system

Abstract

The system according to this embodiment aims to enable the creation of planting plans for reforestation even without specialized knowledge. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a planting plan based on the data analyzed by the analysis unit. The provision unit provides the planting plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 the conventional technology, there is a problem that specialized knowledge is required and it is costly to create a planting plan for reforestation.

[0005] The system according to the embodiment aims to enable the creation of a planting plan for reforestation without specialized knowledge.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a planting plan based on the data analyzed by the analysis unit. The provision unit provides the planting plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for the creation of planting plans for reforestation even without specialized knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] 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), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The reforestation plan creation system according to an embodiment of the present invention is a system that creates planting plans for reforestation in forestry using a generative AI. This system collects forest data after logging, and the generative AI analyzes it to create an optimal planting plan. The reforestation plan creation system provides planting plans in a format that forestry personnel can easily understand and implement. As a result, anyone can create an appropriate planting plan without having to ask a specialist, thereby achieving cost reduction and expansion of afforestation area. For example, the reforestation plan creation system collects forest data after logging. This data includes topographic information, soil conditions, and climate conditions. For example, the current state of the forest can be photographed using a drone, and the image data can be collected. Next, the reforestation plan creation system uses a generative AI to analyze the collected data. Based on the collected data, the generative AI creates an optimal planting plan. At this time, the generative AI has learned from past planting data and expert knowledge, and can select the optimal tree species and set the planting density. For example, it can select tree species suitable for a specific area and set the planting density to match the climate conditions of that area. The generated planting plans are provided in a format that forestry professionals can easily understand and implement. For example, planting locations are indicated on a map, and specific work procedures are described, making it easy for anyone to implement. This system allows anyone to create appropriate planting plans without having to rely on experts. This results in cost reduction and an expansion of afforestation area. For example, by creating and implementing planting plans themselves, local governments and forestry cooperatives can reduce the costs of hiring experts and regenerate more forests. Furthermore, this system contributes to the creation of a circular forest environment maintenance society through forest regeneration. For example, as reforestation progresses, forest biodiversity is preserved and environmental destruction is prevented. It also enables the sustainable use of forest resources, leaving a rich forest asset for future generations. In summary, the reforestation plan creation system allows forestry professionals to create appropriate planting plans without having to rely on experts, achieving cost reduction and an expansion of afforestation area.

[0029] The reforestation plan creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit collects data such as topographic information, soil conditions, and climate conditions. The collection unit can, for example, use a drone to photograph the current state of the forest and collect the image data. The collection unit can also collect topographic maps and elevation data to obtain topographic information. Furthermore, the collection unit can take soil samples and analyze them to understand the soil conditions. For example, the collection unit measures the pH value and nutrient content of the soil. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses a generation AI to select the optimal tree species and set the planting density based on the collected data. The analysis unit, for example, selects tree species suitable for a specific area and sets the planting density according to the climate conditions of that area. The analysis unit has learned from past planting data and expert knowledge, for example, and can create an optimal planting plan. The generation unit generates a planting plan based on the data analyzed by the analysis unit. The generation unit generates an optimal planting plan, for example, using a generation AI. The generation unit makes it easy for anyone to implement the plan by, for example, indicating planting locations on a map and describing specific work procedures. The provision unit provides the planting plan generated by the generation unit. The provision unit provides the planting plan in a format that forestry personnel can easily understand and implement. The provision unit makes it easy for anyone to implement the plan by, for example, indicating planting locations on a map and describing specific work procedures. As a result, the reforestation plan creation system according to this embodiment can perform everything from data collection and analysis to planting plan generation and provision in an integrated manner.

[0030] The data collection unit collects data such as topographic information, soil conditions, and climatic conditions. Specifically, the data collection unit can use drones to photograph the current state of forests and collect image data. Drones are equipped with high-resolution cameras and LiDAR sensors, which allow for detailed understanding of the forest's topographic information, tree density, and health. The data collection unit can also collect topographic maps and elevation data to acquire topographic information. This includes methods of obtaining high-precision topographic information over a wide area using satellite data and aerial photographs. Furthermore, the data collection unit can collect and analyze soil samples to understand soil conditions. Soil samples are collected at specific locations, and the collected samples are analyzed in detail in a laboratory. For example, soil pH, nutrient content, soil particle size distribution, and water retention capacity are measured. This allows the data collection unit to understand soil conditions suitable for reforestation and to select the optimal tree species. In addition, climatic condition data is also important, and the data collection unit collects meteorological data. This includes historical and current weather observation data, collecting information such as temperature, precipitation, wind speed, and sunshine duration. Because this data significantly impacts the success of reforestation, detailed and accurate data collection is essential. The data collection unit centrally manages this diverse data, making it available to the analysis and generation units.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, using generative AI, the analysis unit selects the optimal tree species and sets the optimal planting density based on the collected data. Specifically, the analysis unit integrates data such as topographic information, soil conditions, and climate conditions to select the optimal tree species for reforestation. The generative AI learns from past planting data and expert knowledge, enabling it to select tree species suitable for specific regions. For example, it selects drought-tolerant tree species for arid areas and moisture-loving tree species for humid areas. The analysis unit also sets the planting density according to climate conditions. For example, it optimizes tree growth by increasing the density in areas with high rainfall and decreasing it in dry areas. Furthermore, the analysis unit can determine the type and amount of fertilizer by considering the soil's nutrient content and pH value. This allows the analysis unit to create an optimal reforestation plan based on the collected data. The analysis unit processes data in real time using AI, providing analysis results quickly and accurately. This allows the analysis unit to play a crucial role in creating reforestation plans, improving the overall efficiency and accuracy of the system.

[0032] The generation unit generates planting plans based on data analyzed by the analysis unit. For example, the generation unit uses generation AI to generate optimal planting plans. Specifically, the generation unit makes it easy for anyone to implement the plan by indicating planting locations on a map and including detailed work procedures. For instance, the generation unit considers topographic information, soil conditions, and climate conditions to indicate optimal planting locations on a map. This allows forestry professionals to quickly understand which tree species should be planted where. Furthermore, the generation unit enables efficient planting by including detailed work procedures. For example, it details the planting order, tools to be used, and required personnel to improve work efficiency. The generation unit can also generate post-planting management plans. For example, it plans management tasks such as regular watering, fertilization, and pest and disease control, providing a plan to maintain long-term forest health. In this way, the generation unit provides comprehensive support from the creation to the implementation and management of reforestation plans, increasing the success rate of reforestation. The generation unit uses AI to analyze data and quickly generate optimal plans, thereby improving the efficiency and accuracy of reforestation projects.

[0033] The service provider provides planting plans generated by the generation service provider. The service provider provides planting plans in a format that forestry personnel can easily understand and implement. Specifically, the service provider makes it easy for anyone to implement by showing planting locations on a map and describing specific work procedures. For example, by showing planting locations on a map, the service provider allows forestry personnel to see at a glance where and which tree species should be planted. In addition, by describing specific work procedures, the service provider enables efficient planting work. For example, it describes the order of planting, the tools to be used, and the necessary personnel in detail to improve work efficiency. Furthermore, the service provider can also provide post-planting management plans. For example, it plans management tasks such as regular watering, fertilization, and pest and disease control to provide a plan for maintaining the long-term health of the forest. In this way, the service provider can provide consistent support from the creation to the implementation and management of reforestation plans, thereby increasing the success rate of reforestation. The service provider enables forestry personnel to proceed with their work efficiently by quickly providing plans generated using AI. Furthermore, the provision department will diversify the methods of providing the plans, offering them through paper media and digital devices to improve user convenience. This will enable the provision department to play a crucial role in smoothly implementing the reforestation plans.

[0034] The data collection unit can collect data such as topographic information, soil conditions, and climate conditions. For example, to obtain topographic information, the data collection unit can collect topographic maps and elevation data. For example, the data collection unit can use a drone to photograph the current state of a forest and collect the image data. The data collection unit can also take soil samples and analyze them to understand the soil conditions. For example, the data collection unit can measure the pH value and nutrient content of the soil. Furthermore, the data collection unit can collect data such as temperature, precipitation, and wind speed to understand climate conditions. For example, the data collection unit can acquire meteorological data and analyze climate conditions. By collecting data such as topographic information, soil conditions, and climate conditions, a more accurate planting plan can be created. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a drone into a generating AI and analyze topographic information and soil conditions from the image data.

[0035] The analysis unit can select the optimal tree species and set the planting density based on the collected data. For example, the analysis unit uses a generative AI to select the optimal tree species based on the collected data. For example, the analysis unit can select tree species suitable for a specific region and set the planting density to match the climate conditions of that region. For example, the analysis unit learns from past planting data and expert knowledge, enabling it to create an optimal planting plan. For example, the analysis unit selects the optimal tree species based on the climate conditions and soil conditions of the region. The analysis unit can also set the planting density to match the characteristics of the region. For example, the analysis unit sets the planting density based on appropriate spacing and growth predictions for each tree species. This enables efficient planting by selecting the optimal tree species and setting the planting density based on the collected data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the selection of the optimal tree species and setting the planting density.

[0036] The generation unit learns from past planting data and expert knowledge. For example, the generation unit selects the optimal tree species and sets the planting density based on past planting data. For example, the generation unit learns from expert knowledge and creates an optimal planting plan. For example, the generation unit creates a planting plan tailored to the characteristics of each region based on past planting data and expert knowledge. For example, the generation unit selects the optimal tree species by referring to past planting history and growth data. The generation unit can also create an optimal planting plan based on expert opinions and research papers. In this way, by learning from past planting data and expert knowledge, it is possible to generate a more accurate planting plan. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past planting data and expert knowledge into a generation AI and have the generation AI execute the generation of an optimal planting plan.

[0037] The service provider can indicate planting locations on a map and describe specific work procedures. For example, the service provider can indicate planting locations on a map. For example, the service provider can display planting locations on a map using a GIS (Geographic Information System). For example, the service provider can indicate planting locations based on coordinate data. The service provider can also describe specific work procedures. For example, the service provider can provide planting procedure manuals and work manuals. For example, the service provider can explain the planting procedure step by step. The service provider can also provide work manuals and describe specific work procedures. This makes it easy for anyone to carry out a planting plan by indicating planting locations on a map and describing specific work procedures. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input map data indicating planting locations into a generating AI and have the generating AI perform the process of displaying the planting locations on the map.

[0038] The collection unit can optimize the drone's flight pattern and efficiently collect terrain information and soil conditions. For example, the collection unit can simulate the drone's flight pattern in advance and set the optimal route. For example, the collection unit can adjust the drone's flight altitude to collect detailed terrain information. For example, the collection unit can adjust the drone's flight speed to accurately collect soil conditions. In this way, by optimizing the drone's flight pattern, terrain information and soil conditions can be efficiently collected. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the drone's flight pattern into a generating AI and have the generating AI set the optimal flight route.

[0039] The data collection unit can adjust its data collection method to account for seasonal and weather variations during data collection. For example, in winter, the data collection unit adjusts its data collection method to account for the effects of snow cover. For example, in rainy weather, the data collection unit changes its data collection method to avoid the effects of rainwater. For example, in summer, the data collection unit adjusts the timing of data collection to account for sunshine hours. By adjusting the data collection method to account for seasonal and weather variations, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data into a generating AI and have the generating AI perform adjustments to the data collection method to account for seasonal and weather variations.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from a specific region based on geographical location information. For example, the data collection unit can select and collect highly relevant data based on geographical location information. For example, the data collection unit can set up a route for efficient data collection based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the process of determining the priority of highly relevant data.

[0041] The data collection unit can analyze social media and other external data sources during data collection and collect relevant data. For example, the data collection unit can analyze social media posts and collect relevant data. For example, the data collection unit can analyze other external data sources and collect relevant data. For example, the data collection unit can expand the scope of data collection based on information obtained from social media and external data sources. This allows for the collection of more diverse data by analyzing social media and other external data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a topographic analysis algorithm to topographic information. For example, the analysis unit applies a soil analysis algorithm to soil conditions. For example, the analysis unit applies a climate analysis algorithm to climate conditions. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the analysis schedule based on the data collection period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0046] The generation unit can select the optimal tree species and set the optimal planting density by referring to past planting data during generation. For example, the generation unit can select the optimal tree species based on past planting data. For example, the generation unit can set the optimal planting density based on past planting data. For example, the generation unit can create a planting plan tailored to the characteristics of each region by referring to past planting data. This makes it possible to select the optimal tree species and set the optimal planting density by referring to past planting data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past planting data into a generation AI and have the generation AI perform the selection of the optimal tree species and the setting of the optimal planting density.

[0047] The generation unit can customize planting plans based on regional characteristics during generation. For example, the generation unit creates an optimal planting plan based on regional climate conditions. For example, the generation unit creates an optimal planting plan based on regional soil conditions. For example, the generation unit creates an optimal planting plan based on regional topographic information. This makes it possible to plant in a region that is suitable for that region by customizing the planting plan based on regional characteristics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input regional characteristic data into the generation AI and have the generation AI perform the customization of the planting plan.

[0048] The generation unit can generate an optimal planting plan while considering geographical location information during generation. For example, the generation unit can generate a planting plan suitable for a specific region based on geographical location information. For example, the generation unit can generate a highly relevant planting plan based on geographical location information. For example, the generation unit can set a route for efficiently generating a planting plan based on geographical location information. This makes it possible to plant in a region that is suitable for the area by generating an optimal planting plan while considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI execute the generation of an optimal planting plan.

[0049] The generation unit can improve the accuracy of the planting plan by referring to relevant literature and expert knowledge during the generation process. For example, the generation unit can refer to relevant literature and generate a planting plan that incorporates the latest knowledge. For example, the generation unit can generate a highly accurate planting plan based on expert knowledge. For example, the generation unit can refer to relevant literature and expert knowledge to create a planting plan tailored to the characteristics of each region. In this way, a highly accurate planting plan can be generated by referring to relevant literature and expert knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input relevant literature and expert knowledge into the generation AI and have the generation AI perform the improvement of the planting plan accuracy.

[0050] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may propose the optimal display method based on the user's past operation history. For example, the service provider may recommend a specific display method from the user's past operation history. In this way, by referring to the user's past operation history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0051] The service provider can adjust the level of detail in the explanation at the time of delivery according to the user's level of understanding. For example, if the user is a beginner, the service provider will provide a detailed explanation. If the user is an intermediate user, the service provider will provide a concise explanation. If the user is an advanced user, the service provider will provide a brief explanation. By adjusting the level of detail in the explanation according to the user's level of understanding, it becomes possible to provide explanations that are easy for the user to understand. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanation.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a display method that includes detailed information. In this way, by taking into account the user's device information, a display method optimized for the device can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0053] The service provider can collect user feedback at the time of delivery and continuously improve its delivery method. For example, the service provider can improve the display method based on user feedback. For example, the service provider can improve the operating procedure based on user feedback. For example, the service provider can adjust the level of detail in the explanation based on user feedback. In this way, the service provider can continuously improve its delivery method by collecting user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI execute improvements to the delivery method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reforestation plan creation system can also be equipped with a communication unit. This unit enables two-way communication between forestry professionals and the system. For example, when a forestry professional enters a question into the system, the communication unit provides an answer. The communication unit can also collect feedback from forestry professionals and use it to improve the system. Furthermore, the communication unit can provide real-time support if forestry professionals encounter difficulties operating the system. This allows forestry professionals to use the system more effectively, facilitating the smooth creation of reforestation plans.

[0056] The reforestation plan creation system can also be equipped with a prediction unit. This unit predicts future forest growth based on collected data. For example, it simulates the future state of the forest, taking into account the growth rate and lifespan of specific tree species. It can also predict forest growth under future climatic conditions, considering the effects of climate change. Furthermore, based on historical data, the unit can predict the risk of pest and disease outbreaks and propose preventative measures. This allows forestry professionals to understand the future state of the forest and take appropriate action.

[0057] The reforestation plan creation system can also include an education department. The education department provides knowledge about reforestation to forestry stakeholders. For example, the education department can provide educational materials explaining the basic procedures and precautions for reforestation. It can also provide information on the characteristics of specific tree species and appropriate planting methods. Furthermore, the education department can provide practical advice to help forestry stakeholders implement reforestation plans. This will allow forestry stakeholders to deepen their knowledge of reforestation and implement reforestation plans more effectively.

[0058] The reforestation plan creation system can also be equipped with a monitoring unit. The monitoring unit monitors the implementation status of the reforestation plan in real time. For example, the monitoring unit can periodically check the growth status of planted trees using drones or sensors. The monitoring unit can also continuously monitor soil conditions and weather conditions and issue alerts if abnormalities occur. Furthermore, the monitoring unit can record the progress of the reforestation plan and report it to forestry stakeholders. This allows forestry stakeholders to understand the implementation status of the reforestation plan and take appropriate action as needed.

[0059] The reforestation plan creation system can also be equipped with an evaluation unit. This unit evaluates the effectiveness of the reforestation plan. For example, it can assess the growth and survival rate of planted trees. It can also evaluate the impact of the reforestation plan on the local ecosystem. Furthermore, it can evaluate the economic effects of the reforestation plan and analyze its cost-effectiveness. This allows forestry stakeholders to understand the effectiveness of reforestation plans and utilize this information in future plan development.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data. The data collection unit collects data such as topographic information, soil conditions, and climatic conditions. For example, the data collection unit can use a drone to photograph the current state of the forest and collect the image data. The data collection unit can also collect topographic maps and elevation data to obtain topographic information. Furthermore, the data collection unit can take soil samples and analyze them to understand the soil conditions. For example, the data collection unit can measure the pH value and nutrient content of the soil. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, generative AI to select the optimal tree species and set the planting density based on the collected data. For example, the analysis unit selects tree species suitable for a specific region and sets the planting density to match the climate conditions of that region. The analysis unit learns from, for example, past planting data and expert knowledge, and can create an optimal planting plan. Step 3: The generation unit generates a planting plan based on the data analyzed by the analysis unit. The generation unit generates the optimal planting plan, for example, using a generation AI. The generation unit makes it easy for anyone to implement the plan by, for example, showing planting locations on a map and describing specific work procedures. Step 4: The provider unit provides the planting plan generated by the generator unit. The provider unit provides the planting plan in a format that forestry personnel can easily understand and implement. The provider unit makes it easy for anyone to implement by, for example, showing planting locations on a map or describing specific work procedures.

[0062] (Example of form 2) The reforestation plan creation system according to an embodiment of the present invention is a system that creates planting plans for reforestation in forestry using a generative AI. This system collects forest data after logging, and the generative AI analyzes it to create an optimal planting plan. The reforestation plan creation system provides planting plans in a format that forestry personnel can easily understand and implement. As a result, anyone can create an appropriate planting plan without having to ask a specialist, thereby achieving cost reduction and expansion of afforestation area. For example, the reforestation plan creation system collects forest data after logging. This data includes topographic information, soil conditions, and climate conditions. For example, the current state of the forest can be photographed using a drone, and the image data can be collected. Next, the reforestation plan creation system uses a generative AI to analyze the collected data. Based on the collected data, the generative AI creates an optimal planting plan. At this time, the generative AI has learned from past planting data and expert knowledge, and can select the optimal tree species and set the planting density. For example, it can select tree species suitable for a specific area and set the planting density to match the climate conditions of that area. The generated planting plans are provided in a format that forestry professionals can easily understand and implement. For example, planting locations are indicated on a map, and specific work procedures are described, making it easy for anyone to implement. This system allows anyone to create appropriate planting plans without having to rely on experts. This results in cost reduction and an expansion of afforestation area. For example, by creating and implementing planting plans themselves, local governments and forestry cooperatives can reduce the costs of hiring experts and regenerate more forests. Furthermore, this system contributes to the creation of a circular forest environment maintenance society through forest regeneration. For example, as reforestation progresses, forest biodiversity is preserved and environmental destruction is prevented. It also enables the sustainable use of forest resources, leaving a rich forest asset for future generations. In summary, the reforestation plan creation system allows forestry professionals to create appropriate planting plans without having to rely on experts, achieving cost reduction and an expansion of afforestation area.

[0063] The reforestation plan creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit collects data such as topographic information, soil conditions, and climate conditions. The collection unit can, for example, use a drone to photograph the current state of the forest and collect the image data. The collection unit can also collect topographic maps and elevation data to obtain topographic information. Furthermore, the collection unit can take soil samples and analyze them to understand the soil conditions. For example, the collection unit measures the pH value and nutrient content of the soil. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses a generation AI to select the optimal tree species and set the planting density based on the collected data. The analysis unit, for example, selects tree species suitable for a specific area and sets the planting density according to the climate conditions of that area. The analysis unit has learned from past planting data and expert knowledge, for example, and can create an optimal planting plan. The generation unit generates a planting plan based on the data analyzed by the analysis unit. The generation unit generates an optimal planting plan, for example, using a generation AI. The generation unit makes it easy for anyone to implement the plan by, for example, indicating planting locations on a map and describing specific work procedures. The provision unit provides the planting plan generated by the generation unit. The provision unit provides the planting plan in a format that forestry personnel can easily understand and implement. The provision unit makes it easy for anyone to implement the plan by, for example, indicating planting locations on a map and describing specific work procedures. As a result, the reforestation plan creation system according to this embodiment can perform everything from data collection and analysis to planting plan generation and provision in an integrated manner.

[0064] The data collection unit collects data such as topographic information, soil conditions, and climatic conditions. Specifically, the data collection unit can use drones to photograph the current state of forests and collect image data. Drones are equipped with high-resolution cameras and LiDAR sensors, which allow for detailed understanding of the forest's topographic information, tree density, and health. The data collection unit can also collect topographic maps and elevation data to acquire topographic information. This includes methods of obtaining high-precision topographic information over a wide area using satellite data and aerial photographs. Furthermore, the data collection unit can collect and analyze soil samples to understand soil conditions. Soil samples are collected at specific locations, and the collected samples are analyzed in detail in a laboratory. For example, soil pH, nutrient content, soil particle size distribution, and water retention capacity are measured. This allows the data collection unit to understand soil conditions suitable for reforestation and to select the optimal tree species. In addition, climatic condition data is also important, and the data collection unit collects meteorological data. This includes historical and current weather observation data, collecting information such as temperature, precipitation, wind speed, and sunshine duration. Because this data significantly impacts the success of reforestation, detailed and accurate data collection is essential. The data collection unit centrally manages this diverse data, making it available to the analysis and generation units.

[0065] The analysis unit analyzes the data collected by the data collection unit. For example, using generative AI, the analysis unit selects the optimal tree species and sets the optimal planting density based on the collected data. Specifically, the analysis unit integrates data such as topographic information, soil conditions, and climate conditions to select the optimal tree species for reforestation. The generative AI learns from past planting data and expert knowledge, enabling it to select tree species suitable for specific regions. For example, it selects drought-tolerant tree species for arid areas and moisture-loving tree species for humid areas. The analysis unit also sets the planting density according to climate conditions. For example, it optimizes tree growth by increasing the density in areas with high rainfall and decreasing it in dry areas. Furthermore, the analysis unit can determine the type and amount of fertilizer by considering the soil's nutrient content and pH value. This allows the analysis unit to create an optimal reforestation plan based on the collected data. The analysis unit processes data in real time using AI, providing analysis results quickly and accurately. This allows the analysis unit to play a crucial role in creating reforestation plans, improving the overall efficiency and accuracy of the system.

[0066] The generation unit generates planting plans based on data analyzed by the analysis unit. For example, the generation unit uses generation AI to generate optimal planting plans. Specifically, the generation unit makes it easy for anyone to implement the plan by indicating planting locations on a map and including detailed work procedures. For instance, the generation unit considers topographic information, soil conditions, and climate conditions to indicate optimal planting locations on a map. This allows forestry professionals to quickly understand which tree species should be planted where. Furthermore, the generation unit enables efficient planting by including detailed work procedures. For example, it details the planting order, tools to be used, and required personnel to improve work efficiency. The generation unit can also generate post-planting management plans. For example, it plans management tasks such as regular watering, fertilization, and pest and disease control, providing a plan to maintain long-term forest health. In this way, the generation unit provides comprehensive support from the creation to the implementation and management of reforestation plans, increasing the success rate of reforestation. The generation unit uses AI to analyze data and quickly generate optimal plans, thereby improving the efficiency and accuracy of reforestation projects.

[0067] The service provider provides planting plans generated by the generation service provider. The service provider provides planting plans in a format that forestry personnel can easily understand and implement. Specifically, the service provider makes it easy for anyone to implement by showing planting locations on a map and describing specific work procedures. For example, by showing planting locations on a map, the service provider allows forestry personnel to see at a glance where and which tree species should be planted. In addition, by describing specific work procedures, the service provider enables efficient planting work. For example, it describes the order of planting, the tools to be used, and the necessary personnel in detail to improve work efficiency. Furthermore, the service provider can also provide post-planting management plans. For example, it plans management tasks such as regular watering, fertilization, and pest and disease control to provide a plan for maintaining the long-term health of the forest. In this way, the service provider can provide consistent support from the creation to the implementation and management of reforestation plans, thereby increasing the success rate of reforestation. The service provider enables forestry personnel to proceed with their work efficiently by quickly providing plans generated using AI. Furthermore, the provision department will diversify the methods of providing the plans, offering them through paper media and digital devices to improve user convenience. This will enable the provision department to play a crucial role in smoothly implementing the reforestation plans.

[0068] The data collection unit can collect data such as topographic information, soil conditions, and climate conditions. For example, to obtain topographic information, the data collection unit can collect topographic maps and elevation data. For example, the data collection unit can use a drone to photograph the current state of a forest and collect the image data. The data collection unit can also take soil samples and analyze them to understand the soil conditions. For example, the data collection unit can measure the pH value and nutrient content of the soil. Furthermore, the data collection unit can collect data such as temperature, precipitation, and wind speed to understand climate conditions. For example, the data collection unit can acquire meteorological data and analyze climate conditions. By collecting data such as topographic information, soil conditions, and climate conditions, a more accurate planting plan can be created. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a drone into a generating AI and analyze topographic information and soil conditions from the image data.

[0069] The analysis unit can select the optimal tree species and set the planting density based on the collected data. For example, the analysis unit uses a generative AI to select the optimal tree species based on the collected data. For example, the analysis unit can select tree species suitable for a specific region and set the planting density to match the climate conditions of that region. For example, the analysis unit learns from past planting data and expert knowledge, enabling it to create an optimal planting plan. For example, the analysis unit selects the optimal tree species based on the climate conditions and soil conditions of the region. The analysis unit can also set the planting density to match the characteristics of the region. For example, the analysis unit sets the planting density based on appropriate spacing and growth predictions for each tree species. This enables efficient planting by selecting the optimal tree species and setting the planting density based on the collected data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the selection of the optimal tree species and setting the planting density.

[0070] The generation unit learns from past planting data and expert knowledge. For example, the generation unit selects the optimal tree species and sets the planting density based on past planting data. For example, the generation unit learns from expert knowledge and creates an optimal planting plan. For example, the generation unit creates a planting plan tailored to the characteristics of each region based on past planting data and expert knowledge. For example, the generation unit selects the optimal tree species by referring to past planting history and growth data. The generation unit can also create an optimal planting plan based on expert opinions and research papers. In this way, by learning from past planting data and expert knowledge, it is possible to generate a more accurate planting plan. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past planting data and expert knowledge into a generation AI and have the generation AI execute the generation of an optimal planting plan.

[0071] The service provider can indicate planting locations on a map and describe specific work procedures. For example, the service provider can indicate planting locations on a map. For example, the service provider can display planting locations on a map using a GIS (Geographic Information System). For example, the service provider can indicate planting locations based on coordinate data. The service provider can also describe specific work procedures. For example, the service provider can provide planting procedure manuals and work manuals. For example, the service provider can explain the planting procedure step by step. The service provider can also provide work manuals and describe specific work procedures. This makes it easy for anyone to carry out a planting plan by indicating planting locations on a map and describing specific work procedures. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input map data indicating planting locations into a generating AI and have the generating AI perform the process of displaying the planting locations on the map.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of data collection and start collection when the user is relaxed. For example, if the user is in a hurry, the data collection unit can advance the timing of data collection to complete it quickly. For example, if the user is concentrating, the data collection unit can adjust the timing of data collection so as not to interrupt the user's work. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The collection unit can optimize the drone's flight pattern and efficiently collect terrain information and soil conditions. For example, the collection unit can simulate the drone's flight pattern in advance and set the optimal route. For example, the collection unit can adjust the drone's flight altitude to collect detailed terrain information. For example, the collection unit can adjust the drone's flight speed to accurately collect soil conditions. In this way, by optimizing the drone's flight pattern, terrain information and soil conditions can be efficiently collected. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the drone's flight pattern into a generating AI and have the generating AI set the optimal flight route.

[0074] The data collection unit can adjust its data collection method to account for seasonal and weather variations during data collection. For example, in winter, the data collection unit adjusts its data collection method to account for the effects of snow cover. For example, in rainy weather, the data collection unit changes its data collection method to avoid the effects of rainwater. For example, in summer, the data collection unit adjusts the timing of data collection to account for sunshine hours. By adjusting the data collection method to account for seasonal and weather variations, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather data into a generating AI and have the generating AI perform adjustments to the data collection method to account for seasonal and weather variations.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize data that can be collected quickly. This allows for data collection that meets the user's needs by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of data from a specific region based on geographical location information. For example, the data collection unit can select and collect highly relevant data based on geographical location information. For example, the data collection unit can set up a route for efficient data collection based on geographical location information. This enables efficient data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the process of determining the priority of highly relevant data.

[0077] The data collection unit can analyze social media and other external data sources during data collection and collect relevant data. For example, the data collection unit can analyze social media posts and collect relevant data. For example, the data collection unit can analyze other external data sources and collect relevant data. For example, the data collection unit can expand the scope of data collection based on information obtained from social media and external data sources. This allows for the collection of more diverse data by analyzing social media and other external data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. If the user is excited, the analysis unit provides analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a topographic analysis algorithm to topographic information. For example, the analysis unit applies a soil analysis algorithm to soil conditions. For example, the analysis unit applies a climate analysis algorithm to climate conditions. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is excited, the analysis unit will perform an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide analysis results that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the analysis schedule based on the data collection period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may optimize the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way the generated planting plan is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit provides a detailed planting plan. If the user is in a hurry, the generation unit provides a concise planting plan that gets straight to the point. If the user is excited, the generation unit provides a planting plan with visually stimulating effects. By adjusting the way the planting plan is presented according to the user's emotions, the generation unit can provide a planting plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0085] The generation unit can select the optimal tree species and set the optimal planting density by referring to past planting data during generation. For example, the generation unit can select the optimal tree species based on past planting data. For example, the generation unit can set the optimal planting density based on past planting data. For example, the generation unit can create a planting plan tailored to the characteristics of each region by referring to past planting data. This makes it possible to select the optimal tree species and set the optimal planting density by referring to past planting data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input past planting data into a generation AI and have the generation AI perform the selection of the optimal tree species and the setting of the optimal planting density.

[0086] The generation unit can customize planting plans based on regional characteristics during generation. For example, the generation unit creates an optimal planting plan based on regional climate conditions. For example, the generation unit creates an optimal planting plan based on regional soil conditions. For example, the generation unit creates an optimal planting plan based on regional topographic information. This makes it possible to plant in a region that is suitable for that region by customizing the planting plan based on regional characteristics. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input regional characteristic data into the generation AI and have the generation AI perform the customization of the planting plan.

[0087] The generation unit can estimate the user's emotions and determine the priority of planting plans to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating high-priority planting plans. For example, if the user is relaxed, the generation unit will generate detailed planting plans. For example, if the user is in a hurry, the generation unit will prioritize planting plans that can be generated quickly. In this way, by determining the priority of planting plans according to the user's emotions, planting plans that meet the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0088] The generation unit can generate an optimal planting plan while considering geographical location information during generation. For example, the generation unit can generate a planting plan suitable for a specific region based on geographical location information. For example, the generation unit can generate a highly relevant planting plan based on geographical location information. For example, the generation unit can set a route for efficiently generating a planting plan based on geographical location information. This makes it possible to plant in a region that is suitable for the area by generating an optimal planting plan while considering geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI execute the generation of an optimal planting plan.

[0089] The generation unit can improve the accuracy of the planting plan by referring to relevant literature and expert knowledge during the generation process. For example, the generation unit can refer to relevant literature and generate a planting plan that incorporates the latest knowledge. For example, the generation unit can generate a highly accurate planting plan based on expert knowledge. For example, the generation unit can refer to relevant literature and expert knowledge to create a planting plan tailored to the characteristics of each region. In this way, a highly accurate planting plan can be generated by referring to relevant literature and expert knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input relevant literature and expert knowledge into the generation AI and have the generation AI perform the improvement of the planting plan accuracy.

[0090] The service provider can estimate the user's emotions and adjust the display method of the planting plan based on the estimated emotions. For example, if the user is tense, the service provider provides a simple and highly visible display method. For example, if the user is relaxed, the service provider provides a display method that includes detailed information. For example, if the user is in a hurry, the service provider provides a display method that gets straight to the point. By adjusting the display method of the planting plan according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may propose the optimal display method based on the user's past operation history. For example, the service provider may recommend a specific display method from the user's past operation history. In this way, by referring to the user's past operation history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI select the optimal display method.

[0092] The service provider can adjust the level of detail in the explanation at the time of delivery according to the user's level of understanding. For example, if the user is a beginner, the service provider will provide a detailed explanation. If the user is an intermediate user, the service provider will provide a concise explanation. If the user is an advanced user, the service provider will provide a brief explanation. By adjusting the level of detail in the explanation according to the user's level of understanding, it becomes possible to provide explanations that are easy for the user to understand. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanation.

[0093] The service provider can estimate the user's emotions and adjust the operation procedures for the planting plan based on the estimated emotions. For example, if the user is tense, the service provider can provide simple and intuitive operation procedures. For example, if the user is relaxed, the service provider can provide detailed operation procedures. For example, if the user is in a hurry, the service provider can provide procedures that can be operated quickly. In this way, by adjusting the operation procedures according to the user's emotions, user-friendly operation procedures can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a display method that includes detailed information. In this way, by taking into account the user's device information, a display method optimized for the device can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0095] The service provider can collect user feedback at the time of delivery and continuously improve its delivery method. For example, the service provider can improve the display method based on user feedback. For example, the service provider can improve the operating procedure based on user feedback. For example, the service provider can adjust the level of detail in the explanation based on user feedback. In this way, the service provider can continuously improve its delivery method by collecting user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI execute improvements to the delivery method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reforestation plan creation system can also be equipped with a communication unit. This unit enables two-way communication between forestry professionals and the system. For example, when a forestry professional enters a question into the system, the communication unit provides an answer. The communication unit can also collect feedback from forestry professionals and use it to improve the system. Furthermore, the communication unit can provide real-time support if forestry professionals encounter difficulties operating the system. This allows forestry professionals to use the system more effectively, facilitating the smooth creation of reforestation plans.

[0098] The reforestation plan creation system can also be equipped with a prediction unit. This unit predicts future forest growth based on collected data. For example, it simulates the future state of the forest, taking into account the growth rate and lifespan of specific tree species. It can also predict forest growth under future climatic conditions, considering the effects of climate change. Furthermore, based on historical data, the unit can predict the risk of pest and disease outbreaks and propose preventative measures. This allows forestry professionals to understand the future state of the forest and take appropriate action.

[0099] The reforestation plan creation system can also include an education department. The education department provides knowledge about reforestation to forestry stakeholders. For example, the education department can provide educational materials explaining the basic procedures and precautions for reforestation. It can also provide information on the characteristics of specific tree species and appropriate planting methods. Furthermore, the education department can provide practical advice to help forestry stakeholders implement reforestation plans. This will allow forestry stakeholders to deepen their knowledge of reforestation and implement reforestation plans more effectively.

[0100] The reforestation plan creation system can also be equipped with a monitoring unit. The monitoring unit monitors the implementation status of the reforestation plan in real time. For example, the monitoring unit can periodically check the growth status of planted trees using drones or sensors. The monitoring unit can also continuously monitor soil conditions and weather conditions and issue alerts if abnormalities occur. Furthermore, the monitoring unit can record the progress of the reforestation plan and report it to forestry stakeholders. This allows forestry stakeholders to understand the implementation status of the reforestation plan and take appropriate action as needed.

[0101] The reforestation plan creation system can also be equipped with an evaluation unit. This unit evaluates the effectiveness of the reforestation plan. For example, it can assess the growth and survival rate of planted trees. It can also evaluate the impact of the reforestation plan on the local ecosystem. Furthermore, it can evaluate the economic effects of the reforestation plan and analyze its cost-effectiveness. This allows forestry stakeholders to understand the effectiveness of reforestation plans and utilize this information in future plan development.

[0102] The reforestation plan creation system can further adjust the proposed reforestation plan based on the user's emotions using an emotion estimation function. For example, if the user is stressed, the system will propose a simple and easy-to-implement plan. If the user is relaxed, it can propose a plan with more detailed information. Furthermore, if the user is excited, it can propose a visually appealing plan. This allows the system to provide the optimal plan according to the user's emotions, thereby improving user satisfaction.

[0103] The reforestation plan creation system can further utilize emotion estimation to adjust data collection methods based on the user's emotions. For example, if the user is stressed, the system reduces the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, it can collect more detailed data. Furthermore, if the user is in a hurry, it can choose a method to collect data quickly. By providing data collection methods tailored to the user's emotions, the system can reduce the user's burden and achieve efficient data collection.

[0104] The reforestation plan creation system can further adjust how analysis results are displayed based on the user's emotions using an emotion estimation function. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide analysis results with visually stimulating effects. By providing analysis results that match the user's emotions, it can provide information that is easy for the user to understand.

[0105] The reforestation plan creation system can further adjust the amount of information provided based on the user's emotions using an emotion estimation function. For example, if the user is stressed, the system will provide only the minimum necessary information to reduce the user's burden. If the user is relaxed, it can provide detailed information. Furthermore, if the user is excited, it can provide visually appealing information. By providing information tailored to the user's emotions, this system can improve user satisfaction.

[0106] The reforestation plan creation system can further utilize emotion estimation capabilities to adjust the system interface based on the user's emotions. For example, if the user is stressed, the system provides a simple and intuitive interface. If the user is relaxed, it can provide an interface with more detailed information. Furthermore, if the user is in a hurry, it can provide a quick and easy-to-use interface. By providing an interface that responds to the user's emotions, the system can improve user experience.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data. The data collection unit collects data such as topographic information, soil conditions, and climatic conditions. For example, the data collection unit can use a drone to photograph the current state of the forest and collect the image data. The data collection unit can also collect topographic maps and elevation data to obtain topographic information. Furthermore, the data collection unit can take soil samples and analyze them to understand the soil conditions. For example, the data collection unit can measure the pH value and nutrient content of the soil. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, generative AI to select the optimal tree species and set the planting density based on the collected data. For example, the analysis unit selects tree species suitable for a specific region and sets the planting density to match the climate conditions of that region. The analysis unit learns from, for example, past planting data and expert knowledge, and can create an optimal planting plan. Step 3: The generation unit generates a planting plan based on the data analyzed by the analysis unit. The generation unit generates the optimal planting plan, for example, using a generation AI. The generation unit makes it easy for anyone to implement the plan by, for example, showing planting locations on a map and describing specific work procedures. Step 4: The provider unit provides the planting plan generated by the generator unit. The provider unit provides the planting plan in a format that forestry personnel can easily understand and implement. The provider unit makes it easy for anyone to implement by, for example, showing planting locations on a map or describing specific work procedures.

[0109] 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.

[0110] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 or a drone to photograph the current state of the forest and collect the image data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and selects the optimal tree species and sets the planting density based on the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal planting plan. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the generated planting plan to forestry personnel. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0118] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] 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.

[0120] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 or a drone to photograph the current state of the forest and collect the image data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and selects the optimal tree species and sets the planting density based on the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal planting plan. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated planting plan to forestry personnel. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0134] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 or a drone to photograph the current state of the forest and collect the image data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and selects the optimal tree species and sets the planting density based on the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an optimal planting plan. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, and provides the generated planting plan to forestry personnel. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0150] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] 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.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 or a drone to photograph the current state of the forest and collect the image data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and selects the optimal tree species and sets the planting density based on the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal planting plan. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated planting plan to forestry workers. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] Figure 9 shows the 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.

[0164] 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.

[0165] 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.

[0166] 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, and motorcycles, 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 based, for example, 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.

[0167] 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."

[0168] 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.

[0169] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] 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 other things 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a planting plan based on the data analyzed by the analysis unit, The system includes a providing unit that provides the planting plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as topographic information, soil conditions, and climatic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the optimal tree species and planting density will be selected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It learns from past planting data and expert knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, This involves indicating planting locations on a map and describing specific work procedures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Optimize drone flight patterns to efficiently collect terrain information and soil conditions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, adjust the collection method to take into account seasonal and weather variations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze social media and other external data sources to gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We estimate the user's emotions and adjust the way the planting plan is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, past planting data is referenced to select the optimal tree species and set the optimal planting density. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the planting plan is customized based on local characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of planting plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the optimal planting plan is generated considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, we improve the accuracy of the planting plan by referring to relevant literature and expert knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the planting plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, adjust the level of detail in the explanation according to the user's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the operation procedures for the planting plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We collect user feedback at the time of delivery and continuously improve the delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a planting plan based on the data analyzed by the analysis unit, The system includes a providing unit that provides the planting plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as topographic information, soil conditions, and climatic conditions. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the optimal tree species and planting density will be selected. The system according to feature 1.

4. The generating unit is It learns from past planting data and expert knowledge. The system according to feature 1.

5. The aforementioned supply unit is, This involves indicating planting locations on a map and describing specific work procedures. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Optimize drone flight patterns to efficiently collect terrain information and soil conditions. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, adjust the collection method to take into account seasonal and weather variations. The system according to feature 1.

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