system

Abstract

We provide the system. [Solution] An analysis means for receiving image data acquired by a camera and evaluating the condition of crops, A proposal method that suggests the optimal treatment for crops based on the analysis results, A notification method for informing users of the proposal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are problems that it is difficult for beginners of home gardens to appropriately judge the health conditions of vegetables and fruits and take appropriate measures at the optimal timing. Also, when it becomes difficult to manage due to sudden absence or health problems of those who conduct home gardens, there is a problem that the remaining family members cannot smoothly take over the work. In such a situation, the crops may not grow healthily and the opportunity for harvest may be missed.

Means for Solving the Problems

[0005] This invention solves the above problems by providing an analysis means for receiving image data acquired by a camera and evaluating the condition of crops. Furthermore, by providing a suggestion means that proposes the optimal treatment for crops based on the analysis results, the invention enables users to take appropriate action quickly. This makes it possible to constantly monitor the growth and health of crops in home gardens and to apply appropriate treatment at the optimal time. In addition, the invention provides a system that allows users to understand the situation and take action even when they are far away by providing a notification means that notifies the user of the suggestions.

[0006] A "photography device" is a device used to acquire image data, and includes a camera in a mobile device.

[0007] "Image data" refers to the electronic representation format of visual information acquired using a photographic device.

[0008] "Analysis means" refers to technology that processes received image data and has the functionality to evaluate the growth and health status of crops.

[0009] A "proposal method" is a technology that has the function of determining the optimal treatment for crops based on evaluation results obtained by analytical methods and providing instructions to the user.

[0010] A "notification method" is a mechanism for delivering proposed actions or information to users, and is primarily done through mobile devices.

[0011] A "system" is a combination of multiple interconnected devices or technologies that function to achieve a specific purpose.

[0012] "Artificial intelligence technology" refers to technologies that enable computers to make judgments and learn in a manner similar to human intelligence, and includes deep learning and machine learning. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is an image analysis system to support the management of home gardens, and is primarily operated using a smartphone. Specifically, it begins with the user taking pictures of vegetables and fruits using the smartphone's camera function. The device then transmits the captured images to a server via the internet.

[0035] The server uses artificial intelligence technology powered by machine learning algorithms to analyze the received images. This AI technology has been trained to identify the growth status of many crops, and can determine the health of crops from, for example, the color and shape of their leaves. Using this AI technology, the server evaluates the presence of pests and diseases, nutrient deficiencies, and the appropriate harvest time, and identifies the next course of action.

[0036] The server then generates a notification containing the analysis results and suggested actions, which it sends back to the terminal. The terminal immediately notifies the user of this information and displays it on the screen. This allows the user to care for their home garden based on accurate information. For example, they can keep their crops healthy by watering or adding fertilizer based on the notification. Furthermore, this system provides a means of efficiently and effectively managing crops even when the user is away from their home garden or unable to perform regular maintenance.

[0037] As a concrete example, consider a case where tomato leaves begin to turn yellow. When a user takes a picture and sends it to the system, the server's AI determines that this may indicate a nitrogen deficiency and suggests adding nitrogen-containing fertilizer. The user, upon receiving this information, can then apply the fertilizer at the appropriate time according to the instructions, thereby improving the health of the tomatoes. In this way, the system makes managing home gardens easier and contributes to improving the quality and quantity of the harvest.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. After taking the pictures, they press the send button in the app to start sending the data.

[0041] Step 2:

[0042] The device saves captured images locally and optimizes their size to reduce data volume. This optimization includes adjusting image resolution and compression, and also adds the user's location information and timestamp.

[0043] Step 3:

[0044] The device sends optimized image data to the server using the HTTP protocol. The data transmission is encrypted to ensure security.

[0045] Step 4:

[0046] The server inputs the received image data into an AI model. This AI model uses a deep learning algorithm and is pre-trained to identify crop types, growth stages, and the presence or absence of pests and diseases.

[0047] Step 5:

[0048] The server retrieves analysis results from the AI ​​model and determines suggested actions for the crop. For example, if leaf discoloration indicates a nutrient deficiency, it will select the necessary type of fertilizer and application method.

[0049] Step 6:

[0050] The server generates a notification message containing the suggested action and sends it to the terminal. The message includes the recommended action and the steps to take it.

[0051] Step 7:

[0052] The terminal displays messages received from the server in the user interface. The user can review the notification and take the suggested action.

[0053] Step 8:

[0054] The user follows the suggested actions to manage their home garden, for example, by applying the appropriate fertilizer or providing the necessary amount of water.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In home gardening, users need specialized knowledge and effort to properly manage the health and growth of plants. However, because there is no readily established method for doing so easily and accurately, it is difficult to maintain crop health and grow them efficiently.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the condition of the plant, an AI processing means for evaluating the growth and health status of the plant using an AI model generated using prompt sentences, and a suggestion means for suggesting the optimal treatment for the plant based on the evaluation results of the AI ​​processing means. This makes it possible for users to easily understand the condition of crops and perform appropriate management without specialized knowledge.

[0060] A "photography device" is a device used to acquire image data of plants, and usually refers to a portable information terminal equipped with a camera function.

[0061] "Image data" refers to visual information acquired by a camera, and is a digital file used to evaluate the growth and health status of plants.

[0062] "Analysis means" refers to a method or apparatus for evaluating the condition of a plant using received image data, and usually includes software or algorithms.

[0063] "AI processing means" refers to a method or system that uses a generated AI model to perform computational processing to evaluate the growth and health status of plants.

[0064] "Suggestion means" refers to a method or apparatus for determining the optimal treatment for plants based on the evaluation results of the AI ​​processing means and proposing it to the user.

[0065] "Notification means" refers to a method or device for transmitting information determined by the proposed means to the user and providing information that can be used for managing home gardening.

[0066] A "server" refers to a computing device or system for receiving, analyzing, suggesting, and notifying about image data, and communicates with portable information terminals via a network.

[0067] A "user" refers to an individual who operates the system and manages the plants, typically someone who practices home gardening.

[0068] This invention is a system to support the management of home gardening, primarily utilizing a portable information terminal and a server. The user takes pictures of plants using a camera mounted on the portable information terminal. This terminal generates the captured image data in digital format and transmits it to the server via the internet.

[0069] The server is a computing device that includes AI processing capabilities to analyze image data received from the terminal. The server utilizes generative AI models built with machine learning libraries such as TENSORFLOW® and PyTorch to evaluate the plant's growth and health in detail. These AI models analyze plant characteristics such as leaf color and shape to identify the presence of pests and diseases and nutrient deficiencies. Based on the evaluation results, the server determines necessary actions and creates recommendations for the user.

[0070] The suggested actions are communicated to the user via the device. The device quickly displays the suggestions to the user and provides helpful feedback for managing home gardening. Based on this information, the user can, for example, add the appropriate amount of fertilizer or water the plants. For example, if the AI ​​model detects yellowing of tomato leaves, it might determine that there is a possibility of nitrogen deficiency and suggest adding nitrogen-containing fertilizer.

[0071] An example of a prompt message would be, "Analyze the latest image of the plant and suggest its health status and necessary actions." In this way, the system allows users to properly manage the condition of their home gardens without requiring specialized knowledge.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user takes a picture of a plant using the camera on their portable information terminal. The input for this step is visual information of the plant, and the output is the captured image data. Specifically, the user launches the camera app on their terminal, points the camera at the plant, and presses the shutter button to save the image.

[0075] Step 2:

[0076] The device sends the captured image data to the server using the internet. The input for this step is the captured image data, and the output is the completion of the data transmission to the server. Specifically, the device uses its data communication function to upload the image to the specified server address via the HTTP protocol.

[0077] Step 3:

[0078] The server activates an AI processing system to analyze the image data received from the terminal. The input for this step is the image data received by the server, and the output is information evaluating the plant's condition based on the image analysis. Specifically, the server calls a generating AI model and uses prompt messages to analyze the color and shape of the plant's leaves, detecting signs of pests, diseases, and nutrient deficiencies.

[0079] Step 4:

[0080] The server proposes the optimal treatment for plants based on evaluation information obtained from AI processing. The input is plant condition evaluation information, and the output is a list of proposed actions. Specifically, the server generates concrete advice based on established evaluation criteria, such as suggesting the addition of nitrogen fertilizer if a nitrogen deficiency is detected.

[0081] Step 5:

[0082] The server sends the proposed actions back to the terminal as a notification message. The input is a list of proposed actions, and the output is the notification message sent to the terminal. Specifically, the server formats the results into a notification format and sends the message to the terminal using a communication protocol.

[0083] Step 6:

[0084] The device displays notification messages received from the server to the user. The input for this step is the notification message from the server, and the output is information that the user can view on the screen. Specifically, the device displays a pop-up notification on the screen and holds the notification until the user acknowledges it.

[0085] Step 7:

[0086] The user performs specific care for the plants based on notifications from the device. The input for this step is the information displayed on the device, and the output is the actual farming work performed. Specifically, the user follows the displayed suggestions to apply the necessary amount of fertilizer to the plants or take measures against pests and diseases.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Efficient quality inspection of goods is crucial in manufacturing. However, traditional methods rely on visual inspection by skilled inspectors, which are prone to false positives, missed defects, and human errors. Furthermore, the limited inspection speed slows down the production line. This leads to a decrease in overall production efficiency and an increased likelihood of quality defects.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes an evaluation means for receiving image data acquired by a camera and evaluating the quality status of an article, a suggestion means for proposing improvement measures for the article based on the evaluation results, and a notification means for notifying workers of the suggestion. This makes it possible to improve the accuracy and speed of quality inspection of articles and to efficiently optimize the production process.

[0092] A "photography device" is a device used to acquire image data of an object.

[0093] "Image data" refers to data that digitally represents visual information, including the shape and color information of an object.

[0094] "Evaluation method" refers to the process of analyzing image data to determine the quality status of an item.

[0095] "Quality condition" is a general term for the results of evaluating the shape, color, and presence or absence of defects of an item.

[0096] "Proposed methods" refer to the process of suggesting specific improvement measures for an item based on the results of a quality evaluation.

[0097] "Notification methods" refer to the process of communicating information about corrective measures to workers.

[0098] "Workers" are individuals involved in the production or inspection of goods.

[0099] "Machine learning technology" is a technique that uses algorithms to learn features from data and recognize patterns and trends.

[0100] A system for carrying out this invention includes an automated device for photographing items on a production line, a server for processing the image data obtained thereby, and a terminal for transmitting information to workers.

[0101] The imaging device plays the role of acquiring high-precision images of items moving along the production line. This device accurately captures the shape and color of the items and transmits that visual information to a server in digital format.

[0102] The server is equipped with an evaluation mechanism for analyzing received image data and assessing the quality of the items. This evaluation mechanism utilizes machine learning technology to analyze image data based on patterns learned from numerous datasets. Specifically, machine learning frameworks such as GOOGLE TENSOR® Flow and PyTorch are used to detect color inconsistencies and shape abnormalities in the items.

[0103] Based on the evaluation results, the server generates improvement measures for the item through the suggested means. This information is transmitted to the terminal via the communication line.

[0104] The terminal notifies workers of proposed corrective actions visually or audibly. Through these notifications, workers can quickly identify defects in items and make necessary corrections.

[0105] As a concrete example, consider using this system for inspecting the print quality of canned beverages moving along a production line. A camera captures minute printing inconsistencies on the surface of the cans, and a server analyzes the images to detect the inconsistencies and generate correction instructions. As a result, workers can quickly take action, such as removing problematic cans.

[0106] An example of a prompt message is: "Implement a function to perform real-time quality inspections of items on the production line, identify defective parts, and output improvement suggestions." This is expected to significantly increase automation and efficiency on the production floor.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user-operated camera captures images of items moving along the production line. Input includes information on the shape and color of the items, and output generates digital image data.

[0110] Step 2:

[0111] The imaging device transmits the generated image data to the server. The server receives this data and transfers it to a temporary storage area for data storage. The input is image data, and the output is temporary data stored within the server.

[0112] Step 3:

[0113] The server inputs the received image data into a machine learning model and begins data analysis. The technologies used are Google® TensorFlow and PyTorch, with image data as input and an evaluation result of the quality status of the items as output. Specifically, it analyzes the shape and color variations of the items to detect quality anomalies.

[0114] Step 4:

[0115] The server generates corrective actions based on the evaluation results. These actions include specific suggestions for defects and areas requiring improvement. The input is the quality status evaluation result, and the output is the proposed corrective actions.

[0116] Step 5:

[0117] The server sends the generated corrective actions to the terminal. The terminal receives this information and prepares to notify the user visually or audibly. The input is the proposed corrective actions, and the output is the information prepared for notification.

[0118] Step 6:

[0119] The terminal provides notifications to workers. Specifically, corrective actions are displayed on the screen, or work instructions are communicated via voice notification. The input is the information prepared for notification, and the output is the communication of instructions to the worker.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system for managing home gardens, providing support that also takes user emotions into consideration. Specifically, it is operated using a smartphone, starting with the user taking pictures of vegetables and fruits. The captured images are sent from the smartphone to a server. The server analyzes the received images using artificial intelligence technology with machine learning algorithms to evaluate the growth status, health status, and presence of pests and diseases of the crops.

[0122] This system integrates an emotion engine that recognizes the user's emotions. The terminal collects emotion data through the user's voice or text input. The emotion engine analyzes this data to determine how the user is feeling while using the system. This emotion analysis infers emotions from the tone of the messages the user enters, the words they choose, and even the intonation and tension of their voice.

[0123] The server combines crop analysis results with user sentiment analysis results to suggest the optimal action. For example, if the user's emotions indicate stress, it may suggest simpler and quicker actions. On the other hand, if the user indicates positive emotions, it can suggest a more detailed and comprehensive approach.

[0124] Finally, a notification is sent from the server to the terminal and presented to the user through the notification system. This notification includes a tone and content that corresponds to the user's emotional state, ranging from gentle expressions such as "Why not try this method?" to urgent expressions such as "You need to water your plants urgently." This makes it easier for the user to respond according to their emotional state and to better care for their home garden. For example, if the user's emotional state is indicated as busy, the server will notify them concisely of the minimum necessary actions to help them respond quickly.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. Once the shooting is complete, they press the send button on the app to begin sending the image data.

[0128] Step 2:

[0129] The device temporarily stores the image data and adjusts the image resolution to optimize the data size. This improves transmission efficiency. Once optimization is complete, the image data is sent to the server.

[0130] Step 3:

[0131] The server inputs the received image data into an AI model. This AI model is trained to identify the type of crop, its growth stage, health status, and the presence or absence of pests and diseases. The AI ​​model analyzes the images and evaluates the condition of the crops.

[0132] Step 4:

[0133] The device sends voice and text input from the user to the emotion engine. The emotion engine analyzes this input data to infer the user's emotional state. The emotion data includes the user's voice tone and the content of the entered text.

[0134] Step 5:

[0135] The server integrates AI-based crop condition assessments with user sentiment assessments from an emotion engine to determine the optimal action for the crops. For example, if the user's sentiment is negative, it recommends simple and immediate actions.

[0136] Step 6:

[0137] The server sends a notification message to the device containing the suggested action. This message includes a tone that reflects the user's emotional state.

[0138] Step 7:

[0139] The device displays received notification messages in the user interface. The user reviews the notification and takes the suggested action. If the user is relaxed, a detailed process may be suggested; if they are stressed, only the key points are provided.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] In home gardening, there is a need for assistance in helping users accurately understand the growth status of their crops and manage them appropriately. However, especially for beginners and busy users, emotions can affect work efficiency, making efficient garden management difficult. In this situation, there is a need for management support that takes into account not only the condition of the crops but also the user's emotions.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes an analysis means for evaluating the condition of crops, an emotion analysis means for analyzing the user's emotional data, and a suggestion means for proposing customized treatments based on the crop analysis results and the user's emotional state. This makes it possible to support garden management that is appropriate not only to the condition of the crops but also to the user's emotional state.

[0145] "Image capture device" refers to equipment used to acquire image data, and specifically refers to mobile devices such as handheld terminals.

[0146] "Image data" refers to digital information acquired by a camera or other imaging device to visually record the condition of crops.

[0147] "Analysis means" refers to a system component that has the function of analyzing received image data and evaluating the growth and health status of crops.

[0148] The "proposal means" refers to a system component responsible for indicating the optimal treatment for crops to the user based on the evaluation results obtained by the analytical means.

[0149] A "notification mechanism" is a system element that has the function of communicating proposed measures to users and helping them manage their gardens.

[0150] An "emotion analysis tool" is a system component that collects and analyzes emotional data from users to identify the user's emotional state.

[0151] "User" refers to an individual who operates and uses the system for the purpose of managing a home garden.

[0152] "Treatment" refers to the specific actions or management methods that users should take to improve or maintain the condition of crops.

[0153] This invention is a system for streamlining the management of home gardens, providing appropriate management support that visually assesses the condition of crops and reflects the user's emotional state. The user takes pictures of the crops being grown using a mobile device such as a smartphone. The image data obtained from this device is transmitted to a server via the device.

[0154] The server analyzes the received image data using image recognition algorithms based on machine learning techniques. Specifically, it uses libraries such as TensorFlow and PyTorch to evaluate the growth status, health, and presence of pests and diseases of the crops. This evaluation allows for a detailed understanding of the crop's condition.

[0155] Next, the device collects voice or text input data to determine the user's emotional state. This data is sent to a server and analyzed by an emotion analysis engine. Emotion analysis can utilize commonly used natural language processing techniques. For example, it may involve recognizing the user's current emotional state by examining the tone of the text they input and the intonation of their voice.

[0156] The server combines image analysis results and emotion analysis results to propose specific and efficient crop management methods tailored to the user's current situation. These proposals use flexible language that takes the user's emotions into account, offering concise solutions for users with low moods and a wide range of options for users in a positive state.

[0157] An example of a prompt message might be, "Please suggest the best way to manage your home garden based on the photos and audio." This prompt message allows the system to utilize a generative AI model to receive optimal management suggestions.

[0158] For example, if a user feels busy, the server will provide specific instructions such as, "Please water your plants immediately to ensure they meet the minimum requirements," helping the user take immediate action based on these instructions. This allows users to manage their gardens effectively in line with their emotional state and ensure the health of their crops.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users take photos of crops being grown using mobile devices such as smartphones. The captured image data is input to the device and sent to the server. Here, the image data is temporarily stored in digital format, encrypted, and prepared for transmission.

[0162] Step 2:

[0163] The server receives image data from the terminal as input. Next, it analyzes this image data using machine learning algorithms. Specifically, it uses image recognition technology to evaluate the growth status, health status, and presence of pests and diseases of crops, and outputs the evaluation results. The processing is carried out using libraries such as TensorFlow and PyTorch.

[0164] Step 3:

[0165] Users input their thoughts and feelings into the device via voice or text. This input data is stored on the device as emotion data and prepared to be sent to the server. The input in this step primarily consists of the user's emotional expressions while using the system.

[0166] Step 4:

[0167] The server receives emotional data from the terminal as input. The emotion analysis engine uses natural language processing technology to analyze this data and identify the user's emotional state. It considers the tone of the message, the words selected, the intonation of the voice, etc., to infer the user's emotional state and output the result.

[0168] Step 5:

[0169] The server combines crop condition assessment results obtained from image data with sentiment analysis results obtained from emotion data. Based on this combination, it proposes the optimal treatment for the crop. For example, even if the crop is healthy, if the user is experiencing stress, the server will output a suggestion prioritizing a simple and quick response.

[0170] Step 6:

[0171] The server generates a notification message containing the suggested actions and sends it to the terminal. The notification incorporates a tone that reflects the user's emotions, using softer or more urgent language. The input is the suggested action, and the notification generated based on that is sent to the terminal as output.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In recent years, advancements in information processing technology have created a demand for information provision that takes into account the user's psychological state. However, existing systems focus solely on evaluating the state of objects and are unable to provide optimal suggestions that consider the user's psychological factors, thus hindering improvements in the user experience. Therefore, there is a need for a system that enables suggestions based on an evaluation that combines the state of objects with the user's psychological state.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the state of an object, a suggestion means for proposing the optimal course of action for the object based on the analysis results and the user's psychological state, and a notification means for notifying an external device of the suggestion and making adjustments according to the psychological state. This makes it possible to provide optimal information according to the user's psychological state.

[0177] A "photography device" is a device used to acquire image data, and includes mobile information terminals.

[0178] "Image data" refers to digital data that contains visual information about an object.

[0179] "Object" refers to the tangible substance that is the subject of analysis, such as crops or food products.

[0180] "Analytical means for evaluating the state" refers to methods and systems that use machine learning techniques to evaluate the growth status and health status of an object.

[0181] "Proposal method" refers to a process or system for providing treatment or suggestions regarding an object based on the analysis results and the user's psychological state.

[0182] "Notification means" refers to methods and technologies for transmitting suggestions to external devices and adjusting information according to the user's psychological state.

[0183] "Psychological state" refers to the state of the user's emotions and mood, and is inferred from voice and text data.

[0184] "External devices" refer to devices that receive information and present it to the user, such as smartphones.

[0185] "Treatment" refers to countermeasures or action plans for an object, proposed to improve the object's condition.

[0186] The embodiment of this invention mainly involves a camera, a server, and an external device. A portable information terminal is used as the camera, and image data of an object is acquired. The acquired image data is transmitted to the server, which evaluates the state of the object based on this data.

[0187] The server uses machine learning techniques to determine the growth status, health status, and presence of abnormalities in objects. Specifically, it uses artificial intelligence technologies such as TensorFlow for analysis. Furthermore, it analyzes the user's psychological state from text and voice data and suggests the most appropriate action based on that state. Natural language processing tools such as NLTK are used for sentiment analysis. This makes it possible to recommend new activities when the user is relaxed and suggest simpler actions when the user is stressed.

[0188] These suggested actions are notified from the server to an external device and then transmitted to the user. A smartphone or similar device may be used as the external device. This notification is adjusted according to the situation, providing information appropriate to the user's psychological state.

[0189] As a concrete example, suppose a user takes a picture of food, and the application analyzes its nutritional value. If the application recognizes that the user has had a busy day, it will suggest a simple and healthy recipe. An example of a prompt to the generative AI model might be, "Please give me a simple, nutritious, and delicious recipe for a busy day."

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The user takes a picture of an object (e.g., food) using the camera on their mobile device. This image data becomes the input for the program. As a result, the device prepares to send this image data to the server.

[0193] Step 2:

[0194] The server receives image data sent from the terminal. Using the received image data as input, it evaluates the state of the object using a machine learning algorithm (such as TensorFlow). Specifically, it performs image recognition to identify what the object is and extracts data on its components and nutritional value. The results of this analysis become the output.

[0195] Step 3:

[0196] The user inputs emotional voice or text data into the device. This data becomes the program's input, and the device prepares to send it to the server.

[0197] Step 4:

[0198] The server receives voice or text data from the terminal and analyzes the user's emotional state using natural language processing tools (such as NLTK). It then takes the analyzed emotional data as input and determines the user's psychological state. The results of this analysis become the output.

[0199] Step 5:

[0200] The server combines the evaluation results of the object's state with the analysis results of the user's emotional state. Using these as input, it proposes the optimal course of action and outputs information and advice generated by a generative AI model. For example, when the user is busy, it suggests a simple, nutritious recipe.

[0201] Step 6:

[0202] The server notifies an external device (smartphone) of the proposed content. The tone and content of the notification are adjusted according to the user's emotional state. The notification content becomes the output, and the external device displays it. In this way, the user can take appropriate action based on the information received.

[0203] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

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

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] This invention is an image analysis system to support the management of home gardens, and is primarily operated using a smartphone. Specifically, it begins with the user taking pictures of vegetables and fruits using the smartphone's camera function. The device then transmits the captured images to a server via the internet.

[0220] The server uses artificial intelligence technology powered by machine learning algorithms to analyze the received images. This AI technology has been trained to identify the growth status of many crops, and can determine the health of crops from, for example, the color and shape of their leaves. Using this AI technology, the server evaluates the presence of pests and diseases, nutrient deficiencies, and the appropriate harvest time, and identifies the next course of action.

[0221] The server then generates a notification containing the analysis results and suggested actions, which it sends back to the terminal. The terminal immediately notifies the user of this information and displays it on the screen. This allows the user to care for their home garden based on accurate information. For example, they can keep their crops healthy by watering or adding fertilizer based on the notification. Furthermore, this system provides a means of efficiently and effectively managing crops even when the user is away from their home garden or unable to perform regular maintenance.

[0222] As a concrete example, consider a case where tomato leaves begin to turn yellow. When a user takes a picture and sends it to the system, the server's AI determines that this may indicate a nitrogen deficiency and suggests adding nitrogen-containing fertilizer. The user, upon receiving this information, can then apply the fertilizer at the appropriate time according to the instructions, thereby improving the health of the tomatoes. In this way, the system makes managing home gardens easier and contributes to improving the quality and quantity of the harvest.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. After taking the pictures, they press the send button in the app to start sending the data.

[0226] Step 2:

[0227] The device saves captured images locally and optimizes their size to reduce data volume. This optimization includes adjusting image resolution and compression, and also adds the user's location information and timestamp.

[0228] Step 3:

[0229] The device sends optimized image data to the server using the HTTP protocol. The data transmission is encrypted to ensure security.

[0230] Step 4:

[0231] The server inputs the received image data into an AI model. This AI model uses a deep learning algorithm and is pre-trained to identify crop types, growth stages, and the presence or absence of pests and diseases.

[0232] Step 5:

[0233] The server retrieves analysis results from the AI ​​model and determines suggested actions for the crop. For example, if leaf discoloration indicates a nutrient deficiency, it will select the necessary type of fertilizer and application method.

[0234] Step 6:

[0235] The server generates a notification message containing the suggested action and sends it to the terminal. The message includes the recommended action and the steps to take it.

[0236] Step 7:

[0237] The terminal displays messages received from the server in the user interface. The user can review the notification and take the suggested action.

[0238] Step 8:

[0239] The user follows the suggested actions to manage their home garden, for example, by applying the appropriate fertilizer or providing the necessary amount of water.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] In home gardening, users need specialized knowledge and effort to properly manage the health and growth of plants. However, because there is no readily established method for doing so easily and accurately, it is difficult to maintain crop health and grow them efficiently.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the condition of the plant, an AI processing means for evaluating the growth and health status of the plant using an AI model generated using prompt sentences, and a suggestion means for suggesting the optimal treatment for the plant based on the evaluation results of the AI ​​processing means. This makes it possible for users to easily understand the condition of crops and perform appropriate management without specialized knowledge.

[0245] A "photography device" is a device used to acquire image data of plants, and usually refers to a portable information terminal equipped with a camera function.

[0246] "Image data" refers to visual information acquired by a camera, and is a digital file used to evaluate the growth and health status of plants.

[0247] "Analysis means" refers to a method or apparatus for evaluating the condition of a plant using received image data, and usually includes software or algorithms.

[0248] "AI processing means" refers to a method or system that uses a generated AI model to perform computational processing to evaluate the growth and health status of plants.

[0249] "Suggestion means" refers to a method or apparatus for determining the optimal treatment for plants based on the evaluation results of the AI ​​processing means and proposing it to the user.

[0250] "Notification means" refers to a method or device for transmitting information determined by the proposed means to the user and providing information that can be used for managing home gardening.

[0251] A "server" refers to a computing device or system for receiving, analyzing, suggesting, and notifying about image data, and communicates with portable information terminals via a network.

[0252] A "user" refers to an individual who operates the system and manages the plants, typically someone who practices home gardening.

[0253] This invention is a system to support the management of home gardening, primarily utilizing a portable information terminal and a server. The user takes pictures of plants using a camera mounted on the portable information terminal. This terminal generates the captured image data in digital format and transmits it to the server via the internet.

[0254] The server is a computing device that includes AI processing capabilities to analyze image data received from the terminal. The server utilizes generative AI models built with machine learning libraries such as TensorFlow and PyTorch to evaluate the growth and health status of plants in detail. These AI models analyze characteristics such as the color and shape of plant leaves to identify the presence of pests and diseases and nutrient deficiencies. Based on the evaluation results, the server determines necessary actions and creates recommendations for the user.

[0255] The suggested actions are communicated to the user via the device. The device quickly displays the suggestions to the user and provides helpful feedback for managing home gardening. Based on this information, the user can, for example, add the appropriate amount of fertilizer or water the plants. For example, if the AI ​​model detects yellowing of tomato leaves, it might determine that there is a possibility of nitrogen deficiency and suggest adding nitrogen-containing fertilizer.

[0256] An example of a prompt message would be, "Analyze the latest image of the plant and suggest its health status and necessary actions." In this way, the system allows users to properly manage the condition of their home gardens without requiring specialized knowledge.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user takes a picture of a plant using the camera on their portable information terminal. The input for this step is visual information of the plant, and the output is the captured image data. Specifically, the user launches the camera app on their terminal, points the camera at the plant, and presses the shutter button to save the image.

[0260] Step 2:

[0261] The device sends the captured image data to the server using the internet. The input for this step is the captured image data, and the output is the completion of the data transmission to the server. Specifically, the device uses its data communication function to upload the image to the specified server address via the HTTP protocol.

[0262] Step 3:

[0263] The server activates an AI processing system to analyze the image data received from the terminal. The input for this step is the image data received by the server, and the output is information evaluating the plant's condition based on the image analysis. Specifically, the server calls a generating AI model and uses prompt messages to analyze the color and shape of the plant's leaves, detecting signs of pests, diseases, and nutrient deficiencies.

[0264] Step 4:

[0265] The server proposes the optimal treatment for plants based on evaluation information obtained from AI processing. The input is plant condition evaluation information, and the output is a list of proposed actions. Specifically, the server generates concrete advice based on established evaluation criteria, such as suggesting the addition of nitrogen fertilizer if a nitrogen deficiency is detected.

[0266] Step 5:

[0267] The server sends the proposed actions back to the terminal as a notification message. The input is a list of proposed actions, and the output is the notification message sent to the terminal. Specifically, the server formats the results into a notification format and sends the message to the terminal using a communication protocol.

[0268] Step 6:

[0269] The device displays notification messages received from the server to the user. The input for this step is the notification message from the server, and the output is information that the user can view on the screen. Specifically, the device displays a pop-up notification on the screen and holds the notification until the user acknowledges it.

[0270] Step 7:

[0271] The user performs specific care for the plants based on notifications from the device. The input for this step is the information displayed on the device, and the output is the actual farming work performed. Specifically, the user follows the displayed suggestions to apply the necessary amount of fertilizer to the plants or take measures against pests and diseases.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] Efficient quality inspection of goods is crucial in manufacturing. However, traditional methods rely on visual inspection by skilled inspectors, which are prone to false positives, missed defects, and human errors. Furthermore, the limited inspection speed slows down the production line. This leads to a decrease in overall production efficiency and an increased likelihood of quality defects.

[0275] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0276] In this invention, the server includes an evaluation means for receiving image data acquired by a camera and evaluating the quality status of an article, a suggestion means for proposing improvement measures for the article based on the evaluation results, and a notification means for notifying workers of the suggestion. This makes it possible to improve the accuracy and speed of quality inspection of articles and to efficiently optimize the production process.

[0277] A "photography device" is a device used to acquire image data of an object.

[0278] "Image data" refers to data that digitally represents visual information, including the shape and color information of an object.

[0279] "Evaluation method" refers to the process of analyzing image data to determine the quality status of an item.

[0280] The "quality status" is a general term for the results of evaluating the shape, color, and presence or absence of defects of an article.

[0281] The "proposal means" is a process of presenting specific improvement measures for an article based on the results of quality evaluation.

[0282] The "notification means" is a process for transmitting information about improvement measures to workers.

[0283] A "worker" is a person involved in the production or inspection of an article.

[0284] "Machine learning technology" is a technology that uses algorithms to learn features from data and recognize patterns and trends.

[0285] The system for implementing this invention includes an automatic device for photographing an article on a production line, a server for processing the image data thus obtained, and a terminal for transmitting information to a worker.

[0286] The photographing device serves to accurately acquire an image of an article flowing on the production line. This device accurately captures the shape and color of the article and transmits its visual information to the server in digital form.

[0287] The server includes evaluation means for analyzing the received image data and evaluating the quality status of the article. In this evaluation means, machine learning technology is utilized to analyze the image data based on patterns learned from a large number of data sets. Specifically, machine learning frameworks such as Google TensorFlow or PyTorch are used to detect color unevenness and shape abnormalities in the article.

[0288] Based on the evaluation results, the server generates improvement measures for the article through the proposal means. This information is transmitted to the terminal through a communication line.

[0289] The terminal notifies workers of proposed corrective actions visually or audibly. Through these notifications, workers can quickly identify defects in items and make necessary corrections.

[0290] As a concrete example, consider using this system for inspecting the print quality of canned beverages moving along a production line. A camera captures minute printing inconsistencies on the surface of the cans, and a server analyzes the images to detect the inconsistencies and generate correction instructions. As a result, workers can quickly take action, such as removing problematic cans.

[0291] An example of a prompt message is: "Implement a function to perform real-time quality inspections of items on the production line, identify defective parts, and output improvement suggestions." This is expected to significantly increase automation and efficiency on the production floor.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user-operated camera captures images of items moving along the production line. Input includes information on the shape and color of the items, and output generates digital image data.

[0295] Step 2:

[0296] The imaging device transmits the generated image data to the server. The server receives this data and transfers it to a temporary storage area for data storage. The input is image data, and the output is temporary data stored within the server.

[0297] Step 3:

[0298] The server inputs the received image data into a machine learning model and begins data analysis. The technologies used are Google TensorFlow and PyTorch, with image data as input and an evaluation result of the quality status of the items as output. Specifically, it analyzes the shape and color variations of the items to detect quality anomalies.

[0299] Step 4:

[0300] The server generates corrective actions based on the evaluation results. These actions include specific suggestions for defects and areas requiring improvement. The input is the quality status evaluation result, and the output is the proposed corrective actions.

[0301] Step 5:

[0302] The server sends the generated corrective actions to the terminal. The terminal receives this information and prepares to notify the user visually or audibly. The input is the proposed corrective actions, and the output is the information prepared for notification.

[0303] Step 6:

[0304] The terminal provides notifications to workers. Specifically, corrective actions are displayed on the screen, or work instructions are communicated via voice notification. The input is the information prepared for notification, and the output is the communication of instructions to the worker.

[0305] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0306] This invention is a system for managing a home garden, which provides support considering the user's emotions. Specifically, it starts with the user operating using a smartphone to take pictures of vegetables and fruits. The captured images are sent from the smartphone to the server. The server analyzes the received images using artificial intelligence technology with machine learning algorithms to evaluate the growth state, health state, and presence or absence of pests of the crops.

[0307] This system integrates an emotion engine that recognizes the user's emotions. The terminal collects emotion data through the user's voice input or text input. The emotion engine analyzes this data to determine how the user is using the system emotionally. This emotion analysis infers emotions from the tone of the message the user inputs, the words the user selects, and further, the intonation and tension of the voice.

[0308] The server combines the analysis results of the crops and the analysis results of the user's emotions and proposes optimal actions. For example, when the user's emotion shows stress, it may propose measures that can be implemented more easily and quickly. On the other hand, when the user shows a positive emotion, a more detailed and comprehensive approach can be proposed.

[0309] Finally, a notification is sent from the server to the terminal and presented to the user through the notification means. This notification contains a tone and content corresponding to the emotion, such as a gentle expression like "How about trying this method?" or an urgent expression like "Watering is urgently needed." This makes it easier for the user to take actions according to their emotional situation and enables them to take better care of the home garden. As a specific example, when the user's emotional state indicates being busy, the server simply notifies the minimum necessary actions to support the user to respond promptly.

[0310] The following explains the processing flow.

[0311] Step 1:

[0312] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. Once the shooting is complete, they press the send button on the app to begin sending the image data.

[0313] Step 2:

[0314] The device temporarily stores the image data and adjusts the image resolution to optimize the data size. This improves transmission efficiency. Once optimization is complete, the image data is sent to the server.

[0315] Step 3:

[0316] The server inputs the received image data into an AI model. This AI model is trained to identify the type of crop, its growth stage, health status, and the presence or absence of pests and diseases. The AI ​​model analyzes the images and evaluates the condition of the crops.

[0317] Step 4:

[0318] The device sends voice and text input from the user to the emotion engine. The emotion engine analyzes this input data to infer the user's emotional state. The emotion data includes the user's voice tone and the content of the entered text.

[0319] Step 5:

[0320] The server integrates AI-based crop condition assessments with user sentiment assessments from an emotion engine to determine the optimal action for the crops. For example, if the user's sentiment is negative, it recommends simple and immediate actions.

[0321] Step 6:

[0322] The server sends a notification message to the device containing the suggested action. This message includes a tone that reflects the user's emotional state.

[0323] Step 7:

[0324] The device displays received notification messages in the user interface. The user reviews the notification and takes the suggested action. If the user is relaxed, a detailed process may be suggested; if they are stressed, only the key points are provided.

[0325] (Example 2)

[0326] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0327] In home gardening, there is a need for assistance in helping users accurately understand the growth status of their crops and manage them appropriately. However, especially for beginners and busy users, emotions can affect work efficiency, making efficient garden management difficult. In this situation, there is a need for management support that takes into account not only the condition of the crops but also the user's emotions.

[0328] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0329] In this invention, the server includes an analysis means for evaluating the condition of crops, an emotion analysis means for analyzing the user's emotional data, and a suggestion means for proposing customized treatments based on the crop analysis results and the user's emotional state. This makes it possible to support garden management that is appropriate not only to the condition of the crops but also to the user's emotional state.

[0330] "Image capture device" refers to equipment used to acquire image data, and specifically refers to mobile devices such as handheld terminals.

[0331] "Image data" refers to digital information acquired by a camera or other imaging device to visually record the condition of crops.

[0332] "Analysis means" refers to a system component that has the function of analyzing received image data and evaluating the growth and health status of crops.

[0333] The "proposal means" refers to a system component responsible for indicating the optimal treatment for crops to the user based on the evaluation results obtained by the analytical means.

[0334] A "notification mechanism" is a system element that has the function of communicating proposed measures to users and helping them manage their gardens.

[0335] An "emotion analysis tool" is a system component that collects and analyzes emotional data from users to identify the user's emotional state.

[0336] "User" refers to an individual who operates and uses the system for the purpose of managing a home garden.

[0337] "Treatment" refers to the specific actions or management methods that users should take to improve or maintain the condition of crops.

[0338] This invention is a system for streamlining the management of home gardens, providing appropriate management support that visually assesses the condition of crops and reflects the user's emotional state. The user takes pictures of the crops being grown using a mobile device such as a smartphone. The image data obtained from this device is transmitted to a server via the device.

[0339] The server analyzes the received image data using image recognition algorithms based on machine learning techniques. Specifically, it uses libraries such as TensorFlow and PyTorch to evaluate the growth status, health, and presence of pests and diseases of the crops. This evaluation allows for a detailed understanding of the crop's condition.

[0340] Next, the device collects voice or text input data to determine the user's emotional state. This data is sent to a server and analyzed by an emotion analysis engine. Emotion analysis can utilize commonly used natural language processing techniques. For example, it may involve recognizing the user's current emotional state by examining the tone of the text they input and the intonation of their voice.

[0341] The server combines image analysis results and emotion analysis results to propose specific and efficient crop management methods tailored to the user's current situation. These proposals use flexible language that takes the user's emotions into account, offering concise solutions for users with low moods and a wide range of options for users in a positive state.

[0342] An example of a prompt message might be, "Please suggest the best way to manage your home garden based on the photos and audio." This prompt message allows the system to utilize a generative AI model to receive optimal management suggestions.

[0343] For example, if a user feels busy, the server will provide specific instructions such as, "Please water your plants immediately to ensure they meet the minimum requirements," helping the user take immediate action based on these instructions. This allows users to manage their gardens effectively in line with their emotional state and ensure the health of their crops.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] Users take photos of crops being grown using mobile devices such as smartphones. The captured image data is input to the device and sent to the server. Here, the image data is temporarily stored in digital format, encrypted, and prepared for transmission.

[0347] Step 2:

[0348] The server receives image data from the terminal as input. Next, it analyzes this image data using machine learning algorithms. Specifically, it uses image recognition technology to evaluate the growth status, health status, and presence of pests and diseases of crops, and outputs the evaluation results. The processing is carried out using libraries such as TensorFlow and PyTorch.

[0349] Step 3:

[0350] Users input their thoughts and feelings into the device via voice or text. This input data is stored on the device as emotion data and prepared to be sent to the server. The input in this step primarily consists of the user's emotional expressions while using the system.

[0351] Step 4:

[0352] The server receives emotional data from the terminal as input. The emotion analysis engine uses natural language processing technology to analyze this data and identify the user's emotional state. It considers the tone of the message, the words selected, the intonation of the voice, etc., to infer the user's emotional state and output the result.

[0353] Step 5:

[0354] The server combines crop condition assessment results obtained from image data with sentiment analysis results obtained from emotion data. Based on this combination, it proposes the optimal treatment for the crop. For example, even if the crop is healthy, if the user is experiencing stress, the server will output a suggestion prioritizing a simple and quick response.

[0355] Step 6:

[0356] The server generates a notification message containing the suggested actions and sends it to the terminal. The notification incorporates a tone that reflects the user's emotions, using softer or more urgent language. The input is the suggested action, and the notification generated based on that is sent to the terminal as output.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] In recent years, advancements in information processing technology have created a demand for information provision that takes into account the user's psychological state. However, existing systems focus solely on evaluating the state of objects and are unable to provide optimal suggestions that consider the user's psychological factors, thus hindering improvements in the user experience. Therefore, there is a need for a system that enables suggestions based on an evaluation that combines the state of objects with the user's psychological state.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the state of an object, a suggestion means for proposing the optimal course of action for the object based on the analysis results and the user's psychological state, and a notification means for notifying an external device of the suggestion and making adjustments according to the psychological state. This makes it possible to provide optimal information according to the user's psychological state.

[0362] A "photography device" is a device used to acquire image data, and includes mobile information terminals.

[0363] "Image data" refers to digital data that contains visual information about an object.

[0364] "Object" refers to the tangible substance that is the subject of analysis, such as crops or food products.

[0365] "Analytical means for evaluating the state" refers to methods and systems that use machine learning techniques to evaluate the growth status and health status of an object.

[0366] "Proposal method" refers to a process or system for providing treatment or suggestions regarding an object based on the analysis results and the user's psychological state.

[0367] "Notification means" refers to methods and technologies for transmitting suggestions to external devices and adjusting information according to the user's psychological state.

[0368] "Psychological state" refers to the state of the user's emotions and mood, and is inferred from voice and text data.

[0369] "External devices" refer to devices that receive information and present it to the user, such as smartphones.

[0370] "Treatment" refers to countermeasures or action plans for an object, proposed to improve the object's condition.

[0371] The embodiment of this invention mainly involves a camera, a server, and an external device. A portable information terminal is used as the camera, and image data of an object is acquired. The acquired image data is transmitted to the server, which evaluates the state of the object based on this data.

[0372] The server uses machine learning techniques to determine the growth status, health status, and presence of abnormalities in objects. Specifically, it uses artificial intelligence technologies such as TensorFlow for analysis. Furthermore, it analyzes the user's psychological state from text and voice data and suggests the most appropriate action based on that state. Natural language processing tools such as NLTK are used for sentiment analysis. This makes it possible to recommend new activities when the user is relaxed and suggest simpler actions when the user is stressed.

[0373] These suggested actions are notified from the server to an external device and then transmitted to the user. A smartphone or similar device may be used as the external device. This notification is adjusted according to the situation, providing information appropriate to the user's psychological state.

[0374] As a concrete example, suppose a user takes a picture of food, and the application analyzes its nutritional value. If the application recognizes that the user has had a busy day, it will suggest a simple and healthy recipe. An example of a prompt to the generative AI model might be, "Please give me a simple, nutritious, and delicious recipe for a busy day."

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user takes a picture of an object (e.g., food) using the camera on their mobile device. This image data becomes the input for the program. As a result, the device prepares to send this image data to the server.

[0378] Step 2:

[0379] The server receives image data sent from the terminal. Using the received image data as input, it evaluates the state of the object using a machine learning algorithm (such as TensorFlow). Specifically, it performs image recognition to identify what the object is and extracts data on its components and nutritional value. The results of this analysis become the output.

[0380] Step 3:

[0381] The user inputs emotional voice or text data into the device. This data becomes the program's input, and the device prepares to send it to the server.

[0382] Step 4:

[0383] The server receives voice or text data from the terminal and analyzes the user's emotional state using natural language processing tools (such as NLTK). It then takes the analyzed emotional data as input and determines the user's psychological state. The results of this analysis become the output.

[0384] Step 5:

[0385] The server combines the evaluation results of the object's state with the analysis results of the user's emotional state. Using these as input, it proposes the optimal course of action and outputs information and advice generated by a generative AI model. For example, when the user is busy, it suggests a simple, nutritious recipe.

[0386] Step 6:

[0387] The server notifies an external device (smartphone) of the proposed content. The tone and content of the notification are adjusted according to the user's emotional state. The notification content becomes the output, and the external device displays it. In this way, the user can take appropriate action based on the information received.

[0388] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0394] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0395] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0396] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0397] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0398] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0399] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0400] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0401] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0402] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0403] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0404] This invention is an image analysis system to support the management of home gardens, and is primarily operated using a smartphone. Specifically, it begins with the user taking pictures of vegetables and fruits using the smartphone's camera function. The device then transmits the captured images to a server via the internet.

[0405] The server uses artificial intelligence technology powered by machine learning algorithms to analyze the received images. This AI technology has been trained to identify the growth status of many crops, and can determine the health of crops from, for example, the color and shape of their leaves. Using this AI technology, the server evaluates the presence of pests and diseases, nutrient deficiencies, and the appropriate harvest time, and identifies the next course of action.

[0406] The server then generates a notification containing the analysis results and suggested actions, which it sends back to the terminal. The terminal immediately notifies the user of this information and displays it on the screen. This allows the user to care for their home garden based on accurate information. For example, they can keep their crops healthy by watering or adding fertilizer based on the notification. Furthermore, this system provides a means of efficiently and effectively managing crops even when the user is away from their home garden or unable to perform regular maintenance.

[0407] As a concrete example, consider a case where tomato leaves begin to turn yellow. When a user takes a picture and sends it to the system, the server's AI determines that this may indicate a nitrogen deficiency and suggests adding nitrogen-containing fertilizer. The user, upon receiving this information, can then apply the fertilizer at the appropriate time according to the instructions, thereby improving the health of the tomatoes. In this way, the system makes managing home gardens easier and contributes to improving the quality and quantity of the harvest.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. After taking the pictures, they press the send button in the app to start sending the data.

[0411] Step 2:

[0412] The device saves captured images locally and optimizes their size to reduce data volume. This optimization includes adjusting image resolution and compression, and also adds the user's location information and timestamp.

[0413] Step 3:

[0414] The device sends optimized image data to the server using the HTTP protocol. The data transmission is encrypted to ensure security.

[0415] Step 4:

[0416] The server inputs the received image data into an AI model. This AI model uses a deep learning algorithm and is pre-trained to identify crop types, growth stages, and the presence or absence of pests and diseases.

[0417] Step 5:

[0418] The server retrieves analysis results from the AI ​​model and determines suggested actions for the crop. For example, if leaf discoloration indicates a nutrient deficiency, it will select the necessary type of fertilizer and application method.

[0419] Step 6:

[0420] The server generates a notification message containing the suggested action and sends it to the terminal. The message includes the recommended action and the steps to take it.

[0421] Step 7:

[0422] The terminal displays messages received from the server in the user interface. The user can review the notification and take the suggested action.

[0423] Step 8:

[0424] The user follows the suggested actions to manage their home garden, for example, by applying the appropriate fertilizer or providing the necessary amount of water.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] In home gardening, users need specialized knowledge and effort to properly manage the health and growth of plants. However, because there is no readily established method for doing so easily and accurately, it is difficult to maintain crop health and grow them efficiently.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the condition of the plant, an AI processing means for evaluating the growth and health status of the plant using an AI model generated using prompt sentences, and a suggestion means for suggesting the optimal treatment for the plant based on the evaluation results of the AI ​​processing means. This makes it possible for users to easily understand the condition of crops and perform appropriate management without specialized knowledge.

[0430] A "photography device" is a device used to acquire image data of plants, and usually refers to a portable information terminal equipped with a camera function.

[0431] "Image data" refers to visual information acquired by a camera, and is a digital file used to evaluate the growth and health status of plants.

[0432] "Analysis means" refers to a method or apparatus for evaluating the condition of a plant using received image data, and usually includes software or algorithms.

[0433] "AI processing means" refers to a method or system that uses a generated AI model to perform computational processing to evaluate the growth and health status of plants.

[0434] "Suggestion means" refers to a method or apparatus for determining the optimal treatment for plants based on the evaluation results of the AI ​​processing means and proposing it to the user.

[0435] "Notification means" refers to a method or device for transmitting information determined by the proposed means to the user and providing information that can be used for managing home gardening.

[0436] A "server" refers to a computing device or system for receiving, analyzing, suggesting, and notifying about image data, and communicates with portable information terminals via a network.

[0437] A "user" refers to an individual who operates the system and manages the plants, typically someone who practices home gardening.

[0438] This invention is a system to support the management of home gardening, primarily utilizing a portable information terminal and a server. The user takes pictures of plants using a camera mounted on the portable information terminal. This terminal generates the captured image data in digital format and transmits it to the server via the internet.

[0439] The server is a computing device that includes AI processing capabilities to analyze image data received from the terminal. The server utilizes generative AI models built with machine learning libraries such as TensorFlow and PyTorch to evaluate the growth and health status of plants in detail. These AI models analyze characteristics such as the color and shape of plant leaves to identify the presence of pests and diseases and nutrient deficiencies. Based on the evaluation results, the server determines necessary actions and creates recommendations for the user.

[0440] The suggested actions are communicated to the user via the device. The device quickly displays the suggestions to the user and provides helpful feedback for managing home gardening. Based on this information, the user can, for example, add the appropriate amount of fertilizer or water the plants. For example, if the AI ​​model detects yellowing of tomato leaves, it might determine that there is a possibility of nitrogen deficiency and suggest adding nitrogen-containing fertilizer.

[0441] An example of a prompt message would be, "Analyze the latest image of the plant and suggest its health status and necessary actions." In this way, the system allows users to properly manage the condition of their home gardens without requiring specialized knowledge.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user takes a picture of a plant using the camera on their portable information terminal. The input for this step is visual information of the plant, and the output is the captured image data. Specifically, the user launches the camera app on their terminal, points the camera at the plant, and presses the shutter button to save the image.

[0445] Step 2:

[0446] The device sends the captured image data to the server using the internet. The input for this step is the captured image data, and the output is the completion of the data transmission to the server. Specifically, the device uses its data communication function to upload the image to the specified server address via the HTTP protocol.

[0447] Step 3:

[0448] The server activates an AI processing system to analyze the image data received from the terminal. The input for this step is the image data received by the server, and the output is information evaluating the plant's condition based on the image analysis. Specifically, the server calls a generating AI model and uses prompt messages to analyze the color and shape of the plant's leaves, detecting signs of pests, diseases, and nutrient deficiencies.

[0449] Step 4:

[0450] The server proposes the optimal treatment for plants based on evaluation information obtained from AI processing. The input is plant condition evaluation information, and the output is a list of proposed actions. Specifically, the server generates concrete advice based on established evaluation criteria, such as suggesting the addition of nitrogen fertilizer if a nitrogen deficiency is detected.

[0451] Step 5:

[0452] The server sends the proposed actions back to the terminal as a notification message. The input is a list of proposed actions, and the output is the notification message sent to the terminal. Specifically, the server formats the results into a notification format and sends the message to the terminal using a communication protocol.

[0453] Step 6:

[0454] The device displays notification messages received from the server to the user. The input for this step is the notification message from the server, and the output is information that the user can view on the screen. Specifically, the device displays a pop-up notification on the screen and holds the notification until the user acknowledges it.

[0455] Step 7:

[0456] The user performs specific care for the plants based on notifications from the device. The input for this step is the information displayed on the device, and the output is the actual farming work performed. Specifically, the user follows the displayed suggestions to apply the necessary amount of fertilizer to the plants or take measures against pests and diseases.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] Efficient quality inspection of goods is crucial in manufacturing. However, traditional methods rely on visual inspection by skilled inspectors, which are prone to false positives, missed defects, and human errors. Furthermore, the limited inspection speed slows down the production line. This leads to a decrease in overall production efficiency and an increased likelihood of quality defects.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes an evaluation means for receiving image data acquired by a camera and evaluating the quality status of an article, a suggestion means for proposing improvement measures for the article based on the evaluation results, and a notification means for notifying workers of the suggestion. This makes it possible to improve the accuracy and speed of quality inspection of articles and to efficiently optimize the production process.

[0462] A "photography device" is a device used to acquire image data of an object.

[0463] "Image data" refers to data that digitally represents visual information, including the shape and color information of an object.

[0464] "Evaluation method" refers to the process of analyzing image data to determine the quality status of an item.

[0465] "Quality condition" is a general term for the results of evaluating the shape, color, and presence or absence of defects of an item.

[0466] "Proposed methods" refer to the process of suggesting specific improvement measures for an item based on the results of a quality evaluation.

[0467] "Notification methods" refer to the process of communicating information about corrective measures to workers.

[0468] "Workers" are individuals involved in the production or inspection of goods.

[0469] "Machine learning technology" is a technique that uses algorithms to learn features from data and recognize patterns and trends.

[0470] A system for carrying out this invention includes an automated device for photographing items on a production line, a server for processing the image data obtained thereby, and a terminal for transmitting information to workers.

[0471] The imaging device plays the role of acquiring high-precision images of items moving along the production line. This device accurately captures the shape and color of the items and transmits that visual information to a server in digital format.

[0472] The server is equipped with an evaluation mechanism for analyzing received image data and assessing the quality of the items. This evaluation mechanism utilizes machine learning techniques to analyze image data based on patterns learned from numerous datasets. Specifically, machine learning frameworks such as Google TensorFlow and PyTorch are used to detect color inconsistencies and shape abnormalities in the items.

[0473] Based on the evaluation results, the server generates improvement measures for the item through the suggested means. This information is transmitted to the terminal via the communication line.

[0474] The terminal notifies workers of proposed corrective actions visually or audibly. Through these notifications, workers can quickly identify defects in items and make necessary corrections.

[0475] As a concrete example, consider using this system for inspecting the print quality of canned beverages moving along a production line. A camera captures minute printing inconsistencies on the surface of the cans, and a server analyzes the images to detect the inconsistencies and generate correction instructions. As a result, workers can quickly take action, such as removing problematic cans.

[0476] An example of a prompt message is: "Implement a function to perform real-time quality inspections of items on the production line, identify defective parts, and output improvement suggestions." This is expected to significantly increase automation and efficiency on the production floor.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The user-operated camera captures images of items moving along the production line. Input includes information on the shape and color of the items, and output generates digital image data.

[0480] Step 2:

[0481] The imaging device transmits the generated image data to the server. The server receives this data and transfers it to a temporary storage area for data storage. The input is image data, and the output is temporary data stored within the server.

[0482] Step 3:

[0483] The server inputs the received image data into a machine learning model and begins data analysis. The technologies used are Google TensorFlow and PyTorch, with image data as input and an evaluation result of the quality status of the items as output. Specifically, it analyzes the shape and color variations of the items to detect quality anomalies.

[0484] Step 4:

[0485] The server generates corrective actions based on the evaluation results. These actions include specific suggestions for defects and areas requiring improvement. The input is the quality status evaluation result, and the output is the proposed corrective actions.

[0486] Step 5:

[0487] The server sends the generated corrective actions to the terminal. The terminal receives this information and prepares to notify the user visually or audibly. The input is the proposed corrective actions, and the output is the information prepared for notification.

[0488] Step 6:

[0489] The terminal provides notifications to workers. Specifically, corrective actions are displayed on the screen, or work instructions are communicated via voice notification. The input is the information prepared for notification, and the output is the communication of instructions to the worker.

[0490] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0491] This invention is a system for managing home gardens, providing support that also takes user emotions into consideration. Specifically, it is operated using a smartphone, starting with the user taking pictures of vegetables and fruits. The captured images are sent from the smartphone to a server. The server analyzes the received images using artificial intelligence technology with machine learning algorithms to evaluate the growth status, health status, and presence of pests and diseases of the crops.

[0492] This system integrates an emotion engine that recognizes the user's emotions. The terminal collects emotion data through the user's voice or text input. The emotion engine analyzes this data to determine how the user is feeling while using the system. This emotion analysis infers emotions from the tone of the messages the user enters, the words they choose, and even the intonation and tension of their voice.

[0493] The server combines crop analysis results with user sentiment analysis results to suggest the optimal action. For example, if the user's emotions indicate stress, it may suggest simpler and quicker actions. On the other hand, if the user indicates positive emotions, it can suggest a more detailed and comprehensive approach.

[0494] Finally, a notification is sent from the server to the terminal and presented to the user through the notification system. This notification includes a tone and content that corresponds to the user's emotional state, ranging from gentle expressions such as "Why not try this method?" to urgent expressions such as "You need to water your plants urgently." This makes it easier for the user to respond according to their emotional state and to better care for their home garden. For example, if the user's emotional state is indicated as busy, the server will notify them concisely of the minimum necessary actions to help them respond quickly.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. Once the shooting is complete, they press the send button on the app to begin sending the image data.

[0498] Step 2:

[0499] The device temporarily stores the image data and adjusts the image resolution to optimize the data size. This improves transmission efficiency. Once optimization is complete, the image data is sent to the server.

[0500] Step 3:

[0501] The server inputs the received image data into an AI model. This AI model is trained to identify the type of crop, its growth stage, health status, and the presence or absence of pests and diseases. The AI ​​model analyzes the images and evaluates the condition of the crops.

[0502] Step 4:

[0503] The device sends voice and text input from the user to the emotion engine. The emotion engine analyzes this input data to infer the user's emotional state. The emotion data includes the user's voice tone and the content of the entered text.

[0504] Step 5:

[0505] The server integrates AI-based crop condition assessments with user sentiment assessments from an emotion engine to determine the optimal action for the crops. For example, if the user's sentiment is negative, it recommends simple and immediate actions.

[0506] Step 6:

[0507] The server sends a notification message to the device containing the suggested action. This message includes a tone that reflects the user's emotional state.

[0508] Step 7:

[0509] The device displays received notification messages in the user interface. The user reviews the notification and takes the suggested action. If the user is relaxed, a detailed process may be suggested; if they are stressed, only the key points are provided.

[0510] (Example 2)

[0511] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0512] In home gardening, there is a need for assistance in helping users accurately understand the growth status of their crops and manage them appropriately. However, especially for beginners and busy users, emotions can affect work efficiency, making efficient garden management difficult. In this situation, there is a need for management support that takes into account not only the condition of the crops but also the user's emotions.

[0513] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0514] In this invention, the server includes an analysis means for evaluating the condition of crops, an emotion analysis means for analyzing the user's emotional data, and a suggestion means for proposing customized treatments based on the crop analysis results and the user's emotional state. This makes it possible to support garden management that is appropriate not only to the condition of the crops but also to the user's emotional state.

[0515] "Image capture device" refers to equipment used to acquire image data, and specifically refers to mobile devices such as handheld terminals.

[0516] "Image data" refers to digital information acquired by a camera or other imaging device to visually record the condition of crops.

[0517] "Analysis means" refers to a system component that has the function of analyzing received image data and evaluating the growth and health status of crops.

[0518] The "proposal means" refers to a system component responsible for indicating the optimal treatment for crops to the user based on the evaluation results obtained by the analytical means.

[0519] A "notification mechanism" is a system element that has the function of communicating proposed measures to users and helping them manage their gardens.

[0520] An "emotion analysis tool" is a system component that collects and analyzes emotional data from users to identify the user's emotional state.

[0521] "User" refers to an individual who operates and uses the system for the purpose of managing a home garden.

[0522] "Treatment" refers to the specific actions or management methods that users should take to improve or maintain the condition of crops.

[0523] This invention is a system for streamlining the management of home gardens, providing appropriate management support that visually assesses the condition of crops and reflects the user's emotional state. The user takes pictures of the crops being grown using a mobile device such as a smartphone. The image data obtained from this device is transmitted to a server via the device.

[0524] The server analyzes the received image data using image recognition algorithms based on machine learning techniques. Specifically, it uses libraries such as TensorFlow and PyTorch to evaluate the growth status, health, and presence of pests and diseases of the crops. This evaluation allows for a detailed understanding of the crop's condition.

[0525] Next, the device collects voice or text input data to determine the user's emotional state. This data is sent to a server and analyzed by an emotion analysis engine. Emotion analysis can utilize commonly used natural language processing techniques. For example, it may involve recognizing the user's current emotional state by examining the tone of the text they input and the intonation of their voice.

[0526] The server combines image analysis results and emotion analysis results to propose specific and efficient crop management methods tailored to the user's current situation. These proposals use flexible language that takes the user's emotions into account, offering concise solutions for users with low moods and a wide range of options for users in a positive state.

[0527] An example of a prompt message might be, "Please suggest the best way to manage your home garden based on the photos and audio." This prompt message allows the system to utilize a generative AI model to receive optimal management suggestions.

[0528] For example, if a user feels busy, the server will provide specific instructions such as, "Please water your plants immediately to ensure they meet the minimum requirements," helping the user take immediate action based on these instructions. This allows users to manage their gardens effectively in line with their emotional state and ensure the health of their crops.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] Users take photos of crops being grown using mobile devices such as smartphones. The captured image data is input to the device and sent to the server. Here, the image data is temporarily stored in digital format, encrypted, and prepared for transmission.

[0532] Step 2:

[0533] The server receives image data from the terminal as input. Next, it analyzes this image data using machine learning algorithms. Specifically, it uses image recognition technology to evaluate the growth status, health status, and presence of pests and diseases of crops, and outputs the evaluation results. The processing is carried out using libraries such as TensorFlow and PyTorch.

[0534] Step 3:

[0535] Users input their thoughts and feelings into the device via voice or text. This input data is stored on the device as emotion data and prepared to be sent to the server. The input in this step primarily consists of the user's emotional expressions while using the system.

[0536] Step 4:

[0537] The server receives emotional data from the terminal as input. The emotion analysis engine uses natural language processing technology to analyze this data and identify the user's emotional state. It considers the tone of the message, the words selected, the intonation of the voice, etc., to infer the user's emotional state and output the result.

[0538] Step 5:

[0539] The server combines crop condition assessment results obtained from image data with sentiment analysis results obtained from emotion data. Based on this combination, it proposes the optimal treatment for the crop. For example, even if the crop is healthy, if the user is experiencing stress, the server will output a suggestion prioritizing a simple and quick response.

[0540] Step 6:

[0541] The server generates a notification message containing the suggested actions and sends it to the terminal. The notification incorporates a tone that reflects the user's emotions, using softer or more urgent language. The input is the suggested action, and the notification generated based on that is sent to the terminal as output.

[0542] (Application Example 2)

[0543] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In recent years, advancements in information processing technology have created a demand for information provision that takes into account the user's psychological state. However, existing systems focus solely on evaluating the state of objects and are unable to provide optimal suggestions that consider the user's psychological factors, thus hindering improvements in the user experience. Therefore, there is a need for a system that enables suggestions based on an evaluation that combines the state of objects with the user's psychological state.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0546] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the state of an object, a suggestion means for proposing the optimal course of action for the object based on the analysis results and the user's psychological state, and a notification means for notifying an external device of the suggestion and making adjustments according to the psychological state. This makes it possible to provide optimal information according to the user's psychological state.

[0547] A "photography device" is a device used to acquire image data, and includes mobile information terminals.

[0548] "Image data" refers to digital data that contains visual information about an object.

[0549] "Object" refers to the tangible substance that is the subject of analysis, such as crops or food products.

[0550] "Analytical means for evaluating the state" refers to methods and systems that use machine learning techniques to evaluate the growth status and health status of an object.

[0551] "Proposal method" refers to a process or system for providing treatment or suggestions regarding an object based on the analysis results and the user's psychological state.

[0552] "Notification means" refers to methods and technologies for transmitting suggestions to external devices and adjusting information according to the user's psychological state.

[0553] "Psychological state" refers to the state of the user's emotions and mood, and is inferred from voice and text data.

[0554] "External devices" refer to devices that receive information and present it to the user, such as smartphones.

[0555] "Treatment" refers to countermeasures or action plans for an object, proposed to improve the object's condition.

[0556] The embodiment of this invention mainly involves a camera, a server, and an external device. A portable information terminal is used as the camera, and image data of an object is acquired. The acquired image data is transmitted to the server, which evaluates the state of the object based on this data.

[0557] The server uses machine learning techniques to determine the growth status, health status, and presence of abnormalities in objects. Specifically, it uses artificial intelligence technologies such as TensorFlow for analysis. Furthermore, it analyzes the user's psychological state from text and voice data and suggests the most appropriate action based on that state. Natural language processing tools such as NLTK are used for sentiment analysis. This makes it possible to recommend new activities when the user is relaxed and suggest simpler actions when the user is stressed.

[0558] These suggested actions are notified from the server to an external device and then transmitted to the user. A smartphone or similar device may be used as the external device. This notification is adjusted according to the situation, providing information appropriate to the user's psychological state.

[0559] As a concrete example, suppose a user takes a picture of food, and the application analyzes its nutritional value. If the application recognizes that the user has had a busy day, it will suggest a simple and healthy recipe. An example of a prompt to the generative AI model might be, "Please give me a simple, nutritious, and delicious recipe for a busy day."

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The user takes a picture of an object (e.g., food) using the camera on their mobile device. This image data becomes the input for the program. As a result, the device prepares to send this image data to the server.

[0563] Step 2:

[0564] The server receives image data sent from the terminal. Using the received image data as input, it evaluates the state of the object using a machine learning algorithm (such as TensorFlow). Specifically, it performs image recognition to identify what the object is and extracts data on its components and nutritional value. The results of this analysis become the output.

[0565] Step 3:

[0566] The user inputs emotional voice or text data into the device. This data becomes the program's input, and the device prepares to send it to the server.

[0567] Step 4:

[0568] The server receives voice or text data from the terminal and analyzes the user's emotional state using natural language processing tools (such as NLTK). It then takes the analyzed emotional data as input and determines the user's psychological state. The results of this analysis become the output.

[0569] Step 5:

[0570] The server combines the evaluation results of the object's state with the analysis results of the user's emotional state. Using these as input, it proposes the optimal course of action and outputs information and advice generated by a generative AI model. For example, when the user is busy, it suggests a simple, nutritious recipe.

[0571] Step 6:

[0572] The server notifies an external device (smartphone) of the proposed content. The tone and content of the notification are adjusted according to the user's emotional state. The notification content becomes the output, and the external device displays it. In this way, the user can take appropriate action based on the information received.

[0573] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0574] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0576] [Fourth Embodiment]

[0577] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0578] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0579] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0580] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0581] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0582] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0583] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0584] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0585] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0586] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0587] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0588] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0589] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] This invention is an image analysis system to support the management of home gardens, and is primarily operated using a smartphone. Specifically, it begins with the user taking pictures of vegetables and fruits using the smartphone's camera function. The device then transmits the captured images to a server via the internet.

[0591] The server uses artificial intelligence technology powered by machine learning algorithms to analyze the received images. This AI technology has been trained to identify the growth status of many crops, and can determine the health of crops from, for example, the color and shape of their leaves. Using this AI technology, the server evaluates the presence of pests and diseases, nutrient deficiencies, and the appropriate harvest time, and identifies the next course of action.

[0592] The server then generates a notification containing the analysis results and suggested actions, which it sends back to the terminal. The terminal immediately notifies the user of this information and displays it on the screen. This allows the user to care for their home garden based on accurate information. For example, they can keep their crops healthy by watering or adding fertilizer based on the notification. Furthermore, this system provides a means of efficiently and effectively managing crops even when the user is away from their home garden or unable to perform regular maintenance.

[0593] As a concrete example, consider a case where tomato leaves begin to turn yellow. When a user takes a picture and sends it to the system, the server's AI determines that this may indicate a nitrogen deficiency and suggests adding nitrogen-containing fertilizer. The user, upon receiving this information, can then apply the fertilizer at the appropriate time according to the instructions, thereby improving the health of the tomatoes. In this way, the system makes managing home gardens easier and contributes to improving the quality and quantity of the harvest.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. After taking the pictures, they press the send button in the app to start sending the data.

[0597] Step 2:

[0598] The device saves captured images locally and optimizes their size to reduce data volume. This optimization includes adjusting image resolution and compression, and also adds the user's location information and timestamp.

[0599] Step 3:

[0600] The device sends optimized image data to the server using the HTTP protocol. The data transmission is encrypted to ensure security.

[0601] Step 4:

[0602] The server inputs the received image data into an AI model. This AI model uses a deep learning algorithm and is pre-trained to identify crop types, growth stages, and the presence or absence of pests and diseases.

[0603] Step 5:

[0604] The server retrieves analysis results from the AI ​​model and determines suggested actions for the crop. For example, if leaf discoloration indicates a nutrient deficiency, it will select the necessary type of fertilizer and application method.

[0605] Step 6:

[0606] The server generates a notification message containing the suggested action and sends it to the terminal. The message includes the recommended action and the steps to take it.

[0607] Step 7:

[0608] The terminal displays messages received from the server in the user interface. The user can review the notification and take the suggested action.

[0609] Step 8:

[0610] The user follows the suggested actions to manage their home garden, for example, by applying the appropriate fertilizer or providing the necessary amount of water.

[0611] (Example 1)

[0612] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] In home gardening, users need specialized knowledge and effort to properly manage the health and growth of plants. However, because there is no readily established method for doing so easily and accurately, it is difficult to maintain crop health and grow them efficiently.

[0614] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0615] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the condition of the plant, an AI processing means for evaluating the growth and health status of the plant using an AI model generated using prompt sentences, and a suggestion means for suggesting the optimal treatment for the plant based on the evaluation results of the AI ​​processing means. This makes it possible for users to easily understand the condition of crops and perform appropriate management without specialized knowledge.

[0616] A "photography device" is a device used to acquire image data of plants, and usually refers to a portable information terminal equipped with a camera function.

[0617] "Image data" refers to visual information acquired by a camera, and is a digital file used to evaluate the growth and health status of plants.

[0618] "Analysis means" refers to a method or apparatus for evaluating the condition of a plant using received image data, and usually includes software or algorithms.

[0619] "AI processing means" refers to a method or system that uses a generated AI model to perform computational processing to evaluate the growth and health status of plants.

[0620] "Suggestion means" refers to a method or apparatus for determining the optimal treatment for plants based on the evaluation results of the AI ​​processing means and proposing it to the user.

[0621] "Notification means" refers to a method or device for transmitting information determined by the proposed means to the user and providing information that can be used for managing home gardening.

[0622] A "server" refers to a computing device or system for receiving, analyzing, suggesting, and notifying about image data, and communicates with portable information terminals via a network.

[0623] A "user" refers to an individual who operates the system and manages the plants, typically someone who practices home gardening.

[0624] This invention is a system to support the management of home gardening, primarily utilizing a portable information terminal and a server. The user takes pictures of plants using a camera mounted on the portable information terminal. This terminal generates the captured image data in digital format and transmits it to the server via the internet.

[0625] The server is a computing device that includes AI processing capabilities to analyze image data received from the terminal. The server utilizes generative AI models built with machine learning libraries such as TensorFlow and PyTorch to evaluate the growth and health status of plants in detail. These AI models analyze characteristics such as the color and shape of plant leaves to identify the presence of pests and diseases and nutrient deficiencies. Based on the evaluation results, the server determines necessary actions and creates recommendations for the user.

[0626] The suggested actions are communicated to the user via the device. The device quickly displays the suggestions to the user and provides helpful feedback for managing home gardening. Based on this information, the user can, for example, add the appropriate amount of fertilizer or water the plants. For example, if the AI ​​model detects yellowing of tomato leaves, it might determine that there is a possibility of nitrogen deficiency and suggest adding nitrogen-containing fertilizer.

[0627] An example of a prompt message would be, "Analyze the latest image of the plant and suggest its health status and necessary actions." In this way, the system allows users to properly manage the condition of their home gardens without requiring specialized knowledge.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The user takes a picture of a plant using the camera on their portable information terminal. The input for this step is visual information of the plant, and the output is the captured image data. Specifically, the user launches the camera app on their terminal, points the camera at the plant, and presses the shutter button to save the image.

[0631] Step 2:

[0632] The device sends the captured image data to the server using the internet. The input for this step is the captured image data, and the output is the completion of the data transmission to the server. Specifically, the device uses its data communication function to upload the image to the specified server address via the HTTP protocol.

[0633] Step 3:

[0634] The server activates an AI processing system to analyze the image data received from the terminal. The input for this step is the image data received by the server, and the output is information evaluating the plant's condition based on the image analysis. Specifically, the server calls a generating AI model and uses prompt messages to analyze the color and shape of the plant's leaves, detecting signs of pests, diseases, and nutrient deficiencies.

[0635] Step 4:

[0636] The server proposes the optimal treatment for plants based on evaluation information obtained from AI processing. The input is plant condition evaluation information, and the output is a list of proposed actions. Specifically, the server generates concrete advice based on established evaluation criteria, such as suggesting the addition of nitrogen fertilizer if a nitrogen deficiency is detected.

[0637] Step 5:

[0638] The server sends the proposed actions back to the terminal as a notification message. The input is a list of proposed actions, and the output is the notification message sent to the terminal. Specifically, the server formats the results into a notification format and sends the message to the terminal using a communication protocol.

[0639] Step 6:

[0640] The device displays notification messages received from the server to the user. The input for this step is the notification message from the server, and the output is information that the user can view on the screen. Specifically, the device displays a pop-up notification on the screen and holds the notification until the user acknowledges it.

[0641] Step 7:

[0642] The user performs specific care for the plants based on notifications from the device. The input for this step is the information displayed on the device, and the output is the actual farming work performed. Specifically, the user follows the displayed suggestions to apply the necessary amount of fertilizer to the plants or take measures against pests and diseases.

[0643] (Application Example 1)

[0644] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0645] Efficient quality inspection of goods is crucial in manufacturing. However, traditional methods rely on visual inspection by skilled inspectors, which are prone to false positives, missed defects, and human errors. Furthermore, the limited inspection speed slows down the production line. This leads to a decrease in overall production efficiency and an increased likelihood of quality defects.

[0646] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0647] In this invention, the server includes an evaluation means for receiving image data acquired by a camera and evaluating the quality status of an article, a suggestion means for proposing improvement measures for the article based on the evaluation results, and a notification means for notifying workers of the suggestion. This makes it possible to improve the accuracy and speed of quality inspection of articles and to efficiently optimize the production process.

[0648] A "photography device" is a device used to acquire image data of an object.

[0649] "Image data" refers to data that digitally represents visual information, including the shape and color information of an object.

[0650] "Evaluation method" refers to the process of analyzing image data to determine the quality status of an item.

[0651] "Quality condition" is a general term for the results of evaluating the shape, color, and presence or absence of defects of an item.

[0652] "Proposed methods" refer to the process of suggesting specific improvement measures for an item based on the results of a quality evaluation.

[0653] "Notification methods" refer to the process of communicating information about corrective measures to workers.

[0654] "Workers" are individuals involved in the production or inspection of goods.

[0655] "Machine learning technology" is a technique that uses algorithms to learn features from data and recognize patterns and trends.

[0656] A system for carrying out this invention includes an automated device for photographing items on a production line, a server for processing the image data obtained thereby, and a terminal for transmitting information to workers.

[0657] The imaging device plays the role of acquiring high-precision images of items moving along the production line. This device accurately captures the shape and color of the items and transmits that visual information to a server in digital format.

[0658] The server is equipped with an evaluation mechanism for analyzing received image data and assessing the quality of the items. This evaluation mechanism utilizes machine learning techniques to analyze image data based on patterns learned from numerous datasets. Specifically, machine learning frameworks such as Google TensorFlow and PyTorch are used to detect color inconsistencies and shape abnormalities in the items.

[0659] Based on the evaluation results, the server generates improvement measures for the item through the suggested means. This information is transmitted to the terminal via the communication line.

[0660] The terminal notifies workers of proposed corrective actions visually or audibly. Through these notifications, workers can quickly identify defects in items and make necessary corrections.

[0661] As a concrete example, consider using this system for inspecting the print quality of canned beverages moving along a production line. A camera captures minute printing inconsistencies on the surface of the cans, and a server analyzes the images to detect the inconsistencies and generate correction instructions. As a result, workers can quickly take action, such as removing problematic cans.

[0662] An example of a prompt message is: "Implement a function to perform real-time quality inspections of items on the production line, identify defective parts, and output improvement suggestions." This is expected to significantly increase automation and efficiency on the production floor.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The user-operated camera captures images of items moving along the production line. Input includes information on the shape and color of the items, and output generates digital image data.

[0666] Step 2:

[0667] The imaging device transmits the generated image data to the server. The server receives this data and transfers it to a temporary storage area for data storage. The input is image data, and the output is temporary data stored within the server.

[0668] Step 3:

[0669] The server inputs the received image data into a machine learning model and begins data analysis. The technologies used are Google TensorFlow and PyTorch, with image data as input and an evaluation result of the quality status of the items as output. Specifically, it analyzes the shape and color variations of the items to detect quality anomalies.

[0670] Step 4:

[0671] The server generates corrective actions based on the evaluation results. These actions include specific suggestions for defects and areas requiring improvement. The input is the quality status evaluation result, and the output is the proposed corrective actions.

[0672] Step 5:

[0673] The server sends the generated corrective actions to the terminal. The terminal receives this information and prepares to notify the user visually or audibly. The input is the proposed corrective actions, and the output is the information prepared for notification.

[0674] Step 6:

[0675] The terminal provides notifications to workers. Specifically, corrective actions are displayed on the screen, or work instructions are communicated via voice notification. The input is the information prepared for notification, and the output is the communication of instructions to the worker.

[0676] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0677] This invention is a system for managing home gardens, providing support that also takes user emotions into consideration. Specifically, it is operated using a smartphone, starting with the user taking pictures of vegetables and fruits. The captured images are sent from the smartphone to a server. The server analyzes the received images using artificial intelligence technology with machine learning algorithms to evaluate the growth status, health status, and presence of pests and diseases of the crops.

[0678] This system integrates an emotion engine that recognizes the user's emotions. The terminal collects emotion data through the user's voice or text input. The emotion engine analyzes this data to determine how the user is feeling while using the system. This emotion analysis infers emotions from the tone of the messages the user enters, the words they choose, and even the intonation and tension of their voice.

[0679] The server combines crop analysis results with user sentiment analysis results to suggest the optimal action. For example, if the user's emotions indicate stress, it may suggest simpler and quicker actions. On the other hand, if the user indicates positive emotions, it can suggest a more detailed and comprehensive approach.

[0680] Finally, a notification is sent from the server to the terminal and presented to the user through the notification system. This notification includes a tone and content that corresponds to the user's emotional state, ranging from gentle expressions such as "Why not try this method?" to urgent expressions such as "You need to water your plants urgently." This makes it easier for the user to respond according to their emotional state and to better care for their home garden. For example, if the user's emotional state is indicated as busy, the server will notify them concisely of the minimum necessary actions to help them respond quickly.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The user launches the smartphone app and uses the camera to take pictures of vegetables and fruits in their home garden. Once the shooting is complete, they press the send button on the app to begin sending the image data.

[0684] Step 2:

[0685] The device temporarily stores the image data and adjusts the image resolution to optimize the data size. This improves transmission efficiency. Once optimization is complete, the image data is sent to the server.

[0686] Step 3:

[0687] The server inputs the received image data into an AI model. This AI model is trained to identify the type of crop, its growth stage, health status, and the presence or absence of pests and diseases. The AI ​​model analyzes the images and evaluates the condition of the crops.

[0688] Step 4:

[0689] The device sends voice and text input from the user to the emotion engine. The emotion engine analyzes this input data to infer the user's emotional state. The emotion data includes the user's voice tone and the content of the entered text.

[0690] Step 5:

[0691] The server integrates AI-based crop condition assessments with user sentiment assessments from an emotion engine to determine the optimal action for the crops. For example, if the user's sentiment is negative, it recommends simple and immediate actions.

[0692] Step 6:

[0693] The server sends a notification message to the device containing the suggested action. This message includes a tone that reflects the user's emotional state.

[0694] Step 7:

[0695] The device displays received notification messages in the user interface. The user reviews the notification and takes the suggested action. If the user is relaxed, a detailed process may be suggested; if they are stressed, only the key points are provided.

[0696] (Example 2)

[0697] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] In home gardening, there is a need for assistance in helping users accurately understand the growth status of their crops and manage them appropriately. However, especially for beginners and busy users, emotions can affect work efficiency, making efficient garden management difficult. In this situation, there is a need for management support that takes into account not only the condition of the crops but also the user's emotions.

[0699] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0700] In this invention, the server includes an analysis means for evaluating the condition of crops, an emotion analysis means for analyzing the user's emotional data, and a suggestion means for proposing customized treatments based on the crop analysis results and the user's emotional state. This makes it possible to support garden management that is appropriate not only to the condition of the crops but also to the user's emotional state.

[0701] "Image capture device" refers to equipment used to acquire image data, and specifically refers to mobile devices such as handheld terminals.

[0702] "Image data" refers to digital information acquired by a camera or other imaging device to visually record the condition of crops.

[0703] "Analysis means" refers to a system component that has the function of analyzing received image data and evaluating the growth and health status of crops.

[0704] The "proposal means" refers to a system component responsible for indicating the optimal treatment for crops to the user based on the evaluation results obtained by the analytical means.

[0705] A "notification mechanism" is a system element that has the function of communicating proposed measures to users and helping them manage their gardens.

[0706] An "emotion analysis tool" is a system component that collects and analyzes emotional data from users to identify the user's emotional state.

[0707] "User" refers to an individual who operates and uses the system for the purpose of managing a home garden.

[0708] "Treatment" refers to the specific actions or management methods that users should take to improve or maintain the condition of crops.

[0709] This invention is a system for streamlining the management of home gardens, providing appropriate management support that visually assesses the condition of crops and reflects the user's emotional state. The user takes pictures of the crops being grown using a mobile device such as a smartphone. The image data obtained from this device is transmitted to a server via the device.

[0710] The server analyzes the received image data using image recognition algorithms based on machine learning techniques. Specifically, it uses libraries such as TensorFlow and PyTorch to evaluate the growth status, health, and presence of pests and diseases of the crops. This evaluation allows for a detailed understanding of the crop's condition.

[0711] Next, the device collects voice or text input data to determine the user's emotional state. This data is sent to a server and analyzed by an emotion analysis engine. Emotion analysis can utilize commonly used natural language processing techniques. For example, it may involve recognizing the user's current emotional state by examining the tone of the text they input and the intonation of their voice.

[0712] The server combines image analysis results and emotion analysis results to propose specific and efficient crop management methods tailored to the user's current situation. These proposals use flexible language that takes the user's emotions into account, offering concise solutions for users with low moods and a wide range of options for users in a positive state.

[0713] An example of a prompt message might be, "Please suggest the best way to manage your home garden based on the photos and audio." This prompt message allows the system to utilize a generative AI model to receive optimal management suggestions.

[0714] For example, if a user feels busy, the server will provide specific instructions such as, "Please water your plants immediately to ensure they meet the minimum requirements," helping the user take immediate action based on these instructions. This allows users to manage their gardens effectively in line with their emotional state and ensure the health of their crops.

[0715] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0716] Step 1:

[0717] Users take photos of crops being grown using mobile devices such as smartphones. The captured image data is input to the device and sent to the server. Here, the image data is temporarily stored in digital format, encrypted, and prepared for transmission.

[0718] Step 2:

[0719] The server receives image data from the terminal as input. Next, it analyzes this image data using machine learning algorithms. Specifically, it uses image recognition technology to evaluate the growth status, health status, and presence of pests and diseases of crops, and outputs the evaluation results. The processing is carried out using libraries such as TensorFlow and PyTorch.

[0720] Step 3:

[0721] Users input their thoughts and feelings into the device via voice or text. This input data is stored on the device as emotion data and prepared to be sent to the server. The input in this step primarily consists of the user's emotional expressions while using the system.

[0722] Step 4:

[0723] The server receives emotional data from the terminal as input. The emotion analysis engine uses natural language processing technology to analyze this data and identify the user's emotional state. It considers the tone of the message, the words selected, the intonation of the voice, etc., to infer the user's emotional state and output the result.

[0724] Step 5:

[0725] The server combines crop condition assessment results obtained from image data with sentiment analysis results obtained from emotion data. Based on this combination, it proposes the optimal treatment for the crop. For example, even if the crop is healthy, if the user is experiencing stress, the server will output a suggestion prioritizing a simple and quick response.

[0726] Step 6:

[0727] The server generates a notification message containing the suggested actions and sends it to the terminal. The notification incorporates a tone that reflects the user's emotions, using softer or more urgent language. The input is the suggested action, and the notification generated based on that is sent to the terminal as output.

[0728] (Application Example 2)

[0729] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] In recent years, advancements in information processing technology have created a demand for information provision that takes into account the user's psychological state. However, existing systems focus solely on evaluating the state of objects and are unable to provide optimal suggestions that consider the user's psychological factors, thus hindering improvements in the user experience. Therefore, there is a need for a system that enables suggestions based on an evaluation that combines the state of objects with the user's psychological state.

[0731] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0732] In this invention, the server includes an analysis means for receiving image data acquired by a camera and evaluating the state of an object, a suggestion means for proposing the optimal course of action for the object based on the analysis results and the user's psychological state, and a notification means for notifying an external device of the suggestion and making adjustments according to the psychological state. This makes it possible to provide optimal information according to the user's psychological state.

[0733] A "photography device" is a device used to acquire image data, and includes mobile information terminals.

[0734] "Image data" refers to digital data that contains visual information about an object.

[0735] "Object" refers to the tangible substance that is the subject of analysis, such as crops or food products.

[0736] "Analytical means for evaluating the state" refers to methods and systems that use machine learning techniques to evaluate the growth status and health status of an object.

[0737] "Proposal method" refers to a process or system for providing treatment or suggestions regarding an object based on the analysis results and the user's psychological state.

[0738] "Notification means" refers to methods and technologies for transmitting suggestions to external devices and adjusting information according to the user's psychological state.

[0739] "Psychological state" refers to the state of the user's emotions and mood, and is inferred from voice and text data.

[0740] "External devices" refer to devices that receive information and present it to the user, such as smartphones.

[0741] "Treatment" refers to countermeasures or action plans for an object, proposed to improve the object's condition.

[0742] The embodiment of this invention mainly involves a camera, a server, and an external device. A portable information terminal is used as the camera, and image data of an object is acquired. The acquired image data is transmitted to the server, which evaluates the state of the object based on this data.

[0743] The server uses machine learning techniques to determine the growth status, health status, and presence of abnormalities in objects. Specifically, it uses artificial intelligence technologies such as TensorFlow for analysis. Furthermore, it analyzes the user's psychological state from text and voice data and suggests the most appropriate action based on that state. Natural language processing tools such as NLTK are used for sentiment analysis. This makes it possible to recommend new activities when the user is relaxed and suggest simpler actions when the user is stressed.

[0744] These suggested actions are notified from the server to an external device and then transmitted to the user. A smartphone or similar device may be used as the external device. This notification is adjusted according to the situation, providing information appropriate to the user's psychological state.

[0745] As a concrete example, suppose a user takes a picture of food, and the application analyzes its nutritional value. If the application recognizes that the user has had a busy day, it will suggest a simple and healthy recipe. An example of a prompt to the generative AI model might be, "Please give me a simple, nutritious, and delicious recipe for a busy day."

[0746] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0747] Step 1:

[0748] The user takes a picture of an object (e.g., food) using the camera on their mobile device. This image data becomes the input for the program. As a result, the device prepares to send this image data to the server.

[0749] Step 2:

[0750] The server receives image data sent from the terminal. Using the received image data as input, it evaluates the state of the object using a machine learning algorithm (such as TensorFlow). Specifically, it performs image recognition to identify what the object is and extracts data on its components and nutritional value. The results of this analysis become the output.

[0751] Step 3:

[0752] The user inputs emotional voice or text data into the device. This data becomes the program's input, and the device prepares to send it to the server.

[0753] Step 4:

[0754] The server receives voice or text data from the terminal and analyzes the user's emotional state using natural language processing tools (such as NLTK). It then takes the analyzed emotional data as input and determines the user's psychological state. The results of this analysis become the output.

[0755] Step 5:

[0756] The server combines the evaluation results of the object's state with the analysis results of the user's emotional state. Using these as input, it proposes the optimal course of action and outputs information and advice generated by a generative AI model. For example, when the user is busy, it suggests a simple, nutritious recipe.

[0757] Step 6:

[0758] The server notifies an external device (smartphone) of the proposed content. The tone and content of the notification are adjusted according to the user's emotional state. The notification content becomes the output, and the external device displays it. In this way, the user can take appropriate action based on the information received.

[0759] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0760] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0761] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0762] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0763] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0764] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0765] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0766] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0767] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0768] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0769] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0770] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0771] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0772] 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.

[0773] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0774] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0775] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0776] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0777] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0778] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0779] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] An analysis means for receiving image data acquired by a camera and evaluating the condition of crops,

[0783] A proposal method that suggests the optimal treatment for crops based on the analysis results,

[0784] A notification method for informing users of the proposal,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein a mobile terminal is used as the imaging device.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein artificial intelligence technology is used as an analytical means to evaluate the growth status, health status, and presence or absence of pests and diseases of crops.

[0790] "Example 1"

[0791] (Claim 1)

[0792] An analytical means for receiving image data acquired by a camera and evaluating the condition of the plant,

[0793] The analysis method utilizes an AI model generated using prompt sentences to evaluate the growth and health status of plants, and includes an AI processing method for evaluating the growth and health status of plants.

[0794] A proposal means that suggests the optimal treatment for plants based on the evaluation results of the AI ​​processing means,

[0795] A notification system that informs users of suggestions and provides information to help users manage their home gardening,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein a portable information terminal is used as the imaging device.

[0799] (Claim 3)

[0800] The system according to claim 1, wherein the analytical means uses machine learning technology to evaluate the presence or absence of plant diseases and pests.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] An evaluation means for receiving image data acquired by a camera and evaluating the quality status of an item,

[0804] A proposal mechanism for suggesting improvement measures for items based on evaluation results,

[0805] A means of notifying workers of the proposal,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which uses an automatic device as the imaging device.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the evaluation means uses machine learning technology to evaluate the shape, color, and presence or absence of defects of an article.

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

[0812] (Claim 1)

[0813] An analysis means for receiving image data acquired by a camera and evaluating the condition of crops,

[0814] A proposal method that suggests the optimal treatment for crops based on the analysis results,

[0815] A notification method for informing users of the proposal,

[0816] An emotion analysis method that collects user emotion data, analyzes those emotions, and identifies the user's emotional state,

[0817] Based on the crop analysis results and the user's emotional state, we propose customized treatments.

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein a mobile terminal is used as a shooting device.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the analytical means uses machine learning technology to evaluate the growth status, health status, and presence or absence of pests and diseases of crops.

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

[0824] (Claim 1)

[0825] An analysis means for receiving image data acquired by a camera and evaluating the state of an object,

[0826] A proposal means that suggests the optimal treatment for an object based on the analysis results and the user's psychological state,

[0827] A notification means that notifies an external device of the proposal and makes adjustments according to the psychological state,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein a portable information terminal is used as the imaging device.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the analytical means uses machine learning techniques to evaluate the growth status, health status, and presence or absence of abnormalities of an object. [Explanation of Symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An analysis means for receiving image data acquired by a camera and evaluating the condition of crops, A proposal method that suggests the optimal treatment for crops based on the analysis results, A notification method for informing users of the proposal, A system that includes this.

2. The system according to claim 1, wherein a mobile terminal is used as the imaging device.

3. The system according to claim 1, wherein artificial intelligence technology is used as an analytical means to evaluate the growth status, health status, and presence or absence of pests and diseases of crops.

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