system

Abstract

We provide the system. [Solution] Means for acquiring and analyzing weather information, Based on the obtained weather data, a means for identifying the area and extent of snow removal required for each device, A means for remotely controlling the operation of each device and calculating the optimal snow removal route, A means of automatically performing snow removal work according to a specified route, A means of monitoring the progress of snow removal work and notifying the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 snowy regions, it is physically demanding for the elderly to perform snow removal work, and there are also risks of falling and getting injured. Therefore, there is a need for a means to perform snow removal safely and efficiently. In addition, it is necessary to reduce the inconvenience of life caused by snow accumulation and provide an environment in which the elderly can live with peace of mind. To solve these problems, there is a need to develop a system that can automate snow removal work and be remotely controlled.

Means for Solving the Problems

[0005] This invention provides an automated snow removal system that utilizes weather information to perform snow removal at the appropriate time. By identifying the snow removal area for each device based on weather information and remotely calculating and controlling an efficient and safe snow removal route, it reduces the burden on the elderly. Furthermore, by providing a means to monitor the progress of snow removal and notify the user, users can live their daily lives with peace of mind. In addition, a mechanism is built to improve the efficiency of future operations by accumulating and analyzing snow removal data.

[0006] "Weather information" refers to data such as snowfall, weather changes, and temperature, obtained from weather forecasting services and other sources.

[0007] The "device" refers to a mechanical device installed in each home that automatically performs snow removal work using AI control.

[0008] An "area" refers to a specific region where snow removal work is required.

[0009] A "snow removal route" is a line that indicates the efficient path that the equipment should follow when performing snow removal work.

[0010] "Remote control" means operating or giving instructions to a device via a communication network without physically touching it.

[0011] "Monitoring" refers to the act of monitoring the progress of snow removal work and the operating status of equipment in real time.

[0012] "Notification" refers to the procedure of communicating information to users regarding the progress of snow removal work, any abnormalities, etc.

[0013] "Data accumulation" means recording and saving information about snow removal operations, both past and present.

[0014] "Analysis" is the act of analyzing accumulated data to gain insights that will be useful in improving the efficiency and accuracy of future snow removal operations. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 Example 2 when an 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 an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system for automatically performing snow removal in areas where elderly people live, and consists of terminals installed in each home and a centrally operated server. In this system, the server periodically acquires and analyzes weather information to predict snowfall in each area. The server then determines the need for snow removal based on the amount of snowfall and sends work instructions to each terminal at the appropriate time.

[0037] Based on the received instructions, the terminal automatically powers on and uses surrounding sensors to detect snow conditions. This identifies areas and extents requiring snow removal and transmits the data to the server in real time.

[0038] The server uses AI algorithms based on the received information to calculate the optimal snow removal route. This process takes into account obstacles and areas that should be prioritized for snow removal, while also optimizing the rotation speed of the snow brushes and the movement speed of the terminals. The calculated route information is then transmitted to each terminal, providing instructions to ensure efficient snow removal.

[0039] During snow removal operations, the terminal reports progress and any anomalies to the server in real time, and the server makes necessary adjustments based on this information. Furthermore, users can monitor the snow removal status using their smartphones or PCs and perform manual operations as needed. This notification function allows users to confidently monitor the progress of the snow removal operations.

[0040] Furthermore, this system includes a mechanism that improves the efficiency of future snow removal operations by having the server accumulate and analyze work data. As a result, the labor required for snow removal can be significantly reduced, providing an environment in which elderly people can live with peace of mind. For example, by analyzing past data, it becomes possible to automatically adjust snow removal operations based on specific patterns, further reducing the burden on users.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect snowfall predictions and related data such as temperature for each region.

[0044] Step 2:

[0045] The server analyzes the acquired weather data and calculates the predicted snowfall amount for each region. Based on this, it creates instructions to prepare for snow removal in areas where snowfall exceeds a certain level.

[0046] Step 3:

[0047] The terminal receives instructions from the server and starts up automatically. It uses its sensors to scan the amount and extent of snow in its surroundings, thereby identifying areas that need snow removal. It then sends the obtained data to the server.

[0048] Step 4:

[0049] Based on the data received from the terminal, the server uses an AI algorithm to calculate the optimal snow removal route. Here, the route is determined considering obstacles and areas that should be prioritized for snow removal, and detailed movement instructions are given to the terminal.

[0050] Step 5:

[0051] The terminal automatically starts snow removal work along the designated snow removal route, following the route instructions received from the server. The rotation speed and movement speed of the snow removal brush are optimized according to the server's instructions.

[0052] Step 6:

[0053] The terminal reports its progress and any anomalies to the server in real time. Based on this information, the server can adjust its operation in real time.

[0054] Step 7:

[0055] Users can access a dedicated application on their smartphone or PC to check the current snow removal progress and system status.

[0056] Step 8:

[0057] After the server closes, it stores and analyzes all the work data. This data will be used to further improve the efficiency of future snow removal operations.

[0058] (Example 1)

[0059] 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."

[0060] This invention aims to solve problems related to technology for efficient and automated snow removal in areas where elderly people and others reside. Conventional snow removal requires labor and manpower, which is a heavy burden for the elderly. There were also challenges in removing snow at the appropriate time and selecting the optimal route. There is a need to improve this situation and provide a system that can perform snow removal more efficiently and safely.

[0061] 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.

[0062] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying the areas and extents requiring snow removal at each device based on the obtained weather data, and means for remotely controlling the operation of each device and calculating the optimal travel route. This enables the automation of efficient snow removal work according to weather conditions and reduces the burden on the user.

[0063] "Weather information" refers to data related to weather conditions, specifically including snowfall, rainfall, temperature, humidity, and wind speed.

[0064] "Analysis" refers to the operations and processes involved in processing acquired data and extracting meaningful information.

[0065] "Device" refers to any equipment with a specific function, and in this invention, it means equipment used for snow removal.

[0066] A "region" refers to a specific geographical area, indicating the region or area where snow removal work is required.

[0067] "Scope" refers to the size or extent of the area in which a particular activity takes place.

[0068] "Remote" refers to performing operations from a distance, including operations carried out via communication technology.

[0069] A "route" refers to a path that connects designated points, and in particular, indicates a route that allows for efficient travel.

[0070] "Automatic" refers to a process where operations are performed mechanically without human intervention.

[0071] "Snow removal work" refers to a series of activities to remove snow, specifically including operations such as pushing aside or removing accumulated snow.

[0072] "Progress" refers to the degree or status of an activity's progress.

[0073] "Surveillance" refers to continuously observing and confirming specific things or situations.

[0074] "Notification" refers to the act of transmitting specific information to another person.

[0075] "User" refers to an individual or legal entity that uses the system or service.

[0076] "Intelligent analysis" refers to the process of using technologies such as AI to analyze information in a sophisticated way and identify patterns.

[0077] This invention provides an automated snow removal system for areas inhabited by the elderly. This system includes a process for acquiring weather information, analyzing that data, and performing optimal snow removal operations. Its specific configuration and functions are described below.

[0078] The server periodically acquires weather information using a weather API service. This acquired data is processed by analysis software. Specifically, it analyzes data such as snowfall amount, temperature, and humidity, and uses an AI model to determine how much snow removal is needed in which areas. After the analysis, it sends instructions to terminals to start snow removal remotely, if necessary.

[0079] The terminal automatically activates upon receiving instructions and uses its built-in sensors to measure the surrounding snow depth. Ultrasonic and infrared sensors are used to detect the height and extent of the snow in real time. By sending this information to a server, the server can calculate a more precise snow removal route and instruct efficient snow removal operations.

[0080] Users can monitor the snow removal process in real time via their smartphones or PCs, and can also manually operate the snow removal system as needed. This feature allows administrators, such as elderly individuals, to safely understand the overall snow removal process from their homes.

[0081] Furthermore, the server stores data on completed tasks and performs data analysis to make future snow removal operations even more efficient. Through this analysis, it becomes possible to make predictions based on specific weather patterns and automatically adjust operations based on those predictions.

[0082] For example, by using a prompt such as, "Calculate the optimal snow removal route based on the latest snowfall forecast and past snow removal data for this region," the server can utilize a generated AI model to formulate an effective snow removal plan.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server retrieves weather information from a weather API service. It uses the API's query parameters as input and receives weather data in JSON format as output. Specifically, the server periodically sends requests to the API service and saves the response data for analysis.

[0086] Step 2:

[0087] The server analyzes acquired weather data to predict snowfall. It uses the JSON-formatted weather data received as input to generate the snowfall forecast, which is the result of the analysis. A generative AI model is used for the analysis, processing the data with a specific algorithm.

[0088] Step 3:

[0089] Based on the analysis results, the server identifies areas requiring snow removal and generates instructions. It uses snowfall forecast data as input and generates snow removal instructions for each terminal as output. The server sets a threshold and generates instructions when this threshold is exceeded.

[0090] Step 4:

[0091] The terminal receives snow removal instructions from the server and automatically starts up. It uses instruction data from the server as input and outputs the terminal's startup and the initiation of sensor operation.

[0092] Step 5:

[0093] The device uses sensors to detect the surrounding snow conditions. It uses the physical environment of the site as input and generates data on the height and extent of the snow as output. Specifically, ultrasonic sensors and infrared sensors measure the snow depth and convert the results into digital data.

[0094] Step 6:

[0095] The terminal sends the detected snow depth data to the server. It uses the detected snow depth data as input and generates data packets to send to the server as output. The transmission protocol used is HTTP or MQTT.

[0096] Step 7:

[0097] The server receives snow depth data transmitted from the terminal and uses an AI algorithm to calculate the optimal snow removal route. It uses the received snow depth data as input and generates optimal route information as output.

[0098] Step 8:

[0099] The server sends the calculated route information to the terminals. It uses the route calculation result as input and generates and sends specific route instructions to each terminal as output.

[0100] Step 9:

[0101] The terminal starts snow removal work according to the specified route based on routing instructions from the server. It uses the route information received as input to perform snow removal as a mechanical operation.

[0102] Step 10:

[0103] The terminal reports the progress and any abnormalities of snow removal operations to the server in real time. It uses progress data from sensors and self-diagnosis results as input and generates a status report to send to the server as output.

[0104] Step 11:

[0105] Users monitor the progress of snow removal using smartphones or PCs and perform manual operations as needed. They can use progress information from the server as input and send instructions as output.

[0106] Step 12:

[0107] The server stores work details in a database and performs analysis to improve efficiency in future work. It uses data from completed tasks as input and generates analysis results useful for planning future work.

[0108] (Application Example 1)

[0109] 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."

[0110] In areas with a large elderly population, snow removal during snowfall poses a significant burden on residents. Furthermore, it is difficult to track the progress of snow removal and provide residents with safe routes. Moreover, there is insufficient information available to residents to monitor snow removal status in real time and ensure safe travel. Therefore, it is necessary to improve the efficiency of snow removal operations and streamline information provision.

[0111] 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.

[0112] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas and their extents requiring snow removal at each device based on the obtained weather data, means for monitoring the progress of snow removal work and notifying users, and display means for residents to check the local conditions and confirm safe routes. This enables residents to grasp the snow removal situation in real time and select safe and effective routes.

[0113] "Weather information" refers to data related to weather conditions, including elements such as snowfall, temperature, and wind speed.

[0114] "Analysis" is the process of analyzing and understanding information based on acquired data.

[0115] A "region" is a geographical area defined for a specific function or purpose.

[0116] "Scope" refers to the extent of the area affected by a particular activity or effect.

[0117] "Progress" refers to the process by which things move forward, or the resulting changes in state.

[0118] "Users" refers to individuals or organizations that use this system.

[0119] "Notification" is a means of conveying specific information to a target audience.

[0120] "Display means" refers to devices or methods for visually presenting information.

[0121] To realize this invention, the system operates as follows:

[0122] The server first acquires weather information from various data sources and then processes and analyzes it. For this purpose, the server utilizes AWS® and Google® Cloud Platform to efficiently perform the calculations necessary for data aggregation and analysis. The analyzed data is then used to clarify snowfall conditions in each region.

[0123] The terminal identifies areas requiring snow removal based on analysis results received from the server. To achieve this, the terminal is equipped with a function that uses sensor technology to detect snow conditions in real time. The terminal transmits this data to the server, which uses it as basic information for calculating the optimal snow removal route.

[0124] The server uses an AI algorithm based on the received information to calculate the optimal snow removal route. This algorithm optimizes the operation of the snow removal equipment by considering physical obstacles and areas that should be prioritized for snow removal. The calculated route information is transmitted to the terminal, forming the basis for efficient snow removal work.

[0125] Users can monitor the progress of snow removal in real time via their smartphones and issue manual instructions as needed. This interface is built using mobile app development frameworks such as React Native. Users can also check safe routes in their area within the app. For example, when the app is opened, the day's snowfall forecast and the progress of snow removal are displayed in real time, and push notifications are sent with alerts such as "The sidewalk in front of City Hall has been cleared of snow."

[0126] An example of a given prompt might be: "Design an app that displays today's snow accumulation and snow removal progress. Also, incorporate an interface that allows users to manually specify snow removal locations." This allows the design team to proceed with development with specific functionality in mind.

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The server obtains weather data from external sources. Specifically, it uses a weather API to collect data such as current snowfall, temperature, and wind speed. Based on this input data, it performs data analysis to predict snowfall patterns for each region.

[0130] Step 2:

[0131] The server identifies which areas require snow removal based on the analyzed weather data. Next, it sets priorities for each area. This process utilizes an algorithm that takes the latitude and longitude data of the areas requiring snow removal as input and outputs the priority order.

[0132] Step 3:

[0133] The terminal uses sensors to measure the actual snow depth based on area information received from the server. It uses the data collected by the sensors as input to recognize the thickness and condition of the snow depth, and then sends that data as output to the server.

[0134] Step 4:

[0135] The server receives snow accumulation data from the terminals and uses an AI algorithm to calculate the optimal snow removal route. The input includes information on obstacles and priority areas for snow removal, while the output includes snow removal route information to instruct each terminal.

[0136] Step 5:

[0137] The terminal automatically starts snow removal work based on route information received from the server. At this time, the terminal controls the snow removal equipment according to the instructed route and operates at the specified speed and rotation speed.

[0138] Step 6:

[0139] Users access the system via a smartphone app to check the progress of snow removal and safe routes in real time. User input consists of actions on the app screen, and output includes progress status and notification messages.

[0140] 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.

[0141] This invention provides a system that automatically performs snow removal in areas where elderly people live, while also recognizing the user's emotions and responding appropriately. The system consists of terminals installed in each home, a centrally operated server, and an emotion engine that recognizes the user's emotions.

[0142] When the system starts up, the server periodically acquires and analyzes weather information to predict snowfall in each region. Based on this, the server sends instructions to terminals installed in areas with heavy snowfall to prepare for snow removal.

[0143] Upon receiving instructions from the server, the terminal automatically activates and uses its built-in sensors to scan the surrounding snow conditions. The terminal then sends information about the identified areas requiring snow removal to the server.

[0144] The server receives information from the terminal, calculates the optimal snow removal route using an AI algorithm, and sends it to the terminal. The terminal then automatically performs snow removal based on the specified route according to the instructions. The speed and movement speed of the snow removal brush are optimized based on the instructions from the server.

[0145] Furthermore, the terminal and server recognize the user's emotional state through an emotion engine. Users can not only check the snow removal status on their smartphones or PCs, but also receive notifications and interactions tailored to their psychological state. For example, if a user is feeling anxious, they will be provided with messages that reassure them about the progress of the work and safety.

[0146] Furthermore, the server stores the user's emotional history recognized by the emotion engine, and uses this data to improve future services. By analyzing the data and adjusting the schedule and methods for future snow removal operations, it is possible to increase user satisfaction.

[0147] This system not only improves the efficiency of snow removal but also addresses users' emotional needs, providing a safer and more comfortable living environment. Research suggests that emotion-based feedback can help reduce psychological stress among the elderly.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect and analyze snowfall predictions and temperature information for each region.

[0151] Step 2:

[0152] The server analyzes weather data to determine the need for snow removal in each region. For areas where snowfall exceeds a certain threshold, it sends instructions to each terminal to prepare for snow removal.

[0153] Step 3:

[0154] The terminal automatically starts up upon receiving instructions from the server. It uses its own sensors to scan the amount and extent of snow in the surrounding area and analyze which areas need to be cleared.

[0155] Step 4:

[0156] The terminal sends the acquired information about the snow removal target area to the server. The server receives this information and uses an AI algorithm to calculate the optimal snow removal route.

[0157] Step 5:

[0158] The server sends the calculated optimal snow removal route information to the terminal. Based on this route, the terminal automatically starts snow removal work, appropriately adjusting the brush rotation speed and movement speed.

[0159] Step 6:

[0160] An emotion engine is installed in the device, which recognizes emotions from voice data and input information when the user interacts with the system. The emotion data is sent to a server.

[0161] Step 7:

[0162] The server analyzes the emotional data received from the emotion engine and determines the content of notifications based on the user's psychological state. For example, if the user is feeling anxious, it will create a message that provides reassurance regarding safety.

[0163] Step 8:

[0164] Users can check the progress of snow removal work on their smartphones or PC apps and receive notification messages tailored to their emotions. They can also interact with the system to adjust the work schedule as needed.

[0165] Step 9:

[0166] The server accumulates data on snow removal operations and user sentiment, and uses this data to analyze and improve the efficiency of future snow removal operations and enhance the user experience. The system is then improved based on the accumulated data.

[0167] (Example 2)

[0168] 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".

[0169] Snow removal in areas inhabited by the elderly presents challenges in terms of efficiency, safety, and ensuring the psychological well-being of residents. Conventional systems are insufficient in providing prompt responses based on weather information and services that consider the feelings of users; further improvements are needed.

[0170] 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.

[0171] In this invention, the server includes means for analyzing acquired external data, means for identifying areas requiring snow removal based on the obtained information, means for remotely controlling each processing unit and calculating efficient work routes, means for recognizing the emotional state of users and providing information based on those emotions, and means for accumulating the recognized emotional state and performing analysis to contribute to future service improvements. This enables prompt and accurate snow removal work and the provision of services that take into consideration the psychological state of users, so that elderly people can live with peace of mind.

[0172] "Acquired external data" refers to data obtained from external sources such as weather information, and serves as the basis for identifying areas where snow removal is necessary.

[0173] "Means of analysis" refers to methods and devices for analyzing acquired external data to determine the need for snow removal in a specific area.

[0174] "Areas requiring snow removal" refers to areas with heavy snowfall that may disrupt safe travel and daily life.

[0175] "Means of remotely controlling each processing unit" refers to technology that allows devices located in physically distant locations to operate according to instructions.

[0176] An "efficient work route" refers to a path that allows you to achieve maximum results while minimizing time and energy.

[0177] "User emotional state" refers to the psychological state and feedback exhibited by system users, and is used to improve the individual user experience.

[0178] "Means of recognition" refers to technologies and methods for detecting and understanding information such as emotional states.

[0179] "Means of providing information" refers to systems that transmit the data and messages that users need in a timely manner.

[0180] "Means for accumulating recognized emotional states" refers to a system that records detected emotional information and uses it for later analysis and system improvement.

[0181] "Means of conducting analysis to contribute to service improvement" refers to technologies and methods that analyze accumulated data and use it to improve the quality of services in the future.

[0182] This invention relates to an automated snow removal system for areas inhabited by the elderly, and includes a server, terminals, and emotion recognition capabilities. The system aims to ensure the safety and psychological well-being of the elderly.

[0183] The server obtains weather information from external data provision services. For example, it uses an API to obtain data such as temperature, snowfall, and wind speed, and then analyzes this data using analysis software. This analysis utilizes machine learning algorithms that predict weather data. Based on the results, it identifies areas where snow removal is necessary and sends instructions to terminals.

[0184] The terminal begins operation upon receiving instructions from the server. Specifically, it uses a LiDAR sensor to accurately scan the surrounding snow conditions and identify areas requiring snow removal. The route for snow removal is automatically determined based on optimized route information received from the server. The terminal uses its built-in snow removal function to remove snow along the designated route. The snow removal brush and movement speed are all optimized based on instructions from the server.

[0185] Users can check the progress of snow removal in real time via their smartphones or PCs. During this process, an emotion recognition engine understands the user's emotional state, and customized messages and notifications are sent to improve the user experience. For example, if a user is worried, information to reassure them is provided quickly.

[0186] The system uses AI algorithms to calculate efficient snow removal routes and utilizes an emotion recognition function via an emotion engine to improve services in line with the user's psychological state, thus leading to expected improvements in user satisfaction.

[0187] As a concrete example, when heavy snowfall is forecast in a certain elderly-friendly area, the server immediately analyzes the weather information and instructs the relevant terminals to prepare for snow removal. The terminals begin snow removal, and users can receive reassuring messages while checking the progress of the snowfall. In this way, snow removal tailored to individual situations and responses that take into account the user's feelings are possible.

[0188] An example of a prompt message would be, "Please propose the optimal snow removal plan based on the latest snowfall forecast for this area."

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The server obtains weather information from external data provision services. It receives weather data such as temperature, snowfall, and wind speed from APIs as input, and analyzes this data using analysis software. This data analysis outputs information for predicting snowfall within a certain period and identifying the area.

[0192] Step 2:

[0193] The server identifies areas requiring snow removal based on the analysis results. Receiving this area identification information as input, the generated AI model determines the most efficient priority areas for snow removal. The optimal start time for snow removal and the priority areas are output, and instructions are sent to the relevant terminals.

[0194] Step 3:

[0195] The terminal receives instructions from the server and scans the surrounding snow conditions using its built-in LIDAR sensor. This scanning process acquires information about the surrounding terrain and snow depth as input, and based on this, identifies the exact area that requires snow removal. The identified area information is then sent to the server as output.

[0196] Step 4:

[0197] When the server receives area information from the terminal, it uses an AI algorithm to calculate the optimal snow removal route. Taking the received area information as input, it outputs a route that considers traffic efficiency and energy consumption. This route information is then sent to the terminal.

[0198] Step 5:

[0199] The terminal automatically performs snow removal according to a specified route based on route information received from the server. It uses route information as input to adjust the rotation speed and movement speed of the snow removal brush in real time. As output, information on areas where snow removal has been completed is reported to the server.

[0200] Step 6:

[0201] The server and terminal monitor the user's emotional state using an emotion recognition engine. Data related to the user's psychological state is acquired as input information, and the emotional state is analyzed. The output of this analysis is provided as appropriate notifications and messages tailored to the user's emotions.

[0202] Step 7:

[0203] The server accumulates users' emotional history and uses this data to improve future services. By analyzing the emotional data collected as input and learning methods that contribute to improving user satisfaction, it can produce output that will enhance future system efficiency.

[0204] (Application Example 2)

[0205] 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".

[0206] The problem that this invention aims to solve is to improve the efficiency of life in areas where the elderly live by automating snow removal work, and to enhance psychological reassurance by providing appropriate information according to the user's emotional state. Conventional snow removal systems have limitations in checking work progress and interacting with users, and there has been a need to improve the user experience.

[0207] 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.

[0208] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas requiring snow removal based on the obtained weather data, means for automatically performing snow removal work according to a specified route, and means for recognizing the user's emotional state using an identification engine and providing information corresponding to that emotional state. This makes it possible to perform snow removal work efficiently and quickly while enhancing the user's psychological sense of security.

[0209] "Weather information" refers to data on atmospheric conditions such as temperature, snowfall, rainfall, wind speed, and humidity, and is used to predict changes in weather conditions.

[0210] "Means of analysis" refers to devices that implement computer programs or algorithms for processing acquired data and making necessary judgments or predictions.

[0211] "Area" refers to a specific geographical area, and in this context, it refers to the region where snow removal is required.

[0212] A "device" (each individual device) is a system consisting of hardware and software that has a specific function and performs a specific task.

[0213] A "route" refers to the path taken from a specific point to a destination, and is information used to determine the optimal sequence of tasks.

[0214] "Automatic" refers to a state in which a machine or computer performs a task independently without human intervention.

[0215] "Monitoring methods" refer to mechanisms for continuously monitoring the status of systems and processes and understanding the situation.

[0216] An "identification engine" is an algorithm or program that analyzes data to recognize specific patterns or features, and is used to determine a user's emotional state.

[0217] "Emotional state" refers to the psychological state and attitude of an individual user, and includes categories of emotions such as anxiety, joy, and calmness.

[0218] "Means of providing information" refers to communication technologies and interfaces used to transmit information to users using digital devices.

[0219] The system for carrying out this invention consists of a server, a terminal, and a user. The server periodically acquires weather information via the internet and uses that data to analyze snowfall in a specific area. Based on the analyzed snowfall data, the server instructs the terminal on the areas and extents where snow removal is necessary.

[0220] Based on information received from the server, the terminal uses its built-in sensors and algorithms to scan the surrounding environment in real time and automatically perform snow removal operations as needed. Furthermore, the terminal transmits progress information to the server, enabling efficient management and monitoring of snow removal activities.

[0221] Users can check the real-time progress of snow removal work through an application installed on their smartphone or computer. In addition, an emotion state recognition engine that works in conjunction with the server analyzes the user's psychological state and provides information and feedback according to the results. For example, if the user is feeling anxious, a reassuring message will be sent such as, "Current snow removal work is progressing smoothly and safety is ensured."

[0222] The entire system is designed to provide a safe and comfortable living environment for residents, including the elderly. Furthermore, feedback on emotional state is expected to reduce user psychological stress. A specific example is a scenario where an elderly user, on a day of heavy snowfall, launches the app on their smartphone and receives a message stating, "Current snow removal operations are proceeding smoothly and safely," providing reassurance.

[0223] An example of a prompt is "Design an application to provide progress and emotion-based feedback on snow removal work." By using such prompts, the generative AI model generates and provides information that is appropriate to the user's psychological state.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The server retrieves weather data from a weather information database. The input is the latest weather data, and it uses a prediction algorithm to analyze the likelihood and amount of snowfall, outputting the analysis results. These results include snowfall forecasts and time information for specific areas.

[0227] Step 2:

[0228] The server uses the analyzed snowfall data to determine areas requiring snow removal and sends that information to the terminal. The input is the analysis results obtained from step 1, and based on this, the area selection logic is applied to output snow removal area information.

[0229] Step 3:

[0230] The terminal uses snow removal area information received from the server to measure the surrounding snow conditions with its built-in sensors. The input is area information from the server, and the sensor measurement data is aggregated to identify the specific starting point and area for necessary snow removal work, and this information is output to the server.

[0231] Step 4:

[0232] The server uses an AI algorithm to calculate the optimal snow removal route based on snow condition data received from the terminal. The input is condition data from the terminal, and the calculated snow removal route information is output to the terminal.

[0233] Step 5:

[0234] The terminal receives route information sent from the server and automatically performs snow removal work according to that route. The input is route information from the server, and it controls motors and actuators to operate brushes and snow removal blades appropriately, completing the snow removal work as output.

[0235] Step 6:

[0236] The terminal reports the progress of snow removal work to the server in real time. The progress information includes time, amount of work, and percentage of work completed, and this is output to the server.

[0237] Step 7:

[0238] The server analyzes the user's psychological state data using an emotion state recognition engine, along with progress information, and generates appropriate feedback. The input is the terminal's progress information and the output of the recognition engine, which generates a prompt and outputs a message to the user.

[0239] Step 8:

[0240] Users receive snow removal progress information and emotion-based feedback messages from the server via their smartphones or computers. Input is notification information from the server, which is displayed on the screen as reassuring text messages.

[0241] 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.

[0242] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0243] 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.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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".

[0257] This invention provides a system for automatically performing snow removal in areas where elderly people live, and consists of terminals installed in each home and a centrally operated server. In this system, the server periodically acquires and analyzes weather information to predict snowfall in each area. The server then determines the need for snow removal based on the amount of snowfall and sends work instructions to each terminal at the appropriate time.

[0258] Based on the received instructions, the terminal automatically powers on and uses surrounding sensors to detect snow conditions. This identifies areas and extents requiring snow removal and transmits the data to the server in real time.

[0259] The server uses AI algorithms based on the received information to calculate the optimal snow removal route. This process takes into account obstacles and areas that should be prioritized for snow removal, while also optimizing the rotation speed of the snow brushes and the movement speed of the terminals. The calculated route information is then transmitted to each terminal, providing instructions to ensure efficient snow removal.

[0260] During snow removal operations, the terminal reports progress and any anomalies to the server in real time, and the server makes necessary adjustments based on this information. Furthermore, users can monitor the snow removal status using their smartphones or PCs and perform manual operations as needed. This notification function allows users to confidently monitor the progress of the snow removal operations.

[0261] Furthermore, this system includes a mechanism that improves the efficiency of future snow removal operations by having the server accumulate and analyze work data. As a result, the labor required for snow removal can be significantly reduced, providing an environment in which elderly people can live with peace of mind. For example, by analyzing past data, it becomes possible to automatically adjust snow removal operations based on specific patterns, further reducing the burden on users.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect snowfall predictions and related data such as temperature for each region.

[0265] Step 2:

[0266] The server analyzes the acquired weather data and calculates the predicted snowfall amount for each region. Based on this, it creates instructions to prepare for snow removal in areas where snowfall exceeds a certain level.

[0267] Step 3:

[0268] The terminal receives instructions from the server and starts up automatically. It uses its sensors to scan the amount and extent of snow in its surroundings, thereby identifying areas that need snow removal. It then sends the obtained data to the server.

[0269] Step 4:

[0270] Based on the data received from the terminal, the server uses an AI algorithm to calculate the optimal snow removal route. Here, the route is determined considering obstacles and areas that should be prioritized for snow removal, and detailed movement instructions are given to the terminal.

[0271] Step 5:

[0272] The terminal automatically starts snow removal work along the designated snow removal route, following the route instructions received from the server. The rotation speed and movement speed of the snow removal brush are optimized according to the server's instructions.

[0273] Step 6:

[0274] The terminal reports its progress and any anomalies to the server in real time. Based on this information, the server can adjust its operation in real time.

[0275] Step 7:

[0276] Users can access a dedicated application on their smartphone or PC to check the current snow removal progress and system status.

[0277] Step 8:

[0278] After the server closes, it stores and analyzes all the work data. This data will be used to further improve the efficiency of future snow removal operations.

[0279] (Example 1)

[0280] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0281] The present invention aims to solve the problem related to the technology for efficiently and automatically performing snow removal work in the area where the elderly and others live. Conventional snow removal work requires labor and manpower, which is a heavy burden for the elderly. There is also a problem that it is difficult to perform snow removal at an appropriate timing and select an optimal route. It is required to provide a system for improving such a situation and performing snow removal more efficiently and safely.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0283] In this invention, the server includes means for acquiring and analyzing weather information, means for specifying the area and its range where snow removal is required in each device based on the obtained weather data, and means for remotely controlling the operation of each device and calculating an optimal movement route. Thereby, it becomes possible to automate efficient snow removal work according to the weather situation and reduce the burden on the user.

[0284] "Weather information" refers to data related to the weather, and specifically includes snowfall, rainfall, temperature, humidity, wind speed, etc.

[0285] "Analysis" refers to an operation or process for processing the acquired data and extracting meaningful information.

[0286] "Device" refers to all devices having a specific function, and in the present invention, it means a device used for snow removal.

[0287] "Area" refers to a specific geographical range, and indicates a region or area where snow removal work is required.

[0288] "Range" refers to the scale and spread of the area where a certain specific activity is carried out.

[0289] "Remote" refers to performing operations from a distance, including operations carried out via communication technology.

[0290] A "route" refers to a path that connects designated points, and in particular, indicates a route that allows for efficient travel.

[0291] "Automatic" refers to a process where operations are performed mechanically without human intervention.

[0292] "Snow removal work" refers to a series of activities to remove snow, specifically including operations such as pushing aside or removing accumulated snow.

[0293] "Progress" refers to the degree or status of an activity's progress.

[0294] "Surveillance" refers to continuously observing and confirming specific things or situations.

[0295] "Notification" refers to the act of transmitting specific information to another person.

[0296] "User" refers to an individual or legal entity that uses the system or service.

[0297] "Intelligent analysis" refers to the process of using technologies such as AI to analyze information in a sophisticated way and identify patterns.

[0298] This invention provides an automated snow removal system for areas inhabited by the elderly. This system includes a process for acquiring weather information, analyzing that data, and performing optimal snow removal operations. Its specific configuration and functions are described below.

[0299] The server periodically acquires weather information using a weather API service. This acquired data is processed by analysis software. Specifically, it analyzes data such as snowfall amount, temperature, and humidity, and uses an AI model to determine how much snow removal is needed in which areas. After the analysis, it sends instructions to terminals to start snow removal remotely, if necessary.

[0300] The terminal automatically activates upon receiving instructions and uses its built-in sensors to measure the surrounding snow depth. Ultrasonic and infrared sensors are used to detect the height and extent of the snow in real time. By sending this information to a server, the server can calculate a more precise snow removal route and instruct efficient snow removal operations.

[0301] Users can monitor the snow removal process in real time via their smartphones or PCs, and can also manually operate the snow removal system as needed. This feature allows administrators, such as elderly individuals, to safely understand the overall snow removal process from their homes.

[0302] Furthermore, the server stores data on completed tasks and performs data analysis to make future snow removal operations even more efficient. Through this analysis, it becomes possible to make predictions based on specific weather patterns and automatically adjust operations based on those predictions.

[0303] For example, by using a prompt such as, "Calculate the optimal snow removal route based on the latest snowfall forecast and past snow removal data for this region," the server can utilize a generated AI model to formulate an effective snow removal plan.

[0304] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0305] Step 1:

[0306] The server obtains weather information from the weather API service. It uses the query parameters of the API as input and receives weather data in JSON format as output. Specifically, the server periodically sends requests to the API service and saves the response data for analysis.

[0307] Step 2:

[0308] The server analyzes the obtained weather data and makes a snowfall prediction. It uses the weather data in JSON format received as input and generates a snowfall amount prediction, which is the analysis result, as output. A generative AI model is used for the analysis, and the data is processed with a specific algorithm.

[0309] Step 3:

[0310] Based on the analysis result, the server identifies the areas where snow removal is necessary and generates instructions. It uses the snowfall amount prediction data as input and generates snow removal instructions for each terminal as output. The server sets a threshold and creates instructions when it is exceeded.

[0311] Step 4:

[0312] The terminal receives the snow removal instructions from the server and automatically starts up. It uses the instruction data from the server as input and starts up the terminal and the operation of the sensors as output.

[0313] Step 5:

[0314] The terminal detects the surrounding snow accumulation situation with sensors. It uses the physical environment at the site as input and generates data on the height and range of the snow accumulation as output. Specifically, ultrasonic sensors and infrared sensors measure the snow accumulation and convert the results into digital data.

[0315] Step 6:

[0316] The terminal sends the detected snow depth data to the server. It uses the detected snow depth data as input and generates data packets to send to the server as output. The transmission protocol used is HTTP or MQTT.

[0317] Step 7:

[0318] The server receives snow depth data transmitted from the terminal and uses an AI algorithm to calculate the optimal snow removal route. It uses the received snow depth data as input and generates optimal route information as output.

[0319] Step 8:

[0320] The server sends the calculated route information to the terminals. It uses the route calculation result as input and generates and sends specific route instructions to each terminal as output.

[0321] Step 9:

[0322] The terminal starts snow removal work according to the specified route based on routing instructions from the server. It uses the route information received as input to perform snow removal as a mechanical operation.

[0323] Step 10:

[0324] The terminal reports the progress and any abnormalities of snow removal operations to the server in real time. It uses progress data from sensors and self-diagnosis results as input and generates a status report to send to the server as output.

[0325] Step 11:

[0326] Users monitor the progress of snow removal using smartphones or PCs and perform manual operations as needed. They can use progress information from the server as input and send instructions as output.

[0327] Step 12:

[0328] The server stores work details in a database and performs analysis to improve efficiency in future work. It uses data from completed tasks as input and generates analysis results useful for planning future work.

[0329] (Application Example 1)

[0330] 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."

[0331] In areas with a large elderly population, snow removal during snowfall poses a significant burden on residents. Furthermore, it is difficult to track the progress of snow removal and provide residents with safe routes. Moreover, there is insufficient information available to residents to monitor snow removal status in real time and ensure safe travel. Therefore, it is necessary to improve the efficiency of snow removal operations and streamline information provision.

[0332] 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.

[0333] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas and their extents requiring snow removal at each device based on the obtained weather data, means for monitoring the progress of snow removal work and notifying users, and display means for residents to check the local conditions and confirm safe routes. This enables residents to grasp the snow removal situation in real time and select safe and effective routes.

[0334] "Weather information" refers to data related to weather conditions, including elements such as snowfall, temperature, and wind speed.

[0335] "Analysis" is the process of analyzing and understanding information based on acquired data.

[0336] A "region" is a geographical area defined for a specific function or purpose.

[0337] "Scope" refers to the extent of the area affected by a particular activity or effect.

[0338] "Progress" refers to the process by which things move forward, or the resulting changes in state.

[0339] "Users" refers to individuals or organizations that use this system.

[0340] "Notification" is a means of conveying specific information to a target audience.

[0341] "Display means" refers to devices or methods for visually presenting information.

[0342] To realize this invention, the system operates as follows:

[0343] The server first acquires weather information from various data sources and then processes and analyzes it. For this purpose, the server utilizes AWS and Google Cloud Platform to efficiently perform the calculations necessary for data aggregation and analysis. The analyzed data is then used to clarify snowfall conditions in each region.

[0344] The terminal identifies areas requiring snow removal based on analysis results received from the server. To achieve this, the terminal is equipped with a function that uses sensor technology to detect snow conditions in real time. The terminal transmits this data to the server, which uses it as basic information for calculating the optimal snow removal route.

[0345] The server uses an AI algorithm based on the received information to calculate the optimal snow removal route. This algorithm optimizes the operation of the snow removal equipment by considering physical obstacles and areas that should be prioritized for snow removal. The calculated route information is transmitted to the terminal, forming the basis for efficient snow removal work.

[0346] Users can monitor the progress of snow removal in real time via their smartphones and issue manual instructions as needed. This interface is built using mobile app development frameworks such as React Native. Users can also check safe routes in their area within the app. For example, when the app is opened, the day's snowfall forecast and the progress of snow removal are displayed in real time, and push notifications are sent with alerts such as "The sidewalk in front of City Hall has been cleared of snow."

[0347] An example of a given prompt might be: "Design an app that displays today's snow accumulation and snow removal progress. Also, incorporate an interface that allows users to manually specify snow removal locations." This allows the design team to proceed with development with specific functionality in mind.

[0348] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0349] Step 1:

[0350] The server obtains weather data from external sources. Specifically, it uses a weather API to collect data such as current snowfall, temperature, and wind speed. Based on this input data, it performs data analysis to predict snowfall patterns for each region.

[0351] Step 2:

[0352] The server identifies which areas require snow removal based on the analyzed weather data. Next, it sets priorities for each area. This process utilizes an algorithm that takes the latitude and longitude data of the areas requiring snow removal as input and outputs the priority order.

[0353] Step 3:

[0354] The terminal uses sensors to measure the actual snow depth based on area information received from the server. It uses the data collected by the sensors as input to recognize the thickness and condition of the snow depth, and then sends that data as output to the server.

[0355] Step 4:

[0356] The server receives snow accumulation data from the terminals and uses an AI algorithm to calculate the optimal snow removal route. The input includes information on obstacles and priority areas for snow removal, while the output includes snow removal route information to instruct each terminal.

[0357] Step 5:

[0358] The terminal automatically starts snow removal work based on route information received from the server. At this time, the terminal controls the snow removal equipment according to the instructed route and operates at the specified speed and rotation speed.

[0359] Step 6:

[0360] Users access the system via a smartphone app to check the progress of snow removal and safe routes in real time. User input consists of actions on the app screen, and output includes progress status and notification messages.

[0361] 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.

[0362] This invention provides a system that automatically performs snow removal in areas where elderly people live, while also recognizing the user's emotions and responding appropriately. The system consists of terminals installed in each home, a centrally operated server, and an emotion engine that recognizes the user's emotions.

[0363] When the system starts up, the server periodically acquires and analyzes weather information to predict snowfall in each region. Based on this, the server sends instructions to terminals installed in areas with heavy snowfall to prepare for snow removal.

[0364] Upon receiving instructions from the server, the terminal automatically activates and uses its built-in sensors to scan the surrounding snow conditions. The terminal then sends information about the identified areas requiring snow removal to the server.

[0365] The server receives information from the terminal, calculates the optimal snow removal route using an AI algorithm, and sends it to the terminal. The terminal then automatically performs snow removal based on the specified route according to the instructions. The speed and movement speed of the snow removal brush are optimized based on the instructions from the server.

[0366] Furthermore, the terminal and server recognize the user's emotional state through an emotion engine. Users can not only check the snow removal status on their smartphones or PCs, but also receive notifications and interactions tailored to their psychological state. For example, if a user is feeling anxious, they will be provided with messages that reassure them about the progress of the work and safety.

[0367] Furthermore, the server stores the user's emotional history recognized by the emotion engine, and uses this data to improve future services. By analyzing the data and adjusting the schedule and methods for future snow removal operations, it is possible to increase user satisfaction.

[0368] This system not only improves the efficiency of snow removal but also addresses users' emotional needs, providing a safer and more comfortable living environment. Research suggests that emotion-based feedback can help reduce psychological stress among the elderly.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect and analyze snowfall predictions and temperature information for each region.

[0372] Step 2:

[0373] The server analyzes weather data to determine the need for snow removal in each region. For areas where snowfall exceeds a certain threshold, it sends instructions to each terminal to prepare for snow removal.

[0374] Step 3:

[0375] The terminal automatically starts up upon receiving instructions from the server. It uses its own sensors to scan the amount and extent of snow in the surrounding area and analyze which areas need to be cleared.

[0376] Step 4:

[0377] The terminal sends the acquired information about the snow removal target area to the server. The server receives this information and uses an AI algorithm to calculate the optimal snow removal route.

[0378] Step 5:

[0379] The server sends the calculated optimal snow removal route information to the terminal. Based on this route, the terminal automatically starts snow removal work, appropriately adjusting the brush rotation speed and movement speed.

[0380] Step 6:

[0381] An emotion engine is installed in the device, which recognizes emotions from voice data and input information when the user interacts with the system. The emotion data is sent to a server.

[0382] Step 7:

[0383] The server analyzes the emotional data received from the emotion engine and determines the content of notifications based on the user's psychological state. For example, if the user is feeling anxious, it will create a message that provides reassurance regarding safety.

[0384] Step 8:

[0385] Users can check the progress of snow removal work on their smartphones or PC apps and receive notification messages tailored to their emotions. They can also interact with the system to adjust the work schedule as needed.

[0386] Step 9:

[0387] The server accumulates data on snow removal operations and user sentiment, and uses this data to analyze and improve the efficiency of future snow removal operations and enhance the user experience. The system is then improved based on the accumulated data.

[0388] (Example 2)

[0389] 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".

[0390] Snow removal in areas inhabited by the elderly presents challenges in terms of efficiency, safety, and ensuring the psychological well-being of residents. Conventional systems are insufficient in providing prompt responses based on weather information and services that consider the feelings of users; further improvements are needed.

[0391] 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.

[0392] In this invention, the server includes means for analyzing acquired external data, means for identifying areas requiring snow removal based on the obtained information, means for remotely controlling each processing unit and calculating efficient work routes, means for recognizing the emotional state of users and providing information based on those emotions, and means for accumulating the recognized emotional state and performing analysis to contribute to future service improvements. This enables prompt and accurate snow removal work and the provision of services that take into consideration the psychological state of users, so that elderly people can live with peace of mind.

[0393] "Acquired external data" refers to data obtained from external sources such as weather information, and serves as the basis for identifying areas where snow removal is necessary.

[0394] "Means of analysis" refers to methods and devices for analyzing acquired external data to determine the need for snow removal in a specific area.

[0395] "Areas requiring snow removal" refers to areas with heavy snowfall that may disrupt safe travel and daily life.

[0396] "Means of remotely controlling each processing unit" refers to technology that allows devices located in physically distant locations to operate according to instructions.

[0397] An "efficient work route" refers to a path that allows you to achieve maximum results while minimizing time and energy.

[0398] "User emotional state" refers to the psychological state and feedback exhibited by system users, and is used to improve the individual user experience.

[0399] "Means of recognition" refers to technologies and methods for detecting and understanding information such as emotional states.

[0400] "Means of providing information" refers to systems that transmit the data and messages that users need in a timely manner.

[0401] "Means for accumulating recognized emotional states" refers to a system that records detected emotional information and uses it for later analysis and system improvement.

[0402] "Means of conducting analysis to contribute to service improvement" refers to technologies and methods that analyze accumulated data and use it to improve the quality of services in the future.

[0403] This invention relates to an automated snow removal system for areas inhabited by the elderly, and includes a server, terminals, and emotion recognition capabilities. The system aims to ensure the safety and psychological well-being of the elderly.

[0404] The server obtains weather information from external data provision services. For example, it uses an API to obtain data such as temperature, snowfall, and wind speed, and then analyzes this data using analysis software. This analysis utilizes machine learning algorithms that predict weather data. Based on the results, it identifies areas where snow removal is necessary and sends instructions to terminals.

[0405] The terminal begins operation upon receiving instructions from the server. Specifically, it uses a LiDAR sensor to accurately scan the surrounding snow conditions and identify areas requiring snow removal. The route for snow removal is automatically determined based on optimized route information received from the server. The terminal uses its built-in snow removal function to remove snow along the designated route. The snow removal brush and movement speed are all optimized based on instructions from the server.

[0406] Users can check the progress of snow removal in real time via their smartphones or PCs. During this process, an emotion recognition engine understands the user's emotional state, and customized messages and notifications are sent to improve the user experience. For example, if a user is worried, information to reassure them is provided quickly.

[0407] The system uses AI algorithms to calculate efficient snow removal routes and utilizes an emotion recognition function via an emotion engine to improve services in line with the user's psychological state, thus leading to expected improvements in user satisfaction.

[0408] As a concrete example, when heavy snowfall is forecast in a certain elderly-friendly area, the server immediately analyzes the weather information and instructs the relevant terminals to prepare for snow removal. The terminals begin snow removal, and users can receive reassuring messages while checking the progress of the snowfall. In this way, snow removal tailored to individual situations and responses that take into account the user's feelings are possible.

[0409] An example of a prompt message would be, "Please propose the optimal snow removal plan based on the latest snowfall forecast for this area."

[0410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0411] Step 1:

[0412] The server obtains weather information from external data provision services. It receives weather data such as temperature, snowfall, and wind speed from APIs as input, and analyzes this data using analysis software. This data analysis outputs information for predicting snowfall within a certain period and identifying the area.

[0413] Step 2:

[0414] The server identifies areas requiring snow removal based on the analysis results. Receiving this area identification information as input, the generated AI model determines the most efficient priority areas for snow removal. The optimal start time for snow removal and the priority areas are output, and instructions are sent to the relevant terminals.

[0415] Step 3:

[0416] The terminal receives instructions from the server and scans the surrounding snow conditions using its built-in LIDAR sensor. This scanning process acquires information about the surrounding terrain and snow depth as input, and based on this, identifies the exact area that requires snow removal. The identified area information is then sent to the server as output.

[0417] Step 4:

[0418] When the server receives area information from the terminal, it uses an AI algorithm to calculate the optimal snow removal route. Taking the received area information as input, it outputs a route that considers traffic efficiency and energy consumption. This route information is then sent to the terminal.

[0419] Step 5:

[0420] The terminal automatically performs snow removal according to a specified route based on route information received from the server. It uses route information as input to adjust the rotation speed and movement speed of the snow removal brush in real time. As output, information on areas where snow removal has been completed is reported to the server.

[0421] Step 6:

[0422] The server and terminal monitor the user's emotional state using an emotion recognition engine. Data related to the user's psychological state is acquired as input information, and the emotional state is analyzed. The output of this analysis is provided as appropriate notifications and messages tailored to the user's emotions.

[0423] Step 7:

[0424] The server accumulates users' emotional history and uses this data to improve future services. By analyzing the emotional data collected as input and learning methods that contribute to improving user satisfaction, it can produce output that will enhance future system efficiency.

[0425] (Application Example 2)

[0426] 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."

[0427] The problem that this invention aims to solve is to improve the efficiency of life in areas where the elderly live by automating snow removal work, and to enhance psychological reassurance by providing appropriate information according to the user's emotional state. Conventional snow removal systems have limitations in checking work progress and interacting with users, and there has been a need to improve the user experience.

[0428] 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.

[0429] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas requiring snow removal based on the obtained weather data, means for automatically performing snow removal work according to a specified route, and means for recognizing the user's emotional state using an identification engine and providing information corresponding to that emotional state. This makes it possible to perform snow removal work efficiently and quickly while enhancing the user's psychological sense of security.

[0430] "Weather information" refers to data on atmospheric conditions such as temperature, snowfall, rainfall, wind speed, and humidity, and is used to predict changes in weather conditions.

[0431] "Means of analysis" refers to devices that implement computer programs or algorithms for processing acquired data and making necessary judgments or predictions.

[0432] "Area" refers to a specific geographical area, and in this context, it refers to the region where snow removal is required.

[0433] A "device" (each individual device) is a system consisting of hardware and software that has a specific function and performs a specific task.

[0434] A "route" refers to the path taken from a specific point to a destination, and is information used to determine the optimal sequence of tasks.

[0435] "Automatic" refers to a state in which a machine or computer performs a task independently without human intervention.

[0436] "Monitoring methods" refer to mechanisms for continuously monitoring the status of systems and processes and understanding the situation.

[0437] An "identification engine" is an algorithm or program that analyzes data to recognize specific patterns or features, and is used to determine a user's emotional state.

[0438] "Emotional state" refers to the psychological state and attitude of an individual user, and includes categories of emotions such as anxiety, joy, and calmness.

[0439] "Means of providing information" refers to communication technologies and interfaces used to transmit information to users using digital devices.

[0440] The system for carrying out this invention consists of a server, a terminal, and a user. The server periodically acquires weather information via the internet and uses that data to analyze snowfall in a specific area. Based on the analyzed snowfall data, the server instructs the terminal on the areas and extents where snow removal is necessary.

[0441] Based on information received from the server, the terminal uses its built-in sensors and algorithms to scan the surrounding environment in real time and automatically perform snow removal operations as needed. Furthermore, the terminal transmits progress information to the server, enabling efficient management and monitoring of snow removal activities.

[0442] Users can check the real-time progress of snow removal work through an application installed on their smartphone or computer. In addition, an emotion state recognition engine that works in conjunction with the server analyzes the user's psychological state and provides information and feedback according to the results. For example, if the user is feeling anxious, a reassuring message will be sent such as, "Current snow removal work is progressing smoothly and safety is ensured."

[0443] The entire system is designed to provide a safe and comfortable living environment for residents, including the elderly. Furthermore, feedback on emotional state is expected to reduce user psychological stress. A specific example is a scenario where an elderly user, on a day of heavy snowfall, launches the app on their smartphone and receives a message stating, "Current snow removal operations are proceeding smoothly and safely," providing reassurance.

[0444] An example of a prompt is "Design an application to provide progress and emotion-based feedback on snow removal work." By using such prompts, the generative AI model generates and provides information that is appropriate to the user's psychological state.

[0445] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0446] Step 1:

[0447] The server retrieves weather data from a weather information database. The input is the latest weather data, and it uses a prediction algorithm to analyze the likelihood and amount of snowfall, outputting the analysis results. These results include snowfall forecasts and time information for specific areas.

[0448] Step 2:

[0449] The server uses the analyzed snowfall data to determine areas requiring snow removal and sends that information to the terminal. The input is the analysis results obtained from step 1, and based on this, the area selection logic is applied to output snow removal area information.

[0450] Step 3:

[0451] The terminal uses snow removal area information received from the server to measure the surrounding snow conditions with its built-in sensors. The input is area information from the server, and the sensor measurement data is aggregated to identify the specific starting point and area for necessary snow removal work, and this information is output to the server.

[0452] Step 4:

[0453] The server uses an AI algorithm to calculate the optimal snow removal route based on snow condition data received from the terminal. The input is condition data from the terminal, and the calculated snow removal route information is output to the terminal.

[0454] Step 5:

[0455] The terminal receives route information sent from the server and automatically performs snow removal work according to that route. The input is route information from the server, and it controls motors and actuators to operate brushes and snow removal blades appropriately, completing the snow removal work as output.

[0456] Step 6:

[0457] The terminal reports the progress of snow removal work to the server in real time. The progress information includes time, amount of work, and percentage of work completed, and this is output to the server.

[0458] Step 7:

[0459] The server analyzes the user's psychological state data using an emotion state recognition engine, along with progress information, and generates appropriate feedback. The input is the terminal's progress information and the output of the recognition engine, which generates a prompt and outputs a message to the user.

[0460] Step 8:

[0461] Users receive snow removal progress information and emotion-based feedback messages from the server via their smartphones or computers. Input is notification information from the server, which is displayed on the screen as reassuring text messages.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] [Third Embodiment]

[0466] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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".

[0478] This invention provides a system for automatically performing snow removal in areas where elderly people live, and consists of terminals installed in each home and a centrally operated server. In this system, the server periodically acquires and analyzes weather information to predict snowfall in each area. The server then determines the need for snow removal based on the amount of snowfall and sends work instructions to each terminal at the appropriate time.

[0479] Based on the received instructions, the terminal automatically powers on and uses surrounding sensors to detect snow conditions. This identifies areas and extents requiring snow removal and transmits the data to the server in real time.

[0480] The server uses AI algorithms based on the received information to calculate the optimal snow removal route. This process takes into account obstacles and areas that should be prioritized for snow removal, while also optimizing the rotation speed of the snow brushes and the movement speed of the terminals. The calculated route information is then transmitted to each terminal, providing instructions to ensure efficient snow removal.

[0481] During snow removal operations, the terminal reports progress and any anomalies to the server in real time, and the server makes necessary adjustments based on this information. Furthermore, users can monitor the snow removal status using their smartphones or PCs and perform manual operations as needed. This notification function allows users to confidently monitor the progress of the snow removal operations.

[0482] Furthermore, this system includes a mechanism that improves the efficiency of future snow removal operations by having the server accumulate and analyze work data. As a result, the labor required for snow removal can be significantly reduced, providing an environment in which elderly people can live with peace of mind. For example, by analyzing past data, it becomes possible to automatically adjust snow removal operations based on specific patterns, further reducing the burden on users.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect snowfall predictions and related data such as temperature for each region.

[0486] Step 2:

[0487] The server analyzes the acquired weather data and calculates the predicted snowfall amount for each region. Based on this, it creates instructions to prepare for snow removal in areas where snowfall exceeds a certain level.

[0488] Step 3:

[0489] The terminal receives instructions from the server and starts up automatically. It uses its sensors to scan the amount and extent of snow in its surroundings, thereby identifying areas that need snow removal. It then sends the obtained data to the server.

[0490] Step 4:

[0491] Based on the data received from the terminal, the server uses an AI algorithm to calculate the optimal snow removal route. Here, the route is determined considering obstacles and areas that should be prioritized for snow removal, and detailed movement instructions are given to the terminal.

[0492] Step 5:

[0493] The terminal automatically starts snow removal work along the designated snow removal route, following the route instructions received from the server. The rotation speed and movement speed of the snow removal brush are optimized according to the server's instructions.

[0494] Step 6:

[0495] The terminal reports its progress and any anomalies to the server in real time. Based on this information, the server can adjust its operation in real time.

[0496] Step 7:

[0497] Users can access a dedicated application on their smartphone or PC to check the current snow removal progress and system status.

[0498] Step 8:

[0499] After the server closes, it stores and analyzes all the work data. This data will be used to further improve the efficiency of future snow removal operations.

[0500] (Example 1)

[0501] 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."

[0502] This invention aims to solve problems related to technology for efficient and automated snow removal in areas where elderly people and others reside. Conventional snow removal requires labor and manpower, which is a heavy burden for the elderly. There were also challenges in removing snow at the appropriate time and selecting the optimal route. There is a need to improve this situation and provide a system that can perform snow removal more efficiently and safely.

[0503] 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.

[0504] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying the areas and extents requiring snow removal at each device based on the obtained weather data, and means for remotely controlling the operation of each device and calculating the optimal travel route. This enables the automation of efficient snow removal work according to weather conditions and reduces the burden on the user.

[0505] "Weather information" refers to data related to weather conditions, specifically including snowfall, rainfall, temperature, humidity, and wind speed.

[0506] "Analysis" refers to the operations and processes involved in processing acquired data and extracting meaningful information.

[0507] "Device" refers to any equipment with a specific function, and in this invention, it means equipment used for snow removal.

[0508] A "region" refers to a specific geographical area, indicating the region or area where snow removal work is required.

[0509] "Scope" refers to the size or extent of the area in which a particular activity takes place.

[0510] "Remote" refers to performing operations from a distance, including operations carried out via communication technology.

[0511] A "route" refers to a path that connects designated points, and in particular, indicates a route that allows for efficient travel.

[0512] "Automatic" refers to a process where operations are performed mechanically without human intervention.

[0513] "Snow removal work" refers to a series of activities to remove snow, specifically including operations such as pushing aside or removing accumulated snow.

[0514] "Progress" refers to the degree or status of an activity's progress.

[0515] "Surveillance" refers to continuously observing and confirming specific things or situations.

[0516] "Notification" refers to the act of transmitting specific information to another person.

[0517] "User" refers to an individual or legal entity that uses the system or service.

[0518] "Intelligent analysis" refers to the process of using technologies such as AI to analyze information in a sophisticated way and identify patterns.

[0519] This invention provides an automated snow removal system for areas inhabited by the elderly. This system includes a process for acquiring weather information, analyzing that data, and performing optimal snow removal operations. Its specific configuration and functions are described below.

[0520] The server periodically acquires weather information using a weather API service. This acquired data is processed by analysis software. Specifically, it analyzes data such as snowfall amount, temperature, and humidity, and uses an AI model to determine how much snow removal is needed in which areas. After the analysis, it sends instructions to terminals to start snow removal remotely, if necessary.

[0521] The terminal automatically activates upon receiving instructions and uses its built-in sensors to measure the surrounding snow depth. Ultrasonic and infrared sensors are used to detect the height and extent of the snow in real time. By sending this information to a server, the server can calculate a more precise snow removal route and instruct efficient snow removal operations.

[0522] Users can monitor the snow removal process in real time via their smartphones or PCs, and can also manually operate the snow removal system as needed. This feature allows administrators, such as elderly individuals, to safely understand the overall snow removal process from their homes.

[0523] Furthermore, the server stores data on completed tasks and performs data analysis to make future snow removal operations even more efficient. Through this analysis, it becomes possible to make predictions based on specific weather patterns and automatically adjust operations based on those predictions.

[0524] For example, by using a prompt such as, "Calculate the optimal snow removal route based on the latest snowfall forecast and past snow removal data for this region," the server can utilize a generated AI model to formulate an effective snow removal plan.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] The server retrieves weather information from a weather API service. It uses the API's query parameters as input and receives weather data in JSON format as output. Specifically, the server periodically sends requests to the API service and saves the response data for analysis.

[0528] Step 2:

[0529] The server analyzes acquired weather data to predict snowfall. It uses the JSON-formatted weather data received as input to generate the snowfall forecast, which is the result of the analysis. A generative AI model is used for the analysis, processing the data with a specific algorithm.

[0530] Step 3:

[0531] Based on the analysis results, the server identifies areas requiring snow removal and generates instructions. It uses snowfall forecast data as input and generates snow removal instructions for each terminal as output. The server sets a threshold and generates instructions when this threshold is exceeded.

[0532] Step 4:

[0533] The terminal receives snow removal instructions from the server and automatically starts up. It uses instruction data from the server as input and outputs the terminal's startup and the initiation of sensor operation.

[0534] Step 5:

[0535] The device uses sensors to detect the surrounding snow conditions. It uses the physical environment of the site as input and generates data on the height and extent of the snow as output. Specifically, ultrasonic sensors and infrared sensors measure the snow depth and convert the results into digital data.

[0536] Step 6:

[0537] The terminal sends the detected snow depth data to the server. It uses the detected snow depth data as input and generates data packets to send to the server as output. The transmission protocol used is HTTP or MQTT.

[0538] Step 7:

[0539] The server receives snow depth data transmitted from the terminal and uses an AI algorithm to calculate the optimal snow removal route. It uses the received snow depth data as input and generates optimal route information as output.

[0540] Step 8:

[0541] The server sends the calculated route information to the terminals. It uses the route calculation result as input and generates and sends specific route instructions to each terminal as output.

[0542] Step 9:

[0543] The terminal starts snow removal work according to the specified route based on routing instructions from the server. It uses the route information received as input to perform snow removal as a mechanical operation.

[0544] Step 10:

[0545] The terminal reports the progress and any abnormalities of snow removal operations to the server in real time. It uses progress data from sensors and self-diagnosis results as input and generates a status report to send to the server as output.

[0546] Step 11:

[0547] Users monitor the progress of snow removal using smartphones or PCs and perform manual operations as needed. They can use progress information from the server as input and send instructions as output.

[0548] Step 12:

[0549] The server stores work details in a database and performs analysis to improve efficiency in future work. It uses data from completed tasks as input and generates analysis results useful for planning future work.

[0550] (Application Example 1)

[0551] 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."

[0552] In areas with a large elderly population, snow removal during snowfall poses a significant burden on residents. Furthermore, it is difficult to track the progress of snow removal and provide residents with safe routes. Moreover, there is insufficient information available to residents to monitor snow removal status in real time and ensure safe travel. Therefore, it is necessary to improve the efficiency of snow removal operations and streamline information provision.

[0553] 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.

[0554] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas and their extents requiring snow removal at each device based on the obtained weather data, means for monitoring the progress of snow removal work and notifying users, and display means for residents to check the local conditions and confirm safe routes. This enables residents to grasp the snow removal situation in real time and select safe and effective routes.

[0555] "Weather information" refers to data related to weather conditions, including elements such as snowfall, temperature, and wind speed.

[0556] "Analysis" is the process of analyzing and understanding information based on acquired data.

[0557] A "region" is a geographical area defined for a specific function or purpose.

[0558] "Scope" refers to the extent of the area affected by a particular activity or effect.

[0559] "Progress" refers to the process by which things move forward, or the resulting changes in state.

[0560] "Users" refers to individuals or organizations that use this system.

[0561] "Notification" is a means of conveying specific information to a target audience.

[0562] "Display means" refers to devices or methods for visually presenting information.

[0563] To realize this invention, the system operates as follows:

[0564] The server first acquires weather information from various data sources and then processes and analyzes it. For this purpose, the server utilizes AWS and Google Cloud Platform to efficiently perform the calculations necessary for data aggregation and analysis. The analyzed data is then used to clarify snowfall conditions in each region.

[0565] The terminal identifies areas requiring snow removal based on analysis results received from the server. To achieve this, the terminal is equipped with a function that uses sensor technology to detect snow conditions in real time. The terminal transmits this data to the server, which uses it as basic information for calculating the optimal snow removal route.

[0566] The server uses an AI algorithm based on the received information to calculate the optimal snow removal route. This algorithm optimizes the operation of the snow removal equipment by considering physical obstacles and areas that should be prioritized for snow removal. The calculated route information is transmitted to the terminal, forming the basis for efficient snow removal work.

[0567] Users can monitor the progress of snow removal in real time via their smartphones and issue manual instructions as needed. This interface is built using mobile app development frameworks such as React Native. Users can also check safe routes in their area within the app. For example, when the app is opened, the day's snowfall forecast and the progress of snow removal are displayed in real time, and push notifications are sent with alerts such as "The sidewalk in front of City Hall has been cleared of snow."

[0568] An example of a given prompt might be: "Design an app that displays today's snow accumulation and snow removal progress. Also, incorporate an interface that allows users to manually specify snow removal locations." This allows the design team to proceed with development with specific functionality in mind.

[0569] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0570] Step 1:

[0571] The server obtains weather data from external sources. Specifically, it uses a weather API to collect data such as current snowfall, temperature, and wind speed. Based on this input data, it performs data analysis to predict snowfall patterns for each region.

[0572] Step 2:

[0573] The server identifies which areas require snow removal based on the analyzed weather data. Next, it sets priorities for each area. This process utilizes an algorithm that takes the latitude and longitude data of the areas requiring snow removal as input and outputs the priority order.

[0574] Step 3:

[0575] The terminal uses sensors to measure the actual snow depth based on area information received from the server. It uses the data collected by the sensors as input to recognize the thickness and condition of the snow depth, and then sends that data as output to the server.

[0576] Step 4:

[0577] The server receives snow accumulation data from the terminals and uses an AI algorithm to calculate the optimal snow removal route. The input includes information on obstacles and priority areas for snow removal, while the output includes snow removal route information to instruct each terminal.

[0578] Step 5:

[0579] The terminal automatically starts snow removal work based on route information received from the server. At this time, the terminal controls the snow removal equipment according to the instructed route and operates at the specified speed and rotation speed.

[0580] Step 6:

[0581] Users access the system via a smartphone app to check the progress of snow removal and safe routes in real time. User input consists of actions on the app screen, and output includes progress status and notification messages.

[0582] 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.

[0583] This invention provides a system that automatically performs snow removal in areas where elderly people live, while also recognizing the user's emotions and responding appropriately. The system consists of terminals installed in each home, a centrally operated server, and an emotion engine that recognizes the user's emotions.

[0584] When the system starts up, the server periodically acquires and analyzes weather information to predict snowfall in each region. Based on this, the server sends instructions to terminals installed in areas with heavy snowfall to prepare for snow removal.

[0585] Upon receiving instructions from the server, the terminal automatically activates and uses its built-in sensors to scan the surrounding snow conditions. The terminal then sends information about the identified areas requiring snow removal to the server.

[0586] The server receives information from the terminal, calculates the optimal snow removal route using an AI algorithm, and sends it to the terminal. The terminal then automatically performs snow removal based on the specified route according to the instructions. The speed and movement speed of the snow removal brush are optimized based on the instructions from the server.

[0587] Furthermore, the terminal and server recognize the user's emotional state through an emotion engine. Users can not only check the snow removal status on their smartphones or PCs, but also receive notifications and interactions tailored to their psychological state. For example, if a user is feeling anxious, they will be provided with messages that reassure them about the progress of the work and safety.

[0588] Furthermore, the server stores the user's emotional history recognized by the emotion engine, and uses this data to improve future services. By analyzing the data and adjusting the schedule and methods for future snow removal operations, it is possible to increase user satisfaction.

[0589] This system not only improves the efficiency of snow removal but also addresses users' emotional needs, providing a safer and more comfortable living environment. Research suggests that emotion-based feedback can help reduce psychological stress among the elderly.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect and analyze snowfall predictions and temperature information for each region.

[0593] Step 2:

[0594] The server analyzes weather data to determine the need for snow removal in each region. For areas where snowfall exceeds a certain threshold, it sends instructions to each terminal to prepare for snow removal.

[0595] Step 3:

[0596] The terminal automatically starts up upon receiving instructions from the server. It uses its own sensors to scan the amount and extent of snow in the surrounding area and analyze which areas need to be cleared.

[0597] Step 4:

[0598] The terminal sends the acquired information about the snow removal target area to the server. The server receives this information and uses an AI algorithm to calculate the optimal snow removal route.

[0599] Step 5:

[0600] The server sends the calculated optimal snow removal route information to the terminal. Based on this route, the terminal automatically starts snow removal work, appropriately adjusting the brush rotation speed and movement speed.

[0601] Step 6:

[0602] An emotion engine is installed in the device, which recognizes emotions from voice data and input information when the user interacts with the system. The emotion data is sent to a server.

[0603] Step 7:

[0604] The server analyzes the emotional data received from the emotion engine and determines the content of notifications based on the user's psychological state. For example, if the user is feeling anxious, it will create a message that provides reassurance regarding safety.

[0605] Step 8:

[0606] Users can check the progress of snow removal work on their smartphones or PC apps and receive notification messages tailored to their emotions. They can also interact with the system to adjust the work schedule as needed.

[0607] Step 9:

[0608] The server accumulates data on snow removal operations and user sentiment, and uses this data to analyze and improve the efficiency of future snow removal operations and enhance the user experience. The system is then improved based on the accumulated data.

[0609] (Example 2)

[0610] 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."

[0611] Snow removal in areas inhabited by the elderly presents challenges in terms of efficiency, safety, and ensuring the psychological well-being of residents. Conventional systems are insufficient in providing prompt responses based on weather information and services that consider the feelings of users; further improvements are needed.

[0612] 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.

[0613] In this invention, the server includes means for analyzing acquired external data, means for identifying areas requiring snow removal based on the obtained information, means for remotely controlling each processing unit and calculating efficient work routes, means for recognizing the emotional state of users and providing information based on those emotions, and means for accumulating the recognized emotional state and performing analysis to contribute to future service improvements. This enables prompt and accurate snow removal work and the provision of services that take into consideration the psychological state of users, so that elderly people can live with peace of mind.

[0614] "Acquired external data" refers to data obtained from external sources such as weather information, and serves as the basis for identifying areas where snow removal is necessary.

[0615] "Means of analysis" refers to methods and devices for analyzing acquired external data to determine the need for snow removal in a specific area.

[0616] "Areas requiring snow removal" refers to areas with heavy snowfall that may disrupt safe travel and daily life.

[0617] "Means of remotely controlling each processing unit" refers to technology that allows devices located in physically distant locations to operate according to instructions.

[0618] An "efficient work route" refers to a path that allows you to achieve maximum results while minimizing time and energy.

[0619] "User emotional state" refers to the psychological state and feedback exhibited by system users, and is used to improve the individual user experience.

[0620] "Means of recognition" refers to technologies and methods for detecting and understanding information such as emotional states.

[0621] "Means of providing information" refers to systems that transmit the data and messages that users need in a timely manner.

[0622] "Means for accumulating recognized emotional states" refers to a system that records detected emotional information and uses it for later analysis and system improvement.

[0623] "Means of conducting analysis to contribute to service improvement" refers to technologies and methods that analyze accumulated data and use it to improve the quality of services in the future.

[0624] This invention relates to an automated snow removal system for areas inhabited by the elderly, and includes a server, terminals, and emotion recognition capabilities. The system aims to ensure the safety and psychological well-being of the elderly.

[0625] The server obtains weather information from external data provision services. For example, it uses an API to obtain data such as temperature, snowfall, and wind speed, and then analyzes this data using analysis software. This analysis utilizes machine learning algorithms that predict weather data. Based on the results, it identifies areas where snow removal is necessary and sends instructions to terminals.

[0626] The terminal begins operation upon receiving instructions from the server. Specifically, it uses a LiDAR sensor to accurately scan the surrounding snow conditions and identify areas requiring snow removal. The route for snow removal is automatically determined based on optimized route information received from the server. The terminal uses its built-in snow removal function to remove snow along the designated route. The snow removal brush and movement speed are all optimized based on instructions from the server.

[0627] Users can check the progress of snow removal in real time via their smartphones or PCs. During this process, an emotion recognition engine understands the user's emotional state, and customized messages and notifications are sent to improve the user experience. For example, if a user is worried, information to reassure them is provided quickly.

[0628] The system uses AI algorithms to calculate efficient snow removal routes and utilizes an emotion recognition function via an emotion engine to improve services in line with the user's psychological state, thus leading to expected improvements in user satisfaction.

[0629] As a concrete example, when heavy snowfall is forecast in a certain elderly-friendly area, the server immediately analyzes the weather information and instructs the relevant terminals to prepare for snow removal. The terminals begin snow removal, and users can receive reassuring messages while checking the progress of the snowfall. In this way, snow removal tailored to individual situations and responses that take into account the user's feelings are possible.

[0630] An example of a prompt message would be, "Please propose the optimal snow removal plan based on the latest snowfall forecast for this area."

[0631] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0632] Step 1:

[0633] The server obtains weather information from external data provision services. It receives weather data such as temperature, snowfall, and wind speed from APIs as input, and analyzes this data using analysis software. This data analysis outputs information for predicting snowfall within a certain period and identifying the area.

[0634] Step 2:

[0635] The server identifies areas requiring snow removal based on the analysis results. Receiving this area identification information as input, the generated AI model determines the most efficient priority areas for snow removal. The optimal start time for snow removal and the priority areas are output, and instructions are sent to the relevant terminals.

[0636] Step 3:

[0637] The terminal receives instructions from the server and scans the surrounding snow conditions using its built-in LIDAR sensor. This scanning process acquires information about the surrounding terrain and snow depth as input, and based on this, identifies the exact area that requires snow removal. The identified area information is then sent to the server as output.

[0638] Step 4:

[0639] When the server receives area information from the terminal, it uses an AI algorithm to calculate the optimal snow removal route. Taking the received area information as input, it outputs a route that considers traffic efficiency and energy consumption. This route information is then sent to the terminal.

[0640] Step 5:

[0641] The terminal automatically performs snow removal according to a specified route based on route information received from the server. It uses route information as input to adjust the rotation speed and movement speed of the snow removal brush in real time. As output, information on areas where snow removal has been completed is reported to the server.

[0642] Step 6:

[0643] The server and terminal monitor the user's emotional state using an emotion recognition engine. Data related to the user's psychological state is acquired as input information, and the emotional state is analyzed. The output of this analysis is provided as appropriate notifications and messages tailored to the user's emotions.

[0644] Step 7:

[0645] The server accumulates users' emotional history and uses this data to improve future services. By analyzing the emotional data collected as input and learning methods that contribute to improving user satisfaction, it can produce output that will enhance future system efficiency.

[0646] (Application Example 2)

[0647] 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."

[0648] The problem that this invention aims to solve is to improve the efficiency of life in areas where the elderly live by automating snow removal work, and to enhance psychological reassurance by providing appropriate information according to the user's emotional state. Conventional snow removal systems have limitations in checking work progress and interacting with users, and there has been a need to improve the user experience.

[0649] 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.

[0650] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas requiring snow removal based on the obtained weather data, means for automatically performing snow removal work according to a specified route, and means for recognizing the user's emotional state using an identification engine and providing information corresponding to that emotional state. This makes it possible to perform snow removal work efficiently and quickly while enhancing the user's psychological sense of security.

[0651] "Weather information" refers to data on atmospheric conditions such as temperature, snowfall, rainfall, wind speed, and humidity, and is used to predict changes in weather conditions.

[0652] "Means of analysis" refers to devices that implement computer programs or algorithms for processing acquired data and making necessary judgments or predictions.

[0653] "Area" refers to a specific geographical area, and in this context, it refers to the region where snow removal is required.

[0654] A "device" (each individual device) is a system consisting of hardware and software that has a specific function and performs a specific task.

[0655] A "route" refers to the path taken from a specific point to a destination, and is information used to determine the optimal sequence of tasks.

[0656] "Automatic" refers to a state in which a machine or computer performs a task independently without human intervention.

[0657] "Monitoring methods" refer to mechanisms for continuously monitoring the status of systems and processes and understanding the situation.

[0658] An "identification engine" is an algorithm or program that analyzes data to recognize specific patterns or features, and is used to determine a user's emotional state.

[0659] "Emotional state" refers to the psychological state and attitude of an individual user, and includes categories of emotions such as anxiety, joy, and calmness.

[0660] "Means of providing information" refers to communication technologies and interfaces used to transmit information to users using digital devices.

[0661] The system for carrying out this invention consists of a server, a terminal, and a user. The server periodically acquires weather information via the internet and uses that data to analyze snowfall in a specific area. Based on the analyzed snowfall data, the server instructs the terminal on the areas and extents where snow removal is necessary.

[0662] Based on information received from the server, the terminal uses its built-in sensors and algorithms to scan the surrounding environment in real time and automatically perform snow removal operations as needed. Furthermore, the terminal transmits progress information to the server, enabling efficient management and monitoring of snow removal activities.

[0663] Users can check the real-time progress of snow removal work through an application installed on their smartphone or computer. In addition, an emotion state recognition engine that works in conjunction with the server analyzes the user's psychological state and provides information and feedback according to the results. For example, if the user is feeling anxious, a reassuring message will be sent such as, "Current snow removal work is progressing smoothly and safety is ensured."

[0664] The entire system is designed to provide a safe and comfortable living environment for residents, including the elderly. Furthermore, feedback on emotional state is expected to reduce user psychological stress. A specific example is a scenario where an elderly user, on a day of heavy snowfall, launches the app on their smartphone and receives a message stating, "Current snow removal operations are proceeding smoothly and safely," providing reassurance.

[0665] An example of a prompt is "Design an application to provide progress and emotion-based feedback on snow removal work." By using such prompts, the generative AI model generates and provides information that is appropriate to the user's psychological state.

[0666] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0667] Step 1:

[0668] The server retrieves weather data from a weather information database. The input is the latest weather data, and it uses a prediction algorithm to analyze the likelihood and amount of snowfall, outputting the analysis results. These results include snowfall forecasts and time information for specific areas.

[0669] Step 2:

[0670] The server uses the analyzed snowfall data to determine areas requiring snow removal and sends that information to the terminal. The input is the analysis results obtained from step 1, and based on this, the area selection logic is applied to output snow removal area information.

[0671] Step 3:

[0672] The terminal uses snow removal area information received from the server to measure the surrounding snow conditions with its built-in sensors. The input is area information from the server, and the sensor measurement data is aggregated to identify the specific starting point and area for necessary snow removal work, and this information is output to the server.

[0673] Step 4:

[0674] The server uses an AI algorithm to calculate the optimal snow removal route based on snow condition data received from the terminal. The input is condition data from the terminal, and the calculated snow removal route information is output to the terminal.

[0675] Step 5:

[0676] The terminal receives route information sent from the server and automatically performs snow removal work according to that route. The input is route information from the server, and it controls motors and actuators to operate brushes and snow removal blades appropriately, completing the snow removal work as output.

[0677] Step 6:

[0678] The terminal reports the progress of snow removal work to the server in real time. The progress information includes time, amount of work, and percentage of work completed, and this is output to the server.

[0679] Step 7:

[0680] The server analyzes the user's psychological state data using an emotion state recognition engine, along with progress information, and generates appropriate feedback. The input is the terminal's progress information and the output of the recognition engine, which generates a prompt and outputs a message to the user.

[0681] Step 8:

[0682] Users receive snow removal progress information and emotion-based feedback messages from the server via their smartphones or computers. Input is notification information from the server, which is displayed on the screen as reassuring text messages.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] [Fourth Embodiment]

[0687] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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".

[0700] This invention provides a system for automatically performing snow removal in areas where elderly people live, and consists of terminals installed in each home and a centrally operated server. In this system, the server periodically acquires and analyzes weather information to predict snowfall in each area. The server then determines the need for snow removal based on the amount of snowfall and sends work instructions to each terminal at the appropriate time.

[0701] Based on the received instructions, the terminal automatically powers on and uses surrounding sensors to detect snow conditions. This identifies areas and extents requiring snow removal and transmits the data to the server in real time.

[0702] The server uses AI algorithms based on the received information to calculate the optimal snow removal route. This process takes into account obstacles and areas that should be prioritized for snow removal, while also optimizing the rotation speed of the snow brushes and the movement speed of the terminals. The calculated route information is then transmitted to each terminal, providing instructions to ensure efficient snow removal.

[0703] During snow removal operations, the terminal reports progress and any anomalies to the server in real time, and the server makes necessary adjustments based on this information. Furthermore, users can monitor the snow removal status using their smartphones or PCs and perform manual operations as needed. This notification function allows users to confidently monitor the progress of the snow removal operations.

[0704] Furthermore, this system includes a mechanism that improves the efficiency of future snow removal operations by having the server accumulate and analyze work data. As a result, the labor required for snow removal can be significantly reduced, providing an environment in which elderly people can live with peace of mind. For example, by analyzing past data, it becomes possible to automatically adjust snow removal operations based on specific patterns, further reducing the burden on users.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect snowfall predictions and related data such as temperature for each region.

[0708] Step 2:

[0709] The server analyzes the acquired weather data and calculates the predicted snowfall amount for each region. Based on this, it creates instructions to prepare for snow removal in areas where snowfall exceeds a certain level.

[0710] Step 3:

[0711] The terminal receives instructions from the server and starts up automatically. It uses its sensors to scan the amount and extent of snow in its surroundings, thereby identifying areas that need snow removal. It then sends the obtained data to the server.

[0712] Step 4:

[0713] Based on the data received from the terminal, the server uses an AI algorithm to calculate the optimal snow removal route. Here, the route is determined considering obstacles and areas that should be prioritized for snow removal, and detailed movement instructions are given to the terminal.

[0714] Step 5:

[0715] The terminal automatically starts snow removal work along the designated snow removal route, following the route instructions received from the server. The rotation speed and movement speed of the snow removal brush are optimized according to the server's instructions.

[0716] Step 6:

[0717] The terminal reports its progress and any anomalies to the server in real time. Based on this information, the server can adjust its operation in real time.

[0718] Step 7:

[0719] Users can access a dedicated application on their smartphone or PC to check the current snow removal progress and system status.

[0720] Step 8:

[0721] After the server closes, it stores and analyzes all the work data. This data will be used to further improve the efficiency of future snow removal operations.

[0722] (Example 1)

[0723] 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".

[0724] This invention aims to solve problems related to technology for efficient and automated snow removal in areas where elderly people and others reside. Conventional snow removal requires labor and manpower, which is a heavy burden for the elderly. There were also challenges in removing snow at the appropriate time and selecting the optimal route. There is a need to improve this situation and provide a system that can perform snow removal more efficiently and safely.

[0725] 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.

[0726] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying the areas and extents requiring snow removal at each device based on the obtained weather data, and means for remotely controlling the operation of each device and calculating the optimal travel route. This enables the automation of efficient snow removal work according to weather conditions and reduces the burden on the user.

[0727] "Weather information" refers to data related to weather conditions, specifically including snowfall, rainfall, temperature, humidity, and wind speed.

[0728] "Analysis" refers to the operations and processes involved in processing acquired data and extracting meaningful information.

[0729] "Device" refers to any equipment with a specific function, and in this invention, it means equipment used for snow removal.

[0730] A "region" refers to a specific geographical area, indicating the region or area where snow removal work is required.

[0731] "Scope" refers to the size or extent of the area in which a particular activity takes place.

[0732] "Remote" refers to performing operations from a distance, including operations carried out via communication technology.

[0733] A "route" refers to a path that connects designated points, and in particular, indicates a route that allows for efficient travel.

[0734] "Automatic" refers to a process where operations are performed mechanically without human intervention.

[0735] "Snow removal work" refers to a series of activities to remove snow, specifically including operations such as pushing aside or removing accumulated snow.

[0736] "Progress" refers to the degree or status of an activity's progress.

[0737] "Surveillance" refers to continuously observing and confirming specific things or situations.

[0738] "Notification" refers to the act of transmitting specific information to another person.

[0739] "User" refers to an individual or legal entity that uses the system or service.

[0740] "Intelligent analysis" refers to the process of using technologies such as AI to analyze information in a sophisticated way and identify patterns.

[0741] This invention provides an automated snow removal system for areas inhabited by the elderly. This system includes a process for acquiring weather information, analyzing that data, and performing optimal snow removal operations. Its specific configuration and functions are described below.

[0742] The server periodically acquires weather information using a weather API service. This acquired data is processed by analysis software. Specifically, it analyzes data such as snowfall amount, temperature, and humidity, and uses an AI model to determine how much snow removal is needed in which areas. After the analysis, it sends instructions to terminals to start snow removal remotely, if necessary.

[0743] The terminal automatically activates upon receiving instructions and uses its built-in sensors to measure the surrounding snow depth. Ultrasonic and infrared sensors are used to detect the height and extent of the snow in real time. By sending this information to a server, the server can calculate a more precise snow removal route and instruct efficient snow removal operations.

[0744] Users can monitor the snow removal process in real time via their smartphones or PCs, and can also manually operate the snow removal system as needed. This feature allows administrators, such as elderly individuals, to safely understand the overall snow removal process from their homes.

[0745] Furthermore, the server stores data on completed tasks and performs data analysis to make future snow removal operations even more efficient. Through this analysis, it becomes possible to make predictions based on specific weather patterns and automatically adjust operations based on those predictions.

[0746] For example, by using a prompt such as, "Calculate the optimal snow removal route based on the latest snowfall forecast and past snow removal data for this region," the server can utilize a generated AI model to formulate an effective snow removal plan.

[0747] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0748] Step 1:

[0749] The server retrieves weather information from a weather API service. It uses the API's query parameters as input and receives weather data in JSON format as output. Specifically, the server periodically sends requests to the API service and saves the response data for analysis.

[0750] Step 2:

[0751] The server analyzes acquired weather data to predict snowfall. It uses the JSON-formatted weather data received as input to generate the snowfall forecast, which is the result of the analysis. A generative AI model is used for the analysis, processing the data with a specific algorithm.

[0752] Step 3:

[0753] Based on the analysis results, the server identifies areas requiring snow removal and generates instructions. It uses snowfall forecast data as input and generates snow removal instructions for each terminal as output. The server sets a threshold and generates instructions when this threshold is exceeded.

[0754] Step 4:

[0755] The terminal receives snow removal instructions from the server and automatically starts up. It uses instruction data from the server as input and outputs the terminal's startup and the initiation of sensor operation.

[0756] Step 5:

[0757] The device uses sensors to detect the surrounding snow conditions. It uses the physical environment of the site as input and generates data on the height and extent of the snow as output. Specifically, ultrasonic sensors and infrared sensors measure the snow depth and convert the results into digital data.

[0758] Step 6:

[0759] The terminal sends the detected snow depth data to the server. It uses the detected snow depth data as input and generates data packets to send to the server as output. The transmission protocol used is HTTP or MQTT.

[0760] Step 7:

[0761] The server receives snow depth data transmitted from the terminal and uses an AI algorithm to calculate the optimal snow removal route. It uses the received snow depth data as input and generates optimal route information as output.

[0762] Step 8:

[0763] The server sends the calculated route information to the terminals. It uses the route calculation result as input and generates and sends specific route instructions to each terminal as output.

[0764] Step 9:

[0765] The terminal starts snow removal work according to the specified route based on routing instructions from the server. It uses the route information received as input to perform snow removal as a mechanical operation.

[0766] Step 10:

[0767] The terminal reports the progress and any abnormalities of snow removal operations to the server in real time. It uses progress data from sensors and self-diagnosis results as input and generates a status report to send to the server as output.

[0768] Step 11:

[0769] Users monitor the progress of snow removal using smartphones or PCs and perform manual operations as needed. They can use progress information from the server as input and send instructions as output.

[0770] Step 12:

[0771] The server stores work details in a database and performs analysis to improve efficiency in future work. It uses data from completed tasks as input and generates analysis results useful for planning future work.

[0772] (Application Example 1)

[0773] 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".

[0774] In areas with a large elderly population, snow removal during snowfall poses a significant burden on residents. Furthermore, it is difficult to track the progress of snow removal and provide residents with safe routes. Moreover, there is insufficient information available to residents to monitor snow removal status in real time and ensure safe travel. Therefore, it is necessary to improve the efficiency of snow removal operations and streamline information provision.

[0775] 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.

[0776] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas and their extents requiring snow removal at each device based on the obtained weather data, means for monitoring the progress of snow removal work and notifying users, and display means for residents to check the local conditions and confirm safe routes. This enables residents to grasp the snow removal situation in real time and select safe and effective routes.

[0777] "Weather information" refers to data related to weather conditions, including elements such as snowfall, temperature, and wind speed.

[0778] "Analysis" is the process of analyzing and understanding information based on acquired data.

[0779] A "region" is a geographical area defined for a specific function or purpose.

[0780] "Scope" refers to the extent of the area affected by a particular activity or effect.

[0781] "Progress" refers to the process by which things move forward, or the resulting changes in state.

[0782] "Users" refers to individuals or organizations that use this system.

[0783] "Notification" is a means of conveying specific information to a target audience.

[0784] "Display means" refers to devices or methods for visually presenting information.

[0785] To realize this invention, the system operates as follows:

[0786] The server first acquires weather information from various data sources and then processes and analyzes it. For this purpose, the server utilizes AWS and Google Cloud Platform to efficiently perform the calculations necessary for data aggregation and analysis. The analyzed data is then used to clarify snowfall conditions in each region.

[0787] The terminal identifies areas requiring snow removal based on analysis results received from the server. To achieve this, the terminal is equipped with a function that uses sensor technology to detect snow conditions in real time. The terminal transmits this data to the server, which uses it as basic information for calculating the optimal snow removal route.

[0788] The server uses an AI algorithm based on the received information to calculate the optimal snow removal route. This algorithm optimizes the operation of the snow removal equipment by considering physical obstacles and areas that should be prioritized for snow removal. The calculated route information is transmitted to the terminal, forming the basis for efficient snow removal work.

[0789] Users can monitor the progress of snow removal in real time via their smartphones and issue manual instructions as needed. This interface is built using mobile app development frameworks such as React Native. Users can also check safe routes in their area within the app. For example, when the app is opened, the day's snowfall forecast and the progress of snow removal are displayed in real time, and push notifications are sent with alerts such as "The sidewalk in front of City Hall has been cleared of snow."

[0790] An example of a given prompt might be: "Design an app that displays today's snow accumulation and snow removal progress. Also, incorporate an interface that allows users to manually specify snow removal locations." This allows the design team to proceed with development with specific functionality in mind.

[0791] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0792] Step 1:

[0793] The server obtains weather data from external sources. Specifically, it uses a weather API to collect data such as current snowfall, temperature, and wind speed. Based on this input data, it performs data analysis to predict snowfall patterns for each region.

[0794] Step 2:

[0795] The server identifies which areas require snow removal based on the analyzed weather data. Next, it sets priorities for each area. This process utilizes an algorithm that takes the latitude and longitude data of the areas requiring snow removal as input and outputs the priority order.

[0796] Step 3:

[0797] The terminal uses sensors to measure the actual snow depth based on area information received from the server. It uses the data collected by the sensors as input to recognize the thickness and condition of the snow depth, and then sends that data as output to the server.

[0798] Step 4:

[0799] The server receives snow accumulation data from the terminals and uses an AI algorithm to calculate the optimal snow removal route. The input includes information on obstacles and priority areas for snow removal, while the output includes snow removal route information to instruct each terminal.

[0800] Step 5:

[0801] The terminal automatically starts snow removal work based on route information received from the server. At this time, the terminal controls the snow removal equipment according to the instructed route and operates at the specified speed and rotation speed.

[0802] Step 6:

[0803] Users access the system via a smartphone app to check the progress of snow removal and safe routes in real time. User input consists of actions on the app screen, and output includes progress status and notification messages.

[0804] 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.

[0805] This invention provides a system that automatically performs snow removal in areas where elderly people live, while also recognizing the user's emotions and responding appropriately. The system consists of terminals installed in each home, a centrally operated server, and an emotion engine that recognizes the user's emotions.

[0806] When the system starts up, the server periodically acquires and analyzes weather information to predict snowfall in each region. Based on this, the server sends instructions to terminals installed in areas with heavy snowfall to prepare for snow removal.

[0807] Upon receiving instructions from the server, the terminal automatically activates and uses its built-in sensors to scan the surrounding snow conditions. The terminal then sends information about the identified areas requiring snow removal to the server.

[0808] The server receives information from the terminal, calculates the optimal snow removal route using an AI algorithm, and sends it to the terminal. The terminal then automatically performs snow removal based on the specified route according to the instructions. The speed and movement speed of the snow removal brush are optimized based on the instructions from the server.

[0809] Furthermore, the terminal and server recognize the user's emotional state through an emotion engine. Users can not only check the snow removal status on their smartphones or PCs, but also receive notifications and interactions tailored to their psychological state. For example, if a user is feeling anxious, they will be provided with messages that reassure them about the progress of the work and safety.

[0810] Furthermore, the server stores the user's emotional history recognized by the emotion engine, and uses this data to improve future services. By analyzing the data and adjusting the schedule and methods for future snow removal operations, it is possible to increase user satisfaction.

[0811] This system not only improves the efficiency of snow removal but also addresses users' emotional needs, providing a safer and more comfortable living environment. Research suggests that emotion-based feedback can help reduce psychological stress among the elderly.

[0812] The following describes the processing flow.

[0813] Step 1:

[0814] The server periodically retrieves weather forecast data from a weather information API. This allows it to collect and analyze snowfall predictions and temperature information for each region.

[0815] Step 2:

[0816] The server analyzes weather data to determine the need for snow removal in each region. For areas where snowfall exceeds a certain threshold, it sends instructions to each terminal to prepare for snow removal.

[0817] Step 3:

[0818] The terminal automatically starts up upon receiving instructions from the server. It uses its own sensors to scan the amount and extent of snow in the surrounding area and analyze which areas need to be cleared.

[0819] Step 4:

[0820] The terminal sends the acquired information about the snow removal target area to the server. The server receives this information and uses an AI algorithm to calculate the optimal snow removal route.

[0821] Step 5:

[0822] The server sends the calculated optimal snow removal route information to the terminal. Based on this route, the terminal automatically starts snow removal work, appropriately adjusting the brush rotation speed and movement speed.

[0823] Step 6:

[0824] An emotion engine is installed in the device, which recognizes emotions from voice data and input information when the user interacts with the system. The emotion data is sent to a server.

[0825] Step 7:

[0826] The server analyzes the emotional data received from the emotion engine and determines the content of notifications based on the user's psychological state. For example, if the user is feeling anxious, it will create a message that provides reassurance regarding safety.

[0827] Step 8:

[0828] Users can check the progress of snow removal work on their smartphones or PC apps and receive notification messages tailored to their emotions. They can also interact with the system to adjust the work schedule as needed.

[0829] Step 9:

[0830] The server accumulates data on snow removal operations and user sentiment, and uses this data to analyze and improve the efficiency of future snow removal operations and enhance the user experience. The system is then improved based on the accumulated data.

[0831] (Example 2)

[0832] 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".

[0833] Snow removal in areas inhabited by the elderly presents challenges in terms of efficiency, safety, and ensuring the psychological well-being of residents. Conventional systems are insufficient in providing prompt responses based on weather information and services that consider the feelings of users; further improvements are needed.

[0834] 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.

[0835] In this invention, the server includes means for analyzing acquired external data, means for identifying areas requiring snow removal based on the obtained information, means for remotely controlling each processing unit and calculating efficient work routes, means for recognizing the emotional state of users and providing information based on those emotions, and means for accumulating the recognized emotional state and performing analysis to contribute to future service improvements. This enables prompt and accurate snow removal work and the provision of services that take into consideration the psychological state of users, so that elderly people can live with peace of mind.

[0836] "Acquired external data" refers to data obtained from external sources such as weather information, and serves as the basis for identifying areas where snow removal is necessary.

[0837] "Means of analysis" refers to methods and devices for analyzing acquired external data to determine the need for snow removal in a specific area.

[0838] "Areas requiring snow removal" refers to areas with heavy snowfall that may disrupt safe travel and daily life.

[0839] "Means of remotely controlling each processing unit" refers to technology that allows devices located in physically distant locations to operate according to instructions.

[0840] An "efficient work route" refers to a path that allows you to achieve maximum results while minimizing time and energy.

[0841] "User emotional state" refers to the psychological state and feedback exhibited by system users, and is used to improve the individual user experience.

[0842] "Means of recognition" refers to technologies and methods for detecting and understanding information such as emotional states.

[0843] "Means of providing information" refers to systems that transmit the data and messages that users need in a timely manner.

[0844] "Means for accumulating recognized emotional states" refers to a system that records detected emotional information and uses it for later analysis and system improvement.

[0845] "Means of conducting analysis to contribute to service improvement" refers to technologies and methods that analyze accumulated data and use it to improve the quality of services in the future.

[0846] This invention relates to an automated snow removal system for areas inhabited by the elderly, and includes a server, terminals, and emotion recognition capabilities. The system aims to ensure the safety and psychological well-being of the elderly.

[0847] The server obtains weather information from external data provision services. For example, it uses an API to obtain data such as temperature, snowfall, and wind speed, and then analyzes this data using analysis software. This analysis utilizes machine learning algorithms that predict weather data. Based on the results, it identifies areas where snow removal is necessary and sends instructions to terminals.

[0848] The terminal begins operation upon receiving instructions from the server. Specifically, it uses a LiDAR sensor to accurately scan the surrounding snow conditions and identify areas requiring snow removal. The route for snow removal is automatically determined based on optimized route information received from the server. The terminal uses its built-in snow removal function to remove snow along the designated route. The snow removal brush and movement speed are all optimized based on instructions from the server.

[0849] Users can check the progress of snow removal in real time via their smartphones or PCs. During this process, an emotion recognition engine understands the user's emotional state, and customized messages and notifications are sent to improve the user experience. For example, if a user is worried, information to reassure them is provided quickly.

[0850] The system uses AI algorithms to calculate efficient snow removal routes and utilizes an emotion recognition function via an emotion engine to improve services in line with the user's psychological state, thus leading to expected improvements in user satisfaction.

[0851] As a concrete example, when heavy snowfall is forecast in a certain elderly-friendly area, the server immediately analyzes the weather information and instructs the relevant terminals to prepare for snow removal. The terminals begin snow removal, and users can receive reassuring messages while checking the progress of the snowfall. In this way, snow removal tailored to individual situations and responses that take into account the user's feelings are possible.

[0852] An example of a prompt message would be, "Please propose the optimal snow removal plan based on the latest snowfall forecast for this area."

[0853] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0854] Step 1:

[0855] The server obtains weather information from external data provision services. It receives weather data such as temperature, snowfall, and wind speed from APIs as input, and analyzes this data using analysis software. This data analysis outputs information for predicting snowfall within a certain period and identifying the area.

[0856] Step 2:

[0857] The server identifies areas requiring snow removal based on the analysis results. Receiving this area identification information as input, the generated AI model determines the most efficient priority areas for snow removal. The optimal start time for snow removal and the priority areas are output, and instructions are sent to the relevant terminals.

[0858] Step 3:

[0859] The terminal receives instructions from the server and scans the surrounding snow conditions using its built-in LIDAR sensor. This scanning process acquires information about the surrounding terrain and snow depth as input, and based on this, identifies the exact area that requires snow removal. The identified area information is then sent to the server as output.

[0860] Step 4:

[0861] When the server receives area information from the terminal, it uses an AI algorithm to calculate the optimal snow removal route. Taking the received area information as input, it outputs a route that considers traffic efficiency and energy consumption. This route information is then sent to the terminal.

[0862] Step 5:

[0863] The terminal automatically performs snow removal according to a specified route based on route information received from the server. It uses route information as input to adjust the rotation speed and movement speed of the snow removal brush in real time. As output, information on areas where snow removal has been completed is reported to the server.

[0864] Step 6:

[0865] The server and terminal monitor the user's emotional state using an emotion recognition engine. Data related to the user's psychological state is acquired as input information, and the emotional state is analyzed. The output of this analysis is provided as appropriate notifications and messages tailored to the user's emotions.

[0866] Step 7:

[0867] The server accumulates users' emotional history and uses this data to improve future services. By analyzing the emotional data collected as input and learning methods that contribute to improving user satisfaction, it can produce output that will enhance future system efficiency.

[0868] (Application Example 2)

[0869] 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".

[0870] The problem that this invention aims to solve is to improve the efficiency of life in areas where the elderly live by automating snow removal work, and to enhance psychological reassurance by providing appropriate information according to the user's emotional state. Conventional snow removal systems have limitations in checking work progress and interacting with users, and there has been a need to improve the user experience.

[0871] 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.

[0872] In this invention, the server includes means for acquiring and analyzing weather information, means for identifying areas requiring snow removal based on the obtained weather data, means for automatically performing snow removal work according to a specified route, and means for recognizing the user's emotional state using an identification engine and providing information corresponding to that emotional state. This makes it possible to perform snow removal work efficiently and quickly while enhancing the user's psychological sense of security.

[0873] "Weather information" refers to data on atmospheric conditions such as temperature, snowfall, rainfall, wind speed, and humidity, and is used to predict changes in weather conditions.

[0874] "Means of analysis" refers to devices that implement computer programs or algorithms for processing acquired data and making necessary judgments or predictions.

[0875] "Area" refers to a specific geographical area, and in this context, it refers to the region where snow removal is required.

[0876] A "device" (each individual device) is a system consisting of hardware and software that has a specific function and performs a specific task.

[0877] A "route" refers to the path taken from a specific point to a destination, and is information used to determine the optimal sequence of tasks.

[0878] "Automatic" refers to a state in which a machine or computer performs a task independently without human intervention.

[0879] "Monitoring methods" refer to mechanisms for continuously monitoring the status of systems and processes and understanding the situation.

[0880] An "identification engine" is an algorithm or program that analyzes data to recognize specific patterns or features, and is used to determine a user's emotional state.

[0881] "Emotional state" refers to the psychological state and attitude of an individual user, and includes categories of emotions such as anxiety, joy, and calmness.

[0882] "Means of providing information" refers to communication technologies and interfaces used to transmit information to users using digital devices.

[0883] The system for carrying out this invention consists of a server, a terminal, and a user. The server periodically acquires weather information via the internet and uses that data to analyze snowfall in a specific area. Based on the analyzed snowfall data, the server instructs the terminal on the areas and extents where snow removal is necessary.

[0884] Based on information received from the server, the terminal uses its built-in sensors and algorithms to scan the surrounding environment in real time and automatically perform snow removal operations as needed. Furthermore, the terminal transmits progress information to the server, enabling efficient management and monitoring of snow removal activities.

[0885] Users can check the real-time progress of snow removal work through an application installed on their smartphone or computer. In addition, an emotion state recognition engine that works in conjunction with the server analyzes the user's psychological state and provides information and feedback according to the results. For example, if the user is feeling anxious, a reassuring message will be sent such as, "Current snow removal work is progressing smoothly and safety is ensured."

[0886] The entire system is designed to provide a safe and comfortable living environment for residents, including the elderly. Furthermore, feedback on emotional state is expected to reduce user psychological stress. A specific example is a scenario where an elderly user, on a day of heavy snowfall, launches the app on their smartphone and receives a message stating, "Current snow removal operations are proceeding smoothly and safely," providing reassurance.

[0887] An example of a prompt is "Design an application to provide progress and emotion-based feedback on snow removal work." By using such prompts, the generative AI model generates and provides information that is appropriate to the user's psychological state.

[0888] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0889] Step 1:

[0890] The server retrieves weather data from a weather information database. The input is the latest weather data, and it uses a prediction algorithm to analyze the likelihood and amount of snowfall, outputting the analysis results. These results include snowfall forecasts and time information for specific areas.

[0891] Step 2:

[0892] The server uses the analyzed snowfall data to determine areas requiring snow removal and sends that information to the terminal. The input is the analysis results obtained from step 1, and based on this, the area selection logic is applied to output snow removal area information.

[0893] Step 3:

[0894] The terminal uses snow removal area information received from the server to measure the surrounding snow conditions with its built-in sensors. The input is area information from the server, and the sensor measurement data is aggregated to identify the specific starting point and area for necessary snow removal work, and this information is output to the server.

[0895] Step 4:

[0896] The server uses an AI algorithm to calculate the optimal snow removal route based on snow condition data received from the terminal. The input is condition data from the terminal, and the calculated snow removal route information is output to the terminal.

[0897] Step 5:

[0898] The terminal receives route information sent from the server and automatically performs snow removal work according to that route. The input is route information from the server, and it controls motors and actuators to operate brushes and snow removal blades appropriately, completing the snow removal work as output.

[0899] Step 6:

[0900] The terminal reports the progress of snow removal work to the server in real time. The progress information includes time, amount of work, and percentage of work completed, and this is output to the server.

[0901] Step 7:

[0902] The server analyzes the user's psychological state data using an emotion state recognition engine, along with progress information, and generates appropriate feedback. The input is the terminal's progress information and the output of the recognition engine, which generates a prompt and outputs a message to the user.

[0903] Step 8:

[0904] Users receive snow removal progress information and emotion-based feedback messages from the server via their smartphones or computers. Input is notification information from the server, which is displayed on the screen as reassuring text messages.

[0905] 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.

[0906] 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.

[0907] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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."

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] The following is further disclosed regarding the embodiments described above.

[0927] (Claim 1)

[0928] Means for acquiring and analyzing weather information,

[0929] Based on the obtained weather data, a means for identifying the area and extent of snow removal required for each device,

[0930] A means for remotely controlling the operation of each device and calculating the optimal snow removal route,

[0931] A means of automatically performing snow removal work according to a specified route,

[0932] A means of monitoring the progress of snow removal work and notifying the user,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, characterized in that it has means for prioritizing and controlling the operation of each device based on the weather data obtained.

[0936] (Claim 3)

[0937] The system according to claim 1, characterized in that it has means for accumulating data including the progress of the snow removal work and for performing analysis for use in optimizing the next snow removal work.

[0938] "Example 1"

[0939] (Claim 1)

[0940] Means for acquiring and analyzing weather information,

[0941] Based on the obtained weather data, a means for identifying the areas and extents where snow removal is necessary for each device,

[0942] A means for remotely controlling the operation of each device and calculating the optimal movement path,

[0943] A means for automatically performing snow removal work according to a calculated path,

[0944] A means of monitoring the progress of snow removal work and notifying users,

[0945] A means to perform intelligent analysis based on received data and optimize subsequent tasks based on specific patterns,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, characterized in that it has means for prioritizing and controlling the operation of each device based on the weather data obtained.

[0949] (Claim 3)

[0950] The system according to claim 1, characterized in that it has means for accumulating data including the progress of the snow removal work and for performing analysis to use for improving the efficiency of the next snow removal work.

[0951] "Application Example 1"

[0952] (Claim 1)

[0953] Means for acquiring and analyzing weather information,

[0954] Based on the obtained weather data, a means for identifying the areas and extents where snow removal is necessary for each device,

[0955] A means for remotely controlling the operation of each device and calculating the optimal snow removal route,

[0956] A means of automatically performing snow removal work according to a specified route,

[0957] A means of monitoring the progress of snow removal work and notifying users,

[0958] A means of display that allows residents to check the local situation and confirm safe routes,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, characterized in that it has means for prioritizing and controlling the operation of each device based on the weather data obtained, and provides information according to the local conditions.

[0962] (Claim 3)

[0963] The system according to claim 1, characterized in that it has means for accumulating data including the progress of the snow removal work, performing analysis for use in optimizing the next snow removal work, and accumulating a history of information notifications to residents.

[0964] "Example 2 of combining an emotion engine"

[0965] (Claim 1)

[0966] A means of analyzing acquired external data,

[0967] A means to identify areas requiring snow removal based on the information obtained,

[0968] A means for remotely controlling each processing unit and calculating an efficient work path,

[0969] A means of automatically performing tasks based on a specified route,

[0970] A means of monitoring the progress of the work and notifying the user,

[0971] A means of recognizing the emotional state of a user and providing information based on that emotion,

[0972] A means of accumulating recognized emotional states and conducting analysis to contribute to future service improvements,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, characterized by having means for prioritizing and controlling the operation of each processing unit based on obtained external data.

[0976] (Claim 3)

[0977] The system according to claim 1, characterized in that it has means for accumulating data including the progress of the aforementioned work and for performing analysis for use in improving the efficiency of the next work.

[0978] "Application example 2 when combining with an emotional engine"

[0979] (Claim 1)

[0980] Means for acquiring and analyzing weather information,

[0981] Based on the obtained weather data, a means for identifying the area and extent of snow removal required for each device,

[0982] A means for remotely controlling the operation of each device and calculating the optimal snow removal route,

[0983] A means of automatically performing snow removal work according to a specified route,

[0984] A means of monitoring the progress of snow removal work, displaying it on a computer, and providing notifications,

[0985] A means of recognizing the user's emotional state using an identification engine and providing information corresponding to that emotional state,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, characterized in that it has means for prioritizing and controlling the operation of each device based on the weather data obtained.

[0989] (Claim 3)

[0990] The system according to claim 1, characterized in that it has means for accumulating data including the progress of the snow removal work and for performing analysis for use in optimizing the next snow removal work. [Explanation of symbols]

[0991] 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. Means for acquiring and analyzing weather information, Based on the obtained weather data, a means for identifying the area and extent of snow removal required for each device, A means for remotely controlling the operation of each device and calculating the optimal snow removal route, A means of automatically performing snow removal work according to a specified route, A means of monitoring the progress of snow removal work and notifying the user, A system that includes this.

2. The system according to claim 1, characterized in that it has means for prioritizing and controlling the operation of each device based on the weather data obtained.

3. The system according to claim 1, characterized in that it has means for accumulating data including the progress of the snow removal work and performing analysis for use in optimizing the next snow removal work.

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