System

Abstract

An object of a system according to an embodiment is to predict the occurrence of a disease or pest in a field at an early stage and to quickly handle the disease or pest.SOLUTION: A system according to an embodiment includes a camera, a sensor, a AI learning unit, and a notifying unit. The camera and the sensor collect images or sensor data obtained from the camera and the sensor installed in the field. The data-learning unit causes the AI to learn the AI collected by the camera and the sensor, and predicts the occurrence of specific pests. The notifying unit notifies the farmer of the result predicted by the AI learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to predict the occurrence of pests in fields at an early stage and deal with the problem promptly.

[0005] The system according to the embodiment aims to predict the occurrence of pests in fields at an early stage and take prompt action. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a sensor, an AI learning unit, and a notification unit. The camera and sensor collect video or sensor data obtained from the camera and sensor installed in the fields. The AI ​​learning unit trains the AI ​​using the data collected by the camera and sensor to predict the occurrence of specific pests and diseases. The notification unit notifies the farmer of the results predicted by the AI ​​learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the occurrence of pests in fields at an early stage and deal with the problem promptly. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The pest prediction system according to an embodiment of the present invention is a system that uses AI to learn images and sensor data obtained from cameras and sensors installed in fields, predict the occurrence of specific pests, and take early action. This enables the pest prediction system to predict pest damage in agriculture and take early action.

[0029] A pest prediction system according to an embodiment includes a camera, a sensor, an AI learning unit, and a notification unit. The camera is installed in a field and records the growth status of crops and the presence of pests as video. For example, a fixed camera or a drone camera can be used to collect wide-area video. Alternatively, an infrared camera capable of capturing images at night can also be used. The sensor is installed in the field and collects environmental data such as temperature, humidity, and soil condition. For example, a temperature sensor or a humidity sensor can be used to acquire environmental data in real time. A soil sensor can also be used to measure the moisture content and nutritional status of the soil. The AI ​​learning unit trains the AI ​​based on the data collected by the camera and sensor to predict the occurrence of specific pests. For example, the AI ​​can use deep learning to predict which pests will occur when specific weather and environmental conditions are met based on past data. The AI ​​can also use machine learning algorithms to analyze data and generate a predictive model. The notification unit notifies farmers of the results predicted by the AI ​​learning unit. For example, information can be provided to farmers in real time via smartphone notifications or email notifications. The notification unit can also notify the prediction results by voice using a voice assistant. This allows the pest prediction system according to the embodiment to predict agricultural pest damage and deal with it early. For example, farmers can take appropriate measures such as spraying pesticides or taking physical measures based on the prediction results. The notification unit can also display the prediction results via a web application or a mobile application.

[0030] The pest prediction system is equipped with an automatic adjustment function for dynamically changing the installation locations of cameras and sensors. This automatic adjustment function for dynamically changing the installation locations of cameras and sensors automatically calculates and moves the optimal installation locations based on, for example, the quality of data collected by the sensors. It is also possible to build a system that dynamically changes the installation locations of cameras and sensors using drones. For example, the drone flies to search for the optimal data collection points and install cameras and sensors. It is also possible to develop an algorithm for dynamically changing the data collection points of sensors. For example, the optimal installation location can be calculated in real time depending on environmental conditions and the growth state of crops, and the sensor can be moved. This makes it possible to always maintain the optimal data collection points.

[0031] Pest prediction systems are equipped with a function that automatically adjusts the sensor's data collection frequency according to environmental conditions. This function automatically adjusts the sensor's data collection frequency according to environmental conditions, for example, by changing the data collection frequency in response to changes in temperature or humidity. AI can also be used to develop algorithms that optimize the sensor's data collection frequency. For example, the optimal data collection frequency can be calculated in real time based on past data. Sensors can also be developed that automatically adjust the sensor's data collection frequency according to environmental conditions. For example, the sensor itself can analyze environmental data and set the optimal collection frequency. This maximizes data accuracy and efficiency.

[0032] In addition to cameras and sensors, pest prediction systems also have the ability to collect audio data and predict pest infestations through audio analysis. For example, a function for collecting audio data and predicting pest infestations through audio analysis can be achieved by installing microphones in fields and predicting pest infestations through audio analysis. Audio analysis technology can also be used to develop algorithms for predicting pest infestations. For example, the sounds made when specific pests infest can be analyzed and detected to predict pest infestations. Systems can also be developed to collect audio data and predict pest infestations. For example, audio data can be analyzed in real time and farmers notified if a pest infestation is detected. This allows for more comprehensive predictions of pest infestations.

[0033] In addition to cameras and sensors, pest prediction systems have the ability to add different types of sensors to collect more diversified data. Adding different types of sensors to collect more diversified data is possible, for example, by installing gas sensors and light sensors in fields to collect environmental data. Gas and light sensors can also be used to develop algorithms that predict pest occurrence. For example, the concentration of specific gases or the intensity of light can be analyzed to predict pest occurrence. It is also possible to develop systems that integrate different types of sensors to collect more diversified data. For example, data can be collected by combining temperature sensors, humidity sensors, gas sensors, and light sensors. This enables more diversified data collection.

[0034] The pest prediction system has a function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions. The function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions, for example, reproduces weather and environmental conditions based on past data to improve the prediction accuracy of the AI ​​model. It is also possible to develop algorithms for simulating different weather and environmental conditions. For example, simulations are performed based on data such as temperature, humidity, and precipitation to increase the learning data for the AI ​​model. It is also possible to develop a system that improves the prediction accuracy of the AI ​​model by simulating weather and environmental conditions. For example, the simulation results can be fed back to the AI ​​model to improve prediction accuracy. This improves prediction accuracy.

[0035] The pest prediction system has an AI learning function that reflects not only past pest occurrence patterns but also farmers' experience and knowledge. The AI ​​learning function that reflects not only past pest occurrence patterns but also farmers' experience and knowledge can be achieved by, for example, adding notes and diaries about pest occurrences recorded by farmers to the dataset. It is also possible to develop algorithms that reflect farmers' experience and knowledge in the AI ​​model. For example, information provided by farmers can be analyzed as text data and trained into the AI ​​model. It is also possible to develop a system that combines past pest occurrence patterns with farmers' experience and knowledge to create a learning dataset for the AI ​​model. For example, farmers' knowledge can be compiled into a database and provided to the AI ​​model. This improves prediction accuracy.

[0036] The pest prediction system has an AI learning unit that has the ability to integrate other agriculture-related data to make more comprehensive predictions. The AI ​​learning unit's ability to integrate other agriculture-related data to make more comprehensive predictions can be achieved, for example, by providing crop growth data and harvest data to the AI ​​model. It is also possible to develop an algorithm for integrating crop growth data and harvest data into the AI ​​model. For example, data from different data sources can be integrated and trained by the AI ​​model. It is also possible to develop a system that integrates other agriculture-related data into the AI ​​model to make more comprehensive predictions. For example, crop growth data and harvest data can be provided to the AI ​​model in real time, enabling more comprehensive predictions.

[0037] The pest prediction system has a notification unit that diversifies notification methods and provides information to farmers through a smartphone app, SMS, and voice assistant. The notification unit's function for diversifying notification methods and providing information to farmers through a smartphone app, SMS, and voice assistant is, for example, to develop a smartphone app to notify farmers of the results of AI predictions. It is also possible to build a system that notifies farmers of the results of AI predictions using SMS. For example, the results of pest outbreak predictions can be sent via SMS and countermeasures can be suggested. It is also possible to develop a system that notifies farmers of the results of AI predictions using a voice assistant. For example, the voice assistant can read out the predictions and suggest countermeasures. This allows farmers to receive information in a variety of ways.

[0038] The pest prediction system has a function in which the notification unit includes specific countermeasure procedures and a list of required materials in the notification content, allowing farmers to take immediate action. The function in which the notification unit includes specific countermeasure procedures and a list of required materials in the notification content, allowing farmers to take immediate action, can be achieved, for example, by building a system that notifies farmers of specific countermeasure procedures based on the results of AI predictions. It is also possible to develop a system that provides notification content that includes a list of required materials. For example, a system could notify farmers of a list of pesticides and equipment needed for pest control, allowing them to prepare immediately. It is also possible to build a system that provides notification content that includes specific countermeasure procedures and a list of required materials. For example, a system could notify farmers of the predicted results of pest infestation together with the countermeasure procedures and a list of materials. This would allow farmers to take measures quickly.

[0039] The pest prediction system has a notification unit that has the function of integrating other agricultural-related information and providing comprehensive information. The function of the notification unit that integrates other agricultural-related information and provides comprehensive information is, for example, to build a system that integrates weather forecasts and provides comprehensive information. For example, a weather forecast is notified along with the predicted results of pest outbreaks. It is also possible to develop a system that integrates market price information into the notification system and provides comprehensive information. For example, the market price of crops is notified along with the predicted results of pest outbreaks. It is also possible to build a system that integrates other agricultural-related information into the notification system and provides comprehensive information. For example, a weather forecast and market price information are notified together with the predicted results of pest outbreaks. This allows farmers to receive comprehensive information.

[0040] The pest prediction system has a function in which the notification unit builds a notification system that supports different languages ​​and cultures and is able to accommodate farmers globally. The function in which the notification unit builds a notification system that supports different languages ​​and cultures and is able to accommodate farmers globally, for example, builds a notification system that supports different languages. For example, notification content is provided in multiple languages, such as English, Spanish, and Chinese. It is also possible to develop a notification system that supports different cultures. For example, notification content tailored to the agricultural customs and culture of each country is provided. It is also possible to build a notification system that supports different languages ​​and cultures to accommodate farmers globally. For example, optimal notification content is provided based on agricultural data from each country. This makes it possible to accommodate farmers globally.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] In addition to cameras and sensors, pest prediction systems also have the ability to collect audio data and predict pest infestations through audio analysis. For example, microphones can be installed in fields and pest infestations can be predicted through audio analysis. Audio analysis technology can also be used to develop algorithms that predict pest infestations. For example, the sounds made when specific pests appear can be analyzed and detected to predict pest infestations. Systems can also be developed that collect audio data and predict pest infestations. For example, audio data can be analyzed in real time and farmers notified if a pest infestation is detected. This allows for more comprehensive predictions of pest infestations.

[0043] Pest prediction systems have a function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions. For example, weather and environmental conditions can be reproduced based on past data to improve the prediction accuracy of the AI ​​model. It is also possible to develop algorithms to simulate different weather and environmental conditions. For example, simulations can be performed based on data such as temperature, humidity, and precipitation to increase the learning data for the AI ​​model. It is also possible to develop a system that improves the prediction accuracy of the AI ​​model by simulating weather and environmental conditions. For example, the simulation results can be fed back to the AI ​​model to improve prediction accuracy. This improves prediction accuracy.

[0044] The AI ​​learning section of the pest prediction system has the ability to reflect not only past pest outbreak patterns, but also farmers' experience and knowledge. For example, notes and diaries recorded by farmers about pest outbreaks can be added to the dataset. It is also possible to develop algorithms to reflect farmers' experience and knowledge in the AI ​​model. For example, information provided by farmers can be analyzed as text data and trained into the AI ​​model. It is also possible to develop a system that combines past pest outbreak patterns with farmers' experience and knowledge to create a learning dataset for the AI ​​model. For example, farmers' knowledge can be compiled into a database and provided to the AI ​​model. This improves prediction accuracy.

[0045] Pest prediction systems have a function in which the notification section includes specific countermeasure procedures and a list of necessary materials in the notification content, allowing farmers to take immediate action. For example, a system can be built that notifies farmers of specific countermeasure procedures based on the results of AI predictions. It is also possible to develop a system that provides notification content that includes a list of necessary materials. For example, a system can notify farmers of a list of pesticides and equipment needed for pest control, allowing them to prepare immediately. It is also possible to build a system that provides notification content that includes specific countermeasure procedures and a list of necessary materials. For example, a system can notify farmers of the predicted results of pest infestation together with countermeasure procedures and a list of materials. This allows farmers to take measures quickly.

[0046] The pest prediction system has a notification unit that has the function of integrating other agricultural-related information and providing comprehensive information. For example, a system can be built that integrates weather forecasts and provides comprehensive information. For example, the weather forecast can be notified along with the predicted results of pest outbreaks. It is also possible to develop a system that integrates market price information into the notification system and provides comprehensive information. For example, the market price of crops can be notified along with the predicted results of pest outbreaks. It is also possible to integrate other agricultural-related information into the notification system and build a system that provides comprehensive information. For example, the weather forecast and market price information can be notified together with the predicted results of pest outbreaks. This allows farmers to receive comprehensive information.

[0047] The pest prediction system has a notification unit that can build a notification system that supports different languages ​​and cultures, and has the function of supporting farmers globally. For example, a notification system that supports different languages ​​can be built. For example, notification content can be provided in multiple languages, such as English, Spanish, and Chinese. It is also possible to develop a notification system that supports different cultures. For example, notification content tailored to the agricultural customs and culture of each country can be provided. It is also possible to build a notification system that supports different languages ​​and cultures to support farmers globally. For example, optimal notification content can be provided based on agricultural data from each country. This makes it possible to support farmers globally.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: Cameras and sensors are installed in the fields to collect video or sensor data. The cameras record the growth status of the crops and the presence of pests as video. Fixed cameras, drone cameras, and infrared cameras can be used. The sensors collect environmental data such as temperature, humidity, and soil condition, and data is obtained in real time using temperature, humidity, and soil sensors. Step 2: The AI ​​learning unit trains the AI ​​using data collected by the cameras and sensors to predict the occurrence of specific pests and diseases. Using deep learning and machine learning algorithms, the AI ​​uses past data to predict what types of pests will occur when specific weather and environmental conditions are met, and generates a predictive model. Step 3: The notification unit notifies farmers of the results predicted by the AI ​​learning unit. Notifications are made in real time via smartphone notifications, email notifications, and voice assistants, and prediction results can also be displayed via web or mobile applications.

[0050] (Example 2) The pest prediction system according to an embodiment of the present invention is a system that uses AI to learn images and sensor data obtained from cameras and sensors installed in fields, predict the occurrence of specific pests, and take early action. This enables the pest prediction system to predict pest damage in agriculture and take early action.

[0051] A pest prediction system according to an embodiment includes a camera, a sensor, an AI learning unit, and a notification unit. The camera is installed in a field and records the growth status of crops and the presence of pests as video. For example, a fixed camera or a drone camera can be used to collect wide-area video. Alternatively, an infrared camera capable of capturing images at night can also be used. The sensor is installed in the field and collects environmental data such as temperature, humidity, and soil condition. For example, a temperature sensor or a humidity sensor can be used to acquire environmental data in real time. A soil sensor can also be used to measure the moisture content and nutritional status of the soil. The AI ​​learning unit trains the AI ​​based on the data collected by the camera and sensor to predict the occurrence of specific pests. For example, the AI ​​can use deep learning to predict which pests will occur when specific weather and environmental conditions are met based on past data. The AI ​​can also use machine learning algorithms to analyze data and generate a predictive model. The notification unit notifies farmers of the results predicted by the AI ​​learning unit. For example, information can be provided to farmers in real time via smartphone notifications or email notifications. The notification unit can also notify the prediction results by voice using a voice assistant. This allows the pest prediction system according to the embodiment to predict agricultural pest damage and deal with it early. For example, farmers can take appropriate measures such as spraying pesticides or taking physical measures based on the prediction results. The notification unit can also display the prediction results via a web application or a mobile application.

[0052] The pest prediction system is equipped with an automatic adjustment function for dynamically changing the installation locations of cameras and sensors. This automatic adjustment function for dynamically changing the installation locations of cameras and sensors automatically calculates and moves the optimal installation locations based on, for example, the quality of data collected by the sensors. It is also possible to build a system that dynamically changes the installation locations of cameras and sensors using drones. For example, the drone flies to search for the optimal data collection points and install cameras and sensors. It is also possible to develop an algorithm for dynamically changing the data collection points of sensors. For example, the optimal installation location can be calculated in real time depending on environmental conditions and the growth state of crops, and the sensor can be moved. This makes it possible to always maintain the optimal data collection points.

[0053] Pest prediction systems are equipped with a function that automatically adjusts the sensor's data collection frequency according to environmental conditions. This function automatically adjusts the sensor's data collection frequency according to environmental conditions, for example, by changing the data collection frequency in response to changes in temperature or humidity. AI can also be used to develop algorithms that optimize the sensor's data collection frequency. For example, the optimal data collection frequency can be calculated in real time based on past data. Sensors can also be developed that automatically adjust the sensor's data collection frequency according to environmental conditions. For example, the sensor itself can analyze environmental data and set the optimal collection frequency. This maximizes data accuracy and efficiency.

[0054] In addition to cameras and sensors, pest prediction systems also have the ability to collect audio data and predict pest infestations through audio analysis. For example, a function for collecting audio data and predicting pest infestations through audio analysis can be achieved by installing microphones in fields and predicting pest infestations through audio analysis. Audio analysis technology can also be used to develop algorithms for predicting pest infestations. For example, the sounds made when specific pests infest can be analyzed and detected to predict pest infestations. Systems can also be developed to collect audio data and predict pest infestations. For example, audio data can be analyzed in real time and farmers notified if a pest infestation is detected. This allows for more comprehensive predictions of pest infestations.

[0055] In addition to cameras and sensors, pest prediction systems have the ability to add different types of sensors to collect more diversified data. Adding different types of sensors to collect more diversified data is possible, for example, by installing gas sensors and light sensors in fields to collect environmental data. Gas and light sensors can also be used to develop algorithms that predict pest occurrence. For example, the concentration of specific gases or the intensity of light can be analyzed to predict pest occurrence. It is also possible to develop systems that integrate different types of sensors to collect more diversified data. For example, data can be collected by combining temperature sensors, humidity sensors, gas sensors, and light sensors. This enables more diversified data collection.

[0056] The pest prediction system has a function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions. The function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions, for example, reproduces weather and environmental conditions based on past data to improve the prediction accuracy of the AI ​​model. It is also possible to develop algorithms for simulating different weather and environmental conditions. For example, simulations are performed based on data such as temperature, humidity, and precipitation to increase the learning data for the AI ​​model. It is also possible to develop a system that improves the prediction accuracy of the AI ​​model by simulating weather and environmental conditions. For example, the simulation results can be fed back to the AI ​​model to improve prediction accuracy. This improves prediction accuracy.

[0057] The pest prediction system has an AI learning function that reflects not only past pest occurrence patterns but also farmers' experience and knowledge. The AI ​​learning function that reflects not only past pest occurrence patterns but also farmers' experience and knowledge can be achieved by, for example, adding notes and diaries about pest occurrences recorded by farmers to the dataset. It is also possible to develop algorithms that reflect farmers' experience and knowledge in the AI ​​model. For example, information provided by farmers can be analyzed as text data and trained into the AI ​​model. It is also possible to develop a system that combines past pest occurrence patterns with farmers' experience and knowledge to create a learning dataset for the AI ​​model. For example, farmers' knowledge can be compiled into a database and provided to the AI ​​model. This improves prediction accuracy.

[0058] The pest prediction system has an AI learning unit that has the ability to integrate other agriculture-related data to make more comprehensive predictions. The AI ​​learning unit's ability to integrate other agriculture-related data to make more comprehensive predictions can be achieved, for example, by providing crop growth data and harvest data to the AI ​​model. It is also possible to develop an algorithm for integrating crop growth data and harvest data into the AI ​​model. For example, data from different data sources can be integrated and trained by the AI ​​model. It is also possible to develop a system that integrates other agriculture-related data into the AI ​​model to make more comprehensive predictions. For example, crop growth data and harvest data can be provided to the AI ​​model in real time, enabling more comprehensive predictions.

[0059] The pest prediction system has a notification unit that diversifies notification methods and provides information to farmers through a smartphone app, SMS, and voice assistant. The notification unit's function for diversifying notification methods and providing information to farmers through a smartphone app, SMS, and voice assistant is, for example, to develop a smartphone app to notify farmers of the results of AI predictions. It is also possible to build a system that notifies farmers of the results of AI predictions using SMS. For example, the results of pest outbreak predictions can be sent via SMS and countermeasures can be suggested. It is also possible to develop a system that notifies farmers of the results of AI predictions using a voice assistant. For example, the voice assistant can read out the predictions and suggest countermeasures. This allows farmers to receive information in a variety of ways.

[0060] The pest prediction system has a function in which the notification unit includes specific countermeasure procedures and a list of required materials in the notification content, allowing farmers to take immediate action. The function in which the notification unit includes specific countermeasure procedures and a list of required materials in the notification content, allowing farmers to take immediate action, can be achieved, for example, by building a system that notifies farmers of specific countermeasure procedures based on the results of AI predictions. It is also possible to develop a system that provides notification content that includes a list of required materials. For example, a system could notify farmers of a list of pesticides and equipment needed for pest control, allowing them to prepare immediately. It is also possible to build a system that provides notification content that includes specific countermeasure procedures and a list of required materials. For example, a system could notify farmers of the predicted results of pest infestation together with the countermeasure procedures and a list of materials. This would allow farmers to take measures quickly.

[0061] The pest prediction system has a notification unit that has the function of integrating other agricultural-related information and providing comprehensive information. The function of the notification unit that integrates other agricultural-related information and provides comprehensive information is, for example, to build a system that integrates weather forecasts and provides comprehensive information. For example, a weather forecast is notified along with the predicted results of pest outbreaks. It is also possible to develop a system that integrates market price information into the notification system and provides comprehensive information. For example, the market price of crops is notified along with the predicted results of pest outbreaks. It is also possible to build a system that integrates other agricultural-related information into the notification system and provides comprehensive information. For example, a weather forecast and market price information are notified together with the predicted results of pest outbreaks. This allows farmers to receive comprehensive information.

[0062] The pest prediction system has a function in which the notification unit builds a notification system that supports different languages ​​and cultures and is able to accommodate farmers globally. The function in which the notification unit builds a notification system that supports different languages ​​and cultures and is able to accommodate farmers globally, for example, builds a notification system that supports different languages. For example, notification content is provided in multiple languages, such as English, Spanish, and Chinese. It is also possible to develop a notification system that supports different cultures. For example, notification content tailored to the agricultural customs and culture of each country is provided. It is also possible to build a notification system that supports different languages ​​and cultures to accommodate farmers globally. For example, optimal notification content is provided based on agricultural data from each country. This makes it possible to accommodate farmers globally.

[0063] The pest prediction system includes a function in which the notification unit uses an emotion estimation function to identify a notification format that is most acceptable to farmers and prioritizes its adoption. The function in which the notification unit uses an emotion estimation function to identify a notification format that is most acceptable to farmers and prioritizes its adoption is, for example, to build a system that uses the emotion estimation function to identify a notification format that is most acceptable to farmers. For example, the emotion data of farmers is analyzed to select the optimal notification format. It is also possible to develop an algorithm for identifying a notification format that is most acceptable to farmers. For example, the optimal notification format is selected based on the emotional response of farmers. It is also possible to develop a system that uses the emotion estimation function to identify a notification format that is most acceptable to farmers and prioritizes its adoption. For example, the emotion data of farmers is analyzed in real time to automatically select the optimal notification format. This allows farmers to receive notifications in the format that is most acceptable to them.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] In addition to cameras and sensors, pest prediction systems also have the ability to collect audio data and predict pest infestations through audio analysis. For example, microphones can be installed in fields and pest infestations can be predicted through audio analysis. Audio analysis technology can also be used to develop algorithms that predict pest infestations. For example, the sounds made when specific pests appear can be analyzed and detected to predict pest infestations. Systems can also be developed that collect audio data and predict pest infestations. For example, audio data can be analyzed in real time and farmers notified if a pest infestation is detected. This allows for more comprehensive predictions of pest infestations.

[0066] Pest prediction systems have a function in which the AI ​​learning unit improves prediction accuracy by simulating different weather and environmental conditions. For example, weather and environmental conditions can be reproduced based on past data to improve the prediction accuracy of the AI ​​model. It is also possible to develop algorithms to simulate different weather and environmental conditions. For example, simulations can be performed based on data such as temperature, humidity, and precipitation to increase the learning data for the AI ​​model. It is also possible to develop a system that improves the prediction accuracy of the AI ​​model by simulating weather and environmental conditions. For example, the simulation results can be fed back to the AI ​​model to improve prediction accuracy. This improves prediction accuracy.

[0067] The pest prediction system has a function in which the notification unit uses an emotion estimation function to identify the notification format that farmers find most acceptable and prioritizes its adoption. For example, a system can be built using the emotion estimation function to identify the notification format that farmers find most acceptable. For example, the emotion estimation function can be used to analyze farmers' emotion data and select the optimal notification format. It is also possible to develop an algorithm to identify the notification format that farmers find most acceptable. For example, the optimal notification format can be selected based on farmers' emotional responses. It is also possible to develop a system using the emotion estimation function to identify the notification format that farmers find most acceptable and prioritizes its adoption. For example, the emotion estimation function can be used to analyze farmers' emotion data in real time and automatically select the optimal notification format. This allows farmers to receive notifications in the format that is most acceptable.

[0068] The AI ​​learning section of the pest prediction system has the ability to reflect not only past pest outbreak patterns, but also farmers' experience and knowledge. For example, notes and diaries recorded by farmers about pest outbreaks can be added to the dataset. It is also possible to develop algorithms to reflect farmers' experience and knowledge in the AI ​​model. For example, information provided by farmers can be analyzed as text data and trained into the AI ​​model. It is also possible to develop a system that combines past pest outbreak patterns with farmers' experience and knowledge to create a learning dataset for the AI ​​model. For example, farmers' knowledge can be compiled into a database and provided to the AI ​​model. This improves prediction accuracy.

[0069] Pest prediction systems have a function in which the notification section includes specific countermeasure procedures and a list of necessary materials in the notification content, allowing farmers to take immediate action. For example, a system can be built that notifies farmers of specific countermeasure procedures based on the results of AI predictions. It is also possible to develop a system that provides notification content that includes a list of necessary materials. For example, a system can notify farmers of a list of pesticides and equipment needed for pest control, allowing them to prepare immediately. It is also possible to build a system that provides notification content that includes specific countermeasure procedures and a list of necessary materials. For example, a system can notify farmers of the predicted results of pest infestation together with countermeasure procedures and a list of materials. This allows farmers to take measures quickly.

[0070] The pest prediction system has a notification unit that has the function of integrating other agricultural-related information and providing comprehensive information. For example, a system can be built that integrates weather forecasts and provides comprehensive information. For example, the weather forecast can be notified along with the predicted results of pest outbreaks. It is also possible to develop a system that integrates market price information into the notification system and provides comprehensive information. For example, the market price of crops can be notified along with the predicted results of pest outbreaks. It is also possible to integrate other agricultural-related information into the notification system and build a system that provides comprehensive information. For example, the weather forecast and market price information can be notified together with the predicted results of pest outbreaks. This allows farmers to receive comprehensive information.

[0071] The pest prediction system has a notification unit that can build a notification system that supports different languages ​​and cultures, and has the function of supporting farmers globally. For example, a notification system that supports different languages ​​can be built. For example, notification content can be provided in multiple languages, such as English, Spanish, and Chinese. It is also possible to develop a notification system that supports different cultures. For example, notification content tailored to the agricultural customs and culture of each country can be provided. It is also possible to build a notification system that supports different languages ​​and cultures to support farmers globally. For example, optimal notification content can be provided based on agricultural data from each country. This makes it possible to support farmers globally.

[0072] The pest prediction system has a notification unit that uses an emotion estimation function to evaluate the stress level of farmers and simplify the notification content if the stress level is high. For example, a system that uses the emotion estimation function to evaluate the stress level of farmers can be built. For example, the emotional data of farmers can be analyzed to evaluate the stress level. It is also possible to develop an algorithm to simplify the notification content if the stress level is high. For example, the notification content can be shortened and only important information can be provided depending on the stress level. It is also possible to develop a system that uses the emotion estimation function to evaluate the stress level of farmers and simplify the notification content if the stress level is high. For example, the emotional data of farmers can be analyzed in real time and the notification content can be adjusted according to the stress level. This allows farmers to receive important information without feeling stressed.

[0073] The pest prediction system has a function in which the notification unit uses an emotion estimation function to provide encouragement and advice according to the emotional state of the farmer. For example, a system for evaluating the emotional state of the farmer can be constructed using the emotion estimation function. For example, the emotional data of the farmer can be analyzed to evaluate the emotional state. It is also possible to develop an algorithm for providing encouragement and advice according to the emotional state. For example, appropriate encouragement and advice can be provided according to the emotional state. It is also possible to develop a system using the emotion estimation function to provide encouragement and advice according to the emotional state of the farmer. For example, the emotional data of the farmer can be analyzed in real time and encouragement and advice according to the emotional state can be automatically provided. This provides emotional support to the farmer.

[0074] The pest prediction system has a function in which the notification unit uses an emotion estimation function to adjust the timing of notifications according to the emotional state of the farmer. For example, a system for evaluating the emotional state of the farmer can be constructed using the emotion estimation function. For example, the emotional data of the farmer can be analyzed to evaluate the emotional state. An algorithm can also be developed to adjust the timing of notifications according to the emotional state. For example, the optimal timing of notifications can be selected according to the emotional state. A system can also be developed using the emotion estimation function to adjust the timing of notifications according to the emotional state of the farmer. For example, the emotional data of the farmer can be analyzed in real time to automatically select the optimal timing of notifications according to the emotional state. This allows the farmer to receive notifications at the timing that is most acceptable to the farmer.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: Cameras and sensors are installed in the fields to collect video or sensor data. The cameras record the growth status of the crops and the presence of pests as video. Fixed cameras, drone cameras, and infrared cameras can be used. The sensors collect environmental data such as temperature, humidity, and soil condition, and data is obtained in real time using temperature, humidity, and soil sensors. Step 2: The AI ​​learning unit trains the AI ​​using data collected by the cameras and sensors to predict the occurrence of specific pests and diseases. Using deep learning and machine learning algorithms, the AI ​​uses past data to predict what types of pests will occur when specific weather and environmental conditions are met, and generates a predictive model. Step 3: The notification unit notifies farmers of the results predicted by the AI ​​learning unit. Notifications are made in real time via smartphone notifications, email notifications, and voice assistants, and prediction results can also be displayed via web or mobile applications.

[0077] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0078] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0079] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0083] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0084] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0085] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0086] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0087] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0088] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0091] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0127] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0128] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0129] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0130] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0131] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0132] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0133] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0134] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0135] 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.

[0136] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0137] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0138] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0139] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0140] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0141] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Cameras and sensors installed in the fields that collect images or sensor data; an AI learning unit that makes an AI learn data collected by the camera and the sensor and predicts the occurrence of specific pests; A notification unit that notifies farmers of the results predicted by the AI ​​learning unit. A system characterized by:

2. Equipped with an automatic adjustment function to dynamically change the installation location of the camera and the sensor The system of claim 1 .

3. The sensor has a function to automatically adjust the data collection frequency according to environmental conditions. The system of claim 1 .

4. In addition to the camera and sensor, it also has the function of collecting audio data and predicting pest infestations through audio analysis. The system of claim 1 .

5. In addition to the camera and sensor, it has the ability to add different types of sensors to collect more diverse data. The system of claim 1 .

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