System

Abstract

A system is provided.SOLUTION: A means for acquiring image data in real time by a camera mounted on a mowing robot, a means for processing the image data and generating preprocessed image data, a means for executing a generation AI model for performing identification of plants based on the preprocessed image data, a means for evaluating a determination result of the generation AI model and generating a warning message when an endangered plant is detected, a means for transmitting the warning message to the mowing robot, and a means for causing the mowing robot that has received the warning message to make a voice announcement; A system comprising: means for automatically stopping a mowing operation; and means for displaying a visual warning regarding detection of an endangered plant by the mowing robot.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In mowing work, which is carried out for forest cultivation and environmental protection, there is a need to prevent ecosystem damage caused by insufficient workers mistakenly cutting endangered plants. However, with the current mowing work, there is a problem that the number of knowledgeable veteran workers is decreasing due to aging, and human error is likely to occur due to the misidentification of endangered plants. To solve this problem, a mowing robot that can automatically identify endangered plants and optimize work is needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: A means for acquiring image data in real time using a camera mounted on a grass-cutting robot and processing the image data to generate preprocessed image data, and a means for executing a generative AI model that identifies plants based on the preprocessed image data.

[0006] The system further includes means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected. This warning message is sent to the grass-cutting robot. When the grass-cutting robot receives this warning message, it includes means for making an audio announcement and automatically stopping grass-cutting operations. The grass-cutting robot also includes means for displaying a visual warning regarding the detection of an endangered plant. With these means, a grass-cutting system is provided that prevents endangered plants from being accidentally cut and contributes to environmental protection.

[0007] A "grass-cutting robot" is a robot equipped with a camera, grass cutter, control device, etc., that performs the task of cutting grass autonomously or remotely.

[0008] A "camera" is an imaging device that captures image data and outputs it as digital data.

[0009] "Real-time" refers to processing and operations being performed in synchronization with real time and without delay.

[0010] "Image data" refers to visual information captured by a camera that is expressed in digital form.

[0011] "Preprocessing" refers to a series of processes performed on acquired image data to make it easier to analyze, and specifically includes resizing, noise reduction, and normalization of saturation and brightness.

[0012] A "generative AI model" is an artificial intelligence model that is trained using large amounts of data to generate and analyze images, text, etc.

[0013] "Endangered plants" is a general term for plant species whose populations have drastically decreased in the natural world and are in danger of extinction.

[0014] An "alert message" is a text or audio message that notifies you of a particular condition when it occurs.

[0015] "Voice announcement" refers to a means of communicating information audibly through a speaker or other device.

[0016] "Visual warnings" are signs that visually alert people to danger or caution using displays, lights, etc.

[0017] "Stop mowing" means that the mower stops all rotation and mowing operations. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0040] Grass-cutting robot configuration

[0041] The grass-cutting robot consists of the following main components:

[0042] Camera: Acquires high-resolution image data in real time.

[0043] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0044] Speaker: A device for making voice announcements.

[0045] Display: A monitor for displaying visual warnings.

[0046] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0047] Server processing

[0048] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0049] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0050] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[0051] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[0052] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0053] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0054] Voice announcement and termination process

[0055] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0056] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0057] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0058] User response

[0059] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0060] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0061] Specific examples

[0062] Here, we will provide a specific example. As an example, we will explain the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0063] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0064] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0065] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0066] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0067] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0068] Visual warning: The display will say "Endangered plant detected."

[0069] User confirmation: The user checks the site and repositions the robot if necessary.

[0070] Through the above process flow, the present invention accurately identifies endangered plants during mowing work and reduces the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[0074] Step 2:

[0075] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[0076] Step 3:

[0077] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[0078] Step 4:

[0079] The server evaluates the results of the generated AI model, checking the confidence score included in the results and determining that an endangered plant is present if it exceeds a set threshold.

[0080] Step 5:

[0081] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[0082] Step 6:

[0083] The server sends the generated warning message to the grass-cutting robot (terminal). This message includes instructions for the grass-cutting robot's control device and audio speaker.

[0084] Step 7:

[0085] The terminal (grass-cutting robot) receives a warning message from the server. At the same time, the audio speaker announces, "An endangered plant has been detected."

[0086] Step 8:

[0087] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[0088] Step 9:

[0089] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected," along with information about the type of plant and its location.

[0090] Step 10:

[0091] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[0092] Step 11:

[0093] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[0094] Step 12:

[0095] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] The present invention aims to reduce the risk of accidentally mowing endangered plants during mowing work. In particular, it provides a system that allows a mowing robot to operate autonomously, identify endangered plants in real time, and issue warnings and stop operations as necessary, thereby solving the problem of achieving both efficient mowing work and environmental protection.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes means for acquiring image data in real time using a camera mounted on the mowing robot, means for processing the image data to generate preprocessed image data, means for executing a generative AI model that identifies plants based on the preprocessed image data, means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected, means for transmitting the warning message to the mowing robot, means for the mowing robot to make an audio announcement and automatically stop mowing operation upon receiving the warning message, means for the mowing robot to display a visual warning regarding the detection of an endangered plant, means for the mowing robot to display on a display so that the user can confirm the location where the endangered plant was detected by the mowing robot, and means for the user to adjust the position of the mowing robot and indicate a new working area. This enables the mowing robot to identify endangered plants in real time and prevent them from being mowed by mistake.

[0101] A "grass-cutting robot" is a mechanical device for automatically cutting grass. It is equipped with a high-resolution camera and a control device, and operates based on instructions from a server.

[0102] A "camera" is a photographing device that captures image data in real time and transmits high-resolution images to a server.

[0103] "Preprocessed image data" refers to raw image data received by the server that has undergone preprocessing such as resizing and noise reduction, and has been converted into a form that is easy to input into an AI model.

[0104] A "generative AI model" is a machine learning model used to extract plant characteristics based on preprocessed image data and calculate the degree of match with known endangered species.

[0105] "Evaluation of discrimination results" is the process of analyzing the results of plant identification by the generative AI model and determining whether any endangered plants are included.

[0106] A "warning message" is a message, including an audio announcement and a visual warning, that is generated by the server and sent to the grass-cutting robot when an endangered plant is detected.

[0107] The "voice announcement" is a voice message that is sent to the user through a speaker when the grass-cutting robot receives a warning message.

[0108] "Stopping the mowing operation" refers to an operation in which the control device of the mowing robot stops the blade of the mower to prevent the mowing of endangered plants.

[0109] A "visual warning" is a message that appears on the grass-cutting robot's display indicating that an endangered plant has been detected.

[0110] "Display" refers to a display device mounted on the grass-cutting robot that provides visual warnings and warning messages to the user.

[0111] A "user" is a person who operates and manages the grass-cutting robot, checks warning messages, and adjusts the robot's position as needed.

[0112] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0113] Grass-cutting robot configuration

[0114] The grass-cutting robot consists of the following main components:

[0115] Camera: Acquires high-resolution image data in real time.

[0116] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0117] Speaker: A device for making voice announcements.

[0118] Display: A monitor for displaying visual warnings.

[0119] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0120] Server processing

[0121] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0122] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0123] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal format for use with the AI ​​model. Software such as OpenCV is used.

[0124] 3. Running the AI ​​model: The preprocessed image data is input into a generative AI model (e.g., TensorFlow, PyTorch) to identify plants.

[0125] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0126] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0127] Voice announcement and termination process

[0128] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0129] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0130] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0131] User response

[0132] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0133] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0134] Specific examples

[0135] The following explains the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0136] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0137] Preprocessing: The server resizes the image data and removes noise before passing it to the generative AI model.

[0138] Execution of the AI ​​model: The generative AI model analyzes the image and confirms that it is a "dogtooth violet."

[0139] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0140] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0141] Visual warning: The display will say "Endangered plant detected."

[0142] User confirmation: The user checks the site and resets the position of the grass cutting robot if necessary.

[0143] Example prompts for generative AI models

[0144] "Real-time identification of endangered plants using high-resolution imagery"

[0145] "A method for safely detecting endangered plants from preprocessed image data and reducing the risk of mis-harvesting"

[0146] In this way, the present invention can accurately identify endangered plants during mowing work and reduce the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1:

[0149] Image acquisition

[0150] The server receives image data from the grass-cutting robot's camera in real time. Specifically, high-resolution images taken by the grass-cutting robot's camera are sent to the server via the Internet. The input data is raw image data, and the output is image data stored on the server.

[0151] Step 2:

[0152] Image preprocessing

[0153] The server pre-processes the received image data. This process involves the following steps:

[0154] Resizing: Reduce or increase the image resolution to fit the AI ​​model. For example, resize a 1024x768 pixel image to 256x256 pixel.

[0155] Noise Reduction: Reduces noise in the image. Apply noise reduction algorithms to reduce the effects of glare and shadows.

[0156] The input data is raw image data stored on the server, and the output is pre-processed image data that has been resized and noise-reduced.

[0157] Step 3:

[0158] Image analysis using AI models

[0159] The server inputs the preprocessed image data into a generative AI model. Specifically, a generative AI model (such as TensorFlow or PyTorch) is used to identify plants in the image. This AI model has been trained in advance using a large amount of plant image data. The input data is the preprocessed image data, and the output is the name and species information of the identified plants. For example, the AI ​​model recognizes the shape and color of dogtooth violets leaves and identifies their species.

[0160] Step 4:

[0161] Evaluation of the discrimination results

[0162] The server evaluates the output of the AI ​​model. Specifically, if the AI ​​output identifies the plant as "dogtooth violet," the evaluation engine checks the information and determines whether it meets the criteria for issuing an alert. The input data is the identification result by the AI ​​model, and the output is the decision to issue an alert.

[0163] Step 5:

[0164] Generate and send warning messages

[0165] The server sends a warning message to the grass-cutting robot. Specifically, it sends a digital message such as "Endangered plants have been detected." This message is received by the control device and triggers subsequent actions. The input data is evaluation information based on the discrimination results, and the output is a warning message.

[0166] Step 6:

[0167] Voice announcement and mower stopping

[0168] The grass-cutting robot will take the following actions based on the warning message received from the server:

[0169] Voice announcement: An announcement is made through the speaker saying "Endangered plants detected." This function is implemented using a TTS (text-to-speech) engine. The input data is a warning message, and the output is a voice announcement.

[0170] Stopping the mower: The control unit stops the mower blade. By stopping operation immediately, endangered plants are protected. The input data is a warning message, and the output is the mower stopping action.

[0171] Step 7:

[0172] Visual warning signs

[0173] A warning message is displayed on the display of the grass-cutting robot. The user is notified of the situation by displaying "Endangered plants detected." The input data is the warning message, and the output is a visual warning display.

[0174] Step 8:

[0175] User confirmation and response

[0176] The user sees the warning message on the display and takes the following specific action:

[0177] Field survey: The user actually visits the site and checks the status of endangered plants. The input data is a visual warning display, and the output is the user's confirmation and response.

[0178] Operation instructions: Adjust the position of the grass-cutting robot as needed and indicate a new work area. Typically, the user operates the robot using a remote controller or terminal. The input data are visual warnings and the results of the on-site inspection, and the output is an indication of a new work area.

[0179] The above are the specific processing steps of the program of this system.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] In conventional logistics centers, the identification and management of sensitive packages and expensive items was insufficient due to human error, resulting in mishandling and damage. Furthermore, workers were not given sufficient warnings, resulting in lower customer satisfaction and lower efficiency in logistics operations. To solve these problems, a system is needed that can identify specific items in real time and issue appropriate warnings to workers.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes means for acquiring image data, means for generating pre-processed image data, and means for executing a generative AI model for identifying items based on the pre-processed image data, thereby enabling real-time identification of specific items and providing audio announcements and visual alerts.

[0185] A "grass-mowing robot" is a mechanical device for automatically mowing grass, and in the present invention includes a camera, a control device, and the like.

[0186] A "camera" is a photographing device for acquiring image data in real time.

[0187] "Image data" is data containing visual information acquired by a camera.

[0188] "Preprocessing" refers to a processing step for converting acquired image data into an appropriate form, including resizing and noise reduction.

[0189] A "generative AI model" is an artificial intelligence model that is trained to perform a specific task (e.g., identifying objects or plants).

[0190] "Discrimination results" are the results of analysis and identification output by the generative AI model.

[0191] A "warning message" is notification information that is generated when a specific condition (eg, the detection of an endangered plant) is met.

[0192] "Voice announcement" is a function that provides information through voice, and is used to convey warning messages and the like to users.

[0193] A "visual warning" is warning information that is visually displayed on a display or the like.

[0194] The "logistics sector" is a range of activities that includes the transportation, warehousing, and distribution of goods.

[0195] A "specific item" is an item that has special conditions (e.g., requires careful handling, is expensive) and is the subject of identification in the present invention.

[0196] The present invention is a system that uses image analysis technology to identify and warn about specific items in a logistics center, and applies technology related to grass-cutting robots. Details are provided below.

[0197] composition

[0198] The system consists of the following main components:

[0199] Smart glasses: devices equipped with high-resolution cameras and displays for capturing image data in real time.

[0200] Server: A device that processes acquired image data and uses a generative AI model to identify specific items.

[0201] Voice announcement device: A device built into smart glasses that notifies users of warning messages by voice.

[0202] Visual warning display: A function that provides visual warnings through the smart glasses display.

[0203] Processing steps

[0204] The smart glasses capture image data in real time within the logistics center and send it to a server. The server preprocesses the image data and inputs it into a generative AI model. The generative AI model analyzes it and identifies specific items. Based on the identification results, the server generates a warning message and sends it to the smart glasses. The smart glasses notify workers by audio announcements and visual warning displays.

[0205] Hardware and Software Used

[0206] Hardware:

[0207] Smart glasses (e.g., with a high-resolution camera and display)

[0208] Server (with high-performance processing capabilities)

[0209] software:

[0210] TensorFlow: Used to run generative AI models

[0211] OpenCV: Used for image acquisition and pre-processing

[0212] pyttsx3: Used for voice announcements

[0213] Specific examples

[0214] Suppose a worker at a logistics center is inspecting packages and the smart glasses detect a box with a "Handle with Care" sticker. In this case, the smart glasses take a photo of the box with their camera and send the image to the server. The server preprocesses the image data and passes it to a generative AI model to identify whether it is a specific item. If the identification result is that the box is a "Handle with Care" box, the server generates a warning message and sends it to the smart glasses. The smart glasses then notify the worker via audio and visual means that "handle with care package has been detected."

[0215] Prompt Sentence Examples

[0216] To detect sensitive items in the logistics center, the smart glasses' camera captures images in real time and analyzes them using AI models. If a specific item is detected, a voice announcement and visual warning are issued.

[0217] By implementing the present invention in this manner, specific items can be quickly and accurately identified at the logistics center, and it is expected that mishandling and damage can be prevented.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] The smart glasses use cameras in the logistics center to capture image data in real time. The input is visual information from the site, and the output is high-resolution image data.

[0221] Step 2:

[0222] The smart glasses transmit the acquired image data to the server. The input is high-resolution image data, and the output is the transmission of image data to the server.

[0223] Step 3:

[0224] The server preprocesses the image data received. Specifically, it uses OpenCV to resize the image data, reduce noise, etc. The input is raw image data, and the output is preprocessed image data.

[0225] Step 4:

[0226] The server inputs the preprocessed image data into a generative AI model to identify specific items. The AI ​​model is run using TensorFlow to extract the features of the item and calculate the degree of match with known specific items. The input is the preprocessed image data, and the output is the classification result.

[0227] Step 5:

[0228] The server evaluates the discrimination results of the generated AI model and generates a warning message if a specific item is detected. The input is the discrimination result from the AI ​​model, and the output is the warning message.

[0229] Step 6:

[0230] The server sends a warning message to the smart glasses. The input is the warning message and the output is the transmission to the smart glasses.

[0231] Step 7:

[0232] The smart glasses make a voice announcement based on the received warning message and, if necessary, display a visual warning on the display. The input is the warning message received from the server, and the output is the voice announcement and the visual warning display.

[0233] Step 8:

[0234] The user listens to the audio announcement and visual warning and takes necessary action. The input is the warning from the smart glasses, and the output is the user's response action.

[0235] The above steps realize a system that smoothly identifies specific items and issues warnings in a logistics center.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[0238] Grass-cutting robot configuration

[0239] The grass-cutting robot consists of the following main components:

[0240] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[0241] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0242] Speaker: A device for making voice announcements.

[0243] Display: A monitor for displaying visual warnings.

[0244] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[0245] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0246] Server processing

[0247] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. The specific roles of the server are as follows:

[0248] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0249] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[0250] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[0251] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0252] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0253] 6. Emotion data evaluation: Evaluate the user's emotion data sent from the emotion engine and determine the optimal action according to the user's state.

[0254] Voice announcement and termination process

[0255] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it will make a voice announcement through the speaker saying, "Endangered plants have been detected." The tone and content of the announcement will be adjusted to match the user's emotional state based on the evaluation of the emotion engine.

[0256] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0257] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0258] User response

[0259] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0260] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0261] Emotion Engine Operation

[0262] The emotion engine analyzes the user's facial expression data in real time to obtain information such as:

[0263] Type of emotion (e.g., surprise, joy, anger, sadness)

[0264] Stress Level: Evaluates the user's stress state.

[0265] The data obtained by the emotion engine is sent to the server, which then takes the following actions:

[0266] 1. Adjusting the operating speed: If the user's stress level is high, the server will slow down the operating speed of the grass-cutting robot.

[0267] 2. Adjusting voice announcements: Change the tone and content of voice announcements depending on the emotion, providing information in a way that is easy for users to accept.

[0268] Specific examples

[0269] Here, a specific example will be given. As an example, a processing flow will be described for a case where a grass-cutting robot detects an endangered plant called "dogtooth violet" during work and at the same time the user expresses surprise.

[0270] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[0271] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0272] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0273] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[0274] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[0275] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[0276] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[0277] Through the above process flow, the present invention allows the user to appropriately identify endangered plants during weeding work and to work in an environment that takes into consideration the user's emotional state, thereby further contributing to forest development and environmental protection.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[0281] Step 2:

[0282] The server receives the user's facial expression data from the grass-cutting robot, which is also stored in a receiving buffer.

[0283] Step 3:

[0284] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[0285] Step 4:

[0286] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[0287] Step 5:

[0288] The server evaluates the results of the generated AI model, checks the confidence score included in the results, and determines that an endangered plant is present if it exceeds a set threshold.

[0289] Step 6:

[0290] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[0291] Step 7:

[0292] Based on the user's facial expression data received by the server, the emotion engine analyzes the user's emotions and identifies the type of emotion (e.g., surprise, joy, anger, sadness) and stress level from the user's facial expression.

[0293] Step 8:

[0294] The server sends the generated warning message and the emotion engine's analysis results to the grass-cutting robot (terminal), including instructions to the grass-cutting robot's control device and audio speaker.

[0295] Step 9:

[0296] The terminal (grass-cutting robot) receives the warning message from the server and the analysis results of the emotion engine. At the same time, the voice speaker announces, "Endangered plants have been detected." The tone and speed of the voice are adjusted based on the analysis results of the emotion engine.

[0297] Step 10:

[0298] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[0299] Step 11:

[0300] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected." The warning message includes information about the type of plant and its location.

[0301] Step 12:

[0302] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[0303] Step 13:

[0304] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[0305] Step 14:

[0306] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[0307] Example 2

[0308] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] The present invention aims to simultaneously protect endangered plants during weeding work and optimize the work environment by taking into account the user's emotional state. In particular, conventional weed-mowing robots lack sufficient accuracy in identifying plants and responding to the user's emotional state, resulting in problems such as accidentally mowing endangered plants and increasing user stress. To solve this problem, a system is needed that can accurately recognize the emotions of plants and the user in real time and automatically adjust tasks based on that information.

[0310] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data and generating preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make an audio announcement and automatically stop mowing operations; means for the mowing robot to display a visual warning regarding the detection of an endangered plant; means including an emotion engine that receives and analyzes user facial expression data; means for evaluating the user's emotion based on the generated emotion data; and means for adjusting the operating speed of the mowing robot and changing the tone of the audio announcement in accordance with the user's emotional state. This makes it possible to optimize the work environment while taking into consideration the protection of endangered plants and the user's emotional state.

[0311] 1. A "grass-cutting robot" is an autonomous robotic device equipped with a mechanical part for cutting grass, a camera for identifying the emotions of plants and the user, a speaker for making voice announcements, a display for showing visual warnings, and a control device for controlling its operation.

[0312] 2. "Camera" refers to a high-resolution image capture device that is installed on the grass-cutting robot and that captures image data in real time and recognizes the user's facial expressions.

[0313] 3. A "generative AI model" is an artificial intelligence model that inputs preprocessed image data and performs plant identification, and is constructed using, for example, a deep learning framework.

[0314] 4. "Preprocessed image data" refers to image data that has been processed, such as by resizing or noise reduction, on the raw data sent from the grass-cutting robot.

[0315] 5. "Warning message" means a message that is generated and sent to a grass-cutting robot when an endangered plant is detected, and includes content such as "An endangered plant has been detected."

[0316] 6. "Emotion Engine" means an engine that receives and analyzes a user's facial expression data and is a software or hardware component for evaluating the user's emotions.

[0317] 7. "Emotion data" refers to the results of analysis by the emotion engine based on the user's facial expression data, and includes emotional information such as surprise, joy, anger, and sadness.

[0318] 8. "Visual warning" means a warning message that appears on a display mounted on the grass-cutting robot, visually notifying the user of the detection of an endangered plant.

[0319] 9. "User's Emotional State" means the type and intensity of a user's emotion based on the results of analysis by the user's emotion engine.

[0320] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[0321] Grass-cutting robot configuration

[0322] The grass-cutting robot consists of the following main components:

[0323] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[0324] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0325] Speaker: A device for making voice announcements.

[0326] Display: A monitor for displaying visual warnings.

[0327] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[0328] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0329] Server processing

[0330] The server receives image data sent from the grass-cutting robot and analyzes it using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. Specific hardware used is a high-performance server, and software such as TensorFlow, PyTorch, and OpenCV is used.

[0331] Image data processing

[0332] After receiving the raw data from the grass-cutting robot, the server first resizes it and performs noise reduction. This preprocessing uses OpenCV, for example, resizes the image to (224x224) pixels, and applies a noise reduction filter.

[0333] Plant identification using AI models

[0334] The preprocessed image data is then fed into a generative AI model using deep learning frameworks such as TensorFlow and PyTorch. This AI model is trained to recognize endangered plants, for example, the dogtooth violet.

[0335] Generate and send warning messages

[0336] If the AI ​​model evaluates the identification results and confirms that an endangered plant is present, the server generates a warning message stating "Endangered plant detected" and sends it to the grass-cutting robot. The warning message includes the plant's species name, location information, and a voice announcement.

[0337] Emotional Data Evaluation

[0338] The emotion engine receives the user's facial expression data, analyzes it, and evaluates the user's emotional state. For example, a facial recognition service is used for the emotion engine. By sending the evaluation results to the server, the server can respond according to the user's emotions.

[0339] Adjusting movement speed and voice announcements

[0340] If the user's emotional state indicates a high stress level, the server instructs the grass-cutting robot to slow down. The tone and content of the voice announcements are also adjusted according to the user's emotions. Specifically, the announcement content is generated using the Google Text-to-Speech API.

[0341] Audio announcements and visual warnings

[0342] When the grass-cutting robot receives a warning message, it will make a voice announcement saying "Endangered plants detected" and stop the grass-cutting robot's operation. In addition, a visual warning message saying "Endangered plants detected" will appear on the display.

[0343] User response

[0344] The user checks the warning message on the display, checks the plants on-site, and then adjusts the position of the mowing robot as needed to indicate a new working area.

[0345] Specific examples

[0346] Here is a concrete example. For example, if a grass-cutting robot detects an endangered plant called "dogtooth violet" while working, and the user expresses surprise, the processing flow is as follows:

[0347] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[0348] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0349] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0350] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[0351] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[0352] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[0353] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[0354] Prompt Sentence Examples

[0355] "The grass-cutting robot has just detected an endangered plant called 'dogtooth violet'. At the same time, the emotion engine has recognized that the user has a surprised expression. Please generate the next instruction based on this situation."

[0356] Thus, the present invention aims to provide a system that identifies endangered plants during weeding work and optimizes the work environment according to the user's emotional state, thereby contributing to forest development and environmental protection.

[0357] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0358] Step 1:

[0359] The server acquires image data in real time from the camera on the mowing robot. The input data is a high-resolution image sent from the camera on the mowing robot. The server receives this image data and simultaneously acquires the user's facial expression data captured by the camera on the mowing robot. The output data is the raw image data and facial expression data received by the server.

[0360] Step 2:

[0361] The server preprocesses the received image data. For preprocessing, it uses OpenCV to resize the image and reduce noise. Specifically, it resizes the image to (224x224) pixels and removes noise using a filter. The input data is raw image data, and the output data is preprocessed image data.

[0362] Step 3:

[0363] The server inputs the preprocessed image data into a generative AI model. This AI model uses TensorFlow and PyTorch to identify plants. Specifically, the preprocessed image data is input into the AI ​​model to identify the plant species. The input data is the preprocessed image data, and the output data is the species name of the identified plant. For example, "Erythronium japonicum" may be identified.

[0364] Step 4:

[0365] The server evaluates the AI ​​model's classification results and generates a warning message if an endangered plant is detected. Specifically, it evaluates whether the AI ​​model's output matches the species of the endangered plant. The input data is the species name of the identified plant, and the output data is a warning message. For example, a message saying "Endangered plant detected" is generated.

[0366] Step 5:

[0367] The server sends the generated warning message along with coordinate information and the voice announcement to the grass-cutting robot. Specifically, the warning message is accompanied by the plant species name, location information, and voice announcement, and then sent to the grass-cutting robot. The input data is the warning message and other supplementary information, and the output data is a data packet sent to the grass-cutting robot.

[0368] Step 6:

[0369] The grass-cutting robot receives the warning message sent from the server. Based on the received message, the grass-cutting robot makes an audio announcement through a speaker, notifying the user that "an endangered plant has been detected." The control device then stops the grass-cutting robot. The input data is the warning message from the server, and the output data is the execution of the audio announcement and the stopping of the grass-cutting robot.

[0370] Step 7:

[0371] The grass-cutting robot displays a visual warning on the display. Specifically, it displays "Endangered plants have been detected" on the display to notify the user. The input data is the warning message from the server, and the output data is the warning message displayed on the display.

[0372] Step 8:

[0373] The server receives the user's facial expression data from the emotion engine and analyzes it in real time. The emotion engine obtains the user's emotional data and produces an analysis result. The input data is the user's facial expression data, and the output data is the analyzed emotional data. For example, the emotion "surprise" is detected.

[0374] Step 9:

[0375] The server evaluates the user's stress level based on the analyzed emotional data. Based on the results of the data analysis, it determines whether the user has a high or low stress level. The input data is the analyzed emotional data, and the output data is the user's stress level.

[0376] Step 10:

[0377] If the user's stress level is high, the server sends instructions to the grass-cutting robot to adjust its speed. It also adjusts the tone of the voice announcement according to the user's emotions. Specifically, it uses the Google Text-to-Speech API to instruct the robot to generate a gentler voice announcement. The input data is the user's stress level and emotional data, and the output data is the speed instruction and voice announcement setting information sent to the grass-cutting robot.

[0378] Step 11:

[0379] The user checks the warning message displayed on the display of the mowing robot. They check the presence of plants on-site and adjust the position of the mowing robot as necessary. For example, they manually change the robot's position or use a remote control to specify a new work area. The input data is the warning message displayed on the display, and the output data is the user's instructions to operate the robot.

[0380] (Application example 2)

[0381] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0382] Conventional grass-cutting robots are specialized in the function of mowing grass, and do not sufficiently consider environmental protection or the user's emotional state. As a result, there is a high risk of accidentally mowing endangered plants, and they lack measures to reduce user stress and anxiety. To address these issues, the present invention aims to optimize the working environment according to the user's emotional state while identifying and protecting endangered plants.

[0383] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data to generate preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop mowing; means for receiving user emotion data, equipped with an emotion engine that analyzes user emotions; means for adjusting the tone and content of the voice announcement based on the emotion data; and means for adjusting the operating speed of the mowing robot based on the emotion data. This enables appropriate protection of endangered plants and provides an optimal working environment according to the user's emotional state.

[0384] A "grass-cutting robot" is an autonomous mechanical device that has the function of cutting grass and is capable of acquiring and analyzing images of the environment in real time.

[0385] The "camera" is a photographing device for acquiring image data, and is mounted on the grass-cutting robot to photograph the environment and the user's facial expression in real time.

[0386] "Preprocessing" refers to the process of optimizing acquired image data for analysis by methods such as resizing and noise reduction.

[0387] A "generative AI model" is an artificial intelligence model used to identify plants based on preprocessed image data.

[0388] A "warning message" is notification information generated when the generative AI model identifies an endangered plant.

[0389] "Voice announcement" is a function in which the grass-cutting robot conveys information to the user by voice based on the results of the generated AI model.

[0390] An "emotion engine" is an analysis device that analyzes a user's facial expressions to assess their emotional state.

[0391] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[0392] "Movement speed adjustment" is a function that changes the movement speed of the grass-cutting robot based on the user's emotional data.

[0393] This invention is a system for a grass-cutting robot that identifies endangered plants in real time and optimizes the working environment by recognizing the user's emotions. This system includes the following hardware and software:

[0394] 1. Hardware configuration:

[0395] Grass-cutting robot: It has a grass-cutting function and is equipped with a camera, speaker, display, emotion engine, and control device.

[0396] Camera: Captures the environment and user's facial expressions in real time.

[0397] Server: A centralized device that processes image data, runs AI models, and evaluates emotion data.

[0398] Speaker: Makes voice announcements to notify users.

[0399] Display: Displays a visual warning message.

[0400] 2. Software configuration:

[0401] Generative AI model: Identifies plants based on preprocessed image data.

[0402] Emotion engine: Software for analyzing the user's emotional state.

[0403] Control device: Controls the movement of the grass-cutting robot. Adjusts the movement speed based on instructions from the server.

[0404] 3. Data processing and calculation:

[0405] Image acquisition and preprocessing: The server acquires image data from the grass-cutting robot's camera in real time, and performs resizing and noise reduction.

[0406] Execution of the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants. Based on the identification results, endangered plants are identified and necessary warning messages are generated.

[0407] Emotion data evaluation: The emotion engine analyzes the user's facial expressions in real time to obtain emotional data, which is then used to adjust the tone and content of voice announcements and optimize the grass-cutting robot's operating speed.

[0408] 4. Example:

[0409] Example 1: A grass-cutting robot detects an endangered plant called "Erythronium japonicum" and sends an image to the server. At the same time, the emotion engine analyzes the user's facial expression to determine whether they are surprised. The server issues a voice announcement in a calm tone saying, "An endangered plant has been detected. Please remain calm," stops the grass-cutting robot's operation, and displays a warning message on the display.

[0410] Example prompt sentence:

[0411] "Write a Python program that implements the following system:

[0412] It preprocesses images captured by the camera and uses an AI model to identify emergencies. It analyzes the user's emotions and, if an emergency is detected, issues an appropriate warning voice message and stops the robot's operation. It also displays a warning message on the display.

[0413] As a result, the present invention can realize appropriate protection of endangered plants and provide an optimal working environment according to the user's emotional state.

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] The camera on the grass-cutting robot captures images of the environment and the user's facial expressions in real time, providing image data of the current working area and the user's emotional data as input.

[0417] Step 2:

[0418] The server receives image data acquired from the grass-cutting robot's camera. The received image data is resized and noise-reduced. This preprocessing results in preprocessed image data suitable for the generative AI model.

[0419] Step 3:

[0420] The server inputs the preprocessed image data into the generative AI model, which analyzes the preprocessed image data and extracts plant characteristics. As a result, a plant identification result is output, and a determination is made as to whether an endangered plant has been detected.

[0421] Step 4:

[0422] Based on the results of the generative AI model, the server generates a warning message when an endangered plant is detected. The warning message includes the results of the detection, coordinate information, and a voice announcement.

[0423] Step 5:

[0424] The server transmits the generated warning message to the grass-cutting robot, which receives the transmitted warning message.

[0425] Step 6:

[0426] Based on the warning message received, the grass-cutting robot will make an audio announcement through its speaker saying, "Endangered plants have been detected," and will simultaneously stop the grass-cutting robot's operation. At this point, the grass-cutting robot's blades will stop moving, protecting the endangered plants.

[0427] Step 7:

[0428] The user's facial expression data is analyzed by the emotion engine on the server, which evaluates the user's emotional state (e.g., surprise, joy, anger, sadness) from the user's facial expression and outputs the result.

[0429] Step 8:

[0430] Based on the user's emotional data obtained from the emotion engine, the server adjusts the tone and content of the voice announcements. If the user's stress level is high, the server slows down the grass-cutting robot's movement speed. This process provides information in a way that is easy for the user to accept, resulting in an output that optimizes the work environment.

[0431] Step 9:

[0432] The mowing robot will again notify the user with a tailored audio tone and show a visual warning message on the display, allowing the user to quickly understand the current situation and take appropriate action.

[0433] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0439] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0441] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0443] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0444] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0445] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0446] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0448] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0449] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0450] Grass-cutting robot configuration

[0451] The grass-cutting robot consists of the following main components:

[0452] Camera: Acquires high-resolution image data in real time.

[0453] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0454] Speaker: A device for making voice announcements.

[0455] Display: A monitor for displaying visual warnings.

[0456] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0457] Server processing

[0458] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0459] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0460] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[0461] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[0462] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0463] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0464] Voice announcement and termination process

[0465] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0466] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0467] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0468] User response

[0469] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0470] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0471] Specific examples

[0472] Here, we will provide a specific example. As an example, we will explain the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0473] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0474] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0475] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0476] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0477] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0478] Visual warning: The display will say "Endangered plant detected."

[0479] User confirmation: The user checks the site and repositions the robot if necessary.

[0480] Through the above process flow, the present invention accurately identifies endangered plants during mowing work and reduces the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[0484] Step 2:

[0485] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[0486] Step 3:

[0487] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[0488] Step 4:

[0489] The server evaluates the results of the generated AI model, checking the confidence score included in the results and determining that an endangered plant is present if it exceeds a set threshold.

[0490] Step 5:

[0491] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[0492] Step 6:

[0493] The server sends the generated warning message to the grass-cutting robot (terminal). This message includes instructions for the grass-cutting robot's control device and audio speaker.

[0494] Step 7:

[0495] The terminal (grass-cutting robot) receives a warning message from the server. At the same time, the audio speaker announces, "An endangered plant has been detected."

[0496] Step 8:

[0497] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[0498] Step 9:

[0499] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected," along with information about the type of plant and its location.

[0500] Step 10:

[0501] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[0502] Step 11:

[0503] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[0504] Step 12:

[0505] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[0506] Example 1

[0507] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] The present invention aims to reduce the risk of accidentally mowing endangered plants during mowing work. In particular, it provides a system that allows a mowing robot to operate autonomously, identify endangered plants in real time, and issue warnings and stop operations as necessary, thereby solving the problem of achieving both efficient mowing work and environmental protection.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0510] In this invention, the server includes means for acquiring image data in real time using a camera mounted on the mowing robot, means for processing the image data to generate preprocessed image data, means for executing a generative AI model that identifies plants based on the preprocessed image data, means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected, means for transmitting the warning message to the mowing robot, means for the mowing robot to make an audio announcement and automatically stop mowing operation upon receiving the warning message, means for the mowing robot to display a visual warning regarding the detection of an endangered plant, means for the mowing robot to display on a display so that the user can confirm the location where the endangered plant was detected by the mowing robot, and means for the user to adjust the position of the mowing robot and indicate a new working area. This enables the mowing robot to identify endangered plants in real time and prevent them from being mowed by mistake.

[0511] A "grass-cutting robot" is a mechanical device for automatically cutting grass. It is equipped with a high-resolution camera and a control device, and operates based on instructions from a server.

[0512] A "camera" is a photographing device that captures image data in real time and transmits high-resolution images to a server.

[0513] "Preprocessed image data" refers to raw image data received by the server that has been preprocessed, such as by resizing and reducing noise, to convert it into a form that is easy to input into an AI model.

[0514] A "generative AI model" is a machine learning model used to extract plant characteristics based on preprocessed image data and calculate the degree of match with known endangered species.

[0515] "Evaluation of discrimination results" is the process of analyzing the results of plant identification by the generative AI model and determining whether any endangered plants are included.

[0516] A "warning message" is a message, including an audio announcement and a visual warning, that is generated by the server and sent to the grass-cutting robot when an endangered plant is detected.

[0517] The "voice announcement" is a voice message that is sent to the user through a speaker when the grass-cutting robot receives a warning message.

[0518] "Stopping the mowing operation" refers to an operation in which the control device of the mowing robot stops the blade of the mower to prevent the mowing of endangered plants.

[0519] A "visual warning" is a message that appears on the grass-cutting robot's display indicating that an endangered plant has been detected.

[0520] "Display" refers to a display device mounted on the grass-cutting robot that provides visual warnings and warning messages to the user.

[0521] A "user" is a person who operates and manages the grass-cutting robot, checks warning messages, and adjusts the robot's position as needed.

[0522] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0523] Grass-cutting robot configuration

[0524] The grass-cutting robot consists of the following main components:

[0525] Camera: Acquires high-resolution image data in real time.

[0526] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0527] Speaker: A device for making voice announcements.

[0528] Display: A monitor for displaying visual warnings.

[0529] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0530] Server processing

[0531] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0532] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0533] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal format for use with the AI ​​model. Software such as OpenCV is used.

[0534] 3. Running the AI ​​model: The preprocessed image data is input into a generative AI model (e.g., TensorFlow, PyTorch) to identify plants.

[0535] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0536] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0537] Voice announcement and termination process

[0538] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0539] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0540] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0541] User response

[0542] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0543] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0544] Specific examples

[0545] The following explains the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0546] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0547] Preprocessing: The server resizes the image data and removes noise before passing it to the generative AI model.

[0548] Execution of the AI ​​model: The generative AI model analyzes the image and confirms that it is a "dogtooth violet."

[0549] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0550] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0551] Visual warning: The display will say "Endangered plant detected."

[0552] User confirmation: The user checks the site and resets the position of the grass cutting robot if necessary.

[0553] Example prompts for generative AI models

[0554] "Real-time identification of endangered plants using high-resolution imagery"

[0555] "A method for safely detecting endangered plants from preprocessed image data and reducing the risk of mis-harvesting"

[0556] In this way, the present invention can accurately identify endangered plants during mowing work and reduce the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0557] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0558] Step 1:

[0559] Image acquisition

[0560] The server receives image data from the grass-cutting robot's camera in real time. Specifically, high-resolution images taken by the grass-cutting robot's camera are sent to the server via the Internet. The input data is raw image data, and the output is image data stored on the server.

[0561] Step 2:

[0562] Image preprocessing

[0563] The server pre-processes the received image data. This process involves the following steps:

[0564] Resizing: Reduce or increase the image resolution to fit the AI ​​model. For example, resize a 1024x768 pixel image to 256x256 pixel.

[0565] Noise Reduction: Reduces noise in the image. Apply noise reduction algorithms to reduce the effects of glare and shadows.

[0566] The input data is raw image data stored on the server, and the output is pre-processed image data that has been resized and noise-reduced.

[0567] Step 3:

[0568] Image analysis using AI models

[0569] The server inputs the preprocessed image data into a generative AI model. Specifically, a generative AI model (such as TensorFlow or PyTorch) is used to identify plants in the image. This AI model has been trained in advance using a large amount of plant image data. The input data is the preprocessed image data, and the output is the name and species information of the identified plants. For example, the AI ​​model recognizes the shape and color of dogtooth violets leaves and identifies their species.

[0570] Step 4:

[0571] Evaluation of the discrimination results

[0572] The server evaluates the output of the AI ​​model. Specifically, if the AI ​​output identifies the plant as "dogtooth violet," the evaluation engine checks the information and determines whether it meets the criteria for issuing an alert. The input data is the identification result by the AI ​​model, and the output is the decision to issue an alert.

[0573] Step 5:

[0574] Generate and send warning messages

[0575] The server sends a warning message to the grass-cutting robot. Specifically, it sends a digital message such as "Endangered plants have been detected." This message is received by the control device and triggers subsequent actions. The input data is evaluation information based on the discrimination results, and the output is a warning message.

[0576] Step 6:

[0577] Voice announcement and mower stopping

[0578] The grass-cutting robot will take the following actions based on the warning message received from the server:

[0579] Voice announcement: An announcement is made through the speaker saying "Endangered plants detected." This function is implemented using a TTS (text-to-speech) engine. The input data is a warning message, and the output is a voice announcement.

[0580] Stopping the mower: The control unit stops the mower blade. By stopping operation immediately, endangered plants are protected. The input data is a warning message, and the output is the mower stopping action.

[0581] Step 7:

[0582] Visual warning signs

[0583] A warning message is displayed on the display of the grass-cutting robot. The user is notified of the situation by displaying "Endangered plants detected." The input data is the warning message, and the output is a visual warning display.

[0584] Step 8:

[0585] User confirmation and response

[0586] The user sees the warning message on the display and takes the following specific action:

[0587] Field survey: The user actually visits the site and checks the status of endangered plants. The input data is a visual warning display, and the output is the user's confirmation and response.

[0588] Operation instructions: Adjust the position of the grass-cutting robot as needed and indicate a new work area. Typically, the user operates the robot using a remote controller or terminal. The input data are visual warnings and the results of the on-site inspection, and the output is an indication of a new work area.

[0589] The above are the specific processing steps of the program of this system.

[0590] (Application example 1)

[0591] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0592] In conventional logistics centers, the identification and management of sensitive packages and expensive items was insufficient due to human error, resulting in mishandling and damage. Furthermore, workers were not given sufficient warnings, resulting in lower customer satisfaction and lower efficiency in logistics operations. To solve these problems, a system is needed that can identify specific items in real time and issue appropriate warnings to workers.

[0593] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0594] In this invention, the server includes means for acquiring image data, means for generating pre-processed image data, and means for executing a generative AI model for identifying items based on the pre-processed image data, thereby enabling real-time identification of specific items and providing audio announcements and visual alerts.

[0595] A "grass-mowing robot" is a mechanical device for automatically mowing grass, and in the present invention includes a camera, a control device, and the like.

[0596] A "camera" is a photographing device for acquiring image data in real time.

[0597] "Image data" is data containing visual information acquired by a camera.

[0598] "Preprocessing" refers to a processing step for converting acquired image data into an appropriate form, including resizing and noise reduction.

[0599] A "generative AI model" is an artificial intelligence model that is trained to perform a specific task (e.g., identifying objects or plants).

[0600] "Discrimination results" are the results of analysis and identification output by the generative AI model.

[0601] A "warning message" is notification information that is generated when a specific condition (eg, the detection of an endangered plant) is met.

[0602] "Voice announcement" is a function that provides information through voice, and is used to convey warning messages and the like to users.

[0603] A "visual warning" is warning information that is visually displayed on a display or the like.

[0604] The "logistics sector" is a range of activities that includes the transportation, warehousing, and distribution of goods.

[0605] A "specific item" is an item that has special conditions (e.g., requires careful handling, is expensive) and is the subject of identification in the present invention.

[0606] The present invention is a system that uses image analysis technology to identify and warn about specific items in a logistics center, and applies technology related to grass-cutting robots. Details are provided below.

[0607] composition

[0608] The system consists of the following main components:

[0609] Smart glasses: devices equipped with high-resolution cameras and displays for capturing image data in real time.

[0610] Server: A device that processes acquired image data and uses a generative AI model to identify specific items.

[0611] Voice announcement device: A device built into smart glasses that notifies users of warning messages by voice.

[0612] Visual warning display: A function that provides visual warnings through the smart glasses display.

[0613] Processing steps

[0614] The smart glasses capture image data in real time within the logistics center and send it to a server. The server preprocesses the image data and inputs it into a generative AI model. The generative AI model analyzes it and identifies specific items. Based on the identification results, the server generates a warning message and sends it to the smart glasses. The smart glasses notify workers by audio announcements and visual warning displays.

[0615] Hardware and Software Used

[0616] Hardware:

[0617] Smart glasses (e.g., with a high-resolution camera and display)

[0618] Server (with high-performance processing capabilities)

[0619] software:

[0620] TensorFlow: Used to run generative AI models

[0621] OpenCV: Used for image acquisition and pre-processing

[0622] pyttsx3: Used for voice announcements

[0623] Specific examples

[0624] Suppose a worker at a logistics center is inspecting packages and the smart glasses detect a box with a "Handle with Care" sticker. In this case, the smart glasses take a photo of the box with their camera and send the image to the server. The server preprocesses the image data and passes it to a generative AI model to identify whether it is a specific item. If the identification result is that the box is a "Handle with Care" box, the server generates a warning message and sends it to the smart glasses. The smart glasses then notify the worker via audio and visual means that "handle with care package has been detected."

[0625] Prompt Sentence Examples

[0626] To detect sensitive items in the logistics center, the smart glasses' camera captures images in real time and analyzes them using AI models. If a specific item is detected, a voice announcement and visual warning are issued.

[0627] By implementing the present invention in this manner, specific items can be quickly and accurately identified at the logistics center, and it is expected that mishandling and damage can be prevented.

[0628] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0629] Step 1:

[0630] The smart glasses use cameras in the logistics center to capture image data in real time. The input is visual information from the site, and the output is high-resolution image data.

[0631] Step 2:

[0632] The smart glasses transmit the acquired image data to the server. The input is high-resolution image data, and the output is the transmission of image data to the server.

[0633] Step 3:

[0634] The server preprocesses the image data received. Specifically, it uses OpenCV to resize the image data, reduce noise, etc. The input is raw image data, and the output is preprocessed image data.

[0635] Step 4:

[0636] The server inputs the preprocessed image data into a generative AI model to identify specific items. The AI ​​model is run using TensorFlow to extract the features of the item and calculate the degree of match with known specific items. The input is the preprocessed image data, and the output is the classification result.

[0637] Step 5:

[0638] The server evaluates the discrimination results of the generated AI model and generates a warning message if a specific item is detected. The input is the discrimination result from the AI ​​model, and the output is the warning message.

[0639] Step 6:

[0640] The server sends a warning message to the smart glasses. The input is the warning message and the output is the transmission to the smart glasses.

[0641] Step 7:

[0642] The smart glasses make a voice announcement based on the received warning message and, if necessary, display a visual warning on the display. The input is the warning message received from the server, and the output is the voice announcement and the visual warning display.

[0643] Step 8:

[0644] The user listens to the audio announcement and visual warning and takes necessary action. The input is the warning from the smart glasses, and the output is the user's response action.

[0645] The above steps realize a system that smoothly identifies specific items and issues warnings in a logistics center.

[0646] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0647] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[0648] Grass-cutting robot configuration

[0649] The grass-cutting robot consists of the following main components:

[0650] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[0651] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0652] Speaker: A device for making voice announcements.

[0653] Display: A monitor for displaying visual warnings.

[0654] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[0655] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0656] Server processing

[0657] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. The specific roles of the server are as follows:

[0658] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0659] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[0660] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[0661] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0662] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0663] 6. Emotion data evaluation: Evaluate the user's emotion data sent from the emotion engine and determine the optimal action according to the user's state.

[0664] Voice announcement and termination process

[0665] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it will make a voice announcement through the speaker saying, "Endangered plants have been detected." The tone and content of the announcement will be adjusted to match the user's emotional state based on the evaluation of the emotion engine.

[0666] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0667] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0668] User response

[0669] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0670] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0671] Emotion Engine Operation

[0672] The emotion engine analyzes the user's facial expression data in real time to obtain information such as:

[0673] Type of emotion (e.g., surprise, joy, anger, sadness)

[0674] Stress Level: Evaluates the user's stress state.

[0675] The data obtained by the emotion engine is sent to the server, which then takes the following actions:

[0676] 1. Adjusting the operating speed: If the user's stress level is high, the server will slow down the operating speed of the grass-cutting robot.

[0677] 2. Adjusting voice announcements: Change the tone and content of voice announcements depending on the emotion, providing information in a way that is easy for users to accept.

[0678] Specific examples

[0679] Here, a specific example will be given. As an example, a processing flow will be described for a case where a grass-cutting robot detects an endangered plant called "dogtooth violet" during work and at the same time the user expresses surprise.

[0680] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[0681] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0682] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0683] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[0684] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[0685] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[0686] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[0687] Through the above process flow, the present invention allows the user to appropriately identify endangered plants during weeding work and to work in an environment that takes into consideration the user's emotional state, thereby further contributing to forest development and environmental protection.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[0691] Step 2:

[0692] The server receives the user's facial expression data from the grass-cutting robot, which is also stored in a receiving buffer.

[0693] Step 3:

[0694] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[0695] Step 4:

[0696] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[0697] Step 5:

[0698] The server evaluates the results of the generated AI model, checks the confidence score included in the results, and determines that an endangered plant is present if it exceeds a set threshold.

[0699] Step 6:

[0700] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[0701] Step 7:

[0702] Based on the user's facial expression data received by the server, the emotion engine analyzes the user's emotions and identifies the type of emotion (e.g., surprise, joy, anger, sadness) and stress level from the user's facial expression.

[0703] Step 8:

[0704] The server sends the generated warning message and the emotion engine's analysis results to the grass-cutting robot (terminal), including instructions to the grass-cutting robot's control device and audio speaker.

[0705] Step 9:

[0706] The terminal (grass-cutting robot) receives the warning message from the server and the analysis results of the emotion engine. At the same time, the voice speaker announces, "Endangered plants have been detected." The tone and speed of the voice are adjusted based on the analysis results of the emotion engine.

[0707] Step 10:

[0708] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[0709] Step 11:

[0710] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected." The warning message includes information about the type of plant and its location.

[0711] Step 12:

[0712] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[0713] Step 13:

[0714] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[0715] Step 14:

[0716] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[0717] Example 2

[0718] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0719] The present invention aims to simultaneously protect endangered plants during weeding work and optimize the work environment by taking into account the user's emotional state. In particular, conventional weed-mowing robots lack sufficient accuracy in identifying plants and responding to the user's emotional state, resulting in problems such as accidentally mowing endangered plants and increasing user stress. To solve this problem, a system is needed that can accurately recognize the emotions of plants and the user in real time and automatically adjust tasks based on that information.

[0720] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data and generating preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make an audio announcement and automatically stop mowing operations; means for the mowing robot to display a visual warning regarding the detection of an endangered plant; means including an emotion engine that receives and analyzes user facial expression data; means for evaluating the user's emotion based on the generated emotion data; and means for adjusting the operating speed of the mowing robot and changing the tone of the audio announcement in accordance with the user's emotional state. This makes it possible to optimize the work environment while taking into consideration the protection of endangered plants and the user's emotional state.

[0721] 1. A "grass-cutting robot" is an autonomous robotic device equipped with a mechanical part for cutting grass, a camera for identifying the emotions of plants and the user, a speaker for making voice announcements, a display for showing visual warnings, and a control device for controlling its operation.

[0722] 2. "Camera" refers to a high-resolution image capture device that is installed on the grass-cutting robot and that captures image data in real time and recognizes the user's facial expressions.

[0723] 3. A "generative AI model" is an artificial intelligence model that inputs preprocessed image data and performs plant identification, and is constructed using, for example, a deep learning framework.

[0724] 4. "Preprocessed image data" refers to image data that has been processed, such as by resizing or noise reduction, on the raw data sent from the grass-cutting robot.

[0725] 5. "Warning message" means a message that is generated and sent to a grass-cutting robot when an endangered plant is detected, and includes content such as "An endangered plant has been detected."

[0726] 6. "Emotion Engine" means an engine that receives and analyzes a user's facial expression data and is a software or hardware component for evaluating the user's emotions.

[0727] 7. "Emotion data" refers to the results of analysis by the emotion engine based on the user's facial expression data, and includes emotional information such as surprise, joy, anger, and sadness.

[0728] 8. "Visual warning" means a warning message that appears on a display mounted on the grass-cutting robot, visually notifying the user of the detection of an endangered plant.

[0729] 9. "User's Emotional State" means the type and intensity of a user's emotion based on the results of analysis by the user's emotion engine.

[0730] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[0731] Grass-cutting robot configuration

[0732] The grass-cutting robot consists of the following main components:

[0733] Camera: Captures high-resolution image data in real time. It also doubles as a camera for recognizing the user's facial expressions.

[0734] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0735] Speaker: A device for making voice announcements.

[0736] Display: A monitor for displaying visual warnings.

[0737] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[0738] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0739] Server processing

[0740] The server receives image data sent from the grass-cutting robot and analyzes it using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. Specific hardware used is a high-performance server, and software such as TensorFlow, PyTorch, and OpenCV is used.

[0741] Image data processing

[0742] After receiving the raw data from the grass-cutting robot, the server first resizes it and performs noise reduction. This preprocessing uses OpenCV, for example, resizes the image to (224x224) pixels, and applies a noise reduction filter.

[0743] Plant identification using AI models

[0744] The preprocessed image data is then fed into a generative AI model using deep learning frameworks such as TensorFlow and PyTorch. This AI model is trained to recognize endangered plants, for example, the dogtooth violet.

[0745] Generate and send warning messages

[0746] If the AI ​​model evaluates the identification results and confirms that an endangered plant is present, the server generates a warning message stating "Endangered plant detected" and sends it to the grass-cutting robot. The warning message includes the plant's species name, location information, and a voice announcement.

[0747] Emotional Data Evaluation

[0748] The emotion engine receives the user's facial expression data, analyzes it, and evaluates the user's emotional state. For example, a facial recognition service is used for the emotion engine. By sending the evaluation results to the server, the server can respond according to the user's emotions.

[0749] Adjusting movement speed and voice announcements

[0750] If the user's emotional state indicates a high stress level, the server instructs the grass-cutting robot to slow down. The tone and content of the voice announcements are also adjusted according to the user's emotions. Specifically, the announcement content is generated using the Google Text-to-Speech API.

[0751] Audio announcements and visual warnings

[0752] When the grass-cutting robot receives a warning message, it will make a voice announcement saying "Endangered plants detected" and stop the grass-cutting robot's operation. In addition, a visual warning message saying "Endangered plants detected" will appear on the display.

[0753] User response

[0754] The user checks the warning message on the display, checks the plants on-site, and then adjusts the position of the mowing robot as needed to indicate a new working area.

[0755] Specific examples

[0756] Here is a concrete example. For example, if a grass-cutting robot detects an endangered plant called "dogtooth violet" while working, and the user expresses surprise, the processing flow is as follows:

[0757] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[0758] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0759] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0760] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[0761] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[0762] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[0763] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[0764] Prompt Sentence Examples

[0765] "The grass-cutting robot has just detected an endangered plant called 'dogtooth violet'. At the same time, the emotion engine has recognized that the user has a surprised expression. Please generate the next instruction based on this situation."

[0766] Thus, the present invention aims to provide a system that identifies endangered plants during weeding work and optimizes the work environment according to the user's emotional state, thereby contributing to forest development and environmental protection.

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1:

[0769] The server acquires image data in real time from the camera on the mowing robot. The input data is a high-resolution image sent from the camera on the mowing robot. The server receives this image data and simultaneously acquires the user's facial expression data captured by the camera on the mowing robot. The output data is the raw image data and facial expression data received by the server.

[0770] Step 2:

[0771] The server preprocesses the received image data. For preprocessing, it uses OpenCV to resize the image and reduce noise. Specifically, it resizes the image to (224x224) pixels and removes noise using a filter. The input data is raw image data, and the output data is preprocessed image data.

[0772] Step 3:

[0773] The server inputs the preprocessed image data into a generative AI model. This AI model uses TensorFlow and PyTorch to identify plants. Specifically, the preprocessed image data is input into the AI ​​model to identify the plant species. The input data is the preprocessed image data, and the output data is the species name of the identified plant. For example, "Erythronium japonicum" may be identified.

[0774] Step 4:

[0775] The server evaluates the AI ​​model's classification results and generates a warning message if an endangered plant is detected. Specifically, it evaluates whether the AI ​​model's output matches the species of the endangered plant. The input data is the species name of the identified plant, and the output data is a warning message. For example, a message saying "Endangered plant detected" is generated.

[0776] Step 5:

[0777] The server sends the generated warning message along with coordinate information and the voice announcement to the grass-cutting robot. Specifically, the warning message is accompanied by the plant species name, location information, and voice announcement, and then sent to the grass-cutting robot. The input data is the warning message and other supplementary information, and the output data is a data packet sent to the grass-cutting robot.

[0778] Step 6:

[0779] The grass-cutting robot receives the warning message sent from the server. Based on the received message, the grass-cutting robot makes an audio announcement through a speaker, notifying the user that "an endangered plant has been detected." The control device then stops the grass-cutting robot. The input data is the warning message from the server, and the output data is the execution of the audio announcement and the stopping of the grass-cutting robot.

[0780] Step 7:

[0781] The grass-cutting robot displays a visual warning on the display. Specifically, it displays "Endangered plants have been detected" on the display to notify the user. The input data is the warning message from the server, and the output data is the warning message displayed on the display.

[0782] Step 8:

[0783] The server receives the user's facial expression data from the emotion engine and analyzes it in real time. The emotion engine obtains the user's emotional data and produces an analysis result. The input data is the user's facial expression data, and the output data is the analyzed emotional data. For example, the emotion "surprise" is detected.

[0784] Step 9:

[0785] The server evaluates the user's stress level based on the analyzed emotional data. Based on the results of the data analysis, it determines whether the user has a high or low stress level. The input data is the analyzed emotional data, and the output data is the user's stress level.

[0786] Step 10:

[0787] If the user's stress level is high, the server sends instructions to the grass-cutting robot to adjust its speed. It also adjusts the tone of the voice announcement according to the user's emotions. Specifically, it uses the Google Text-to-Speech API to instruct the robot to generate a gentler voice announcement. The input data is the user's stress level and emotional data, and the output data is the speed instruction and voice announcement setting information sent to the grass-cutting robot.

[0788] Step 11:

[0789] The user checks the warning message displayed on the display of the mowing robot. They check the presence of plants on-site and adjust the position of the mowing robot as necessary. For example, they manually change the robot's position or use a remote control to specify a new work area. The input data is the warning message displayed on the display, and the output data is the user's instructions to operate the robot.

[0790] (Application example 2)

[0791] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] Conventional grass-cutting robots are specialized in the function of mowing grass, and do not sufficiently consider environmental protection or the user's emotional state. As a result, there is a high risk of accidentally mowing endangered plants, and they lack measures to reduce user stress and anxiety. To address these issues, the present invention aims to optimize the working environment according to the user's emotional state while identifying and protecting endangered plants.

[0793] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data to generate preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop mowing; means for receiving user emotion data, equipped with an emotion engine that analyzes user emotions; means for adjusting the tone and content of the voice announcement based on the emotion data; and means for adjusting the operating speed of the mowing robot based on the emotion data. This enables appropriate protection of endangered plants and provides an optimal working environment according to the user's emotional state.

[0794] A "grass-cutting robot" is an autonomous mechanical device that has the function of cutting grass and is capable of acquiring and analyzing images of the environment in real time.

[0795] The "camera" is a photographing device for acquiring image data, and is mounted on the grass-cutting robot to photograph the environment and the user's facial expression in real time.

[0796] "Preprocessing" refers to the process of optimizing acquired image data for analysis by methods such as resizing and noise reduction.

[0797] A "generative AI model" is an artificial intelligence model used to identify plants based on preprocessed image data.

[0798] A "warning message" is notification information generated when the generative AI model identifies an endangered plant.

[0799] "Voice announcement" is a function in which the grass-cutting robot conveys information to the user by voice based on the results of the generated AI model.

[0800] An "emotion engine" is an analysis device that analyzes a user's facial expressions to assess their emotional state.

[0801] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[0802] "Movement speed adjustment" is a function that changes the movement speed of the grass-cutting robot based on the user's emotional data.

[0803] This invention is a system for a grass-cutting robot to identify endangered plants in real time and optimize the working environment by recognizing the user's emotions. This system includes the following hardware and software:

[0804] 1. Hardware configuration:

[0805] Grass-cutting robot: It has a grass-cutting function and is equipped with a camera, speaker, display, emotion engine, and control device.

[0806] Camera: Captures the environment and user's facial expressions in real time.

[0807] Server: A centralized device that processes image data, runs AI models, and evaluates emotion data.

[0808] Speaker: Makes voice announcements to notify users.

[0809] Display: Displays a visual warning message.

[0810] 2. Software configuration:

[0811] Generative AI model: Identifies plants based on preprocessed image data.

[0812] Emotion engine: Software for analyzing the user's emotional state.

[0813] Control device: Controls the movement of the grass-cutting robot. Adjusts the movement speed based on instructions from the server.

[0814] 3. Data processing and calculation:

[0815] Image acquisition and preprocessing: The server acquires image data from the grass-cutting robot's camera in real time, and performs resizing and noise reduction.

[0816] Execution of the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants. Based on the identification results, endangered plants are identified and necessary warning messages are generated.

[0817] Emotion data evaluation: The emotion engine analyzes the user's facial expressions in real time to obtain emotional data, which is then used to adjust the tone and content of voice announcements and optimize the grass-cutting robot's operating speed.

[0818] 4. Example:

[0819] Example 1: A grass-cutting robot detects an endangered plant called "Erythronium japonicum" and sends an image to the server. At the same time, the emotion engine analyzes the user's facial expression to determine whether they are surprised. The server issues a voice announcement in a calm tone saying, "An endangered plant has been detected. Please remain calm," stops the grass-cutting robot's operation, and displays a warning message on the display.

[0820] Example prompt sentence:

[0821] "Write a Python program that implements the following system:

[0822] It preprocesses images captured by the camera and uses an AI model to identify emergencies. It analyzes the user's emotions and, if an emergency is detected, issues an appropriate warning voice message and stops the robot's operation. It also displays a warning message on the display.

[0823] As a result, the present invention can realize appropriate protection of endangered plants and provide an optimal working environment according to the user's emotional state.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] The camera on the grass-cutting robot captures images of the environment and the user's facial expressions in real time, providing image data of the current working area and the user's emotional data as input.

[0827] Step 2:

[0828] The server receives image data acquired from the grass-cutting robot's camera. The received image data is resized and noise-reduced. This preprocessing results in preprocessed image data suitable for the generative AI model.

[0829] Step 3:

[0830] The server inputs the preprocessed image data into the generative AI model, which analyzes the preprocessed image data and extracts plant characteristics. As a result, a plant identification result is output, and a determination is made as to whether an endangered plant has been detected.

[0831] Step 4:

[0832] Based on the results of the generative AI model, the server generates a warning message when an endangered plant is detected. The warning message includes the results of the detection, coordinate information, and a voice announcement.

[0833] Step 5:

[0834] The server transmits the generated warning message to the grass-cutting robot, which receives the transmitted warning message.

[0835] Step 6:

[0836] Based on the warning message received, the grass-cutting robot will make an audio announcement through its speaker saying, "Endangered plants have been detected," and will simultaneously stop the grass-cutting robot's operation. At this point, the grass-cutting robot's blades will stop moving, protecting the endangered plants.

[0837] Step 7:

[0838] The user's facial expression data is analyzed by the emotion engine on the server, which evaluates the user's emotional state (e.g., surprise, joy, anger, sadness) from the user's facial expression and outputs the result.

[0839] Step 8:

[0840] Based on the user's emotional data obtained from the emotion engine, the server adjusts the tone and content of the voice announcements. If the user's stress level is high, the server slows down the grass-cutting robot's movement speed. This process provides information in a way that is easy for the user to accept, resulting in an output that optimizes the work environment.

[0841] Step 9:

[0842] The mowing robot will again notify the user with a tailored audio tone and show a visual warning message on the display, allowing the user to quickly understand the current situation and take appropriate action.

[0843] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0844] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0846] [Third embodiment]

[0847] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0848] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0849] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0850] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0851] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0852] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0853] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0854] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0855] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0856] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0857] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0858] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0859] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0860] Grass-cutting robot configuration

[0861] The grass-cutting robot consists of the following main components:

[0862] Camera: Acquires high-resolution image data in real time.

[0863] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0864] Speaker: A device for making voice announcements.

[0865] Display: A monitor for displaying visual warnings.

[0866] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0867] Server processing

[0868] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0869] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0870] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[0871] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[0872] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0873] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0874] Voice announcement and termination process

[0875] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0876] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0877] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0878] User response

[0879] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0880] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0881] Specific examples

[0882] Here, we will provide a specific example. As an example, we will explain the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0883] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0884] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[0885] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[0886] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0887] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0888] Visual warning: The display will say "Endangered plant detected."

[0889] User confirmation: The user checks the site and repositions the robot if necessary.

[0890] Through the above process flow, the present invention accurately identifies endangered plants during mowing work and reduces the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0891] The processing flow will be explained below.

[0892] Step 1:

[0893] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[0894] Step 2:

[0895] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[0896] Step 3:

[0897] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[0898] Step 4:

[0899] The server evaluates the results of the generated AI model, checking the confidence score included in the results and determining that an endangered plant is present if it exceeds a set threshold.

[0900] Step 5:

[0901] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[0902] Step 6:

[0903] The server sends the generated warning message to the grass-cutting robot (terminal). This message includes instructions for the grass-cutting robot's control device and audio speaker.

[0904] Step 7:

[0905] The terminal (grass-cutting robot) receives a warning message from the server. At the same time, the audio speaker announces, "An endangered plant has been detected."

[0906] Step 8:

[0907] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[0908] Step 9:

[0909] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected," along with information about the type of plant and its location.

[0910] Step 10:

[0911] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[0912] Step 11:

[0913] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[0914] Step 12:

[0915] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[0916] Example 1

[0917] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0918] The present invention aims to reduce the risk of accidentally mowing endangered plants during mowing work. In particular, it provides a system that allows a mowing robot to operate autonomously, identify endangered plants in real time, and issue warnings and stop operations as necessary, thereby solving the problem of achieving both efficient mowing work and environmental protection.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0920] In this invention, the server includes means for acquiring image data in real time using a camera mounted on the mowing robot, means for processing the image data to generate preprocessed image data, means for executing a generative AI model that identifies plants based on the preprocessed image data, means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected, means for transmitting the warning message to the mowing robot, means for the mowing robot to make an audio announcement and automatically stop mowing operation upon receiving the warning message, means for the mowing robot to display a visual warning regarding the detection of an endangered plant, means for the mowing robot to display on a display so that the user can confirm the location where the endangered plant was detected by the mowing robot, and means for the user to adjust the position of the mowing robot and indicate a new working area. This enables the mowing robot to identify endangered plants in real time and prevent them from being mowed by mistake.

[0921] A "grass-cutting robot" is a mechanical device for automatically cutting grass. It is equipped with a high-resolution camera and a control device, and operates based on instructions from a server.

[0922] A "camera" is a photographing device that captures image data in real time and transmits high-resolution images to a server.

[0923] "Preprocessed image data" refers to raw image data received by the server that has undergone preprocessing such as resizing and noise reduction, and has been converted into a form that is easy to input into an AI model.

[0924] A "generative AI model" is a machine learning model used to extract plant characteristics based on preprocessed image data and calculate the degree of match with known endangered species.

[0925] "Evaluation of discrimination results" is the process of analyzing the results of plant identification by the generative AI model and determining whether any endangered plants are included.

[0926] A "warning message" is a message, including an audio announcement and a visual warning, that is generated by the server and sent to the grass-cutting robot when an endangered plant is detected.

[0927] The "voice announcement" is a voice message that is sent to the user through a speaker when the grass-cutting robot receives a warning message.

[0928] "Stopping the mowing operation" refers to an operation in which the control device of the mowing robot stops the blade of the mower to prevent the mowing of endangered plants.

[0929] A "visual warning" is a message that appears on the grass-cutting robot's display indicating that an endangered plant has been detected.

[0930] "Display" refers to a display device mounted on the grass-cutting robot that provides visual warnings and warning messages to the user.

[0931] A "user" is a person who operates and manages the grass-cutting robot, checks warning messages, and adjusts the robot's position as needed.

[0932] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[0933] Grass-cutting robot configuration

[0934] The grass-cutting robot consists of the following main components:

[0935] Camera: Acquires high-resolution image data in real time.

[0936] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[0937] Speaker: A device for making voice announcements.

[0938] Display: A monitor for displaying visual warnings.

[0939] Control device: Controls the operation of the grass cutter based on instructions from the server.

[0940] Server processing

[0941] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[0942] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[0943] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal format for use with the AI ​​model. Software such as OpenCV is used.

[0944] 3. Running the AI ​​model: The preprocessed image data is input into a generative AI model (e.g., TensorFlow, PyTorch) to identify plants.

[0945] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[0946] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[0947] Voice announcement and termination process

[0948] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[0949] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[0950] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[0951] User response

[0952] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[0953] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[0954] Specific examples

[0955] The following explains the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[0956] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[0957] Preprocessing: The server resizes the image data and removes noise before passing it to the generative AI model.

[0958] Execution of the AI ​​model: The generative AI model analyzes the image and confirms that it is a "dogtooth violet."

[0959] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[0960] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[0961] Visual warning: The display will say "Endangered plant detected."

[0962] User confirmation: The user checks the site and resets the position of the grass cutting robot if necessary.

[0963] Example prompts for generative AI models

[0964] "Real-time identification of endangered plants using high-resolution imagery"

[0965] "A method for safely detecting endangered plants from preprocessed image data and reducing the risk of mis-harvesting"

[0966] In this way, the present invention can accurately identify endangered plants during mowing work and reduce the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[0967] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] Image acquisition

[0970] The server receives image data from the grass-cutting robot's camera in real time. Specifically, high-resolution images taken by the grass-cutting robot's camera are sent to the server via the Internet. The input data is raw image data, and the output is image data stored on the server.

[0971] Step 2:

[0972] Image preprocessing

[0973] The server pre-processes the received image data. This process involves the following steps:

[0974] Resizing: Reduce or increase the image resolution to fit the AI ​​model. For example, resize a 1024x768 pixel image to 256x256 pixel.

[0975] Noise Reduction: Reduces noise in the image. Apply noise reduction algorithms to reduce the effects of glare and shadows.

[0976] The input data is raw image data stored on the server, and the output is pre-processed image data that has been resized and noise-reduced.

[0977] Step 3:

[0978] Image analysis using AI models

[0979] The server inputs the preprocessed image data into a generative AI model. Specifically, a generative AI model (such as TensorFlow or PyTorch) is used to identify plants in the image. This AI model has been trained in advance using a large amount of plant image data. The input data is the preprocessed image data, and the output is the name and species information of the identified plants. For example, the AI ​​model recognizes the shape and color of dogtooth violets leaves and identifies their species.

[0980] Step 4:

[0981] Evaluation of discrimination results

[0982] The server evaluates the output of the AI ​​model. Specifically, if the AI ​​output identifies the plant as "dogtooth violet," the evaluation engine checks the information and determines whether it meets the criteria for issuing an alert. The input data is the identification result by the AI ​​model, and the output is the decision to issue an alert.

[0983] Step 5:

[0984] Generate and send warning messages

[0985] The server sends a warning message to the grass-cutting robot. Specifically, it sends a digital message such as "Endangered plants have been detected." This message is received by the control device and triggers subsequent actions. The input data is evaluation information based on the discrimination results, and the output is a warning message.

[0986] Step 6:

[0987] Voice announcement and mower stopping

[0988] The grass-cutting robot will take the following actions based on the warning message received from the server:

[0989] Voice announcement: An announcement is made through the speaker saying "Endangered plants detected." This function is implemented using a TTS (text-to-speech) engine. The input data is a warning message, and the output is a voice announcement.

[0990] Stopping the mower: The control unit stops the mower blade. By stopping operation immediately, endangered plants are protected. The input data is a warning message, and the output is the mower stopping action.

[0991] Step 7:

[0992] Visual warning signs

[0993] A warning message is displayed on the display of the grass-cutting robot. The user is notified of the situation by displaying "Endangered plants detected." The input data is the warning message, and the output is a visual warning display.

[0994] Step 8:

[0995] User confirmation and response

[0996] The user sees the warning message on the display and takes the following specific action:

[0997] Field survey: The user actually visits the site and checks the status of endangered plants. The input data is a visual warning display, and the output is the user's confirmation and response.

[0998] Operation instructions: Adjust the position of the grass-cutting robot as needed and indicate a new work area. Typically, the user operates the robot using a remote controller or terminal. The input data are visual warnings and the results of the on-site inspection, and the output is an indication of a new work area.

[0999] The above are the specific processing steps of the program of this system.

[1000] (Application example 1)

[1001] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1002] In conventional logistics centers, the identification and management of sensitive packages and expensive items was insufficient due to human error, resulting in mishandling and damage. Furthermore, workers were not given sufficient warnings, resulting in lower customer satisfaction and lower efficiency in logistics operations. To solve these problems, a system is needed that can identify specific items in real time and issue appropriate warnings to workers.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1004] In this invention, the server includes means for acquiring image data, means for generating pre-processed image data, and means for executing a generative AI model for identifying items based on the pre-processed image data, thereby enabling real-time identification of specific items and providing audio announcements and visual alerts.

[1005] A "grass-mowing robot" is a mechanical device for automatically mowing grass, and in the present invention includes a camera, a control device, and the like.

[1006] A "camera" is a photographing device for acquiring image data in real time.

[1007] "Image data" is data containing visual information acquired by a camera.

[1008] "Preprocessing" refers to a processing step for converting acquired image data into an appropriate form, including resizing and noise reduction.

[1009] A "generative AI model" is an artificial intelligence model that is trained to perform a specific task (e.g., identifying objects or plants).

[1010] "Discrimination results" are the results of analysis and identification output by the generative AI model.

[1011] A "warning message" is notification information that is generated when a specific condition (eg, the detection of an endangered plant) is met.

[1012] "Voice announcement" is a function that provides information through voice, and is used to convey warning messages and the like to users.

[1013] A "visual warning" is warning information that is visually displayed on a display or the like.

[1014] The "logistics sector" is a range of activities that includes the transportation, warehousing, and distribution of goods.

[1015] A "specific item" is an item that has special conditions (e.g., requires careful handling, is expensive) and is the subject of identification in the present invention.

[1016] The present invention is a system that uses image analysis technology to identify specific items and issue warnings at logistics centers, and applies technology related to grass-cutting robots. Details are provided below.

[1017] composition

[1018] The system consists of the following main components:

[1019] Smart glasses: devices equipped with high-resolution cameras and displays for capturing image data in real time.

[1020] Server: A device that processes acquired image data and uses a generative AI model to identify specific items.

[1021] Voice announcement device: A device built into smart glasses that notifies users of warning messages by voice.

[1022] Visual warning display: A function that provides visual warnings through the smart glasses display.

[1023] Processing steps

[1024] The smart glasses capture image data in real time within the logistics center and send it to a server. The server preprocesses the image data and inputs it into a generative AI model. The generative AI model analyzes it and identifies specific items. Based on the identification results, the server generates a warning message and sends it to the smart glasses. The smart glasses notify workers by audio announcements and visual warning displays.

[1025] Hardware and Software Used

[1026] Hardware:

[1027] Smart glasses (e.g., with a high-resolution camera and display)

[1028] Server (with high-performance processing capabilities)

[1029] software:

[1030] TensorFlow: Used to run generative AI models

[1031] OpenCV: Used for image acquisition and pre-processing

[1032] pyttsx3: Used for voice announcements

[1033] Specific examples

[1034] Suppose a worker at a logistics center is inspecting packages and the smart glasses detect a box with a "Handle with Care" sticker. In this case, the smart glasses take a photo of the box with their camera and send the image to the server. The server preprocesses the image data and passes it to a generative AI model to identify whether it is a specific item. If the identification result is that the box is a "Handle with Care" box, the server generates a warning message and sends it to the smart glasses. The smart glasses then notify the worker via audio and visual means that "handle with care package has been detected."

[1035] Prompt Sentence Examples

[1036] To detect sensitive items in the logistics center, the smart glasses' camera captures images in real time and analyzes them using AI models. If a specific item is detected, a voice announcement and visual warning are issued.

[1037] By implementing the present invention in this manner, specific items can be quickly and accurately identified at the logistics center, and it is expected that mishandling and damage can be prevented.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1:

[1040] The smart glasses use cameras in the logistics center to capture image data in real time. The input is visual information from the site, and the output is high-resolution image data.

[1041] Step 2:

[1042] The smart glasses transmit the acquired image data to the server. The input is high-resolution image data, and the output is the transmission of image data to the server.

[1043] Step 3:

[1044] The server preprocesses the image data received. Specifically, it uses OpenCV to resize the image data, reduce noise, etc. The input is raw image data, and the output is preprocessed image data.

[1045] Step 4:

[1046] The server inputs the preprocessed image data into a generative AI model to identify specific items. The AI ​​model is run using TensorFlow to extract the features of the item and calculate the degree of match with known specific items. The input is the preprocessed image data, and the output is the classification result.

[1047] Step 5:

[1048] The server evaluates the discrimination results of the generated AI model and generates a warning message if a specific item is detected. The input is the discrimination result from the AI ​​model, and the output is the warning message.

[1049] Step 6:

[1050] The server sends a warning message to the smart glasses. The input is the warning message and the output is the transmission to the smart glasses.

[1051] Step 7:

[1052] The smart glasses make a voice announcement based on the received warning message and, if necessary, display a visual warning on the display. The input is the warning message received from the server, and the output is the voice announcement and the visual warning display.

[1053] Step 8:

[1054] The user listens to the audio announcement and visual warning and takes necessary action. The input is the warning from the smart glasses, and the output is the user's response action.

[1055] The above steps realize a system that smoothly identifies specific items and issues warnings in a logistics center.

[1056] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1057] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[1058] Grass-cutting robot configuration

[1059] The grass-cutting robot consists of the following main components:

[1060] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[1061] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1062] Speaker: A device for making voice announcements.

[1063] Display: A monitor for displaying visual warnings.

[1064] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[1065] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1066] Server processing

[1067] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. The specific roles of the server are as follows:

[1068] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[1069] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[1070] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[1071] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[1072] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[1073] 6. Emotion data evaluation: Evaluate the user's emotion data sent from the emotion engine and determine the optimal action according to the user's state.

[1074] Voice announcement and termination process

[1075] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it will make a voice announcement through the speaker saying, "Endangered plants have been detected." The tone and content of the announcement will be adjusted to match the user's emotional state based on the evaluation of the emotion engine.

[1076] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[1077] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[1078] User response

[1079] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[1080] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[1081] Emotion Engine Operation

[1082] The emotion engine analyzes the user's facial expression data in real time to obtain information such as:

[1083] Type of emotion (e.g., surprise, joy, anger, sadness)

[1084] Stress Level: Evaluates the user's stress state.

[1085] The data obtained by the emotion engine is sent to the server, which then takes the following actions:

[1086] 1. Adjusting the operating speed: If the user's stress level is high, the server will slow down the operating speed of the grass-cutting robot.

[1087] 2. Adjusting voice announcements: Change the tone and content of voice announcements depending on the emotion, providing information in a way that is easy for users to accept.

[1088] Specific examples

[1089] Here, a specific example will be given. As an example, a processing flow will be described for a case where a grass-cutting robot detects an endangered plant called "dogtooth violet" during work and at the same time the user expresses surprise.

[1090] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[1091] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[1092] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[1093] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[1094] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[1095] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[1096] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[1097] Through the above process flow, the present invention allows the user to appropriately identify endangered plants during weeding work and to work in an environment that takes into consideration the user's emotional state, thereby further contributing to forest development and environmental protection.

[1098] The processing flow will be explained below.

[1099] Step 1:

[1100] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[1101] Step 2:

[1102] The server receives the user's facial expression data from the grass-cutting robot, which is also stored in a receiving buffer.

[1103] Step 3:

[1104] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[1105] Step 4:

[1106] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[1107] Step 5:

[1108] The server evaluates the generated AI model's classification results, checks the confidence score included in the classification results, and determines that an endangered plant is present if it exceeds a set threshold.

[1109] Step 6:

[1110] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[1111] Step 7:

[1112] Based on the user's facial expression data received by the server, the emotion engine analyzes the user's emotions and identifies the type of emotion (e.g., surprise, joy, anger, sadness) and stress level from the user's facial expression.

[1113] Step 8:

[1114] The server sends the generated warning message and the emotion engine's analysis results to the grass-cutting robot (terminal), including instructions to the grass-cutting robot's control device and audio speaker.

[1115] Step 9:

[1116] The terminal (grass-cutting robot) receives the warning message from the server and the analysis results of the emotion engine. At the same time, the voice speaker announces, "Endangered plants have been detected." The tone and speed of the voice are adjusted based on the analysis results of the emotion engine.

[1117] Step 10:

[1118] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[1119] Step 11:

[1120] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected." The warning message includes information about the type of plant and its location.

[1121] Step 12:

[1122] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[1123] Step 13:

[1124] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[1125] Step 14:

[1126] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[1127] Example 2

[1128] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1129] The present invention aims to simultaneously protect endangered plants during weeding work and optimize the work environment by taking into account the user's emotional state. In particular, conventional weed-mowing robots lack sufficient accuracy in identifying plants and responding to the user's emotional state, resulting in problems such as accidentally mowing endangered plants and increasing user stress. To solve this problem, a system is needed that can accurately recognize the emotions of plants and the user in real time and automatically adjust tasks based on that information.

[1130] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data and generating preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make an audio announcement and automatically stop mowing operations; means for the mowing robot to display a visual warning regarding the detection of an endangered plant; means including an emotion engine that receives and analyzes user facial expression data; means for evaluating the user's emotion based on the generated emotion data; and means for adjusting the operating speed of the mowing robot and changing the tone of the audio announcement in accordance with the user's emotional state. This makes it possible to optimize the work environment while taking into consideration the protection of endangered plants and the user's emotional state.

[1131] 1. A "grass-cutting robot" is an autonomous robotic device equipped with a mechanical part for cutting grass, a camera for identifying the emotions of plants and the user, a speaker for making voice announcements, a display for showing visual warnings, and a control device for controlling its operation.

[1132] 2. "Camera" refers to a high-resolution image capture device that is installed on the grass-cutting robot and that captures image data in real time and recognizes the user's facial expressions.

[1133] 3. A "generative AI model" is an artificial intelligence model that inputs preprocessed image data and performs plant identification, and is constructed using, for example, a deep learning framework.

[1134] 4. "Preprocessed image data" refers to image data that has been processed, such as by resizing or noise reduction, on the raw data sent from the grass-cutting robot.

[1135] 5. "Warning message" means a message that is generated and sent to a grass-cutting robot when an endangered plant is detected, and includes content such as "An endangered plant has been detected."

[1136] 6. "Emotion Engine" means an engine that receives and analyzes a user's facial expression data and is a software or hardware component for evaluating the user's emotions.

[1137] 7. "Emotion data" refers to the results of analysis by the emotion engine based on the user's facial expression data, and includes emotional information such as surprise, joy, anger, and sadness.

[1138] 8. "Visual warning" means a warning message that appears on a display mounted on the grass-cutting robot, visually notifying the user of the detection of an endangered plant.

[1139] 9. "User's Emotional State" means the type and intensity of a user's emotion based on the results of analysis by the user's emotion engine.

[1140] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[1141] Grass-cutting robot configuration

[1142] The grass-cutting robot consists of the following main components:

[1143] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[1144] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1145] Speaker: A device for making voice announcements.

[1146] Display: A monitor for displaying visual warnings.

[1147] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[1148] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1149] Server processing

[1150] The server receives image data sent from the grass-cutting robot and analyzes it using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. Specific hardware used is a high-performance server, and software such as TensorFlow, PyTorch, and OpenCV is used.

[1151] Image data processing

[1152] After receiving the raw data from the grass-cutting robot, the server first resizes it and performs noise reduction. This preprocessing uses OpenCV, for example, resizes the image to (224x224) pixels, and applies a noise reduction filter.

[1153] Plant identification using AI models

[1154] The preprocessed image data is then fed into a generative AI model using deep learning frameworks such as TensorFlow and PyTorch. This AI model is trained to recognize endangered plants, for example, the dogtooth violet.

[1155] Generate and send warning messages

[1156] If the AI ​​model evaluates the identification results and confirms that an endangered plant is present, the server generates a warning message stating "Endangered plant detected" and sends it to the grass-cutting robot. The warning message includes the plant's species name, location information, and a voice announcement.

[1157] Emotional Data Evaluation

[1158] The emotion engine receives the user's facial expression data, analyzes it, and evaluates the user's emotional state. For example, a facial recognition service is used for the emotion engine. By sending the evaluation results to the server, the server can respond according to the user's emotions.

[1159] Adjusting movement speed and voice announcements

[1160] If the user's emotional state indicates a high stress level, the server instructs the grass-cutting robot to slow down. The tone and content of the voice announcements are also adjusted according to the user's emotions. Specifically, the announcement content is generated using the Google Text-to-Speech API.

[1161] Audio announcements and visual warnings

[1162] When the grass-cutting robot receives a warning message, it will make a voice announcement saying "Endangered plants detected" and stop the grass-cutting robot's operation. In addition, a visual warning message saying "Endangered plants detected" will appear on the display.

[1163] User response

[1164] The user checks the warning message on the display, checks the plants on-site, and then adjusts the position of the mowing robot as needed to indicate a new working area.

[1165] Specific examples

[1166] Here is a concrete example. For example, if a grass-cutting robot detects an endangered plant called "dogtooth violet" while working, and the user expresses surprise, the processing flow is as follows:

[1167] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[1168] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[1169] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[1170] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[1171] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[1172] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[1173] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[1174] Prompt Sentence Examples

[1175] "The grass-cutting robot has just detected an endangered plant called 'dogtooth violet'. At the same time, the emotion engine has recognized that the user has a surprised expression. Please generate the next instruction based on this situation."

[1176] Thus, the present invention aims to provide a system that identifies endangered plants during weeding work and optimizes the work environment according to the user's emotional state, thereby contributing to forest development and environmental protection.

[1177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] The server acquires image data in real time from the camera on the mowing robot. The input data is a high-resolution image sent from the camera on the mowing robot. The server receives this image data and simultaneously acquires the user's facial expression data captured by the camera on the mowing robot. The output data is the raw image data and facial expression data received by the server.

[1180] Step 2:

[1181] The server preprocesses the received image data. For preprocessing, it uses OpenCV to resize the image and reduce noise. Specifically, it resizes the image to (224x224) pixels and removes noise using a filter. The input data is raw image data, and the output data is preprocessed image data.

[1182] Step 3:

[1183] The server inputs the preprocessed image data into a generative AI model. This AI model uses TensorFlow and PyTorch to identify plants. Specifically, the preprocessed image data is input into the AI ​​model to identify the plant species. The input data is the preprocessed image data, and the output data is the species name of the identified plant. For example, "Erythronium japonicum" may be identified.

[1184] Step 4:

[1185] The server evaluates the AI ​​model's classification results and generates a warning message if an endangered plant is detected. Specifically, it evaluates whether the AI ​​model's output matches the species of the endangered plant. The input data is the species name of the identified plant, and the output data is a warning message. For example, a message saying "Endangered plant detected" is generated.

[1186] Step 5:

[1187] The server sends the generated warning message along with coordinate information and the voice announcement to the grass-cutting robot. Specifically, the warning message is accompanied by the plant species name, location information, and voice announcement, and then sent to the grass-cutting robot. The input data is the warning message and other supplementary information, and the output data is a data packet sent to the grass-cutting robot.

[1188] Step 6:

[1189] The grass-cutting robot receives the warning message sent from the server. Based on the received message, the grass-cutting robot makes an audio announcement through a speaker, notifying the user that "an endangered plant has been detected." The control device then stops the grass-cutting robot. The input data is the warning message from the server, and the output data is the execution of the audio announcement and the stopping of the grass-cutting robot.

[1190] Step 7:

[1191] The grass-cutting robot displays a visual warning on the display. Specifically, it displays "Endangered plants have been detected" on the display to notify the user. The input data is the warning message from the server, and the output data is the warning message displayed on the display.

[1192] Step 8:

[1193] The server receives the user's facial expression data from the emotion engine and analyzes it in real time. The emotion engine obtains the user's emotional data and produces an analysis result. The input data is the user's facial expression data, and the output data is the analyzed emotional data. For example, the emotion "surprise" is detected.

[1194] Step 9:

[1195] The server evaluates the user's stress level based on the analyzed emotional data. Based on the results of the data analysis, it determines whether the user has a high or low stress level. The input data is the analyzed emotional data, and the output data is the user's stress level.

[1196] Step 10:

[1197] If the user's stress level is high, the server sends instructions to the grass-cutting robot to adjust its speed. It also adjusts the tone of the voice announcement according to the user's emotions. Specifically, it uses the Google Text-to-Speech API to instruct the robot to generate a gentler voice announcement. The input data is the user's stress level and emotional data, and the output data is the speed instruction and voice announcement setting information sent to the grass-cutting robot.

[1198] Step 11:

[1199] The user checks the warning message displayed on the display of the mowing robot. They check the presence of plants on-site and adjust the position of the mowing robot as necessary. For example, they manually change the robot's position or use a remote control to specify a new work area. The input data is the warning message displayed on the display, and the output data is the user's instructions to operate the robot.

[1200] (Application example 2)

[1201] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1202] Conventional grass-cutting robots are specialized in the function of mowing grass, and do not sufficiently consider environmental protection or the user's emotional state. As a result, there is a high risk of accidentally mowing endangered plants, and they lack measures to reduce user stress and anxiety. To address these issues, the present invention aims to optimize the working environment according to the user's emotional state while identifying and protecting endangered plants.

[1203] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data to generate preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop mowing; means for receiving user emotion data, equipped with an emotion engine that analyzes user emotions; means for adjusting the tone and content of the voice announcement based on the emotion data; and means for adjusting the operating speed of the mowing robot based on the emotion data. This enables appropriate protection of endangered plants and provides an optimal working environment according to the user's emotional state.

[1204] A "grass-cutting robot" is an autonomous mechanical device that has the function of cutting grass and is capable of acquiring and analyzing images of the environment in real time.

[1205] The "camera" is a photographing device for acquiring image data, and is mounted on the grass-cutting robot to photograph the environment and the user's facial expression in real time.

[1206] "Preprocessing" refers to the process of optimizing acquired image data for analysis by methods such as resizing and noise reduction.

[1207] A "generative AI model" is an artificial intelligence model used to identify plants based on preprocessed image data.

[1208] A "warning message" is notification information generated when the generative AI model identifies an endangered plant.

[1209] "Voice announcement" is a function in which the grass-cutting robot conveys information to the user by voice based on the results of the generated AI model.

[1210] An "emotion engine" is an analysis device that analyzes a user's facial expressions to assess their emotional state.

[1211] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[1212] "Movement speed adjustment" is a function that changes the movement speed of the grass-cutting robot based on the user's emotional data.

[1213] This invention is a system for a grass-cutting robot that identifies endangered plants in real time and optimizes the working environment by recognizing the user's emotions. This system includes the following hardware and software:

[1214] 1. Hardware configuration:

[1215] Grass-cutting robot: It has a grass-cutting function and is equipped with a camera, speaker, display, emotion engine, and control device.

[1216] Camera: Captures the environment and user's facial expressions in real time.

[1217] Server: A centralized device that processes image data, runs AI models, and evaluates emotion data.

[1218] Speaker: Makes voice announcements to notify users.

[1219] Display: Displays a visual warning message.

[1220] 2. Software configuration:

[1221] Generative AI model: Identifies plants based on preprocessed image data.

[1222] Emotion engine: Software for analyzing the user's emotional state.

[1223] Control device: Controls the movement of the grass-cutting robot. Adjusts the movement speed based on instructions from the server.

[1224] 3. Data processing and calculation:

[1225] Image acquisition and preprocessing: The server acquires image data from the grass-cutting robot's camera in real time, and performs resizing and noise reduction.

[1226] Execution of the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants. Based on the identification results, endangered plants are identified and necessary warning messages are generated.

[1227] Emotion data evaluation: The emotion engine analyzes the user's facial expressions in real time to obtain emotional data, which is then used to adjust the tone and content of voice announcements and optimize the grass-cutting robot's operating speed.

[1228] 4. Example:

[1229] Example 1: A grass-cutting robot detects an endangered plant called "Erythronium japonicum" and sends an image to the server. At the same time, the emotion engine analyzes the user's facial expression to determine whether they are surprised. The server issues a voice announcement in a calm tone saying, "An endangered plant has been detected. Please remain calm," stops the grass-cutting robot's operation, and displays a warning message on the display.

[1230] Example prompt sentence:

[1231] "Write a Python program that implements the following system:

[1232] It preprocesses images captured by the camera and uses an AI model to identify emergencies. It analyzes the user's emotions and, if an emergency is detected, issues an appropriate warning voice message and stops the robot's operation. It also displays a warning message on the display.

[1233] As a result, the present invention can realize appropriate protection of endangered plants and provide an optimal working environment according to the user's emotional state.

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1:

[1236] The camera on the grass-cutting robot captures images of the environment and the user's facial expressions in real time, providing image data of the current working area and the user's emotional data as input.

[1237] Step 2:

[1238] The server receives image data acquired from the grass-cutting robot's camera. The received image data is resized and noise-reduced. This preprocessing results in preprocessed image data suitable for the generative AI model.

[1239] Step 3:

[1240] The server inputs the preprocessed image data into the generative AI model, which analyzes the preprocessed image data and extracts plant characteristics. As a result, a plant identification result is output, and a determination is made as to whether an endangered plant has been detected.

[1241] Step 4:

[1242] Based on the results of the generative AI model, the server generates a warning message when an endangered plant is detected. The warning message includes the results of the detection, coordinate information, and a voice announcement.

[1243] Step 5:

[1244] The server transmits the generated warning message to the grass-cutting robot, which receives the transmitted warning message.

[1245] Step 6:

[1246] Based on the warning message received, the grass-cutting robot will make an audio announcement through its speaker saying, "Endangered plants have been detected," and will simultaneously stop the grass-cutting robot's operation. At this point, the grass-cutting robot's blades will stop moving, protecting the endangered plants.

[1247] Step 7:

[1248] The user's facial expression data is analyzed by the emotion engine on the server, which evaluates the user's emotional state (e.g., surprise, joy, anger, sadness) from the user's facial expression and outputs the result.

[1249] Step 8:

[1250] Based on the user's emotional data obtained from the emotion engine, the server adjusts the tone and content of the voice announcements. If the user's stress level is high, the server slows down the grass-cutting robot's movement speed. This process provides information in a way that is easy for the user to accept, resulting in an output that optimizes the work environment.

[1251] Step 9:

[1252] The mowing robot will again notify the user with a tailored audio tone and show a visual warning message on the display, allowing the user to quickly understand the current situation and take appropriate action.

[1253] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1254] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1255] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1256] [Fourth embodiment]

[1257] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1258] 7, a 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.

[1259] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1260] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1261] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1262] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1263] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1264] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1265] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1266] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1267] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1268] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1269] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1270] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[1271] Grass-cutting robot configuration

[1272] The grass-cutting robot consists of the following main components:

[1273] Camera: Acquires high-resolution image data in real time.

[1274] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1275] Speaker: A device for making voice announcements.

[1276] Display: A monitor for displaying visual warnings.

[1277] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1278] Server processing

[1279] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[1280] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[1281] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[1282] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[1283] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[1284] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[1285] Voice announcement and termination process

[1286] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[1287] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[1288] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[1289] User response

[1290] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[1291] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[1292] Specific examples

[1293] Here, we will provide a specific example. As an example, we will explain the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[1294] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[1295] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[1296] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[1297] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[1298] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[1299] Visual warning: The display will say "Endangered plant detected."

[1300] User confirmation: The user checks the site and repositions the robot if necessary.

[1301] Through the above process flow, the present invention accurately identifies endangered plants during mowing work and reduces the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[1302] The processing flow will be explained below.

[1303] Step 1:

[1304] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[1305] Step 2:

[1306] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[1307] Step 3:

[1308] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[1309] Step 4:

[1310] The server evaluates the results of the generated AI model, checking the confidence score included in the results and determining that an endangered plant is present if it exceeds a set threshold.

[1311] Step 5:

[1312] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[1313] Step 6:

[1314] The server sends the generated warning message to the grass-cutting robot (terminal). This message includes instructions for the grass-cutting robot's control device and audio speaker.

[1315] Step 7:

[1316] The terminal (grass-cutting robot) receives a warning message from the server. At the same time, the audio speaker announces, "An endangered plant has been detected."

[1317] Step 8:

[1318] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[1319] Step 9:

[1320] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected," along with information about the type of plant and its location.

[1321] Step 10:

[1322] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[1323] Step 11:

[1324] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[1325] Step 12:

[1326] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[1327] Example 1

[1328] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1329] The present invention aims to reduce the risk of accidentally mowing endangered plants during mowing work. In particular, it provides a system that allows a mowing robot to operate autonomously, identify endangered plants in real time, and issue warnings and stop operations as necessary, thereby solving the problem of achieving both efficient mowing work and environmental protection.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1331] In this invention, the server includes means for acquiring image data in real time using a camera mounted on the mowing robot, means for processing the image data to generate preprocessed image data, means for executing a generative AI model that identifies plants based on the preprocessed image data, means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected, means for transmitting the warning message to the mowing robot, means for the mowing robot to make an audio announcement and automatically stop mowing operation upon receiving the warning message, means for the mowing robot to display a visual warning regarding the detection of an endangered plant, means for the mowing robot to display on a display so that the user can confirm the location where the endangered plant was detected by the mowing robot, and means for the user to adjust the position of the mowing robot and indicate a new working area. This enables the mowing robot to identify endangered plants in real time and prevent them from being mowed by mistake.

[1332] A "grass-cutting robot" is a mechanical device for automatically cutting grass. It is equipped with a high-resolution camera and a control device, and operates based on instructions from a server.

[1333] A "camera" is a photographing device that captures image data in real time and transmits high-resolution images to a server.

[1334] "Preprocessed image data" refers to raw image data received by the server that has undergone preprocessing such as resizing and noise reduction, and has been converted into a form that is easy to input into an AI model.

[1335] A "generative AI model" is a machine learning model used to extract plant characteristics based on preprocessed image data and calculate the degree of match with known endangered species.

[1336] "Evaluation of discrimination results" is the process of analyzing the results of plant identification by the generative AI model and determining whether any endangered plants are included.

[1337] A "warning message" is a message, including an audio announcement and a visual warning, that is generated by the server and sent to the grass-cutting robot when an endangered plant is detected.

[1338] The "voice announcement" is a voice message that is sent to the user through a speaker when the grass-cutting robot receives a warning message.

[1339] "Stopping the mowing operation" refers to an operation in which the control device of the mowing robot stops the blade of the mower to prevent the mowing of endangered plants.

[1340] A "visual warning" is a message that appears on the grass-cutting robot's display indicating that an endangered plant has been detected.

[1341] "Display" refers to a display device mounted on the grass-cutting robot that provides visual warnings and warning messages to the user.

[1342] A "user" is a person who operates and manages the grass-cutting robot, checks warning messages, and adjusts the robot's position as needed.

[1343] The present invention provides a system for enabling a grass-mowing robot to identify endangered plants in real time and reduce the risk of accidentally mowing those plants. The system is composed of a grass-mowing robot, a server, and a user.

[1344] Grass-cutting robot configuration

[1345] The grass-cutting robot consists of the following main components:

[1346] Camera: Acquires high-resolution image data in real time.

[1347] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1348] Speaker: A device for making voice announcements.

[1349] Display: A monitor for displaying visual warnings.

[1350] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1351] Server processing

[1352] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. The specific roles of the server are as follows:

[1353] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[1354] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal format for use with the AI ​​model. Software such as OpenCV is used.

[1355] 3. Running the AI ​​model: The preprocessed image data is input into a generative AI model (e.g., TensorFlow, PyTorch) to identify plants.

[1356] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[1357] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[1358] Voice announcement and termination process

[1359] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it makes a voice announcement through the speaker saying, "Endangered plants have been detected."

[1360] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[1361] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[1362] User response

[1363] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[1364] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[1365] Specific examples

[1366] The following explains the processing flow when a grass-cutting robot detects an endangered plant called "Erythronium japonicum" during work.

[1367] Image acquisition: The camera on the grass-cutting robot takes pictures of the dogtooth violets and sends them to the server.

[1368] Preprocessing: The server resizes the image data and removes noise before passing it to the generative AI model.

[1369] Execution of the AI ​​model: The generative AI model analyzes the image and confirms that it is a "dogtooth violet."

[1370] Evaluation of the discrimination results and sending of a warning message: The server confirms the presence of dogtooth violets and sends a warning message to the grass-cutting robot.

[1371] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants have been detected" and stop the grass-cutting machine.

[1372] Visual warning: The display will say "Endangered plant detected."

[1373] User confirmation: The user checks the site and resets the position of the grass cutting robot if necessary.

[1374] Example prompts for generative AI models

[1375] "Real-time identification of endangered plants using high-resolution imagery"

[1376] "A method for safely detecting endangered plants from preprocessed image data and reducing the risk of mis-harvesting"

[1377] In this way, the present invention can accurately identify endangered plants during mowing work and reduce the risk of them being mowed by mistake, thereby contributing to forest development and environmental protection.

[1378] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1379] Step 1:

[1380] Image acquisition

[1381] The server receives image data from the grass-cutting robot's camera in real time. Specifically, high-resolution images taken by the grass-cutting robot's camera are sent to the server via the Internet. The input data is raw image data, and the output is image data stored on the server.

[1382] Step 2:

[1383] Image preprocessing

[1384] The server pre-processes the received image data. This process involves the following steps:

[1385] Resizing: Reduce or increase the image resolution to fit the AI ​​model. For example, resize a 1024x768 pixel image to 256x256 pixel.

[1386] Noise Reduction: Reduces noise in the image. Apply noise reduction algorithms to reduce the effects of glare and shadows.

[1387] The input data is raw image data stored on the server, and the output is pre-processed image data that has been resized and noise-reduced.

[1388] Step 3:

[1389] Image analysis using AI models

[1390] The server inputs the preprocessed image data into a generative AI model. Specifically, a generative AI model (such as TensorFlow or PyTorch) is used to identify plants in the image. This AI model has been trained in advance using a large amount of plant image data. The input data is the preprocessed image data, and the output is the name and species information of the identified plants. For example, the AI ​​model recognizes the shape and color of dogtooth violets leaves and identifies their species.

[1391] Step 4:

[1392] Evaluation of the discrimination results

[1393] The server evaluates the output of the AI ​​model. Specifically, if the AI ​​output identifies the plant as "dogtooth violet," the evaluation engine checks the information and determines whether it meets the criteria for issuing an alert. The input data is the identification result by the AI ​​model, and the output is the decision to issue an alert.

[1394] Step 5:

[1395] Generate and send warning messages

[1396] The server sends a warning message to the grass-cutting robot. Specifically, it sends a digital message such as "Endangered plants have been detected." This message is received by the control device and triggers subsequent actions. The input data is evaluation information based on the discrimination results, and the output is a warning message.

[1397] Step 6:

[1398] Voice announcement and mower stopping

[1399] The grass-cutting robot will take the following actions based on the warning message received from the server:

[1400] Voice announcement: An announcement is made through the speaker saying "Endangered plants detected." This function is implemented using a TTS (text-to-speech) engine. The input data is a warning message, and the output is a voice announcement.

[1401] Stopping the mower: The control unit stops the mower blade. By stopping operation immediately, endangered plants are protected. The input data is a warning message, and the output is the mower stopping action.

[1402] Step 7:

[1403] Visual warning signs

[1404] A warning message is displayed on the display of the grass-cutting robot. The user is notified of the situation by displaying "Endangered plants detected." The input data is the warning message, and the output is a visual warning display.

[1405] Step 8:

[1406] User confirmation and response

[1407] The user sees the warning message on the display and takes the following specific action:

[1408] Field survey: The user actually visits the site and checks the status of endangered plants. The input data is a visual warning display, and the output is the user's confirmation and response.

[1409] Operation instructions: Adjust the position of the grass-cutting robot as needed and indicate a new work area. Typically, the user operates the robot using a remote controller or terminal. The input data are visual warnings and the results of the on-site inspection, and the output is an indication of a new work area.

[1410] The above are the specific processing steps of the program of this system.

[1411] (Application example 1)

[1412] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1413] In conventional logistics centers, the identification and management of sensitive packages and expensive items was insufficient due to human error, resulting in mishandling and damage. Furthermore, workers were not given sufficient warnings, resulting in lower customer satisfaction and lower efficiency in logistics operations. To solve these problems, a system is needed that can identify specific items in real time and issue appropriate warnings to workers.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1415] In this invention, the server includes means for acquiring image data, means for generating pre-processed image data, and means for executing a generative AI model for identifying items based on the pre-processed image data, thereby enabling real-time identification of specific items and providing audio announcements and visual alerts.

[1416] A "grass-mowing robot" is a mechanical device for automatically mowing grass, and in the present invention includes a camera, a control device, and the like.

[1417] A "camera" is a photographing device for acquiring image data in real time.

[1418] "Image data" is data containing visual information acquired by a camera.

[1419] "Preprocessing" refers to a processing step for converting acquired image data into an appropriate form, including resizing and noise reduction.

[1420] A "generative AI model" is an artificial intelligence model that is trained to perform a specific task (e.g., identifying objects or plants).

[1421] "Discrimination results" are the results of analysis and identification output by the generative AI model.

[1422] A "warning message" is notification information that is generated when a specific condition (eg, the detection of an endangered plant) is met.

[1423] "Voice announcement" is a function that provides information through voice, and is used to convey warning messages and the like to users.

[1424] A "visual warning" is warning information that is visually displayed on a display or the like.

[1425] The "logistics sector" is a range of activities that includes the transportation, warehousing, and distribution of goods.

[1426] A "specific item" is an item that has special conditions (e.g., requires careful handling, is expensive) and is the subject of identification in the present invention.

[1427] The present invention is a system that uses image analysis technology to identify and warn about specific items in a logistics center, and applies technology related to grass-cutting robots. Details are provided below.

[1428] composition

[1429] The system consists of the following main components:

[1430] Smart glasses: devices equipped with high-resolution cameras and displays for capturing image data in real time.

[1431] Server: A device that processes acquired image data and uses a generative AI model to identify specific items.

[1432] Voice announcement device: A device built into smart glasses that notifies users of warning messages by voice.

[1433] Visual warning display: A function that provides visual warnings through the smart glasses display.

[1434] Processing steps

[1435] The smart glasses capture image data in real time within the logistics center and send it to a server. The server preprocesses the image data and inputs it into a generative AI model. The generative AI model analyzes it and identifies specific items. Based on the identification results, the server generates a warning message and sends it to the smart glasses. The smart glasses notify workers by audio announcements and visual warning displays.

[1436] Hardware and Software Used

[1437] Hardware:

[1438] Smart glasses (e.g., with a high-resolution camera and display)

[1439] Server (with high-performance processing capabilities)

[1440] software:

[1441] TensorFlow: Used to run generative AI models

[1442] OpenCV: Used for image acquisition and pre-processing

[1443] pyttsx3: Used for voice announcements

[1444] Specific examples

[1445] Suppose a worker at a logistics center is inspecting packages and the smart glasses detect a box with a "Handle with Care" sticker. In this case, the smart glasses take a photo of the box with their camera and send the image to the server. The server preprocesses the image data and passes it to a generative AI model to identify whether it is a specific item. If the identification result is that the box is a "Handle with Care" box, the server generates a warning message and sends it to the smart glasses. The smart glasses then notify the worker via audio and visual means that "handle with care package has been detected."

[1446] Prompt Sentence Examples

[1447] To detect sensitive items in the logistics center, the smart glasses' camera captures images in real time and analyzes them using AI models. If a specific item is detected, a voice announcement and visual warning are issued.

[1448] By implementing the present invention in this manner, specific items can be quickly and accurately identified at the logistics center, and it is expected that mishandling and damage can be prevented.

[1449] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1450] Step 1:

[1451] The smart glasses use cameras in the logistics center to capture image data in real time. The input is visual information from the site, and the output is high-resolution image data.

[1452] Step 2:

[1453] The smart glasses transmit the acquired image data to the server. The input is high-resolution image data, and the output is the transmission of image data to the server.

[1454] Step 3:

[1455] The server preprocesses the image data received. Specifically, it uses OpenCV to resize the image data, reduce noise, etc. The input is raw image data, and the output is preprocessed image data.

[1456] Step 4:

[1457] The server inputs the preprocessed image data into a generative AI model to identify specific items. The AI ​​model is run using TensorFlow to extract the features of the item and calculate the degree of match with known specific items. The input is the preprocessed image data, and the output is the classification result.

[1458] Step 5:

[1459] The server evaluates the discrimination results of the generated AI model and generates a warning message if a specific item is detected. The input is the discrimination result from the AI ​​model, and the output is the warning message.

[1460] Step 6:

[1461] The server sends a warning message to the smart glasses. The input is the warning message and the output is the transmission to the smart glasses.

[1462] Step 7:

[1463] The smart glasses make a voice announcement based on the received warning message and, if necessary, display a visual warning on the display. The input is the warning message received from the server, and the output is the voice announcement and the visual warning display.

[1464] Step 8:

[1465] The user listens to the audio announcement and visual warning and takes necessary action. The input is the warning from the smart glasses, and the output is the user's response action.

[1466] The above steps realize a system that smoothly identifies specific items and issues warnings in a logistics center.

[1467] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1468] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[1469] Grass-cutting robot configuration

[1470] The grass-cutting robot consists of the following main components:

[1471] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[1472] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1473] Speaker: A device for making voice announcements.

[1474] Display: A monitor for displaying visual warnings.

[1475] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[1476] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1477] Server processing

[1478] The server receives image data sent from the grass-cutting robot and analyzes the data using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. The specific roles of the server are as follows:

[1479] 1. Image acquisition: The server receives image data sent from the grass-cutting robot's camera in real time.

[1480] 2. Preprocessing: The received image data is preprocessed by resizing, noise reduction, etc. to convert it into an optimal form for use with the AI ​​model.

[1481] 3. Running the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants.

[1482] 4. Evaluation of the classification results: Evaluate the classification results of the AI ​​model to determine whether endangered plants have been detected.

[1483] 5. Generating and sending a warning message: If an endangered plant is detected, a warning message is generated and sent to the grass-cutting robot.

[1484] 6. Emotion data evaluation: Evaluate the user's emotion data sent from the emotion engine and determine the optimal action according to the user's state.

[1485] Voice announcement and termination process

[1486] 1. Voice announcement: When the grass-cutting robot receives a warning message from the server, it will make a voice announcement through the speaker saying, "Endangered plants have been detected." The tone and content of the announcement will be adjusted to match the user's emotional state based on the evaluation of the emotion engine.

[1487] 2. Stopping the mower: At the same time, the control device stops the mower, preventing the endangered plants from being cut.

[1488] 3. Visual warning display: A warning message is displayed on the mowing robot's display to notify the user.

[1489] User response

[1490] 1. Warning confirmation: The user confirms the warning message displayed on the display and investigates the site.

[1491] 2. Operational Command: The user adjusts the robot's position as needed and commands a new working area.

[1492] Emotion Engine Operation

[1493] The emotion engine analyzes the user's facial expression data in real time to obtain information such as:

[1494] Type of emotion (e.g., surprise, joy, anger, sadness)

[1495] Stress Level: Evaluates the user's stress state.

[1496] The data obtained by the emotion engine is sent to the server, which then takes the following actions:

[1497] 1. Adjusting the operating speed: If the user's stress level is high, the server will slow down the operating speed of the grass-cutting robot.

[1498] 2. Adjusting voice announcements: Change the tone and content of voice announcements depending on the emotion, providing information in a way that is easy for users to accept.

[1499] Specific examples

[1500] Here, a specific example will be given. As an example, a processing flow will be described for a case where a grass-cutting robot detects an endangered plant called "dogtooth violet" during work and at the same time the user expresses surprise.

[1501] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[1502] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[1503] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[1504] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[1505] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[1506] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[1507] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[1508] Through the above process flow, the present invention allows the user to appropriately identify endangered plants during weeding work and to work in an environment that takes into consideration the user's emotional state, thereby further contributing to forest development and environmental protection.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] The server receives real-time image data captured by the camera of the grass-cutting robot, and stores the image data in a receiving buffer.

[1512] Step 2:

[1513] The server receives the user's facial expression data from the grass-cutting robot, which is also stored in a receiving buffer.

[1514] Step 3:

[1515] The server performs preprocessing on the received image data, specifically resizing the image, reducing noise, and adjusting saturation and brightness, providing the data in an optimal form for the generative AI model.

[1516] Step 4:

[1517] The server inputs the preprocessed image data into a generative AI model, which extracts plant characteristics and calculates the degree of match with known endangered plants.

[1518] Step 5:

[1519] The server evaluates the results of the generated AI model, checks the confidence score included in the results, and determines that an endangered plant is present if it exceeds a set threshold.

[1520] Step 6:

[1521] When the server detects an endangered plant, it generates a warning message that includes the detection result, coordinate information, and a voice announcement.

[1522] Step 7:

[1523] Based on the user's facial expression data received by the server, the emotion engine analyzes the user's emotions and identifies the type of emotion (e.g., surprise, joy, anger, sadness) and stress level from the user's facial expression.

[1524] Step 8:

[1525] The server sends the generated warning message and the emotion engine's analysis results to the grass-cutting robot (terminal), including instructions to the grass-cutting robot's control device and audio speaker.

[1526] Step 9:

[1527] The terminal (grass-cutting robot) receives the warning message from the server and the analysis results of the emotion engine. At the same time, the voice speaker announces, "Endangered plants have been detected." The tone and speed of the voice are adjusted based on the analysis results of the emotion engine.

[1528] Step 10:

[1529] The terminal (grass-cutting robot) stops the grass cutter through the control device, thereby preventing the endangered plants from being cut.

[1530] Step 11:

[1531] The terminal (weed-cutting robot) displays a visual warning on its display saying, "Endangered plants detected." The warning message includes information about the type of plant and its location.

[1532] Step 12:

[1533] The user checks the warning information displayed on the grass-cutting robot's display, and then investigates the area based on the warning to identify the location of endangered plants.

[1534] Step 13:

[1535] The user adjusts the position of the mowing robot and designates a new safe working area, thereby avoiding mowing in the wrong area.

[1536] Step 14:

[1537] The terminal (grass-cutting robot) resumes grass-cutting work in a new work area based on the user's instructions, and the series of processes is repeated again from step 1.

[1538] Example 2

[1539] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1540] The present invention aims to simultaneously protect endangered plants during weeding work and optimize the work environment by taking into account the user's emotional state. In particular, conventional weed-mowing robots lack sufficient accuracy in identifying plants and responding to the user's emotional state, resulting in problems such as accidentally mowing endangered plants and increasing user stress. To solve this problem, a system is needed that can accurately recognize the emotions of plants and the user in real time and automatically adjust tasks based on that information.

[1541] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data and generating preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make an audio announcement and automatically stop mowing operations; means for the mowing robot to display a visual warning regarding the detection of an endangered plant; means including an emotion engine that receives and analyzes user facial expression data; means for evaluating the user's emotion based on the generated emotion data; and means for adjusting the operating speed of the mowing robot and changing the tone of the audio announcement in accordance with the user's emotional state. This makes it possible to optimize the work environment while taking into consideration the protection of endangered plants and the user's emotional state.

[1542] 1. A "grass-cutting robot" is an autonomous robotic device equipped with a mechanical part for cutting grass, a camera for identifying the emotions of plants and the user, a speaker for making voice announcements, a display for showing visual warnings, and a control device for controlling its operation.

[1543] 2. "Camera" refers to a high-resolution image capture device that is installed on the grass-cutting robot and that captures image data in real time and recognizes the user's facial expressions.

[1544] 3. A "generative AI model" is an artificial intelligence model that inputs preprocessed image data and performs plant identification, and is constructed using, for example, a deep learning framework.

[1545] 4. "Preprocessed image data" refers to image data that has been processed, such as by resizing or noise reduction, on the raw data sent from the grass-cutting robot.

[1546] 5. "Warning message" means a message that is generated and sent to a grass-cutting robot when an endangered plant is detected, and includes content such as "An endangered plant has been detected."

[1547] 6. "Emotion Engine" means an engine that receives and analyzes a user's facial expression data and is a software or hardware component for evaluating the user's emotions.

[1548] 7. "Emotion data" refers to the results of analysis by the emotion engine based on the user's facial expression data, and includes emotional information such as surprise, joy, anger, and sadness.

[1549] 8. "Visual warning" means a warning message that appears on a display mounted on the grass-cutting robot, visually notifying the user of the detection of an endangered plant.

[1550] 9. "User's Emotional State" means the type and intensity of a user's emotion based on the results of analysis by the user's emotion engine.

[1551] The present invention is a system that enables a grass-cutting robot to identify and protect endangered plants in real time, while recognizing the user's emotions and optimizing the working environment. This system is composed of a grass-cutting robot, a server, a user, and an emotion engine.

[1552] Grass-cutting robot configuration

[1553] The grass-cutting robot consists of the following main components:

[1554] Camera: Captures high-resolution image data in real time. It also doubles as a camera to recognize the user's facial expressions.

[1555] Grass trimmer: A mechanical part with blades for cutting grass, the operation of which is stopped by a control device.

[1556] Speaker: A device for making voice announcements.

[1557] Display: A monitor for displaying visual warnings.

[1558] Emotion engine: A device that recognizes the user's facial expressions and analyzes their emotions.

[1559] Control device: Controls the operation of the grass cutter based on instructions from the server.

[1560] Server processing

[1561] The server receives image data sent from the grass-cutting robot and analyzes it using an AI model. At the same time, the emotion engine analyzes the user's facial expressions and sends emotional data to the server. Specific hardware used is a high-performance server, and software such as TensorFlow, PyTorch, and OpenCV is used.

[1562] Image data processing

[1563] After receiving the raw data from the grass-cutting robot, the server first resizes it and performs noise reduction. This preprocessing uses OpenCV, for example, resizes the image to (224x224) pixels, and applies a noise reduction filter.

[1564] Plant identification using AI models

[1565] The preprocessed image data is then fed into a generative AI model using deep learning frameworks such as TensorFlow and PyTorch. This AI model is trained to recognize endangered plants, for example, the dogtooth violet.

[1566] Generate and send warning messages

[1567] If the AI ​​model evaluates the identification results and confirms that an endangered plant is present, the server generates a warning message stating "Endangered plant detected" and sends it to the grass-cutting robot. The warning message includes the plant's species name, location information, and a voice announcement.

[1568] Emotional Data Evaluation

[1569] The emotion engine receives the user's facial expression data, analyzes it, and evaluates the user's emotional state. For example, a facial recognition service is used for the emotion engine. By sending the evaluation results to the server, the server can respond according to the user's emotions.

[1570] Adjusting movement speed and voice announcements

[1571] If the user's emotional state indicates a high stress level, the server instructs the grass-cutting robot to slow down. The tone and content of the voice announcements are also adjusted according to the user's emotions. Specifically, the announcement content is generated using the Google Text-to-Speech API.

[1572] Audio announcements and visual warnings

[1573] When the grass-cutting robot receives a warning message, it will make a voice announcement saying "Endangered plants detected" and stop the grass-cutting robot's operation. In addition, a visual warning message saying "Endangered plants detected" will appear on the display.

[1574] User response

[1575] The user checks the warning message on the display, checks the plants on-site, and then adjusts the position of the mowing robot as needed to indicate a new working area.

[1576] Specific examples

[1577] Here is a concrete example. For example, if a grass-cutting robot detects an endangered plant called "dogtooth violet" while working, and the user expresses surprise, the processing flow is as follows:

[1578] Image acquisition: The camera on the grass-cutting robot takes a picture of the dogtooth violets and sends it to the server. The camera also captures the user's facial expression data, which the emotion engine then begins analyzing.

[1579] Preprocessing: The server resizes the image data, removes noise, and then passes it to the AI ​​model.

[1580] Running the AI ​​model: The AI ​​model analyzes the image and confirms that it is a "dogtooth violet."

[1581] Evaluation of the discrimination result and sending of a warning message: The server confirms the presence of the dogtooth violet and sends a warning message to the grass-cutting robot. At the same time, the emotion engine determines the user's emotion as "surprise" and sends this information to the server.

[1582] Voice announcement and stop: The grass-cutting robot will make a voice announcement saying "Endangered plants detected" and adjust the tone gently based on the emotion engine's judgment. It will also stop the grass-cutting robot.

[1583] Visual warning: The display will say "Endangered plant detected" along with an emotionally sensitive message.

[1584] User confirmation: The user confirms the situation on-site and takes action to take appropriate action.

[1585] Prompt Sentence Examples

[1586] "The grass-cutting robot has just detected an endangered plant called 'dogtooth violet'. At the same time, the emotion engine has recognized that the user has a surprised expression. Please generate the next instruction based on this situation."

[1587] Thus, the present invention aims to provide a system that identifies endangered plants during weeding work and optimizes the work environment according to the user's emotional state, thereby contributing to forest development and environmental protection.

[1588] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1589] Step 1:

[1590] The server acquires image data in real time from the camera on the mowing robot. The input data is a high-resolution image sent from the camera on the mowing robot. The server receives this image data and simultaneously acquires the user's facial expression data captured by the camera on the mowing robot. The output data is the raw image data and facial expression data received by the server.

[1591] Step 2:

[1592] The server preprocesses the received image data. For preprocessing, it uses OpenCV to resize the image and reduce noise. Specifically, it resizes the image to (224x224) pixels and removes noise using a filter. The input data is raw image data, and the output data is preprocessed image data.

[1593] Step 3:

[1594] The server inputs the preprocessed image data into a generative AI model. This AI model uses TensorFlow and PyTorch to identify plants. Specifically, the preprocessed image data is input into the AI ​​model to identify the plant species. The input data is the preprocessed image data, and the output data is the species name of the identified plant. For example, "Erythronium japonicum" may be identified.

[1595] Step 4:

[1596] The server evaluates the AI ​​model's classification results and generates a warning message if an endangered plant is detected. Specifically, it evaluates whether the AI ​​model's output matches the species of the endangered plant. The input data is the species name of the identified plant, and the output data is a warning message. For example, a message saying "Endangered plant detected" is generated.

[1597] Step 5:

[1598] The server sends the generated warning message along with coordinate information and the voice announcement to the grass-cutting robot. Specifically, the warning message is accompanied by the plant species name, location information, and voice announcement, and then sent to the grass-cutting robot. The input data is the warning message and other supplementary information, and the output data is a data packet sent to the grass-cutting robot.

[1599] Step 6:

[1600] The grass-cutting robot receives the warning message sent from the server. Based on the received message, the grass-cutting robot makes an audio announcement through a speaker, notifying the user that "an endangered plant has been detected." The control device then stops the grass-cutting robot. The input data is the warning message from the server, and the output data is the execution of the audio announcement and the stopping of the grass-cutting robot.

[1601] Step 7:

[1602] The grass-cutting robot displays a visual warning on the display. Specifically, it displays "Endangered plants have been detected" on the display to notify the user. The input data is the warning message from the server, and the output data is the warning message displayed on the display.

[1603] Step 8:

[1604] The server receives the user's facial expression data from the emotion engine and analyzes it in real time. The emotion engine obtains the user's emotional data and produces an analysis result. The input data is the user's facial expression data, and the output data is the analyzed emotional data. For example, the emotion "surprise" is detected.

[1605] Step 9:

[1606] The server evaluates the user's stress level based on the analyzed emotional data. Based on the results of the data analysis, it determines whether the user has a high or low stress level. The input data is the analyzed emotional data, and the output data is the user's stress level.

[1607] Step 10:

[1608] If the user's stress level is high, the server sends instructions to the grass-cutting robot to adjust its speed. It also adjusts the tone of the voice announcement according to the user's emotions. Specifically, it uses the Google Text-to-Speech API to instruct the robot to generate a gentler voice announcement. The input data is the user's stress level and emotional data, and the output data is the speed instruction and voice announcement setting information sent to the grass-cutting robot.

[1609] Step 11:

[1610] The user checks the warning message displayed on the display of the mowing robot. They check the presence of plants on-site and adjust the position of the mowing robot as necessary. For example, they manually change the robot's position or use a remote control to specify a new work area. The input data is the warning message displayed on the display, and the output data is the user's instructions to operate the robot.

[1611] (Application example 2)

[1612] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] Conventional grass-cutting robots are specialized in the function of mowing grass, and do not sufficiently consider environmental protection or the user's emotional state. As a result, there is a high risk of accidentally mowing endangered plants, and they lack measures to reduce user stress and anxiety. To address these issues, the present invention aims to optimize the working environment according to the user's emotional state while identifying and protecting endangered plants.

[1614] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring image data in real time using a camera mounted on the mowing robot; means for processing the image data to generate preprocessed image data; means for executing a generative AI model that identifies plants based on the preprocessed image data; means for evaluating the discrimination results of the generative AI model and generating a warning message if an endangered plant is detected; means for transmitting the warning message to the mowing robot; means for the mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop mowing; means for receiving user emotion data, equipped with an emotion engine that analyzes user emotions; means for adjusting the tone and content of the voice announcement based on the emotion data; and means for adjusting the operating speed of the mowing robot based on the emotion data. This enables appropriate protection of endangered plants and provides an optimal working environment according to the user's emotional state.

[1615] A "grass-cutting robot" is an autonomous mechanical device that has the function of cutting grass and is capable of acquiring and analyzing images of the environment in real time.

[1616] The "camera" is a photographing device for acquiring image data, and is mounted on the grass-cutting robot to photograph the environment and the user's facial expression in real time.

[1617] "Preprocessing" refers to the process of optimizing acquired image data for analysis by methods such as resizing and noise reduction.

[1618] A "generative AI model" is an artificial intelligence model used to identify plants based on preprocessed image data.

[1619] A "warning message" is notification information generated when the generative AI model identifies an endangered plant.

[1620] "Voice announcement" is a function in which the grass-cutting robot conveys information to the user by voice based on the results of the generated AI model.

[1621] An "emotion engine" is an analysis device that analyzes a user's facial expressions to assess their emotional state.

[1622] "Emotion data" is information about the user's emotional state analyzed by the emotion engine.

[1623] "Movement speed adjustment" is a function that changes the movement speed of the grass-cutting robot based on the user's emotional data.

[1624] This invention is a system for a grass-cutting robot to identify endangered plants in real time and optimize the working environment by recognizing the user's emotions. This system includes the following hardware and software:

[1625] 1. Hardware configuration:

[1626] Grass-cutting robot: It has a grass-cutting function and is equipped with a camera, speaker, display, emotion engine, and control device.

[1627] Camera: Captures the environment and user's facial expressions in real time.

[1628] Server: A centralized device that processes image data, runs AI models, and evaluates emotion data.

[1629] Speaker: Makes voice announcements to notify users.

[1630] Display: Displays a visual warning message.

[1631] 2. Software configuration:

[1632] Generative AI model: Identifies plants based on preprocessed image data.

[1633] Emotion engine: Software for analyzing the user's emotional state.

[1634] Control device: Controls the movement of the grass-cutting robot. Adjusts the movement speed based on instructions from the server.

[1635] 3. Data processing and calculation:

[1636] Image acquisition and preprocessing: The server acquires image data from the grass-cutting robot's camera in real time, and performs resizing and noise reduction.

[1637] Execution of the AI ​​model: The preprocessed image data is input into the AI ​​model to identify the plants. Based on the identification results, endangered plants are identified and necessary warning messages are generated.

[1638] Emotion data evaluation: The emotion engine analyzes the user's facial expressions in real time to obtain emotional data, which is then used to adjust the tone and content of voice announcements and optimize the grass-cutting robot's operating speed.

[1639] 4. Example:

[1640] Example 1: A grass-cutting robot detects an endangered plant called "Erythronium japonicum" and sends an image to the server. At the same time, the emotion engine analyzes the user's facial expression to determine whether they are surprised. The server issues a voice announcement in a calm tone saying, "An endangered plant has been detected. Please remain calm," stops the grass-cutting robot's operation, and displays a warning message on the display.

[1641] Example prompt sentence:

[1642] "Write a Python program that implements the following system:

[1643] It preprocesses images captured by the camera and uses an AI model to identify emergencies. It analyzes the user's emotions and, if an emergency is detected, issues an appropriate warning voice message and stops the robot's operation. It also displays a warning message on the display.

[1644] As a result, the present invention can realize appropriate protection of endangered plants and provide an optimal working environment according to the user's emotional state.

[1645] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1646] Step 1:

[1647] The camera on the grass-cutting robot captures images of the environment and the user's facial expressions in real time, providing image data of the current working area and the user's emotional data as input.

[1648] Step 2:

[1649] The server receives image data acquired from the grass-cutting robot's camera. The received image data is resized and noise-reduced. This preprocessing results in preprocessed image data suitable for the generative AI model.

[1650] Step 3:

[1651] The server inputs the preprocessed image data into the generative AI model, which analyzes the preprocessed image data and extracts plant characteristics. As a result, a plant identification result is output, and a determination is made as to whether an endangered plant has been detected.

[1652] Step 4:

[1653] Based on the results of the generative AI model, the server generates a warning message when an endangered plant is detected. The warning message includes the results of the detection, coordinate information, and a voice announcement.

[1654] Step 5:

[1655] The server transmits the generated warning message to the grass-cutting robot, which receives the transmitted warning message.

[1656] Step 6:

[1657] Based on the warning message received, the grass-cutting robot will make an audio announcement through its speaker saying, "Endangered plants have been detected," and will simultaneously stop the grass-cutting robot's operation. At this point, the grass-cutting robot's blades will stop moving, protecting the endangered plants.

[1658] Step 7:

[1659] The user's facial expression data is analyzed by the emotion engine on the server, which evaluates the user's emotional state (e.g., surprise, joy, anger, sadness) from the user's facial expression and outputs the result.

[1660] Step 8:

[1661] Based on the user's emotional data obtained from the emotion engine, the server adjusts the tone and content of the voice announcements. If the user's stress level is high, the server slows down the grass-cutting robot's movement speed. This process provides information in a way that is easy for the user to accept, resulting in an output that optimizes the work environment.

[1662] Step 9:

[1663] The mowing robot will again notify the user with a tailored audio tone and show a visual warning message on the display, allowing the user to quickly understand the current situation and take appropriate action.

[1664] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1665] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1666] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1667] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1668] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1669] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1670] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1671] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1672] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1673] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1674] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1675] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1676] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1677] 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.

[1678] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1679] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1680] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1681] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1682] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1683] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1684] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1685] The following is further disclosed regarding the above embodiment.

[1686] (Claim 1)

[1687] A means for acquiring image data in real time using a camera mounted on the grass-cutting robot;

[1688] means for processing the image data to generate preprocessed image data;

[1689] means for executing a generative AI model that performs plant identification based on the preprocessed image data;

[1690] means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected;

[1691] means for transmitting the warning message to the grass-cutting robot;

[1692] a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation;

[1693] means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant;

[1694] A system including:

[1695] (Claim 2)

[1696] 2. The system according to claim 1, wherein the warning message includes a determination result, coordinate information, and a voice announcement content.

[1697] (Claim 3)

[1698] The system of claim 1, wherein the generative AI model extracts plant characteristics and calculates a match to known endangered species.

[1699] "Example 1"

[1700] (Claim 1)

[1701] A means for acquiring image data in real time using a camera mounted on the grass-cutting robot;

[1702] means for processing the image data to generate preprocessed image data;

[1703] means for executing a generative AI model that performs plant identification based on the preprocessed image data;

[1704] means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected;

[1705] means for transmitting the warning message to the grass-cutting robot;

[1706] a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation;

[1707] means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant;

[1708] a means for displaying on a display the location where the grass-mowing robot has detected the endangered plant so that a user can confirm the location;

[1709] a means for the user to adjust the position of the grass-cutting robot and indicate a new working area;

[1710] A system including:

[1711] (Claim 2)

[1712] 2. The system according to claim 1, wherein the warning message includes a determination result, coordinate information, and a voice announcement content.

[1713] (Claim 3)

[1714] The system of claim 1, wherein the generative AI model extracts plant characteristics and calculates a match to known endangered species.

[1715] "Application Example 1"

[1716] (Claim 1)

[1717] A means for acquiring image data in real time using a camera mounted on the grass-cutting robot;

[1718] means for processing the image data to generate preprocessed image data;

[1719] means for executing a generative AI model that performs plant identification based on the preprocessed image data;

[1720] means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected;

[1721] means for transmitting the warning message to the grass-cutting robot;

[1722] a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation;

[1723] means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant;

[1724] A means in the field of logistics that uses image analysis to identify specific items and warn workers;

[1725] a means for providing audio announcements and visual warnings;

[1726] A system including:

[1727] (Claim 2)

[1728] 2. The system according to claim 1, wherein the warning message includes a determination result, coordinate information, and a voice announcement content.

[1729] (Claim 3)

[1730] The system of claim 1, wherein the generative AI model extracts features of an item and calculates a match with a known specific item.

[1731] "Example 2: Combining Emotion Engines"

[1732] (Claim 1)

[1733] A means for acquiring image data in real time using a camera mounted on the grass-cutting robot;

[1734] means for processing the image data to generate preprocessed image data;

[1735] means for executing a generative AI model that performs plant identification based on the preprocessed image data;

[1736] means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected;

[1737] means for transmitting the warning message to the grass-cutting robot;

[1738] a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation;

[1739] means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant;

[1740] means including an emotion engine for receiving and analyzing a user's facial expression data;

[1741] means for evaluating the user's emotion based on the generated emotion data;

[1742] a means for adjusting the operating speed of the grass-cutting robot and changing the tone of the voice announcement according to the emotional state of the user;

[1743] A system including:

[1744] (Claim 2)

[1745] 2. The system according to claim 1, wherein the warning message includes a determination result, coordinate information, and a voice announcement content.

[1746] (Claim 3)

[1747] The system of claim 1, wherein the generative AI model extracts plant characteristics and calculates a match to known endangered species.

[1748] "Application example 2 when combining emotion engines"

[1749] (Claim 1)

[1750] A means for acquiring image data in real time using a camera mounted on the grass-cutting robot;

[1751] means for processing the image data to generate preprocessed image data;

[1752] means for executing a generative AI model that performs plant identification based on the preprocessed image data;

[1753] means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected;

[1754] means for transmitting the warning message to the grass-cutting robot;

[1755] a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation;

[1756] means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant;

[1757] A device equipped with an emotion engine for analyzing a user's emotion and receiving the user's emotion data;

[1758] means for adjusting the tone and content of the voice announcement based on the emotion data;

[1759] a means for adjusting the operating speed of the grass-cutting robot based on the emotion data;

[1760] A system including:

[1761] (Claim 2)

[1762] 2. The system according to claim 1, wherein the warning message includes a determination result, coordinate information, and a voice announcement content.

[1763] (Claim 3)

[1764] The system of claim 1, wherein the generative AI model extracts plant characteristics and calculates a match to known endangered species. [Explanation of symbols]

[1765] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring image data in real time using a camera mounted on the grass-cutting robot; means for processing the image data to generate preprocessed image data; means for executing a generative AI model that performs plant identification based on the preprocessed image data; means for evaluating the discrimination result of the generation AI model and generating a warning message when an endangered plant is detected; means for transmitting the warning message to the grass-cutting robot; a means for the grass-mowing robot, upon receiving the warning message, to make a voice announcement and automatically stop the grass-mowing operation; means for the grass-cutting robot to display a visual warning regarding the detection of an endangered plant; A system including:

2. The system according to claim 1 , wherein the warning message includes the determination result, coordinate information, and voice announcement content.

3. The system of claim 1 , wherein the generative AI model extracts plant characteristics and calculates a match to known endangered species.

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