system

Abstract

システムを提供する。【解決手段】自律駆動型除去装置が多数の施設に配備されて、データ処理装置が気象情報や地形情報を解析して除去経路を立案し、該除去経路を自律駆動型除去装置に配信して作業を指示する手段と、端末装置が除去作業中に取得した情報をデータ処理装置に送信してフィードバックし、データ処理装置がフィードバックされた情報を基に除去アルゴリズムを再解析して経路を調整する手段と、利用者が通信機器を通じてシステムを操作し、除去作業の進行状況を監視し制御する手段と、データ処理装置が端末装置の状態を監視し、異常が検出された場合には警告を利用者に通知する手段と、複数の自律駆動型除去装置を統合管理し、都市全域の除去作業を効率的に行う手段と、を含むシステム。

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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, snow removal work in snowy areas is a great burden for the elderly and involves the risk of falling and injury, which is a factor reducing the quality of life and safety. Furthermore, if snow removal is not properly carried out, the entrance and roads will be covered with snow, directly affecting life, so regular and efficient snow removal work is required. To solve such problems, there is a need for new means to provide an environment in which the elderly can live safely and reduce the burden of snow removal work.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system in which an automated snowblower is installed in each home, a server analyzes weather and terrain data to plan a snow removal route, and transmits that route to the snowblower to instruct it to work. The snowblower feeds back the data it collects during operation to the server, which then re-analyzes the snow removal algorithm and readjusts the route based on that data. In addition, the user can operate the system via an application to check the progress of the snow removal work, and the server monitors the status of the terminal and notifies the user of a warning if a malfunction occurs. This system can significantly reduce the burden of snow removal work on the elderly and provide them with an environment in which they can live safely and comfortably.

[0006] An "automatic snow removal machine" is a machine installed in each home that automatically performs snow removal work and operates according to instructions from a server.

[0007] A "server" is a computer system that analyzes weather and terrain data to plan snow removal routes and then sends work instructions to snow removal machines based on that plan.

[0008] "Weather data" refers to data that provides information about the weather, such as snowfall, temperature, and wind direction.

[0009] "Topographic data" refers to information about the terrain of the area to be cleared of snow, including data such as the layout of roads and properties, and elevation differences.

[0010] A "snow removal route" is a plan that outlines the path that snow removal machines will take to efficiently remove snow.

[0011] "Feedback" is the process by which snowplows send back data collected during operation to a server to help improve and adjust the system.

[0012] "Reanalysis" refers to re-analyzing the data based on the feedback received and adjusting the snow removal algorithm accordingly.

[0013] An "application" is software that allows users to operate and monitor snow removal systems using their smartphones or tablets.

[0014] "Monitoring" is the process by which a server continuously monitors the status of a terminal to check for any abnormalities.

[0015] A "warning" is information that is sent to the user when a malfunction or abnormality occurs. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled 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.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention aims to effectively and efficiently perform snow removal tasks for households, including those of the elderly, by constructing an automated snow removal system. This system mainly consists of a server, terminals (autonomous snow removal machines), and a user application.

[0038] The server acts as the central hub, collecting and analyzing weather and terrain data in real time. Based on this data, the server plans the optimal snow removal route for each household and transmits the schedule to the automated snowplows. The server also constantly monitors the overall system's functionality by monitoring the real-time status information of each terminal.

[0039] The automated snowplow terminal operates based on instructions from the server. It efficiently removes snow by detecting ground conditions and surrounding obstacles using built-in sensors and cameras. The terminal also feeds back environmental data collected during operation to the server. This data is then re-analyzed on the server to improve the terminal's operational accuracy and enhance future work efficiency.

[0040] Users can access the system via a dedicated smartphone or tablet application. This application displays the operating status of the snowblower in real time, and allows users to manually control snow removal operations as needed. Users can also receive alerts from the system, enabling immediate action in case of malfunctions, for example.

[0041] For example, when snowfall is predicted, the server automatically sets the schedule for the next snow removal operation based on weather data and sends route information to the terminal. The terminal efficiently performs the work according to the designated route while checking the condition of the snow removal area in real time. If the terminal detects an obstacle, it will either avoid it or send data to the server for a retry. This provides a safe environment for the elderly and enables flexible snow removal responses according to the season and conditions.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects weather and terrain data and analyzes it in real time. This allows it to identify areas and times when snow removal is needed and plan optimal snow removal routes and schedules.

[0045] Step 2:

[0046] The server sends the planned snow removal route and schedule to each terminal. The terminal then prepares for snow removal work based on the received information.

[0047] Step 3:

[0048] The terminal starts snow removal work based on the route received from the server. It automatically removes snow while monitoring the surrounding conditions with its built-in sensors and camera.

[0049] Step 4:

[0050] The terminal feeds back data collected during operation (e.g., obstacle information, work progress) to the server. This allows the server to obtain data to improve the accuracy and efficiency of snow removal operations.

[0051] Step 5:

[0052] The server analyzes the data fed back from the terminals and readjusts snow removal routes and schedules as needed. This enables more efficient snow removal operations.

[0053] Step 6:

[0054] Users can check the status of snow removal operations through a dedicated application. The application displays the location of the snowblower and the progress of the work in real time, and users can also take action as needed.

[0055] Step 7:

[0056] The server constantly monitors the status of the terminal and immediately notifies the user if any abnormalities or malfunctions are detected. This establishes a system that allows for a rapid response.

[0057] (Example 1)

[0058] 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."

[0059] In an aging society, snow removal at home is a significant burden, especially in snowy regions. Traditional snow removal methods require manual labor, raising concerns about safety and effectiveness, particularly for the elderly and those with physical disabilities. Furthermore, snow removal is not performed efficiently in real time, making it difficult to respond quickly to operational errors or equipment malfunctions.

[0060] 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.

[0061] In this invention, the server includes means for autonomous operating machines to be deployed at individual facilities, a central processing unit to analyze weather and terrain information and plan operating routes, means for transmitting the operating routes to the autonomous operating machines to instruct them to perform tasks, means for terminals to feed back information collected during the work to the central processing unit, the central processing unit to re-analyze the operation algorithm based on the feedback information and readjust the route, and means for users to monitor the information using a display device and issue manual instructions as needed. This enables safe and efficient automated snow removal for all users, including the elderly.

[0062] An "autonomous machine" is a device that can automatically perform predetermined tasks in response to instructions from an external source.

[0063] A "centralized processing unit" is a computer system that collects multiple data sets, analyzes them, and derives results.

[0064] "Weather information" refers to data related to the weather, such as temperature, precipitation, and wind speed.

[0065] "Topographic information" refers to information about the shape and characteristics of the ground surface in a specific area.

[0066] A "movement path" is the route that an autonomous machine should follow when performing a task.

[0067] "Feedback" is the process of incorporating information sent from one system to an external source back into that system.

[0068] An "operation algorithm" is a set of steps or computational methods that enable an autonomous machine to efficiently perform a task.

[0069] A "display device" is a device used to present information to a user visually.

[0070] This invention employs a configuration in which a central processing unit (server), an autonomous machine (terminal), and a user display device (application) work together to efficiently operate an autonomous machine. The purpose of this system is to reduce the burden on users, including the elderly, by automatically planning and adjusting the operating route based primarily on weather and terrain information.

[0071] The server acquires weather and topographic information in real time from external data sources. For example, it uses weather information APIs on the internet to obtain weather data and analyzes the operation path using machine learning models based on that data. The analysis results are sent as instructions to the autonomous operating machine.

[0072] The autonomous operating machine, acting as the terminal, uses built-in hardware such as sensors and cameras to detect the surrounding terrain and obstacles in real time. This allows it to efficiently and safely carry out tasks while following the instructed operating path. Information on obstacles detected during the operation is fed back to the server in real time, and the server readjusts the operating path as needed based on this information.

[0073] Users can monitor the operation status of autonomous machinery in real time using a dedicated application installed on their smartphone or tablet. This application allows them to check the progress of snow removal work and the status of the autonomous machinery, and to issue manual instructions as needed. In the event of a malfunction or abnormality, the app will notify the user with a warning, enabling a quick response.

[0074] As a concrete example, consider a situation where snowfall is predicted. The server automatically sets a snow removal schedule based on real-time weather data, and this schedule and route information are transmitted to the terminal. The terminal efficiently removes snow while following the designated route, and when it detects an obstacle, it sends that information to the server and adjusts the route accordingly.

[0075] Examples of prompts for a generative AI model:

[0076] "Please explain the specific operational flow of an automated snow removal system for elderly households when snowfall is expected the following day."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server receives real-time weather data from a weather information API and topographic data from a topographic information database as input. Using this data, the server identifies areas requiring snow removal and plans the optimal route for each household. In this process, it utilizes a generative AI model to select efficient routes based on historical data. As output, it generates a snow removal schedule and route information for each household and transmits it to autonomous operating machines.

[0080] Step 2:

[0081] The autonomous operating machine, acting as the terminal, receives snow removal schedule and route information from the server as input. Using its built-in sensors and camera, the terminal detects the surrounding terrain and obstacles in real time, and based on the data obtained, begins work according to the designated route. Specifically, when the terminal detects an obstacle, it adjusts its direction of travel and continues snow removal safely. As output, it feeds back environmental information collected during the operation to the server.

[0082] Step 3:

[0083] The server receives environmental information fed back from the terminal as input and reanalyzes the snow removal algorithm based on this information. If necessary, the server generates newly optimized route instructions and sends them back to the terminal. This process makes it possible to continuously improve the efficiency of snow removal work. Specifically, the server analyzes the real-time data obtained and updates the data to be useful for subsequent operations.

[0084] Step 4:

[0085] Users monitor snow removal progress and terminal operating status received from the server via an application installed on their smartphone or tablet. Users can manually issue commands to the terminal via the app as needed. For example, if a user wants to prioritize snow removal in a specific area, they can send a command via the app, and the terminal will respond immediately. As output, users can receive visual operational feedback.

[0086] (Application Example 1)

[0087] 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."

[0088] In modern cities, snow removal during snowfall is often labor-intensive and inefficient, making it particularly difficult in areas with aging populations. Furthermore, flexible snow removal that can adapt to fluctuating weather conditions is required, but conventional methods are sometimes insufficient. Therefore, the challenge is to enable efficient snow removal throughout urban areas and provide a safe and comfortable living environment.

[0089] 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.

[0090] In this invention, the server includes means for analyzing weather information and topographic information to plan a removal route, means for distributing the removal route to an autonomously driven removal device to instruct it to perform the work, and means for re-analyzing the removal algorithm based on the feedback information to adjust the route. This enables efficient and flexible snow removal work throughout the entire city.

[0091] An "autonomous removal device" is a mobile mechanical device that operates automatically according to weather conditions and has the function of removing snow and obstacles.

[0092] A "data processing device" is a computer system that analyzes collected weather and topographic information and calculates the optimal route for removal work.

[0093] A "removal route" is a route set up for carrying out removal work, and it is a route plan designed to ensure efficient work.

[0094] A "terminal device" is a device mounted on an autonomous removal device that has communication capabilities to transmit information acquired during operation to a data processing device.

[0095] "Users" are individuals who have the authority to operate and monitor the removal system, and who can use communication devices to check the system's status and issue instructions as needed.

[0096] "Communication equipment" refers to a system for sending and receiving information between a data processing device and a terminal device, and is a device that assists users in operating the system.

[0097] "Feedback" refers to information transmitted from a terminal device to a data processing device, including environmental data and work status acquired during the work process.

[0098] "Weather information" refers to data related to weather conditions such as snowfall, temperature, and wind speed, and is a factor that influences the plan for removal work.

[0099] "Topographic information" refers to data about the terrain of the work area, including changes in terrain and the location of obstacles.

[0100] The system implementing this invention is realized by a central data processing unit (hereinafter referred to as the server) controlling multiple autonomous removal devices (hereinafter referred to as terminals). The server executes a program written in a programming language such as Python to analyze weather information and terrain information in real time. This allows it to plan removal routes and send optimal instructions to each terminal. The server also reanalyzes the removal algorithm based on feedback information from the terminals and adjusts the route as necessary.

[0101] The terminal has autonomous driving capabilities and uses ROS (Robot Operating System) and various sensors (such as LIDAR and cameras) to automatically perform tasks according to the instructed path. During the task, it transmits data acquired by the sensors to the server and provides real-time feedback on the environment. If an obstacle is detected, it reports that information to the server and confirms the next action.

[0102] Users access the system via smartphones and tablets to check the progress of their tasks and the status of their devices. This is done using an application developed with Flutter®, and information is provided by a real-time database such as Firebase. Users can also manually instruct the device to operate as needed.

[0103] As a concrete example, on a night when heavy snowfall is expected, the server uses a weather forecast API to obtain weather information and plans routes so that all major roads remain clear until the following morning. Terminals then operate based on this plan, efficiently clearing snow. As a result, citizens can commute to work and school with peace of mind.

[0104] An example of a prompt to input into the generated AI model would be, "Explain how an autonomous snowplow system works, using ROS and sensors to effectively remove snow."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects weather data. It uses real-time data obtained from weather forecast APIs (e.g., OpenWeatherMap API) as input. This data includes temperature, snowfall, and wind speed, and is used to assess the need for snow removal. The output determines the required snow removal area and time. Specifically, the system periodically sends requests to the API to retrieve updated weather information.

[0108] Step 2:

[0109] The server acquires and analyzes terrain information. It uses geographic information APIs (e.g., Google® Maps API) or built-in map data as input. It analyzes terrain gradients, road layouts, and anticipated obstacles to plan effective routes for removal. The output is detailed route information for instructing terminals. Specifically, it uses analytical algorithms to determine road priorities and safe routes.

[0110] Step 3:

[0111] The server sends the calculated removal route to the terminal. The input uses data that integrates the judgment and analysis results obtained in steps 1 and 2. The output to the terminal includes the specific travel route and the start time of the work. In terms of operation, it communicates with each terminal via the network and transfers instruction data.

[0112] Step 4:

[0113] The terminal autonomously begins snow removal work according to the route information it receives. Using route information transmitted from the server as input, the terminal uses its built-in sensors (e.g., LiDAR and camera) to check its current location and terrain details in real time. The output is feedback information such as obstacle avoidance and confirmation of work completion. Specific operations include controlling the motor to travel along a designated route and operating the snow removal blade as needed.

[0114] Step 5:

[0115] The terminal feeds back environmental data acquired during operation to the server. The input is terrain and obstacle information acquired in real time by the terminal's sensors. The output sent to the server is feedback information to improve work efficiency. Specifically, it periodically generates data packets and feeds them back to the server.

[0116] Step 6:

[0117] Users check the progress of their devices using a smartphone app. The input is real-time device status information transmitted from a server. The output is the progress status and warning messages displayed to the user. Specifically, the app displays the progress of the task and the estimated remaining time, and allows for manual operation as needed.

[0118] 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.

[0119] This invention is a system that combines an automated snowblower installed in a home, a server and user application to control it, and an emotion engine that recognizes the user's emotions. The system aims to enable efficient snow removal while simultaneously reducing the psychological burden on the user.

[0120] The server analyzes weather and terrain data to formulate a snow removal plan. It transmits the planned snow removal route to each terminal and notifies users through a dedicated application. At this time, an emotion engine analyzes the user's voice and facial expressions in real time and evaluates the user's emotional state online.

[0121] Users can check the progress of snow removal work in real time using a dedicated application. The application uses an emotion engine to analyze the user's emotions if they feel anxious or stressed. For example, if the user raises their voice or makes a displeased face, it will suggest adjusting the work schedule accordingly. It can also display situation-appropriate messages and advice to reduce the user's psychological burden.

[0122] The terminal operates as an autonomous snowplow, feeding back data from its operation to the server. The server then re-analyzes the data to improve the efficiency of snow removal operations. The feedback information is used to improve future work plans.

[0123] For example, if the application detects user anxiety while the user is checking the progress of snow removal work, the emotion engine will display a reassuring message on the screen stating that "the work is progressing smoothly." Furthermore, especially when severe weather conditions are expected, the application can send a notification urging the user to take early action, thus preparing for unexpected situations.

[0124] Thus, this invention not only streamlines snow removal operations but also contributes to creating a safer and more secure living environment by providing services that take into consideration the user's feelings.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects and analyzes weather and topographic data. It identifies areas requiring snow removal and develops optimal snow removal routes and schedules.

[0128] Step 2:

[0129] The server sends the formulated snow removal plan to the terminal (autonomous snowplow). It also notifies the user's application of this information.

[0130] Step 3:

[0131] The terminal automatically starts snow removal work according to the received snow removal route. During the work, it continuously feeds back data on the ground and obstacles to the server.

[0132] Step 4:

[0133] Users can check the progress of snow removal work in real time through the application, and an emotion engine analyzes the user's emotions by analyzing voice, facial expressions, etc.

[0134] Step 5:

[0135] If the emotion engine detects user anxiety or stress, the server adjusts the snow removal schedule based on that information and revises the work plan if necessary.

[0136] Step 6:

[0137] Users receive reassuring messages and specific advice from the application, which reduces their psychological burden.

[0138] Step 7:

[0139] The server re-analyzes the snow removal algorithm based on the feedback data and makes adjustments to improve the accuracy of the next work plan.

[0140] As a result, the system not only improves the efficiency of snow removal but also enhances the user's quality of life through emotional care.

[0141] (Example 2)

[0142] 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".

[0143] Autonomous snow removal operations require the development of efficient snow removal plans, but it is also necessary to consider the psychological burden on users. However, conventional systems lacked sufficient consideration for the mental well-being of users, often leading to situations where users felt anxious or stressed. Furthermore, there was room for improvement in the mechanism for providing real-time feedback on snow removal data and immediately optimizing the algorithm.

[0144] 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.

[0145] In this invention, the server includes means for analyzing weather and topographic information to plan snow removal routes, means for re-analyzing the snow removal algorithm based on the collected information to readjust the routes, and means for evaluating the user's emotional state using an emotion analysis engine and providing feedback. This enables efficient snow removal work, reduces the psychological burden on users, and provides an environment in which the system can be used with peace of mind.

[0146] An "autonomous snow removal system" is a mechanical device installed at each facility that automatically performs snow removal work according to a programmed route.

[0147] A "data processing device" is a computer system that analyzes weather and topographic information to formulate snow removal route plans and to re-analyze algorithms based on the collected information.

[0148] An "information terminal" is an electronic device used to feed back information collected during snow removal work to a data processing device.

[0149] An "emotion analysis engine" is a software technology that evaluates the user's emotional state based on voice and facial expression data and generates appropriate feedback.

[0150] "Snow removal route" refers to the specific route and schedule that an autonomous snow removal system should follow when performing snow removal operations.

[0151] This invention is a system for achieving efficient and user-friendly snow removal using an autonomous snow removal device installed at a specific facility. The system's core, a data processing unit, analyzes weather data obtained from weather information services and topographic data obtained from geographic information services to plan the optimal snow removal route. This process utilizes data feeds such as the "OpenWeatherMap API" and the "Google Maps API".

[0152] The server transmits the planned snow removal route to the autonomous snow removal device, which then performs physical snow removal operations according to the device's instructions. This allows the snow removal device to efficiently remove snow from the facility. During snow removal, the information terminal feeds its progress back to the data processing device in real time, and the data processing device re-analyzes this information to adjust the route and schedule, thereby further improving efficiency.

[0153] Furthermore, users can check the progress of snow removal work through a dedicated mobile application. This application incorporates an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, if a user is worried about delays in the work, the application will generate and display a reassuring message such as, "The work is progressing as scheduled."

[0154] A possible concrete example is to input a prompt sentence such as, "What message should be displayed if a user feels uneasy about snow removal?" into an emotion analysis engine, and then use a generative AI model to automatically provide appropriate feedback.

[0155] This system not only makes snow removal more efficient, but also minimizes the psychological burden on users. As a result, users can confidently incorporate this system into their daily lives.

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

[0157] Step 1:

[0158] The server obtains weather data and topographic data from weather information services and geographic information services, respectively. Based on this input data, it plans snow removal routes using an analysis algorithm. Specifically, the server obtains snowfall forecasts from the "OpenWeatherMap API" and topographic information from the "Google Maps API". As a result of the analysis, the optimal snow removal route and work schedule are output.

[0159] Step 2:

[0160] The server transmits the planned snow removal route to an information terminal. The information terminal forwards this data to the autonomous snow removal device, which then begins snow removal according to the specified route. The input is route data from the server, and the output is route instructions to the autonomous snow removal device. In practice, the snow removal device physically moves along the route and removes the snow.

[0161] Step 3:

[0162] The terminal feeds back progress data collected during snow removal operations to the server. Based on this data, the server evaluates the current work efficiency and readjusts the snow removal route and work schedule as needed. The input is progress data, and the output is optimized new route information. In specific operations, the terminal transmits information such as the distance and time the device has traveled to the server via voice.

[0163] Step 4:

[0164] Users can check the progress of snow removal work and receive feedback through a dedicated application. The application has an emotion analysis engine that analyzes voice and facial expression input to evaluate the user's emotional state. Specifically, it performs emotion analysis using the user's voice input and provides a reassuring message as output.

[0165] Step 5:

[0166] The server generates feedback messages using a generative AI model based on the user's emotional state obtained from the emotion analysis engine. The prompt is "What message should be displayed if the user feels anxious about the snow removal work?", and the output is a specific support message. For example, based on the emotion analysis results, it might send a reassuring message such as "The work is progressing as planned."

[0167] (Application Example 2)

[0168] 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".

[0169] The goal is not only to improve the efficiency of snow removal operations using autonomous snowplows, but also to reduce the anxiety and stress users feel regarding the progress of the work. However, conventional systems have struggled to provide feedback and appropriate messages that take user emotions into consideration. As a result, users sometimes feel anxious about the progress of the work or changes in the weather, leading to significant psychological burden.

[0170] 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.

[0171] In this invention, the server includes emotion recognition means for detecting the user's emotional state, means for optimizing the work schedule based on the user's emotional state evaluated by the emotion recognition means, and means for providing messages to reduce the user's psychological burden. This reduces the user's psychological burden by adjusting the work content and information provided according to the user's emotions, making it possible to monitor snow removal work with peace of mind.

[0172] An "autonomous snow removal machine" is a mechanical device installed in homes or other locations that has the function of autonomously performing snow removal work.

[0173] A "server" is a computing device that analyzes data and issues instructions to snowplows regarding their work routes.

[0174] "Weather data" refers to data that shows information about weather conditions.

[0175] "Topographic data" refers to data that shows information about the characteristics of the ground surface in a given area.

[0176] A "snow removal route" is a route that indicates the path a snow removal machine will take to perform its work.

[0177] A "terminal" is a device installed on a snowblower that collects information during operation and transmits it to a server.

[0178] "Feedback" is the process of sending data back from a terminal to a server.

[0179] A "snow removal algorithm" is a computational method and program for efficiently performing snow removal work.

[0180] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and voice to determine their emotional state.

[0181] A "work schedule" is a plan outlining the time and sequence of snow removal operations.

[0182] "Psychological burden" is a concept that refers to the anxiety and stress that users experience.

[0183] A "message" is a form of communication consisting of text or audio used to convey information to a user.

[0184] This invention is a snow removal system using an automated snowblower installed in a home. The system utilizes emotion recognition technology to improve the user experience.

[0185] The server receives weather and terrain data, analyzes this data to plan an efficient snow removal route. The server transmits the planned route information to the snowplow and instructs it to perform snow removal. During snow removal, the terminals collect work data and feed it back to the server, which then re-evaluates and adjusts the snow removal route and snow removal algorithm based on that data.

[0186] Furthermore, emotion recognition functionality is provided through the user's smartphone or smart glasses. The user's voice and facial expressions are captured using a camera and microphone, and these are analyzed by the emotion recognition system. For this purpose, voice processing software is used for voice recognition, and image recognition software is used for facial expressions. Based on the analysis results, encouraging messages and advice tailored to the user's emotional state are provided.

[0187] For example, if severe weather is detected during snow removal and the user becomes anxious, a prompt message is sent to the generating AI model saying, "A user with an anxious expression is checking this. Please think of some words of encouragement:" and the user is shown a message such as, "Everything is going smoothly, so please don't worry." This process reduces the user's psychological burden while ensuring safe and comfortable snow removal work.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server receives weather data from a weather data provider and terrain data from a terrain provider. Using this data, it performs data analysis to plan snow removal routes. The input data consists of weather and terrain data, and the output generates efficient snow removal routes.

[0191] Step 2:

[0192] The server transmits the generated snow removal route information to the automated snowblower, instructing it to perform snow removal work. The route information includes the direction of travel and the amount of snow to be removed, which allows the snowblower to automatically follow the specified path.

[0193] Step 3:

[0194] The terminal continuously collects operational and environmental data from the snowblower and feeds this data back to the server. The input is sensor data from the snowblower, and the output is data transmission to the server.

[0195] Step 4:

[0196] The server analyzes the feedback data and optimizes the snow removal algorithm. This allows for readjustment of snow removal routes, enabling more efficient work. The analysis results are output as a new snow removal route.

[0197] Step 5:

[0198] Users operate a dedicated application using their smartphone or smart glasses to check the progress of snow removal work. The application displays snow removal status data as input information. Users use this information to monitor the progress of the work.

[0199] Step 6:

[0200] The user terminal uses a camera and microphone to collect the user's facial expressions and voice. An emotion recognition system analyzes this data to determine the user's emotional state. The input data consists of audio and visual data, and the output is an evaluation of the emotional state.

[0201] Step 7:

[0202] The server uses a generative AI model to send appropriate messages to the user based on the evaluated emotional state. The input is the emotional state and a prompt, and the output is a message of encouragement or caution. For example, based on the prompt "A user with an anxious expression is checking this. Please think of some words of encouragement:", it will provide a message of reassurance.

[0203] 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.

[0204] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention aims to effectively and efficiently perform snow removal tasks for households, including those of the elderly, by constructing an automated snow removal system. This system mainly consists of a server, terminals (autonomous snow removal machines), and a user application.

[0220] The server acts as the central hub, collecting and analyzing weather and terrain data in real time. Based on this data, the server plans the optimal snow removal route for each household and transmits the schedule to the automated snowplows. The server also constantly monitors the overall system's functionality by monitoring the real-time status information of each terminal.

[0221] The automated snowplow terminal operates based on instructions from the server. It efficiently removes snow by detecting ground conditions and surrounding obstacles using built-in sensors and cameras. The terminal also feeds back environmental data collected during operation to the server. This data is then re-analyzed on the server to improve the terminal's operational accuracy and enhance future work efficiency.

[0222] Users can access the system via a dedicated smartphone or tablet application. This application displays the operating status of the snowblower in real time, and allows users to manually control snow removal operations as needed. Users can also receive alerts from the system, enabling immediate action in case of malfunctions, for example.

[0223] For example, when snowfall is predicted, the server automatically sets the schedule for the next snow removal operation based on weather data and sends route information to the terminal. The terminal efficiently performs the work according to the designated route while checking the condition of the snow removal area in real time. If the terminal detects an obstacle, it will either avoid it or send data to the server for a retry. This provides a safe environment for the elderly and enables flexible snow removal responses according to the season and conditions.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server collects weather and terrain data and analyzes it in real time. This allows it to identify areas and times when snow removal is needed and plan optimal snow removal routes and schedules.

[0227] Step 2:

[0228] The server sends the planned snow removal route and schedule to each terminal. The terminal then prepares for snow removal work based on the received information.

[0229] Step 3:

[0230] The terminal starts snow removal work based on the route received from the server. It automatically removes snow while monitoring the surrounding conditions with its built-in sensors and camera.

[0231] Step 4:

[0232] The terminal feeds back data collected during operation (e.g., obstacle information, work progress) to the server. This allows the server to obtain data to improve the accuracy and efficiency of snow removal operations.

[0233] Step 5:

[0234] The server analyzes the data fed back from the terminals and readjusts snow removal routes and schedules as needed. This enables more efficient snow removal operations.

[0235] Step 6:

[0236] Users can check the status of snow removal operations through a dedicated application. The application displays the location of the snowblower and the progress of the work in real time, and users can also take action as needed.

[0237] Step 7:

[0238] The server constantly monitors the status of the terminal and immediately notifies the user if any abnormalities or malfunctions are detected. This establishes a system that allows for a rapid response.

[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] In an aging society, snow removal at home is a significant burden, especially in snowy regions. Traditional snow removal methods require manual labor, raising concerns about safety and effectiveness, particularly for the elderly and those with physical disabilities. Furthermore, snow removal is not performed efficiently in real time, making it difficult to respond quickly to operational errors or equipment malfunctions.

[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 means for autonomous operating machines to be deployed at individual facilities, a central processing unit to analyze weather and terrain information and plan operating routes, means for transmitting the operating routes to the autonomous operating machines to instruct them to perform tasks, means for terminals to feed back information collected during the work to the central processing unit, the central processing unit to re-analyze the operation algorithm based on the feedback information and readjust the route, and means for users to monitor the information using a display device and issue manual instructions as needed. This enables safe and efficient automated snow removal for all users, including the elderly.

[0244] An "autonomous machine" is a device that can automatically perform predetermined tasks in response to instructions from an external source.

[0245] A "centralized processing unit" is a computer system that collects multiple data sets, analyzes them, and derives results.

[0246] "Weather information" refers to data related to the weather, such as temperature, precipitation, and wind speed.

[0247] "Topographic information" refers to information about the shape and characteristics of the ground surface in a specific area.

[0248] A "movement path" is the route that an autonomous machine should follow when performing a task.

[0249] "Feedback" is the process of incorporating information sent from one system to an external source back into that system.

[0250] An "operation algorithm" is a set of steps or computational methods that enable an autonomous machine to efficiently perform a task.

[0251] A "display device" is a device used to present information to a user visually.

[0252] This invention employs a configuration in which a central processing unit (server), an autonomous machine (terminal), and a user display device (application) work together to efficiently operate an autonomous machine. The purpose of this system is to reduce the burden on users, including the elderly, by automatically planning and adjusting the operating route based primarily on weather and terrain information.

[0253] The server acquires weather and topographic information in real time from external data sources. For example, it uses weather information APIs on the internet to obtain weather data and analyzes the operation path using machine learning models based on that data. The analysis results are sent as instructions to the autonomous operating machine.

[0254] The autonomous operating machine, acting as the terminal, uses built-in hardware such as sensors and cameras to detect the surrounding terrain and obstacles in real time. This allows it to efficiently and safely carry out tasks while following the instructed operating path. Information on obstacles detected during the operation is fed back to the server in real time, and the server readjusts the operating path as needed based on this information.

[0255] Users can monitor the operation status of autonomous machinery in real time using a dedicated application installed on their smartphone or tablet. This application allows them to check the progress of snow removal work and the status of the autonomous machinery, and to issue manual instructions as needed. In the event of a malfunction or abnormality, the app will notify the user with a warning, enabling a quick response.

[0256] As a concrete example, consider a situation where snowfall is predicted. The server automatically sets a snow removal schedule based on real-time weather data, and this schedule and route information are transmitted to the terminal. The terminal efficiently removes snow while following the designated route, and when it detects an obstacle, it sends that information to the server and adjusts the route accordingly.

[0257] Examples of prompts for a generative AI model:

[0258] "Please explain the specific operational flow of an automated snow removal system for elderly households when snowfall is expected the following day."

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server receives real-time weather data from a weather information API and topographic data from a topographic information database as input. Using this data, the server identifies areas requiring snow removal and plans the optimal route for each household. In this process, it utilizes a generative AI model to select efficient routes based on historical data. As output, it generates a snow removal schedule and route information for each household and transmits it to autonomous operating machines.

[0262] Step 2:

[0263] The autonomous operating machine, acting as the terminal, receives snow removal schedule and route information from the server as input. Using its built-in sensors and camera, the terminal detects the surrounding terrain and obstacles in real time, and based on the data obtained, begins work according to the designated route. Specifically, when the terminal detects an obstacle, it adjusts its direction of travel and continues snow removal safely. As output, it feeds back environmental information collected during the operation to the server.

[0264] Step 3:

[0265] The server receives environmental information fed back from the terminal as input and reanalyzes the snow removal algorithm based on this information. If necessary, the server generates newly optimized route instructions and sends them back to the terminal. This process makes it possible to continuously improve the efficiency of snow removal work. Specifically, the server analyzes the real-time data obtained and updates the data to be useful for subsequent operations.

[0266] Step 4:

[0267] Users monitor snow removal progress and terminal operating status received from the server via an application installed on their smartphone or tablet. Users can manually issue commands to the terminal via the app as needed. For example, if a user wants to prioritize snow removal in a specific area, they can send a command via the app, and the terminal will respond immediately. As output, users can receive visual operational feedback.

[0268] (Application Example 1)

[0269] 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."

[0270] In modern cities, snow removal during snowfall is often labor-intensive and inefficient, making it particularly difficult in areas with aging populations. Furthermore, flexible snow removal that can adapt to fluctuating weather conditions is required, but conventional methods are sometimes insufficient. Therefore, the challenge is to enable efficient snow removal throughout urban areas and provide a safe and comfortable living environment.

[0271] 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.

[0272] In this invention, the server includes means for analyzing weather information and topographic information to plan a removal route, means for distributing the removal route to an autonomously driven removal device to instruct it to perform the work, and means for re-analyzing the removal algorithm based on the feedback information to adjust the route. This enables efficient and flexible snow removal work throughout the entire city.

[0273] An "autonomous removal device" is a mobile mechanical device that operates automatically according to weather conditions and has the function of removing snow and obstacles.

[0274] A "data processing device" is a computer system that analyzes collected weather and topographic information and calculates the optimal route for removal work.

[0275] A "removal route" is a route set up for carrying out removal work, and it is a route plan designed to ensure efficient work.

[0276] A "terminal device" is a device mounted on an autonomous removal device that has communication capabilities to transmit information acquired during operation to a data processing device.

[0277] "Users" are individuals who have the authority to operate and monitor the removal system, and who can use communication devices to check the system's status and issue instructions as needed.

[0278] "Communication equipment" refers to a system for sending and receiving information between a data processing device and a terminal device, and is a device that assists users in operating the system.

[0279] "Feedback" refers to information transmitted from a terminal device to a data processing device, including environmental data and work status acquired during the work process.

[0280] "Weather information" refers to data related to weather conditions such as snowfall, temperature, and wind speed, and is a factor that influences the plan for removal work.

[0281] "Topographic information" refers to data on the terrain of the area where the work is carried out, including terrain changes and the positions of obstacles.

[0282] The system for implementing this invention is realized by a central data processing device (hereinafter referred to as the server) controlling a plurality of autonomous driving type removal devices (hereinafter referred to as terminals). The server executes a program constructed in a programming language such as Python in order to analyze meteorological information and topographic information in real time. Thereby, it plans the removal route and sends an optimal instruction to each terminal. The server also re-analyzes the removal algorithm based on the feedback information from the terminals and adjusts the route as necessary.

[0283] The terminal has an autonomous driving function and uses ROS (Robot Operating System) and various sensors (such as LIDAR and cameras) to automatically perform work according to the instructed route. It sends the data acquired by the sensors during work to the server and feeds back the environmental information in real time. When an obstacle is detected, it reports that information to the server and confirms the next operation.

[0284] The user accesses the system through a communication device such as a smartphone or tablet to check the progress of the work and the status of the terminals. For this, an application developed with Flutter is used, and information is provided by a real-time database such as Firebase. If necessary, the user can also manually instruct the operation of the terminal.

[0285] As a specific example, on a night when heavy snow is predicted, the server uses a weather forecast API to obtain meteorological information and plans the route so that all major roads are cleared by the next morning. The terminal operates based on that plan and efficiently performs snow removal. As a result, citizens can commute and go to school with peace of mind.

[0286] Examples of prompt sentences input to the generative AI model include "Please explain the mechanism of a system in which an autonomous driving snow removal machine effectively removes snow using ROS and sensors."

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server collects meteorological data. As input, it uses real-time data obtained from a weather forecast API (e.g., OpenWeatherMap API). The data includes temperature, snowfall, wind speed, etc., and based on this, the necessity of snow removal is evaluated. As output, the necessary snow removal area and time are determined. As a specific operation, it periodically sends requests to the API to obtain updated meteorological information.

[0290] Step 2:

[0291] The server obtains and analyzes terrain information. As input, it uses a geographical information API (e.g., Google Maps API) and built-in map data. It analyzes the terrain gradient, road layout, expected obstacle information, etc., and formulates an effective removal route. The output is detailed route information for instructing the terminal. Specifically, it uses an analysis algorithm to determine the priority of roads and safe routes.

[0292] Step 3:

[0293] The server sends the calculated removal route to the terminal. As input, it uses the data integrated with the judgments and analysis results obtained in Step 1 and Step 2. The output to the terminal is the specific driving route and the start time of the operation. As a specific operation, it communicates with each terminal through the network and transfers the instruction data.

[0294] Step 4:

[0295] The terminal autonomously begins snow removal work according to the route information it receives. Using route information transmitted from the server as input, the terminal uses its built-in sensors (e.g., LiDAR and camera) to check its current location and terrain details in real time. The output is feedback information such as obstacle avoidance and confirmation of work completion. Specific operations include controlling the motor to travel along a designated route and operating the snow removal blade as needed.

[0296] Step 5:

[0297] The terminal feeds back environmental data acquired during operation to the server. The input is terrain and obstacle information acquired in real time by the terminal's sensors. The output sent to the server is feedback information to improve work efficiency. Specifically, it periodically generates data packets and feeds them back to the server.

[0298] Step 6:

[0299] Users check the progress of their devices using a smartphone app. The input is real-time device status information transmitted from a server. The output is the progress status and warning messages displayed to the user. Specifically, the app displays the progress of the task and the estimated remaining time, and allows for manual operation as needed.

[0300] 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.

[0301] This invention is a system that combines an automated snowblower installed in a home, a server and user application to control it, and an emotion engine that recognizes the user's emotions. The system aims to enable efficient snow removal while simultaneously reducing the psychological burden on the user.

[0302] The server analyzes weather data and terrain data to formulate a snow removal plan. It transmits the planned snow removal route to each terminal and notifies the user through a dedicated application. At this time, the emotion engine analyzes the user's voice and expression in real time and evaluates the user's emotional state online.

[0303] The user can check the progress of the snow removal work in real time through the dedicated application. When the user feels anxious or stressed, the application uses the emotion engine to analyze that emotion. For example, when the user raises their voice or shows a displeased expression, a proposal to adjust the work schedule is made accordingly. In addition, it is possible to display messages and advice according to the situation to reduce the user's psychological burden.

[0304] The terminal executes the work as an autonomous snow removal machine and feeds back the data during the work to the server. Thereby, the server re-analyzes the data to improve the efficiency of the snow removal work. The feedback information is utilized to improve future work plans.

[0305] As a specific example, when the user is checking the progress of the snow removal work and the application detects the user's anxiety, the emotion engine displays a message on the screen that gives a sense of reassurance such as "The work is progressing smoothly". Also, when a particularly severe weather situation is expected, by sending a notice urging the user to take early countermeasures, it is possible to prepare for unexpected situations.

[0306] In this way, the present invention not only improves the efficiency of the snow removal work, but also provides a service that takes into account the emotions of the user, thereby realizing a more reassuring and safe living environment.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The server collects and analyzes weather and topographic data. It identifies areas requiring snow removal and develops optimal snow removal routes and schedules.

[0310] Step 2:

[0311] The server sends the formulated snow removal plan to the terminal (autonomous snowplow). It also notifies the user's application of this information.

[0312] Step 3:

[0313] The terminal automatically starts snow removal work according to the received snow removal route. During the work, it continuously feeds back data on the ground and obstacles to the server.

[0314] Step 4:

[0315] Users can check the progress of snow removal work in real time through the application, and an emotion engine analyzes the user's emotions by analyzing voice, facial expressions, etc.

[0316] Step 5:

[0317] If the emotion engine detects user anxiety or stress, the server adjusts the snow removal schedule based on that information and revises the work plan if necessary.

[0318] Step 6:

[0319] Users receive reassuring messages and specific advice from the application, which reduces their psychological burden.

[0320] Step 7:

[0321] The server re-analyzes the snow removal algorithm based on the feedback data and makes adjustments to improve the accuracy of the next work plan.

[0322] As a result, the system not only improves the efficiency of snow removal but also enhances the user's quality of life through emotional care.

[0323] (Example 2)

[0324] 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".

[0325] Autonomous snow removal operations require the development of efficient snow removal plans, but it is also necessary to consider the psychological burden on users. However, conventional systems lacked sufficient consideration for the mental well-being of users, often leading to situations where users felt anxious or stressed. Furthermore, there was room for improvement in the mechanism for providing real-time feedback on snow removal data and immediately optimizing the algorithm.

[0326] 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.

[0327] In this invention, the server includes means for analyzing weather and topographic information to plan snow removal routes, means for re-analyzing the snow removal algorithm based on the collected information to readjust the routes, and means for evaluating the user's emotional state using an emotion analysis engine and providing feedback. This enables efficient snow removal work, reduces the psychological burden on users, and provides an environment in which the system can be used with peace of mind.

[0328] An "autonomous snow removal system" is a mechanical device installed at each facility that automatically performs snow removal work according to a programmed route.

[0329] A "data processing device" is a computer system that analyzes weather and topographic information to formulate snow removal route plans and to re-analyze algorithms based on the collected information.

[0330] An "information terminal" is an electronic device used to feed back information collected during snow removal work to a data processing device.

[0331] An "emotion analysis engine" is a software technology that evaluates the user's emotional state based on voice and facial expression data and generates appropriate feedback.

[0332] "Snow removal route" refers to the specific route and schedule that an autonomous snow removal system should follow when performing snow removal operations.

[0333] This invention is a system for achieving efficient and user-friendly snow removal using an autonomous snow removal device installed at a specific facility. The system's core, a data processing unit, analyzes weather data obtained from weather information services and topographic data obtained from geographic information services to plan the optimal snow removal route. This process utilizes data feeds such as the "OpenWeatherMap API" and the "Google Maps API".

[0334] The server transmits the planned snow removal route to the autonomous snow removal device, which then performs physical snow removal operations according to the device's instructions. This allows the snow removal device to efficiently remove snow from the facility. During snow removal, the information terminal feeds its progress back to the data processing device in real time, and the data processing device re-analyzes this information to adjust the route and schedule, thereby further improving efficiency.

[0335] Furthermore, users can check the progress of snow removal work through a dedicated mobile application. This application incorporates an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, if a user is worried about delays in the work, the application will generate and display a reassuring message such as, "The work is progressing as scheduled."

[0336] A possible concrete example is to input a prompt sentence such as, "What message should be displayed if a user feels uneasy about snow removal?" into an emotion analysis engine, and then use a generative AI model to automatically provide appropriate feedback.

[0337] This system not only makes snow removal more efficient, but also minimizes the psychological burden on users. As a result, users can confidently incorporate this system into their daily lives.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The server obtains weather data and topographic data from weather information services and geographic information services, respectively. Based on this input data, it plans snow removal routes using an analysis algorithm. Specifically, the server obtains snowfall forecasts from the "OpenWeatherMap API" and topographic information from the "Google Maps API". As a result of the analysis, the optimal snow removal route and work schedule are output.

[0341] Step 2:

[0342] The server transmits the planned snow removal route to an information terminal. The information terminal forwards this data to the autonomous snow removal device, which then begins snow removal according to the specified route. The input is route data from the server, and the output is route instructions to the autonomous snow removal device. In practice, the snow removal device physically moves along the route and removes the snow.

[0343] Step 3:

[0344] The terminal feeds back progress data collected during snow removal operations to the server. Based on this data, the server evaluates the current work efficiency and readjusts the snow removal route and work schedule as needed. The input is progress data, and the output is optimized new route information. In specific operations, the terminal transmits information such as the distance and time the device has traveled to the server via voice.

[0345] Step 4:

[0346] Users can check the progress of snow removal work and receive feedback through a dedicated application. The application has an emotion analysis engine that analyzes voice and facial expression input to evaluate the user's emotional state. Specifically, it performs emotion analysis using the user's voice input and provides a reassuring message as output.

[0347] Step 5:

[0348] The server generates feedback messages using a generative AI model based on the user's emotional state obtained from the emotion analysis engine. The prompt is "What message should be displayed if the user feels anxious about the snow removal work?", and the output is a specific support message. For example, based on the emotion analysis results, it might send a reassuring message such as "The work is progressing as planned."

[0349] (Application Example 2)

[0350] 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 will be referred to as the "terminal."

[0351] The goal is not only to improve the efficiency of snow removal operations using autonomous snowplows, but also to reduce the anxiety and stress users feel regarding the progress of the work. However, conventional systems have struggled to provide feedback and appropriate messages that take user emotions into consideration. As a result, users sometimes feel anxious about the progress of the work or changes in the weather, leading to significant psychological burden.

[0352] 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.

[0353] In this invention, the server includes emotion recognition means for detecting the user's emotional state, means for optimizing the work schedule based on the user's emotional state evaluated by the emotion recognition means, and means for providing messages to reduce the user's psychological burden. This reduces the user's psychological burden by adjusting the work content and information provided according to the user's emotions, making it possible to monitor snow removal work with peace of mind.

[0354] An "autonomous snow removal machine" is a mechanical device installed in homes or other locations that has the function of autonomously performing snow removal work.

[0355] A "server" is a computing device that analyzes data and issues instructions to snowplows regarding their work routes.

[0356] "Weather data" refers to data that shows information about weather conditions.

[0357] "Topographic data" refers to data that shows information about the characteristics of the ground surface in a given area.

[0358] A "snow removal route" is a route that indicates the path a snow removal machine will take to perform its work.

[0359] A "terminal" is a device installed on a snowblower that collects information during operation and transmits it to a server.

[0360] "Feedback" is the process of sending data back from a terminal to a server.

[0361] A "snow removal algorithm" is a computational method and program for efficiently performing snow removal work.

[0362] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and voice to determine their emotional state.

[0363] A "work schedule" is a plan outlining the time and sequence of snow removal operations.

[0364] "Psychological burden" is a concept that refers to the anxiety and stress that users experience.

[0365] A "message" is a form of communication consisting of text or audio used to convey information to a user.

[0366] This invention is a snow removal system using an automated snowblower installed in a home. The system utilizes emotion recognition technology to improve the user experience.

[0367] The server receives weather and terrain data, analyzes this data to plan an efficient snow removal route. The server transmits the planned route information to the snowplow and instructs it to perform snow removal. During snow removal, the terminals collect work data and feed it back to the server, which then re-evaluates and adjusts the snow removal route and snow removal algorithm based on that data.

[0368] Furthermore, emotion recognition functionality is provided through the user's smartphone or smart glasses. The user's voice and facial expressions are captured using a camera and microphone, and these are analyzed by the emotion recognition system. For this purpose, voice processing software is used for voice recognition, and image recognition software is used for facial expressions. Based on the analysis results, encouraging messages and advice tailored to the user's emotional state are provided.

[0369] For example, if severe weather is detected during snow removal and the user becomes anxious, a prompt message is sent to the generating AI model saying, "A user with an anxious expression is checking this. Please think of some words of encouragement:" and the user is shown a message such as, "Everything is going smoothly, so please don't worry." This process reduces the user's psychological burden while ensuring safe and comfortable snow removal work.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The server receives weather data from a weather data provider and terrain data from a terrain provider. Using this data, it performs data analysis to plan snow removal routes. The input data consists of weather and terrain data, and the output generates efficient snow removal routes.

[0373] Step 2:

[0374] The server transmits the generated snow removal route information to the automated snowblower, instructing it to perform snow removal work. The route information includes the direction of travel and the amount of snow to be removed, which allows the snowblower to automatically follow the specified path.

[0375] Step 3:

[0376] The terminal continuously collects operational and environmental data from the snowblower and feeds this data back to the server. The input is sensor data from the snowblower, and the output is data transmission to the server.

[0377] Step 4:

[0378] The server analyzes the feedback data and optimizes the snow removal algorithm. This allows for readjustment of snow removal routes, enabling more efficient work. The analysis results are output as a new snow removal route.

[0379] Step 5:

[0380] Users operate a dedicated application using their smartphone or smart glasses to check the progress of snow removal work. The application displays snow removal status data as input information. Users use this information to monitor the progress of the work.

[0381] Step 6:

[0382] The user terminal uses a camera and microphone to collect the user's facial expressions and voice. An emotion recognition system analyzes this data to determine the user's emotional state. The input data consists of audio and visual data, and the output is an evaluation of the emotional state.

[0383] Step 7:

[0384] The server uses a generative AI model to send appropriate messages to the user based on the evaluated emotional state. The input is the emotional state and a prompt, and the output is a message of encouragement or caution. For example, based on the prompt "A user with an anxious expression is checking this. Please think of some words of encouragement:", it will provide a message of reassurance.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention aims to effectively and efficiently perform snow removal tasks for households, including those of the elderly, by constructing an automated snow removal system. This system mainly consists of a server, terminals (autonomous snow removal machines), and a user application.

[0402] The server acts as the central hub, collecting and analyzing weather and terrain data in real time. Based on this data, the server plans the optimal snow removal route for each household and transmits the schedule to the automated snowplows. The server also constantly monitors the overall system's functionality by monitoring the real-time status information of each terminal.

[0403] The automated snowplow terminal operates based on instructions from the server. It efficiently removes snow by detecting ground conditions and surrounding obstacles using built-in sensors and cameras. The terminal also feeds back environmental data collected during operation to the server. This data is then re-analyzed on the server to improve the terminal's operational accuracy and enhance future work efficiency.

[0404] Users can access the system via a dedicated smartphone or tablet application. This application displays the operating status of the snowblower in real time, and allows users to manually control snow removal operations as needed. Users can also receive alerts from the system, enabling immediate action in case of malfunctions, for example.

[0405] For example, when snowfall is predicted, the server automatically sets the schedule for the next snow removal operation based on weather data and sends route information to the terminal. The terminal efficiently performs the work according to the designated route while checking the condition of the snow removal area in real time. If the terminal detects an obstacle, it will either avoid it or send data to the server for a retry. This provides a safe environment for the elderly and enables flexible snow removal responses according to the season and conditions.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The server collects weather and terrain data and analyzes it in real time. This allows it to identify areas and times when snow removal is needed and plan optimal snow removal routes and schedules.

[0409] Step 2:

[0410] The server sends the planned snow removal route and schedule to each terminal. The terminal then prepares for snow removal work based on the received information.

[0411] Step 3:

[0412] The terminal starts snow removal work based on the route received from the server. It automatically removes snow while monitoring the surrounding conditions with its built-in sensors and camera.

[0413] Step 4:

[0414] The terminal feeds back data collected during operation (e.g., obstacle information, work progress) to the server. This allows the server to obtain data to improve the accuracy and efficiency of snow removal operations.

[0415] Step 5:

[0416] The server analyzes the data fed back from the terminals and readjusts snow removal routes and schedules as needed. This enables more efficient snow removal operations.

[0417] Step 6:

[0418] Users can check the status of snow removal operations through a dedicated application. The application displays the location of the snowblower and the progress of the work in real time, and users can also take action as needed.

[0419] Step 7:

[0420] The server constantly monitors the status of the terminal and immediately notifies the user if any abnormalities or malfunctions are detected. This establishes a system that allows for a rapid response.

[0421] (Example 1)

[0422] 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."

[0423] In an aging society, snow removal at home is a significant burden, especially in snowy regions. Traditional snow removal methods require manual labor, raising concerns about safety and effectiveness, particularly for the elderly and those with physical disabilities. Furthermore, snow removal is not performed efficiently in real time, making it difficult to respond quickly to operational errors or equipment malfunctions.

[0424] 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.

[0425] In this invention, the server includes means for autonomous operating machines to be deployed at individual facilities, a central processing unit to analyze weather and terrain information and plan operating routes, means for transmitting the operating routes to the autonomous operating machines to instruct them to perform tasks, means for terminals to feed back information collected during the work to the central processing unit, the central processing unit to re-analyze the operation algorithm based on the feedback information and readjust the route, and means for users to monitor the information using a display device and issue manual instructions as needed. This enables safe and efficient automated snow removal for all users, including the elderly.

[0426] An "autonomous machine" is a device that can automatically perform predetermined tasks in response to instructions from an external source.

[0427] A "centralized processing unit" is a computer system that collects multiple data sets, analyzes them, and derives results.

[0428] "Weather information" refers to data related to the weather, such as temperature, precipitation, and wind speed.

[0429] "Topographic information" refers to information about the shape and characteristics of the ground surface in a specific area.

[0430] A "movement path" is the route that an autonomous machine should follow when performing a task.

[0431] "Feedback" is the process of incorporating information sent from one system to an external source back into that system.

[0432] An "operation algorithm" is a set of steps or computational methods that enable an autonomous machine to efficiently perform a task.

[0433] A "display device" is a device used to present information to a user visually.

[0434] This invention employs a configuration in which a central processing unit (server), an autonomous machine (terminal), and a user display device (application) work together to efficiently operate an autonomous machine. The purpose of this system is to reduce the burden on users, including the elderly, by automatically planning and adjusting the operating route based primarily on weather and terrain information.

[0435] The server acquires weather and topographic information in real time from external data sources. For example, it uses weather information APIs on the internet to obtain weather data and analyzes the operation path using machine learning models based on that data. The analysis results are sent as instructions to the autonomous operating machine.

[0436] The autonomous operating machine, acting as the terminal, uses built-in hardware such as sensors and cameras to detect the surrounding terrain and obstacles in real time. This allows it to efficiently and safely carry out tasks while following the instructed operating path. Information on obstacles detected during the operation is fed back to the server in real time, and the server readjusts the operating path as needed based on this information.

[0437] Users can monitor the operation status of autonomous machinery in real time using a dedicated application installed on their smartphone or tablet. This application allows them to check the progress of snow removal work and the status of the autonomous machinery, and to issue manual instructions as needed. In the event of a malfunction or abnormality, the app will notify the user with a warning, enabling a quick response.

[0438] As a concrete example, consider a situation where snowfall is predicted. The server automatically sets a snow removal schedule based on real-time weather data, and this schedule and route information are transmitted to the terminal. The terminal efficiently removes snow while following the designated route, and when it detects an obstacle, it sends that information to the server and adjusts the route accordingly.

[0439] Examples of prompts for a generative AI model:

[0440] "Please explain the specific operational flow of an automated snow removal system for elderly households when snowfall is expected the following day."

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

[0442] Step 1:

[0443] The server receives real-time weather data from a weather information API and topographic data from a topographic information database as input. Using this data, the server identifies areas requiring snow removal and plans the optimal route for each household. In this process, it utilizes a generative AI model to select efficient routes based on historical data. As output, it generates a snow removal schedule and route information for each household and transmits it to autonomous operating machines.

[0444] Step 2:

[0445] The autonomous operating machine, acting as the terminal, receives snow removal schedule and route information from the server as input. Using its built-in sensors and camera, the terminal detects the surrounding terrain and obstacles in real time, and based on the data obtained, begins work according to the designated route. Specifically, when the terminal detects an obstacle, it adjusts its direction of travel and continues snow removal safely. As output, it feeds back environmental information collected during the operation to the server.

[0446] Step 3:

[0447] The server receives environmental information fed back from the terminal as input and reanalyzes the snow removal algorithm based on this information. If necessary, the server generates newly optimized route instructions and sends them back to the terminal. This process makes it possible to continuously improve the efficiency of snow removal work. Specifically, the server analyzes the real-time data obtained and updates the data to be useful for subsequent operations.

[0448] Step 4:

[0449] Users monitor snow removal progress and terminal operating status received from the server via an application installed on their smartphone or tablet. Users can manually issue commands to the terminal via the app as needed. For example, if a user wants to prioritize snow removal in a specific area, they can send a command via the app, and the terminal will respond immediately. As output, users can receive visual operational feedback.

[0450] (Application Example 1)

[0451] 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."

[0452] In modern cities, snow removal during snowfall is often labor-intensive and inefficient, making it particularly difficult in areas with aging populations. Furthermore, flexible snow removal that can adapt to fluctuating weather conditions is required, but conventional methods are sometimes insufficient. Therefore, the challenge is to enable efficient snow removal throughout urban areas and provide a safe and comfortable living environment.

[0453] 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.

[0454] In this invention, the server includes means for analyzing weather information and topographic information to plan a removal route, means for distributing the removal route to an autonomously driven removal device to instruct it to perform the work, and means for re-analyzing the removal algorithm based on the feedback information to adjust the route. This enables efficient and flexible snow removal work throughout the entire city.

[0455] An "autonomous removal device" is a mobile mechanical device that operates automatically according to weather conditions and has the function of removing snow and obstacles.

[0456] A "data processing device" is a computer system that analyzes collected weather and topographic information and calculates the optimal route for removal work.

[0457] A "removal route" is a route set up for carrying out removal work, and it is a route plan designed to ensure efficient work.

[0458] A "terminal device" is a device mounted on an autonomous removal device that has communication capabilities to transmit information acquired during operation to a data processing device.

[0459] "Users" are individuals who have the authority to operate and monitor the removal system, and who can use communication devices to check the system's status and issue instructions as needed.

[0460] "Communication equipment" refers to a system for sending and receiving information between a data processing device and a terminal device, and is a device that assists users in operating the system.

[0461] "Feedback" refers to information transmitted from a terminal device to a data processing device, including environmental data and work status acquired during the work process.

[0462] "Weather information" refers to data related to weather conditions such as snowfall, temperature, and wind speed, and is a factor that influences the plan for removal work.

[0463] "Topographic information" refers to data about the terrain of the work area, including changes in terrain and the location of obstacles.

[0464] The system implementing this invention is realized by a central data processing unit (hereinafter referred to as the server) controlling multiple autonomous removal devices (hereinafter referred to as terminals). The server executes a program written in a programming language such as Python to analyze weather information and terrain information in real time. This allows it to plan removal routes and send optimal instructions to each terminal. The server also reanalyzes the removal algorithm based on feedback information from the terminals and adjusts the route as necessary.

[0465] The terminal has autonomous driving capabilities and uses ROS (Robot Operating System) and various sensors (such as LIDAR and cameras) to automatically perform tasks according to the instructed path. During the task, it transmits data acquired by the sensors to the server and provides real-time feedback on the environment. If an obstacle is detected, it reports that information to the server and confirms the next action.

[0466] Users access the system via smartphones and tablets to check the progress of their tasks and the status of their devices. This uses an application developed with Flutter, and information is provided by a real-time database such as Firebase. Users can also manually instruct the device to operate as needed.

[0467] As a concrete example, on a night when heavy snowfall is expected, the server uses a weather forecast API to obtain weather information and plans routes so that all major roads remain clear until the following morning. Terminals then operate based on this plan, efficiently clearing snow. As a result, citizens can commute to work and school with peace of mind.

[0468] An example of a prompt to input into the generated AI model would be, "Explain how an autonomous snowplow system works, using ROS and sensors to effectively remove snow."

[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 data. It uses real-time data obtained from weather forecast APIs (e.g., OpenWeatherMap API) as input. This data includes temperature, snowfall, and wind speed, and is used to assess the need for snow removal. The output determines the required snow removal area and time. Specifically, the system periodically sends requests to the API to retrieve updated weather information.

[0472] Step 2:

[0473] The server acquires and analyzes terrain information. It uses geographic information APIs (e.g., Google Maps API) or built-in map data as input. It analyzes terrain gradients, road layouts, and anticipated obstacles to plan effective routes for removal. The output is detailed route information for instructing terminals. Specifically, it uses analytical algorithms to determine road priorities and safe routes.

[0474] Step 3:

[0475] The server sends the calculated removal route to the terminal. The input uses data that integrates the judgment and analysis results obtained in steps 1 and 2. The output to the terminal includes the specific travel route and the start time of the work. In terms of operation, it communicates with each terminal via the network and transfers instruction data.

[0476] Step 4:

[0477] The terminal autonomously begins snow removal work according to the route information it receives. Using route information transmitted from the server as input, the terminal uses its built-in sensors (e.g., LiDAR and camera) to check its current location and terrain details in real time. The output is feedback information such as obstacle avoidance and confirmation of work completion. Specific operations include controlling the motor to travel along a designated route and operating the snow removal blade as needed.

[0478] Step 5:

[0479] The terminal feeds back environmental data acquired during operation to the server. The input is terrain and obstacle information acquired in real time by the terminal's sensors. The output sent to the server is feedback information to improve work efficiency. Specifically, it periodically generates data packets and feeds them back to the server.

[0480] Step 6:

[0481] Users check the progress of their devices using a smartphone app. The input is real-time device status information transmitted from a server. The output is the progress status and warning messages displayed to the user. Specifically, the app displays the progress of the task and the estimated remaining time, and allows for manual operation as needed.

[0482] 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.

[0483] This invention is a system that combines an automated snowblower installed in a home, a server and user application to control it, and an emotion engine that recognizes the user's emotions. The system aims to enable efficient snow removal while simultaneously reducing the psychological burden on the user.

[0484] The server analyzes weather and terrain data to formulate a snow removal plan. It transmits the planned snow removal route to each terminal and notifies users through a dedicated application. At this time, an emotion engine analyzes the user's voice and facial expressions in real time and evaluates the user's emotional state online.

[0485] Users can check the progress of snow removal work in real time using a dedicated application. The application uses an emotion engine to analyze the user's emotions if they feel anxious or stressed. For example, if the user raises their voice or makes a displeased face, it will suggest adjusting the work schedule accordingly. It can also display situation-appropriate messages and advice to reduce the user's psychological burden.

[0486] The terminal operates as an autonomous snowplow, feeding back data from its operation to the server. The server then re-analyzes the data to improve the efficiency of snow removal operations. The feedback information is used to improve future work plans.

[0487] For example, if the application detects user anxiety while the user is checking the progress of snow removal work, the emotion engine will display a reassuring message on the screen stating that "the work is progressing smoothly." Furthermore, especially when severe weather conditions are expected, the application can send a notification urging the user to take early action, thus preparing for unexpected situations.

[0488] Thus, this invention not only streamlines snow removal operations but also contributes to creating a safer and more secure living environment by providing services that take into consideration the user's feelings.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The server collects and analyzes weather and topographic data. It identifies areas requiring snow removal and develops optimal snow removal routes and schedules.

[0492] Step 2:

[0493] The server sends the formulated snow removal plan to the terminal (autonomous snowplow). It also notifies the user's application of this information.

[0494] Step 3:

[0495] The terminal automatically starts snow removal work according to the received snow removal route. During the work, it continuously feeds back data on the ground and obstacles to the server.

[0496] Step 4:

[0497] Users can check the progress of snow removal work in real time through the application, and an emotion engine analyzes the user's emotions by analyzing voice, facial expressions, etc.

[0498] Step 5:

[0499] If the emotion engine detects user anxiety or stress, the server adjusts the snow removal schedule based on that information and revises the work plan if necessary.

[0500] Step 6:

[0501] Users receive reassuring messages and specific advice from the application, which reduces their psychological burden.

[0502] Step 7:

[0503] The server re-analyzes the snow removal algorithm based on the feedback data and makes adjustments to improve the accuracy of the next work plan.

[0504] As a result, the system not only improves the efficiency of snow removal but also enhances the user's quality of life through emotional care.

[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] Autonomous snow removal operations require the development of efficient snow removal plans, but it is also necessary to consider the psychological burden on users. However, conventional systems lacked sufficient consideration for the mental well-being of users, often leading to situations where users felt anxious or stressed. Furthermore, there was room for improvement in the mechanism for providing real-time feedback on snow removal data and immediately optimizing the algorithm.

[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 means for analyzing weather and topographic information to plan snow removal routes, means for re-analyzing the snow removal algorithm based on the collected information to readjust the routes, and means for evaluating the user's emotional state using an emotion analysis engine and providing feedback. This enables efficient snow removal work, reduces the psychological burden on users, and provides an environment in which the system can be used with peace of mind.

[0510] An "autonomous snow removal system" is a mechanical device installed at each facility that automatically performs snow removal work according to a programmed route.

[0511] A "data processing device" is a computer system that analyzes weather and topographic information to formulate snow removal route plans and to re-analyze algorithms based on the collected information.

[0512] An "information terminal" is an electronic device used to feed back information collected during snow removal work to a data processing device.

[0513] An "emotion analysis engine" is a software technology that evaluates the user's emotional state based on voice and facial expression data and generates appropriate feedback.

[0514] "Snow removal route" refers to the specific route and schedule that an autonomous snow removal system should follow when performing snow removal operations.

[0515] This invention is a system for achieving efficient and user-friendly snow removal using an autonomous snow removal device installed at a specific facility. The system's core, a data processing unit, analyzes weather data obtained from weather information services and topographic data obtained from geographic information services to plan the optimal snow removal route. This process utilizes data feeds such as the "OpenWeatherMap API" and the "Google Maps API".

[0516] The server transmits the planned snow removal route to the autonomous snow removal device, which then performs physical snow removal operations according to the device's instructions. This allows the snow removal device to efficiently remove snow from the facility. During snow removal, the information terminal feeds its progress back to the data processing device in real time, and the data processing device re-analyzes this information to adjust the route and schedule, thereby further improving efficiency.

[0517] Furthermore, users can check the progress of snow removal work through a dedicated mobile application. This application incorporates an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, if a user is worried about delays in the work, the application will generate and display a reassuring message such as, "The work is progressing as scheduled."

[0518] A possible concrete example is to input a prompt sentence such as, "What message should be displayed if a user feels uneasy about snow removal?" into an emotion analysis engine, and then use a generative AI model to automatically provide appropriate feedback.

[0519] This system not only makes snow removal more efficient, but also minimizes the psychological burden on users. As a result, users can confidently incorporate this system into their daily lives.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] The server obtains weather data and topographic data from weather information services and geographic information services, respectively. Based on this input data, it plans snow removal routes using an analysis algorithm. Specifically, the server obtains snowfall forecasts from the "OpenWeatherMap API" and topographic information from the "Google Maps API". As a result of the analysis, the optimal snow removal route and work schedule are output.

[0523] Step 2:

[0524] The server transmits the planned snow removal route to an information terminal. The information terminal forwards this data to the autonomous snow removal device, which then begins snow removal according to the specified route. The input is route data from the server, and the output is route instructions to the autonomous snow removal device. In practice, the snow removal device physically moves along the route and removes the snow.

[0525] Step 3:

[0526] The terminal feeds back progress data collected during snow removal operations to the server. Based on this data, the server evaluates the current work efficiency and readjusts the snow removal route and work schedule as needed. The input is progress data, and the output is optimized new route information. In specific operations, the terminal transmits information such as the distance and time the device has traveled to the server via voice.

[0527] Step 4:

[0528] Users can check the progress of snow removal work and receive feedback through a dedicated application. The application has an emotion analysis engine that analyzes voice and facial expression input to evaluate the user's emotional state. Specifically, it performs emotion analysis using the user's voice input and provides a reassuring message as output.

[0529] Step 5:

[0530] The server generates feedback messages using a generative AI model based on the user's emotional state obtained from the emotion analysis engine. The prompt is "What message should be displayed if the user feels anxious about the snow removal work?", and the output is a specific support message. For example, based on the emotion analysis results, it might send a reassuring message such as "The work is progressing as planned."

[0531] (Application Example 2)

[0532] 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."

[0533] The goal is not only to improve the efficiency of snow removal operations using autonomous snowplows, but also to reduce the anxiety and stress users feel regarding the progress of the work. However, conventional systems have struggled to provide feedback and appropriate messages that take user emotions into consideration. As a result, users sometimes feel anxious about the progress of the work or changes in the weather, leading to significant psychological burden.

[0534] 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.

[0535] In this invention, the server includes emotion recognition means for detecting the user's emotional state, means for optimizing the work schedule based on the user's emotional state evaluated by the emotion recognition means, and means for providing messages to reduce the user's psychological burden. This reduces the user's psychological burden by adjusting the work content and information provided according to the user's emotions, making it possible to monitor snow removal work with peace of mind.

[0536] An "autonomous snow removal machine" is a mechanical device installed in homes or other locations that has the function of autonomously performing snow removal work.

[0537] A "server" is a computing device that analyzes data and issues instructions to snowplows regarding their work routes.

[0538] "Weather data" refers to data that shows information about weather conditions.

[0539] "Topographic data" refers to data that shows information about the characteristics of the ground surface in a given area.

[0540] A "snow removal route" is a route that indicates the path a snow removal machine will take to perform its work.

[0541] A "terminal" is a device installed on a snowblower that collects information during operation and transmits it to a server.

[0542] "Feedback" is the process of sending data back from a terminal to a server.

[0543] A "snow removal algorithm" is a computational method and program for efficiently performing snow removal work.

[0544] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and voice to determine their emotional state.

[0545] A "work schedule" is a plan outlining the time and sequence of snow removal operations.

[0546] "Psychological burden" is a concept that refers to the anxiety and stress that users experience.

[0547] A "message" is a form of communication consisting of text or audio used to convey information to a user.

[0548] This invention is a snow removal system using an automated snowblower installed in a home. The system utilizes emotion recognition technology to improve the user experience.

[0549] The server receives weather and terrain data, analyzes this data to plan an efficient snow removal route. The server transmits the planned route information to the snowplow and instructs it to perform snow removal. During snow removal, the terminals collect work data and feed it back to the server, which then re-evaluates and adjusts the snow removal route and snow removal algorithm based on that data.

[0550] Furthermore, emotion recognition functionality is provided through the user's smartphone or smart glasses. The user's voice and facial expressions are captured using a camera and microphone, and these are analyzed by the emotion recognition system. For this purpose, voice processing software is used for voice recognition, and image recognition software is used for facial expressions. Based on the analysis results, encouraging messages and advice tailored to the user's emotional state are provided.

[0551] For example, if severe weather is detected during snow removal and the user becomes anxious, a prompt message is sent to the generating AI model saying, "A user with an anxious expression is checking this. Please think of some words of encouragement:" and the user is shown a message such as, "Everything is going smoothly, so please don't worry." This process reduces the user's psychological burden while ensuring safe and comfortable snow removal work.

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

[0553] Step 1:

[0554] The server receives weather data from a weather data provider and terrain data from a terrain provider. Using this data, it performs data analysis to plan snow removal routes. The input data consists of weather and terrain data, and the output generates efficient snow removal routes.

[0555] Step 2:

[0556] The server transmits the generated snow removal route information to the automated snowblower, instructing it to perform snow removal work. The route information includes the direction of travel and the amount of snow to be removed, which allows the snowblower to automatically follow the specified path.

[0557] Step 3:

[0558] The terminal continuously collects operational and environmental data from the snowblower and feeds this data back to the server. The input is sensor data from the snowblower, and the output is data transmission to the server.

[0559] Step 4:

[0560] The server analyzes the feedback data and optimizes the snow removal algorithm. This allows for readjustment of snow removal routes, enabling more efficient work. The analysis results are output as a new snow removal route.

[0561] Step 5:

[0562] Users operate a dedicated application using their smartphone or smart glasses to check the progress of snow removal work. The application displays snow removal status data as input information. Users use this information to monitor the progress of the work.

[0563] Step 6:

[0564] The user terminal uses a camera and microphone to collect the user's facial expressions and voice. An emotion recognition system analyzes this data to determine the user's emotional state. The input data consists of audio and visual data, and the output is an evaluation of the emotional state.

[0565] Step 7:

[0566] The server uses a generative AI model to send appropriate messages to the user based on the evaluated emotional state. The input is the emotional state and a prompt, and the output is a message of encouragement or caution. For example, based on the prompt "A user with an anxious expression is checking this. Please think of some words of encouragement:", it will provide a message of reassurance.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention aims to effectively and efficiently perform snow removal tasks for households, including those of the elderly, by constructing an automated snow removal system. This system mainly consists of a server, terminals (autonomous snow removal machines), and a user application.

[0585] The server acts as the central hub, collecting and analyzing weather and terrain data in real time. Based on this data, the server plans the optimal snow removal route for each household and transmits the schedule to the automated snowplows. The server also constantly monitors the overall system's functionality by monitoring the real-time status information of each terminal.

[0586] The automated snowplow terminal operates based on instructions from the server. It efficiently removes snow by detecting ground conditions and surrounding obstacles using built-in sensors and cameras. The terminal also feeds back environmental data collected during operation to the server. This data is then re-analyzed on the server to improve the terminal's operational accuracy and enhance future work efficiency.

[0587] Users can access the system via a dedicated smartphone or tablet application. This application displays the operating status of the snowblower in real time, and allows users to manually control snow removal operations as needed. Users can also receive alerts from the system, enabling immediate action in case of malfunctions, for example.

[0588] For example, when snowfall is predicted, the server automatically sets the schedule for the next snow removal operation based on weather data and sends route information to the terminal. The terminal efficiently performs the work according to the designated route while checking the condition of the snow removal area in real time. If the terminal detects an obstacle, it will either avoid it or send data to the server for a retry. This provides a safe environment for the elderly and enables flexible snow removal responses according to the season and conditions.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server collects weather and terrain data and analyzes it in real time. This allows it to identify areas and times when snow removal is needed and plan optimal snow removal routes and schedules.

[0592] Step 2:

[0593] The server sends the planned snow removal route and schedule to each terminal. The terminal then prepares for snow removal work based on the received information.

[0594] Step 3:

[0595] The terminal starts snow removal work based on the route received from the server. It automatically removes snow while monitoring the surrounding conditions with its built-in sensors and camera.

[0596] Step 4:

[0597] The terminal feeds back data collected during operation (e.g., obstacle information, work progress) to the server. This allows the server to obtain data to improve the accuracy and efficiency of snow removal operations.

[0598] Step 5:

[0599] The server analyzes the data fed back from the terminals and readjusts snow removal routes and schedules as needed. This enables more efficient snow removal operations.

[0600] Step 6:

[0601] Users can check the status of snow removal operations through a dedicated application. The application displays the location of the snowblower and the progress of the work in real time, and users can also take action as needed.

[0602] Step 7:

[0603] The server constantly monitors the status of the terminal and immediately notifies the user if any abnormalities or malfunctions are detected. This establishes a system that allows for a rapid response.

[0604] (Example 1)

[0605] 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".

[0606] In an aging society, snow removal at home is a significant burden, especially in snowy regions. Traditional snow removal methods require manual labor, raising concerns about safety and effectiveness, particularly for the elderly and those with physical disabilities. Furthermore, snow removal is not performed efficiently in real time, making it difficult to respond quickly to operational errors or equipment malfunctions.

[0607] 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.

[0608] In this invention, the server includes means for autonomous operating machines to be deployed at individual facilities, a central processing unit to analyze weather and terrain information and plan operating routes, means for transmitting the operating routes to the autonomous operating machines to instruct them to perform tasks, means for terminals to feed back information collected during the work to the central processing unit, the central processing unit to re-analyze the operation algorithm based on the feedback information and readjust the route, and means for users to monitor the information using a display device and issue manual instructions as needed. This enables safe and efficient automated snow removal for all users, including the elderly.

[0609] An "autonomous machine" is a device that can automatically perform predetermined tasks in response to instructions from an external source.

[0610] A "centralized processing unit" is a computer system that collects multiple data sets, analyzes them, and derives results.

[0611] "Weather information" refers to data related to the weather, such as temperature, precipitation, and wind speed.

[0612] "Topographic information" refers to information about the shape and characteristics of the ground surface in a specific area.

[0613] A "movement path" is the route that an autonomous machine should follow when performing a task.

[0614] "Feedback" is the process of incorporating information sent from one system to an external source back into that system.

[0615] An "operation algorithm" is a set of steps or computational methods that enable an autonomous machine to efficiently perform a task.

[0616] A "display device" is a device used to present information to a user visually.

[0617] This invention employs a configuration in which a central processing unit (server), an autonomous machine (terminal), and a user display device (application) work together to efficiently operate an autonomous machine. The purpose of this system is to reduce the burden on users, including the elderly, by automatically planning and adjusting the operating route based primarily on weather and terrain information.

[0618] The server acquires weather and topographic information in real time from external data sources. For example, it uses weather information APIs on the internet to obtain weather data and analyzes the operation path using machine learning models based on that data. The analysis results are sent as instructions to the autonomous operating machine.

[0619] The autonomous operating machine, acting as the terminal, uses built-in hardware such as sensors and cameras to detect the surrounding terrain and obstacles in real time. This allows it to efficiently and safely carry out tasks while following the instructed operating path. Information on obstacles detected during the operation is fed back to the server in real time, and the server readjusts the operating path as needed based on this information.

[0620] Users can monitor the operation status of autonomous machinery in real time using a dedicated application installed on their smartphone or tablet. This application allows them to check the progress of snow removal work and the status of the autonomous machinery, and to issue manual instructions as needed. In the event of a malfunction or abnormality, the app will notify the user with a warning, enabling a quick response.

[0621] As a concrete example, consider a situation where snowfall is predicted. The server automatically sets a snow removal schedule based on real-time weather data, and this schedule and route information are transmitted to the terminal. The terminal efficiently removes snow while following the designated route, and when it detects an obstacle, it sends that information to the server and adjusts the route accordingly.

[0622] Examples of prompts for a generative AI model:

[0623] "Please explain the specific operational flow of an automated snow removal system for elderly households when snowfall is expected the following day."

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] The server receives real-time weather data from a weather information API and topographic data from a topographic information database as input. Using this data, the server identifies areas requiring snow removal and plans the optimal route for each household. In this process, it utilizes a generative AI model to select efficient routes based on historical data. As output, it generates a snow removal schedule and route information for each household and transmits it to autonomous operating machines.

[0627] Step 2:

[0628] The autonomous operating machine, acting as the terminal, receives snow removal schedule and route information from the server as input. Using its built-in sensors and camera, the terminal detects the surrounding terrain and obstacles in real time, and based on the data obtained, begins work according to the designated route. Specifically, when the terminal detects an obstacle, it adjusts its direction of travel and continues snow removal safely. As output, it feeds back environmental information collected during the operation to the server.

[0629] Step 3:

[0630] The server receives environmental information fed back from the terminal as input and reanalyzes the snow removal algorithm based on this information. If necessary, the server generates newly optimized route instructions and sends them back to the terminal. This process makes it possible to continuously improve the efficiency of snow removal work. Specifically, the server analyzes the real-time data obtained and updates the data to be useful for subsequent operations.

[0631] Step 4:

[0632] Users monitor snow removal progress and terminal operating status received from the server via an application installed on their smartphone or tablet. Users can manually issue commands to the terminal via the app as needed. For example, if a user wants to prioritize snow removal in a specific area, they can send a command via the app, and the terminal will respond immediately. As output, users can receive visual operational feedback.

[0633] (Application Example 1)

[0634] 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".

[0635] In modern cities, snow removal during snowfall is often labor-intensive and inefficient, making it particularly difficult in areas with aging populations. Furthermore, flexible snow removal that can adapt to fluctuating weather conditions is required, but conventional methods are sometimes insufficient. Therefore, the challenge is to enable efficient snow removal throughout urban areas and provide a safe and comfortable living environment.

[0636] 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.

[0637] In this invention, the server includes means for analyzing weather information and topographic information to plan a removal route, means for distributing the removal route to an autonomously driven removal device to instruct it to perform the work, and means for re-analyzing the removal algorithm based on the feedback information to adjust the route. This enables efficient and flexible snow removal work throughout the entire city.

[0638] An "autonomous removal device" is a mobile mechanical device that operates automatically according to weather conditions and has the function of removing snow and obstacles.

[0639] A "data processing device" is a computer system that analyzes collected weather and topographic information and calculates the optimal route for removal work.

[0640] A "removal route" is a route set up for carrying out removal work, and it is a route plan designed to ensure efficient work.

[0641] A "terminal device" is a device mounted on an autonomous removal device that has communication capabilities to transmit information acquired during operation to a data processing device.

[0642] "Users" are individuals who have the authority to operate and monitor the removal system, and who can use communication devices to check the system's status and issue instructions as needed.

[0643] "Communication equipment" refers to a system for sending and receiving information between a data processing device and a terminal device, and is a device that assists users in operating the system.

[0644] "Feedback" refers to information transmitted from a terminal device to a data processing device, including environmental data and work status acquired during the work process.

[0645] "Weather information" refers to data related to weather conditions such as snowfall, temperature, and wind speed, and is a factor that influences the plan for removal work.

[0646] "Topographic information" refers to data about the terrain of the work area, including changes in terrain and the location of obstacles.

[0647] The system implementing this invention is realized by a central data processing unit (hereinafter referred to as the server) controlling multiple autonomous removal devices (hereinafter referred to as terminals). The server executes a program written in a programming language such as Python to analyze weather information and terrain information in real time. This allows it to plan removal routes and send optimal instructions to each terminal. The server also reanalyzes the removal algorithm based on feedback information from the terminals and adjusts the route as necessary.

[0648] The terminal has autonomous driving capabilities and uses ROS (Robot Operating System) and various sensors (such as LIDAR and cameras) to automatically perform tasks according to the instructed path. During the task, it transmits data acquired by the sensors to the server and provides real-time feedback on the environment. If an obstacle is detected, it reports that information to the server and confirms the next action.

[0649] Users access the system via smartphones and tablets to check the progress of their tasks and the status of their devices. This uses an application developed with Flutter, and information is provided by a real-time database such as Firebase. Users can also manually instruct the device to operate as needed.

[0650] As a concrete example, on a night when heavy snowfall is expected, the server uses a weather forecast API to obtain weather information and plans routes so that all major roads remain clear until the following morning. Terminals then operate based on this plan, efficiently clearing snow. As a result, citizens can commute to work and school with peace of mind.

[0651] An example of a prompt to input into the generated AI model would be, "Explain how an autonomous snowplow system works, using ROS and sensors to effectively remove snow."

[0652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0653] Step 1:

[0654] The server collects weather data. It uses real-time data obtained from weather forecast APIs (e.g., OpenWeatherMap API) as input. This data includes temperature, snowfall, and wind speed, and is used to assess the need for snow removal. The output determines the required snow removal area and time. Specifically, the system periodically sends requests to the API to retrieve updated weather information.

[0655] Step 2:

[0656] The server acquires and analyzes terrain information. It uses geographic information APIs (e.g., Google Maps API) or built-in map data as input. It analyzes terrain gradients, road layouts, and anticipated obstacles to plan effective routes for removal. The output is detailed route information for instructing terminals. Specifically, it uses analytical algorithms to determine road priorities and safe routes.

[0657] Step 3:

[0658] The server sends the calculated removal route to the terminal. The input uses data that integrates the judgment and analysis results obtained in steps 1 and 2. The output to the terminal includes the specific travel route and the start time of the work. In terms of operation, it communicates with each terminal via the network and transfers instruction data.

[0659] Step 4:

[0660] The terminal autonomously begins snow removal work according to the route information it receives. Using route information transmitted from the server as input, the terminal uses its built-in sensors (e.g., LiDAR and camera) to check its current location and terrain details in real time. The output is feedback information such as obstacle avoidance and confirmation of work completion. Specific operations include controlling the motor to travel along a designated route and operating the snow removal blade as needed.

[0661] Step 5:

[0662] The terminal feeds back environmental data acquired during operation to the server. The input is terrain and obstacle information acquired in real time by the terminal's sensors. The output sent to the server is feedback information to improve work efficiency. Specifically, it periodically generates data packets and feeds them back to the server.

[0663] Step 6:

[0664] Users check the progress of their devices using a smartphone app. The input is real-time device status information transmitted from a server. The output is the progress status and warning messages displayed to the user. Specifically, the app displays the progress of the task and the estimated remaining time, and allows for manual operation as needed.

[0665] 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.

[0666] This invention is a system that combines an automated snowblower installed in a home, a server and user application to control it, and an emotion engine that recognizes the user's emotions. The system aims to enable efficient snow removal while simultaneously reducing the psychological burden on the user.

[0667] The server analyzes weather and terrain data to formulate a snow removal plan. It transmits the planned snow removal route to each terminal and notifies users through a dedicated application. At this time, an emotion engine analyzes the user's voice and facial expressions in real time and evaluates the user's emotional state online.

[0668] Users can check the progress of snow removal work in real time using a dedicated application. The application uses an emotion engine to analyze the user's emotions if they feel anxious or stressed. For example, if the user raises their voice or makes a displeased face, it will suggest adjusting the work schedule accordingly. It can also display situation-appropriate messages and advice to reduce the user's psychological burden.

[0669] The terminal operates as an autonomous snowplow, feeding back data from its operation to the server. The server then re-analyzes the data to improve the efficiency of snow removal operations. The feedback information is used to improve future work plans.

[0670] For example, if the application detects user anxiety while the user is checking the progress of snow removal work, the emotion engine will display a reassuring message on the screen stating that "the work is progressing smoothly." Furthermore, especially when severe weather conditions are expected, the application can send a notification urging the user to take early action, thus preparing for unexpected situations.

[0671] Thus, this invention not only streamlines snow removal operations but also contributes to creating a safer and more secure living environment by providing services that take into consideration the user's feelings.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server collects and analyzes weather and topographic data. It identifies areas requiring snow removal and develops optimal snow removal routes and schedules.

[0675] Step 2:

[0676] The server sends the formulated snow removal plan to the terminal (autonomous snowplow). It also notifies the user's application of this information.

[0677] Step 3:

[0678] The terminal automatically starts snow removal work according to the received snow removal route. During the work, it continuously feeds back data on the ground and obstacles to the server.

[0679] Step 4:

[0680] Users can check the progress of snow removal work in real time through the application, and an emotion engine analyzes the user's emotions by analyzing voice, facial expressions, etc.

[0681] Step 5:

[0682] If the emotion engine detects user anxiety or stress, the server adjusts the snow removal schedule based on that information and revises the work plan if necessary.

[0683] Step 6:

[0684] Users receive reassuring messages and specific advice from the application, which reduces their psychological burden.

[0685] Step 7:

[0686] The server re-analyzes the snow removal algorithm based on the feedback data and makes adjustments to improve the accuracy of the next work plan.

[0687] As a result, the system not only improves the efficiency of snow removal but also enhances the user's quality of life through emotional care.

[0688] (Example 2)

[0689] 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".

[0690] Autonomous snow removal operations require the development of efficient snow removal plans, but it is also necessary to consider the psychological burden on users. However, conventional systems lacked sufficient consideration for the mental well-being of users, often leading to situations where users felt anxious or stressed. Furthermore, there was room for improvement in the mechanism for providing real-time feedback on snow removal data and immediately optimizing the algorithm.

[0691] 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.

[0692] In this invention, the server includes means for analyzing weather and topographic information to plan snow removal routes, means for re-analyzing the snow removal algorithm based on the collected information to readjust the routes, and means for evaluating the user's emotional state using an emotion analysis engine and providing feedback. This enables efficient snow removal work, reduces the psychological burden on users, and provides an environment in which the system can be used with peace of mind.

[0693] An "autonomous snow removal system" is a mechanical device installed at each facility that automatically performs snow removal work according to a programmed route.

[0694] A "data processing device" is a computer system that analyzes weather and topographic information to formulate snow removal route plans and to re-analyze algorithms based on the collected information.

[0695] An "information terminal" is an electronic device used to feed back information collected during snow removal work to a data processing device.

[0696] An "emotion analysis engine" is a software technology that evaluates the user's emotional state based on voice and facial expression data and generates appropriate feedback.

[0697] "Snow removal route" refers to the specific route and schedule that an autonomous snow removal system should follow when performing snow removal operations.

[0698] This invention is a system for achieving efficient and user-friendly snow removal using an autonomous snow removal device installed at a specific facility. The system's core, a data processing unit, analyzes weather data obtained from weather information services and topographic data obtained from geographic information services to plan the optimal snow removal route. This process utilizes data feeds such as the "OpenWeatherMap API" and the "Google Maps API".

[0699] The server transmits the planned snow removal route to the autonomous snow removal device, which then performs physical snow removal operations according to the device's instructions. This allows the snow removal device to efficiently remove snow from the facility. During snow removal, the information terminal feeds its progress back to the data processing device in real time, and the data processing device re-analyzes this information to adjust the route and schedule, thereby further improving efficiency.

[0700] Furthermore, users can check the progress of snow removal work through a dedicated mobile application. This application incorporates an emotion analysis engine that analyzes the user's emotional state from their voice and facial expressions. For example, if a user is worried about delays in the work, the application will generate and display a reassuring message such as, "The work is progressing as scheduled."

[0701] A possible concrete example is to input a prompt sentence such as, "What message should be displayed if a user feels uneasy about snow removal?" into an emotion analysis engine, and then use a generative AI model to automatically provide appropriate feedback.

[0702] This system not only makes snow removal more efficient, but also minimizes the psychological burden on users. As a result, users can confidently incorporate this system into their daily lives.

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] The server obtains weather data and topographic data from weather information services and geographic information services, respectively. Based on this input data, it plans snow removal routes using an analysis algorithm. Specifically, the server obtains snowfall forecasts from the "OpenWeatherMap API" and topographic information from the "Google Maps API". As a result of the analysis, the optimal snow removal route and work schedule are output.

[0706] Step 2:

[0707] The server transmits the planned snow removal route to an information terminal. The information terminal forwards this data to the autonomous snow removal device, which then begins snow removal according to the specified route. The input is route data from the server, and the output is route instructions to the autonomous snow removal device. In practice, the snow removal device physically moves along the route and removes the snow.

[0708] Step 3:

[0709] The terminal feeds back progress data collected during snow removal operations to the server. Based on this data, the server evaluates the current work efficiency and readjusts the snow removal route and work schedule as needed. The input is progress data, and the output is optimized new route information. In specific operations, the terminal transmits information such as the distance and time the device has traveled to the server via voice.

[0710] Step 4:

[0711] Users can check the progress of snow removal work and receive feedback through a dedicated application. The application has an emotion analysis engine that analyzes voice and facial expression input to evaluate the user's emotional state. Specifically, it performs emotion analysis using the user's voice input and provides a reassuring message as output.

[0712] Step 5:

[0713] The server generates feedback messages using a generative AI model based on the user's emotional state obtained from the emotion analysis engine. The prompt is "What message should be displayed if the user feels anxious about the snow removal work?", and the output is a specific support message. For example, based on the emotion analysis results, it might send a reassuring message such as "The work is progressing as planned."

[0714] (Application Example 2)

[0715] 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".

[0716] The goal is not only to improve the efficiency of snow removal operations using autonomous snowplows, but also to reduce the anxiety and stress users feel regarding the progress of the work. However, conventional systems have struggled to provide feedback and appropriate messages that take user emotions into consideration. As a result, users sometimes feel anxious about the progress of the work or changes in the weather, leading to significant psychological burden.

[0717] 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.

[0718] In this invention, the server includes emotion recognition means for detecting the user's emotional state, means for optimizing the work schedule based on the user's emotional state evaluated by the emotion recognition means, and means for providing messages to reduce the user's psychological burden. This reduces the user's psychological burden by adjusting the work content and information provided according to the user's emotions, making it possible to monitor snow removal work with peace of mind.

[0719] An "autonomous snow removal machine" is a mechanical device installed in homes or other locations that has the function of autonomously performing snow removal work.

[0720] A "server" is a computing device that analyzes data and issues instructions to snowplows regarding their work routes.

[0721] "Weather data" refers to data that shows information about weather conditions.

[0722] "Topographic data" refers to data that shows information about the characteristics of the ground surface in a given area.

[0723] A "snow removal route" is a route that indicates the path a snow removal machine will take to perform its work.

[0724] A "terminal" is a device installed on a snowblower that collects information during operation and transmits it to a server.

[0725] "Feedback" is the process of sending data back from a terminal to a server.

[0726] A "snow removal algorithm" is a computational method and program for efficiently performing snow removal work.

[0727] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and voice to determine their emotional state.

[0728] A "work schedule" is a plan outlining the time and sequence of snow removal operations.

[0729] "Psychological burden" is a concept that refers to the anxiety and stress that users experience.

[0730] A "message" is a form of communication consisting of text or audio used to convey information to a user.

[0731] This invention is a snow removal system using an automated snowblower installed in a home. The system utilizes emotion recognition technology to improve the user experience.

[0732] The server receives weather and terrain data, analyzes this data to plan an efficient snow removal route. The server transmits the planned route information to the snowplow and instructs it to perform snow removal. During snow removal, the terminals collect work data and feed it back to the server, which then re-evaluates and adjusts the snow removal route and snow removal algorithm based on that data.

[0733] Furthermore, emotion recognition functionality is provided through the user's smartphone or smart glasses. The user's voice and facial expressions are captured using a camera and microphone, and these are analyzed by the emotion recognition system. For this purpose, voice processing software is used for voice recognition, and image recognition software is used for facial expressions. Based on the analysis results, encouraging messages and advice tailored to the user's emotional state are provided.

[0734] For example, if severe weather is detected during snow removal and the user becomes anxious, a prompt message is sent to the generating AI model saying, "A user with an anxious expression is checking this. Please think of some words of encouragement:" and the user is shown a message such as, "Everything is going smoothly, so please don't worry." This process reduces the user's psychological burden while ensuring safe and comfortable snow removal work.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] The server receives weather data from a weather data provider and terrain data from a terrain provider. Using this data, it performs data analysis to plan snow removal routes. The input data consists of weather and terrain data, and the output generates efficient snow removal routes.

[0738] Step 2:

[0739] The server transmits the generated snow removal route information to the automated snowblower, instructing it to perform snow removal work. The route information includes the direction of travel and the amount of snow to be removed, which allows the snowblower to automatically follow the specified path.

[0740] Step 3:

[0741] The terminal continuously collects operational and environmental data from the snowblower and feeds this data back to the server. The input is sensor data from the snowblower, and the output is data transmission to the server.

[0742] Step 4:

[0743] The server analyzes the feedback data and optimizes the snow removal algorithm. This allows for readjustment of snow removal routes, enabling more efficient work. The analysis results are output as a new snow removal route.

[0744] Step 5:

[0745] Users operate a dedicated application using their smartphone or smart glasses to check the progress of snow removal work. The application displays snow removal status data as input information. Users use this information to monitor the progress of the work.

[0746] Step 6:

[0747] The user terminal uses a camera and microphone to collect the user's facial expressions and voice. An emotion recognition system analyzes this data to determine the user's emotional state. The input data consists of audio and visual data, and the output is an evaluation of the emotional state.

[0748] Step 7:

[0749] The server uses a generative AI model to send appropriate messages to the user based on the evaluated emotional state. The input is the emotional state and a prompt, and the output is a message of encouragement or caution. For example, based on the prompt "A user with an anxious expression is checking this. Please think of some words of encouragement:", it will provide a message of reassurance.

[0750] 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.

[0751] 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.

[0752] 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 robot 414.

[0753] 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.

[0754] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] Automatic snowplows are being installed in each home,

[0774] The server analyzes weather and terrain data to plan snow removal routes.

[0775] A means for transmitting the snow removal route to an automated snow removal machine and instructing it to perform the work,

[0776] The terminal feeds back the data it collects during snow removal work to the server.

[0777] A system that includes a mechanism for the server to reanalyze the snow removal algorithm and readjust the route based on the data it has received as feedback.

[0778] (Claim 2)

[0779] The system according to claim 1, further comprising a function that allows the user to operate the system through an application and to check and operate the status of snow removal work.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the server has a function to monitor the status of the terminal and notify the user of a warning if a failure occurs.

[0782] "Example 1"

[0783] (Claim 1)

[0784] Autonomous machines are deployed to individual facilities,

[0785] The central processing unit analyzes weather information and terrain information to plan the route,

[0786] A means for transmitting the operation path to an autonomous machine and instructing it to perform the task,

[0787] The terminal feeds back the information it collects during operation to the central processing unit.

[0788] A means by which a central processing unit reanalyzes the operation algorithm and readjusts the path based on the information received as feedback,

[0789] A means by which the user can monitor information using a display device and issue manual instructions as needed,

[0790] ...

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the central processing unit has a function to monitor the status of terminals and notify the user of a warning if an abnormality is detected.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the user has the ability to check the progress of the work through a display device and perform remote operation in real time.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] Autonomous removal devices have been deployed to numerous facilities.

[0799] The data processing unit analyzes weather information and topographic information to plan removal routes.

[0800] A means for distributing the removal route to an autonomously driven removal device and instructing it to perform the work,

[0801] The terminal device transmits the information acquired during the removal process to the data processing device for feedback.

[0802] A means by which a data processing device reanalyzes the removal algorithm based on the feedback information and adjusts the path,

[0803] A means by which users operate the system via communication devices to monitor and control the progress of the removal work,

[0804] A data processing device monitors the status of terminal devices and, if an abnormality is detected, provides a means to notify the user of a warning.

[0805] A means of efficiently carrying out removal work throughout an entire city by integrating and managing multiple autonomous removal devices,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, comprising a function for acquiring and analyzing weather information for the entire city in real time.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising a function that allows a user to manually instruct the operation of any autonomous removal device using a communication device.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] Autonomous snow removal devices are installed at each facility.

[0814] The data processing unit analyzes weather and topographic information to plan snow removal routes.

[0815] A means for transmitting the snow removal route to an autonomous snow removal device and instructing it to perform the work,

[0816] The information terminal feeds back the information it collects during snow removal work to the data processing unit.

[0817] A means by which a data processing unit reanalyzes the snow removal algorithm and readjusts the route based on the feedback information,

[0818] A system that includes a means of providing feedback to reduce the user's psychological burden, based on an emotion analysis engine that evaluates the user's emotional state through voice and facial expressions.

[0819] (Claim 2)

[0820] The system according to claim 1, which includes a function that allows the user to operate the system through an application, check the status of snow removal work, and receive appropriate messages regarding mental stress using an emotion analysis engine.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the data processing device has a function to monitor the status of an information terminal and notify the user of a warning if an abnormality occurs.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] Automatic snowplows are being installed in each home,

[0826] The server analyzes weather and terrain data to plan snow removal routes.

[0827] A means for transmitting the snow removal route to an automated snow removal machine and instructing it to perform the work,

[0828] The terminal feeds back the data it collects during snow removal work to the server.

[0829] A means of re-analyzing the snow removal algorithm and readjusting the route based on the data the server has received as feedback,

[0830] An emotion recognition means for detecting the user's emotional state,

[0831] A means for optimizing the work schedule based on the user's emotional state evaluated by an emotion recognition means,

[0832] A system that includes means of providing messages that reduce the user's psychological burden.

[0833] (Claim 2)

[0834] The system according to claim 1, which includes a function that allows the user to operate the system through an application, check and operate the status of snow removal work, and receive feedback according to their emotional state.

[0835] (Claim 3)

[0836] The system according to claim 1, which includes a function for the server to monitor the status of the terminal and notify the user of a warning if a failure occurs, as well as a function to prompt the user to take appropriate action based on their emotional state. [Explanation of Symbols]

[0837] 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. Autonomous removal devices have been deployed to numerous facilities. The data processing unit analyzes weather information and topographic information to plan removal routes. A means for distributing the removal route to an autonomously driven removal device and instructing it to perform the work, The terminal device transmits the information acquired during the removal process to the data processing device for feedback. A means by which a data processing device reanalyzes the removal algorithm based on the feedback information and adjusts the path, A means by which users operate the system via communication devices to monitor and control the progress of the removal work, A data processing device monitors the status of terminal devices and, if an abnormality is detected, provides a means to notify the user of a warning. A means of efficiently carrying out removal work throughout an entire city by integrating and managing multiple autonomous removal devices, A system that includes this.

2. The system according to claim 1, comprising a function for acquiring and analyzing weather information for the entire city in real time.

3. The system according to claim 1, further comprising a function that allows a user to manually instruct the operation of any autonomous removal device using a communication device.

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