System

Abstract

An object of a system according to an embodiment is to find pests in farmland at an early stage and take appropriate measures.SOLUTION: A system includes a reception unit, an analysis unit, a warning unit, and a storage unit. The reception unit takes a photograph of insects and uploads the photograph to the AI. The analysis unit analyzes the photograph uploaded by the reception unit and identifies the insect. The warning unit issues a warning when the insects identified by the analysis unit are pests or when the number of pests is rapidly increasing. The storage unit stores the result identified by the analysis unit in a database.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] With conventional technology, it is difficult to detect and deal with pests in farmland early, which may lead to the spread of pest damage.

[0005] The system according to the embodiment aims to detect pests in farmland at an early stage and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a warning unit, and a storage unit. The reception unit takes photos of insects and uploads them to the AI. The analysis unit analyzes the photos uploaded by the reception unit and identifies the insects. The warning unit issues a warning if the insects identified by the analysis unit are pests or if their numbers are rapidly increasing. The storage unit stores the results of identification by the analysis unit in a database. [Effects of the Invention]

[0007] The system according to the embodiment can detect pests in farmland at an early stage and take appropriate measures. [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 AI ​​insect identification system according to an embodiment of the present invention is useful for all farmers facing the challenge of controlling insects, especially pests, that infest their fields. This system involves taking photos of insects and uploading them to an AI system. The AI ​​then identifies the insects and alerts farmers if they are pests or their numbers are rapidly increasing. This allows farmers to eliminate pests early. For example, farmers can take photos of insects and upload them to the AI ​​system. To do this, they use a smartphone or camera to take photos of the insects and upload them to the AI ​​system through a dedicated application. The AI ​​then analyzes the uploaded photos and identifies the insects. The AI ​​uses image recognition technology to analyze the insects' characteristics and identify them. If the identified insects are pests or their numbers are rapidly increasing, the AI ​​alerts farmers. For example, if the AI ​​identifies a specific pest and its numbers exceed a certain threshold, it issues an alert to farmers. This alert is communicated to farmers via smartphone notifications or email, allowing farmers to eliminate pests early. Furthermore, the AI ​​stores the insect identification results in a database, allowing farmers to refer to past identification results. This allows farmers to understand past pest outbreaks and take effective measures, making the AI ​​insect identification system a useful tool for farmers to eradicate pests early and minimize damage to crops.

[0029] An AI insect identification system according to an embodiment includes a reception unit, an analysis unit, a warning unit, and a storage unit. The reception unit takes photos of insects and uploads them to the AI. For example, the reception unit takes photos of insects using a smartphone or camera and uploads them to the AI ​​through a dedicated application. The reception unit can also estimate a user's emotions and adjust the timing of taking photos of insects based on the estimated user's emotions. The analysis unit uses AI to analyze the photos uploaded by the reception unit and identify the insects. For example, the analysis unit can use image recognition technology to analyze the characteristics of the insects and identify their identity. The analysis unit can also estimate a user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The warning unit issues a warning if the insect identified by the analysis unit is a pest or its population is rapidly increasing. For example, the warning unit can alert farmers via smartphone notifications or email if the identified insect is a pest or its population is rapidly increasing. The warning unit can also estimate a user's emotions and adjust the presentation method of the warning based on the estimated user's emotions. The storage unit stores the results of identification by the analysis unit in a database. For example, the storage unit stores the identification results in a database so that farmers can refer to past identification results. The storage unit can also estimate the user's emotions and select stored data based on the estimated user's emotions. As a result, the AI ​​insect identification system according to the embodiment allows farmers to exterminate pests early and minimize damage to crops.

[0030] The analysis unit can use image recognition technology to analyze the characteristics of an insect and identify what it is. Image recognition technology includes, for example, deep learning and computer vision technology. The analysis unit can use, for example, deep learning to analyze the characteristics of an insect, such as its shape, color, and pattern, and identify what it is. The analysis unit can also use computer vision technology to analyze the characteristics of an insect and identify what it is. Furthermore, the analysis unit can use AI to analyze the characteristics of an insect and identify what it is. This allows the characteristics of an insect to be accurately analyzed and identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo of an insect into AI, which analyzes the characteristics of the insect and identifies what it is.

[0031] The warning unit can warn farmers via smartphone notifications or emails if the identified insects are pests or their numbers are rapidly increasing. For example, the warning unit can warn farmers via smartphone notifications if the identified insects are pests or their numbers are rapidly increasing. The warning unit can also warn farmers via email. Furthermore, the warning unit can use AI to warn farmers if the identified insects are pests or their numbers are rapidly increasing. This allows farmers to quickly become aware of pest infestations and take measures. Some or all of the above-mentioned processing in the warning unit can be performed using AI, or can be performed without using AI. For example, the warning unit can warn farmers based on information about pests identified by AI.

[0032] The storage unit can store the identification results in a database, allowing farmers to refer to past identification results. The storage unit, for example, stores the identification results in a relational database. The storage unit can also store the identification results in a NoSQL database. Furthermore, the storage unit can use AI to store the identification results in a database. This allows farmers to understand past pest infestations and take effective measures. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI, or can be performed without using AI. For example, the storage unit can store the results of AI identification in a database, allowing farmers to refer to past identification results.

[0033] The reception unit can take photos of insects using a smartphone or camera and upload them to the AI ​​through a dedicated application. The reception unit can, for example, take photos of insects using a smartphone and upload them to the AI ​​through a dedicated application. The reception unit can also take photos of insects using a camera and upload them to the AI ​​through a dedicated application. The reception unit can also use AI to take photos of insects and upload them to the AI. This allows farmers to easily take photos of insects and upload them to the AI. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can upload photos of insects taken with a smartphone to AI, and the AI ​​can analyze the photos.

[0034] When taking a photo of an insect, the reception unit can analyze the user's past photography history and select an appropriate photography method. For example, the reception unit can automatically apply similar settings based on a photography method that the user has used successfully in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past photography history. Furthermore, the reception unit can automatically select the optimal photography method by referring to camera settings used by the user in the past. This makes it possible to select the optimal photography method based on the user's past photography history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past photography history data into AI, which can then select the optimal photography method.

[0035] When taking photos of insects, the reception unit can filter the photos based on the user's current farm work status and area of ​​interest. For example, the reception unit prioritizes photographing insects related to the farm work the user is currently performing. The reception unit can also filter and photograph specific insects based on the user's area of ​​interest. Furthermore, the reception unit can grasp the user's farm work status in real time and select the optimal insect to photograph. This makes it possible to take photos of insects that are optimal for the user's farm work status and area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's farm work status data into AI, which can select the optimal insect to photograph.

[0036] The reception unit can select an appropriate photographing means according to the user's input method when taking a photo of an insect. For example, when the user gives instructions by voice, the reception unit can select the optimal photographing means using voice recognition technology. Furthermore, when the user gives instructions by text, the reception unit can also select the optimal photographing means using text analysis technology. Furthermore, when the user provides an image, the reception unit can also select the optimal photographing means using image analysis technology. This makes it possible to select the optimal photographing means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data into AI, which can then select the optimal photographing means.

[0037] When taking photos of insects, the reception unit can prioritize photographing insects that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize photographing insects that are commonly seen in the user's current location based on the user's current location. The reception unit can also prioritize photographing locations where specific pests are likely to occur based on the user's geographical location information. Furthermore, the reception unit can update the user's location information in real time and select the optimal insect to photograph. This makes it possible to take optimal insect photos based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's GPS data into AI, which can select the optimal insect to photograph.

[0038] When taking a photo of an insect, the reception unit can analyze the user's social media activity and photograph related insects. For example, the reception unit can prioritize photographing related insects based on photos of insects shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and photograph related insects. Furthermore, the reception unit can also photograph related insects based on the activity of the user's friends on social media. This makes it possible to take optimal photos of insects based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related insects.

[0039] The reception unit can customize the photography method based on the user's past feedback when taking photos of insects. For example, the reception unit automatically applies the optimal photography method based on feedback provided by the user in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past feedback. Furthermore, the reception unit can automatically select the optimal photography method by referring to camera settings used by the user in the past. This makes it possible to customize the optimal photography method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can then select the optimal photography method.

[0040] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the insect. For example, the analysis unit provides detailed analysis results for important pests. The analysis unit can also provide concise analysis results for less important insects. Furthermore, the analysis unit can automatically adjust the level of detail of the analysis according to the importance of the insect. This makes it possible to provide optimal analysis results according to the importance of the insect. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input insect importance data into AI, which can then adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the insect category. For example, in the case of pest insects, the analysis unit can apply an analysis algorithm specialized for a specific pest insect. Furthermore, in the case of beneficial insects, the analysis unit can also apply an analysis algorithm specialized for beneficial insects. Furthermore, the analysis unit can automatically select the optimal analysis algorithm depending on the insect category. This makes it possible to apply the optimal analysis algorithm depending on the insect category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect category data into AI, which can select the optimal analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy for specific insects based on the user's past analysis results. Furthermore, the analysis unit can automatically improve the analysis accuracy by referring to the user's past analysis results. This makes it possible to improve the analysis accuracy based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the analysis accuracy.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the insect was discovered. For example, the analysis unit prioritizes analysis if the insect was discovered recently. The analysis unit can also lower the priority of analysis if the insect was discovered a long time ago. Furthermore, the analysis unit can automatically determine the priority of analysis based on the time when the insect was discovered. This makes it possible to determine the optimal analysis priority based on the time when the insect was discovered. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the insect was discovered into AI, and the AI ​​can determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the insects. For example, the analysis unit prioritizes analysis of important pests. The analysis unit can also postpone the order of analysis of less important insects. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the insects. This makes it possible to adjust the optimal order of analysis based on the relevance of the insects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect relevance data into AI, which can then adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results using concise and easy-to-understand terminology. Furthermore, the analysis unit can automatically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows optimal analysis results to be provided according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the analysis.

[0046] The warning unit can adjust the level of detail of the warning based on the danger level of the insect when issuing the warning. For example, the warning unit displays a detailed warning for a highly dangerous pest. The warning unit can also display a concise warning for a less dangerous insect. Furthermore, the warning unit can automatically adjust the level of detail of the warning based on the danger level of the insect. This makes it possible to provide an optimal level of detail of the warning depending on the danger level of the insect. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect danger level data into AI, which can then adjust the level of detail of the warning.

[0047] When issuing a warning, the warning unit can apply different warning algorithms depending on the frequency of insect occurrence. For example, the warning unit applies a specific warning algorithm to pests that occur frequently. The warning unit can also apply a different warning algorithm to insects that occur infrequently. Furthermore, the warning unit can automatically select the optimal warning algorithm depending on the frequency of insect occurrence. This makes it possible to apply the optimal warning algorithm depending on the frequency of insect occurrence. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect occurrence frequency data into AI, which then selects the optimal warning algorithm.

[0048] When issuing a warning, the warning unit can improve the accuracy of the warning based on the user's past warning history. The warning unit, for example, optimizes a warning algorithm based on the user's past warning history. The warning unit can also improve the accuracy of warnings for specific pests based on the user's past warning history. Furthermore, the warning unit can automatically improve the accuracy of the warning by referring to the user's past warning history. This makes it possible to improve the accuracy of warnings based on the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's past warning history data into AI, which can improve the accuracy of the warning.

[0049] When issuing a warning, the warning unit can adjust the order of warnings based on the location of insect infestation. For example, if the location of insect infestation is in the center of farmland, the warning unit can prioritize displaying a warning. Also, if the location of insect infestation is on the periphery of farmland, the warning unit can postpone the order of warnings. Furthermore, the warning unit can automatically adjust the order of warnings based on the location of insect infestation. This makes it possible to adjust the optimal order of warnings based on the location of insect infestation. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect infestation location data into AI, which can adjust the order of warnings.

[0050] The warning unit can improve the accuracy of the warning by referring to insect-related literature when issuing a warning. The warning unit can, for example, optimize a warning algorithm based on insect-related literature. The warning unit can also improve the accuracy of warnings for specific pests based on insect-related literature. Furthermore, the warning unit can automatically improve the accuracy of the warning by referring to insect-related literature. This makes it possible to improve the accuracy of warnings based on insect-related literature. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input insect-related literature data into AI, which can improve the accuracy of the warning.

[0051] When issuing a warning, the warning unit can issue a warning based on the market value of the insect. For example, the warning unit can display a detailed warning for pests with high market value. The warning unit can also display a concise warning for insects with low market value. Furthermore, the warning unit can automatically adjust the level of detail of the warning based on the market value of the insect. This allows for an optimal warning to be issued based on the market value of the insect. Some or all of the above-mentioned processing in the warning unit can be performed using, or without, AI, for example. For example, the warning unit can input market value data of the insect into AI, which can then adjust the level of detail of the warning.

[0052] The storage unit can optimize the storage algorithm based on past stored data when storing data. The storage unit, for example, optimizes the storage algorithm based on past stored data. The storage unit can also improve storage accuracy for specific data from past stored data. Furthermore, the storage unit can automatically optimize the storage algorithm by referring to past stored data. This makes it possible to optimize the storage algorithm based on past stored data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past stored data into AI, which then optimizes the storage algorithm.

[0053] The storage unit can update the stored data to reflect user feedback when storing the data. The storage unit updates the stored data based on, for example, user feedback. The storage unit can also improve the storage accuracy for specific data based on user feedback. Furthermore, the storage unit can automatically update the stored data by referring to user feedback. This allows the stored data to be updated based on user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input user feedback data into AI, which then updates the stored data.

[0054] The storage unit can weight the stored data based on the time the insect was discovered when storing the data. For example, if the insect was discovered recently, the storage unit can weight the stored data higher. Also, if the insect was discovered older, the storage unit can weight the stored data lower. Furthermore, the storage unit can automatically adjust the weighting of the stored data based on the time the insect was discovered. This allows optimal weighting of the stored data based on the time the insect was discovered. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input data on the time the insect was discovered into AI, which can adjust the weighting of the stored data.

[0055] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also improve the accuracy of the stored data based on information from different data sources. Furthermore, the storage unit can automatically enrich the stored data by referring to information from different data sources. This makes it possible to enrich the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input information from different data sources into AI, which can enrich the stored data.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When taking a photo of an insect, the reception unit can analyze the user's past photography history and select an appropriate photography method. For example, the reception unit can automatically apply similar settings based on the photography method that the user used successfully in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past photography history. Furthermore, the reception unit can automatically select the optimal photography method by referring to the camera settings the user used in the past. This makes it possible to select the optimal photography method based on the user's past photography history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past photography history data into AI, which can then select the optimal photography method.

[0058] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the insect. For example, in the case of an important pest, a detailed analysis result is provided. The analysis unit can also provide a concise analysis result for an insect of low importance. Furthermore, the analysis unit can automatically adjust the level of detail of the analysis according to the importance of the insect. This makes it possible to provide optimal analysis results according to the importance of the insect. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect importance data into AI, which can then adjust the level of detail of the analysis.

[0059] When issuing a warning, the warning unit can apply different warning algorithms depending on the frequency of insect occurrence. For example, a specific warning algorithm is applied for pests with a high frequency of occurrence. The warning unit can also apply a different warning algorithm for insects with a low frequency of occurrence. Furthermore, the warning unit can automatically select the optimal warning algorithm depending on the frequency of insect occurrence. This makes it possible to apply the optimal warning algorithm depending on the frequency of insect occurrence. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect occurrence frequency data into AI, which then selects the optimal warning algorithm.

[0060] The storage unit can optimize the storage algorithm based on past stored data when storing data. For example, the storage algorithm is optimized based on past stored data. The storage unit can also improve the storage accuracy for specific data from past stored data. Furthermore, the storage unit can automatically optimize the storage algorithm by referring to past stored data. This makes it possible to optimize the storage algorithm based on past stored data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past stored data into AI, which then optimizes the storage algorithm.

[0061] When taking photos of insects, the reception unit can prioritize photographing insects that are highly relevant based on the user's geographical location information. For example, based on the user's current location, it can prioritize photographing insects that are commonly seen in that area. The reception unit can also prioritize photographing locations where specific pests are likely to occur based on the user's geographical location information. Furthermore, the reception unit can update the user's location information in real time and select the optimal insect to photograph. This makes it possible to take optimal insect photos based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's GPS data into AI, which can select the optimal insect to photograph.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit takes a photo of the insect and uploads it to the AI. For example, the reception unit takes a photo of the insect using a smartphone or camera and uploads it to the AI ​​through a dedicated application. The reception unit can also estimate the user's emotions and adjust the timing of taking the insect photo based on the estimated user emotions. Step 2: The analysis unit uses AI to analyze the photo uploaded by the reception unit and identify the insect. For example, the analysis unit uses image recognition technology to analyze the insect's characteristics and identify what it is. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The warning unit issues a warning if the insect identified by the analysis unit is a pest or if its number is rapidly increasing. For example, if the identified insect is a pest or if its number is rapidly increasing, the warning unit issues a warning to the farmer via a smartphone notification or email. The warning unit can also estimate the user's emotions and adjust the way the warning is displayed based on the estimated user's emotions. Step 4: The storage unit stores the results identified by the analysis unit in a database. For example, the storage unit stores the identification results in a database so that farmers can refer to past identification results. The storage unit can also estimate the user's emotions and select data to be stored based on the estimated user's emotions.

[0064] (Example 2) The AI ​​insect identification system according to an embodiment of the present invention is useful for all farmers facing the challenge of controlling insects, especially pests, that infest their fields. This system involves taking photos of insects and uploading them to an AI system. The AI ​​then identifies the insects and alerts farmers if they are pests or their numbers are rapidly increasing. This allows farmers to eliminate pests early. For example, farmers can take photos of insects and upload them to the AI ​​system. To do this, they use a smartphone or camera to take photos of the insects and upload them to the AI ​​system through a dedicated application. The AI ​​then analyzes the uploaded photos and identifies the insects. The AI ​​uses image recognition technology to analyze the insects' characteristics and identify them. If the identified insects are pests or their numbers are rapidly increasing, the AI ​​alerts farmers. For example, if the AI ​​identifies a specific pest and its numbers exceed a certain threshold, it issues an alert to farmers. This alert is communicated to farmers via smartphone notifications or email, allowing farmers to eliminate pests early. Furthermore, the AI ​​stores the insect identification results in a database, allowing farmers to refer to past identification results. This allows farmers to understand past pest outbreaks and take effective measures, making the AI ​​insect identification system a useful tool for farmers to eradicate pests early and minimize damage to crops.

[0065] An AI insect identification system according to an embodiment includes a reception unit, an analysis unit, a warning unit, and a storage unit. The reception unit takes photos of insects and uploads them to the AI. For example, the reception unit takes photos of insects using a smartphone or camera and uploads them to the AI ​​through a dedicated application. The reception unit can also estimate a user's emotions and adjust the timing of taking photos of insects based on the estimated user's emotions. The analysis unit uses AI to analyze the photos uploaded by the reception unit and identify the insects. For example, the analysis unit can use image recognition technology to analyze the characteristics of the insects and identify their identity. The analysis unit can also estimate a user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The warning unit issues a warning if the insect identified by the analysis unit is a pest or its population is rapidly increasing. For example, the warning unit can alert farmers via smartphone notifications or email if the identified insect is a pest or its population is rapidly increasing. The warning unit can also estimate a user's emotions and adjust the presentation method of the warning based on the estimated user's emotions. The storage unit stores the results of identification by the analysis unit in a database. For example, the storage unit stores the identification results in a database so that farmers can refer to past identification results. The storage unit can also estimate the user's emotions and select stored data based on the estimated user's emotions. As a result, the AI ​​insect identification system according to the embodiment allows farmers to exterminate pests early and minimize damage to crops.

[0066] The analysis unit can use image recognition technology to analyze the characteristics of an insect and identify what it is. Image recognition technology includes, for example, deep learning and computer vision technology. The analysis unit can use, for example, deep learning to analyze the characteristics of an insect, such as its shape, color, and pattern, and identify what it is. The analysis unit can also use computer vision technology to analyze the characteristics of an insect and identify what it is. Furthermore, the analysis unit can use AI to analyze the characteristics of an insect and identify what it is. This allows the characteristics of an insect to be accurately analyzed and identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo of an insect into AI, which analyzes the characteristics of the insect and identifies what it is.

[0067] The warning unit can warn farmers via smartphone notifications or emails if the identified insects are pests or their numbers are rapidly increasing. For example, the warning unit can warn farmers via smartphone notifications if the identified insects are pests or their numbers are rapidly increasing. The warning unit can also warn farmers via email. Furthermore, the warning unit can use AI to warn farmers if the identified insects are pests or their numbers are rapidly increasing. This allows farmers to quickly become aware of pest infestations and take measures. Some or all of the above-mentioned processing in the warning unit can be performed using AI, or can be performed without using AI. For example, the warning unit can warn farmers based on information about pests identified by AI.

[0068] The storage unit can store the identification results in a database, allowing farmers to refer to past identification results. The storage unit, for example, stores the identification results in a relational database. The storage unit can also store the identification results in a NoSQL database. Furthermore, the storage unit can use AI to store the identification results in a database. This allows farmers to understand past pest infestations and take effective measures. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI, or can be performed without using AI. For example, the storage unit can store the results of AI identification in a database, allowing farmers to refer to past identification results.

[0069] The reception unit can take photos of insects using a smartphone or camera and upload them to the AI ​​through a dedicated application. The reception unit can, for example, take photos of insects using a smartphone and upload them to the AI ​​through a dedicated application. The reception unit can also take photos of insects using a camera and upload them to the AI ​​through a dedicated application. The reception unit can also use AI to take photos of insects and upload them to the AI. This allows farmers to easily take photos of insects and upload them to the AI. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can upload photos of insects taken with a smartphone to AI, and the AI ​​can analyze the photos.

[0070] The reception unit can estimate the user's emotions and adjust the timing of taking photos of insects based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can have the AI ​​automatically take photos of insects at the optimal timing. Furthermore, if the user is relaxed, the reception unit can also provide an interface that allows the user to select the timing of taking photos. Furthermore, if the user is in a hurry, the reception unit can have the AI ​​quickly take photos of insects and prompt the user to upload them. This allows photos of insects to be taken at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the timing of taking photos based on the results.

[0071] When taking a photo of an insect, the reception unit can analyze the user's past photography history and select an appropriate photography method. For example, the reception unit can automatically apply similar settings based on a photography method that the user has used successfully in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past photography history. Furthermore, the reception unit can automatically select the optimal photography method by referring to camera settings used by the user in the past. This makes it possible to select the optimal photography method based on the user's past photography history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past photography history data into AI, which can then select the optimal photography method.

[0072] When taking photos of insects, the reception unit can filter the photos based on the user's current farm work status and area of ​​interest. For example, the reception unit prioritizes photographing insects related to the farm work the user is currently performing. The reception unit can also filter and photograph specific insects based on the user's area of ​​interest. Furthermore, the reception unit can grasp the user's farm work status in real time and select the optimal insect to photograph. This makes it possible to take photos of insects that are optimal for the user's farm work status and area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's farm work status data into AI, which can select the optimal insect to photograph.

[0073] The reception unit can select an appropriate photographing means according to the user's input method when taking a photo of an insect. For example, when the user gives instructions by voice, the reception unit can select the optimal photographing means using voice recognition technology. Furthermore, when the user gives instructions by text, the reception unit can also select the optimal photographing means using text analysis technology. Furthermore, when the user provides an image, the reception unit can also select the optimal photographing means using image analysis technology. This makes it possible to select the optimal photographing means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice data into AI, which can then select the optimal photographing means.

[0074] The reception unit can estimate the user's emotions and prioritize the insects to be photographed based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​automatically prioritizes photographing the most important insects. Furthermore, if the user is relaxed, the reception unit can provide an interface that allows the user to select the priority of the insects to be photographed. Furthermore, if the user is in a hurry, the reception unit can quickly prioritize photographing the most important insects. This allows the optimal priority of insects to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and prioritizes the insects to be photographed based on the result.

[0075] When taking photos of insects, the reception unit can prioritize photographing insects that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize photographing insects that are commonly seen in the user's current location based on the user's current location. The reception unit can also prioritize photographing locations where specific pests are likely to occur based on the user's geographical location information. Furthermore, the reception unit can update the user's location information in real time and select the optimal insect to photograph. This makes it possible to take optimal insect photos based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's GPS data into AI, which can select the optimal insect to photograph.

[0076] When taking a photo of an insect, the reception unit can analyze the user's social media activity and photograph related insects. For example, the reception unit can prioritize photographing related insects based on photos of insects shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and photograph related insects. Furthermore, the reception unit can also photograph related insects based on the activity of the user's friends on social media. This makes it possible to take optimal photos of insects based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can select related insects.

[0077] The reception unit can customize the photography method based on the user's past feedback when taking photos of insects. For example, the reception unit automatically applies the optimal photography method based on feedback provided by the user in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past feedback. Furthermore, the reception unit can automatically select the optimal photography method by referring to camera settings used by the user in the past. This makes it possible to customize the optimal photography method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can then select the optimal photography method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the optimal analysis result to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the presentation method of the analysis based on the result.

[0079] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the insect. For example, the analysis unit provides detailed analysis results for important pests. The analysis unit can also provide concise analysis results for less important insects. Furthermore, the analysis unit can automatically adjust the level of detail of the analysis according to the importance of the insect. This makes it possible to provide optimal analysis results according to the importance of the insect. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input insect importance data into AI, which can then adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the insect category. For example, in the case of pest insects, the analysis unit can apply an analysis algorithm specialized for a specific pest insect. Furthermore, in the case of beneficial insects, the analysis unit can also apply an analysis algorithm specialized for beneficial insects. Furthermore, the analysis unit can automatically select the optimal analysis algorithm depending on the insect category. This makes it possible to apply the optimal analysis algorithm depending on the insect category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect category data into AI, which can select the optimal analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy for specific insects based on the user's past analysis results. Furthermore, the analysis unit can automatically improve the analysis accuracy by referring to the user's past analysis results. This makes it possible to improve the analysis accuracy based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the analysis accuracy.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the optimal length of the analysis result to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the length of the analysis based on the result.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time when the insect was discovered. For example, the analysis unit prioritizes analysis if the insect was discovered recently. The analysis unit can also lower the priority of analysis if the insect was discovered a long time ago. Furthermore, the analysis unit can automatically determine the priority of analysis based on the time when the insect was discovered. This makes it possible to determine the optimal analysis priority based on the time when the insect was discovered. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the insect was discovered into AI, and the AI ​​can determine the priority of analysis.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the insects. For example, the analysis unit prioritizes analysis of important pests. The analysis unit can also postpone the order of analysis of less important insects. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the insects. This makes it possible to adjust the optimal order of analysis based on the relevance of the insects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect relevance data into AI, which can then adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results using detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results using concise and easy-to-understand terminology. Furthermore, the analysis unit can automatically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows optimal analysis results to be provided according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the analysis.

[0086] The warning unit can estimate the user's emotions and adjust the warning display method based on the estimated user emotions. For example, if the user is nervous, the warning unit can display a simple, highly visible warning. Furthermore, if the user is relaxed, the warning unit can also display a detailed warning. Furthermore, if the user is in a hurry, the warning unit can display a warning that focuses on the main points. This makes it possible to provide an optimal warning display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the warning display method based on the result.

[0087] The warning unit can adjust the level of detail of the warning based on the danger level of the insect when issuing the warning. For example, the warning unit displays a detailed warning for a highly dangerous pest. The warning unit can also display a concise warning for a less dangerous insect. Furthermore, the warning unit can automatically adjust the level of detail of the warning based on the danger level of the insect. This makes it possible to provide an optimal level of detail of the warning depending on the danger level of the insect. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect danger level data into AI, which can then adjust the level of detail of the warning.

[0088] When issuing a warning, the warning unit can apply different warning algorithms depending on the frequency of insect occurrence. For example, the warning unit applies a specific warning algorithm to pests that occur frequently. The warning unit can also apply a different warning algorithm to insects that occur infrequently. Furthermore, the warning unit can automatically select the optimal warning algorithm depending on the frequency of insect occurrence. This makes it possible to apply the optimal warning algorithm depending on the frequency of insect occurrence. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect occurrence frequency data into AI, which then selects the optimal warning algorithm.

[0089] When issuing a warning, the warning unit can improve the accuracy of the warning based on the user's past warning history. The warning unit, for example, optimizes a warning algorithm based on the user's past warning history. The warning unit can also improve the accuracy of warnings for specific pests based on the user's past warning history. Furthermore, the warning unit can automatically improve the accuracy of the warning by referring to the user's past warning history. This makes it possible to improve the accuracy of warnings based on the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's past warning history data into AI, which can improve the accuracy of the warning.

[0090] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated user emotions. For example, when the user is feeling stressed, the AI ​​automatically prioritizes and displays the most important warning. The warning unit can also provide an interface that allows the user to select the priority of warnings when the user is relaxed. Furthermore, when the user is in a hurry, the AI ​​can quickly prioritize and display the most important warning. This allows the optimal priority of warnings to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit can be performed using, for example, an AI, or without an AI. For example, the warning unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of warnings based on the result.

[0091] When issuing a warning, the warning unit can adjust the order of warnings based on the location of insect infestation. For example, if the location of insect infestation is in the center of farmland, the warning unit can prioritize displaying a warning. Also, if the location of insect infestation is on the periphery of farmland, the warning unit can postpone the order of warnings. Furthermore, the warning unit can automatically adjust the order of warnings based on the location of insect infestation. This makes it possible to adjust the optimal order of warnings based on the location of insect infestation. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect infestation location data into AI, which can adjust the order of warnings.

[0092] The warning unit can improve the accuracy of the warning by referring to insect-related literature when issuing a warning. The warning unit can, for example, optimize a warning algorithm based on insect-related literature. The warning unit can also improve the accuracy of warnings for specific pests based on insect-related literature. Furthermore, the warning unit can automatically improve the accuracy of the warning by referring to insect-related literature. This makes it possible to improve the accuracy of warnings based on insect-related literature. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input insect-related literature data into AI, which can improve the accuracy of the warning.

[0093] When issuing a warning, the warning unit can issue a warning based on the market value of the insect. For example, the warning unit can display a detailed warning for pests with high market value. The warning unit can also display a concise warning for insects with low market value. Furthermore, the warning unit can automatically adjust the level of detail of the warning based on the market value of the insect. This allows for an optimal warning to be issued based on the market value of the insect. Some or all of the above-mentioned processing in the warning unit can be performed using, or without, AI, for example. For example, the warning unit can input market value data of the insect into AI, which can then adjust the level of detail of the warning.

[0094] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. For example, when the user is stressed, the storage unit can save only important data. Furthermore, when the user is relaxed, the storage unit can save detailed data. Furthermore, when the user is in a hurry, the storage unit can save only data that highlights the essential points. This allows optimal data to be selected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and select data to be saved based on the results.

[0095] The storage unit can optimize the storage algorithm based on past stored data when storing data. The storage unit, for example, optimizes the storage algorithm based on past stored data. The storage unit can also improve storage accuracy for specific data from past stored data. Furthermore, the storage unit can automatically optimize the storage algorithm by referring to past stored data. This makes it possible to optimize the storage algorithm based on past stored data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past stored data into AI, which then optimizes the storage algorithm.

[0096] The storage unit can update the stored data to reflect user feedback when storing the data. The storage unit updates the stored data based on, for example, user feedback. The storage unit can also improve the storage accuracy for specific data based on user feedback. Furthermore, the storage unit can automatically update the stored data by referring to user feedback. This allows the stored data to be updated based on user feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input user feedback data into AI, which then updates the stored data.

[0097] The storage unit can estimate the user's emotions and adjust the frequency of saving based on the estimated user emotions. For example, the storage unit can reduce the frequency of saving when the user is stressed. The storage unit can also increase the frequency of saving when the user is relaxed. Furthermore, the storage unit can frequently save only key data when the user is in a hurry. This allows the optimal frequency of saving to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the frequency of saving based on the result.

[0098] The storage unit can weight the stored data based on the time the insect was discovered when storing the data. For example, if the insect was discovered recently, the storage unit can weight the stored data higher. Also, if the insect was discovered older, the storage unit can weight the stored data lower. Furthermore, the storage unit can automatically adjust the weighting of the stored data based on the time the insect was discovered. This allows optimal weighting of the stored data based on the time the insect was discovered. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input data on the time the insect was discovered into AI, which can adjust the weighting of the stored data.

[0099] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also improve the accuracy of the stored data based on information from different data sources. Furthermore, the storage unit can automatically enrich the stored data by referring to information from different data sources. This makes it possible to enrich the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input information from different data sources into AI, which can enrich the stored data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, warning unit, and storage unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit takes a photo of an insect using the camera 42 of the smart device 14 and uploads the photo to the AI ​​via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the insect using image recognition technology. The warning unit is realized by the specific processing unit 290 of the data processing device 12 and alerts the farmer of the identified pest information via the notification function of the smart device 14. The storage unit stores the identification results in the database 24 of the data processing device 12, allowing farmers to refer to past identification results. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, warning unit, and storage unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit takes a photo of an insect using the camera 42 of the smart glasses 214 and uploads it to the AI ​​via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the insect using image recognition technology. The warning unit is realized by the specific processing unit 290 of the data processing device 12 and alerts the farmer of the identified pest information via the notification function of the smart glasses 214. The storage unit stores the identification results in the database 24 of the data processing device 12, allowing farmers to refer to past identification results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, warning unit, and storage unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit takes a photo of an insect using the camera 42 of the headset terminal 314 and uploads the photo to the AI ​​via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the insect using image recognition technology. The warning unit is realized by the specific processing unit 290 of the data processing device 12 and alerts the farmer of information about the identified pest insect via the notification function of the headset terminal 314. The storage unit stores the identification results in the database 24 of the data processing device 12, allowing farmers to refer to past identification results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, warning unit, and storage unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit takes a photo of an insect using the camera 42 of the robot 414 and uploads the photo to the AI ​​via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the insect using image recognition technology. The warning unit is realized by the specific processing unit 290 of the data processing device 12 and alerts the farmer of information about the identified pest insect via the notification function of the robot 414. The storage unit stores the identification results in the database 24 of the data processing device 12, allowing farmers to refer to past identification results.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When taking a photo of an insect, the reception unit can analyze the user's past photography history and select an appropriate photography method. For example, the reception unit can automatically apply similar settings based on the photography method that the user used successfully in the past. The reception unit can also suggest the most effective photography angle and distance based on the user's past photography history. Furthermore, the reception unit can automatically select the optimal photography method by referring to the camera settings the user used in the past. This makes it possible to select the optimal photography method based on the user's past photography history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past photography history data into AI, which can then select the optimal photography method.

[0102] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the insect. For example, in the case of an important pest, a detailed analysis result is provided. The analysis unit can also provide a concise analysis result for an insect of low importance. Furthermore, the analysis unit can automatically adjust the level of detail of the analysis according to the importance of the insect. This makes it possible to provide optimal analysis results according to the importance of the insect. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input insect importance data into AI, which can then adjust the level of detail of the analysis.

[0103] When issuing a warning, the warning unit can apply different warning algorithms depending on the frequency of insect occurrence. For example, a specific warning algorithm is applied for pests with a high frequency of occurrence. The warning unit can also apply a different warning algorithm for insects with a low frequency of occurrence. Furthermore, the warning unit can automatically select the optimal warning algorithm depending on the frequency of insect occurrence. This makes it possible to apply the optimal warning algorithm depending on the frequency of insect occurrence. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input insect occurrence frequency data into AI, which then selects the optimal warning algorithm.

[0104] The storage unit can optimize the storage algorithm based on past stored data when storing data. For example, the storage algorithm is optimized based on past stored data. The storage unit can also improve the storage accuracy for specific data from past stored data. Furthermore, the storage unit can automatically optimize the storage algorithm by referring to past stored data. This makes it possible to optimize the storage algorithm based on past stored data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past stored data into AI, which then optimizes the storage algorithm.

[0105] When taking photos of insects, the reception unit can prioritize photographing insects that are highly relevant based on the user's geographical location information. For example, based on the user's current location, it can prioritize photographing insects that are commonly seen in that area. The reception unit can also prioritize photographing locations where specific pests are likely to occur based on the user's geographical location information. Furthermore, the reception unit can update the user's location information in real time and select the optimal insect to photograph. This makes it possible to take optimal insect photos based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's GPS data into AI, which can select the optimal insect to photograph.

[0106] The reception unit can estimate the user's emotions and adjust the timing of taking photos of insects based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​automatically takes photos of insects at the optimal timing. Furthermore, if the user is relaxed, the reception unit can provide an interface that allows the user to select the timing of taking photos. Furthermore, if the user is in a hurry, the reception unit can prompt the AI ​​to quickly take photos of insects and upload them. This allows photos of insects to be taken at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the timing of taking photos based on the results.

[0107] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the optimal analysis result to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and adjust the presentation method of the analysis based on the result.

[0108] The warning unit can estimate the user's emotions and adjust the warning display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible warning is displayed. The warning unit can also display a detailed warning if the user is relaxed. Furthermore, if the user is in a hurry, the warning unit can display a warning that focuses on the main points. This makes it possible to provide an optimal warning display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the warning display method based on the result.

[0109] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. For example, if the user is feeling stressed, only important data is saved. Furthermore, if the user is relaxed, the storage unit can save detailed data. Furthermore, if the user is in a hurry, the storage unit can save only data that summarizes the key points. This allows optimal data to be selected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and select data to be saved based on the results.

[0110] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can automatically prioritize and display the most important warning. The warning unit can also provide an interface that allows the user to select the priority of warnings when the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can quickly prioritize and display the most important warning. This allows the optimal priority of warnings to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit can be performed using, for example, an AI, or without an AI. For example, the warning unit can input the user's facial expression data into the generation AI, which can estimate the user's emotions and determine the priority of warnings based on the result.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit takes a photo of the insect and uploads it to the AI. For example, the reception unit takes a photo of the insect using a smartphone or camera and uploads it to the AI ​​through a dedicated application. The reception unit can also estimate the user's emotions and adjust the timing of taking the insect photo based on the estimated user emotions. Step 2: The analysis unit uses AI to analyze the photo uploaded by the reception unit and identify the insect. For example, the analysis unit uses image recognition technology to analyze the insect's characteristics and identify what it is. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Step 3: The warning unit issues a warning if the insect identified by the analysis unit is a pest or if its number is rapidly increasing. For example, if the identified insect is a pest or if its number is rapidly increasing, the warning unit issues a warning to the farmer via a smartphone notification or email. The warning unit can also estimate the user's emotions and adjust the way the warning is displayed based on the estimated user's emotions. Step 4: The storage unit stores the results identified by the analysis unit in a database. For example, the storage unit stores the identification results in a database so that farmers can refer to past identification results. The storage unit can also estimate the user's emotions and select data to be stored based on the estimated user's emotions.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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. A reception desk where photos of insects are taken and uploaded to the AI. an analysis unit that analyzes the photo uploaded by the reception unit and identifies insects; a warning unit that issues a warning when the insect identified by the analysis unit is a pest or when the number of pests is rapidly increasing; a storage unit that stores the results identified by the analysis unit in a database. A system characterized by:

2. The analysis unit Use image recognition technology to analyze insect features and identify what they are 2. The system of claim 1.

3. The warning unit Alert farmers via smartphone notifications or email if identified insects are pests or are rapidly increasing in number 2. The system of claim 1.

4. The storage unit The identification results are stored in a database so that farmers can refer to past identification results.

2. The system of claim 1.

5. The reception unit Take a photo of the insect using your smartphone or camera and upload it to the AI ​​via a dedicated application.

2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of taking photos of insects based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When taking photos of insects, the system analyzes the user's past photography history and selects the appropriate photography method.

2. The system of claim 1.

8. The reception unit Filtering insect photos based on the user's current farming situation or area of ​​interest 2. The system of claim 1.

Images