for real-time weather data acquisition, optimization of removal routes based on data analysis, automated removal operations, and reflecting feedback from local residents.

[0283] The server utilizes cloud services such as AWS to collect information from weather sensors and satellite data in real time. These data are analyzed by a machine learning model described in Python (e.g., using TensorFlow or PyTorch), and an optimal snow removal route is generated. Google Maps API or the like can be used for route optimization.

[0284] The snow removal vehicle serving as the terminal is equipped with a GPS device and various sensors (e.g., LiDAR and camera sensors), and automatically performs removal operations based on the optimal route generated by the server. This reduces the manpower on-site and enables efficient operations. A smartphone app with a real-time feedback function is developed in React Native, and users can send information, and the feedback is utilized in the next plan.

[0285] As a specific example, the operator of a certain city can grasp the progress of snow removal through the app and confirm emergency requests from residents. This enables efficient responses. Examples of prompt texts for the generated AI model include sentences such as "Please show me the recent snow removal work situation in the area where I live and teach me the procedure for reporting if there are any problems. Also, please explain how this will be reflected in the next snow removal plan."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server collects weather information in real time from weather sensors and satellite data. Input data includes information such as temperature, snowfall, and wind speed received from various sensors. Output data includes integrated real-time data of this weather information. In this process, data is retrieved from various weather information providers via APIs to create an integrated dataset.

[0289] Step 2:

[0290] The server uses a machine learning model to predict future snowfall and weather changes based on collected real-time weather data. In this step, the input is the integrated weather data collected in step 1. For data processing, machine learning algorithms are used to make short-term and medium-term weather forecasts. The output is information such as predicted snowfall and snowfall timing. This information is used in subsequent steps.

[0291] Step 3:

[0292] The server generates the optimal snow removal route based on predicted weather data. The input data is the weather information predicted in step 2. Here, a route optimization algorithm is used to calculate the route, taking traffic data into consideration. The output is route information that allows for efficient and rapid snow removal. Specifically, the route is determined prioritizing major roads and areas with heavy traffic.

[0293] Step 4:

[0294] The snowplow, acting as the terminal, begins snow removal work automatically based on optimal route information transmitted from the server. The input is the optimal route information generated in step 3. As output, actual snow removal progress information is sent to the server. Throughout this process, the vehicle's position is constantly monitored using a GPS device, and the work proceeds while monitoring snow conditions in real time with sensors.

[0295] Step 5:

[0296] Users check the progress of snow removal and provide feedback through a smartphone app. The input information is the actual data accumulated on the server in step 4. User feedback is collected through the app and reflected in the next plan. The output is information on adjusting routes and work schedules based on the feedback. For example, a user can request areas where snow removal is insufficient through the app, and that information will be incorporated into the next snow removal plan.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention relates to an automated snow removal system that combines AI technology with emotion analysis. Specific embodiments are described below.

[0299] First, the server collects weather data and snow depth data in real time and stores it in a database. Next, it analyzes the collected data and uses a machine learning model to predict snow depth. The predicted data is used to optimize snow removal routes.

[0300] Next, the server also uses user feedback and emotional data for analysis. Users can provide feedback via voice or text through a smartphone app. This information is analyzed by the emotion engine, and emotional states (e.g., disillusionment, relief, dissatisfaction) are extracted. This allows for adjustments to priority snow removal areas based on the user's specific emotions.

[0301] The emotion engine extracts features from audio data and classifies emotions from user utterances. This classification result influences the prioritization and scheduling of snow removal.

[0302] After that, the server sends the optimized snow removal route information to the snow removal vehicle, which is the terminal. The terminal performs snow removal operations in the automatic driving mode and sends back the progress status and sensor information to the server in real time. As a result, the snow removal plan is adjusted sequentially as needed.

[0303] Users can use the app to check the snow removal status and continuously provide feedback. This feedback is used in the next route optimization process together with the sentiment analysis results.

[0304] In this way, the present invention realizes an efficient snow removal operation considering the actual needs of the region and the emotions of users. By analyzing emotions, user satisfaction can be improved and the quality of the snow removal service can be enhanced.

[0305] The processing flow will be described below.

[0306] Step 1:

[0307] The server obtains real-time data from the weather API and regional sensors. This includes snowfall amount, temperature, wind speed, etc. The obtained data is saved in the database and prepared for analysis.

[0308] Step 2:

[0309] The server applies a machine learning model to the saved weather data to predict snow accumulation. This predicts which regions will have more snow accumulation in the future and serves as the basis for the snow removal plan.

[0310] Step 3:

[0311] Users send feedback by voice or text through the smartphone app. The app provides an interface for extracting emotions from the user's speech and input.

[0312] Step 4:

[0313] The server analyzes the received user feedback using an emotion engine. In the case of audio data, features are extracted from the audio, and an emotion classification algorithm is used to identify the user's emotions. The resulting emotion data is used to inform snow removal plans.

[0314] Step 5:

[0315] The server integrates predictive data from machine learning models with user sentiment data from an emotion engine to calculate the optimal snow removal route. This takes into account factors such as road importance, traffic volume, and the user's emotional needs.

[0316] Step 6:

[0317] The server transmits the calculated route information to the snowplow, which acts as the terminal. The terminal then performs snow removal automatically according to the specified route.

[0318] Step 7:

[0319] The terminal transmits sensor information and location data collected during operation to the server in real time. This allows the server to dynamically adjust routes and work content according to the situation.

[0320] Step 8:

[0321] Users can check the current snow removal status through the app and provide additional feedback. This feedback will be used to inform future analysis and planning.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0324] In autonomous snow removal operations, efficient and flexible route optimization is required, taking into account not only local snow conditions and weather, but also user emotions and feedback. However, conventional systems have difficulty effectively incorporating these elements, which may result in decreased user satisfaction. Furthermore, insufficient real-time monitoring and adjustment of work status can lead to problems in responding to sudden changes in conditions.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes information acquisition means for collecting weather information and snow depth information, data analysis means for analyzing the collected information and generating snow depth predictions using an AI model, and feedback processing means for analyzing sentiment data from users and adjusting priorities. This enables the setting of efficient snow removal routes that take into account snow depth conditions and user sentiment, as well as real-time monitoring and adjustment of work progress.

[0327] "Information acquisition means" refers to components for collecting weather information and snow depth information from sensors and web services and preparing them for the system.

[0328] "Data analysis methods" refer to the process of using a generative AI model to predict snowfall based on collected weather information and snow depth data.

[0329] "Route setting means" refers to a procedure for designing the optimal snow removal route based on the results of data analysis, thereby improving work efficiency.

[0330] A "feedback processing system" is a mechanism that analyzes emotional data and feedback provided by users and uses it to adjust the areas that should be prioritized for snow removal.

[0331] "Machine control means" refers to control technology for vehicles or machines that automatically perform snow removal work based on a set optimal route.

[0332] A "progress management system" is a function that monitors the progress of snow removal work and information from sensors in real time, and adjusts the plan as needed.

[0333] This invention is an advanced snow removal system that integrates autonomous driving technology with generative AI models. At the heart of this system is a server that collects weather information and snow depth information in real time and integrates it into a database. Specifically, it uses weather sensors and external weather APIs to accurately acquire the necessary information.

[0334] The server further analyzes the acquired information and uses generative AI models (e.g., machine learning algorithms such as LSTM and BERT) to predict snow conditions, and then designs the optimal snow removal route based on those predictions. This data analysis process makes snow removal operations more efficient.

[0335] Furthermore, users can provide feedback through a dedicated mobile application, sending emotional data via voice or text. This feedback is analyzed on a server and used to determine priority snow removal areas. Emotion recognition and natural language processing technologies are essential for feedback analysis. Specifically, multiple emotional states (e.g., reassurance, dissatisfaction) are automatically extracted and reflected in the system's route determination.

[0336] The optimized snow removal route information is transmitted to the snowplow, which acts as a terminal. The snowplow operates in autonomous driving mode based on this information, using LiDAR and GPS to recognize obstacles and conditions in real time as it works. During this process, the work status and data obtained from the sensors are continuously returned to the server, and the work plan is adjusted as needed.

[0337] For example, if a sudden increase in snowfall is predicted on a major road in a region, a user can send feedback such as, "I'm worried about the snow on the main road, so please clear it as soon as possible." The server analyzes this information using sentiment analysis and incorporates it into optimizing snow removal routes. In this way, rapid and flexible snow removal operations tailored to the specific circumstances of the region are realized.

[0338] An example of a prompt message might be: "Please explain how you will use user sentiment data to optimize the snow removal service."

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] The server collects weather information and snow depth information. Input is raw data obtained from weather sensors and weather APIs. This data undergoes consistency checks and preprocessing before being stored in an integrated database. Specific operations include sending information retrieval requests from weather APIs and periodic data polling from sensors. Output is weather and snow depth information converted into an analyzable format.

[0342] Step 2:

[0343] The server analyzes the collected data. The input is the data accumulated in Step 1. A generative AI model (such as BERT or LSTM) is used on this data to predict snow conditions. Specifically, it refers to past weather data and estimates future snowfall amounts using a machine learning algorithm. The output is the predicted snowfall data.

[0344] Step 3:

[0345] Users submit feedback using a smartphone app. Input is either voice or text feedback from the user. This feedback is sent to a server for sentiment analysis. Specifically, this involves converting speech to text using a speech recognition system and analyzing the sentiment using natural language processing. The output is the analyzed emotional state (e.g., reassured, dissatisfied).

[0346] Step 4:

[0347] The server optimizes snow removal routes based on sentiment analysis results. The inputs are the predicted data from step 2 and the sentiment state from step 3. This allows the server to determine priority snow removal areas and recalculate the route. Specifically, the server weights the sentiment analysis results and inputs them into the route optimization algorithm. The output is the optimized snow removal route.

[0348] Step 5:

[0349] The server sends optimized route information to the snowplow, which acts as the terminal. The input is the route information generated in step 4. Based on this, the snowplow starts operating in automatic driving mode. Specifically, it downloads route data using the vehicle's communication module and initializes the vehicle control system based on it. The output is the work command to be executed by the snowplow.

[0350] Step 6:

[0351] The terminal performs snow removal and sends progress and sensor information back to the server in real time. Input is various sensor data collected during snow removal. Specific operations include obstacle detection and position tracking using LiDAR and GPS devices, and recording of work progress. Output is progress and current route information stored in the database.

[0352] Step 7:

[0353] Users can check the snow removal status in real time through the app and provide additional feedback. The input is current work progress information provided by the server. Specifically, the user interface displays the current progress and a feedback input form. The output is the latest information on the snow removal status, which impacts user satisfaction.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0356] Conventional automated snow removal systems simply optimize routes and perform tasks based on weather data, and no system yet exists that can adjust to user emotions and feedback. Therefore, it has been difficult to contribute to improving resident satisfaction. Thus, the present invention aims to provide an efficient and flexible automated work system that reflects user emotions.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes data collection means for acquiring environmental information in real time, route setting means for analyzing the information obtained by the data collection means and optimizing the work route, machine control means for automatically performing work based on the generated optimal route, and emotion analysis means for analyzing user emotion information and dynamically adjusting the priority of snow removal work. This makes it possible to implement an optimal snow removal work plan that incorporates user emotions and feedback.

[0359] "Data collection methods" refer to technologies for acquiring environmental information and related data in real time.

[0360] "Route setting means" refers to a technology that optimizes and sets the work route based on acquired information.

[0361] "Machine control means" refers to technology for automatically performing tasks according to a set optimal path.

[0362] "Emotional analysis tools" are technologies that analyze users' emotional information and dynamically adjust task priorities and plans.

[0363] The system used to implement this application consists of a server, a terminal, and a user.

[0364] The server acquires weather data and related information through data collection mechanisms for collecting environmental information in real time. This data is efficiently processed using AWS Lambda, which operates on a cloud platform. Subsequently, a machine learning model using TensorFlow analyzes the acquired data and optimizes the workflow.

[0365] The terminal is equipped with machine control means for performing automated tasks. This allows the system to automatically continue tasks based on optimized path information. This control means utilizes sensing technology that can monitor progress and status in real time.

[0366] Furthermore, users can interact with the system through a smartphone app. This application uses Google Cloud Speech-to-Text to convert speech data into text and then supplies that data to an emotion analysis tool using NLTK. Users can check the progress of their work and easily provide emotion feedback through this application.

[0367] For example, if feedback is received in a certain area stating, "It's inconvenient because the street in front of my house hasn't been cleared of snow yet," this negative emotional data is read through an emotion analysis tool and reflected in the route planning tool. As a result, work in that area is prioritized for scheduling.

[0368] An example of a prompt for a generating AI model would be: "Three hours after the snowfall, dissatisfaction is increasing in the southern part of the city. Please suggest the optimal plan for which areas should be prioritized for snow removal." Such prompts allow the system to present an efficient work plan that takes people's emotions into account.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server collects environmental information, particularly weather data and snow cover information, in real time using data collection methods. The input to this process is data provided by various sensors and the internet, and the output is a set of raw data for analysis.

[0372] Step 2:

[0373] The server utilizes a generative AI model based on the collected data and executes an optimization algorithm using TensorFlow. This optimizes the work path. The input is the set of raw data obtained in step 1, and the output is optimized work path information. Specifically, it recalculates the path priority using predictive modeling.

[0374] Step 3:

[0375] The terminal receives optimized work path information transmitted from the server and automatically performs the work via machine control means. The input is work path information provided by the server, and the output is actual snow removal work data. The specific operation of the terminal is to perform snow removal work according to the path.

[0376] Step 4:

[0377] Users use a smartphone app to monitor their work status in real time and provide feedback via voice or text. Inputs include work status and feedback data, while output is user emotion information. Specific actions include inputting emotion feedback within the app.

[0378] Step 5:

[0379] The server uses Google Cloud Speech-to-Text and NLTK to convert user voice data into text and analyze it using sentiment analysis tools. Input is user voice and text feedback, and output is analyzed sentiment data. Specifically, it extracts features from the audio to facilitate analysis.

[0380] Step 6:

[0381] The server re-evaluates which areas should be prioritized for snow removal using prompt messages, based on the sentiment analysis results and the generative AI model, and adjusts the route priorities. The input is the sentiment analysis results and prompt messages, and the output is the adjusted priority route information. Dynamic adjustment of priority settings is performed in this step.

[0382] 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.

[0383] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0389] 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.

[0390] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0391] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0392] 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.

[0393] 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.

[0394] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The 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.

[0396] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0397] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0398] This invention relates to an automated snow removal system utilizing AI technology, and is implemented as follows: First, the server collects real-time weather information by integrating weather sensors and satellite data. This makes it possible to continuously monitor conditions such as snowfall and temperature.

[0399] Next, the server aggregates and analyzes the collected weather data and snow depth data from fixed sensors installed in the area. This analysis uses a predictive model based on machine learning, which can accurately predict future snow depth and weather changes. For example, it can predict the timing of the start and end of snowfall, making it possible to create an efficient snow removal schedule.

[0400] Based on the analysis results, the server optimizes the snow removal route by considering multiple parameters. It uses an algorithm that determines the most efficient route while prioritizing major roads and areas with heavy traffic. The server then transmits the optimized route information to the snowplow, which acts as a terminal.

[0401] The terminal performs snow removal in autonomous driving mode based on the received route information. Specifically, it uses the built-in GPS and various sensors to accurately determine its location as it moves. For example, it can sense the thickness of the snow on the road in real time and adjust the height and angle of the snow removal equipment as needed.

[0402] Furthermore, progress updates and vehicle status are transmitted to a server and used for further analysis and route adjustments. This enables flexible operations that can cope with unexpected weather changes and obstacles.

[0403] Users can use a dedicated smartphone app to check snow removal information and provide feedback. This allows local residents to understand the progress of snow removal work in real time and to communicate their requests. The feedback will be reflected in the next snow removal plan.

[0404] As described above, the present invention can realize efficient and sustainable snow removal operations and address the problem of labor shortages.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The server obtains the latest weather data in real time via a weather API, including snowfall, temperature, and wind speed. It also periodically collects snow depth data from installed sensors.

[0408] Step 2:

[0409] The server stores the collected weather and snow depth data in a database and then performs preprocessing to remove outliers and noise. This improves the reliability of the data.

[0410] Step 3:

[0411] The server uses a trained machine learning model to predict future weather conditions and snow cover patterns. Based on the prediction results, it creates snow cover forecast maps for each region.

[0412] Step 4:

[0413] The server optimizes snow removal routes based on multiple factors (e.g., road importance, traffic volume), taking into account predictive maps and traffic information. The optimal route is calculated by an algorithm.

[0414] Step 5:

[0415] The server transmits optimized route information to the snowplow terminal, which includes specific geographical information and snow removal patterns.

[0416] Step 6:

[0417] The terminal (snowplow) automatically performs snow removal work while using its onboard GPS and sensors to precisely control its position according to the received route information.

[0418] Step 7:

[0419] The terminal transmits information such as the progress of the work and the presence of obstacles to the server in real time. This allows the server to dynamically adjust the route and work pattern as needed.

[0420] Step 8:

[0421] Users can check the snow removal status and provide feedback through a dedicated app. The server uses the collected feedback to optimize the next route.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] Automated snow removal systems require the design of efficient snow removal routes that can quickly respond to changes in snowfall and traffic volume, as well as flexible system operation that reflects real-time feedback from users. However, existing systems often lack sufficient collection and analysis of weather data, resulting in lengthy route optimization processes. Furthermore, the lack of mechanisms to effectively utilize user information leads to a decrease in overall work efficiency.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes an information acquisition means for collecting weather data, a data analysis means for integrating the data obtained from the information acquisition means and analyzing it using a machine learning model, and a route communication means for designing high-priority routes based on the analysis results and transmitting route information to an autonomous vehicle via a communication means. This enables flexible response to real-time weather changes and efficient and safe snow removal operations that reflect user feedback.

[0427] "Meteorological data" refers to information about weather conditions such as temperature, precipitation, wind speed, and humidity, and is used to determine weather conditions.

[0428] "Information acquisition means" refers to devices or systems for collecting weather data and other necessary data, and this includes sensors and data provision services.

[0429] "Data analysis tools" refer to systems that integrate collected data, analyze it using machine learning models and statistical methods, and extract information for prediction and optimization.

[0430] A "machine learning model" is a type of algorithm used for data analysis that learns from past data to make predictions and classifications about unknown data.

[0431] "Route communication means" refers to a communication system or device for transmitting route data, determined based on analyzed information, to an autonomous vehicle.

[0432] An "autonomous vehicle" refers to a vehicle equipped with the ability to automatically operate according to received route information, and is used for snow removal work.

[0433] A "location tracking device" is a device used to measure the current position of a vehicle or object with high precision, and refers to devices that utilize technologies such as GPS.

[0434] A "surrounding object recognition device" refers to a sensor system that detects objects present around a vehicle and recognizes their position and shape.

[0435] This invention is an automated snow removal system utilizing AI technology, which achieves efficient snow removal by collecting and analyzing weather data. The system consists of three main components: a server, an automated vehicle (the terminal), and a user interface.

[0436] The server first collects weather data obtained from weather sensors and data provision services using information acquisition methods. In this process, comprehensive data including temperature, precipitation, and wind speed at each location is acquired, specifically via APIs.

[0437] Next, the server processes the collected weather data using data analysis tools. Specifically, it analyzes the data using programming languages ​​such as Python and machine learning frameworks such as Scikit-learn and TensorFlow. This analysis predicts future snowfall patterns and temperature changes, and designs the optimal snow removal route.

[0438] The designed route is transmitted to the autonomous vehicle, which acts as the terminal, via a route communication system. This communication utilizes the latest high-speed communication technology, and the vehicle performs autonomous driving based on the received information. The terminal is equipped with a GPS module and LiDAR sensors, which are used to monitor the vehicle's current location and surrounding conditions in real time. For example, the LiDAR sensor can detect the thickness of snow during snow removal, and the device height can be automatically adjusted for efficient snow removal.

[0439] Users can use a dedicated application to operate this system to check the progress of snow removal and the current location of vehicles. Users can also provide feedback via their smartphones, using text input such as prompts like "Please tell me the progress of snow removal." This feedback is collected by the server and used to optimize future snow removal plans.

[0440] As described above, the system of the present invention enables real-time analysis of weather data and agile vehicle control, supporting efficient and flexible snow removal operations. This is expected to significantly improve the efficiency of snow damage countermeasures in local communities.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server collects weather data in real time from weather sensors and data provision services. It receives data such as temperature, precipitation, and wind speed at various locations via APIs as input. This data is stored in a database, and missing values ​​are imputed by cleaning the sensor data. This process outputs a clean and reliable weather dataset.

[0444] Step 2:

[0445] The server analyzes the collected weather data using a machine learning model. The weather data obtained in step 1 is supplied to the model as input. Specifically, it utilizes frameworks such as TensorFlow and Scikit-learn to predict snowfall patterns and temperature fluctuations through analysis. The output of this analysis is predictive information for optimizing future snow removal routes.

[0446] Step 3:

[0447] The server designs the optimal snow removal route based on the forecast information. Using the forecast information obtained in step 2 as input, it considers traffic volume and road priority using the Google Maps API, etc. This process calculates the shortest route using algorithms such as the A algorithm and outputs optimized route information.

[0448] Step 4:

[0449] The server transmits optimized route information to the autonomous vehicle, which is the terminal, using a communication method. As input, the route information generated in step 3 is prepared and transmitted to the vehicle via 5G communication. Through this communication, the terminal receives instructions to start snow removal work.

[0450] Step 5:

[0451] The terminal starts autonomous driving based on the received route information. The route information received in step 4 is used as input. The GPS module and LiDAR sensor mounted on the vehicle are activated to accurately determine its own position and surrounding environment as it proceeds. During snow removal, the snow thickness is detected by sensors, and the settings of the snow removal equipment are adjusted as needed. As output, data on the progress of the snow removal work and the status of the vehicle are generated in real time.

[0452] Step 6:

[0453] The terminal sends progress and vehicle status to the server. As input, it sends progress and vehicle data generated in step 5 to the server. The server analyzes this data and outputs information to issue new instructions as needed, and in emergencies, it issues immediate rerouting instructions.

[0454] Step 7:

[0455] Users can use a smartphone app to check the progress of snow removal and provide feedback. Input involves viewing real-time data displayed on the app. Feedback is also provided by sending prompt messages such as "Please tell me the progress of snow removal." This information is then sent to the server, and a response indicating that it will be reflected in the next snow removal plan is output.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] This invention aims to maintain the smooth flow of urban life by combining real-time collection and analysis of weather information, generation of optimized snow removal routes, automated snow removal operations, and user feedback, in order to carry out snow removal work efficiently and effectively. However, with previous technologies, it was difficult to respond to sudden changes in weather and to quickly reflect user feedback, which limited the realization of efficient snow removal plans.

[0459] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0460] In this invention, the server includes data acquisition means for acquiring weather data in real time, route optimization means for analyzing the information obtained by the data acquisition means and optimizing the removal route, equipment control means for automatically performing removal work based on the generated optimal route, and feedback processing means for collecting information from local residents and reflecting it in the next operation. This makes it possible to respond flexibly to rapid weather changes and to efficiently feed back the opinions of local residents into the snow removal plan.

[0461] A "data acquisition method for obtaining weather data in real time" refers to a function that collects the latest weather information moment by moment and uses it for analysis and decision-making within the system.

[0462] "Route optimization means that analyzes information obtained by the data acquisition means to optimize the removal route" refers to a method for processing collected weather data and calculating the optimal route for efficient removal work.

[0463] "Equipment control means that automatically performs removal work based on the generated optimal route" refers to a function that performs operations to mechanically and automatically carry out removal work based on optimized route information.

[0464] A "feedback processing method for collecting information from local residents and reflecting it in future work" is an information processing method that incorporates opinions and requests from residents and utilizes them in future planning.

[0465] The embodiments for carrying out this invention include real-time acquisition of weather data, optimization of removal routes based on data analysis, automated removal work, and incorporation of feedback from local residents.

[0466] The server utilizes cloud services such as AWS to collect information from weather sensors and satellite data in real time. This data is analyzed by machine learning models written in Python (e.g., using TensorFlow or PyTorch) to generate the optimal snow removal route. Google Maps API and other tools can be used for route optimization.

[0467] The snowplows, which serve as terminals, are equipped with GPS devices and various sensors (such as LiDAR and camera sensors), and automatically perform snow removal work based on the optimal route generated by the server. This reduces the amount of manpower required on site and enables more efficient work. A smartphone app with real-time feedback functionality is developed using React Native, allowing users to send information, and this feedback is used to plan future operations.

[0468] As a concrete example, the administrators of a certain city can use an app to monitor the progress of snow removal and check for urgent requests from residents. This enables a more efficient response. An example of a prompt from the generated AI model would be: "Please tell me how to check the recent snow removal status in my area and report any problems. Also, please explain how this will be reflected in the next snow removal plan."

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The server collects weather information in real time from weather sensors and satellite data. Input data includes information such as temperature, snowfall, and wind speed received from various sensors. Output data includes integrated real-time data of this weather information. In this process, data is retrieved from various weather information providers via APIs to create an integrated dataset.

[0472] Step 2:

[0473] The server uses a machine learning model to predict future snowfall and weather changes based on collected real-time weather data. In this step, the input is the integrated weather data collected in step 1. For data processing, machine learning algorithms are used to make short-term and medium-term weather forecasts. The output is information such as predicted snowfall and snowfall timing. This information is used in subsequent steps.

[0474] Step 3:

[0475] The server generates the optimal snow removal route based on predicted weather data. The input data is the weather information predicted in step 2. Here, a route optimization algorithm is used to calculate the route, taking traffic data into consideration. The output is route information that allows for efficient and rapid snow removal. Specifically, the route is determined prioritizing major roads and areas with heavy traffic.

[0476] Step 4:

[0477] The snowplow, acting as the terminal, begins snow removal work automatically based on optimal route information transmitted from the server. The input is the optimal route information generated in step 3. As output, actual snow removal progress information is sent to the server. Throughout this process, the vehicle's position is constantly monitored using a GPS device, and the work proceeds while monitoring snow conditions in real time with sensors.

[0478] Step 5:

[0479] Users check the progress of snow removal and provide feedback through a smartphone app. The input information is the actual data accumulated on the server in step 4. User feedback is collected through the app and reflected in the next plan. The output is information on adjusting routes and work schedules based on the feedback. For example, a user can request areas where snow removal is insufficient through the app, and that information will be incorporated into the next snow removal plan.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] This invention relates to an automated snow removal system that combines AI technology with emotion analysis. Specific embodiments are described below.

[0482] First, the server collects weather data and snow depth data in real time and stores it in a database. Next, it analyzes the collected data and uses a machine learning model to predict snow depth. The predicted data is used to optimize snow removal routes.

[0483] Next, the server also uses user feedback and emotional data for analysis. Users can provide feedback via voice or text through a smartphone app. This information is analyzed by the emotion engine, and emotional states (e.g., disillusionment, relief, dissatisfaction) are extracted. This allows for adjustments to priority snow removal areas based on the user's specific emotions.

[0484] The emotion engine extracts features from audio data and classifies emotions from user utterances. This classification result influences the prioritization and scheduling of snow removal.

[0485] Subsequently, the server transmits optimized snow removal route information to the snowplow, which acts as the terminal. The terminal performs snow removal in autonomous driving mode, sending progress and sensor information back to the server in real time. This allows the snow removal plan to be adjusted sequentially as needed.

[0486] Users can use the app to check the snow removal status and provide ongoing feedback. This feedback, along with sentiment analysis results, is used in the next route optimization process.

[0487] In this way, the present invention realizes efficient snow removal operations that take into account the actual needs of the region and the emotions of the users. By analyzing emotions, it is possible to improve user satisfaction and enhance the quality of snow removal services.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The server retrieves real-time data from weather APIs and local sensors. This includes snowfall, temperature, and wind speed. The obtained data is stored in a database and prepared for analysis.

[0491] Step 2:

[0492] The server applies machine learning models to stored weather data to predict snowfall. This allows it to predict which areas will experience heavy snowfall in the future, forming the basis for snow removal planning.

[0493] Step 3:

[0494] Users send voice or text feedback through a smartphone app. The app provides an interface for extracting emotions from the user's speech and input.

[0495] Step 4:

[0496] The server analyzes the received user feedback using an emotion engine. In the case of audio data, features are extracted from the audio, and an emotion classification algorithm is used to identify the user's emotions. The resulting emotion data is used to inform snow removal plans.

[0497] Step 5:

[0498] The server integrates predictive data from machine learning models with user sentiment data from an emotion engine to calculate the optimal snow removal route. This takes into account factors such as road importance, traffic volume, and the user's emotional needs.

[0499] Step 6:

[0500] The server transmits the calculated route information to the snowplow, which acts as the terminal. The terminal then performs snow removal automatically according to the specified route.

[0501] Step 7:

[0502] The terminal transmits sensor information and location data collected during operation to the server in real time. This allows the server to dynamically adjust routes and work content according to the situation.

[0503] Step 8:

[0504] Users can check the current snow removal status through the app and provide additional feedback. This feedback will be used to inform future analysis and planning.

[0505] (Example 2)

[0506] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0507] In autonomous snow removal operations, efficient and flexible route optimization is required, taking into account not only local snow conditions and weather, but also user emotions and feedback. However, conventional systems have difficulty effectively incorporating these elements, which may result in decreased user satisfaction. Furthermore, insufficient real-time monitoring and adjustment of work status can lead to problems in responding to sudden changes in conditions.

[0508] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0509] In this invention, the server includes information acquisition means for collecting weather information and snow depth information, data analysis means for analyzing the collected information and generating snow depth predictions using an AI model, and feedback processing means for analyzing sentiment data from users and adjusting priorities. This enables the setting of efficient snow removal routes that take into account snow depth conditions and user sentiment, as well as real-time monitoring and adjustment of work progress.

[0510] "Information acquisition means" refers to components for collecting weather information and snow depth information from sensors and web services and preparing them for the system.

[0511] "Data analysis methods" refer to the process of using a generative AI model to predict snowfall based on collected weather information and snow depth data.

[0512] "Route setting means" refers to a procedure for designing the optimal snow removal route based on the results of data analysis, thereby improving work efficiency.

[0513] A "feedback processing system" is a mechanism that analyzes emotional data and feedback provided by users and uses it to adjust the areas that should be prioritized for snow removal.

[0514] "Machine control means" refers to control technology for vehicles or machines that automatically perform snow removal work based on a set optimal route.

[0515] A "progress management system" is a function that monitors the progress of snow removal work and information from sensors in real time, and adjusts the plan as needed.

[0516] This invention is an advanced snow removal system that integrates autonomous driving technology with generative AI models. At the heart of this system is a server that collects weather information and snow depth information in real time and integrates it into a database. Specifically, it uses weather sensors and external weather APIs to accurately acquire the necessary information.

[0517] The server further analyzes the acquired information and uses generative AI models (e.g., machine learning algorithms such as LSTM and BERT) to predict snow conditions, and then designs the optimal snow removal route based on those predictions. This data analysis process makes snow removal operations more efficient.

[0518] Furthermore, users can provide feedback through a dedicated mobile application, sending emotional data via voice or text. This feedback is analyzed on a server and used to determine priority snow removal areas. Emotion recognition and natural language processing technologies are essential for feedback analysis. Specifically, multiple emotional states (e.g., reassurance, dissatisfaction) are automatically extracted and reflected in the system's route determination.

[0519] The optimized snow removal route information is transmitted to the snowplow, which acts as a terminal. The snowplow operates in autonomous driving mode based on this information, using LiDAR and GPS to recognize obstacles and conditions in real time as it works. During this process, the work status and data obtained from the sensors are continuously returned to the server, and the work plan is adjusted as needed.

[0520] For example, if a sudden increase in snowfall is predicted on a major road in a region, a user can send feedback such as, "I'm worried about the snow on the main road, so please clear it as soon as possible." The server analyzes this information using sentiment analysis and incorporates it into optimizing snow removal routes. In this way, rapid and flexible snow removal operations tailored to the specific circumstances of the region are realized.

[0521] An example of a prompt message might be: "Please explain how you will use user sentiment data to optimize the snow removal service."

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The server collects weather information and snow depth information. Input is raw data obtained from weather sensors and weather APIs. This data undergoes consistency checks and preprocessing before being stored in an integrated database. Specific operations include sending information retrieval requests from weather APIs and periodic data polling from sensors. Output is weather and snow depth information converted into an analyzable format.

[0525] Step 2:

[0526] The server analyzes the collected data. The input is the data accumulated in Step 1. A generative AI model (such as BERT or LSTM) is used on this data to predict snow conditions. Specifically, it refers to past weather data and estimates future snowfall amounts using a machine learning algorithm. The output is the predicted snowfall data.

[0527] Step 3:

[0528] Users submit feedback using a smartphone app. Input is either voice or text feedback from the user. This feedback is sent to a server for sentiment analysis. Specifically, this involves converting speech to text using a speech recognition system and analyzing the sentiment using natural language processing. The output is the analyzed emotional state (e.g., reassured, dissatisfied).

[0529] Step 4:

[0530] The server optimizes snow removal routes based on sentiment analysis results. The inputs are the predicted data from step 2 and the sentiment state from step 3. This allows the server to determine priority snow removal areas and recalculate the route. Specifically, the server weights the sentiment analysis results and inputs them into the route optimization algorithm. The output is the optimized snow removal route.

[0531] Step 5:

[0532] The server sends optimized route information to the snowplow, which acts as the terminal. The input is the route information generated in step 4. Based on this, the snowplow starts operating in automatic driving mode. Specifically, it downloads route data using the vehicle's communication module and initializes the vehicle control system based on it. The output is the work command to be executed by the snowplow.

[0533] Step 6:

[0534] The terminal performs snow removal and sends progress and sensor information back to the server in real time. Input is various sensor data collected during snow removal. Specific operations include obstacle detection and position tracking using LiDAR and GPS devices, and recording of work progress. Output is progress and current route information stored in the database.

[0535] Step 7:

[0536] Users can check the snow removal status in real time through the app and provide additional feedback. The input is current work progress information provided by the server. Specifically, the user interface displays the current progress and a feedback input form. The output is the latest information on the snow removal status, which impacts user satisfaction.

[0537] (Application Example 2)

[0538] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] Conventional automated snow removal systems simply optimize routes and perform tasks based on weather data, and no system yet exists that can adjust to user emotions and feedback. Therefore, it has been difficult to contribute to improving resident satisfaction. Thus, the present invention aims to provide an efficient and flexible automated work system that reflects user emotions.

[0540] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0541] In this invention, the server includes data collection means for acquiring environmental information in real time, route setting means for analyzing the information obtained by the data collection means and optimizing the work route, machine control means for automatically performing work based on the generated optimal route, and emotion analysis means for analyzing user emotion information and dynamically adjusting the priority of snow removal work. This makes it possible to implement an optimal snow removal work plan that incorporates user emotions and feedback.

[0542] "Data collection methods" refer to technologies for acquiring environmental information and related data in real time.

[0543] "Route setting means" refers to a technology that optimizes and sets the work route based on acquired information.

[0544] "Machine control means" refers to technology for automatically performing tasks according to a set optimal path.

[0545] "Emotional analysis tools" are technologies that analyze users' emotional information and dynamically adjust task priorities and plans.

[0546] The system used to implement this application consists of a server, a terminal, and a user.

[0547] The server acquires weather data and related information through data collection mechanisms for collecting environmental information in real time. This data is efficiently processed using AWS Lambda, which operates on a cloud platform. Subsequently, a machine learning model using TensorFlow analyzes the acquired data and optimizes the workflow.

[0548] The terminal is equipped with machine control means for performing automated tasks. This allows the system to automatically continue tasks based on optimized path information. This control means utilizes sensing technology that can monitor progress and status in real time.

[0549] Furthermore, users can interact with the system through a smartphone app. This application uses Google Cloud Speech-to-Text to convert speech data into text and then supplies that data to an emotion analysis tool using NLTK. Users can check the progress of their work and easily provide emotion feedback through this application.

[0550] For example, if feedback is received in a certain area stating, "It's inconvenient because the street in front of my house hasn't been cleared of snow yet," this negative emotional data is read through an emotion analysis tool and reflected in the route planning tool. As a result, work in that area is prioritized for scheduling.

[0551] An example of a prompt for a generating AI model would be: "Three hours after the snowfall, dissatisfaction is increasing in the southern part of the city. Please suggest the optimal plan for which areas should be prioritized for snow removal." Such prompts allow the system to present an efficient work plan that takes people's emotions into account.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The server collects environmental information, particularly weather data and snow cover information, in real time using data collection methods. The input to this process is data provided by various sensors and the internet, and the output is a set of raw data for analysis.

[0555] Step 2:

[0556] The server utilizes a generative AI model based on the collected data and executes an optimization algorithm using TensorFlow. This optimizes the work path. The input is the set of raw data obtained in step 1, and the output is optimized work path information. Specifically, it recalculates the path priority using predictive modeling.

[0557] Step 3:

[0558] The terminal receives optimized work path information transmitted from the server and automatically performs the work via machine control means. The input is work path information provided by the server, and the output is actual snow removal work data. The specific operation of the terminal is to perform snow removal work according to the path.

[0559] Step 4:

[0560] Users use a smartphone app to monitor their work status in real time and provide feedback via voice or text. Inputs include work status and feedback data, while output is user emotion information. Specific actions include inputting emotion feedback within the app.

[0561] Step 5:

[0562] The server uses Google Cloud Speech-to-Text and NLTK to convert user voice data into text and analyze it using sentiment analysis tools. Input is user voice and text feedback, and output is analyzed sentiment data. Specifically, it extracts features from the audio to facilitate analysis.

[0563] Step 6:

[0564] The server re-evaluates which areas should be prioritized for snow removal using prompt messages, based on the sentiment analysis results and the generative AI model, and adjusts the route priorities. The input is the sentiment analysis results and prompt messages, and the output is the adjusted priority route information. Dynamic adjustment of priority settings is performed in this step.

[0565] 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.

[0566] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0567] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0572] 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.

[0573] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0574] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0575] 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.

[0576] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0577] 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.

[0578] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0579] The 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.

[0580] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0581] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] This invention relates to an automated snow removal system utilizing AI technology, and is implemented as follows: First, the server collects real-time weather information by integrating weather sensors and satellite data. This makes it possible to continuously monitor conditions such as snowfall and temperature.

[0583] Next, the server aggregates and analyzes the collected weather data and snow depth data from fixed sensors installed in the area. This analysis uses a predictive model based on machine learning, which can accurately predict future snow depth and weather changes. For example, it can predict the timing of the start and end of snowfall, making it possible to create an efficient snow removal schedule.

[0584] Based on the analysis results, the server optimizes the snow removal route by considering multiple parameters. It uses an algorithm that determines the most efficient route while prioritizing major roads and areas with heavy traffic. The server then transmits the optimized route information to the snowplow, which acts as a terminal.

[0585] The terminal performs snow removal in autonomous driving mode based on the received route information. Specifically, it uses the built-in GPS and various sensors to accurately determine its location as it moves. For example, it can sense the thickness of the snow on the road in real time and adjust the height and angle of the snow removal equipment as needed.

[0586] Furthermore, progress updates and vehicle status are transmitted to a server and used for further analysis and route adjustments. This enables flexible operations that can cope with unexpected weather changes and obstacles.

[0587] Users can use a dedicated smartphone app to check snow removal information and provide feedback. This allows local residents to understand the progress of snow removal work in real time and to communicate their requests. The feedback will be reflected in the next snow removal plan.

[0588] As described above, the present invention can realize efficient and sustainable snow removal operations and address the problem of labor shortages.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server obtains the latest weather data in real time via a weather API, including snowfall, temperature, and wind speed. It also periodically collects snow depth data from installed sensors.

[0592] Step 2:

[0593] The server stores the collected weather and snow depth data in a database and then performs preprocessing to remove outliers and noise. This improves the reliability of the data.

[0594] Step 3:

[0595] The server uses a trained machine learning model to predict future weather conditions and snow cover patterns. Based on the prediction results, it creates snow cover forecast maps for each region.

[0596] Step 4:

[0597] The server optimizes snow removal routes based on multiple factors (e.g., road importance, traffic volume), taking into account predictive maps and traffic information. The optimal route is calculated by an algorithm.

[0598] Step 5:

[0599] The server transmits optimized route information to the snowplow terminal, which includes specific geographical information and snow removal patterns.

[0600] Step 6:

[0601] The terminal (snowplow) automatically performs snow removal work while using its onboard GPS and sensors to precisely control its position according to the received route information.

[0602] Step 7:

[0603] The terminal transmits information such as the progress of the work and the presence of obstacles to the server in real time. This allows the server to dynamically adjust the route and work pattern as needed.

[0604] Step 8:

[0605] Users can check the snow removal status and provide feedback through a dedicated app. The server uses the collected feedback to optimize the next route.

[0606] (Example 1)

[0607] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] Automated snow removal systems require the design of efficient snow removal routes that can quickly respond to changes in snowfall and traffic volume, as well as flexible system operation that reflects real-time feedback from users. However, existing systems often lack sufficient collection and analysis of weather data, resulting in lengthy route optimization processes. Furthermore, the lack of mechanisms to effectively utilize user information leads to a decrease in overall work efficiency.

[0609] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0610] In this invention, the server includes an information acquisition means for collecting weather data, a data analysis means for integrating the data obtained from the information acquisition means and analyzing it using a machine learning model, and a route communication means for designing high-priority routes based on the analysis results and transmitting route information to an autonomous vehicle via a communication means. This enables flexible response to real-time weather changes and efficient and safe snow removal operations that reflect user feedback.

[0611] "Meteorological data" refers to information about weather conditions such as temperature, precipitation, wind speed, and humidity, and is used to determine weather conditions.

[0612] "Information acquisition means" refers to devices or systems for collecting weather data and other necessary data, and this includes sensors and data provision services.

[0613] "Data analysis tools" refer to systems that integrate collected data, analyze it using machine learning models and statistical methods, and extract information for prediction and optimization.

[0614] A "machine learning model" is a type of algorithm used for data analysis that learns from past data to make predictions and classifications about unknown data.

[0615] "Route communication means" refers to a communication system or device for transmitting route data, determined based on analyzed information, to an autonomous vehicle.

[0616] An "autonomous vehicle" refers to a vehicle equipped with the ability to automatically operate according to received route information, and is used for snow removal work.

[0617] A "location tracking device" is a device used to measure the current position of a vehicle or object with high precision, and refers to devices that utilize technologies such as GPS.

[0618] A "surrounding object recognition device" refers to a sensor system that detects objects present around a vehicle and recognizes their position and shape.

[0619] This invention is an automated snow removal system utilizing AI technology, which achieves efficient snow removal by collecting and analyzing weather data. The system consists of three main components: a server, an automated vehicle (the terminal), and a user interface.

[0620] The server first collects weather data obtained from weather sensors and data provision services using information acquisition methods. In this process, comprehensive data including temperature, precipitation, and wind speed at each location is acquired, specifically via APIs.

[0621] Next, the server processes the collected weather data using data analysis tools. Specifically, it analyzes the data using programming languages ​​such as Python and machine learning frameworks such as Scikit-learn and TensorFlow. This analysis predicts future snowfall patterns and temperature changes, and designs the optimal snow removal route.

[0622] The designed route is transmitted to the autonomous vehicle, which acts as the terminal, via a route communication system. This communication utilizes the latest high-speed communication technology, and the vehicle performs autonomous driving based on the received information. The terminal is equipped with a GPS module and LiDAR sensors, which are used to monitor the vehicle's current location and surrounding conditions in real time. For example, the LiDAR sensor can detect the thickness of snow during snow removal, and the device height can be automatically adjusted for efficient snow removal.

[0623] Users can use a dedicated application to operate this system to check the progress of snow removal and the current location of vehicles. Users can also provide feedback via their smartphones, using text input such as prompts like "Please tell me the progress of snow removal." This feedback is collected by the server and used to optimize future snow removal plans.

[0624] As described above, the system of the present invention enables real-time analysis of weather data and agile vehicle control, supporting efficient and flexible snow removal operations. This is expected to significantly improve the efficiency of snow damage countermeasures in local communities.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server collects weather data in real time from weather sensors and data provision services. It receives data such as temperature, precipitation, and wind speed at various locations via APIs as input. This data is stored in a database, and missing values ​​are imputed by cleaning the sensor data. This process outputs a clean and reliable weather dataset.

[0628] Step 2:

[0629] The server analyzes the collected weather data using a machine learning model. The weather data obtained in step 1 is supplied to the model as input. Specifically, it utilizes frameworks such as TensorFlow and Scikit-learn to predict snowfall patterns and temperature fluctuations through analysis. The output of this analysis is predictive information for optimizing future snow removal routes.

[0630] Step 3:

[0631] The server designs the optimal snow removal route based on the forecast information. Using the forecast information obtained in step 2 as input, it considers traffic volume and road priority using the Google Maps API, etc. This process calculates the shortest route using algorithms such as the A algorithm and outputs optimized route information.

[0632] Step 4:

[0633] The server transmits optimized route information to the autonomous vehicle, which is the terminal, using a communication method. As input, the route information generated in step 3 is prepared and transmitted to the vehicle via 5G communication. Through this communication, the terminal receives instructions to start snow removal work.

[0634] Step 5:

[0635] The terminal starts autonomous driving based on the received route information. The route information received in step 4 is used as input. The GPS module and LiDAR sensor mounted on the vehicle are activated to accurately determine its own position and surrounding environment as it proceeds. During snow removal, the snow thickness is detected by sensors, and the settings of the snow removal equipment are adjusted as needed. As output, data on the progress of the snow removal work and the status of the vehicle are generated in real time.

[0636] Step 6:

[0637] The terminal sends progress and vehicle status to the server. As input, it sends progress and vehicle data generated in step 5 to the server. The server analyzes this data and outputs information to issue new instructions as needed, and in emergencies, it issues immediate rerouting instructions.

[0638] Step 7:

[0639] Users can use a smartphone app to check the progress of snow removal and provide feedback. Input involves viewing real-time data displayed on the app. Feedback is also provided by sending prompt messages such as "Please tell me the progress of snow removal." This information is then sent to the server, and a response indicating that it will be reflected in the next snow removal plan is output.

[0640] (Application Example 1)

[0641] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] This invention aims to maintain the smooth flow of urban life by combining real-time collection and analysis of weather information, generation of optimized snow removal routes, automated snow removal operations, and user feedback, in order to carry out snow removal work efficiently and effectively. However, with previous technologies, it was difficult to respond to sudden changes in weather and to quickly reflect user feedback, which limited the realization of efficient snow removal plans.

[0643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0644] In this invention, the server includes data acquisition means for acquiring weather data in real time, route optimization means for analyzing the information obtained by the data acquisition means and optimizing the removal route, equipment control means for automatically performing removal work based on the generated optimal route, and feedback processing means for collecting information from local residents and reflecting it in the next operation. This makes it possible to respond flexibly to rapid weather changes and to efficiently feed back the opinions of local residents into the snow removal plan.

[0645] A "data acquisition method for obtaining weather data in real time" refers to a function that collects the latest weather information moment by moment and uses it for analysis and decision-making within the system.

[0646] "Route optimization means that analyzes information obtained by the data acquisition means to optimize the removal route" refers to a method for processing collected weather data and calculating the optimal route for efficient removal work.

[0647] "Equipment control means that automatically performs removal work based on the generated optimal route" refers to a function that performs operations to mechanically and automatically carry out removal work based on optimized route information.

[0648] A "feedback processing method for collecting information from local residents and reflecting it in future work" is an information processing method that incorporates opinions and requests from residents and utilizes them in future planning.

[0649] The embodiments for carrying out this invention include real-time acquisition of weather data, optimization of removal routes based on data analysis, automated removal work, and incorporation of feedback from local residents.

[0650] The server utilizes cloud services such as AWS to collect information from weather sensors and satellite data in real time. This data is analyzed by machine learning models written in Python (e.g., using TensorFlow or PyTorch) to generate the optimal snow removal route. Google Maps API and other tools can be used for route optimization.

[0651] The snowplows, which serve as terminals, are equipped with GPS devices and various sensors (such as LiDAR and camera sensors), and automatically perform snow removal work based on the optimal route generated by the server. This reduces the amount of manpower required on site and enables more efficient work. A smartphone app with real-time feedback functionality is developed using React Native, allowing users to send information, and this feedback is used to plan future operations.

[0652] As a concrete example, the administrators of a certain city can use an app to monitor the progress of snow removal and check for urgent requests from residents. This enables a more efficient response. An example of a prompt from the generated AI model would be: "Please tell me how to check the recent snow removal status in my area and report any problems. Also, please explain how this will be reflected in the next snow removal plan."

[0653] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0654] Step 1:

[0655] The server collects weather information in real time from weather sensors and satellite data. Input data includes information such as temperature, snowfall, and wind speed received from various sensors. Output data includes integrated real-time data of this weather information. In this process, data is retrieved from various weather information providers via APIs to create an integrated dataset.

[0656] Step 2:

[0657] The server uses a machine learning model to predict future snowfall and weather changes based on collected real-time weather data. In this step, the input is the integrated weather data collected in step 1. For data processing, machine learning algorithms are used to make short-term and medium-term weather forecasts. The output is information such as predicted snowfall and snowfall timing. This information is used in subsequent steps.

[0658] Step 3:

[0659] The server generates the optimal snow removal route based on predicted weather data. The input data is the weather information predicted in step 2. Here, a route optimization algorithm is used to calculate the route, taking traffic data into consideration. The output is route information that allows for efficient and rapid snow removal. Specifically, the route is determined prioritizing major roads and areas with heavy traffic.

[0660] Step 4:

[0661] The snowplow, acting as the terminal, begins snow removal work automatically based on optimal route information transmitted from the server. The input is the optimal route information generated in step 3. As output, actual snow removal progress information is sent to the server. Throughout this process, the vehicle's position is constantly monitored using a GPS device, and the work proceeds while monitoring snow conditions in real time with sensors.

[0662] Step 5:

[0663] Users check the progress of snow removal and provide feedback through a smartphone app. The input information is the actual data accumulated on the server in step 4. User feedback is collected through the app and reflected in the next plan. The output is information on adjusting routes and work schedules based on the feedback. For example, a user can request areas where snow removal is insufficient through the app, and that information will be incorporated into the next snow removal plan.

[0664] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0665] This invention relates to an automated snow removal system that combines AI technology with emotion analysis. Specific embodiments are described below.

[0666] First, the server collects weather data and snow depth data in real time and stores it in a database. Next, it analyzes the collected data and uses a machine learning model to predict snow depth. The predicted data is used to optimize snow removal routes.

[0667] Next, the server also uses user feedback and emotional data for analysis. Users can provide feedback via voice or text through a smartphone app. This information is analyzed by the emotion engine, and emotional states (e.g., disillusionment, relief, dissatisfaction) are extracted. This allows for adjustments to priority snow removal areas based on the user's specific emotions.

[0668] The emotion engine extracts features from audio data and classifies emotions from user utterances. This classification result influences the prioritization and scheduling of snow removal.

[0669] Subsequently, the server transmits optimized snow removal route information to the snowplow, which acts as the terminal. The terminal performs snow removal in autonomous driving mode, sending progress and sensor information back to the server in real time. This allows the snow removal plan to be adjusted sequentially as needed.

[0670] Users can use the app to check the snow removal status and provide ongoing feedback. This feedback, along with sentiment analysis results, is used in the next route optimization process.

[0671] In this way, the present invention realizes efficient snow removal operations that take into account the actual needs of the region and the emotions of the users. By analyzing emotions, it is possible to improve user satisfaction and enhance the quality of snow removal services.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server retrieves real-time data from weather APIs and local sensors. This includes snowfall, temperature, and wind speed. The obtained data is stored in a database and prepared for analysis.

[0675] Step 2:

[0676] The server applies machine learning models to stored weather data to predict snowfall. This allows it to predict which areas will experience heavy snowfall in the future, forming the basis for snow removal planning.

[0677] Step 3:

[0678] Users send voice or text feedback through a smartphone app. The app provides an interface for extracting emotions from the user's speech and input.

[0679] Step 4:

[0680] The server analyzes the received user feedback using an emotion engine. In the case of audio data, features are extracted from the audio, and an emotion classification algorithm is used to identify the user's emotions. The resulting emotion data is used to inform snow removal plans.

[0681] Step 5:

[0682] The server integrates predictive data from machine learning models with user sentiment data from an emotion engine to calculate the optimal snow removal route. This takes into account factors such as road importance, traffic volume, and the user's emotional needs.

[0683] Step 6:

[0684] The server transmits the calculated route information to the snowplow, which acts as the terminal. The terminal then performs snow removal automatically according to the specified route.

[0685] Step 7:

[0686] The terminal transmits sensor information and location data collected during operation to the server in real time. This allows the server to dynamically adjust routes and work content according to the situation.

[0687] Step 8:

[0688] Users can check the current snow removal status through the app and provide additional feedback. This feedback will be used to inform future analysis and planning.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] In autonomous snow removal operations, efficient and flexible route optimization is required, taking into account not only local snow conditions and weather, but also user emotions and feedback. However, conventional systems have difficulty effectively incorporating these elements, which may result in decreased user satisfaction. Furthermore, insufficient real-time monitoring and adjustment of work status can lead to problems in responding to sudden changes in conditions.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes information acquisition means for collecting weather information and snow depth information, data analysis means for analyzing the collected information and generating snow depth predictions using an AI model, and feedback processing means for analyzing sentiment data from users and adjusting priorities. This enables the setting of efficient snow removal routes that take into account snow depth conditions and user sentiment, as well as real-time monitoring and adjustment of work progress.

[0694] "Information acquisition means" refers to components for collecting weather information and snow depth information from sensors and web services and preparing them for the system.

[0695] "Data analysis methods" refer to the process of using a generative AI model to predict snowfall based on collected weather information and snow depth data.

[0696] "Route setting means" refers to a procedure for designing the optimal snow removal route based on the results of data analysis, thereby improving work efficiency.

[0697] A "feedback processing system" is a mechanism that analyzes emotional data and feedback provided by users and uses it to adjust the areas that should be prioritized for snow removal.

[0698] "Machine control means" refers to control technology for vehicles or machines that automatically perform snow removal work based on a set optimal route.

[0699] A "progress management system" is a function that monitors the progress of snow removal work and information from sensors in real time, and adjusts the plan as needed.

[0700] This invention is an advanced snow removal system that integrates autonomous driving technology with generative AI models. At the heart of this system is a server that collects weather information and snow depth information in real time and integrates it into a database. Specifically, it uses weather sensors and external weather APIs to accurately acquire the necessary information.

[0701] The server further analyzes the acquired information and uses generative AI models (e.g., machine learning algorithms such as LSTM and BERT) to predict snow conditions, and then designs the optimal snow removal route based on those predictions. This data analysis process makes snow removal operations more efficient.

[0702] Furthermore, users can provide feedback through a dedicated mobile application, sending emotional data via voice or text. This feedback is analyzed on a server and used to determine priority snow removal areas. Emotion recognition and natural language processing technologies are essential for feedback analysis. Specifically, multiple emotional states (e.g., reassurance, dissatisfaction) are automatically extracted and reflected in the system's route determination.

[0703] The optimized snow removal route information is transmitted to the snowplow, which acts as a terminal. The snowplow operates in autonomous driving mode based on this information, using LiDAR and GPS to recognize obstacles and conditions in real time as it works. During this process, the work status and data obtained from the sensors are continuously returned to the server, and the work plan is adjusted as needed.

[0704] For example, if a sudden increase in snowfall is predicted on a major road in a region, a user can send feedback such as, "I'm worried about the snow on the main road, so please clear it as soon as possible." The server analyzes this information using sentiment analysis and incorporates it into optimizing snow removal routes. In this way, rapid and flexible snow removal operations tailored to the specific circumstances of the region are realized.

[0705] An example of a prompt message might be: "Please explain how you will use user sentiment data to optimize the snow removal service."

[0706] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0707] Step 1:

[0708] The server collects weather information and snow depth information. Input is raw data obtained from weather sensors and weather APIs. This data undergoes consistency checks and preprocessing before being stored in an integrated database. Specific operations include sending information retrieval requests from weather APIs and periodic data polling from sensors. Output is weather and snow depth information converted into an analyzable format.

[0709] Step 2:

[0710] The server analyzes the collected data. The input is the data accumulated in Step 1. A generative AI model (such as BERT or LSTM) is used on this data to predict snow conditions. Specifically, it refers to past weather data and estimates future snowfall amounts using a machine learning algorithm. The output is the predicted snowfall data.

[0711] Step 3:

[0712] Users submit feedback using a smartphone app. Input is either voice or text feedback from the user. This feedback is sent to a server for sentiment analysis. Specifically, this involves converting speech to text using a speech recognition system and analyzing the sentiment using natural language processing. The output is the analyzed emotional state (e.g., reassured, dissatisfied).

[0713] Step 4:

[0714] The server optimizes snow removal routes based on sentiment analysis results. The inputs are the predicted data from step 2 and the sentiment state from step 3. This allows the server to determine priority snow removal areas and recalculate the route. Specifically, the server weights the sentiment analysis results and inputs them into the route optimization algorithm. The output is the optimized snow removal route.

[0715] Step 5:

[0716] The server sends optimized route information to the snowplow, which acts as the terminal. The input is the route information generated in step 4. Based on this, the snowplow starts operating in automatic driving mode. Specifically, it downloads route data using the vehicle's communication module and initializes the vehicle control system based on it. The output is the work command to be executed by the snowplow.

[0717] Step 6:

[0718] The terminal performs snow removal and sends progress and sensor information back to the server in real time. Input is various sensor data collected during snow removal. Specific operations include obstacle detection and position tracking using LiDAR and GPS devices, and recording of work progress. Output is progress and current route information stored in the database.

[0719] Step 7:

[0720] Users can check the snow removal status in real time through the app and provide additional feedback. The input is current work progress information provided by the server. Specifically, the user interface displays the current progress and a feedback input form. The output is the latest information on the snow removal status, which impacts user satisfaction.

[0721] (Application Example 2)

[0722] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] Conventional automated snow removal systems simply optimize routes and perform tasks based on weather data, and no system yet exists that can adjust to user emotions and feedback. Therefore, it has been difficult to contribute to improving resident satisfaction. Thus, the present invention aims to provide an efficient and flexible automated work system that reflects user emotions.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0725] In this invention, the server includes data collection means for acquiring environmental information in real time, route setting means for analyzing the information obtained by the data collection means and optimizing the work route, machine control means for automatically performing work based on the generated optimal route, and emotion analysis means for analyzing user emotion information and dynamically adjusting the priority of snow removal work. This makes it possible to implement an optimal snow removal work plan that incorporates user emotions and feedback.

[0726] "Data collection methods" refer to technologies for acquiring environmental information and related data in real time.

[0727] "Route setting means" refers to a technology that optimizes and sets the work route based on acquired information.

[0728] "Machine control means" refers to technology for automatically performing tasks according to a set optimal path.

[0729] "Emotional analysis tools" are technologies that analyze users' emotional information and dynamically adjust task priorities and plans.

[0730] The system used to implement this application consists of a server, a terminal, and a user.

[0731] The server acquires weather data and related information through data collection mechanisms for collecting environmental information in real time. This data is efficiently processed using AWS Lambda, which operates on a cloud platform. Subsequently, a machine learning model using TensorFlow analyzes the acquired data and optimizes the workflow.

[0732] The terminal is equipped with machine control means for performing automated tasks. This allows the system to automatically continue tasks based on optimized path information. This control means utilizes sensing technology that can monitor progress and status in real time.

[0733] Furthermore, users can interact with the system through a smartphone app. This application uses Google Cloud Speech-to-Text to convert speech data into text and then supplies that data to an emotion analysis tool using NLTK. Users can check the progress of their work and easily provide emotion feedback through this application.

[0734] For example, if feedback is received in a certain area stating, "It's inconvenient because the street in front of my house hasn't been cleared of snow yet," this negative emotional data is read through an emotion analysis tool and reflected in the route planning tool. As a result, work in that area is prioritized for scheduling.

[0735] An example of a prompt for a generating AI model would be: "Three hours after the snowfall, dissatisfaction is increasing in the southern part of the city. Please suggest the optimal plan for which areas should be prioritized for snow removal." Such prompts allow the system to present an efficient work plan that takes people's emotions into account.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server collects environmental information, particularly weather data and snow cover information, in real time using data collection methods. The input to this process is data provided by various sensors and the internet, and the output is a set of raw data for analysis.

[0739] Step 2:

[0740] The server utilizes a generative AI model based on the collected data and executes an optimization algorithm using TensorFlow. This optimizes the work path. The input is the set of raw data obtained in step 1, and the output is optimized work path information. Specifically, it recalculates the path priority using predictive modeling.

[0741] Step 3:

[0742] The terminal receives optimized work path information transmitted from the server and automatically performs the work via machine control means. The input is work path information provided by the server, and the output is actual snow removal work data. The specific operation of the terminal is to perform snow removal work according to the path.

[0743] Step 4:

[0744] Users use a smartphone app to monitor their work status in real time and provide feedback via voice or text. Inputs include work status and feedback data, while output is user emotion information. Specific actions include inputting emotion feedback within the app.

[0745] Step 5:

[0746] The server uses Google Cloud Speech-to-Text and NLTK to convert user voice data into text and analyze it using sentiment analysis tools. Input is user voice and text feedback, and output is analyzed sentiment data. Specifically, it extracts features from the audio to facilitate analysis.

[0747] Step 6:

[0748] The server re-evaluates which areas should be prioritized for snow removal using prompt messages, based on the sentiment analysis results and the generative AI model, and adjusts the route priorities. The input is the sentiment analysis results and prompt messages, and the output is the adjusted priority route information. Dynamic adjustment of priority settings is performed in this step.

[0749] 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.

[0750] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0751] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0752] 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.

[0753] Figure 9 shows an 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0754] 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.

[0755] 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.

[0756] 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, motorcycles, etc., 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, for example, based 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.

[0757] 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."

[0758] 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.

[0759] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0760] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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 the like 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.

[0769] 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.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] A means of collecting information to obtain weather information in real time,

[0773] A route optimization means that analyzes the data obtained by the information gathering means to optimize the snow removal route,

[0774] A vehicle control means that automatically performs snow removal work based on the generated optimal route,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, wherein the route optimization means adjusts priority snow removal areas based on user feedback.

[0778] (Claim 3)

[0779] The system according to claim 1, wherein the vehicle control means monitors the snow removal progress using sensors and adjusts the route in real time.

[0780] "Example 1"

[0781] (Claim 1)

[0782] Information acquisition methods for collecting weather data,

[0783] A data analysis means that integrates the data obtained from the aforementioned information acquisition means and analyzes it using a machine learning model,

[0784] Based on the analysis results, a route communication means designs a high-priority route and transmits route information to the autonomous vehicle via a communication means.

[0785] A vehicle operating means that automatically performs snow removal work using an onboard positioning device and surrounding object recognition device based on received route information,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the routing communication means dynamically reconfigures high-priority areas based on information obtained from the user.

[0789] (Claim 3)

[0790] The system according to claim 1, wherein the vehicle operating means monitors the progress of snow removal using a measuring device and automatically changes the work route in response to changes in the external environment.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A data acquisition method for obtaining weather data in real time,

[0794] A route optimization means that analyzes the information obtained by the data acquisition means and optimizes the removal path,

[0795] Equipment control means that automatically performs removal work based on the generated optimal route,

[0796] A feedback processing method that collects information from local residents and incorporates it into the next work,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the route optimization means adjusts overpriority areas based on user feedback.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the equipment control means monitors the progress of the work using a detector and adjusts the route in real time.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] Information acquisition means for collecting weather information and snow depth information,

[0805] A data analysis method that analyzes acquired information and uses a generated AI model to predict snowfall,

[0806] Route setting means for optimizing snow removal routes based on analysis,

[0807] A feedback processing means that analyzes emotional data from users and adjusts priorities,

[0808] A machine control means that automatically performs snow removal work based on the generated optimal path,

[0809] A progress management system that detects information during work and adjusts the plan according to the acquired information,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, which adjusts the priority of snow removal routes based on user feedback and sentiment data.

[0813] (Claim 3)

[0814] The system according to claim 1, which acquires information and detector information in real time during work and adjusts the route and work schedule based on them.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A data collection method for acquiring environmental information in real time,

[0818] A route setting means that analyzes the information obtained by the data collection means and optimizes the work route,

[0819] A machine control means that automatically performs the task based on the generated optimal path,

[0820] A sentiment analysis method that analyzes user sentiment information and dynamically adjusts the priority of snow removal work,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the route setting means adjusts priority work areas based on emotional feedback from the user.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the machine control means monitors the progress of the work using sensing technology and adjusts the route in real time. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data acquisition method for obtaining weather data in real time, A route optimization means that analyzes the information obtained by the data acquisition means and optimizes the removal path, Equipment control means that automatically performs removal work based on the generated optimal route, A feedback processing method that collects information from local residents and incorporates it into the next work, A system that includes this.

2. The system according to claim 1, wherein the route optimization means adjusts overpriority areas based on user feedback.

3. The system according to claim 1, wherein the equipment control means monitors the progress of the work using a detector and adjusts the route in real time.

Images