system

Abstract

Provide a system. 【Solution means】 Means for collecting climate information and market information and storing them in a database, Means for analyzing the above information to identify risk factors and market trends of crops, Means for presenting profitable plants and optimal planting times to crop producers based on the identified information, Means for analyzing the individual data of crop producers and optimizing the production method, Means for proposing new sales channels and high-demand products based on the above analysis, Means for providing an educational program to improve crop cultivation techniques, Means for activating information exchange, Means for individualizing information on plant production in the urban environment and providing it through digital devices, A system including the above.

Description

Technical Field

[0001] The technology of this 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] In conventional agriculture, it is difficult to quickly respond to climate change and market changes. As a result, there is a risk of reduced profitability of agricultural crops. Also, the instability of crop yields and quality due to natural factors such as weather and pests has been a major issue. Furthermore, it is difficult to accurately grasp consumers' needs and find effective sales channels, thus hindering the improvement of the income of agricultural workers.

Means for Solving the Problems

[0005] This invention includes means for collecting climate and market information and storing it in a database. Furthermore, it includes means for analyzing this information and identifying risk factors and market trends for agricultural crops. Based on the identified information, it provides means for suggesting highly profitable crops and optimal planting times to farmers, thereby preventing a decline in profitability. It also mitigates risks such as weather and pests through means for analyzing individual farmer data and optimizing production methods. Furthermore, it supports effective market development by proposing new sales channels and high-demand products. By providing educational programs, improving agricultural technology, and fostering community building to promote information exchange, it aims to enhance the skills of farmers and realize sustainable agricultural management.

[0006] "Climate information" refers to data about local weather conditions such as temperature, precipitation, and sunshine duration.

[0007] "Market information" refers to data on agricultural product price trends, supply and demand balance, distribution channels, and so on.

[0008] A "database" is an information management system that efficiently stores collected information and performs necessary analysis on it.

[0009] "Risk factors" are elements that can affect profitability in agricultural production, and mainly include climate change and the occurrence of pests and diseases.

[0010] "Market trends" are indicators that show market trends related to agricultural products, including price fluctuations and the emergence of new demand.

[0011] A "highly profitable crop" is an agricultural product that is expected to yield relatively high profits based on current and future market demand and prices.

[0012] "Planting season" refers to the optimal time to plant a particular crop, and is selected considering weather conditions and soil conditions.

[0013] "Individual data of agricultural workers" refers to unique information specific to each farm, such as land conditions, materials used, and management style.

[0014] "Optimizing production methods" is the process of improving individual production activities with the aim of efficient resource utilization and increased profitability.

[0015] "Sales channels" refer to the distribution routes through which agricultural products reach the market, including retailers and wholesale markets.

[0016] "High-demand products" are agricultural products that have a high volume of transactions in the market and are popular with consumers, thus having the potential to generate stable revenue.

[0017] An "educational program" refers to learning content and training designed to help agricultural workers improve their skills.

[0018] A "community" is a group of people who come together with common goals or interests to share information and support each other. [Brief explanation of the drawing]

[0019] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable operations, and is implemented as follows.

[0041] This system includes multiple modules for collecting and analyzing weather and market data. The server periodically retrieves data from external weather information providers and market information systems and stores it in a database. This database holds a wide range of information, including temperature, precipitation, sunshine hours, regional market trends, and price fluctuations.

[0042] The server uses AI algorithms to analyze collected information and identify risk factors and market trends in crop cultivation. The AI ​​algorithms leverage historical data to predict future weather conditions and market trends. This makes it possible to determine which crops are best suited for specific regions and times.

[0043] Users can provide data about their farms to the cloud through an interface. This could include information about soil composition and available resources. The server uses this information to create production methods and fertilization plans optimized for each individual farm, and delivers them to the user via their terminal. This allows users to receive concrete action plans, minimizing waste and improving productivity.

[0044] Furthermore, the server forecasts market demand and suggests products best suited to the environment. The terminal presents users with recommended sales methods and destinations, supporting them in developing new sales channels. Specifically, it can provide crop cultivation plans based on seasonal demand forecasts.

[0045] In addition, the system provides users with new agricultural techniques and knowledge through educational programs. This allows farmers to improve their skills and stimulate information exchange within the community. The terminal notifies users of registration information for online workshops and training sessions, providing learning opportunities.

[0046] For example, if high temperatures are predicted in a certain region, the server may recommend crops that are highly heat-resistant and in high market demand. Furthermore, based on the user's data, it may suggest efficient irrigation methods and fertilizer usage, providing measures to ensure profitability while reducing environmental impact.

[0047] Thus, this invention aims to provide AI-driven, data-driven support to address the various challenges faced by agricultural workers, thereby enabling sustainable and profitable agricultural management.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server periodically collects weather and market information from external weather data providers and market information systems. This includes data on temperature, precipitation, sunshine hours, and market prices and demand for agricultural products. The collected data is stored in a database.

[0051] Step 2:

[0052] The server uses AI algorithms to analyze accumulated data and identify regional weather variability patterns and market trends. This allows for the analysis of current and future risk factors and the development of optimal crop selection guidelines.

[0053] Step 3:

[0054] Users upload individual data related to their farm (e.g., soil composition, available resources, etc.) via a cloud-based platform. This data is necessary to conduct highly accurate individual analyses.

[0055] Step 4:

[0056] The server generates optimized production methods and fertilization plans tailored to the characteristics of each farm, based on the uploaded individual data. These suggestions are created based on the results of AI-driven data analysis.

[0057] Step 5:

[0058] The terminal notifies the user of the production methods and fertilization plans generated by the server. Based on this information, the user can create a specific farming schedule.

[0059] Step 6:

[0060] The server analyzes market demand and identifies crops and sales channels that are expected to be in high demand during specific seasons. This allows users to efficiently develop sales channels for their crops.

[0061] Step 7:

[0062] The terminal provides users with sales strategies recommended by the server and new market information. Based on this, users plan and implement effective sales activities.

[0063] Step 8:

[0064] The server helps improve agricultural techniques by collecting and providing users with information on online educational programs and workshops.

[0065] Step 9:

[0066] The device notifies users of opportunities to participate in educational programs and provides links to access learning content. Through this, users can acquire the latest knowledge and techniques related to agriculture.

[0067] (Example 1)

[0068] 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."

[0069] In the agricultural sector, it is difficult to achieve sustainable and profitable management in response to rapid changes in weather conditions and market trends. In particular, there are challenges in developing production plans optimized for individual farms and selecting sales channels appropriate for the times. Furthermore, there is a need for improvements in agricultural technology and the promotion of efficient information exchange.

[0070] 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.

[0071] In this invention, the server includes means for acquiring climate information and market information from information providers and information systems and storing it in a storage device; means for analyzing the stored information using a learning model and predicting crop risk factors and market trends; and means for indicating highly profitable crops and appropriate planting times to agricultural workers based on the predicted results. This enables agricultural workers to respond immediately to weather and market fluctuations and adopt strategies optimized for individual farms.

[0072] "Information provider" refers to a third-party organization or system that provides climate information or market information.

[0073] An "information system" is a technological foundation for aggregating information and providing it to users as a service.

[0074] A "storage device" is a device or system designed to store data and allow it to be quickly retrieved as needed.

[0075] A "learning model" is an algorithm designed to analyze patterns and trends based on past data in order to make predictions and classifications about the future.

[0076] "Crop risk factors" refer to uncertain factors such as climatic conditions and pests and diseases that may affect the growth and harvest of crops.

[0077] "Market trends" refer to the ever-changing market conditions, including changes in the supply and demand of agricultural products, price trends, and distribution conditions.

[0078] "Profitability" refers to the degree of economic benefit derived from agricultural work and production activities.

[0079] "Planting season" refers to the time when crops are planted at the most suitable time to obtain the best harvest.

[0080] "Workers engaged in agricultural activities" refers to those who engage in agricultural activities as a profession.

[0081] "Sales channels" refer to the distribution routes used to deliver products to consumers or the market.

[0082] "High-demand products" refer to goods or crops that are considered to be in high demand among consumers in the market.

[0083] A "learning program" refers to a structured educational program designed to help individuals acquire new knowledge and skills.

[0084] A "group" refers to an assembly of people or organizations that share a common purpose or characteristics.

[0085] "Information exchange" is a communication activity in which individuals or organizations share necessary information with each other.

[0086] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable farming operations. This system is primarily built through the interaction of servers, terminals, and users.

[0087] The server periodically acquires climate and market data from climate information providers and market information systems, and stores it in a database. High-performance servers are used as hardware, and the software includes APIs for data collection and a database system (e.g., PostgreSQL) for data management. The collected data is analyzed using AI algorithms. The AI ​​model uses machine learning frameworks (e.g., TENSORFLOW®, PyTorch) to predict future weather conditions and market trends based on historical data.

[0088] Users can provide individual farm data to the cloud via smart devices. This data includes soil composition, available resources, and past harvest yields, and is transmitted using secure communication protocols (e.g., SSL / TLS). Based on the user's data, the server generates individually optimized production plans and fertilization schedules.

[0089] The terminal's role is to deliver plans and analysis results generated on the server to the user. Furthermore, it also provides sales strategies and sales channel recommendations based on market demand. The terminal includes scheduling functions to allow users to participate in online workshops and learning programs.

[0090] For example, if high temperatures are predicted in a particular region in the near future, the server will proactively recommend heat-resistant crops to the user. Furthermore, based on user-provided data, it will suggest optimal irrigation methods and fertilizer usage, supporting reduced environmental impact and improved profitability. This system allows farmers to obtain concrete and actionable plans, even while being affected by market fluctuations and climate changes.

[0091] An example of a prompt message is, "Based on weather data and market trend data from the past 10 years, please tell me the best crops for next summer and how to grow them."

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The server acquires weather and market information from information providers and market information systems. Input includes raw data obtained via APIs, such as temperature, precipitation, sunshine hours, and price trends. The data is initially stored locally, then formatted before being stored in a relational database. This ensures the data is readily available for subsequent analysis.

[0095] Step 2:

[0096] The server executes an AI algorithm based on accumulated data. The input consists of historical weather and market data stored in a database. Using a generative AI model, the server extracts features from the input data and predicts weather conditions and market trends. The output provides predictive information regarding future risk factors and market changes.

[0097] Step 3:

[0098] Users provide individual farm data to the cloud through an interface. Inputs include soil composition, available water resources, and crop types. This user-provided data is securely stored on the server and used for individual analysis. Outputs include confirmation of the provided information and a list of data to be provided next.

[0099] Step 4:

[0100] The server integrates user-provided data and forecast information to generate individually optimized production plans. The inputs used are the user's individual data and the server's forecast results. Through data calculations, specific production plans and action plans, such as planting timing, fertilization schedules, and irrigation methods, are created. The output consists of recommended measures for the user and their predicted effects.

[0101] Step 5:

[0102] The terminal notifies the user of production plans and analysis results generated by the server. Inputs include recommendations and planning data from the server. The terminal organizes this information and presents it to the user in an intuitively understandable format. The output is a detailed plan including the optimal actions the user should take and the steps involved.

[0103] Step 6:

[0104] The device provides users with learning programs and event information for further learning and knowledge enhancement. Input includes information on online workshops and training sessions. The device organizes this information using a calendar function and encourages user participation. Output is the user's learning schedule and its progress.

[0105] (Application Example 1)

[0106] 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."

[0107] In today's urban environment, agricultural producers need to respond quickly to changes in climate and market conditions in order to achieve sustainable and profitable plant production. However, plant producers in urban areas face challenges such as dispersed information and difficulty in obtaining timely advice. Furthermore, developing new sales channels and formulating strategies based on market trends is difficult due to a lack of expertise and resources. Solving these challenges is an urgent necessity.

[0108] 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.

[0109] In this invention, the server includes means for collecting climate information and market information and storing it in a database; means for analyzing the information and identifying risk factors and market trends for crops; and means for personalizing information on plant production in urban environments and providing it through a digital device. This enables plant producers to receive accurate and timely appropriate production methods and sales strategies.

[0110] "Climate information" refers to data on environmental factors such as temperature, precipitation, and solar radiation, tailored to specific regions and time periods.

[0111] "Market information" refers to data on price trends and the balance of supply and demand for agricultural products.

[0112] A "database" refers to a collection of information that stores and manages diverse information gathered, making it easy to search and analyze.

[0113] "Risk factors" refer to potential unfavorable conditions or events that may occur in agricultural activities.

[0114] "Market trends" refer to time-series movements related to fluctuations in the demand and supply of goods and services, as well as price movements.

[0115] "Agricultural producer" refers to an individual or organization engaged in the work of growing and harvesting agricultural products.

[0116] "Individual data" refers to a set of information related to a specific crop producer or their production activities.

[0117] "Production method" refers to the specific techniques and processes related to the cultivation and harvesting of agricultural products.

[0118] "Sales channels" refer to the distribution routes through which a product or service reaches the consumer.

[0119] An "educational program" refers to a series of planned educational activities aimed at improving specific skills or knowledge.

[0120] "Information exchange" refers to the act of sharing data and knowledge between individuals or organizations through communication.

[0121] The term "urban environment" refers to the conditions under which geographical and social factors in a city or its surrounding area interact with each other.

[0122] A "digital device" refers to an electronic device that can receive, process, transmit, and store information in digital format.

[0123] The system for realizing this invention has functions to support agricultural producers in urban environments. The server collects climate and market information via APIs and stores this information in a database. The specific software uses Python and its libraries, Pandas and NumPy, to efficiently organize and process data. For weather data and market trend prediction, an AI model is built using TensorFlow to analyze risk factors and market trends from the data.

[0124] The device plays a role in receiving specific advice and suggestions tailored to the urban environment, for example, through the user's smartphone or smart glasses. Users can receive notifications via the device regarding optimal planting times and fertilization plans for crops. In this case, a web application using the Flask framework is useful, enabling real-time information delivery.

[0125] Users can also upload their farmland information and production activity data to the cloud, which is then analyzed by a server to provide individually optimized fertilization plans and production methods. This enables efficient agriculture that is adapted to the constraints unique to urban environments. Furthermore, users can receive notifications of educational programs through digital devices and participate in learning new technologies and community activities.

[0126] For example, if high temperatures are predicted for a particular week, the server can recommend heat-resistant crops and develop a corresponding sales strategy. An example of a prompt would be, "Please suggest urban agricultural crops suitable for the high temperatures expected in the next week. Focus on high-demand, heat-resistant crops." This data is then input into the generating AI model, and the prediction results are presented to the user.

[0127] These features enable crop producers to conduct their activities based on accurate forecasts and appropriate strategies, thereby achieving sustainable production.

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The server connects to external climate information providers and market information systems to periodically collect climate and market data. Specifically, it uses APIs to obtain data such as temperature, precipitation, solar radiation, and crop price trends, and stores them in a database on the server. The input is information from external data providers, and the output is organized database entries.

[0131] Step 2:

[0132] The server uses Python and libraries to preprocess the collected data. This process includes imputing missing values ​​and removing outliers. The input is raw, unprocessed data from the database, and the output is a cleaned, analyzable dataset.

[0133] Step 3:

[0134] The server analyzes data collected and preprocessed using TensorFlow and predicts future climate conditions and market trends using an AI model. The generative AI model performs predictive calculations and generates forecast data for climate change risk and market demand. The input is a preprocessed dataset, and the output is risk factors and trend information based on the prediction results.

[0135] Step 4:

[0136] Based on the analysis results, the server calculates the optimal crops and planting times for agricultural producers and creates personalized recommendations. This includes creating specific fertilization plans and efficient production methods. The input is predicted risk factors and trend information, and the output is customized agricultural advice.

[0137] Step 5:

[0138] The terminal displays personalized advice and market information transmitted from the server in real time. Based on this, the user can decide on appropriate farming and sales activities. The input is advice from the server, and the output is visualized information provided to the user.

[0139] Step 6:

[0140] Users upload farmland information and production activity data to the cloud. This data is then used for further analysis on the server. The input consists of various data provided by the user, and the output is information stored in a database on the server.

[0141] Step 7:

[0142] The server generates and sends notifications for educational programs and community activities to each crop producer, encouraging participation. Input is program and event information, while output is notification messages for the user.

[0143] 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.

[0144] This invention is an agricultural support system that utilizes AI technology to improve profitability using climate and market information, and also features a user emotion recognition function using an emotion engine. This enables the provision of more personalized services based on user behavior and feedback.

[0145] This system consists of multiple modules. First, the server collects weather and market data from external sources and stores it in an internal database. This data includes detailed information such as regional weather conditions, soil information, and crop market prices. The server uses AI algorithms to analyze this data, identify risk factors, and suggest optimal crops and planting times to farmers.

[0146] In addition, the server uses an emotion engine to recognize the user's emotions. Users upload data related to their farm information and production activities on the platform. The system analyzes the user's input and behavior patterns to evaluate their emotional state. This emotional data is used as a factor in customizing the agricultural advice and educational programs provided.

[0147] The device provides user-specific feedback based on evaluation results from the emotion engine. Specifically, if it is determined that the content is not understood or that the user's interest and motivation are low, it can increase user engagement by providing content with adjusted difficulty levels or information that will pique their interest.

[0148] Furthermore, the server analyzes users' emotional tendencies to inform predicted market trends and sales strategies. For example, if a large number of farmers in a particular region exhibit similar emotional shifts, it may reveal specific market needs and risks in that region. This information can be used to improve marketing strategies and production plans.

[0149] Thus, this invention, which combines an emotion engine, aims not only to provide data-driven support but also to achieve a high level of personalization that takes into account the user's emotions, thereby improving the profitability of agricultural workers in the long term.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The server periodically collects weather and market data from external sources and stores it in a database. This process includes temperature, precipitation, sunshine hours, market prices, and supply and demand information.

[0153] Step 2:

[0154] The server analyzes the collected data using AI algorithms to identify regional weather risks and market trends. This allows it to calculate the most profitable crops and the appropriate planting times for farmers.

[0155] Step 3:

[0156] Users upload individual data about their farms (e.g., soil composition, resource status, etc.) to the cloud platform. Since the accuracy of the information affects the accuracy of subsequent analysis, it is recommended to provide detailed and up-to-date data.

[0157] Step 4:

[0158] The server generates optimized production methods and fertilization plans based on the individual data provided by the user. These results are then delivered to the user, and specific action plans are proposed.

[0159] Step 5:

[0160] User input and behavioral patterns are continuously monitored by the emotion engine. The server analyzes this data to evaluate the user's emotional state and feeds that information back to other processing functions.

[0161] Step 6:

[0162] The device provides personalized feedback to the user based on the emotions it recognizes. For example, if the user is showing unstable emotions, it will present additional support information or motivational content.

[0163] Step 7:

[0164] The server analyzes emotional trends at the regional level based on sentiment data, and uses this information to predict market trends and sales strategies. This information is used to support the strategic decision-making of agricultural workers.

[0165] Step 8:

[0166] The device notifies the user of recommended educational programs and marketing strategies. Users can then follow these recommendations to further improve their skills and expand their sales opportunities.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0169] Traditional agricultural support systems only provided advice based on weather and market data, failing to adequately consider users' emotional factors and individual needs. Furthermore, they did not optimize market strategies using regional emotional trends, making it difficult to improve profitability in the long term. Additionally, traditional systems lacked personalization of services based on the behavior and feedback of farmers, making it difficult to sustain user motivation and engagement.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for acquiring weather data and market data from an external source and storing it in a storage device; means for analyzing the acquired data and detecting risk factors for agricultural products and market fluctuations; and means for collecting and analyzing emotional data of agricultural workers using an emotion recognition function and personalizing guidance content based on the results. This makes it possible to provide personalized agricultural advice that takes into account the emotional state of the user, and to achieve optimization of market strategies that utilize the emotional trends of the entire region. Furthermore, it is possible to improve profitability and optimize production techniques in the long term while maintaining the motivation of agricultural workers.

[0172] "Weather data" refers to information about the weather, and is a numerical representation of atmospheric phenomena such as temperature, precipitation, wind speed, and humidity.

[0173] "Market data" refers to information related to agricultural product transactions, including crop prices, demand, and supply conditions.

[0174] A "storage device" is hardware used to store digital information, and may include databases and storage servers.

[0175] "Analysis" refers to information processing activities aimed at deriving specific patterns or results based on collected data.

[0176] "Agricultural products" is a general term for cultivated crops and agricultural products, including fruits, vegetables, and grains.

[0177] "Risk factors" refer to elements or conditions that can cause uncertainty or losses in agricultural production.

[0178] "Market fluctuations" refer to the phenomenon in which the price and demand for agricultural products change over time.

[0179] "Emotion recognition functionality" is a technology that analyzes input information and behavior to identify emotions in order to understand the user's emotional state.

[0180] "Guidance content" refers to the advice and recommended activity plans provided to agricultural workers.

[0181] "Personalization" refers to the process of adjusting content and responses based on each user's characteristics and needs.

[0182] "Area-wide emotional trends" refers to aggregating the emotional states of multiple users in a specific area to understand trends and developments.

[0183] "Market strategy" refers to the plans and methods for effectively disseminating products or services in a specific market.

[0184] This invention is an agricultural support system that utilizes AI technology and is realized through collaboration among three parties: a server, a terminal, and a user.

[0185] The server periodically collects weather and market data from external data sources and stores it in cloud-based storage. The hardware used includes a cluster of high-speed servers, and APIs are utilized for data ingestion. This server analyzes the collected data using machine learning models and data mining techniques. Through this analysis, it identifies and predicts risk factors related to agricultural products. For example, an AI model can identify changes in rainfall patterns and assess their impact on crops. Furthermore, it can analyze sentiment data provided by users and diagnose their emotional state through sentiment recognition capabilities.

[0186] Users can input data about their land and production activities on the system's platform and upload it to the cloud to receive personalized agricultural advice from the server. Based on the user's actions and feedback, emotional data is collected and analyzed and recognized by the server. Using the results of this analysis, the server proposes the optimal fertilization plan and cultivation method to the user.

[0187] The terminal provides feedback to the user based on analytical data received from the server. The terminal evaluates the user's level of understanding and interest, and adjusts the content displayed accordingly. For example, if the user shows interest in a particular crop, the terminal recommends relevant educational materials and videos. In this way, it enhances user engagement while supporting the acquisition of optimal agricultural techniques.

[0188] As a concrete example, here is an example of a prompt:

[0189] "I'm currently growing tomatoes. When would be the ideal time for the next harvest? Also, what's the market price like?"

[0190] "Lately, I haven't been feeling very motivated to do farming. How can I make it more interesting?"

[0191] The present invention aims to improve the profitability and efficiency of agricultural workers in the long term by combining the information collected, analyzed, and presented in this manner.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The server collects weather and market data using external APIs. Inputs include regional information and target crop varieties. Based on these inputs, the server performs database queries and API calls to obtain the latest weather forecasts and market price information. The output is stored in cloud-based storage as saved weather and market datasets. Specifically, it stores information such as "Rainfall forecast for the next 3 days: 20%, Market price: Average price of tomatoes is 50 yen / kg."

[0195] Step 2:

[0196] The server applies machine learning algorithms to the collected data to identify risk factors. The inputs are the weather and market data saved in Step 1. The server uses this data to run a model and identify various risk patterns (e.g., the possibility of drought or a sharp drop in market prices). The output is generated as a risk report, which includes specific risk factors and predictions of their impact. Specifically, it might generate a report stating, "High temperatures next week could reduce tomato yields by 20%."

[0197] Step 3:

[0198] Users log in to the platform, enter their farm information, and provide emotional feedback. This information includes the types of crops grown, cultivated area, daily activity logs, and emotional comments. Once users upload this data to the system, the server records it for further analysis. The output is registered as a user profile, and the user's emotional state is evaluated. For example, the analysis might conclude that "the user is experiencing stress due to the prolonged high temperatures."

[0199] Step 4:

[0200] The server analyzes the emotional data provided by the user using an emotion recognition engine to personalize the guidance. The input is the user's emotional data obtained in step 3. The server uses an emotion analysis algorithm to generate advice tailored to the user's current situation. The output is personalized advice and recommended actions, which are displayed on the user interface. For example, the advice might be, "It is recommended to irrigate in the early morning or evening to reduce stress."

[0201] Step 5:

[0202] The terminal provides feedback tailored to the user's understanding and interests, based on analysis results and advice from the server. Input consists of advice received from the server and the user profile. The terminal considers these factors and customizes the information displayed on the screen. Output includes content suitable for the user's control panel, specifically suggesting, "You can learn about effective tomato irrigation methods in the following video content."

[0203] This entire process allows users to receive personalized agricultural support tailored to their individual needs, leading to improved profitability and efficiency.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0206] As urbanization progresses, urban residents are required to produce agricultural products efficiently and profitably in limited spaces. Maintaining and improving residents' motivation and interest in gardening activities is also a crucial challenge. Existing systems struggle to accurately reflect the emotional state and interests of individual users and provide personalized advice, and developing optimal management plans for public garden spaces is also cumbersome. This invention aims to solve these problems and optimize urban agriculture in smart cities.

[0207] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0208] This invention includes a server that collects weather and economic information and stores it in a data storage unit, a means for determining the user's emotional state using emotion recognition technology and providing personalized advice based on that, and a means for providing residents with an optimal management plan for urban public garden spaces. This enables urban residents to produce crops efficiently and profitably in limited spaces, and also helps maintain and improve residents' motivation and interest.

[0209] "Weather information" refers to data related to climate, such as weather and temperature, and is a factor that influences crop cultivation and agricultural planning.

[0210] "Economic information" refers to data related to market trends and supply and demand, and is used to determine crop profitability and formulate sales strategies.

[0211] A "data storage unit" is a system structure for accumulating information, a place where collected information is stored for later use.

[0212] "Emotion recognition technology" is a technology used to analyze and judge a user's emotional state, and is used to personalize advice and improve the user experience.

[0213] "Personalized advice" refers to individualized suggestions and instructions that are optimized and provided according to each user's emotional state and interests.

[0214] "Urban public gardening spaces" are areas within cities where communities and residents jointly use gardening and crop cultivation.

[0215] A "management plan" is a systematic framework of operations and policies aimed at the efficient use of resources and the environment, and serves as a roadmap to enable smooth operations.

[0216] This invention is a system aimed at improving the efficiency of urban agriculture and enhancing the user experience, providing personalized advice by utilizing various data. The server acquires weather and economic information from external sources and stores it in the data storage unit. Specifically, it is recommended to use Python and TensorFlow for data analysis and spaCy for sentiment recognition.

[0217] The server, based on the results of information analysis, formulates an optimal cultivation plan tailored to the farmland information and emotional state provided by the user, and presents it to the user. The terminal operates based on user input and feedback, generating advice that is more suitable for individual needs. In this process, a web application can be built using Flask.

[0218] As a concrete example, the terminal proposes a fertilization plan necessary for tomato cultivation in a public garden space to urban residents of Tokyo and provides local gardening event information based on the user's sentiment data. In this system, the following inputs to the generative AI model are used as prompts.

[0219] Example of a prompt:

[0220] "I'm growing tomatoes, and through emotional analysis, what events or activities would you recommend to boost my motivation?"

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The server acquires weather and economic information from external sources and stores it in its data storage unit. It uses weather data and market price information obtained via an API as input and stores it in a database in JSON format.

[0224] Step 2:

[0225] The server analyzes the stored information using Python and TensorFlow. Specifically, it identifies risk factors and elements that affect crop profitability from input data such as weather patterns and market trends, and outputs them as analysis results.

[0226] Step 3:

[0227] The server compares the analysis results with the user's individual farmland information and presents a personalized cultivation plan to the terminal via the Flask application. This plan includes recommended planting times and appropriate fertilization schedules.

[0228] Step 4:

[0229] The device receives user input and collects user feedback and input data. This data is analyzed using emotion recognition technology with spaCy to determine the user's emotional state. Here, user responses and operation history are used as input, and emotional tendencies are obtained as output.

[0230] Step 5:

[0231] The server uses the sentiment data obtained in step 4 to again provide the user with optimal gardening event information and engaging content through the Flask application. This aims to maintain user interest and increase motivation.

[0232] Step 6:

[0233] Based on suggestions received from the server, the user plans activities in urban public garden spaces. Further advice can be sought using prompts provided by the generative AI model.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable operations, and is implemented as follows.

[0251] This system includes multiple modules for collecting and analyzing weather and market data. The server periodically retrieves data from external weather information providers and market information systems and stores it in a database. This database holds a wide range of information, including temperature, precipitation, sunshine hours, regional market trends, and price fluctuations.

[0252] The server uses AI algorithms to analyze collected information and identify risk factors and market trends in crop cultivation. The AI ​​algorithms leverage historical data to predict future weather conditions and market trends. This makes it possible to determine which crops are best suited for specific regions and times.

[0253] Users can provide data about their farms to the cloud through an interface. This could include information about soil composition and available resources. The server uses this information to create production methods and fertilization plans optimized for each individual farm, and delivers them to the user via their terminal. This allows users to receive concrete action plans, minimizing waste and improving productivity.

[0254] Furthermore, the server forecasts market demand and suggests products best suited to the environment. The terminal presents users with recommended sales methods and destinations, supporting them in developing new sales channels. Specifically, it can provide crop cultivation plans based on seasonal demand forecasts.

[0255] In addition, the system provides users with new agricultural techniques and knowledge through educational programs. This allows farmers to improve their skills and stimulate information exchange within the community. The terminal notifies users of registration information for online workshops and training sessions, providing learning opportunities.

[0256] For example, if high temperatures are predicted in a certain region, the server may recommend crops that are highly heat-resistant and in high market demand. Furthermore, based on the user's data, it may suggest efficient irrigation methods and fertilizer usage, providing measures to ensure profitability while reducing environmental impact.

[0257] Thus, this invention aims to provide AI-driven, data-driven support to address the various challenges faced by agricultural workers, thereby enabling sustainable and profitable agricultural management.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The server periodically collects weather and market information from external weather data providers and market information systems. This includes data on temperature, precipitation, sunshine hours, and market prices and demand for agricultural products. The collected data is stored in a database.

[0261] Step 2:

[0262] The server uses AI algorithms to analyze accumulated data and identify regional weather variability patterns and market trends. This allows for the analysis of current and future risk factors and the development of optimal crop selection guidelines.

[0263] Step 3:

[0264] Users upload individual data related to their farm (e.g., soil composition, available resources, etc.) via a cloud-based platform. This data is necessary to conduct highly accurate individual analyses.

[0265] Step 4:

[0266] The server generates optimized production methods and fertilization plans tailored to the characteristics of each farm, based on the uploaded individual data. These suggestions are created based on the results of AI-driven data analysis.

[0267] Step 5:

[0268] The terminal notifies the user of the production methods and fertilization plans generated by the server. Based on this information, the user can create a specific farming schedule.

[0269] Step 6:

[0270] The server analyzes market demand and identifies crops and sales channels that are expected to be in high demand during specific seasons. This allows users to efficiently develop sales channels for their crops.

[0271] Step 7:

[0272] The terminal provides users with sales strategies recommended by the server and new market information. Based on this, users plan and implement effective sales activities.

[0273] Step 8:

[0274] The server helps improve agricultural techniques by collecting and providing users with information on online educational programs and workshops.

[0275] Step 9:

[0276] The device notifies users of opportunities to participate in educational programs and provides links to access learning content. Through this, users can acquire the latest knowledge and techniques related to agriculture.

[0277] (Example 1)

[0278] 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".

[0279] In the agricultural sector, it is difficult to achieve sustainable and profitable management in response to rapid changes in weather conditions and market trends. In particular, there are challenges in developing production plans optimized for individual farms and selecting sales channels appropriate for the times. Furthermore, there is a need for improvements in agricultural technology and the promotion of efficient information exchange.

[0280] 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.

[0281] In this invention, the server includes means for acquiring climate information and market information from information providers and information systems and storing them in a storage device, means for analyzing the stored information using a learning model to predict crop risk factors and market trends, and means for indicating to workers highly profitable crops and appropriate planting times based on the predicted results. As a result, farmers can respond immediately to changes in weather and the market and adopt strategies optimized for individual farms.

[0282] "Information provider" refers to a third-party organization or system that provides climate information and market information.

[0283] "Information system" is a technical infrastructure for aggregating information and providing it as a service to users.

[0284] "Storage device" is a device or system for storing data and enabling it to be retrieved quickly as needed.

[0285] "Learning model" is an algorithm designed to analyze patterns and trends based on past data for future prediction and classification.

[0286] "Crop risk factors" refer to uncertain factors such as climate conditions and pests that may affect the growth and harvest of crops.

[0287] "Market trends" indicate the ever-changing market situation, such as changes in supply and demand of agricultural-related goods, price trends, and distribution status.

[0288] "Profitability" refers to the degree of economic profit brought by farming operations and production activities.

[0289] "Planting time" refers to the time when the best harvest can be obtained by planting crops at the most suitable timing.

[0290] "Worker" refers to a person who engages in agricultural activities as a profession.

[0291] "Sales channels" refer to the distribution routes used to deliver products to consumers or the market.

[0292] "High-demand products" refer to goods or crops that are considered to be in high demand among consumers in the market.

[0293] A "learning program" refers to a structured educational program designed to help individuals acquire new knowledge and skills.

[0294] A "group" refers to an assembly of people or organizations that share a common purpose or characteristics.

[0295] "Information exchange" is a communication activity in which individuals or organizations share necessary information with each other.

[0296] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable farming operations. This system is primarily built through the interaction of servers, terminals, and users.

[0297] The server periodically acquires climate and market data from climate information providers and market information systems, and stores it in a database. High-performance servers are used as hardware, and the software includes APIs for data collection and a database system (e.g., PostgreSQL) for data management. The collected data is analyzed using AI algorithms. The AI ​​model uses machine learning frameworks (e.g., TensorFlow, PyTorch) to predict future weather conditions and market trends based on historical data.

[0298] Users can provide individual farm data to the cloud via smart devices. This data includes soil composition, available resources, and past harvest yields, and is transmitted using secure communication protocols (e.g., SSL / TLS). Based on the user's data, the server generates individually optimized production plans and fertilization schedules.

[0299] The terminal's role is to deliver plans and analysis results generated on the server to the user. Furthermore, it also provides sales strategies and sales channel recommendations based on market demand. The terminal includes scheduling functions to allow users to participate in online workshops and learning programs.

[0300] For example, if high temperatures are predicted in a particular region in the near future, the server will proactively recommend heat-resistant crops to the user. Furthermore, based on user-provided data, it will suggest optimal irrigation methods and fertilizer usage, supporting reduced environmental impact and improved profitability. This system allows farmers to obtain concrete and actionable plans, even while being affected by market fluctuations and climate changes.

[0301] An example of a prompt message is, "Based on weather data and market trend data from the past 10 years, please tell me the best crops for next summer and how to grow them."

[0302] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0303] Step 1:

[0304] The server acquires weather and market information from information providers and market information systems. Input includes raw data obtained via APIs, such as temperature, precipitation, sunshine hours, and price trends. The data is initially stored locally, then formatted before being stored in a relational database. This ensures the data is readily available for subsequent analysis.

[0305] Step 2:

[0306] The server executes an AI algorithm based on the accumulated data. At this time, the input is past weather data and market data accumulated in the database. The server uses a generative AI model to extract the features of the input data and predict weather conditions and market trends. As output, prediction information regarding future risk factors and market changes can be obtained.

[0307] Step 3:

[0308] The user provides individual data related to the farm to the cloud through the interface. What is included as input are the soil components, available water resources, crop types, etc. These data provided by the user are securely stored on the server side and used for individual analysis. As output, the confirmation result of the provided information and a list of data to be provided next are shown.

[0309] Step 4:

[0310] The server integrates the data provided by the user and the prediction information, and generates an individually optimized production plan. As input, the user's individual data and the prediction results by the server are used. Through data calculations, specific production plans and action plans such as sowing time, fertilization schedule, and irrigation method are created. The output is the recommended measures for the user and the prediction of their effects.

[0311] Step 5:

[0312] The terminal notifies the user of the production plan and analysis results generated by the server. As input, there is the recommended information and planned data from the server. The terminal organizes it and presents it to the user in a form that is intuitive and easy to understand. The output is a detailed plan including the optimal actions the user should take and the procedures.

[0313] Step 6:

[0314] The device provides users with learning programs and event information for further learning and knowledge enhancement. Input includes information on online workshops and training sessions. The device organizes this information using a calendar function and encourages user participation. Output is the user's learning schedule and its progress.

[0315] (Application Example 1)

[0316] 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."

[0317] In today's urban environment, agricultural producers need to respond quickly to changes in climate and market conditions in order to achieve sustainable and profitable plant production. However, plant producers in urban areas face challenges such as dispersed information and difficulty in obtaining timely advice. Furthermore, developing new sales channels and formulating strategies based on market trends is difficult due to a lack of expertise and resources. Solving these challenges is an urgent necessity.

[0318] 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.

[0319] In this invention, the server includes means for collecting climate information and market information and storing it in a database; means for analyzing the information and identifying risk factors and market trends for crops; and means for personalizing information on plant production in urban environments and providing it through a digital device. This enables plant producers to receive accurate and timely appropriate production methods and sales strategies.

[0320] "Climate information" refers to data on environmental factors such as temperature, precipitation, and solar radiation, tailored to specific regions and time periods.

[0321] "Market information" refers to data on price trends and the balance of supply and demand for agricultural products.

[0322] A "database" refers to a collection of information that stores and manages diverse information gathered, making it easy to search and analyze.

[0323] "Risk factors" refer to potential unfavorable conditions or events that may occur in agricultural activities.

[0324] "Market trends" refer to time-series movements related to fluctuations in the demand and supply of goods and services, as well as price movements.

[0325] "Agricultural producer" refers to an individual or organization engaged in the work of growing and harvesting agricultural products.

[0326] "Individual data" refers to a set of information related to a specific crop producer or their production activities.

[0327] "Production method" refers to the specific techniques and processes related to the cultivation and harvesting of agricultural products.

[0328] "Sales channels" refer to the distribution routes through which a product or service reaches the consumer.

[0329] An "educational program" refers to a series of planned educational activities aimed at improving specific skills or knowledge.

[0330] "Information exchange" refers to the act of sharing data and knowledge between individuals or organizations through communication.

[0331] The term "urban environment" refers to the conditions under which geographical and social factors in a city or its surrounding area interact with each other.

[0332] A "digital device" refers to an electronic device that can receive, process, transmit, and store information in digital format.

[0333] The system for realizing this invention has functions to support agricultural producers in urban environments. The server collects climate and market information via APIs and stores this information in a database. The specific software uses Python and its libraries, Pandas and NumPy, to efficiently organize and process data. For weather data and market trend prediction, an AI model is built using TensorFlow to analyze risk factors and market trends from the data.

[0334] The device plays a role in receiving specific advice and suggestions tailored to the urban environment, for example, through the user's smartphone or smart glasses. Users can receive notifications via the device regarding optimal planting times and fertilization plans for crops. In this case, a web application using the Flask framework is useful, enabling real-time information delivery.

[0335] Users can also upload their farmland information and production activity data to the cloud, which is then analyzed by a server to provide individually optimized fertilization plans and production methods. This enables efficient agriculture that is adapted to the constraints unique to urban environments. Furthermore, users can receive notifications of educational programs through digital devices and participate in learning new technologies and community activities.

[0336] For example, if high temperatures are predicted for a particular week, the server can recommend heat-resistant crops and develop a corresponding sales strategy. An example of a prompt would be, "Please suggest urban agricultural crops suitable for the high temperatures expected in the next week. Focus on high-demand, heat-resistant crops." This data is then input into the generating AI model, and the prediction results are presented to the user.

[0337] These features enable crop producers to conduct their activities based on accurate forecasts and appropriate strategies, thereby achieving sustainable production.

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] The server connects to external climate information providers and market information systems to periodically collect climate and market data. Specifically, it uses APIs to obtain data such as temperature, precipitation, solar radiation, and crop price trends, and stores them in a database on the server. The input is information from external data providers, and the output is organized database entries.

[0341] Step 2:

[0342] The server uses Python and libraries to preprocess the collected data. This process includes imputing missing values ​​and removing outliers. The input is raw, unprocessed data from the database, and the output is a cleaned, analyzable dataset.

[0343] Step 3:

[0344] The server analyzes data collected and preprocessed using TensorFlow and predicts future climate conditions and market trends using an AI model. The generative AI model performs predictive calculations and generates forecast data for climate change risk and market demand. The input is a preprocessed dataset, and the output is risk factors and trend information based on the prediction results.

[0345] Step 4:

[0346] Based on the analysis results, the server calculates the optimal crops and planting times for agricultural producers and creates personalized recommendations. This includes creating specific fertilization plans and efficient production methods. The input is predicted risk factors and trend information, and the output is customized agricultural advice.

[0347] Step 5:

[0348] The terminal displays personalized advice and market information transmitted from the server in real time. Based on this, the user can decide on appropriate farming and sales activities. The input is advice from the server, and the output is visualized information provided to the user.

[0349] Step 6:

[0350] Users upload farmland information and production activity data to the cloud. This data is then used for further analysis on the server. The input consists of various data provided by the user, and the output is information stored in a database on the server.

[0351] Step 7:

[0352] The server generates and sends notifications for educational programs and community activities to each crop producer, encouraging participation. Input is program and event information, while output is notification messages for the user.

[0353] 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.

[0354] This invention is an agricultural support system that utilizes AI technology to improve profitability using climate and market information, and also features a user emotion recognition function using an emotion engine. This enables the provision of more personalized services based on user behavior and feedback.

[0355] This system consists of multiple modules. First, the server collects weather and market data from external sources and stores it in an internal database. This data includes detailed information such as regional weather conditions, soil information, and crop market prices. The server uses AI algorithms to analyze this data, identify risk factors, and suggest optimal crops and planting times to farmers.

[0356] In addition, the server uses an emotion engine to recognize the user's emotions. Users upload data related to their farm information and production activities on the platform. The system analyzes the user's input and behavior patterns to evaluate their emotional state. This emotional data is used as a factor in customizing the agricultural advice and educational programs provided.

[0357] The device provides user-specific feedback based on evaluation results from the emotion engine. Specifically, if it is determined that the content is not understood or that the user's interest and motivation are low, it can increase user engagement by providing content with adjusted difficulty levels or information that will pique their interest.

[0358] Furthermore, the server analyzes users' emotional tendencies to inform predicted market trends and sales strategies. For example, if a large number of farmers in a particular region exhibit similar emotional shifts, it may reveal specific market needs and risks in that region. This information can be used to improve marketing strategies and production plans.

[0359] Thus, this invention, which combines an emotion engine, aims not only to provide data-driven support but also to achieve a high level of personalization that takes into account the user's emotions, thereby improving the profitability of agricultural workers in the long term.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The server periodically collects weather and market data from external sources and stores it in a database. This process includes temperature, precipitation, sunshine hours, market prices, and supply and demand information.

[0363] Step 2:

[0364] The server analyzes the collected data using AI algorithms to identify regional weather risks and market trends. This allows it to calculate the most profitable crops and the appropriate planting times for farmers.

[0365] Step 3:

[0366] Users upload individual data about their farms (e.g., soil composition, resource status, etc.) to the cloud platform. Since the accuracy of the information affects the accuracy of subsequent analysis, it is recommended to provide detailed and up-to-date data.

[0367] Step 4:

[0368] The server generates optimized production methods and fertilization plans based on the individual data provided by the user. These results are then delivered to the user, and specific action plans are proposed.

[0369] Step 5:

[0370] User input and behavioral patterns are continuously monitored by the emotion engine. The server analyzes this data to evaluate the user's emotional state and feeds that information back to other processing functions.

[0371] Step 6:

[0372] The device provides personalized feedback to the user based on the emotions it recognizes. For example, if the user is showing unstable emotions, it will present additional support information or motivational content.

[0373] Step 7:

[0374] The server analyzes emotional trends at the regional level based on sentiment data, and uses this information to predict market trends and sales strategies. This information is used to support the strategic decision-making of agricultural workers.

[0375] Step 8:

[0376] The device notifies the user of recommended educational programs and marketing strategies. Users can then follow these recommendations to further improve their skills and expand their sales opportunities.

[0377] (Example 2)

[0378] 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".

[0379] Traditional agricultural support systems only provided advice based on weather and market data, failing to adequately consider users' emotional factors and individual needs. Furthermore, they did not optimize market strategies using regional emotional trends, making it difficult to improve profitability in the long term. Additionally, traditional systems lacked personalization of services based on the behavior and feedback of farmers, making it difficult to sustain user motivation and engagement.

[0380] 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.

[0381] In this invention, the server includes means for acquiring weather data and market data from an external source and storing it in a storage device; means for analyzing the acquired data and detecting risk factors for agricultural products and market fluctuations; and means for collecting and analyzing emotional data of agricultural workers using an emotion recognition function and personalizing guidance content based on the results. This makes it possible to provide personalized agricultural advice that takes into account the emotional state of the user, and to achieve optimization of market strategies that utilize the emotional trends of the entire region. Furthermore, it is possible to improve profitability and optimize production techniques in the long term while maintaining the motivation of agricultural workers.

[0382] "Weather data" refers to information about the weather, and is a numerical representation of atmospheric phenomena such as temperature, precipitation, wind speed, and humidity.

[0383] "Market data" refers to information related to agricultural product transactions, including crop prices, demand, and supply conditions.

[0384] A "storage device" is hardware used to store digital information, and may include databases and storage servers.

[0385] "Analysis" refers to information processing activities aimed at deriving specific patterns or results based on collected data.

[0386] "Agricultural products" is a general term for cultivated crops and agricultural products, including fruits, vegetables, and grains.

[0387] "Risk factors" refer to elements or conditions that can cause uncertainty or losses in agricultural production.

[0388] "Market fluctuations" refer to the phenomenon in which the price and demand for agricultural products change over time.

[0389] "Emotion recognition functionality" is a technology that analyzes input information and behavior to identify emotions in order to understand the user's emotional state.

[0390] "Guidance content" refers to the advice and recommended activity plans provided to agricultural workers.

[0391] "Personalization" refers to the process of adjusting content and responses based on each user's characteristics and needs.

[0392] "Area-wide emotional trends" refers to aggregating the emotional states of multiple users in a specific area to understand trends and developments.

[0393] "Market strategy" refers to the plans and methods for effectively disseminating products or services in a specific market.

[0394] This invention is an agricultural support system that utilizes AI technology and is realized through collaboration among three parties: a server, a terminal, and a user.

[0395] The server periodically collects weather and market data from external data sources and stores it in cloud-based storage. The hardware used includes a cluster of high-speed servers, and APIs are utilized for data ingestion. This server analyzes the collected data using machine learning models and data mining techniques. Through this analysis, it identifies and predicts risk factors related to agricultural products. For example, an AI model can identify changes in rainfall patterns and assess their impact on crops. Furthermore, it can analyze sentiment data provided by users and diagnose their emotional state through sentiment recognition capabilities.

[0396] Users can input data about their land and production activities on the system's platform and upload it to the cloud to receive personalized agricultural advice from the server. Based on the user's actions and feedback, emotional data is collected and analyzed and recognized by the server. Using the results of this analysis, the server proposes the optimal fertilization plan and cultivation method to the user.

[0397] The terminal provides feedback to the user based on analytical data received from the server. The terminal evaluates the user's level of understanding and interest, and adjusts the content displayed accordingly. For example, if the user shows interest in a particular crop, the terminal recommends relevant educational materials and videos. In this way, it enhances user engagement while supporting the acquisition of optimal agricultural techniques.

[0398] As a concrete example, here is an example of a prompt:

[0399] "I'm currently growing tomatoes. When would be the ideal time for the next harvest? Also, what's the market price like?"

[0400] "Lately, I haven't been feeling very motivated to do farming. How can I make it more interesting?"

[0401] The present invention aims to improve the profitability and efficiency of agricultural workers in the long term by combining the information collected, analyzed, and presented in this manner.

[0402] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0403] Step 1:

[0404] The server collects weather and market data using external APIs. Inputs include regional information and target crop varieties. Based on these inputs, the server performs database queries and API calls to obtain the latest weather forecasts and market price information. The output is stored in cloud-based storage as saved weather and market datasets. Specifically, it stores information such as "Rainfall forecast for the next 3 days: 20%, Market price: Average price of tomatoes is 50 yen / kg."

[0405] Step 2:

[0406] The server applies machine learning algorithms to the collected data to identify risk factors. The inputs are the weather and market data saved in Step 1. The server uses this data to run a model and identify various risk patterns (e.g., the possibility of drought or a sharp drop in market prices). The output is generated as a risk report, which includes specific risk factors and predictions of their impact. Specifically, it might generate a report stating, "High temperatures next week could reduce tomato yields by 20%."

[0407] Step 3:

[0408] Users log in to the platform, enter their farm information, and provide emotional feedback. This information includes the types of crops grown, cultivated area, daily activity logs, and emotional comments. Once users upload this data to the system, the server records it for further analysis. The output is registered as a user profile, and the user's emotional state is evaluated. For example, the analysis might conclude that "the user is experiencing stress due to the prolonged high temperatures."

[0409] Step 4:

[0410] The server analyzes the emotional data provided by the user using an emotion recognition engine to personalize the guidance. The input is the user's emotional data obtained in step 3. The server uses an emotion analysis algorithm to generate advice tailored to the user's current situation. The output is personalized advice and recommended actions, which are displayed on the user interface. For example, the advice might be, "It is recommended to irrigate in the early morning or evening to reduce stress."

[0411] Step 5:

[0412] The terminal provides feedback tailored to the user's understanding and interests, based on analysis results and advice from the server. Input consists of advice received from the server and the user profile. The terminal considers these factors and customizes the information displayed on the screen. Output includes content suitable for the user's control panel, specifically suggesting, "You can learn about effective tomato irrigation methods in the following video content."

[0413] This entire process allows users to receive personalized agricultural support tailored to their individual needs, leading to improved profitability and efficiency.

[0414] (Application Example 2)

[0415] 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."

[0416] As urbanization progresses, urban residents are required to produce agricultural products efficiently and profitably in limited spaces. Maintaining and improving residents' motivation and interest in gardening activities is also a crucial challenge. Existing systems struggle to accurately reflect the emotional state and interests of individual users and provide personalized advice, and developing optimal management plans for public garden spaces is also cumbersome. This invention aims to solve these problems and optimize urban agriculture in smart cities.

[0417] 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.

[0418] This invention includes a server that collects weather and economic information and stores it in a data storage unit, a means for determining the user's emotional state using emotion recognition technology and providing personalized advice based on that, and a means for providing residents with an optimal management plan for urban public garden spaces. This enables urban residents to produce crops efficiently and profitably in limited spaces, and also helps maintain and improve residents' motivation and interest.

[0419] "Weather information" refers to data related to climate, such as weather and temperature, and is a factor that influences crop cultivation and agricultural planning.

[0420] "Economic information" refers to data related to market trends and supply and demand, and is used to determine crop profitability and formulate sales strategies.

[0421] A "data storage unit" is a system structure for accumulating information, a place where collected information is stored for later use.

[0422] "Emotion recognition technology" is a technology used to analyze and judge a user's emotional state, and is used to personalize advice and improve the user experience.

[0423] "Personalized advice" refers to individualized suggestions and instructions that are optimized and provided according to each user's emotional state and interests.

[0424] "Urban public gardening spaces" are areas within cities where communities and residents jointly use gardening and crop cultivation.

[0425] A "management plan" is a systematic framework of operations and policies aimed at the efficient use of resources and the environment, and serves as a roadmap to enable smooth operations.

[0426] This invention is a system aimed at improving the efficiency of urban agriculture and enhancing the user experience, providing personalized advice by utilizing various data. The server acquires weather and economic information from external sources and stores it in the data storage unit. Specifically, it is recommended to use Python and TensorFlow for data analysis and spaCy for sentiment recognition.

[0427] The server, based on the results of information analysis, formulates an optimal cultivation plan tailored to the farmland information and emotional state provided by the user, and presents it to the user. The terminal operates based on user input and feedback, generating advice that is more suitable for individual needs. In this process, a web application can be built using Flask.

[0428] As a concrete example, the terminal proposes a fertilization plan necessary for tomato cultivation in a public garden space to urban residents of Tokyo and provides local gardening event information based on the user's sentiment data. In this system, the following inputs to the generative AI model are used as prompts.

[0429] Example of a prompt:

[0430] "I'm growing tomatoes, and through emotional analysis, what events or activities would you recommend to boost my motivation?"

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The server acquires weather and economic information from external sources and stores it in its data storage unit. It uses weather data and market price information obtained via an API as input and stores it in a database in JSON format.

[0434] Step 2:

[0435] The server analyzes the stored information using Python and TensorFlow. Specifically, it identifies risk factors and elements that affect crop profitability from input data such as weather patterns and market trends, and outputs them as analysis results.

[0436] Step 3:

[0437] The server compares the analysis results with the user's individual farmland information and presents a personalized cultivation plan to the terminal via the Flask application. This plan includes recommended planting times and appropriate fertilization schedules.

[0438] Step 4:

[0439] The device receives user input and collects user feedback and input data. This data is analyzed using emotion recognition technology with spaCy to determine the user's emotional state. Here, user responses and operation history are used as input, and emotional tendencies are obtained as output.

[0440] Step 5:

[0441] The server uses the sentiment data obtained in step 4 to again provide the user with optimal gardening event information and engaging content through the Flask application. This aims to maintain user interest and increase motivation.

[0442] Step 6:

[0443] Based on suggestions received from the server, the user plans activities in urban public garden spaces. Further advice can be sought using prompts provided by the generative AI model.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable operations, and is implemented as follows.

[0461] This system includes multiple modules for collecting and analyzing weather and market data. The server periodically retrieves data from external weather information providers and market information systems and stores it in a database. This database holds a wide range of information, including temperature, precipitation, sunshine hours, regional market trends, and price fluctuations.

[0462] The server uses AI algorithms to analyze collected information and identify risk factors and market trends in crop cultivation. The AI ​​algorithms leverage historical data to predict future weather conditions and market trends. This makes it possible to determine which crops are best suited for specific regions and times.

[0463] Users can provide data about their farms to the cloud through an interface. This could include information about soil composition and available resources. The server uses this information to create production methods and fertilization plans optimized for each individual farm, and delivers them to the user via their terminal. This allows users to receive concrete action plans, minimizing waste and improving productivity.

[0464] Furthermore, the server forecasts market demand and suggests products best suited to the environment. The terminal presents users with recommended sales methods and destinations, supporting them in developing new sales channels. Specifically, it can provide crop cultivation plans based on seasonal demand forecasts.

[0465] In addition, the system provides users with new agricultural techniques and knowledge through educational programs. This allows farmers to improve their skills and stimulate information exchange within the community. The terminal notifies users of registration information for online workshops and training sessions, providing learning opportunities.

[0466] For example, if high temperatures are predicted in a certain region, the server may recommend crops that are highly heat-resistant and in high market demand. Furthermore, based on the user's data, it may suggest efficient irrigation methods and fertilizer usage, providing measures to ensure profitability while reducing environmental impact.

[0467] Thus, this invention aims to provide AI-driven, data-driven support to address the various challenges faced by agricultural workers, thereby enabling sustainable and profitable agricultural management.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The server periodically collects weather and market information from external weather data providers and market information systems. This includes data on temperature, precipitation, sunshine hours, and market prices and demand for agricultural products. The collected data is stored in a database.

[0471] Step 2:

[0472] The server uses AI algorithms to analyze accumulated data and identify regional weather variability patterns and market trends. This allows for the analysis of current and future risk factors and the development of optimal crop selection guidelines.

[0473] Step 3:

[0474] Users upload individual data related to their farm (e.g., soil composition, available resources, etc.) via a cloud-based platform. This data is necessary to conduct highly accurate individual analyses.

[0475] Step 4:

[0476] The server generates optimized production methods and fertilization plans tailored to the characteristics of each farm, based on the uploaded individual data. These suggestions are created based on the results of AI-driven data analysis.

[0477] Step 5:

[0478] The terminal notifies the user of the production methods and fertilization plans generated by the server. Based on this information, the user can create a specific farming schedule.

[0479] Step 6:

[0480] The server analyzes market demand and identifies crops and sales channels that are expected to be in high demand during specific seasons. This allows users to efficiently develop sales channels for their crops.

[0481] Step 7:

[0482] The terminal provides users with sales strategies recommended by the server and new market information. Based on this, users plan and implement effective sales activities.

[0483] Step 8:

[0484] The server helps improve agricultural techniques by collecting and providing users with information on online educational programs and workshops.

[0485] Step 9:

[0486] The device notifies users of opportunities to participate in educational programs and provides links to access learning content. Through this, users can acquire the latest knowledge and techniques related to agriculture.

[0487] (Example 1)

[0488] 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."

[0489] In the agricultural sector, it is difficult to achieve sustainable and profitable management in response to rapid changes in weather conditions and market trends. In particular, there are challenges in developing production plans optimized for individual farms and selecting sales channels appropriate for the times. Furthermore, there is a need for improvements in agricultural technology and the promotion of efficient information exchange.

[0490] 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.

[0491] In this invention, the server includes means for acquiring climate information and market information from information providers and information systems and storing it in a storage device; means for analyzing the stored information using a learning model and predicting crop risk factors and market trends; and means for indicating highly profitable crops and appropriate planting times to agricultural workers based on the predicted results. This enables agricultural workers to respond immediately to weather and market fluctuations and adopt strategies optimized for individual farms.

[0492] "Information provider" refers to a third-party organization or system that provides climate information or market information.

[0493] An "information system" is a technological foundation for aggregating information and providing it to users as a service.

[0494] A "storage device" is a device or system designed to store data and allow it to be quickly retrieved as needed.

[0495] A "learning model" is an algorithm designed to analyze patterns and trends based on past data in order to make predictions and classifications about the future.

[0496] "Crop risk factors" refer to uncertain factors such as climatic conditions and pests and diseases that may affect the growth and harvest of crops.

[0497] "Market trends" refer to the ever-changing market conditions, including changes in the supply and demand of agricultural products, price trends, and distribution conditions.

[0498] "Profitability" refers to the degree of economic benefit derived from agricultural work and production activities.

[0499] "Planting season" refers to the time when crops are planted at the most suitable time to obtain the best harvest.

[0500] "Workers engaged in agricultural activities" refers to those who engage in agricultural activities as a profession.

[0501] "Sales channels" refer to the distribution routes used to deliver products to consumers or the market.

[0502] "High-demand products" refer to goods or crops that are considered to be in high demand among consumers in the market.

[0503] A "learning program" refers to a structured educational program designed to help individuals acquire new knowledge and skills.

[0504] A "group" refers to an assembly of people or organizations that share a common purpose or characteristics.

[0505] "Information exchange" is a communication activity in which individuals or organizations share necessary information with each other.

[0506] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable farming operations. This system is primarily built through the interaction of servers, terminals, and users.

[0507] The server periodically acquires climate and market data from climate information providers and market information systems, and stores it in a database. High-performance servers are used as hardware, and the software includes APIs for data collection and a database system (e.g., PostgreSQL) for data management. The collected data is analyzed using AI algorithms. The AI ​​model uses machine learning frameworks (e.g., TensorFlow, PyTorch) to predict future weather conditions and market trends based on historical data.

[0508] Users can provide individual farm data to the cloud via smart devices. This data includes soil composition, available resources, and past harvest yields, and is transmitted using secure communication protocols (e.g., SSL / TLS). Based on the user's data, the server generates individually optimized production plans and fertilization schedules.

[0509] The terminal's role is to deliver plans and analysis results generated on the server to the user. Furthermore, it also provides sales strategies and sales channel recommendations based on market demand. The terminal includes scheduling functions to allow users to participate in online workshops and learning programs.

[0510] For example, if high temperatures are predicted in a particular region in the near future, the server will proactively recommend heat-resistant crops to the user. Furthermore, based on user-provided data, it will suggest optimal irrigation methods and fertilizer usage, supporting reduced environmental impact and improved profitability. This system allows farmers to obtain concrete and actionable plans, even while being affected by market fluctuations and climate changes.

[0511] An example of a prompt message is, "Based on weather data and market trend data from the past 10 years, please tell me the best crops for next summer and how to grow them."

[0512] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0513] Step 1:

[0514] The server acquires weather and market information from information providers and market information systems. Input includes raw data obtained via APIs, such as temperature, precipitation, sunshine hours, and price trends. The data is initially stored locally, then formatted before being stored in a relational database. This ensures the data is readily available for subsequent analysis.

[0515] Step 2:

[0516] The server executes an AI algorithm based on accumulated data. The input consists of historical weather and market data stored in a database. Using a generative AI model, the server extracts features from the input data and predicts weather conditions and market trends. The output provides predictive information regarding future risk factors and market changes.

[0517] Step 3:

[0518] Users provide individual farm data to the cloud through an interface. Inputs include soil composition, available water resources, and crop types. This user-provided data is securely stored on the server and used for individual analysis. Outputs include confirmation of the provided information and a list of data to be provided next.

[0519] Step 4:

[0520] The server integrates user-provided data and forecast information to generate individually optimized production plans. The inputs used are the user's individual data and the server's forecast results. Through data calculations, specific production plans and action plans, such as planting timing, fertilization schedules, and irrigation methods, are created. The output consists of recommended measures for the user and their predicted effects.

[0521] Step 5:

[0522] The terminal notifies the user of production plans and analysis results generated by the server. Inputs include recommendations and planning data from the server. The terminal organizes this information and presents it to the user in an intuitively understandable format. The output is a detailed plan including the optimal actions the user should take and the steps involved.

[0523] Step 6:

[0524] The device provides users with learning programs and event information for further learning and knowledge enhancement. Input includes information on online workshops and training sessions. The device organizes this information using a calendar function and encourages user participation. Output is the user's learning schedule and its progress.

[0525] (Application Example 1)

[0526] 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."

[0527] In today's urban environment, agricultural producers need to respond quickly to changes in climate and market conditions in order to achieve sustainable and profitable plant production. However, plant producers in urban areas face challenges such as dispersed information and difficulty in obtaining timely advice. Furthermore, developing new sales channels and formulating strategies based on market trends is difficult due to a lack of expertise and resources. Solving these challenges is an urgent necessity.

[0528] 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.

[0529] In this invention, the server includes means for collecting climate information and market information and storing it in a database; means for analyzing the information and identifying risk factors and market trends for crops; and means for personalizing information on plant production in urban environments and providing it through a digital device. This enables plant producers to receive accurate and timely appropriate production methods and sales strategies.

[0530] "Climate information" refers to data on environmental factors such as temperature, precipitation, and solar radiation, tailored to specific regions and time periods.

[0531] "Market information" refers to data on price trends and the balance of supply and demand for agricultural products.

[0532] A "database" refers to a collection of information that stores and manages diverse information gathered, making it easy to search and analyze.

[0533] "Risk factors" refer to potential unfavorable conditions or events that may occur in agricultural activities.

[0534] "Market trends" refer to time-series movements related to fluctuations in the demand and supply of goods and services, as well as price movements.

[0535] "Agricultural producer" refers to an individual or organization engaged in the work of growing and harvesting agricultural products.

[0536] "Individual data" refers to a set of information related to a specific crop producer or their production activities.

[0537] "Production method" refers to the specific techniques and processes related to the cultivation and harvesting of agricultural products.

[0538] "Sales channels" refer to the distribution routes through which a product or service reaches the consumer.

[0539] An "educational program" refers to a series of planned educational activities aimed at improving specific skills or knowledge.

[0540] "Information exchange" refers to the act of sharing data and knowledge between individuals or organizations through communication.

[0541] The term "urban environment" refers to the conditions under which geographical and social factors in a city or its surrounding area interact with each other.

[0542] A "digital device" refers to an electronic device that can receive, process, transmit, and store information in digital format.

[0543] The system for realizing this invention has functions to support agricultural producers in urban environments. The server collects climate and market information via APIs and stores this information in a database. The specific software uses Python and its libraries, Pandas and NumPy, to efficiently organize and process data. For weather data and market trend prediction, an AI model is built using TensorFlow to analyze risk factors and market trends from the data.

[0544] The device plays a role in receiving specific advice and suggestions tailored to the urban environment, for example, through the user's smartphone or smart glasses. Users can receive notifications via the device regarding optimal planting times and fertilization plans for crops. In this case, a web application using the Flask framework is useful, enabling real-time information delivery.

[0545] Users can also upload their farmland information and production activity data to the cloud, which is then analyzed by a server to provide individually optimized fertilization plans and production methods. This enables efficient agriculture that is adapted to the constraints unique to urban environments. Furthermore, users can receive notifications of educational programs through digital devices and participate in learning new technologies and community activities.

[0546] For example, if high temperatures are predicted for a particular week, the server can recommend heat-resistant crops and develop a corresponding sales strategy. An example of a prompt would be, "Please suggest urban agricultural crops suitable for the high temperatures expected in the next week. Focus on high-demand, heat-resistant crops." This data is then input into the generating AI model, and the prediction results are presented to the user.

[0547] These features enable crop producers to conduct their activities based on accurate forecasts and appropriate strategies, thereby achieving sustainable production.

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The server connects to external climate information providers and market information systems to periodically collect climate and market data. Specifically, it uses APIs to obtain data such as temperature, precipitation, solar radiation, and crop price trends, and stores them in a database on the server. The input is information from external data providers, and the output is organized database entries.

[0551] Step 2:

[0552] The server uses Python and libraries to preprocess the collected data. This process includes imputing missing values ​​and removing outliers. The input is raw, unprocessed data from the database, and the output is a cleaned, analyzable dataset.

[0553] Step 3:

[0554] The server analyzes data collected and preprocessed using TensorFlow and predicts future climate conditions and market trends using an AI model. The generative AI model performs predictive calculations and generates forecast data for climate change risk and market demand. The input is a preprocessed dataset, and the output is risk factors and trend information based on the prediction results.

[0555] Step 4:

[0556] Based on the analysis results, the server calculates the optimal crops and planting times for agricultural producers and creates personalized recommendations. This includes creating specific fertilization plans and efficient production methods. The input is predicted risk factors and trend information, and the output is customized agricultural advice.

[0557] Step 5:

[0558] The terminal displays personalized advice and market information transmitted from the server in real time. Based on this, the user can decide on appropriate farming and sales activities. The input is advice from the server, and the output is visualized information provided to the user.

[0559] Step 6:

[0560] Users upload farmland information and production activity data to the cloud. This data is then used for further analysis on the server. The input consists of various data provided by the user, and the output is information stored in a database on the server.

[0561] Step 7:

[0562] The server generates and sends notifications for educational programs and community activities to each crop producer, encouraging participation. Input is program and event information, while output is notification messages for the user.

[0563] 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.

[0564] This invention is an agricultural support system that utilizes AI technology to improve profitability using climate and market information, and also features a user emotion recognition function using an emotion engine. This enables the provision of more personalized services based on user behavior and feedback.

[0565] This system consists of multiple modules. First, the server collects weather and market data from external sources and stores it in an internal database. This data includes detailed information such as regional weather conditions, soil information, and crop market prices. The server uses AI algorithms to analyze this data, identify risk factors, and suggest optimal crops and planting times to farmers.

[0566] In addition, the server uses an emotion engine to recognize the user's emotions. Users upload data related to their farm information and production activities on the platform. The system analyzes the user's input and behavior patterns to evaluate their emotional state. This emotional data is used as a factor in customizing the agricultural advice and educational programs provided.

[0567] The device provides user-specific feedback based on evaluation results from the emotion engine. Specifically, if it is determined that the content is not understood or that the user's interest and motivation are low, it can increase user engagement by providing content with adjusted difficulty levels or information that will pique their interest.

[0568] Furthermore, the server analyzes users' emotional tendencies to inform predicted market trends and sales strategies. For example, if a large number of farmers in a particular region exhibit similar emotional shifts, it may reveal specific market needs and risks in that region. This information can be used to improve marketing strategies and production plans.

[0569] Thus, this invention, which combines an emotion engine, aims not only to provide data-driven support but also to achieve a high level of personalization that takes into account the user's emotions, thereby improving the profitability of agricultural workers in the long term.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] The server periodically collects weather and market data from external sources and stores it in a database. This process includes temperature, precipitation, sunshine hours, market prices, and supply and demand information.

[0573] Step 2:

[0574] The server analyzes the collected data using AI algorithms to identify regional weather risks and market trends. This allows it to calculate the most profitable crops and the appropriate planting times for farmers.

[0575] Step 3:

[0576] Users upload individual data about their farms (e.g., soil composition, resource status, etc.) to the cloud platform. Since the accuracy of the information affects the accuracy of subsequent analysis, it is recommended to provide detailed and up-to-date data.

[0577] Step 4:

[0578] The server generates optimized production methods and fertilization plans based on the individual data provided by the user. These results are then delivered to the user, and specific action plans are proposed.

[0579] Step 5:

[0580] User input and behavioral patterns are continuously monitored by the emotion engine. The server analyzes this data to evaluate the user's emotional state and feeds that information back to other processing functions.

[0581] Step 6:

[0582] The device provides personalized feedback to the user based on the emotions it recognizes. For example, if the user is showing unstable emotions, it will present additional support information or motivational content.

[0583] Step 7:

[0584] The server analyzes emotional trends at the regional level based on sentiment data, and uses this information to predict market trends and sales strategies. This information is used to support the strategic decision-making of agricultural workers.

[0585] Step 8:

[0586] The device notifies the user of recommended educational programs and marketing strategies. Users can then follow these recommendations to further improve their skills and expand their sales opportunities.

[0587] (Example 2)

[0588] 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."

[0589] Traditional agricultural support systems only provided advice based on weather and market data, failing to adequately consider users' emotional factors and individual needs. Furthermore, they did not optimize market strategies using regional emotional trends, making it difficult to improve profitability in the long term. Additionally, traditional systems lacked personalization of services based on the behavior and feedback of farmers, making it difficult to sustain user motivation and engagement.

[0590] 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.

[0591] In this invention, the server includes means for acquiring weather data and market data from an external source and storing it in a storage device; means for analyzing the acquired data and detecting risk factors for agricultural products and market fluctuations; and means for collecting and analyzing emotional data of agricultural workers using an emotion recognition function and personalizing guidance content based on the results. This makes it possible to provide personalized agricultural advice that takes into account the emotional state of the user, and to achieve optimization of market strategies that utilize the emotional trends of the entire region. Furthermore, it is possible to improve profitability and optimize production techniques in the long term while maintaining the motivation of agricultural workers.

[0592] "Weather data" refers to information about the weather, and is a numerical representation of atmospheric phenomena such as temperature, precipitation, wind speed, and humidity.

[0593] "Market data" refers to information related to agricultural product transactions, including crop prices, demand, and supply conditions.

[0594] A "storage device" is hardware used to store digital information, and may include databases and storage servers.

[0595] "Analysis" refers to information processing activities aimed at deriving specific patterns or results based on collected data.

[0596] "Agricultural products" is a general term for cultivated crops and agricultural products, including fruits, vegetables, and grains.

[0597] "Risk factors" refer to elements or conditions that can cause uncertainty or losses in agricultural production.

[0598] "Market fluctuations" refer to the phenomenon in which the price and demand for agricultural products change over time.

[0599] "Emotion recognition functionality" is a technology that analyzes input information and behavior to identify emotions in order to understand the user's emotional state.

[0600] "Guidance content" refers to the advice and recommended activity plans provided to agricultural workers.

[0601] "Personalization" refers to the process of adjusting content and responses based on each user's characteristics and needs.

[0602] "Area-wide emotional trends" refers to aggregating the emotional states of multiple users in a specific area to understand trends and developments.

[0603] "Market strategy" refers to the plans and methods for effectively disseminating products or services in a specific market.

[0604] This invention is an agricultural support system that utilizes AI technology and is realized through collaboration among three parties: a server, a terminal, and a user.

[0605] The server periodically collects weather and market data from external data sources and stores it in cloud-based storage. The hardware used includes a cluster of high-speed servers, and APIs are utilized for data ingestion. This server analyzes the collected data using machine learning models and data mining techniques. Through this analysis, it identifies and predicts risk factors related to agricultural products. For example, an AI model can identify changes in rainfall patterns and assess their impact on crops. Furthermore, it can analyze sentiment data provided by users and diagnose their emotional state through sentiment recognition capabilities.

[0606] Users can input data about their land and production activities on the system's platform and upload it to the cloud to receive personalized agricultural advice from the server. Based on the user's actions and feedback, emotional data is collected and analyzed and recognized by the server. Using the results of this analysis, the server proposes the optimal fertilization plan and cultivation method to the user.

[0607] The terminal provides feedback to the user based on analytical data received from the server. The terminal evaluates the user's level of understanding and interest, and adjusts the content displayed accordingly. For example, if the user shows interest in a particular crop, the terminal recommends relevant educational materials and videos. In this way, it enhances user engagement while supporting the acquisition of optimal agricultural techniques.

[0608] As a concrete example, here is an example of a prompt:

[0609] "I'm currently growing tomatoes. When would be the ideal time for the next harvest? Also, what's the market price like?"

[0610] "Lately, I haven't been feeling very motivated to do farming. How can I make it more interesting?"

[0611] The present invention aims to improve the profitability and efficiency of agricultural workers in the long term by combining the information collected, analyzed, and presented in this manner.

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] The server collects weather and market data using external APIs. Inputs include regional information and target crop varieties. Based on these inputs, the server performs database queries and API calls to obtain the latest weather forecasts and market price information. The output is stored in cloud-based storage as saved weather and market datasets. Specifically, it stores information such as "Rainfall forecast for the next 3 days: 20%, Market price: Average price of tomatoes is 50 yen / kg."

[0615] Step 2:

[0616] The server applies machine learning algorithms to the collected data to identify risk factors. The inputs are the weather and market data saved in Step 1. The server uses this data to run a model and identify various risk patterns (e.g., the possibility of drought or a sharp drop in market prices). The output is generated as a risk report, which includes specific risk factors and predictions of their impact. Specifically, it might generate a report stating, "High temperatures next week could reduce tomato yields by 20%."

[0617] Step 3:

[0618] Users log in to the platform, enter their farm information, and provide emotional feedback. This information includes the types of crops grown, cultivated area, daily activity logs, and emotional comments. Once users upload this data to the system, the server records it for further analysis. The output is registered as a user profile, and the user's emotional state is evaluated. For example, the analysis might conclude that "the user is experiencing stress due to the prolonged high temperatures."

[0619] Step 4:

[0620] The server analyzes the emotional data provided by the user using an emotion recognition engine to personalize the guidance. The input is the user's emotional data obtained in step 3. The server uses an emotion analysis algorithm to generate advice tailored to the user's current situation. The output is personalized advice and recommended actions, which are displayed on the user interface. For example, the advice might be, "It is recommended to irrigate in the early morning or evening to reduce stress."

[0621] Step 5:

[0622] The terminal provides feedback tailored to the user's understanding and interests, based on analysis results and advice from the server. Input consists of advice received from the server and the user profile. The terminal considers these factors and customizes the information displayed on the screen. Output includes content suitable for the user's control panel, specifically suggesting, "You can learn about effective tomato irrigation methods in the following video content."

[0623] This entire process allows users to receive personalized agricultural support tailored to their individual needs, leading to improved profitability and efficiency.

[0624] (Application Example 2)

[0625] 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."

[0626] As urbanization progresses, urban residents are required to produce agricultural products efficiently and profitably in limited spaces. Maintaining and improving residents' motivation and interest in gardening activities is also a crucial challenge. Existing systems struggle to accurately reflect the emotional state and interests of individual users and provide personalized advice, and developing optimal management plans for public garden spaces is also cumbersome. This invention aims to solve these problems and optimize urban agriculture in smart cities.

[0627] 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.

[0628] This invention includes a server that collects weather and economic information and stores it in a data storage unit, a means for determining the user's emotional state using emotion recognition technology and providing personalized advice based on that, and a means for providing residents with an optimal management plan for urban public garden spaces. This enables urban residents to produce crops efficiently and profitably in limited spaces, and also helps maintain and improve residents' motivation and interest.

[0629] "Weather information" refers to data related to climate, such as weather and temperature, and is a factor that influences crop cultivation and agricultural planning.

[0630] "Economic information" refers to data related to market trends and supply and demand, and is used to determine crop profitability and formulate sales strategies.

[0631] A "data storage unit" is a system structure for accumulating information, a place where collected information is stored for later use.

[0632] "Emotion recognition technology" is a technology used to analyze and judge a user's emotional state, and is used to personalize advice and improve the user experience.

[0633] "Personalized advice" refers to individualized suggestions and instructions that are optimized and provided according to each user's emotional state and interests.

[0634] "Urban public gardening spaces" are areas within cities where communities and residents jointly use gardening and crop cultivation.

[0635] A "management plan" is a systematic framework of operations and policies aimed at the efficient use of resources and the environment, and serves as a roadmap to enable smooth operations.

[0636] This invention is a system aimed at improving the efficiency of urban agriculture and enhancing the user experience, providing personalized advice by utilizing various data. The server acquires weather and economic information from external sources and stores it in the data storage unit. Specifically, it is recommended to use Python and TensorFlow for data analysis and spaCy for sentiment recognition.

[0637] The server, based on the results of information analysis, formulates an optimal cultivation plan tailored to the farmland information and emotional state provided by the user, and presents it to the user. The terminal operates based on user input and feedback, generating advice that is more suitable for individual needs. In this process, a web application can be built using Flask.

[0638] As a concrete example, the terminal proposes a fertilization plan necessary for tomato cultivation in a public garden space to urban residents of Tokyo and provides local gardening event information based on the user's sentiment data. In this system, the following inputs to the generative AI model are used as prompts.

[0639] Example of a prompt:

[0640] "I'm growing tomatoes, and through emotional analysis, what events or activities would you recommend to boost my motivation?"

[0641] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0642] Step 1:

[0643] The server acquires weather and economic information from external sources and stores it in its data storage unit. It uses weather data and market price information obtained via an API as input and stores it in a database in JSON format.

[0644] Step 2:

[0645] The server analyzes the stored information using Python and TensorFlow. Specifically, it identifies risk factors and elements that affect crop profitability from input data such as weather patterns and market trends, and outputs them as analysis results.

[0646] Step 3:

[0647] The server compares the analysis results with the user's individual farmland information and presents a personalized cultivation plan to the terminal via the Flask application. This plan includes recommended planting times and appropriate fertilization schedules.

[0648] Step 4:

[0649] The device receives user input and collects user feedback and input data. This data is analyzed using emotion recognition technology with spaCy to determine the user's emotional state. Here, user responses and operation history are used as input, and emotional tendencies are obtained as output.

[0650] Step 5:

[0651] The server uses the sentiment data obtained in step 4 to again provide the user with optimal gardening event information and engaging content through the Flask application. This aims to maintain user interest and increase motivation.

[0652] Step 6:

[0653] Based on suggestions received from the server, the user plans activities in urban public garden spaces. Further advice can be sought using prompts provided by the generative AI model.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] [Fourth Embodiment]

[0658] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0659] 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.

[0660] 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).

[0661] 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.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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".

[0671] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable operations, and is implemented as follows.

[0672] This system includes multiple modules for collecting and analyzing weather and market data. The server periodically retrieves data from external weather information providers and market information systems and stores it in a database. This database holds a wide range of information, including temperature, precipitation, sunshine hours, regional market trends, and price fluctuations.

[0673] The server uses AI algorithms to analyze collected information and identify risk factors and market trends in crop cultivation. The AI ​​algorithms leverage historical data to predict future weather conditions and market trends. This makes it possible to determine which crops are best suited for specific regions and times.

[0674] Users can provide data about their farms to the cloud through an interface. This could include information about soil composition and available resources. The server uses this information to create production methods and fertilization plans optimized for each individual farm, and delivers them to the user via their terminal. This allows users to receive concrete action plans, minimizing waste and improving productivity.

[0675] Furthermore, the server forecasts market demand and suggests products best suited to the environment. The terminal presents users with recommended sales methods and destinations, supporting them in developing new sales channels. Specifically, it can provide crop cultivation plans based on seasonal demand forecasts.

[0676] In addition, the system provides users with new agricultural techniques and knowledge through educational programs. This allows farmers to improve their skills and stimulate information exchange within the community. The terminal notifies users of registration information for online workshops and training sessions, providing learning opportunities.

[0677] For example, if high temperatures are predicted in a certain region, the server may recommend crops that are highly heat-resistant and in high market demand. Furthermore, based on the user's data, it may suggest efficient irrigation methods and fertilizer usage, providing measures to ensure profitability while reducing environmental impact.

[0678] Thus, this invention aims to provide AI-driven, data-driven support to address the various challenges faced by agricultural workers, thereby enabling sustainable and profitable agricultural management.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The server periodically collects weather and market information from external weather data providers and market information systems. This includes data on temperature, precipitation, sunshine hours, and market prices and demand for agricultural products. The collected data is stored in a database.

[0682] Step 2:

[0683] The server uses AI algorithms to analyze accumulated data and identify regional weather variability patterns and market trends. This allows for the analysis of current and future risk factors and the development of optimal crop selection guidelines.

[0684] Step 3:

[0685] Users upload individual data related to their farm (e.g., soil composition, available resources, etc.) via a cloud-based platform. This data is necessary to conduct highly accurate individual analyses.

[0686] Step 4:

[0687] The server generates optimized production methods and fertilization plans tailored to the characteristics of each farm, based on the uploaded individual data. These suggestions are created based on the results of AI-driven data analysis.

[0688] Step 5:

[0689] The terminal notifies the user of the production methods and fertilization plans generated by the server. Based on this information, the user can create a specific farming schedule.

[0690] Step 6:

[0691] The server analyzes market demand and identifies crops and sales channels that are expected to be in high demand during specific seasons. This allows users to efficiently develop sales channels for their crops.

[0692] Step 7:

[0693] The terminal provides users with sales strategies recommended by the server and new market information. Based on this, users plan and implement effective sales activities.

[0694] Step 8:

[0695] The server helps improve agricultural techniques by collecting and providing users with information on online educational programs and workshops.

[0696] Step 9:

[0697] The device notifies users of opportunities to participate in educational programs and provides links to access learning content. Through this, users can acquire the latest knowledge and techniques related to agriculture.

[0698] (Example 1)

[0699] 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".

[0700] In the agricultural sector, it is difficult to achieve sustainable and profitable management in response to rapid changes in weather conditions and market trends. In particular, there are challenges in developing production plans optimized for individual farms and selecting sales channels appropriate for the times. Furthermore, there is a need for improvements in agricultural technology and the promotion of efficient information exchange.

[0701] 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.

[0702] In this invention, the server includes means for acquiring climate information and market information from information providers and information systems and storing it in a storage device; means for analyzing the stored information using a learning model and predicting crop risk factors and market trends; and means for indicating highly profitable crops and appropriate planting times to agricultural workers based on the predicted results. This enables agricultural workers to respond immediately to weather and market fluctuations and adopt strategies optimized for individual farms.

[0703] "Information provider" refers to a third-party organization or system that provides climate information or market information.

[0704] An "information system" is a technological foundation for aggregating information and providing it to users as a service.

[0705] A "storage device" is a device or system designed to store data and allow it to be quickly retrieved as needed.

[0706] A "learning model" is an algorithm designed to analyze patterns and trends based on past data in order to make predictions and classifications about the future.

[0707] "Crop risk factors" refer to uncertain factors such as climatic conditions and pests and diseases that may affect the growth and harvest of crops.

[0708] "Market trends" refer to the ever-changing market conditions, including changes in the supply and demand of agricultural products, price trends, and distribution conditions.

[0709] "Profitability" refers to the degree of economic benefit derived from agricultural work and production activities.

[0710] "Planting season" refers to the time when crops are planted at the most suitable time to obtain the best harvest.

[0711] "Workers engaged in agricultural activities" refers to those who engage in agricultural activities as a profession.

[0712] "Sales channels" refer to the distribution routes used to deliver products to consumers or the market.

[0713] "High-demand products" refer to goods or crops that are considered to be in high demand among consumers in the market.

[0714] A "learning program" refers to a structured educational program designed to help individuals acquire new knowledge and skills.

[0715] A "group" refers to an assembly of people or organizations that share a common purpose or characteristics.

[0716] "Information exchange" is a communication activity in which individuals or organizations share necessary information with each other.

[0717] This invention is a comprehensive support system using AI technology to enable agricultural workers to conduct sustainable and profitable farming operations. This system is primarily built through the interaction of servers, terminals, and users.

[0718] The server periodically acquires climate and market data from climate information providers and market information systems, and stores it in a database. High-performance servers are used as hardware, and the software includes APIs for data collection and a database system (e.g., PostgreSQL) for data management. The collected data is analyzed using AI algorithms. The AI ​​model uses machine learning frameworks (e.g., TensorFlow, PyTorch) to predict future weather conditions and market trends based on historical data.

[0719] Users can provide individual farm data to the cloud via smart devices. This data includes soil composition, available resources, and past harvest yields, and is transmitted using secure communication protocols (e.g., SSL / TLS). Based on the user's data, the server generates individually optimized production plans and fertilization schedules.

[0720] The terminal's role is to deliver plans and analysis results generated on the server to the user. Furthermore, it also provides sales strategies and sales channel recommendations based on market demand. The terminal includes scheduling functions to allow users to participate in online workshops and learning programs.

[0721] For example, if high temperatures are predicted in a particular region in the near future, the server will proactively recommend heat-resistant crops to the user. Furthermore, based on user-provided data, it will suggest optimal irrigation methods and fertilizer usage, supporting reduced environmental impact and improved profitability. This system allows farmers to obtain concrete and actionable plans, even while being affected by market fluctuations and climate changes.

[0722] An example of a prompt message is, "Based on weather data and market trend data from the past 10 years, please tell me the best crops for next summer and how to grow them."

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] The server acquires weather and market information from information providers and market information systems. Input includes raw data obtained via APIs, such as temperature, precipitation, sunshine hours, and price trends. The data is initially stored locally, then formatted before being stored in a relational database. This ensures the data is readily available for subsequent analysis.

[0726] Step 2:

[0727] The server executes an AI algorithm based on accumulated data. The input consists of historical weather and market data stored in a database. Using a generative AI model, the server extracts features from the input data and predicts weather conditions and market trends. The output provides predictive information regarding future risk factors and market changes.

[0728] Step 3:

[0729] Users provide individual farm data to the cloud through an interface. Inputs include soil composition, available water resources, and crop types. This user-provided data is securely stored on the server and used for individual analysis. Outputs include confirmation of the provided information and a list of data to be provided next.

[0730] Step 4:

[0731] The server integrates user-provided data and forecast information to generate individually optimized production plans. The inputs used are the user's individual data and the server's forecast results. Through data calculations, specific production plans and action plans, such as planting timing, fertilization schedules, and irrigation methods, are created. The output consists of recommended measures for the user and their predicted effects.

[0732] Step 5:

[0733] The terminal notifies the user of production plans and analysis results generated by the server. Inputs include recommendations and planning data from the server. The terminal organizes this information and presents it to the user in an intuitively understandable format. The output is a detailed plan including the optimal actions the user should take and the steps involved.

[0734] Step 6:

[0735] The device provides users with learning programs and event information for further learning and knowledge enhancement. Input includes information on online workshops and training sessions. The device organizes this information using a calendar function and encourages user participation. Output is the user's learning schedule and its progress.

[0736] (Application Example 1)

[0737] 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".

[0738] In today's urban environment, agricultural producers need to respond quickly to changes in climate and market conditions in order to achieve sustainable and profitable plant production. However, plant producers in urban areas face challenges such as dispersed information and difficulty in obtaining timely advice. Furthermore, developing new sales channels and formulating strategies based on market trends is difficult due to a lack of expertise and resources. Solving these challenges is an urgent necessity.

[0739] 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.

[0740] In this invention, the server includes means for collecting climate information and market information and storing it in a database; means for analyzing the information and identifying risk factors and market trends for crops; and means for personalizing information on plant production in urban environments and providing it through a digital device. This enables plant producers to receive accurate and timely appropriate production methods and sales strategies.

[0741] "Climate information" refers to data on environmental factors such as temperature, precipitation, and solar radiation, tailored to specific regions and time periods.

[0742] "Market information" refers to data on price trends and the balance of supply and demand for agricultural products.

[0743] A "database" refers to a collection of information that stores and manages diverse information gathered, making it easy to search and analyze.

[0744] "Risk factors" refer to potential unfavorable conditions or events that may occur in agricultural activities.

[0745] "Market trends" refer to time-series movements related to fluctuations in the demand and supply of goods and services, as well as price movements.

[0746] "Agricultural producer" refers to an individual or organization engaged in the work of growing and harvesting agricultural products.

[0747] "Individual data" refers to a set of information related to a specific crop producer or their production activities.

[0748] "Production method" refers to the specific techniques and processes related to the cultivation and harvesting of agricultural products.

[0749] "Sales channels" refer to the distribution routes through which a product or service reaches the consumer.

[0750] An "educational program" refers to a series of planned educational activities aimed at improving specific skills or knowledge.

[0751] "Information exchange" refers to the act of sharing data and knowledge between individuals or organizations through communication.

[0752] The term "urban environment" refers to the conditions under which geographical and social factors in a city or its surrounding area interact with each other.

[0753] A "digital device" refers to an electronic device that can receive, process, transmit, and store information in digital format.

[0754] The system for realizing this invention has functions to support agricultural producers in urban environments. The server collects climate and market information via APIs and stores this information in a database. The specific software uses Python and its libraries, Pandas and NumPy, to efficiently organize and process data. For weather data and market trend prediction, an AI model is built using TensorFlow to analyze risk factors and market trends from the data.

[0755] The device plays a role in receiving specific advice and suggestions tailored to the urban environment, for example, through the user's smartphone or smart glasses. Users can receive notifications via the device regarding optimal planting times and fertilization plans for crops. In this case, a web application using the Flask framework is useful, enabling real-time information delivery.

[0756] Users can also upload their farmland information and production activity data to the cloud, which is then analyzed by a server to provide individually optimized fertilization plans and production methods. This enables efficient agriculture that is adapted to the constraints unique to urban environments. Furthermore, users can receive notifications of educational programs through digital devices and participate in learning new technologies and community activities.

[0757] For example, if high temperatures are predicted for a particular week, the server can recommend heat-resistant crops and develop a corresponding sales strategy. An example of a prompt would be, "Please suggest urban agricultural crops suitable for the high temperatures expected in the next week. Focus on high-demand, heat-resistant crops." This data is then input into the generating AI model, and the prediction results are presented to the user.

[0758] These features enable crop producers to conduct their activities based on accurate forecasts and appropriate strategies, thereby achieving sustainable production.

[0759] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0760] Step 1:

[0761] The server connects to external climate information providers and market information systems to periodically collect climate and market data. Specifically, it uses APIs to obtain data such as temperature, precipitation, solar radiation, and crop price trends, and stores them in a database on the server. The input is information from external data providers, and the output is organized database entries.

[0762] Step 2:

[0763] The server uses Python and libraries to preprocess the collected data. This process includes imputing missing values ​​and removing outliers. The input is raw, unprocessed data from the database, and the output is a cleaned, analyzable dataset.

[0764] Step 3:

[0765] The server analyzes data collected and preprocessed using TensorFlow and predicts future climate conditions and market trends using an AI model. The generative AI model performs predictive calculations and generates forecast data for climate change risk and market demand. The input is a preprocessed dataset, and the output is risk factors and trend information based on the prediction results.

[0766] Step 4:

[0767] Based on the analysis results, the server calculates the optimal crops and planting times for agricultural producers and creates personalized recommendations. This includes creating specific fertilization plans and efficient production methods. The input is predicted risk factors and trend information, and the output is customized agricultural advice.

[0768] Step 5:

[0769] The terminal displays personalized advice and market information transmitted from the server in real time. Based on this, the user can decide on appropriate farming and sales activities. The input is advice from the server, and the output is visualized information provided to the user.

[0770] Step 6:

[0771] Users upload farmland information and production activity data to the cloud. This data is then used for further analysis on the server. The input consists of various data provided by the user, and the output is information stored in a database on the server.

[0772] Step 7:

[0773] The server generates and sends notifications for educational programs and community activities to each crop producer, encouraging participation. Input is program and event information, while output is notification messages for the user.

[0774] 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.

[0775] This invention is an agricultural support system that utilizes AI technology to improve profitability using climate and market information, and also features a user emotion recognition function using an emotion engine. This enables the provision of more personalized services based on user behavior and feedback.

[0776] This system consists of multiple modules. First, the server collects weather and market data from external sources and stores it in an internal database. This data includes detailed information such as regional weather conditions, soil information, and crop market prices. The server uses AI algorithms to analyze this data, identify risk factors, and suggest optimal crops and planting times to farmers.

[0777] In addition, the server uses an emotion engine to recognize the user's emotions. Users upload data related to their farm information and production activities on the platform. The system analyzes the user's input and behavior patterns to evaluate their emotional state. This emotional data is used as a factor in customizing the agricultural advice and educational programs provided.

[0778] The device provides user-specific feedback based on evaluation results from the emotion engine. Specifically, if it is determined that the content is not understood or that the user's interest and motivation are low, it can increase user engagement by providing content with adjusted difficulty levels or information that will pique their interest.

[0779] Furthermore, the server analyzes users' emotional tendencies to inform predicted market trends and sales strategies. For example, if a large number of farmers in a particular region exhibit similar emotional shifts, it may reveal specific market needs and risks in that region. This information can be used to improve marketing strategies and production plans.

[0780] Thus, this invention, which combines an emotion engine, aims not only to provide data-driven support but also to achieve a high level of personalization that takes into account the user's emotions, thereby improving the profitability of agricultural workers in the long term.

[0781] The following describes the processing flow.

[0782] Step 1:

[0783] The server periodically collects weather and market data from external sources and stores it in a database. This process includes temperature, precipitation, sunshine hours, market prices, and supply and demand information.

[0784] Step 2:

[0785] The server analyzes the collected data using AI algorithms to identify regional weather risks and market trends. This allows it to calculate the most profitable crops and the appropriate planting times for farmers.

[0786] Step 3:

[0787] Users upload individual data about their farms (e.g., soil composition, resource status, etc.) to the cloud platform. Since the accuracy of the information affects the accuracy of subsequent analysis, it is recommended to provide detailed and up-to-date data.

[0788] Step 4:

[0789] The server generates optimized production methods and fertilization plans based on the individual data provided by the user. These results are then delivered to the user, and specific action plans are proposed.

[0790] Step 5:

[0791] User input and behavioral patterns are continuously monitored by the emotion engine. The server analyzes this data to evaluate the user's emotional state and feeds that information back to other processing functions.

[0792] Step 6:

[0793] The device provides personalized feedback to the user based on the emotions it recognizes. For example, if the user is showing unstable emotions, it will present additional support information or motivational content.

[0794] Step 7:

[0795] The server analyzes emotional trends at the regional level based on sentiment data, and uses this information to predict market trends and sales strategies. This information is used to support the strategic decision-making of agricultural workers.

[0796] Step 8:

[0797] The device notifies the user of recommended educational programs and marketing strategies. Users can then follow these recommendations to further improve their skills and expand their sales opportunities.

[0798] (Example 2)

[0799] 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".

[0800] Traditional agricultural support systems only provided advice based on weather and market data, failing to adequately consider users' emotional factors and individual needs. Furthermore, they did not optimize market strategies using regional emotional trends, making it difficult to improve profitability in the long term. Additionally, traditional systems lacked personalization of services based on the behavior and feedback of farmers, making it difficult to sustain user motivation and engagement.

[0801] 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.

[0802] In this invention, the server includes means for acquiring weather data and market data from an external source and storing it in a storage device; means for analyzing the acquired data and detecting risk factors for agricultural products and market fluctuations; and means for collecting and analyzing emotional data of agricultural workers using an emotion recognition function and personalizing guidance content based on the results. This makes it possible to provide personalized agricultural advice that takes into account the emotional state of the user, and to achieve optimization of market strategies that utilize the emotional trends of the entire region. Furthermore, it is possible to improve profitability and optimize production techniques in the long term while maintaining the motivation of agricultural workers.

[0803] "Weather data" refers to information about the weather, and is a numerical representation of atmospheric phenomena such as temperature, precipitation, wind speed, and humidity.

[0804] "Market data" refers to information related to agricultural product transactions, including crop prices, demand, and supply conditions.

[0805] A "storage device" is hardware used to store digital information, and may include databases and storage servers.

[0806] "Analysis" refers to information processing activities aimed at deriving specific patterns or results based on collected data.

[0807] "Agricultural products" is a general term for cultivated crops and agricultural products, including fruits, vegetables, and grains.

[0808] "Risk factors" refer to elements or conditions that can cause uncertainty or losses in agricultural production.

[0809] "Market fluctuations" refer to the phenomenon in which the price and demand for agricultural products change over time.

[0810] "Emotion recognition functionality" is a technology that analyzes input information and behavior to identify emotions in order to understand the user's emotional state.

[0811] "Guidance content" refers to the advice and recommended activity plans provided to agricultural workers.

[0812] "Personalization" refers to the process of adjusting content and responses based on each user's characteristics and needs.

[0813] "Area-wide emotional trends" refers to aggregating the emotional states of multiple users in a specific area to understand trends and developments.

[0814] "Market strategy" refers to the plans and methods for effectively disseminating products or services in a specific market.

[0815] This invention is an agricultural support system that utilizes AI technology and is realized through collaboration among three parties: a server, a terminal, and a user.

[0816] The server periodically collects weather and market data from external data sources and stores it in cloud-based storage. The hardware used includes a cluster of high-speed servers, and APIs are utilized for data ingestion. This server analyzes the collected data using machine learning models and data mining techniques. Through this analysis, it identifies and predicts risk factors related to agricultural products. For example, an AI model can identify changes in rainfall patterns and assess their impact on crops. Furthermore, it can analyze sentiment data provided by users and diagnose their emotional state through sentiment recognition capabilities.

[0817] Users can input data about their land and production activities on the system's platform and upload it to the cloud to receive personalized agricultural advice from the server. Based on the user's actions and feedback, emotional data is collected and analyzed and recognized by the server. Using the results of this analysis, the server proposes the optimal fertilization plan and cultivation method to the user.

[0818] The terminal provides feedback to the user based on analytical data received from the server. The terminal evaluates the user's level of understanding and interest, and adjusts the content displayed accordingly. For example, if the user shows interest in a particular crop, the terminal recommends relevant educational materials and videos. In this way, it enhances user engagement while supporting the acquisition of optimal agricultural techniques.

[0819] As a concrete example, here is an example of a prompt:

[0820] "I'm currently growing tomatoes. When would be the ideal time for the next harvest? Also, what's the market price like?"

[0821] "Lately, I haven't been feeling very motivated to do farming. How can I make it more interesting?"

[0822] The present invention aims to improve the profitability and efficiency of agricultural workers in the long term by combining the information collected, analyzed, and presented in this manner.

[0823] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0824] Step 1:

[0825] The server collects weather and market data using external APIs. Inputs include regional information and target crop varieties. Based on these inputs, the server performs database queries and API calls to obtain the latest weather forecasts and market price information. The output is stored in cloud-based storage as saved weather and market datasets. Specifically, it stores information such as "Rainfall forecast for the next 3 days: 20%, Market price: Average price of tomatoes is 50 yen / kg."

[0826] Step 2:

[0827] The server applies machine learning algorithms to the collected data to identify risk factors. The inputs are the weather and market data saved in Step 1. The server uses this data to run a model and identify various risk patterns (e.g., the possibility of drought or a sharp drop in market prices). The output is generated as a risk report, which includes specific risk factors and predictions of their impact. Specifically, it might generate a report stating, "High temperatures next week could reduce tomato yields by 20%."

[0828] Step 3:

[0829] Users log in to the platform, enter their farm information, and provide emotional feedback. This information includes the types of crops grown, cultivated area, daily activity logs, and emotional comments. Once users upload this data to the system, the server records it for further analysis. The output is registered as a user profile, and the user's emotional state is evaluated. For example, the analysis might conclude that "the user is experiencing stress due to the prolonged high temperatures."

[0830] Step 4:

[0831] The server analyzes the emotional data provided by the user using an emotion recognition engine to personalize the guidance. The input is the user's emotional data obtained in step 3. The server uses an emotion analysis algorithm to generate advice tailored to the user's current situation. The output is personalized advice and recommended actions, which are displayed on the user interface. For example, the advice might be, "It is recommended to irrigate in the early morning or evening to reduce stress."

[0832] Step 5:

[0833] The terminal provides feedback tailored to the user's understanding and interests, based on analysis results and advice from the server. Input consists of advice received from the server and the user profile. The terminal considers these factors and customizes the information displayed on the screen. Output includes content suitable for the user's control panel, specifically suggesting, "You can learn about effective tomato irrigation methods in the following video content."

[0834] This entire process allows users to receive personalized agricultural support tailored to their individual needs, leading to improved profitability and efficiency.

[0835] (Application Example 2)

[0836] 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".

[0837] As urbanization progresses, urban residents are required to produce agricultural products efficiently and profitably in limited spaces. Maintaining and improving residents' motivation and interest in gardening activities is also a crucial challenge. Existing systems struggle to accurately reflect the emotional state and interests of individual users and provide personalized advice, and developing optimal management plans for public garden spaces is also cumbersome. This invention aims to solve these problems and optimize urban agriculture in smart cities.

[0838] 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.

[0839] This invention includes a server that collects weather and economic information and stores it in a data storage unit, a means for determining the user's emotional state using emotion recognition technology and providing personalized advice based on that, and a means for providing residents with an optimal management plan for urban public garden spaces. This enables urban residents to produce crops efficiently and profitably in limited spaces, and also helps maintain and improve residents' motivation and interest.

[0840] "Weather information" refers to data related to climate, such as weather and temperature, and is a factor that influences crop cultivation and agricultural planning.

[0841] "Economic information" refers to data related to market trends and supply and demand, and is used to determine crop profitability and formulate sales strategies.

[0842] A "data storage unit" is a system structure for accumulating information, a place where collected information is stored for later use.

[0843] "Emotion recognition technology" is a technology used to analyze and judge a user's emotional state, and is used to personalize advice and improve the user experience.

[0844] "Personalized advice" refers to individualized suggestions and instructions that are optimized and provided according to each user's emotional state and interests.

[0845] "Urban public gardening spaces" are areas within cities where communities and residents jointly use gardening and crop cultivation.

[0846] A "management plan" is a systematic framework of operations and policies aimed at the efficient use of resources and the environment, and serves as a roadmap to enable smooth operations.

[0847] This invention is a system aimed at improving the efficiency of urban agriculture and enhancing the user experience, providing personalized advice by utilizing various data. The server acquires weather and economic information from external sources and stores it in the data storage unit. Specifically, it is recommended to use Python and TensorFlow for data analysis and spaCy for sentiment recognition.

[0848] The server, based on the results of information analysis, formulates an optimal cultivation plan tailored to the farmland information and emotional state provided by the user, and presents it to the user. The terminal operates based on user input and feedback, generating advice that is more suitable for individual needs. In this process, a web application can be built using Flask.

[0849] As a concrete example, the terminal proposes a fertilization plan necessary for tomato cultivation in a public garden space to urban residents of Tokyo and provides local gardening event information based on the user's sentiment data. In this system, the following inputs to the generative AI model are used as prompts.

[0850] Example of a prompt:

[0851] "I'm growing tomatoes, and through emotional analysis, what events or activities would you recommend to boost my motivation?"

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] The server acquires weather and economic information from external sources and stores it in its data storage unit. It uses weather data and market price information obtained via an API as input and stores it in a database in JSON format.

[0855] Step 2:

[0856] The server analyzes the stored information using Python and TensorFlow. Specifically, it identifies risk factors and elements that affect crop profitability from input data such as weather patterns and market trends, and outputs them as analysis results.

[0857] Step 3:

[0858] The server compares the analysis results with the user's individual farmland information and presents a personalized cultivation plan to the terminal via the Flask application. This plan includes recommended planting times and appropriate fertilization schedules.

[0859] Step 4:

[0860] The device receives user input and collects user feedback and input data. This data is analyzed using emotion recognition technology with spaCy to determine the user's emotional state. Here, user responses and operation history are used as input, and emotional tendencies are obtained as output.

[0861] Step 5:

[0862] The server uses the sentiment data obtained in step 4 to again provide the user with optimal gardening event information and engaging content through the Flask application. This aims to maintain user interest and increase motivation.

[0863] Step 6:

[0864] Based on suggestions received from the server, the user plans activities in urban public garden spaces. Further advice can be sought using prompts provided by the generative AI model.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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."

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] The following is further disclosed regarding the embodiments described above.

[0887] (Claim 1)

[0888] A means of collecting climate information and market information and storing it in a database,

[0889] A means of analyzing the aforementioned information to identify risk factors and market trends for agricultural products,

[0890] A means of presenting agricultural workers with highly profitable crops and optimal planting times based on identified information,

[0891] A means of analyzing individual data of agricultural workers and optimizing production methods,

[0892] Based on the above analysis, means of proposing new sales channels and high-demand products,

[0893] To provide educational programs and means to improve agricultural technology,

[0894] A means of forming a community and promoting information exchange,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] Farmland information provided by agricultural workers is uploaded to the cloud.

[0898] The system according to claim 1, which proposes individually optimized fertilization plans and production methods based on the aforementioned farmland information.

[0899] (Claim 3)

[0900] The system according to claim 1, which proposes specific sales strategies and pricing to agricultural workers based on market trend analysis.

[0901] "Example 1"

[0902] (Claim 1)

[0903] A means for acquiring climate information and market information from information providers and information systems and storing it in a storage device,

[0904] A means for analyzing the accumulated information using a learning model to predict crop risk factors and market trends,

[0905] Based on the predicted results, a means to indicate to workers which crops are most profitable and when they are most suitable for planting,

[0906] A means of optimizing production plans based on individual data provided by employees,

[0907] The aforementioned plan provides a means to introduce new sales channels and products with high demand,

[0908] We provide learning programs and means to improve technical skills,

[0909] A means of forming a group and promoting information exchange,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] Register the land information provided by the employees in the online environment.

[0913] The system according to claim 1, which presents individually optimized fertilization plans and work methods based on the aforementioned land information.

[0914] (Claim 3)

[0915] The system according to claim 1, which proposes specific sales methods and price adjustments to business personnel based on market trend analysis.

[0916] "Application Example 1"

[0917] (Claim 1)

[0918] A means of collecting climate information and market information and storing it in a database,

[0919] A means of analyzing the aforementioned information to identify risk factors and market trends for agricultural products,

[0920] A means of presenting crop producers with highly profitable plants and optimal planting times based on identified information,

[0921] A means of analyzing individual data of agricultural producers and optimizing production methods,

[0922] Based on the above analysis, the means of proposing new sales channels and high-demand products are as follows:

[0923] To provide educational programs and means to improve crop cultivation techniques,

[0924] Means to revitalize information exchange,

[0925] A means of providing personalized information on plant production in urban environments through digital devices,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] Land information provided by agricultural producers is uploaded to the network.

[0929] The system according to claim 1, which proposes individually optimized fertilization plans and production methods based on the aforementioned land information, and also provides information specific to urban agriculture.

[0930] (Claim 3)

[0931] The system according to claim 1, which proposes specific sales strategies and pricing to agricultural producers based on market trend analysis, and provides additional information based on urban supply and demand conditions.

[0932] "Example 2 of combining an emotion engine"

[0933] (Claim 1)

[0934] A means for acquiring weather data and market data from an external source and storing it in a storage device,

[0935] The aforementioned means for analyzing the acquired data and detecting risk factors and market fluctuations for agricultural products,

[0936] A means for presenting agricultural producers with agricultural products that are expected to increase profitability and the optimal timing for starting cultivation, based on the detection results.

[0937] A means of analyzing individual information provided by agricultural workers and optimizing production techniques,

[0938] Based on the aforementioned analysis, a means of introducing new sales channels and high-demand products,

[0939] To provide educational content and support the improvement of agricultural technology,

[0940] A means of forming a community and facilitating information exchange through a communication network,

[0941] A means of collecting and analyzing emotional data of agricultural workers using emotion recognition functions, and personalizing guidance content based on the results,

[0942] A means of analyzing emotional trends in each region and adjusting market trends and sales strategies accordingly.

[0943] A system that includes this.

[0944] (Claim 2)

[0945] Agricultural workers upload land information they provide to an online platform.

[0946] The system according to claim 1, which provides individually optimized fertilization plans and production methods based on the aforementioned land information.

[0947] (Claim 3)

[0948] Based on market analysis results, we will present specific sales strategies and pricing strategies to agricultural workers.

[0949] Furthermore, the system according to claim 1 optimizes marketing strategies based on emotional data analysis.

[0950] "Application example 2 when combining with an emotional engine"

[0951] (Claim 1)

[0952] A means for collecting weather information and economic information and storing it in a data storage unit,

[0953] A means of analyzing the aforementioned information and identifying risk factors and market trends for crops,

[0954] A means of presenting farmers with highly profitable crops and optimal planting times based on identified information,

[0955] A means of analyzing individual data of agricultural workers and optimizing production methods,

[0956] Based on the above analysis, the means of proposing new sales channels and high-demand products are as follows:

[0957] To provide educational programs and means to improve agricultural technology,

[0958] A means of determining a user's emotional state using emotion recognition technology and providing personalized advice based on that,

[0959] Means of providing interesting information and events to increase user motivation,

[0960] Means of providing residents with optimal management plans for public horticultural spaces in urban areas,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] Farmers upload farmland information they provide to the internet.

[0964] The system according to claim 1, which proposes individually optimized fertilizer application plans and production methods based on the aforementioned farmland information.

[0965] (Claim 3)

[0966] The system according to claim 1, which proposes specific sales strategies and pricing to agricultural workers based on market trend analysis. [Explanation of Symbols]

[0967] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting climate information and market information and storing it in a database, A means of analyzing the aforementioned information to identify risk factors and market trends for agricultural products, A means of presenting crop producers with highly profitable plants and optimal planting times based on identified information, A means of analyzing individual data of agricultural producers and optimizing production methods, Based on the above analysis, the means of proposing new sales channels and high-demand products are as follows: To provide educational programs and means to improve crop cultivation techniques, Means to revitalize information exchange, A means of providing personalized information on plant production in urban environments through digital devices, A system that includes this.

2. Land information provided by agricultural producers is uploaded to the network. The system according to claim 1, which proposes individually optimized fertilization plans and production methods based on the aforementioned land information, and also provides information specific to urban agriculture.

3. The system according to claim 1, which proposes specific sales strategies and pricing to agricultural producers based on market trend analysis, and provides additional information based on urban supply and demand conditions.

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