system

Abstract

We provide the system. [Solution] A means of acquiring information for obtaining snow conditions remotely, A means for determining the necessity of snow removal by analyzing acquired snow depth information, A plan generation means that automatically generates a snow removal work plan based on the judgment result, Control means for controlling the snowplow according to the generated plan, A communication means for monitoring the progress of snow removal work and transmitting progress information to user terminals, 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 small-scale stores in snowy regions, snow removal in the parking lot and in front of the store during snowfall is a significant problem for operators. Snow removal work is heavy labor, and early morning work requires a lot of time and labor. Furthermore, if the snow removal work is not properly carried out, the convenience for customers when using the store may be impaired. The present invention aims to provide a system for efficiently performing snow removal while reducing the burden on operators in order to solve these problems.

Means for Solving the Problems

[0005] This invention provides a means for remotely acquiring snow conditions and determining the necessity of snow removal based on that data. Furthermore, it uses AI to automatically generate a snow removal plan, thereby planning optimal snow removal operations. By controlling snow removal machines based on this plan, business owners can automate all snow removal tasks, allowing them to focus on preparing the store for opening. This system also includes a function to monitor the progress of snow removal operations and report it to the user's terminal in real time, thereby improving the efficiency and reliability of the work.

[0006] "Information acquisition means" refers to sensors and cameras used to accurately acquire snow conditions remotely.

[0007] "Determination means" refers to a device or program that uses an AI algorithm to analyze acquired snow depth information and automatically determine the necessity of snow removal.

[0008] A "plan generation system" refers to a mechanism that automatically creates a schedule and route for carrying out optimal snow removal work based on the judgment results.

[0009] "Control means" refers to a function that remotely operates the snowplow based on the generated plan to efficiently carry out snow removal work.

[0010] "Communication means" refers to network technology used to report the progress of work to the user's terminal and to receive approvals and instructions from the user. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs 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, etc.

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

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

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

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0032] This invention is a system for automating snow removal work in front of stores and parking lots, and mainly consists of three elements: a server, a terminal, and a user.

[0033] First, the server acquires real-time snow conditions through sensors and cameras installed in the store. The data is stored in the cloud, and an AI algorithm automatically determines the extent of snowfall and the need for snow removal. The AI ​​then refers to past data and weather forecasts to plan the optimal snow removal schedule.

[0034] Next, the server notifies the user's terminal of the details of the planned snow removal work. The user can check the schedule via the terminal and make any modifications or approvals. Once the snow removal plan is approved by the user, the server sends it to the snowblower. The snowblower then automatically starts snow removal work according to the plan.

[0035] During operation, the server monitors the movement of the snowplow and the progress of the work in real time, and provides this information to the user's terminal. Users can check the progress through their terminal and, if necessary, instruct the expansion of the work area or extension of the work time.

[0036] As an example, consider the case of a hair salon owner who efficiently carried out snow removal using this system. The server detects snowfall in the early morning, and the AI ​​determines that the snow accumulation exceeds the scheduled threshold. Subsequently, it plans the optimal route and time, which the owner approves on a terminal. The server controls the snowplow and completes snow removal of the parking lot within the specified time. The owner can then focus on opening preparations without any additional manual work. In this way, the present invention reduces the burden on store managers and improves the efficiency and accuracy of snow removal work.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server collects real-time data from sensors and cameras installed in the store. This includes environmental information such as snow depth, temperature, and the start and end times of snowfall.

[0040] Step 2:

[0041] The server stores the collected data in a cloud database, then activates an AI algorithm to analyze the snow conditions. The AI ​​also refers to past data to determine the need for snow removal based on the degree of snowfall.

[0042] Step 3:

[0043] If the server determines that snow removal is necessary, it will create an optimal snow removal plan. The plan includes the route, the start time of snow removal, and the estimated work time, and is designed to ensure efficient operation.

[0044] Step 4:

[0045] The server notifies users of planned snow removal operations on their devices. Users can then view the schedule and plan details on their devices and make modifications or approvals.

[0046] Step 5:

[0047] After user approval, the server sends remote control commands to the snowblower, initiating snow removal work according to the plan. The snowblower then travels along the automatically configured route, efficiently removing snow.

[0048] Step 6:

[0049] During operation, the server receives feedback data from the snowblower and monitors the progress in real time. The server transfers this information to the terminal, allowing the user to check the progress and issue additional instructions as needed.

[0050] Step 7:

[0051] After snow removal is complete, the server sends a completion notification to the user's terminal, reporting that the work was completed within the timeframe specified in the plan. The server then uses the collected data to train an AI algorithm in preparation for the next snowfall forecast.

[0052] (Example 1)

[0053] 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."

[0054] Snow removal is an essential task in commercial facilities and residential areas, but it requires a large amount of manpower and time. Current manual snow removal methods are inefficient and make it difficult to respond quickly to changing weather conditions; therefore, an automated and effective snow removal system is needed.

[0055] 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.

[0056] In this invention, the server includes an information gathering mechanism for acquiring snowfall conditions remotely, an evaluation mechanism for analyzing the collected snowfall information and determining the necessity of snow removal, a schedule creation mechanism for automatically generating a snow removal plan based on the evaluation results, and an operation mechanism for operating snow removal equipment based on the generated plan. This enables the rapid and efficient automation of snow removal work and flexible responses to changes in weather.

[0057] An "information gathering mechanism" is a device or system that acquires snowfall conditions remotely using sensors and cameras and transmits the data to a server.

[0058] An "evaluation mechanism" is a device or system that analyzes collected snowfall data and uses AI algorithms to make decisions regarding the necessity of snow removal.

[0059] A "schedule creation mechanism" is a device or system that automatically generates an optimal snow removal plan based on the evaluation results from an evaluation mechanism.

[0060] An "operating mechanism" is a device or system used to control snow removal equipment and perform work according to a plan generated by a scheduling mechanism.

[0061] A "communication mechanism" is a device or system that transmits information on the progress of snow removal work to user terminals and communicates to obtain approval from users.

[0062] This invention is a system that automatically manages snow accumulation in stores and residential areas and performs snow removal work efficiently. The system mainly consists of three elements: a server, terminals, and users.

[0063] The server acquires real-time information on snow conditions at stores and parking lots using a data collection mechanism that utilizes sensors and cameras. Various sensors measure the physical amount of snow, while cameras capture the visual data. This acquired data is stored in cloud storage. The server uses AI software such as "TENSORFLOW®" and "PyTorch" to analyze this data through an evaluation mechanism and determine the need for snow removal. Here, the AI ​​makes the optimal decision based on machine learning algorithms that utilize historical data and weather forecast information.

[0064] Based on the evaluation results, the server automatically generates a snow removal plan using the scheduling mechanism. This plan includes the start and end times and the optimal route. Using the generated plan, the server automatically controls the snow removal equipment using the operation mechanism, ensuring the work proceeds according to the plan.

[0065] The server uses a communication mechanism to notify users of the work progress on their terminals. This allows users to check the work status in real time and make necessary instructions or schedule changes. A specific example is a case where a hair salon owner uses the system. The server detects snowfall in the early morning, and after the AI ​​determines the need for snow removal, it plans the optimal snow removal route and schedule. Once the owner approves on their terminal, the snowplow automatically starts work and completes the task within the specified time.

[0066] An example of a prompt to input into a generating AI model is: "Explain the process by which the server automatically controls the snowplow to complete snow removal in the parking lot within the specified time."

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server collects snow depth data from sensors and cameras installed in the store. Sensors physically measure the amount of snow, and cameras capture the video. This data undergoes noise reduction filtering as an initial processing step and is stored in the cloud as clean data. The input is raw data from sensors and cameras, and the output is filtered snow depth data.

[0070] Step 2:

[0071] The server passes filtered snow depth data from the cloud to an AI algorithm, which uses an evaluation mechanism to determine the need for snow removal. The AI ​​uses a machine learning model to analyze the current situation based on past snowfall patterns and weather forecasts to determine whether snow removal is necessary. The input is clean snow depth data, and the output is a determination indicating whether snow removal is necessary or not.

[0072] Step 3:

[0073] The server uses a scheduling mechanism to create an optimal snow removal plan based on the AI's assessment results. This plan includes the start and end times of the work and the route to be used. The AI ​​performs simulations and selects the best plan. The input is the assessment result, and the output is a detailed snow removal schedule.

[0074] Step 4:

[0075] The server notifies the user's terminal of the plan details. The user can check the schedule through their terminal, make any necessary corrections, and then approve it. This process allows for adjustments based on the user's schedule and plans. The input is the snow removal schedule, and the output is the user's approval or correction instructions.

[0076] Step 5:

[0077] The server sends the user-approved schedule to the control mechanism, which then controls the snow removal equipment. Based on the specified route, the snow removal equipment automatically starts operating, and the work is carried out according to plan. The input is the user-approved schedule, and the output is the operating status of the snow removal equipment.

[0078] Step 6:

[0079] The server updates the progress of snow removal work in real time to the user's terminal via a communication mechanism. Based on this information, the user can check the progress and, if necessary, instruct the expansion of the work area or extension of the work time. The input is data on the progress of the snow removal work, and the output is progress information notified to the user.

[0080] (Application Example 1)

[0081] 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."

[0082] The present invention aims to provide a system for efficient and rapid snow removal in urban areas to prevent traffic disruptions and a decline in the quality of life for citizens caused by snowfall. This will reduce citizens' anxiety about snowfall and create an environment where they can request priority snow removal in specific locations.

[0083] 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.

[0084] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal plan based on the determination result, control means for controlling a snow removal machine according to the generated plan, communication means for monitoring the progress of snow removal work and transmitting progress information to a user terminal, and user interface means for citizens to check the progress of snow removal and request snow removal at a specific location. This makes it possible to streamline snow removal response in urban areas and improve the sense of security in citizens' lives.

[0085] "Information acquisition means" refers to devices or software that remotely acquire real-time information on snow conditions in front of stores or parking lots.

[0086] "Determination means" refers to algorithms or devices used to determine the necessity of snow removal based on acquired snow depth information.

[0087] "Plan generation means" refers to a system that automatically creates the optimal snow removal work schedule based on the judgment results.

[0088] "Control means" refers to a system or device that operates a snow removal machine and performs the work based on the generated snow removal plan.

[0089] "Communication means" refers to the network and communication equipment used to transmit the progress of snow removal work to the user's terminal and provide progress information.

[0090] "User interface means" refers to an interface that allows citizens to check the progress of snow removal and request snow removal for specific locations.

[0091] To implement this invention, three elements are necessary: ​​a server, a terminal, and a user. Each of these elements will be described in detail below.

[0092] The server acquires snow accumulation data through sensors and cameras installed in the city. This requires real-time data processing, and data collection and storage are performed using cloud platforms such as AWS® and Google® Cloud. The acquired data is analyzed using machine learning algorithms built with Python to determine the level of snow accumulation and the need for snow removal. In this process, past snowfall data and weather forecasts are also referenced to generate an optimal snow removal schedule.

[0093] The terminal provides an interface that allows users to check the progress of snow removal and request snow removal for specific locations. The application running on the terminal is developed using React Native and is compatible with iOS and Android® devices. Through the terminal, users can check the progress of snow removal work in real time and, if necessary, instruct the expansion of the snow removal area or extension of the time.

[0094] Users can utilize this system as citizens. For example, they can request snow removal for a specific park via a smartphone app. In response to this request, the server reorganizes the snow removal schedule to prioritize and efficiently controls the snow removal equipment.

[0095] As a concrete example, consider a case where a citizen requests snow removal for an event being held in a local park on a holiday. In this case, the citizen can request snow removal for the park and check the progress via a smartphone app. The server receives this request, uses an AI algorithm to plan the optimal snow removal operation, and issues instructions to the snowplow.

[0096] An example of a prompt message for the generating AI model might be: "Based on snow accumulation data in the city, determine the areas that require snow removal and create a snow removal schedule according to priority. Propose a system that updates the progress in real time and notifies users."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server acquires snow accumulation data from sensors and cameras installed throughout the city. The input is real-time sensor data, and the output is a dataset showing the snow accumulation conditions. This clearly reveals the snow accumulation situation in each area.

[0100] Step 2:

[0101] The server analyzes the acquired snow depth data using a machine learning algorithm. The input consists of snow depth data, historical snowfall data, and weather forecast information, and the output is a determination of the necessity and priority of snow removal. Through data processing, the AI ​​determines a snow removal schedule for each amount of snow depth.

[0102] Step 3:

[0103] The server automatically generates a snow removal plan based on the analysis results. The input is the determination of the need for snow removal, and the output is a specific snow removal schedule and route. The generated schedule includes optimal route calculation and time allocation.

[0104] Step 4:

[0105] The server sends the planned snow removal schedule to the user's terminal. The input is the generated schedule, and the output is data for display on the user's terminal. The user can review this information on their terminal and make corrections or approvals as needed.

[0106] Step 5:

[0107] The terminal displays the snow removal plan to the user and obtains their approval. Input is schedule information sent from the server, and output is the user's approval or modification request. The user can view the schedule details through the interface.

[0108] Step 6:

[0109] The server issues instructions to control the snowblower based on a user-approved schedule. The input is the approved schedule, and the output is the control command to the snowblower. The snowblower follows the instructions and begins work along the specified route.

[0110] Step 7:

[0111] The server monitors the progress of snow removal operations and transmits progress information to terminals in real time. Input is work progress data from the snow removal machine, and output is progress information displayed on the user's terminal. Through the terminal, the user can monitor the work status and issue additional instructions as needed.

[0112] 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.

[0113] This invention is a system for automating snow removal work in stores and parking lots, and for providing an optimal plan that takes user emotions into consideration. It mainly consists of a server, terminals, users, and an emotion engine.

[0114] In this system, the server first acquires snow accumulation information from sensors and cameras. The server analyzes the data and uses AI to determine the need for snow removal. This process utilizes historical snowfall data and machine learning algorithms. If the AI ​​determines that snow removal is necessary, the server automatically generates a snow removal plan, considering the optimal route and time.

[0115] The emotion engine is installed on the user's device and recognizes emotions from the user's voice and text data. For example, if the system determines that the user is stressed, the server can change the priority of the snow removal plan based on the emotion data. If positive user feedback is confirmed, it will be used to adjust future plans and machine learning algorithms.

[0116] For example, if the server detects snowfall and the AI ​​determines that snow removal is necessary, the emotion engine will quickly prioritize the plan and proceed if it determines that the user is busy. The notified snow removal plan is displayed on the user's terminal, and once the user approves it, the server controls the snowplow and starts the work. The progress is monitored in real time during the work, and information is provided to the user. Once the work is completed, the user is notified by the server, and the emotion data is used for future operations. This entire process enables efficient and flexible snow removal management for the user.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server acquires real-time data on snow accumulation from sensors and cameras installed in the stores. This includes detailed information on snow depth and current snowfall conditions.

[0120] Step 2:

[0121] The server stores the acquired data in a cloud-based database, analyzes the data using an AI algorithm, and evaluates the degree of snowfall. It also refers to past snowfall data to determine whether snow removal is necessary.

[0122] Step 3:

[0123] If the server determines that snow removal is necessary, it uses AI to generate an optimal snow removal plan. This plan includes the snow removal route, start time, and estimated work time.

[0124] Step 4:

[0125] The emotion engine on the device analyzes the user's voice and text input to evaluate their current emotional state. For example, if the user is busy and stressed, it provides that information to the server.

[0126] Step 5:

[0127] The server adjusts the priority of snow removal plans based on sentiment data. If necessary, it modifies the plan to match the user's sentiment and sends notifications to the user's device.

[0128] Step 6:

[0129] The user reviews the snow removal plan displayed on their terminal and makes any necessary modifications or approvals. Once the user approves, the server proceeds to the next step.

[0130] Step 7:

[0131] The server sends remote control signals to the snowblower, instructing it to begin snow removal work according to the plan. The snowblower automatically travels along the set route and efficiently removes snow.

[0132] Step 8:

[0133] During operation, the server receives real-time progress data from the snowblower, monitors it, and provides feedback to the user's terminal. The user can check the progress and issue additional instructions as needed.

[0134] Step 9:

[0135] Once the snow removal work is complete, the server sends a completion notification to the user's terminal, reporting that the work was carried out as planned. The server uses the user's feedback obtained from the emotion engine to plan for future operations and adjust the AI ​​algorithm.

[0136] (Example 2)

[0137] 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".

[0138] There is a need for a system that can efficiently remove snow from large areas such as stores and parking lots. In particular, a challenge is how to adjust the priority of snow removal according to the amount of snowfall and the emotional state of the users, thereby minimizing delays and waste in the work.

[0139] 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.

[0140] In this invention, the server includes information gathering means, determination means for analyzing collected snow depth information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal work plan based on the determination result, emotion recognition means for recognizing the user's emotions and adjusting the priority of the snow removal plan, control means for controlling work equipment according to the generated plan, and communication means for monitoring the progress of the work and transmitting progress information to the user device. This enables the automatic generation and execution of a snow removal work plan optimized for the snow depth conditions, and achieves flexible work adjustments according to the user's situation.

[0141] "Information gathering means" refers to methods for remotely acquiring snow conditions using sensors and cameras.

[0142] "Decision-making tools" refer to methods for analyzing collected data and determining the necessity of snow removal through machine learning algorithms.

[0143] A "plan generation means" is a means for automatically generating an optimal snow removal plan based on the results of determining the necessity of snow removal.

[0144] "Emotion recognition means" refers to a method for recognizing emotions from the user's voice and text data and adjusting the priorities of the snow removal plan.

[0145] "Control means" refers to means for controlling work equipment based on the generated snow removal plan.

[0146] "Communication means" refers to a means for monitoring the progress of snow removal work and transmitting that information to user devices in real time.

[0147] This invention is a system for automating snow removal work in large areas such as stores and parking lots, and for providing an efficient plan that takes user emotions into consideration. The system mainly consists of a server, terminals, users, and an emotion engine.

[0148] The server acquires snow depth information using data collection devices such as weather sensors and surveillance cameras. Specifically, general weather sensors and network cameras are used. This data is analyzed on the server using software libraries such as TensorFlow to execute machine learning algorithms. This allows the server to compare the current snow depth with past data to determine the need for snow removal.

[0149] The terminal is equipped with an emotion engine that analyzes the user's emotions from their voice and text data. Natural language processing technologies such as Google Cloud Speech-to-Text API and IBM Watson® Natural Language Understanding are used for this purpose. The recognized emotion data is sent to a server and used to adjust the priority of plans.

[0150] The server automatically generates a snow removal plan based on the assessment results. The generated plan is designed to take into account the optimal route and work time, and is notified to the user's device using Firebase Cloud Messaging. Once the user approves the plan, the server controls the snowplow via an IoT control board such as a Raspberry Pi and starts the work.

[0151] For example, on a snowy morning, if the server immediately determines the need for snow removal and the emotion engine recognizes the user's busy schedule, the plan will be prioritized and executed accordingly. In this way, information about the work in progress is monitored in real time and fed back to the user sequentially.

[0152] Examples of prompts to input into a generative AI model include questions such as, "How do you adjust priorities when a user is feeling emotionally stressed?" or "Please explain in detail the algorithm by which the AI ​​determines the need for snow removal based on collected weather data."

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server acquires snow depth information from data collection devices. Specifically, weather sensors and network cameras function as input devices, collecting data such as snow depth, temperature, and wind speed. The input data undergoes initial processing, including format conversion and noise reduction, and is organized into a dataset for analysis. This organized dataset becomes the input for the next processing step.

[0156] Step 2:

[0157] The server analyzes the acquired data to determine the need for snow removal. In this step, machine learning libraries such as TensorFlow are used to compare the current data with past snowfall data and to perform analysis using pattern recognition techniques. The analysis results output a decision on whether or not snow removal is necessary. Based on this result, a subsequent snow removal plan is created.

[0158] Step 3:

[0159] The server automatically generates a snow removal plan if it determines that snow removal is necessary. Using an AI algorithm, it calculates the optimal snow removal route and working time, and generates a detailed work instruction sheet. The input is the result of the previous step's decision, and the output is a detailed plan for operating the work equipment.

[0160] Step 4:

[0161] The device's emotion engine recognizes emotions from user voice and text input. Input includes user utterances and text information from the interface, which is analyzed through text analysis software and speech recognition APIs. The output provides a judgment of the user's current emotional state. This information is sent to a server and used to adjust plan priorities.

[0162] Step 5:

[0163] The server adjusts the priority of snow removal plans based on the emotion recognition results. If a plan has a higher priority, the execution order of the plan is changed. The adjusted plan is notified to the user's device, and a notification is sent to the user using Firebase Cloud Messaging. Once the user approves, this information is passed on to the next step, and the execution of the approved plan is prepared.

[0164] Step 6:

[0165] Once the user approves the snow removal plan notified via their terminal, the server begins controlling the work equipment. Inputs include the approved plan and user instructions. The control system sends commands to the snowplow via devices such as a Raspberry Pi, and the specific snow removal work is carried out. During this process, the progress is observed in real time, and this information is output to the next step.

[0166] Step 7:

[0167] The server monitors the progress of snow removal work and provides feedback to the user. This monitoring utilizes data feeds from various sensors to detect delays and anomalies. The obtained progress information is provided to the user through a dedicated app, allowing the user to monitor the situation. This facilitates user confirmation of work completion and provides feedback.

[0168] (Application Example 2)

[0169] 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".

[0170] In modern society, snowfall has become a major problem, causing disruptions to transportation and stagnation of commercial activities. Snow removal, in particular, is a time-consuming and labor-intensive task for busy individuals and businesses, requiring efficient management. Furthermore, it is necessary to appropriately adjust the priority of snow removal plans based on user sentiment and respond flexibly, but current systems are unable to do so.

[0171] 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.

[0172] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, and emotion adjustment means for analyzing the user's emotional information and adjusting the priority of the snow removal plan based on those emotions. This enables the execution of an efficient and flexible snow removal plan in real time.

[0173] "Information acquisition means" refers to various devices and sensors used to understand snow conditions from a remote location.

[0174] "Determination means" refers to algorithms or systems that analyze acquired snow depth information to automatically determine whether snow removal work is necessary.

[0175] "Plan generation means" refers to a system or software that automatically generates an optimal snow removal plan based on the judgment results of the determination means.

[0176] "Control means" refers to a mechanism for operating and controlling snowplows and related equipment according to the generated plan.

[0177] "Communication means" refers to the network and protocols used to monitor the progress of snow removal work and transmit progress information to the user's terminal.

[0178] "Emotional adjustment tools" refer to systems and tools that analyze users' emotional information and adjust the priority of snow removal plans based on those emotions.

[0179] The system for implementing this invention consists of a server, a user terminal, and snow removal equipment. The server aggregates information from geographically distributed sensors and cameras to grasp the snow accumulation situation in real time. As a result, the information acquisition means collects snow accumulation data. Based on the acquired data, the server uses a determination means to analyze the degree of snow accumulation and uses an AI algorithm to determine whether snow removal is necessary. If a determination is made, the plan generation means automatically generates an optimal snow removal plan using past data and machine learning. This uses AI technologies such as TensorFlow.

[0180] The user terminal functions as an emotion regulation mechanism, analyzing the user's emotions from their voice and text. This uses tools such as Google Cloud Natural Language. Based on the emotion data obtained from this analysis, the server adjusts the priority of snow removal tasks. For example, if the user is feeling stressed, the system can adjust the tasks to proceed more quickly.

[0181] Once a snow removal plan is decided, the server remotely operates the actual snow removal equipment using control devices. 5G communication technology makes this control possible, and progress is notified to the user's terminal in real time.

[0182] As a concrete example, consider a process where a server automatically checks snow accumulation information at night when heavy snowfall is expected, and plans to efficiently complete snow removal work before the start of commuting and commercial activities the following morning. If a user prompts at night, "I would like the snow removed so that it will be ready in time for my commute tomorrow morning," this will be reflected in the snow removal plan along with emotional data. In this way, the system of the present invention can provide rational snow removal work suggestions while also meeting the emotional needs of users.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server acquires real-time snow conditions from sensors and cameras placed in various locations. The acquired input data includes image data and environmental data such as temperature and humidity. The server analyzes this data to perform initial data processing to determine the amount and extent of snow accumulation.

[0186] Step 2:

[0187] The server processes the analyzed snow depth data using an AI algorithm to determine the need for snow removal. The input consists of sensor information and historical weather data, and a machine learning model based on this data outputs whether or not snow removal is necessary. This process utilizes a machine learning model based on TensorFlow.

[0188] Step 3:

[0189] If the server determines that snow removal is necessary, it automatically creates an optimal snow removal work schedule using a planning generation system. The inputs are the need for snow removal and past work data, and the AI ​​model generates the optimal route and time. The plan includes implementation time and route information.

[0190] Step 4:

[0191] The user terminal receives voice and text data as a means of emotion regulation and analyzes the user's emotions. The input is user information from the terminal, and this data is processed by Google Cloud Natural Language and output as data representing the user's emotional state.

[0192] Step 5:

[0193] The server adjusts the priority of the snow removal plan based on the analyzed user sentiment data. The inputs are sentiment information and the initial work plan, and the output is a prioritized plan tailored to the user's needs.

[0194] Step 6:

[0195] After the final snow removal plan is determined, the server uses control devices to send commands to the snow removal equipment, initiating actual work. The input is the prioritized snow removal plan, and the output is the actual operating status of the snow removal equipment. 5G technology is used for communication, enabling real-time monitoring and feedback.

[0196] Step 7:

[0197] The server monitors the progress of snow removal work and notifies user terminals. Progress data is updated in real time, and users receive confirmation that the work has been completed. This allows users to check the progress of the snow removal work.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention is a system for automating snow removal work in front of stores and parking lots, and mainly consists of three elements: a server, a terminal, and a user.

[0215] First, the server acquires real-time snow conditions through sensors and cameras installed in the store. The data is stored in the cloud, and an AI algorithm automatically determines the extent of snowfall and the need for snow removal. The AI ​​then refers to past data and weather forecasts to plan the optimal snow removal schedule.

[0216] Next, the server notifies the user's terminal of the details of the planned snow removal work. The user can check the schedule via the terminal and make any modifications or approvals. Once the snow removal plan is approved by the user, the server sends it to the snowblower. The snowblower then automatically starts snow removal work according to the plan.

[0217] During operation, the server monitors the movement of the snowplow and the progress of the work in real time, and provides this information to the user's terminal. Users can check the progress through their terminal and, if necessary, instruct the expansion of the work area or extension of the work time.

[0218] As an example, consider the case of a hair salon owner who efficiently carried out snow removal using this system. The server detects snowfall in the early morning, and the AI ​​determines that the snow accumulation exceeds the scheduled threshold. Subsequently, it plans the optimal route and time, which the owner approves on a terminal. The server controls the snowplow and completes snow removal of the parking lot within the specified time. The owner can then focus on opening preparations without any additional manual work. In this way, the present invention reduces the burden on store managers and improves the efficiency and accuracy of snow removal work.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server collects real-time data from sensors and cameras installed in the store. This includes environmental information such as snow depth, temperature, and the start and end times of snowfall.

[0222] Step 2:

[0223] The server stores the collected data in a cloud database, then activates an AI algorithm to analyze the snow conditions. The AI ​​also refers to past data to determine the need for snow removal based on the degree of snowfall.

[0224] Step 3:

[0225] If the server determines that snow removal is necessary, it will create an optimal snow removal plan. The plan includes the route, the start time of snow removal, and the estimated work time, and is designed to ensure efficient operation.

[0226] Step 4:

[0227] The server notifies users of planned snow removal operations on their devices. Users can then view the schedule and plan details on their devices and make modifications or approvals.

[0228] Step 5:

[0229] After user approval, the server sends remote control commands to the snowblower, initiating snow removal work according to the plan. The snowblower then travels along the automatically configured route, efficiently removing snow.

[0230] Step 6:

[0231] During operation, the server receives feedback data from the snowblower and monitors the progress in real time. The server transfers this information to the terminal, allowing the user to check the progress and issue additional instructions as needed.

[0232] Step 7:

[0233] After snow removal is complete, the server sends a completion notification to the user's terminal, reporting that the work was completed within the timeframe specified in the plan. The server then uses the collected data to train an AI algorithm in preparation for the next snowfall forecast.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] Snow removal is an essential task in commercial facilities and residential areas, but it requires a large amount of manpower and time. Current manual snow removal methods are inefficient and make it difficult to respond quickly to changing weather conditions; therefore, an automated and effective snow removal system is needed.

[0237] 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.

[0238] In this invention, the server includes an information gathering mechanism for acquiring snowfall conditions remotely, an evaluation mechanism for analyzing the collected snowfall information and determining the necessity of snow removal, a schedule creation mechanism for automatically generating a snow removal plan based on the evaluation results, and an operation mechanism for operating snow removal equipment based on the generated plan. This enables the rapid and efficient automation of snow removal work and flexible responses to changes in weather.

[0239] An "information gathering mechanism" is a device or system that acquires snowfall conditions remotely using sensors and cameras and transmits the data to a server.

[0240] An "evaluation mechanism" is a device or system that analyzes collected snowfall data and uses AI algorithms to make decisions regarding the necessity of snow removal.

[0241] A "schedule creation mechanism" is a device or system that automatically generates an optimal snow removal plan based on the evaluation results from an evaluation mechanism.

[0242] An "operating mechanism" is a device or system used to control snow removal equipment and perform work according to a plan generated by a scheduling mechanism.

[0243] A "communication mechanism" is a device or system that transmits information on the progress of snow removal work to user terminals and communicates to obtain approval from users.

[0244] This invention is a system that automatically manages snow accumulation in stores and residential areas and performs snow removal work efficiently. The system mainly consists of three elements: a server, terminals, and users.

[0245] The server acquires real-time information on snow conditions at stores and parking lots using a data collection mechanism that utilizes sensors and cameras. Various sensors measure the physical amount of snow, while cameras capture visual data. This acquired data is stored in cloud storage. The server then uses AI software such as TensorFlow and PyTorch to analyze this data through an evaluation mechanism and determine the need for snow removal. Here, the AI ​​makes the optimal decision based on machine learning algorithms that utilize historical data and weather forecast information.

[0246] Based on the evaluation results, the server automatically generates a snow removal plan using the scheduling mechanism. This plan includes the start and end times and the optimal route. Using the generated plan, the server automatically controls the snow removal equipment using the operation mechanism, ensuring the work proceeds according to the plan.

[0247] The server uses a communication mechanism to notify users of the work progress on their terminals. This allows users to check the work status in real time and make necessary instructions or schedule changes. A specific example is a case where a hair salon owner uses the system. The server detects snowfall in the early morning, and after the AI ​​determines the need for snow removal, it plans the optimal snow removal route and schedule. Once the owner approves on their terminal, the snowplow automatically starts work and completes the task within the specified time.

[0248] An example of a prompt to input into a generating AI model is: "Explain the process by which the server automatically controls the snowplow to complete snow removal in the parking lot within the specified time."

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The server collects snow depth data from sensors and cameras installed in the store. Sensors physically measure the amount of snow, and cameras capture the video. This data undergoes noise reduction filtering as an initial processing step and is stored in the cloud as clean data. The input is raw data from sensors and cameras, and the output is filtered snow depth data.

[0252] Step 2:

[0253] The server passes filtered snow depth data from the cloud to an AI algorithm, which uses an evaluation mechanism to determine the need for snow removal. The AI ​​uses a machine learning model to analyze the current situation based on past snowfall patterns and weather forecasts to determine whether snow removal is necessary. The input is clean snow depth data, and the output is a determination indicating whether snow removal is necessary or not.

[0254] Step 3:

[0255] The server uses a scheduling mechanism to create an optimal snow removal plan based on the AI's assessment results. This plan includes the start and end times of the work and the route to be used. The AI ​​performs simulations and selects the best plan. The input is the assessment result, and the output is a detailed snow removal schedule.

[0256] Step 4:

[0257] The server notifies the user's terminal of the plan details. The user can check the schedule through their terminal, make any necessary corrections, and then approve it. This process allows for adjustments based on the user's schedule and plans. The input is the snow removal schedule, and the output is the user's approval or correction instructions.

[0258] Step 5:

[0259] The server sends the user-approved schedule to the control mechanism, which then controls the snow removal equipment. Based on the specified route, the snow removal equipment automatically starts operating, and the work is carried out according to plan. The input is the user-approved schedule, and the output is the operating status of the snow removal equipment.

[0260] Step 6:

[0261] The server updates the progress of snow removal work in real time to the user's terminal via a communication mechanism. Based on this information, the user can check the progress and, if necessary, instruct the expansion of the work area or extension of the work time. The input is data on the progress of the snow removal work, and the output is progress information notified to the user.

[0262] (Application Example 1)

[0263] 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."

[0264] The present invention aims to provide a system for efficient and rapid snow removal in urban areas to prevent traffic disruptions and a decline in the quality of life for citizens caused by snowfall. This will reduce citizens' anxiety about snowfall and create an environment where they can request priority snow removal in specific locations.

[0265] 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.

[0266] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal plan based on the determination result, control means for controlling a snow removal machine according to the generated plan, communication means for monitoring the progress of snow removal work and transmitting progress information to a user terminal, and user interface means for citizens to check the progress of snow removal and request snow removal at a specific location. This makes it possible to streamline snow removal response in urban areas and improve the sense of security in citizens' lives.

[0267] "Information acquisition means" refers to devices or software that remotely acquire real-time information on snow conditions in front of stores or parking lots.

[0268] "Determination means" refers to algorithms or devices used to determine the necessity of snow removal based on acquired snow depth information.

[0269] "Plan generation means" refers to a system that automatically creates the optimal snow removal work schedule based on the judgment results.

[0270] "Control means" refers to a system or device that operates a snow removal machine and performs the work based on the generated snow removal plan.

[0271] "Communication means" refers to the network and communication equipment used to transmit the progress of snow removal work to the user's terminal and provide progress information.

[0272] "User interface means" refers to an interface that allows citizens to check the progress of snow removal and request snow removal for specific locations.

[0273] To implement this invention, three elements are necessary: ​​a server, a terminal, and a user. Each of these elements will be described in detail below.

[0274] The server acquires snow accumulation data through sensors and cameras installed in the city. This requires real-time data processing, and data collection and storage are performed using cloud platforms such as AWS and Google Cloud. The acquired data is analyzed using machine learning algorithms built with Python to determine the level of snow accumulation and the need for snow removal. In this process, past snowfall data and weather forecasts are also referenced to generate an optimal snow removal schedule.

[0275] The terminal provides an interface that allows users to check the progress of snow removal and request snow removal for specific locations. The application running on the terminal is developed using React Native and is compatible with iOS and Android devices. Through the terminal, users can check the progress of snow removal work in real time and, if necessary, instruct the expansion of the snow removal area or extension of the time.

[0276] Users can utilize this system as citizens. For example, they can request snow removal for a specific park via a smartphone app. In response to this request, the server reorganizes the snow removal schedule to prioritize and efficiently controls the snow removal equipment.

[0277] As a concrete example, consider a case where a citizen requests snow removal for an event being held in a local park on a holiday. In this case, the citizen can request snow removal for the park and check the progress via a smartphone app. The server receives this request, uses an AI algorithm to plan the optimal snow removal operation, and issues instructions to the snowplow.

[0278] An example of a prompt message for the generating AI model might be: "Based on snow accumulation data in the city, determine the areas that require snow removal and create a snow removal schedule according to priority. Propose a system that updates the progress in real time and notifies users."

[0279] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0280] Step 1:

[0281] The server acquires data on the snow accumulation situation from sensors and cameras installed within the city. The input is real-time sensor data, and the output is a dataset indicating the snow accumulation situation. This clarifies the snow accumulation situation in each area.

[0282] Step 2:

[0283] The server analyzes the acquired snow accumulation data using a machine learning algorithm. The input is the snow accumulation data, past snowfall data, and weather forecast information, and the output is the result of determining the necessity and priority of snow removal. Through data processing, AI determines the snow removal schedule for each amount of snow accumulation.

[0284] Step 3:

[0285] The server automatically generates a plan for the snow removal operation based on the analysis results. The input is the determination result of the necessity of snow removal, and the output is a specific snow removal schedule and route. The generated schedule includes optimal route calculation and time distribution.

[0286] Step 4:

[0287] The server transmits the planned snow removal schedule to the user terminal. The input is the generated schedule, and the output is data for display on the user terminal. The user can check this information on the terminal and make corrections or approvals as necessary.

[0288] Step 5:

[0289] The terminal displays the snow removal plan to the user and receives approval. The input is the schedule information transmitted from the server, and the output is the user's approval or modification request. The user can check the details of the schedule through the interface.

[0290] Step 6:

[0291] The server issues instructions to control the snowblower based on a user-approved schedule. The input is the approved schedule, and the output is the control command to the snowblower. The snowblower follows the instructions and begins work along the specified route.

[0292] Step 7:

[0293] The server monitors the progress of snow removal operations and transmits progress information to terminals in real time. Input is work progress data from the snow removal machine, and output is progress information displayed on the user's terminal. Through the terminal, the user can monitor the work status and issue additional instructions as needed.

[0294] 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.

[0295] This invention is a system for automating snow removal work in stores and parking lots, and for providing an optimal plan that takes user emotions into consideration. It mainly consists of a server, terminals, users, and an emotion engine.

[0296] In this system, the server first acquires snow accumulation information from sensors and cameras. The server analyzes the data and uses AI to determine the need for snow removal. This process utilizes historical snowfall data and machine learning algorithms. If the AI ​​determines that snow removal is necessary, the server automatically generates a snow removal plan, considering the optimal route and time.

[0297] The emotion engine is installed on the user's device and recognizes emotions from the user's voice and text data. For example, if the system determines that the user is stressed, the server can change the priority of the snow removal plan based on the emotion data. If positive user feedback is confirmed, it will be used to adjust future plans and machine learning algorithms.

[0298] For example, if the server detects snowfall and the AI ​​determines that snow removal is necessary, the emotion engine will quickly prioritize the plan and proceed if it determines that the user is busy. The notified snow removal plan is displayed on the user's terminal, and once the user approves it, the server controls the snowplow and starts the work. The progress is monitored in real time during the work, and information is provided to the user. Once the work is completed, the user is notified by the server, and the emotion data is used for future operations. This entire process enables efficient and flexible snow removal management for the user.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server acquires real-time data on snow accumulation from sensors and cameras installed in the stores. This includes detailed information on snow depth and current snowfall conditions.

[0302] Step 2:

[0303] The server stores the acquired data in a cloud-based database, analyzes the data using an AI algorithm, and evaluates the degree of snowfall. It also refers to past snowfall data to determine whether snow removal is necessary.

[0304] Step 3:

[0305] If the server determines that snow removal is necessary, it uses AI to generate an optimal snow removal plan. This plan includes the snow removal route, start time, and estimated work time.

[0306] Step 4:

[0307] The emotion engine on the terminal analyzes the user's voice input and text input, and evaluates the current emotional state. For example, if the user is busy and feeling stressed, it provides that information to the server.

[0308] Step 5:

[0309] The server adjusts the priority of the snow removal plan according to the emotional data. If necessary, it changes the plan according to the user's emotion and sends a notification to the user's terminal.

[0310] Step 6:

[0311] The user checks the snow removal plan displayed on the terminal and makes corrections or approvals as necessary. When the user's approval is obtained, the server proceeds to the next step.

[0312] Step 7:

[0313] The server sends a remote control signal to the snowplow and instructs it to start the snow removal work according to the plan. The snowplow travels along the automatically set route and efficiently removes snow.

[0314] Step 8:

[0315] During the operation, the server receives real-time progress data from the snowplow and provides feedback to the user's terminal while monitoring it. The user can check the progress of the work and give additional instructions if necessary.

[0316] Step 9:

[0317] When the snow removal work is completed, the server sends a completion notification to the user's terminal and reports that the work has been carried out as planned. The server uses this feedback obtained from the emotion engine for the adjustment of future plans and AI algorithms.

[0318] (Example 2)

[0319] 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".

[0320] There is a need for a system that can efficiently remove snow from large areas such as stores and parking lots. In particular, a challenge is how to adjust the priority of snow removal according to the amount of snowfall and the emotional state of the users, thereby minimizing delays and waste in the work.

[0321] 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.

[0322] In this invention, the server includes information gathering means, determination means for analyzing collected snow depth information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal work plan based on the determination result, emotion recognition means for recognizing the user's emotions and adjusting the priority of the snow removal plan, control means for controlling work equipment according to the generated plan, and communication means for monitoring the progress of the work and transmitting progress information to the user device. This enables the automatic generation and execution of a snow removal work plan optimized for the snow depth conditions, and achieves flexible work adjustments according to the user's situation.

[0323] "Information gathering means" refers to methods for remotely acquiring snow conditions using sensors and cameras.

[0324] "Decision-making tools" refer to methods for analyzing collected data and determining the necessity of snow removal through machine learning algorithms.

[0325] A "plan generation means" is a means for automatically generating an optimal snow removal plan based on the results of determining the necessity of snow removal.

[0326] "Emotion recognition means" refers to a method for recognizing emotions from the user's voice and text data and adjusting the priorities of the snow removal plan.

[0327] "Control means" refers to means for controlling work equipment based on the generated snow removal plan.

[0328] "Communication means" refers to a means for monitoring the progress of snow removal work and transmitting that information to user devices in real time.

[0329] This invention is a system for automating snow removal work in large areas such as stores and parking lots, and for providing an efficient plan that takes user emotions into consideration. The system mainly consists of a server, terminals, users, and an emotion engine.

[0330] The server acquires snow depth information using data collection devices such as weather sensors and surveillance cameras. Specifically, general weather sensors and network cameras are used. This data is analyzed on the server using software libraries such as TensorFlow to execute machine learning algorithms. This allows the server to compare the current snow depth with past data to determine the need for snow removal.

[0331] The device is equipped with an emotion engine that analyzes the user's emotions from their voice and text data. Natural language processing technologies such as Google Cloud Speech-to-Text API and IBM Watson Natural Language Understanding are used for this purpose. The recognized emotion data is sent to a server and used to adjust the priority of plans.

[0332] The server automatically generates a snow removal plan based on the assessment results. The generated plan is designed to take into account the optimal route and work time, and is notified to the user's device using Firebase Cloud Messaging. Once the user approves the plan, the server controls the snowplow via an IoT control board such as a Raspberry Pi and starts the work.

[0333] For example, on a snowy morning, if the server immediately determines the need for snow removal and the emotion engine recognizes the user's busy schedule, the plan will be prioritized and executed accordingly. In this way, information about the work in progress is monitored in real time and fed back to the user sequentially.

[0334] Examples of prompts to input into a generative AI model include questions such as, "How do you adjust priorities when a user is feeling emotionally stressed?" or "Please explain in detail the algorithm by which the AI ​​determines the need for snow removal based on collected weather data."

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The server acquires snow depth information from data collection devices. Specifically, weather sensors and network cameras function as input devices, collecting data such as snow depth, temperature, and wind speed. The input data undergoes initial processing, including format conversion and noise reduction, and is organized into a dataset for analysis. This organized dataset becomes the input for the next processing step.

[0338] Step 2:

[0339] The server analyzes the acquired data to determine the need for snow removal. In this step, machine learning libraries such as TensorFlow are used to compare the current data with past snowfall data and to perform analysis using pattern recognition techniques. The analysis results output a decision on whether or not snow removal is necessary. Based on this result, a subsequent snow removal plan is created.

[0340] Step 3:

[0341] The server automatically generates a snow removal plan if it determines that snow removal is necessary. Using an AI algorithm, it calculates the optimal snow removal route and working time, and generates a detailed work instruction sheet. The input is the result of the previous step's decision, and the output is a detailed plan for operating the work equipment.

[0342] Step 4:

[0343] The device's emotion engine recognizes emotions from user voice and text input. Input includes user utterances and text information from the interface, which is analyzed through text analysis software and speech recognition APIs. The output provides a judgment of the user's current emotional state. This information is sent to a server and used to adjust plan priorities.

[0344] Step 5:

[0345] The server adjusts the priority of snow removal plans based on the emotion recognition results. If a plan has a higher priority, the execution order of the plan is changed. The adjusted plan is notified to the user's device, and a notification is sent to the user using Firebase Cloud Messaging. Once the user approves, this information is passed on to the next step, and the execution of the approved plan is prepared.

[0346] Step 6:

[0347] Once the user approves the snow removal plan notified via their terminal, the server begins controlling the work equipment. Inputs include the approved plan and user instructions. The control system sends commands to the snowplow via devices such as a Raspberry Pi, and the specific snow removal work is carried out. During this process, the progress is observed in real time, and this information is output to the next step.

[0348] Step 7:

[0349] The server monitors the progress of snow removal work and provides feedback to the user. This monitoring utilizes data feeds from various sensors to detect delays and anomalies. The obtained progress information is provided to the user through a dedicated app, allowing the user to monitor the situation. This facilitates user confirmation of work completion and provides feedback.

[0350] (Application Example 2)

[0351] 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."

[0352] In modern society, snowfall has become a major problem, causing disruptions to transportation and stagnation of commercial activities. Snow removal, in particular, is a time-consuming and labor-intensive task for busy individuals and businesses, requiring efficient management. Furthermore, it is necessary to appropriately adjust the priority of snow removal plans based on user sentiment and respond flexibly, but current systems are unable to do so.

[0353] 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.

[0354] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, and emotion adjustment means for analyzing the user's emotional information and adjusting the priority of the snow removal plan based on those emotions. This enables the execution of an efficient and flexible snow removal plan in real time.

[0355] "Information acquisition means" refers to various devices and sensors used to understand snow conditions from a remote location.

[0356] "Determination means" refers to algorithms or systems that analyze acquired snow depth information to automatically determine whether snow removal work is necessary.

[0357] "Plan generation means" refers to a system or software that automatically generates an optimal snow removal plan based on the judgment results of the determination means.

[0358] "Control means" refers to a mechanism for operating and controlling snowplows and related equipment according to the generated plan.

[0359] "Communication means" refers to the network and protocols used to monitor the progress of snow removal work and transmit progress information to the user's terminal.

[0360] "Emotional adjustment tools" refer to systems and tools that analyze users' emotional information and adjust the priority of snow removal plans based on those emotions.

[0361] The system for implementing this invention consists of a server, a user terminal, and snow removal equipment. The server aggregates information from geographically distributed sensors and cameras to grasp the snow accumulation situation in real time. As a result, the information acquisition means collects snow accumulation data. Based on the acquired data, the server uses a determination means to analyze the degree of snow accumulation and uses an AI algorithm to determine whether snow removal is necessary. If a determination is made, the plan generation means automatically generates an optimal snow removal plan using past data and machine learning. This uses AI technologies such as TensorFlow.

[0362] The user terminal functions as an emotion regulation mechanism, analyzing the user's emotions from their voice and text. This uses tools such as Google Cloud Natural Language. Based on the emotion data obtained from this analysis, the server adjusts the priority of snow removal tasks. For example, if the user is feeling stressed, the system can adjust the tasks to proceed more quickly.

[0363] Once a snow removal plan is decided, the server remotely operates the actual snow removal equipment using control devices. 5G communication technology makes this control possible, and progress is notified to the user's terminal in real time.

[0364] As a concrete example, consider a process where a server automatically checks snow accumulation information at night when heavy snowfall is expected, and plans to efficiently complete snow removal work before the start of commuting and commercial activities the following morning. If a user prompts at night, "I would like the snow removed so that it will be ready in time for my commute tomorrow morning," this will be reflected in the snow removal plan along with emotional data. In this way, the system of the present invention can provide rational snow removal work suggestions while also meeting the emotional needs of users.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server acquires real-time snow conditions from sensors and cameras placed in various locations. The acquired input data includes image data and environmental data such as temperature and humidity. The server analyzes this data to perform initial data processing to determine the amount and extent of snow accumulation.

[0368] Step 2:

[0369] The server processes the analyzed snow depth data using an AI algorithm to determine the need for snow removal. The input consists of sensor information and historical weather data, and a machine learning model based on this data outputs whether or not snow removal is necessary. This process utilizes a machine learning model based on TensorFlow.

[0370] Step 3:

[0371] If the server determines that snow removal is necessary, it automatically creates an optimal snow removal work schedule using a planning generation system. The inputs are the need for snow removal and past work data, and the AI ​​model generates the optimal route and time. The plan includes implementation time and route information.

[0372] Step 4:

[0373] The user terminal receives voice and text data as a means of emotion regulation and analyzes the user's emotions. The input is user information from the terminal, and this data is processed by Google Cloud Natural Language and output as data representing the user's emotional state.

[0374] Step 5:

[0375] The server adjusts the priority of the snow removal plan based on the analyzed user sentiment data. The inputs are sentiment information and the initial work plan, and the output is a prioritized plan tailored to the user's needs.

[0376] Step 6:

[0377] After the final snow removal plan is determined, the server uses control devices to send commands to the snow removal equipment, initiating actual work. The input is the prioritized snow removal plan, and the output is the actual operating status of the snow removal equipment. 5G technology is used for communication, enabling real-time monitoring and feedback.

[0378] Step 7:

[0379] The server monitors the progress of snow removal work and notifies user terminals. Progress data is updated in real time, and users receive confirmation that the work has been completed. This allows users to check the progress of the snow removal work.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] This invention is a system for automating snow removal work in front of stores and parking lots, and mainly consists of three elements: a server, a terminal, and a user.

[0397] First, the server acquires real-time snow conditions through sensors and cameras installed in the store. The data is stored in the cloud, and an AI algorithm automatically determines the extent of snowfall and the need for snow removal. The AI ​​then refers to past data and weather forecasts to plan the optimal snow removal schedule.

[0398] Next, the server notifies the user's terminal of the details of the planned snow removal work. The user can check the schedule via the terminal and make any modifications or approvals. Once the snow removal plan is approved by the user, the server sends it to the snowblower. The snowblower then automatically starts snow removal work according to the plan.

[0399] During operation, the server monitors the movement of the snowplow and the progress of the work in real time, and provides this information to the user's terminal. Users can check the progress through their terminal and, if necessary, instruct the expansion of the work area or extension of the work time.

[0400] As an example, consider the case of a hair salon owner who efficiently carried out snow removal using this system. The server detects snowfall in the early morning, and the AI ​​determines that the snow accumulation exceeds the scheduled threshold. Subsequently, it plans the optimal route and time, which the owner approves on a terminal. The server controls the snowplow and completes snow removal of the parking lot within the specified time. The owner can then focus on opening preparations without any additional manual work. In this way, the present invention reduces the burden on store managers and improves the efficiency and accuracy of snow removal work.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The server collects real-time data from sensors and cameras installed in the store. This includes environmental information such as snow depth, temperature, and the start and end times of snowfall.

[0404] Step 2:

[0405] The server stores the collected data in a cloud database, then activates an AI algorithm to analyze the snow conditions. The AI ​​also refers to past data to determine the need for snow removal based on the degree of snowfall.

[0406] Step 3:

[0407] If the server determines that snow removal is necessary, it will create an optimal snow removal plan. The plan includes the route, the start time of snow removal, and the estimated work time, and is designed to ensure efficient operation.

[0408] Step 4:

[0409] The server notifies users of planned snow removal operations on their devices. Users can then view the schedule and plan details on their devices and make modifications or approvals.

[0410] Step 5:

[0411] After user approval, the server sends remote control commands to the snowblower, initiating snow removal work according to the plan. The snowblower then travels along the automatically configured route, efficiently removing snow.

[0412] Step 6:

[0413] During operation, the server receives feedback data from the snowblower and monitors the progress in real time. The server transfers this information to the terminal, allowing the user to check the progress and issue additional instructions as needed.

[0414] Step 7:

[0415] After snow removal is complete, the server sends a completion notification to the user's terminal, reporting that the work was completed within the timeframe specified in the plan. The server then uses the collected data to train an AI algorithm in preparation for the next snowfall forecast.

[0416] (Example 1)

[0417] 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."

[0418] Snow removal is an essential task in commercial facilities and residential areas, but it requires a large amount of manpower and time. Current manual snow removal methods are inefficient and make it difficult to respond quickly to changing weather conditions; therefore, an automated and effective snow removal system is needed.

[0419] 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.

[0420] In this invention, the server includes an information gathering mechanism for acquiring snowfall conditions remotely, an evaluation mechanism for analyzing the collected snowfall information and determining the necessity of snow removal, a schedule creation mechanism for automatically generating a snow removal plan based on the evaluation results, and an operation mechanism for operating snow removal equipment based on the generated plan. This enables the rapid and efficient automation of snow removal work and flexible responses to changes in weather.

[0421] An "information gathering mechanism" is a device or system that acquires snowfall conditions remotely using sensors and cameras and transmits the data to a server.

[0422] An "evaluation mechanism" is a device or system that analyzes collected snowfall data and uses AI algorithms to make decisions regarding the necessity of snow removal.

[0423] A "schedule creation mechanism" is a device or system that automatically generates an optimal snow removal plan based on the evaluation results from an evaluation mechanism.

[0424] An "operating mechanism" is a device or system used to control snow removal equipment and perform work according to a plan generated by a scheduling mechanism.

[0425] A "communication mechanism" is a device or system that transmits information on the progress of snow removal work to user terminals and communicates to obtain approval from users.

[0426] This invention is a system that automatically manages snow accumulation in stores and residential areas and performs snow removal work efficiently. The system mainly consists of three elements: a server, terminals, and users.

[0427] The server acquires real-time information on snow conditions at stores and parking lots using a data collection mechanism that utilizes sensors and cameras. Various sensors measure the physical amount of snow, while cameras capture visual data. This acquired data is stored in cloud storage. The server then uses AI software such as TensorFlow and PyTorch to analyze this data through an evaluation mechanism and determine the need for snow removal. Here, the AI ​​makes the optimal decision based on machine learning algorithms that utilize historical data and weather forecast information.

[0428] Based on the evaluation results, the server automatically generates a snow removal plan using the scheduling mechanism. This plan includes the start and end times and the optimal route. Using the generated plan, the server automatically controls the snow removal equipment using the operation mechanism, ensuring the work proceeds according to the plan.

[0429] The server uses a communication mechanism to notify users of the work progress on their terminals. This allows users to check the work status in real time and make necessary instructions or schedule changes. A specific example is a case where a hair salon owner uses the system. The server detects snowfall in the early morning, and after the AI ​​determines the need for snow removal, it plans the optimal snow removal route and schedule. Once the owner approves on their terminal, the snowplow automatically starts work and completes the task within the specified time.

[0430] An example of a prompt to input into a generating AI model is: "Explain the process by which the server automatically controls the snowplow to complete snow removal in the parking lot within the specified time."

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The server collects snow depth data from sensors and cameras installed in the store. Sensors physically measure the amount of snow, and cameras capture the video. This data undergoes noise reduction filtering as an initial processing step and is stored in the cloud as clean data. The input is raw data from sensors and cameras, and the output is filtered snow depth data.

[0434] Step 2:

[0435] The server passes filtered snow depth data from the cloud to an AI algorithm, which uses an evaluation mechanism to determine the need for snow removal. The AI ​​uses a machine learning model to analyze the current situation based on past snowfall patterns and weather forecasts to determine whether snow removal is necessary. The input is clean snow depth data, and the output is a determination indicating whether snow removal is necessary or not.

[0436] Step 3:

[0437] The server uses a scheduling mechanism to create an optimal snow removal plan based on the AI's assessment results. This plan includes the start and end times of the work and the route to be used. The AI ​​performs simulations and selects the best plan. The input is the assessment result, and the output is a detailed snow removal schedule.

[0438] Step 4:

[0439] The server notifies the user's terminal of the plan details. The user can check the schedule through their terminal, make any necessary corrections, and then approve it. This process allows for adjustments based on the user's schedule and plans. The input is the snow removal schedule, and the output is the user's approval or correction instructions.

[0440] Step 5:

[0441] The server sends the user-approved schedule to the control mechanism, which then controls the snow removal equipment. Based on the specified route, the snow removal equipment automatically starts operating, and the work is carried out according to plan. The input is the user-approved schedule, and the output is the operating status of the snow removal equipment.

[0442] Step 6:

[0443] The server updates the progress of snow removal work in real time to the user's terminal via a communication mechanism. Based on this information, the user can check the progress and, if necessary, instruct the expansion of the work area or extension of the work time. The input is data on the progress of the snow removal work, and the output is progress information notified to the user.

[0444] (Application Example 1)

[0445] 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."

[0446] The present invention aims to provide a system for efficient and rapid snow removal in urban areas to prevent traffic disruptions and a decline in the quality of life for citizens caused by snowfall. This will reduce citizens' anxiety about snowfall and create an environment where they can request priority snow removal in specific locations.

[0447] 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.

[0448] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal plan based on the determination result, control means for controlling a snow removal machine according to the generated plan, communication means for monitoring the progress of snow removal work and transmitting progress information to a user terminal, and user interface means for citizens to check the progress of snow removal and request snow removal at a specific location. This makes it possible to streamline snow removal response in urban areas and improve the sense of security in citizens' lives.

[0449] "Information acquisition means" refers to devices or software that remotely acquire real-time information on snow conditions in front of stores or parking lots.

[0450] "Determination means" refers to algorithms or devices used to determine the necessity of snow removal based on acquired snow depth information.

[0451] "Plan generation means" refers to a system that automatically creates the optimal snow removal work schedule based on the judgment results.

[0452] "Control means" refers to a system or device that operates a snow removal machine and performs the work based on the generated snow removal plan.

[0453] "Communication means" refers to the network and communication equipment used to transmit the progress of snow removal work to the user's terminal and provide progress information.

[0454] "User interface means" refers to an interface that allows citizens to check the progress of snow removal and request snow removal for specific locations.

[0455] To implement this invention, three elements are necessary: ​​a server, a terminal, and a user. Each of these elements will be described in detail below.

[0456] The server acquires snow accumulation data through sensors and cameras installed in the city. This requires real-time data processing, and data collection and storage are performed using cloud platforms such as AWS and Google Cloud. The acquired data is analyzed using machine learning algorithms built with Python to determine the level of snow accumulation and the need for snow removal. In this process, past snowfall data and weather forecasts are also referenced to generate an optimal snow removal schedule.

[0457] The terminal provides an interface that allows users to check the progress of snow removal and request snow removal for specific locations. The application running on the terminal is developed using React Native and is compatible with iOS and Android devices. Through the terminal, users can check the progress of snow removal work in real time and, if necessary, instruct the expansion of the snow removal area or extension of the time.

[0458] Users can utilize this system as citizens. For example, they can request snow removal for a specific park via a smartphone app. In response to this request, the server reorganizes the snow removal schedule to prioritize and efficiently controls the snow removal equipment.

[0459] As a concrete example, consider a case where a citizen requests snow removal for an event being held in a local park on a holiday. In this case, the citizen can request snow removal for the park and check the progress via a smartphone app. The server receives this request, uses an AI algorithm to plan the optimal snow removal operation, and issues instructions to the snowplow.

[0460] An example of a prompt message for the generating AI model might be: "Based on snow accumulation data in the city, determine the areas that require snow removal and create a snow removal schedule according to priority. Propose a system that updates the progress in real time and notifies users."

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] The server acquires snow accumulation data from sensors and cameras installed throughout the city. The input is real-time sensor data, and the output is a dataset showing the snow accumulation conditions. This clearly reveals the snow accumulation situation in each area.

[0464] Step 2:

[0465] The server analyzes the acquired snow depth data using a machine learning algorithm. The input consists of snow depth data, historical snowfall data, and weather forecast information, and the output is a determination of the necessity and priority of snow removal. Through data processing, the AI ​​determines a snow removal schedule for each amount of snow depth.

[0466] Step 3:

[0467] The server automatically generates a snow removal plan based on the analysis results. The input is the determination of the need for snow removal, and the output is a specific snow removal schedule and route. The generated schedule includes optimal route calculation and time allocation.

[0468] Step 4:

[0469] The server sends the planned snow removal schedule to the user's terminal. The input is the generated schedule, and the output is data for display on the user's terminal. The user can review this information on their terminal and make corrections or approvals as needed.

[0470] Step 5:

[0471] The terminal displays the snow removal plan to the user and obtains their approval. Input is schedule information sent from the server, and output is the user's approval or modification request. The user can view the schedule details through the interface.

[0472] Step 6:

[0473] The server issues instructions to control the snowblower based on a user-approved schedule. The input is the approved schedule, and the output is the control command to the snowblower. The snowblower follows the instructions and begins work along the specified route.

[0474] Step 7:

[0475] The server monitors the progress of snow removal operations and transmits progress information to terminals in real time. Input is work progress data from the snow removal machine, and output is progress information displayed on the user's terminal. Through the terminal, the user can monitor the work status and issue additional instructions as needed.

[0476] 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.

[0477] This invention is a system for automating snow removal work in stores and parking lots, and for providing an optimal plan that takes user emotions into consideration. It mainly consists of a server, terminals, users, and an emotion engine.

[0478] In this system, the server first acquires snow accumulation information from sensors and cameras. The server analyzes the data and uses AI to determine the need for snow removal. This process utilizes historical snowfall data and machine learning algorithms. If the AI ​​determines that snow removal is necessary, the server automatically generates a snow removal plan, considering the optimal route and time.

[0479] The emotion engine is installed on the user's device and recognizes emotions from the user's voice and text data. For example, if the system determines that the user is stressed, the server can change the priority of the snow removal plan based on the emotion data. If positive user feedback is confirmed, it will be used to adjust future plans and machine learning algorithms.

[0480] For example, if the server detects snowfall and the AI ​​determines that snow removal is necessary, the emotion engine will quickly prioritize the plan and proceed if it determines that the user is busy. The notified snow removal plan is displayed on the user's terminal, and once the user approves it, the server controls the snowplow and starts the work. The progress is monitored in real time during the work, and information is provided to the user. Once the work is completed, the user is notified by the server, and the emotion data is used for future operations. This entire process enables efficient and flexible snow removal management for the user.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The server acquires real-time data on snow accumulation from sensors and cameras installed in the stores. This includes detailed information on snow depth and current snowfall conditions.

[0484] Step 2:

[0485] The server stores the acquired data in a cloud-based database, analyzes the data using an AI algorithm, and evaluates the degree of snowfall. It also refers to past snowfall data to determine whether snow removal is necessary.

[0486] Step 3:

[0487] If the server determines that snow removal is necessary, it uses AI to generate an optimal snow removal plan. This plan includes the snow removal route, start time, and estimated work time.

[0488] Step 4:

[0489] The emotion engine on the device analyzes the user's voice and text input to evaluate their current emotional state. For example, if the user is busy and stressed, it provides that information to the server.

[0490] Step 5:

[0491] The server adjusts the priority of snow removal plans based on sentiment data. If necessary, it modifies the plan to match the user's sentiment and sends notifications to the user's device.

[0492] Step 6:

[0493] The user reviews the snow removal plan displayed on their terminal and makes any necessary modifications or approvals. Once the user approves, the server proceeds to the next step.

[0494] Step 7:

[0495] The server sends remote control signals to the snowblower, instructing it to begin snow removal work according to the plan. The snowblower automatically travels along the set route and efficiently removes snow.

[0496] Step 8:

[0497] During operation, the server receives real-time progress data from the snowblower, monitors it, and provides feedback to the user's terminal. The user can check the progress and issue additional instructions as needed.

[0498] Step 9:

[0499] Once the snow removal work is complete, the server sends a completion notification to the user's terminal, reporting that the work was carried out as planned. The server uses the user's feedback obtained from the emotion engine to plan for future operations and adjust the AI ​​algorithm.

[0500] (Example 2)

[0501] 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."

[0502] There is a need for a system that can efficiently remove snow from large areas such as stores and parking lots. In particular, a challenge is how to adjust the priority of snow removal according to the amount of snowfall and the emotional state of the users, thereby minimizing delays and waste in the work.

[0503] 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.

[0504] In this invention, the server includes information gathering means, determination means for analyzing collected snow depth information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal work plan based on the determination result, emotion recognition means for recognizing the user's emotions and adjusting the priority of the snow removal plan, control means for controlling work equipment according to the generated plan, and communication means for monitoring the progress of the work and transmitting progress information to the user device. This enables the automatic generation and execution of a snow removal work plan optimized for the snow depth conditions, and achieves flexible work adjustments according to the user's situation.

[0505] "Information gathering means" refers to methods for remotely acquiring snow conditions using sensors and cameras.

[0506] "Decision-making tools" refer to methods for analyzing collected data and determining the necessity of snow removal through machine learning algorithms.

[0507] A "plan generation means" is a means for automatically generating an optimal snow removal plan based on the results of determining the necessity of snow removal.

[0508] "Emotion recognition means" refers to a method for recognizing emotions from the user's voice and text data and adjusting the priorities of the snow removal plan.

[0509] "Control means" refers to means for controlling work equipment based on the generated snow removal plan.

[0510] "Communication means" refers to a means for monitoring the progress of snow removal work and transmitting that information to user devices in real time.

[0511] This invention is a system for automating snow removal work in large areas such as stores and parking lots, and for providing an efficient plan that takes user emotions into consideration. The system mainly consists of a server, terminals, users, and an emotion engine.

[0512] The server acquires snow depth information using data collection devices such as weather sensors and surveillance cameras. Specifically, general weather sensors and network cameras are used. This data is analyzed on the server using software libraries such as TensorFlow to execute machine learning algorithms. This allows the server to compare the current snow depth with past data to determine the need for snow removal.

[0513] The device is equipped with an emotion engine that analyzes the user's emotions from their voice and text data. Natural language processing technologies such as Google Cloud Speech-to-Text API and IBM Watson Natural Language Understanding are used for this purpose. The recognized emotion data is sent to a server and used to adjust the priority of plans.

[0514] The server automatically generates a snow removal plan based on the assessment results. The generated plan is designed to take into account the optimal route and work time, and is notified to the user's device using Firebase Cloud Messaging. Once the user approves the plan, the server controls the snowplow via an IoT control board such as a Raspberry Pi and starts the work.

[0515] For example, on a snowy morning, if the server immediately determines the need for snow removal and the emotion engine recognizes the user's busy schedule, the plan will be prioritized and executed accordingly. In this way, information about the work in progress is monitored in real time and fed back to the user sequentially.

[0516] Examples of prompts to input into a generative AI model include questions such as, "How do you adjust priorities when a user is feeling emotionally stressed?" or "Please explain in detail the algorithm by which the AI ​​determines the need for snow removal based on collected weather data."

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The server acquires snow depth information from data collection devices. Specifically, weather sensors and network cameras function as input devices, collecting data such as snow depth, temperature, and wind speed. The input data undergoes initial processing, including format conversion and noise reduction, and is organized into a dataset for analysis. This organized dataset becomes the input for the next processing step.

[0520] Step 2:

[0521] The server analyzes the acquired data to determine the need for snow removal. In this step, machine learning libraries such as TensorFlow are used to compare the current data with past snowfall data and to perform analysis using pattern recognition techniques. The analysis results output a decision on whether or not snow removal is necessary. Based on this result, a subsequent snow removal plan is created.

[0522] Step 3:

[0523] The server automatically generates a snow removal plan if it determines that snow removal is necessary. Using an AI algorithm, it calculates the optimal snow removal route and working time, and generates a detailed work instruction sheet. The input is the result of the previous step's decision, and the output is a detailed plan for operating the work equipment.

[0524] Step 4:

[0525] The device's emotion engine recognizes emotions from user voice and text input. Input includes user utterances and text information from the interface, which is analyzed through text analysis software and speech recognition APIs. The output provides a judgment of the user's current emotional state. This information is sent to a server and used to adjust plan priorities.

[0526] Step 5:

[0527] The server adjusts the priority of snow removal plans based on the emotion recognition results. If a plan has a higher priority, the execution order of the plan is changed. The adjusted plan is notified to the user's device, and a notification is sent to the user using Firebase Cloud Messaging. Once the user approves, this information is passed on to the next step, and the execution of the approved plan is prepared.

[0528] Step 6:

[0529] Once the user approves the snow removal plan notified via their terminal, the server begins controlling the work equipment. Inputs include the approved plan and user instructions. The control system sends commands to the snowplow via devices such as a Raspberry Pi, and the specific snow removal work is carried out. During this process, the progress is observed in real time, and this information is output to the next step.

[0530] Step 7:

[0531] The server monitors the progress of snow removal work and provides feedback to the user. This monitoring utilizes data feeds from various sensors to detect delays and anomalies. The obtained progress information is provided to the user through a dedicated app, allowing the user to monitor the situation. This facilitates user confirmation of work completion and provides feedback.

[0532] (Application Example 2)

[0533] 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."

[0534] In modern society, snowfall has become a major problem, causing disruptions to transportation and stagnation of commercial activities. Snow removal, in particular, is a time-consuming and labor-intensive task for busy individuals and businesses, requiring efficient management. Furthermore, it is necessary to appropriately adjust the priority of snow removal plans based on user sentiment and respond flexibly, but current systems are unable to do so.

[0535] 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.

[0536] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, and emotion adjustment means for analyzing the user's emotional information and adjusting the priority of the snow removal plan based on those emotions. This enables the execution of an efficient and flexible snow removal plan in real time.

[0537] "Information acquisition means" refers to various devices and sensors used to understand snow conditions from a remote location.

[0538] "Determination means" refers to algorithms or systems that analyze acquired snow depth information to automatically determine whether snow removal work is necessary.

[0539] "Plan generation means" refers to a system or software that automatically generates an optimal snow removal plan based on the judgment results of the determination means.

[0540] "Control means" refers to a mechanism for operating and controlling snowplows and related equipment according to the generated plan.

[0541] "Communication means" refers to the network and protocols used to monitor the progress of snow removal work and transmit progress information to the user's terminal.

[0542] "Emotional adjustment tools" refer to systems and tools that analyze users' emotional information and adjust the priority of snow removal plans based on those emotions.

[0543] The system for implementing this invention consists of a server, a user terminal, and snow removal equipment. The server aggregates information from geographically distributed sensors and cameras to grasp the snow accumulation situation in real time. As a result, the information acquisition means collects snow accumulation data. Based on the acquired data, the server uses a determination means to analyze the degree of snow accumulation and uses an AI algorithm to determine whether snow removal is necessary. If a determination is made, the plan generation means automatically generates an optimal snow removal plan using past data and machine learning. This uses AI technologies such as TensorFlow.

[0544] The user terminal functions as an emotion regulation mechanism, analyzing the user's emotions from their voice and text. This uses tools such as Google Cloud Natural Language. Based on the emotion data obtained from this analysis, the server adjusts the priority of snow removal tasks. For example, if the user is feeling stressed, the system can adjust the tasks to proceed more quickly.

[0545] Once a snow removal plan is decided, the server remotely operates the actual snow removal equipment using control devices. 5G communication technology makes this control possible, and progress is notified to the user's terminal in real time.

[0546] As a concrete example, consider a process where a server automatically checks snow accumulation information at night when heavy snowfall is expected, and plans to efficiently complete snow removal work before the start of commuting and commercial activities the following morning. If a user prompts at night, "I would like the snow removed so that it will be ready in time for my commute tomorrow morning," this will be reflected in the snow removal plan along with emotional data. In this way, the system of the present invention can provide rational snow removal work suggestions while also meeting the emotional needs of users.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The server acquires real-time snow conditions from sensors and cameras placed in various locations. The acquired input data includes image data and environmental data such as temperature and humidity. The server analyzes this data to perform initial data processing to determine the amount and extent of snow accumulation.

[0550] Step 2:

[0551] The server processes the analyzed snow depth data using an AI algorithm to determine the need for snow removal. The input consists of sensor information and historical weather data, and a machine learning model based on this data outputs whether or not snow removal is necessary. This process utilizes a machine learning model based on TensorFlow.

[0552] Step 3:

[0553] If the server determines that snow removal is necessary, it automatically creates an optimal snow removal work schedule using a planning generation system. The inputs are the need for snow removal and past work data, and the AI ​​model generates the optimal route and time. The plan includes implementation time and route information.

[0554] Step 4:

[0555] The user terminal receives voice and text data as a means of emotion regulation and analyzes the user's emotions. The input is user information from the terminal, and this data is processed by Google Cloud Natural Language and output as data representing the user's emotional state.

[0556] Step 5:

[0557] The server adjusts the priority of the snow removal plan based on the analyzed user sentiment data. The inputs are sentiment information and the initial work plan, and the output is a prioritized plan tailored to the user's needs.

[0558] Step 6:

[0559] After the final snow removal plan is determined, the server uses control devices to send commands to the snow removal equipment, initiating actual work. The input is the prioritized snow removal plan, and the output is the actual operating status of the snow removal equipment. 5G technology is used for communication, enabling real-time monitoring and feedback.

[0560] Step 7:

[0561] The server monitors the progress of snow removal work and notifies user terminals. Progress data is updated in real time, and users receive confirmation that the work has been completed. This allows users to check the progress of the snow removal work.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] This invention is a system for automating snow removal work in front of stores and parking lots, and mainly consists of three elements: a server, a terminal, and a user.

[0580] First, the server acquires real-time snow conditions through sensors and cameras installed in the store. The data is stored in the cloud, and an AI algorithm automatically determines the extent of snowfall and the need for snow removal. The AI ​​then refers to past data and weather forecasts to plan the optimal snow removal schedule.

[0581] Next, the server notifies the user's terminal of the details of the planned snow removal work. The user can check the schedule via the terminal and make any modifications or approvals. Once the snow removal plan is approved by the user, the server sends it to the snowblower. The snowblower then automatically starts snow removal work according to the plan.

[0582] During operation, the server monitors the movement of the snowplow and the progress of the work in real time, and provides this information to the user's terminal. Users can check the progress through their terminal and, if necessary, instruct the expansion of the work area or extension of the work time.

[0583] As an example, consider the case of a hair salon owner who efficiently carried out snow removal using this system. The server detects snowfall in the early morning, and the AI ​​determines that the snow accumulation exceeds the scheduled threshold. Subsequently, it plans the optimal route and time, which the owner approves on a terminal. The server controls the snowplow and completes snow removal of the parking lot within the specified time. The owner can then focus on opening preparations without any additional manual work. In this way, the present invention reduces the burden on store managers and improves the efficiency and accuracy of snow removal work.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server collects real-time data from sensors and cameras installed in the store. This includes environmental information such as snow depth, temperature, and the start and end times of snowfall.

[0587] Step 2:

[0588] The server stores the collected data in a cloud database, then activates an AI algorithm to analyze the snow conditions. The AI ​​also refers to past data to determine the need for snow removal based on the degree of snowfall.

[0589] Step 3:

[0590] If the server determines that snow removal is necessary, it will create an optimal snow removal plan. The plan includes the route, the start time of snow removal, and the estimated work time, and is designed to ensure efficient operation.

[0591] Step 4:

[0592] The server notifies users of planned snow removal operations on their devices. Users can then view the schedule and plan details on their devices and make modifications or approvals.

[0593] Step 5:

[0594] After user approval, the server sends remote control commands to the snowblower, initiating snow removal work according to the plan. The snowblower then travels along the automatically configured route, efficiently removing snow.

[0595] Step 6:

[0596] During operation, the server receives feedback data from the snowblower and monitors the progress in real time. The server transfers this information to the terminal, allowing the user to check the progress and issue additional instructions as needed.

[0597] Step 7:

[0598] After snow removal is complete, the server sends a completion notification to the user's terminal, reporting that the work was completed within the timeframe specified in the plan. The server then uses the collected data to train an AI algorithm in preparation for the next snowfall forecast.

[0599] (Example 1)

[0600] 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".

[0601] Snow removal is an essential task in commercial facilities and residential areas, but it requires a large amount of manpower and time. Current manual snow removal methods are inefficient and make it difficult to respond quickly to changing weather conditions; therefore, an automated and effective snow removal system is needed.

[0602] 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.

[0603] In this invention, the server includes an information gathering mechanism for acquiring snowfall conditions remotely, an evaluation mechanism for analyzing the collected snowfall information and determining the necessity of snow removal, a schedule creation mechanism for automatically generating a snow removal plan based on the evaluation results, and an operation mechanism for operating snow removal equipment based on the generated plan. This enables the rapid and efficient automation of snow removal work and flexible responses to changes in weather.

[0604] An "information gathering mechanism" is a device or system that acquires snowfall conditions remotely using sensors and cameras and transmits the data to a server.

[0605] An "evaluation mechanism" is a device or system that analyzes collected snowfall data and uses AI algorithms to make decisions regarding the necessity of snow removal.

[0606] A "schedule creation mechanism" is a device or system that automatically generates an optimal snow removal plan based on the evaluation results from an evaluation mechanism.

[0607] An "operating mechanism" is a device or system used to control snow removal equipment and perform work according to a plan generated by a scheduling mechanism.

[0608] A "communication mechanism" is a device or system that transmits information on the progress of snow removal work to user terminals and communicates to obtain approval from users.

[0609] This invention is a system that automatically manages snow accumulation in stores and residential areas and performs snow removal work efficiently. The system mainly consists of three elements: a server, terminals, and users.

[0610] The server acquires real-time information on snow conditions at stores and parking lots using a data collection mechanism that utilizes sensors and cameras. Various sensors measure the physical amount of snow, while cameras capture visual data. This acquired data is stored in cloud storage. The server then uses AI software such as TensorFlow and PyTorch to analyze this data through an evaluation mechanism and determine the need for snow removal. Here, the AI ​​makes the optimal decision based on machine learning algorithms that utilize historical data and weather forecast information.

[0611] Based on the evaluation results, the server automatically generates a snow removal plan using the scheduling mechanism. This plan includes the start and end times and the optimal route. Using the generated plan, the server automatically controls the snow removal equipment using the operation mechanism, ensuring the work proceeds according to the plan.

[0612] The server uses a communication mechanism to notify users of the work progress on their terminals. This allows users to check the work status in real time and make necessary instructions or schedule changes. A specific example is a case where a hair salon owner uses the system. The server detects snowfall in the early morning, and after the AI ​​determines the need for snow removal, it plans the optimal snow removal route and schedule. Once the owner approves on their terminal, the snowplow automatically starts work and completes the task within the specified time.

[0613] An example of a prompt to input into a generating AI model is: "Explain the process by which the server automatically controls the snowplow to complete snow removal in the parking lot within the specified time."

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The server collects snow depth data from sensors and cameras installed in the store. Sensors physically measure the amount of snow, and cameras capture the video. This data undergoes noise reduction filtering as an initial processing step and is stored in the cloud as clean data. The input is raw data from sensors and cameras, and the output is filtered snow depth data.

[0617] Step 2:

[0618] The server passes filtered snow depth data from the cloud to an AI algorithm, which uses an evaluation mechanism to determine the need for snow removal. The AI ​​uses a machine learning model to analyze the current situation based on past snowfall patterns and weather forecasts to determine whether snow removal is necessary. The input is clean snow depth data, and the output is a determination indicating whether snow removal is necessary or not.

[0619] Step 3:

[0620] The server uses a scheduling mechanism to create an optimal snow removal plan based on the AI's assessment results. This plan includes the start and end times of the work and the route to be used. The AI ​​performs simulations and selects the best plan. The input is the assessment result, and the output is a detailed snow removal schedule.

[0621] Step 4:

[0622] The server notifies the user's terminal of the plan details. The user can check the schedule through their terminal, make any necessary corrections, and then approve it. This process allows for adjustments based on the user's schedule and plans. The input is the snow removal schedule, and the output is the user's approval or correction instructions.

[0623] Step 5:

[0624] The server sends the user-approved schedule to the control mechanism, which then controls the snow removal equipment. Based on the specified route, the snow removal equipment automatically starts operating, and the work is carried out according to plan. The input is the user-approved schedule, and the output is the operating status of the snow removal equipment.

[0625] Step 6:

[0626] The server updates the progress of snow removal work in real time to the user's terminal via a communication mechanism. Based on this information, the user can check the progress and, if necessary, instruct the expansion of the work area or extension of the work time. The input is data on the progress of the snow removal work, and the output is progress information notified to the user.

[0627] (Application Example 1)

[0628] 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".

[0629] The present invention aims to provide a system for efficient and rapid snow removal in urban areas to prevent traffic disruptions and a decline in the quality of life for citizens caused by snowfall. This will reduce citizens' anxiety about snowfall and create an environment where they can request priority snow removal in specific locations.

[0630] 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.

[0631] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal plan based on the determination result, control means for controlling a snow removal machine according to the generated plan, communication means for monitoring the progress of snow removal work and transmitting progress information to a user terminal, and user interface means for citizens to check the progress of snow removal and request snow removal at a specific location. This makes it possible to streamline snow removal response in urban areas and improve the sense of security in citizens' lives.

[0632] "Information acquisition means" refers to devices or software that remotely acquire real-time information on snow conditions in front of stores or parking lots.

[0633] "Determination means" refers to algorithms or devices used to determine the necessity of snow removal based on acquired snow depth information.

[0634] "Plan generation means" refers to a system that automatically creates the optimal snow removal work schedule based on the judgment results.

[0635] "Control means" refers to a system or device that operates a snow removal machine and performs the work based on the generated snow removal plan.

[0636] "Communication means" refers to the network and communication equipment used to transmit the progress of snow removal work to the user's terminal and provide progress information.

[0637] "User interface means" refers to an interface that allows citizens to check the progress of snow removal and request snow removal for specific locations.

[0638] To implement this invention, three elements are necessary: ​​a server, a terminal, and a user. Each of these elements will be described in detail below.

[0639] The server acquires snow accumulation data through sensors and cameras installed in the city. This requires real-time data processing, and data collection and storage are performed using cloud platforms such as AWS and Google Cloud. The acquired data is analyzed using machine learning algorithms built with Python to determine the level of snow accumulation and the need for snow removal. In this process, past snowfall data and weather forecasts are also referenced to generate an optimal snow removal schedule.

[0640] The terminal provides an interface that allows users to check the progress of snow removal and request snow removal for specific locations. The application running on the terminal is developed using React Native and is compatible with iOS and Android devices. Through the terminal, users can check the progress of snow removal work in real time and, if necessary, instruct the expansion of the snow removal area or extension of the time.

[0641] Users can utilize this system as citizens. For example, they can request snow removal for a specific park via a smartphone app. In response to this request, the server reorganizes the snow removal schedule to prioritize and efficiently controls the snow removal equipment.

[0642] As a concrete example, consider a case where a citizen requests snow removal for an event being held in a local park on a holiday. In this case, the citizen can request snow removal for the park and check the progress via a smartphone app. The server receives this request, uses an AI algorithm to plan the optimal snow removal operation, and issues instructions to the snowplow.

[0643] An example of a prompt message for the generating AI model might be: "Based on snow accumulation data in the city, determine the areas that require snow removal and create a snow removal schedule according to priority. Propose a system that updates the progress in real time and notifies users."

[0644] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0645] Step 1:

[0646] The server acquires snow accumulation data from sensors and cameras installed throughout the city. The input is real-time sensor data, and the output is a dataset showing the snow accumulation conditions. This clearly reveals the snow accumulation situation in each area.

[0647] Step 2:

[0648] The server analyzes the acquired snow depth data using a machine learning algorithm. The input consists of snow depth data, historical snowfall data, and weather forecast information, and the output is a determination of the necessity and priority of snow removal. Through data processing, the AI ​​determines a snow removal schedule for each amount of snow depth.

[0649] Step 3:

[0650] The server automatically generates a snow removal plan based on the analysis results. The input is the determination of the need for snow removal, and the output is a specific snow removal schedule and route. The generated schedule includes optimal route calculation and time allocation.

[0651] Step 4:

[0652] The server sends the planned snow removal schedule to the user's terminal. The input is the generated schedule, and the output is data for display on the user's terminal. The user can review this information on their terminal and make corrections or approvals as needed.

[0653] Step 5:

[0654] The terminal displays the snow removal plan to the user and obtains their approval. Input is schedule information sent from the server, and output is the user's approval or modification request. The user can view the schedule details through the interface.

[0655] Step 6:

[0656] The server issues instructions to control the snowblower based on a user-approved schedule. The input is the approved schedule, and the output is the control command to the snowblower. The snowblower follows the instructions and begins work along the specified route.

[0657] Step 7:

[0658] The server monitors the progress of snow removal operations and transmits progress information to terminals in real time. Input is work progress data from the snow removal machine, and output is progress information displayed on the user's terminal. Through the terminal, the user can monitor the work status and issue additional instructions as needed.

[0659] 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.

[0660] This invention is a system for automating snow removal work in stores and parking lots, and for providing an optimal plan that takes user emotions into consideration. It mainly consists of a server, terminals, users, and an emotion engine.

[0661] In this system, the server first acquires snow accumulation information from sensors and cameras. The server analyzes the data and uses AI to determine the need for snow removal. This process utilizes historical snowfall data and machine learning algorithms. If the AI ​​determines that snow removal is necessary, the server automatically generates a snow removal plan, considering the optimal route and time.

[0662] The emotion engine is installed on the user's device and recognizes emotions from the user's voice and text data. For example, if the system determines that the user is stressed, the server can change the priority of the snow removal plan based on the emotion data. If positive user feedback is confirmed, it will be used to adjust future plans and machine learning algorithms.

[0663] For example, if the server detects snowfall and the AI ​​determines that snow removal is necessary, the emotion engine will quickly prioritize the plan and proceed if it determines that the user is busy. The notified snow removal plan is displayed on the user's terminal, and once the user approves it, the server controls the snowplow and starts the work. The progress is monitored in real time during the work, and information is provided to the user. Once the work is completed, the user is notified by the server, and the emotion data is used for future operations. This entire process enables efficient and flexible snow removal management for the user.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The server acquires real-time data on snow accumulation from sensors and cameras installed in the stores. This includes detailed information on snow depth and current snowfall conditions.

[0667] Step 2:

[0668] The server stores the acquired data in a cloud-based database, analyzes the data using an AI algorithm, and evaluates the degree of snowfall. It also refers to past snowfall data to determine whether snow removal is necessary.

[0669] Step 3:

[0670] If the server determines that snow removal is necessary, it uses AI to generate an optimal snow removal plan. This plan includes the snow removal route, start time, and estimated work time.

[0671] Step 4:

[0672] The emotion engine on the device analyzes the user's voice and text input to evaluate their current emotional state. For example, if the user is busy and stressed, it provides that information to the server.

[0673] Step 5:

[0674] The server adjusts the priority of snow removal plans based on sentiment data. If necessary, it modifies the plan to match the user's sentiment and sends notifications to the user's device.

[0675] Step 6:

[0676] The user reviews the snow removal plan displayed on their terminal and makes any necessary modifications or approvals. Once the user approves, the server proceeds to the next step.

[0677] Step 7:

[0678] The server sends remote control signals to the snowblower, instructing it to begin snow removal work according to the plan. The snowblower automatically travels along the set route and efficiently removes snow.

[0679] Step 8:

[0680] During operation, the server receives real-time progress data from the snowblower, monitors it, and provides feedback to the user's terminal. The user can check the progress and issue additional instructions as needed.

[0681] Step 9:

[0682] Once the snow removal work is complete, the server sends a completion notification to the user's terminal, reporting that the work was carried out as planned. The server uses the user's feedback obtained from the emotion engine to plan for future operations and adjust the AI ​​algorithm.

[0683] (Example 2)

[0684] 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".

[0685] There is a need for a system that can efficiently remove snow from large areas such as stores and parking lots. In particular, a challenge is how to adjust the priority of snow removal according to the amount of snowfall and the emotional state of the users, thereby minimizing delays and waste in the work.

[0686] 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.

[0687] In this invention, the server includes information gathering means, determination means for analyzing collected snow depth information and determining the necessity of snow removal, plan generation means for automatically generating a snow removal work plan based on the determination result, emotion recognition means for recognizing the user's emotions and adjusting the priority of the snow removal plan, control means for controlling work equipment according to the generated plan, and communication means for monitoring the progress of the work and transmitting progress information to the user device. This enables the automatic generation and execution of a snow removal work plan optimized for the snow depth conditions, and achieves flexible work adjustments according to the user's situation.

[0688] "Information gathering means" refers to methods for remotely acquiring snow conditions using sensors and cameras.

[0689] "Decision-making tools" refer to methods for analyzing collected data and determining the necessity of snow removal through machine learning algorithms.

[0690] A "plan generation means" is a means for automatically generating an optimal snow removal plan based on the results of determining the necessity of snow removal.

[0691] "Emotion recognition means" refers to a method for recognizing emotions from the user's voice and text data and adjusting the priorities of the snow removal plan.

[0692] "Control means" refers to means for controlling work equipment based on the generated snow removal plan.

[0693] "Communication means" refers to a means for monitoring the progress of snow removal work and transmitting that information to user devices in real time.

[0694] This invention is a system for automating snow removal work in large areas such as stores and parking lots, and for providing an efficient plan that takes user emotions into consideration. The system mainly consists of a server, terminals, users, and an emotion engine.

[0695] The server acquires snow depth information using data collection devices such as weather sensors and surveillance cameras. Specifically, general weather sensors and network cameras are used. This data is analyzed on the server using software libraries such as TensorFlow to execute machine learning algorithms. This allows the server to compare the current snow depth with past data to determine the need for snow removal.

[0696] The device is equipped with an emotion engine that analyzes the user's emotions from their voice and text data. Natural language processing technologies such as Google Cloud Speech-to-Text API and IBM Watson Natural Language Understanding are used for this purpose. The recognized emotion data is sent to a server and used to adjust the priority of plans.

[0697] The server automatically generates a snow removal plan based on the assessment results. The generated plan is designed to take into account the optimal route and work time, and is notified to the user's device using Firebase Cloud Messaging. Once the user approves the plan, the server controls the snowplow via an IoT control board such as a Raspberry Pi and starts the work.

[0698] For example, on a snowy morning, if the server immediately determines the need for snow removal and the emotion engine recognizes the user's busy schedule, the plan will be prioritized and executed accordingly. In this way, information about the work in progress is monitored in real time and fed back to the user sequentially.

[0699] Examples of prompts to input into a generative AI model include questions such as, "How do you adjust priorities when a user is feeling emotionally stressed?" or "Please explain in detail the algorithm by which the AI ​​determines the need for snow removal based on collected weather data."

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server acquires snow depth information from data collection devices. Specifically, weather sensors and network cameras function as input devices, collecting data such as snow depth, temperature, and wind speed. The input data undergoes initial processing, including format conversion and noise reduction, and is organized into a dataset for analysis. This organized dataset becomes the input for the next processing step.

[0703] Step 2:

[0704] The server analyzes the acquired data to determine the need for snow removal. In this step, machine learning libraries such as TensorFlow are used to compare the current data with past snowfall data and to perform analysis using pattern recognition techniques. The analysis results output a decision on whether or not snow removal is necessary. Based on this result, a subsequent snow removal plan is created.

[0705] Step 3:

[0706] The server automatically generates a snow removal plan if it determines that snow removal is necessary. Using an AI algorithm, it calculates the optimal snow removal route and working time, and generates a detailed work instruction sheet. The input is the result of the previous step's decision, and the output is a detailed plan for operating the work equipment.

[0707] Step 4:

[0708] The device's emotion engine recognizes emotions from user voice and text input. Input includes user utterances and text information from the interface, which is analyzed through text analysis software and speech recognition APIs. The output provides a judgment of the user's current emotional state. This information is sent to a server and used to adjust plan priorities.

[0709] Step 5:

[0710] The server adjusts the priority of snow removal plans based on the emotion recognition results. If a plan has a higher priority, the execution order of the plan is changed. The adjusted plan is notified to the user's device, and a notification is sent to the user using Firebase Cloud Messaging. Once the user approves, this information is passed on to the next step, and the execution of the approved plan is prepared.

[0711] Step 6:

[0712] Once the user approves the snow removal plan notified via their terminal, the server begins controlling the work equipment. Inputs include the approved plan and user instructions. The control system sends commands to the snowplow via devices such as a Raspberry Pi, and the specific snow removal work is carried out. During this process, the progress is observed in real time, and this information is output to the next step.

[0713] Step 7:

[0714] The server monitors the progress of snow removal work and provides feedback to the user. This monitoring utilizes data feeds from various sensors to detect delays and anomalies. The obtained progress information is provided to the user through a dedicated app, allowing the user to monitor the situation. This facilitates user confirmation of work completion and provides feedback.

[0715] (Application Example 2)

[0716] 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".

[0717] In modern society, snowfall has become a major problem, causing disruptions to transportation and stagnation of commercial activities. Snow removal, in particular, is a time-consuming and labor-intensive task for busy individuals and businesses, requiring efficient management. Furthermore, it is necessary to appropriately adjust the priority of snow removal plans based on user sentiment and respond flexibly, but current systems are unable to do so.

[0718] 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.

[0719] In this invention, the server includes information acquisition means for obtaining snow conditions remotely, determination means for analyzing the acquired snow information and determining the necessity of snow removal, and emotion adjustment means for analyzing the user's emotional information and adjusting the priority of the snow removal plan based on those emotions. This enables the execution of an efficient and flexible snow removal plan in real time.

[0720] "Information acquisition means" refers to various devices and sensors used to understand snow conditions from a remote location.

[0721] "Determination means" refers to algorithms or systems that analyze acquired snow depth information to automatically determine whether snow removal work is necessary.

[0722] "Plan generation means" refers to a system or software that automatically generates an optimal snow removal plan based on the judgment results of the determination means.

[0723] "Control means" refers to a mechanism for operating and controlling snowplows and related equipment according to the generated plan.

[0724] "Communication means" refers to the network and protocols used to monitor the progress of snow removal work and transmit progress information to the user's terminal.

[0725] "Emotional adjustment tools" refer to systems and tools that analyze users' emotional information and adjust the priority of snow removal plans based on those emotions.

[0726] The system for implementing this invention consists of a server, a user terminal, and snow removal equipment. The server aggregates information from geographically distributed sensors and cameras to grasp the snow accumulation situation in real time. As a result, the information acquisition means collects snow accumulation data. Based on the acquired data, the server uses a determination means to analyze the degree of snow accumulation and uses an AI algorithm to determine whether snow removal is necessary. If a determination is made, the plan generation means automatically generates an optimal snow removal plan using past data and machine learning. This uses AI technologies such as TensorFlow.

[0727] The user terminal functions as an emotion regulation mechanism, analyzing the user's emotions from their voice and text. This uses tools such as Google Cloud Natural Language. Based on the emotion data obtained from this analysis, the server adjusts the priority of snow removal tasks. For example, if the user is feeling stressed, the system can adjust the tasks to proceed more quickly.

[0728] Once a snow removal plan is decided, the server remotely operates the actual snow removal equipment using control devices. 5G communication technology makes this control possible, and progress is notified to the user's terminal in real time.

[0729] As a concrete example, consider a process where a server automatically checks snow accumulation information at night when heavy snowfall is expected, and plans to efficiently complete snow removal work before the start of commuting and commercial activities the following morning. If a user prompts at night, "I would like the snow removed so that it will be ready in time for my commute tomorrow morning," this will be reflected in the snow removal plan along with emotional data. In this way, the system of the present invention can provide rational snow removal work suggestions while also meeting the emotional needs of users.

[0730] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0731] Step 1:

[0732] The server acquires real-time snow conditions from sensors and cameras placed in various locations. The acquired input data includes image data and environmental data such as temperature and humidity. The server analyzes this data to perform initial data processing to determine the amount and extent of snow accumulation.

[0733] Step 2:

[0734] The server processes the analyzed snow depth data using an AI algorithm to determine the need for snow removal. The input consists of sensor information and historical weather data, and a machine learning model based on this data outputs whether or not snow removal is necessary. This process utilizes a machine learning model based on TensorFlow.

[0735] Step 3:

[0736] If the server determines that snow removal is necessary, it automatically creates an optimal snow removal work schedule using a planning generation system. The inputs are the need for snow removal and past work data, and the AI ​​model generates the optimal route and time. The plan includes implementation time and route information.

[0737] Step 4:

[0738] The user terminal receives voice and text data as a means of emotion regulation and analyzes the user's emotions. The input is user information from the terminal, and this data is processed by Google Cloud Natural Language and output as data representing the user's emotional state.

[0739] Step 5:

[0740] The server adjusts the priority of the snow removal plan based on the analyzed user sentiment data. The inputs are sentiment information and the initial work plan, and the output is a prioritized plan tailored to the user's needs.

[0741] Step 6:

[0742] After the final snow removal plan is determined, the server uses control devices to send commands to the snow removal equipment, initiating actual work. The input is the prioritized snow removal plan, and the output is the actual operating status of the snow removal equipment. 5G technology is used for communication, enabling real-time monitoring and feedback.

[0743] Step 7:

[0744] The server monitors the progress of snow removal work and notifies user terminals. Progress data is updated in real time, and users receive confirmation that the work has been completed. This allows users to check the progress of the snow removal work.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] A means of acquiring information for obtaining snow conditions remotely,

[0769] A means for determining the necessity of snow removal by analyzing acquired snow depth information,

[0770] A plan generation means that automatically generates a snow removal work plan based on the judgment result,

[0771] Control means for controlling the snowplow according to the generated plan,

[0772] A communication means for monitoring the progress of snow removal work and transmitting progress information to user terminals,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, wherein the communication means obtains authorization from the user terminal and initiates an operation by the control means.

[0776] (Claim 3)

[0777] The system according to claim 1, wherein the determination means uses a machine learning algorithm that utilizes past snowfall data when determining the necessity of snow removal.

[0778] "Example 1"

[0779] (Claim 1)

[0780] A remote information gathering mechanism for obtaining snowfall conditions,

[0781] An evaluation mechanism that analyzes collected snowfall information and determines the necessity of snow removal,

[0782] A scheduling mechanism that automatically generates a snow removal plan based on evaluation results,

[0783] An operating mechanism for operating the snow removal equipment based on the generated plan,

[0784] A communication mechanism that monitors the progress of snow removal work and transmits progress information to user terminals,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the communication mechanism obtains approval from the user terminal and the operation mechanism starts work.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the evaluation mechanism uses a machine learning algorithm based on past snowfall data when determining the need for snow removal.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] A means of acquiring information for obtaining snow conditions remotely,

[0793] A means for determining the necessity of snow removal by analyzing acquired snow depth information,

[0794] A plan generation means that automatically generates a snow removal work plan based on the judgment result,

[0795] Control means for controlling the snowplow according to the generated plan,

[0796] A communication means for monitoring the progress of snow removal work and transmitting progress information to user terminals,

[0797] A user interface that allows citizens to check the progress of snow removal and request snow removal for specific locations,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, wherein the communication means obtains authorization from the user terminal and initiates an operation by the control means.

[0801] (Claim 3)

[0802] The system according to claim 1, wherein the determination means uses a machine learning algorithm based on past snowfall data, as well as real-time data, when determining the necessity of snow removal.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] Information gathering methods,

[0806] A means of determining the necessity of snow removal by analyzing collected snow accumulation information,

[0807] A plan generation means that automatically generates a snow removal work plan based on the judgment result,

[0808] An emotion recognition means that recognizes the user's emotions and adjusts the priority of the snow removal plan,

[0809] A control means for controlling work equipment according to the generated plan,

[0810] A communication means for monitoring the progress of the work and transmitting progress information to the user device,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the communication means obtains approval from the user device and initiates operation by the control means.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the decision-making means uses a machine learning algorithm that utilizes past weather data when determining the necessity of snow removal.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] A means of acquiring information for obtaining snow conditions remotely,

[0819] A means for determining the necessity of snow removal by analyzing acquired snow depth information,

[0820] A plan generation means that automatically generates a snow removal work plan based on the judgment result,

[0821] Control means for controlling the snowplow according to the generated plan,

[0822] A communication means for monitoring the progress of snow removal work and transmitting progress information to user terminals,

[0823] An emotion adjustment mechanism that analyzes user emotion information and adjusts the priority of the snow removal plan based on those emotions,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the communication means obtains authorization from the user terminal and initiates an operation by the control means.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the determination means uses a machine learning algorithm based on past snowfall data to determine the necessity of snow removal, and takes into account the user's emotional information. [Explanation of symbols]

[0829] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring information for obtaining snow conditions remotely, A means for determining the necessity of snow removal by analyzing acquired snow depth information, A plan generation means that automatically generates a snow removal work plan based on the judgment result, Control means for controlling the snowplow according to the generated plan, A communication means for monitoring the progress of snow removal work and transmitting progress information to user terminals, A system that includes this.

2. The system according to claim 1, wherein the communication means obtains authorization from the user terminal and initiates an operation by the control means.

3. The system according to claim 1, wherein the determination means uses a machine learning algorithm that utilizes past snowfall data when determining the necessity of snow removal.

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