system

Abstract

The system according to this embodiment aims to identify bacteria growing on the surface of food and automatically apply appropriate chemicals. [Solution] The system according to the embodiment comprises an imaging unit, an analysis unit, an identification unit, a selection unit, and a coating unit. The imaging unit photographs the surface of the food. The analysis unit analyzes the image captured by the imaging unit. The identification unit identifies the type and growth status of bacteria based on the image analyzed by the analysis unit. The selection unit selects an appropriate chemical based on the bacteria identified by the identification unit. The coating unit automatically applies the chemical selected by the selection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and accurately identify bacteria growing on the surface of food and take appropriate measures.

[0005] The system according to the embodiment aims to identify bacteria growing on the surface of food and automatically apply appropriate chemicals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an imaging unit, an analysis unit, an identification unit, a selection unit, and a coating unit. The imaging unit photographs the surface of the food. The analysis unit analyzes the image captured by the imaging unit. The identification unit identifies the type and growth status of bacteria based on the image analyzed by the analysis unit. The selection unit selects an appropriate chemical based on the bacteria identified by the identification unit. The coating unit automatically applies the chemical selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify bacteria growing on the surface of food and automatically apply appropriate chemicals. [Brief explanation of the drawing]

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

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

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

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The food shelf-life extension system according to an embodiment of the present invention is a system that extends the shelf life of food by utilizing a high-precision camera and AI. This system photographs the surface of food and monitors the growth of bacteria in real time. Next, the AI ​​analyzes the captured image to identify the type of bacteria and their growth status. Based on pre-learned data, the AI ​​determines the appearance and reproductive capacity of the bacteria. If bacterial growth is detected, the AI ​​selects an appropriate chemical and automatically calculates the required amount. Next, a device that automatically applies the selected chemical is activated and applies the chemical to the surface of the food. This suppresses bacterial growth and extends the shelf life of the food. Because this system selects the optimal chemical according to the type and condition of the food and applies the appropriate amount, it can extend the shelf life while maintaining the quality of the food. Furthermore, because it monitors bacterial growth in real time and applies chemical only when necessary, it can prevent the unnecessary use of chemicals. In addition, this system contributes to reducing food waste. Much of the cause of food spoilage is bacterial growth, and by suppressing bacterial growth, food waste can be reduced. Thus, a system utilizing high-precision cameras and AI is an effective means of extending the shelf life of food and reducing food waste. For example, a high-precision camera is used to photograph the surface of food and monitor bacterial growth in real time. Next, AI analyzes the captured images to identify the type of bacteria and their growth status. Based on pre-trained data, the AI ​​determines the appearance and reproductive capacity of the bacteria. If bacterial growth is detected, the AI ​​selects the appropriate chemical and automatically calculates the required amount. Next, a device that automatically applies the selected chemical is activated and applied to the surface of the food. This suppresses bacterial growth and extends the shelf life of the food. Because this system selects the optimal chemical and applies the appropriate amount according to the type and condition of the food, it can extend the shelf life while maintaining the quality of the food. In addition, because it monitors bacterial growth in real time and applies chemical only when necessary, it prevents the use of unnecessary chemicals. Furthermore, this system also contributes to reducing food waste. Much of the cause of food spoilage is bacterial growth, so suppressing bacterial growth can reduce food waste.Thus, a system utilizing high-precision cameras and AI is an effective means of extending the shelf life of food and reducing food waste. Therefore, a food shelf-life extension system can extend the shelf life of food and reduce food waste.

[0029] The food shelf life extension system according to this embodiment comprises an imaging unit, an analysis unit, an identification unit, a selection unit, and a coating unit. The imaging unit photographs the surface of the food. The imaging unit can, for example, use a high-precision camera to photograph the surface of the food. The imaging unit can also use light of different wavelengths to make it easier to identify types of bacteria. For example, ultraviolet light is used to highlight specific bacteria, and AI identifies their types. The analysis unit analyzes the images captured by the imaging unit. The analysis unit analyzes the captured images using, for example, AI to identify the types of bacteria and their growth status. The identification unit identifies the types of bacteria and their growth status based on the images analyzed by the analysis unit. The identification unit identifies the types of bacteria and their growth status using, for example, AI. The selection unit selects an appropriate chemical based on the bacteria identified by the identification unit. The selection unit selects an appropriate chemical using, for example, AI. The coating unit automatically applies the chemical selected by the selection unit. The coating unit automatically applies the chemical selected using, for example, AI. As a result, the food shelf life extension system according to this embodiment can extend the shelf life of food.

[0030] The imaging unit photographs the surface of food. For example, it can use a high-precision camera to photograph the food surface. Specifically, the camera acquires high-resolution images, allowing for detailed capture of minute bacteria and the surface condition of the food. The imaging unit can also use different wavelengths of light to facilitate the identification of bacterial species. For example, ultraviolet light can be used to highlight specific bacteria, and AI can identify their species. Ultraviolet light enhances the fluorescence emitted by certain bacteria, improving the accuracy of bacterial detection. Furthermore, by combining different wavelengths of light, such as infrared and visible light, the imaging unit can more accurately determine the type and growth status of bacteria. This allows the imaging unit to capture the surface condition of food from multiple angles and collect high-quality data to provide to the analysis unit.

[0031] The analysis unit analyzes images captured by the imaging unit. For example, the analysis unit uses AI to analyze the captured images and identify the type and growth status of bacteria. Specifically, the AI ​​uses image recognition technology to analyze the shape, color, and pattern of bacteria from the captured images to identify the type of bacteria. Furthermore, the AI ​​can evaluate the growth status and progression of bacteria by comparing it with a past database. For example, the AI ​​analyzes the density and distribution of bacteria in the image to predict the rate and extent of bacterial growth. The AI ​​can also use anomaly detection algorithms to detect unusual bacterial patterns or abnormal growth conditions. This allows the analysis unit to quickly and accurately analyze captured images and understand the type and growth status of bacteria in real time. Finally, the analysis unit provides the analysis results to the identification unit to support processing in the next step.

[0032] The identification unit identifies the type and growth status of bacteria based on the images analyzed by the analysis unit. The identification unit uses AI, for example, to identify the type and growth status of bacteria. Specifically, the AI ​​identifies the type of bacteria and evaluates its growth status based on the data provided by the analysis unit. The AI ​​analyzes the shape, color, and pattern of the bacteria in detail to identify the characteristics of specific bacteria. In addition, the AI ​​can more accurately identify the type and growth status of bacteria by comparing it with past databases. For example, the AI ​​analyzes the environments in which a particular bacterium has grown in the past and compares it with the current situation to predict the growth rate and range of the bacteria. As a result, the identification unit can quickly and accurately identify the type and growth status of bacteria and generate information to provide to the selection unit.

[0033] The selection unit selects the appropriate drug based on the bacteria identified by the identification unit. The selection unit uses AI, for example, to select the appropriate drug. Specifically, the AI ​​selects the optimal drug based on data on the type and growth status of the bacteria provided by the identification unit. The AI ​​utilizes past databases and expertise to identify drugs that are effective against specific bacteria. The AI ​​can also comprehensively evaluate the effects, side effects, and usage conditions of drugs to select the optimal drug. For example, the AI ​​generates a list of drugs that are effective against a specific bacteria and selects the most suitable drug from among them. This allows the selection unit to quickly and accurately select the optimal drug according to the type and growth status of the bacteria and generate information to provide to the application unit.

[0034] The application unit automatically applies the chemical selected by the selection unit. For example, the application unit automatically applies the chemical selected using AI. Specifically, the application unit applies the chemical in the appropriate amount and method based on the chemical information provided by the selection unit. The AI ​​optimizes the timing, amount, and area of ​​application, enabling effective chemical application. For example, the AI ​​monitors the bacterial growth status and the surface condition of the food in real time and applies the chemical at the optimal time. Furthermore, the application unit can apply the chemical uniformly using automated machinery. As a result, the application unit can apply the selected chemical efficiently and effectively, extending the shelf life of the food. In addition, the application unit can monitor the effect after application and perform additional application or adjustments as needed. As a result, the application unit can maximize the shelf life of the food and maintain its quality.

[0035] The monitoring unit can monitor bacterial growth in real time. The monitoring unit can, for example, use AI to monitor bacterial growth in real time. The monitoring unit can, for example, adjust the monitoring frequency to monitor the bacterial growth status in real time. The monitoring unit can, for example, select the type of sensor to use to monitor the bacterial growth status in real time. The monitoring unit can, for example, adjust the data update interval to monitor the bacterial growth status in real time. This allows for the application of chemicals at the appropriate time by monitoring bacterial growth in real time.

[0036] The calculation unit can automatically calculate the required amount of chemicals. For example, the calculation unit can automatically calculate the required amount of chemicals using AI. For example, the calculation unit can select the algorithm to be used and automatically calculate the required amount of chemicals. For example, the calculation unit can adjust the accuracy of the calculation and automatically calculate the required amount of chemicals. For example, the calculation unit can adjust the calculation time and automatically calculate the required amount of chemicals. This allows for the automatic calculation of the required amount of chemicals, preventing the wasteful use of chemicals.

[0037] The analysis unit can determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can use AI to determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can select a dataset to use and determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can select a learning algorithm and determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can adjust the learning period and determine the appearance and reproductive capacity of bacteria based on pre-trained data. This improves the accuracy of the analysis by determining the appearance and reproductive capacity of bacteria based on pre-trained data.

[0038] The coating unit can automatically apply selected chemicals. For example, the coating unit can automatically apply chemicals selected using AI. For example, the coating unit can select the type of coating device and automatically apply the selected chemicals. For example, the coating unit can adjust the application amount and automatically apply the selected chemicals. For example, the coating unit can adjust the application range and automatically apply the selected chemicals. This allows for the automatic application of selected chemicals, thereby suppressing bacterial growth and extending the shelf life of food.

[0039] The imaging unit can simultaneously measure the surface temperature of food during imaging and adjust the imaging frequency based on temperature changes. For example, the imaging unit can use AI to simultaneously measure the surface temperature of food during imaging and adjust the imaging frequency based on temperature changes. For example, if the surface temperature of food changes rapidly, the imaging unit's AI will increase the imaging frequency to acquire detailed data. For example, if the temperature is stable, the imaging unit's AI will set a lower imaging frequency to avoid acquiring unnecessary data. For example, if the temperature change is gradual, the imaging unit's AI will take pictures at appropriate intervals to efficiently collect the necessary information. In this way, by adjusting the imaging frequency based on the surface temperature of food, detailed data can be efficiently acquired.

[0040] The imaging unit can make it easier to identify types of bacteria by using light of different wavelengths during imaging. For example, the imaging unit can use AI to make it easier to identify types of bacteria by using light of different wavelengths during imaging. For example, the imaging unit can use ultraviolet light to highlight specific bacteria, and AI can identify their type. For example, the imaging unit can use infrared light to understand the bacterial growth status in detail, and AI can analyze it. For example, the imaging unit can combine visible light with light of different wavelengths to identify types of bacteria from multiple angles. In this way, it is possible to make it easier to identify types of bacteria by using light of different wavelengths.

[0041] The camera unit can automatically adjust the camera position and angle according to the shape and size of the food during shooting. For example, the camera unit can use AI to automatically adjust the camera position and angle according to the shape and size of the food during shooting. For example, for large food items, the camera unit's AI will set the camera position high to capture the whole thing. For example, for small food items, the camera unit's AI will set the camera position low to capture details. For example, for irregularly shaped food items, the camera unit's AI will automatically adjust to the optimal angle to capture the whole thing. In this way, optimal shooting is possible by automatically adjusting the camera position and angle according to the shape and size of the food.

[0042] The photography unit can select the optimal shooting method while considering the packaging condition of the food. For example, the photography unit can use AI to select the optimal shooting method while considering the packaging condition of the food. For example, if the packaging is transparent, the photography unit will take the picture as is using AI. For example, if the packaging is opaque, the photography unit will open the packaging before taking the picture using AI. For example, if the food is partially packaged, the photography unit will take the picture at an angle that minimizes the impact of the packaging. By selecting the optimal shooting method while considering the packaging condition of the food, accurate data can be obtained.

[0043] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can use AI to optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can use AI to automatically adjust the analysis algorithm based on past data. For example, the analysis unit can use AI to improve accuracy by referring to past analysis results. For example, the analysis unit can use AI to select the optimal analysis method by analyzing past data. This improves the accuracy of the analysis by optimizing the analysis algorithm by referring to past analysis data.

[0044] The analysis unit can apply different analysis methods to different types of food during analysis. For example, the analysis unit can use AI to apply different analysis methods to different types of food during analysis. For example, in the case of meat, the analysis unit can apply an analysis method in which the AI ​​focuses on specific bacteria. For example, in the case of vegetables, the analysis unit can apply an analysis method in which the AI ​​analyzes the growth status of different bacteria. For example, in the case of seafood, the analysis unit can apply an analysis method in which the AI ​​operates under specific temperature conditions. By applying different analysis methods to different types of food, the accuracy of the analysis is improved.

[0045] The analysis unit can improve the accuracy of its analysis by referring to food storage environment data during the analysis. For example, the analysis unit can use AI to improve the accuracy of its analysis by referring to food storage environment data during the analysis. For example, the AI ​​improves the accuracy of the analysis based on storage temperature data. For example, the AI ​​corrects the analysis results by referring to storage humidity data. For example, the AI ​​adjusts the analysis method by considering storage period data. This allows for more accurate analysis by improving the accuracy of the analysis by referring to food storage environment data.

[0046] The analysis unit can perform analysis while considering the food's manufacturing date and expiration date information. For example, the analysis unit can use AI to perform analysis while considering the food's manufacturing date and expiration date information. For example, the analysis unit can use AI to adjust the analysis method based on manufacturing date data. For example, the analysis unit can use AI to correct the analysis results by referring to expiration date information. For example, the analysis unit can use AI to select the optimal analysis method by considering both the manufacturing date and expiration date. As a result, the accuracy of the analysis is improved by performing analysis while considering the food's manufacturing date and expiration date information.

[0047] The identification unit can predict the growth rate of bacteria at a specific time and identify future reproduction conditions. The identification unit can, for example, use AI to predict the growth rate of bacteria at a specific time and identify future reproduction conditions. The identification unit can, for example, use AI to predict future reproduction conditions based on the current growth rate of bacteria. The identification unit can, for example, refer to past data and use AI to identify the growth pattern of bacteria. The identification unit can, for example, consider changes in temperature and humidity and use AI to predict future reproduction conditions of bacteria. By predicting the growth rate of bacteria and identifying future reproduction conditions, appropriate countermeasures can be taken.

[0048] The identification unit can improve the accuracy of identification by analyzing the genetic information of the fungus at the time of identification. The identification unit can improve the accuracy of identification by analyzing the genetic information of the fungus at the time of identification, for example, using AI. The identification unit can improve the accuracy of identification by using AI based on the genetic information of the fungus. The identification unit can improve the accuracy of identification by using AI to analyze the genetic information and use AI to identify the type of fungus in detail. The identification unit can improve the accuracy of identification by using AI to combine the genetic information and reproduction status, for example. As a result, by analyzing the genetic information of the fungus and improving the accuracy of identification, more accurate identification becomes possible.

[0049] The identification unit can improve the accuracy of identification by referring to food storage environment data at the time of identification. The identification unit can improve the accuracy of identification by referring to food storage environment data at the time of identification, for example, using AI. The identification unit can improve the accuracy of identification by using AI based on storage temperature data, for example. The identification unit can correct the identification results by referring to storage humidity data, for example. The identification unit can adjust the identification method by using AI considering storage period data, for example. As a result, more accurate identification becomes possible by improving the accuracy of identification by referring to food storage environment data.

[0050] The identification unit can perform identification by referring to the fungus's past reproduction data at the time of identification. The identification unit can, for example, use AI to refer to the fungus's past reproduction data at the time of identification. The identification unit can, for example, adjust the identification method based on past reproduction data using AI. The identification unit can, for example, refer to past data and the AI ​​can identify the type of fungus. The identification unit can, for example, combine reproduction data with the current situation to improve the accuracy of identification using AI. As a result, the accuracy of identification is improved by referring to the fungus's past reproduction data.

[0051] The selection unit can optimize its selection algorithm by referring to past selection data during the selection process. For example, the selection unit can use AI to optimize its selection algorithm by referring to past selection data during the selection process. For example, the selection unit can use AI to automatically adjust the selection algorithm based on past data. For example, the selection unit can use AI to improve accuracy by referring to past selection results. For example, the selection unit can analyze past data and use AI to select the optimal selection method. This improves selection accuracy by optimizing the selection algorithm by referring to past selection data.

[0052] The selection unit can select the optimal drug by considering bacterial resistance information during the selection process. For example, the selection unit can use AI to select the optimal drug by considering bacterial resistance information during the selection process. For example, the selection unit uses AI to select the optimal drug based on bacterial resistance information. For example, the selection unit analyzes resistance information and uses AI to select an effective drug against the bacteria. For example, the selection unit combines resistance information with the reproduction status and uses AI to select the optimal drug. This allows for the selection of an effective drug by considering bacterial resistance information.

[0053] The selection unit can improve the accuracy of its selection process by referring to food storage environment data during the selection process. For example, the selection unit can use AI to improve the accuracy of its selection process by referring to food storage environment data during the selection process. For example, the selection unit can use AI to improve selection accuracy based on storage temperature data. For example, the selection unit can use AI to correct selection results by referring to storage humidity data. For example, the selection unit can use AI to adjust the selection method by considering storage period data. This allows for more accurate selection by improving the accuracy of the selection process by referring to food storage environment data.

[0054] The selection unit can make selections while considering drug inventory information. The selection unit can, for example, use AI to make selections while considering drug inventory information. The selection unit can, for example, use AI to select the optimal drug based on inventory information. The selection unit can, for example, use AI to suggest an alternative drug if inventory is low. The selection unit can, for example, use AI to select the optimal drug by combining inventory information and bacterial resistance information. As a result, inventory management becomes more efficient by making selections while considering drug inventory information.

[0055] The coating unit can monitor the surface condition of the food in real time during coating and adjust the optimal coating amount. For example, the coating unit can use AI to monitor the surface condition of the food in real time during coating and adjust the optimal coating amount. For example, if the surface condition of the food changes, the AI ​​will automatically adjust the coating amount. For example, if the surface is dry, the AI ​​will increase the coating amount. For example, if the surface is wet, the AI ​​will decrease the coating amount. In this way, by monitoring the surface condition of the food in real time and adjusting the optimal coating amount, it is possible to prevent the unnecessary use of chemicals.

[0056] The coating unit can maximize its effect by simultaneously applying different chemicals during application. For example, the coating unit can maximize its effect by simultaneously applying different chemicals using AI. For example, the coating unit can apply multiple chemicals simultaneously, with AI maximizing the effect. For example, the coating unit can apply different chemicals sequentially, with AI maximizing the effect. For example, the coating unit can mix and apply multiple chemicals, with AI maximizing the effect. In this way, the effect can be maximized by simultaneously applying different chemicals.

[0057] The coating unit can automatically adjust the position and angle of the coating device according to the shape and size of the food during coating. For example, the coating unit can use AI to automatically adjust the position and angle of the coating device according to the shape and size of the food during coating. For example, in the case of a large food item, the AI ​​will set the position of the coating device higher and coat the entire item. For example, in the case of a small food item, the AI ​​will set the position of the coating device lower and coat the details. For example, in the case of an irregularly shaped food item, the AI ​​will automatically adjust the optimal angle and coat the entire item. In this way, optimal coating is possible by automatically adjusting the position and angle of the coating device according to the shape and size of the food item.

[0058] The coating unit can apply different coating methods depending on the type of chemical during coating. For example, the coating unit can use AI to apply different coating methods depending on the type of chemical during coating. For example, for a specific chemical, the AI ​​can apply spray coating. For example, for a different chemical, the AI ​​can apply brush coating. For example, the AI ​​can select the optimal coating method according to the characteristics of the chemical. This enables effective coating by applying different coating methods for each type of chemical.

[0059] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can use AI to optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can use AI to automatically adjust the monitoring algorithm based on past data. For example, the monitoring unit can use AI to improve accuracy by referring to past monitoring results. For example, the monitoring unit can use AI to select the optimal monitoring method by analyzing past data. As a result, monitoring accuracy is improved by optimizing the monitoring algorithm by referring to past monitoring data.

[0060] The monitoring unit can improve the accuracy of monitoring by referring to food storage environment data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to food storage environment data during monitoring, for example, using AI. The monitoring unit can improve monitoring accuracy using AI based on storage temperature data, for example. The monitoring unit can correct monitoring results using AI by referring to storage humidity data, for example. The monitoring unit can adjust the monitoring method using AI by considering storage period data, for example. As a result, more accurate monitoring becomes possible by improving monitoring accuracy by referring to food storage environment data.

[0061] The calculation unit can optimize the calculation algorithm by referring to past calculation data during calculation. For example, the calculation unit can use AI to optimize the calculation algorithm by referring to past calculation data during calculation. For example, the calculation unit can use AI to automatically adjust the calculation algorithm based on past data. For example, the calculation unit can use AI to improve accuracy by referring to past calculation results. For example, the calculation unit can analyze past data and use AI to select the optimal calculation method. This improves calculation accuracy by optimizing the calculation algorithm by referring to past calculation data.

[0062] The calculation unit can improve the accuracy of calculations by referring to food storage environment data during calculations. For example, the calculation unit can improve the accuracy of calculations by referring to food storage environment data during calculations using AI. For example, the calculation unit can improve the accuracy of calculations using AI based on storage temperature data. For example, the calculation unit can correct the calculation results using AI by referring to storage humidity data. For example, the calculation unit can adjust the calculation method using AI by considering storage period data. In this way, by improving the accuracy of calculations by referring to food storage environment data, more accurate calculations become possible.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The food shelf-life extension system can also be equipped with a temperature control unit. This unit can monitor the food's storage temperature in real time and adjust it to an optimal temperature. For example, it can control the cooling device to keep the food's storage temperature low. It can also control the heating device to raise the food's storage temperature appropriately. Furthermore, the temperature control unit can monitor the food's surface temperature using a temperature sensor and cool or heat it according to temperature changes. This allows for optimal storage of the food, suppressing bacterial growth and extending its shelf life.

[0065] The food shelf-life extension system can also be equipped with a humidity control unit. This unit can monitor the humidity of the food storage environment in real time and adjust it to an optimal level. For example, it can control a humidifier to maintain a high humidity level, or control a dehumidifier to maintain a low humidity level. Furthermore, the unit can monitor the humidity level using a humidity sensor and adjust the humidity level accordingly. By maintaining an optimal humidity level in the food storage environment, bacterial growth can be suppressed, extending the food's shelf life.

[0066] The food shelf-life extension system may also include a light adjustment unit. This unit can monitor the light intensity of the food storage environment in real time and adjust it to the optimal level. For example, it can control the lighting device to maintain an appropriate light intensity in the food storage environment. It can also control the light-blocking device to keep the light intensity low. Furthermore, the light adjustment unit can monitor the light intensity of the food storage environment using a light sensor and adjust lighting or shading in response to changes in light intensity. This allows for optimal light intensity in the food storage environment, suppressing bacterial growth and extending the food's shelf life.

[0067] The food shelf-life extension system can also be equipped with a vibration detection unit. This unit can monitor vibrations in the food storage environment in real time and issue a warning if vibrations occur. For example, the vibration detection unit can use a vibration sensor to monitor vibrations in the food storage environment and issue a warning if the vibration exceeds a certain threshold. Furthermore, the vibration detection unit can identify the source of the vibration and take appropriate countermeasures. In addition, the vibration detection unit can record vibration data for later analysis. This allows for monitoring vibrations in the food storage environment and preventing quality deterioration due to vibrations, thereby extending the food's shelf life.

[0068] The food shelf-life extension system can also be equipped with a gas detection unit. The gas detection unit can monitor the gas concentration in the food storage environment in real time and issue a warning if an abnormal gas concentration is detected. For example, the gas detection unit can use a gas sensor to monitor the gas concentration in the food storage environment and issue a warning if the abnormal gas concentration exceeds a certain threshold. Furthermore, the gas detection unit can identify the type of gas and take appropriate countermeasures. In addition, the gas detection unit can record gas data for later analysis. This allows for monitoring the gas concentration in the food storage environment and preventing quality deterioration due to gases, thereby extending the food's shelf life.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The imaging unit photographs the surface of the food. The imaging unit can photograph the surface of the food using a high-precision camera and can also use light of different wavelengths to make it easier to identify types of bacteria. For example, ultraviolet light is used to highlight specific bacteria, and AI identifies their type. Step 2: The analysis unit analyzes the images captured by the imaging unit. The analysis unit uses AI to analyze the captured images and identify the type of fungus and its growth stage. Step 3: The identification unit identifies the type of bacteria and their growth status based on the images analyzed by the analysis unit. The identification unit uses AI to identify the type of bacteria and their growth status. Step 4: The selection unit selects the appropriate drug based on the bacteria identified by the identification unit. The selection unit uses AI to select the appropriate drug. Step 5: The application unit automatically applies the chemical selected by the selection unit. The application unit automatically applies the chemical selected using AI.

[0071] (Example of form 2) The food shelf-life extension system according to an embodiment of the present invention is a system that extends the shelf life of food by utilizing a high-precision camera and AI. This system photographs the surface of food and monitors the growth of bacteria in real time. Next, the AI ​​analyzes the captured image to identify the type of bacteria and their growth status. Based on pre-learned data, the AI ​​determines the appearance and reproductive capacity of the bacteria. If bacterial growth is detected, the AI ​​selects an appropriate chemical and automatically calculates the required amount. Next, a device that automatically applies the selected chemical is activated and applies the chemical to the surface of the food. This suppresses bacterial growth and extends the shelf life of the food. Because this system selects the optimal chemical according to the type and condition of the food and applies the appropriate amount, it can extend the shelf life while maintaining the quality of the food. Furthermore, because it monitors bacterial growth in real time and applies chemical only when necessary, it can prevent the unnecessary use of chemicals. In addition, this system contributes to reducing food waste. Much of the cause of food spoilage is bacterial growth, and by suppressing bacterial growth, food waste can be reduced. Thus, a system utilizing high-precision cameras and AI is an effective means of extending the shelf life of food and reducing food waste. For example, a high-precision camera is used to photograph the surface of food and monitor bacterial growth in real time. Next, AI analyzes the captured images to identify the type of bacteria and their growth status. Based on pre-trained data, the AI ​​determines the appearance and reproductive capacity of the bacteria. If bacterial growth is detected, the AI ​​selects the appropriate chemical and automatically calculates the required amount. Next, a device that automatically applies the selected chemical is activated and applied to the surface of the food. This suppresses bacterial growth and extends the shelf life of the food. Because this system selects the optimal chemical and applies the appropriate amount according to the type and condition of the food, it can extend the shelf life while maintaining the quality of the food. In addition, because it monitors bacterial growth in real time and applies chemical only when necessary, it prevents the use of unnecessary chemicals. Furthermore, this system also contributes to reducing food waste. Much of the cause of food spoilage is bacterial growth, so suppressing bacterial growth can reduce food waste.Thus, a system utilizing high-precision cameras and AI is an effective means of extending the shelf life of food and reducing food waste. Therefore, a food shelf-life extension system can extend the shelf life of food and reduce food waste.

[0072] The food shelf life extension system according to this embodiment comprises an imaging unit, an analysis unit, an identification unit, a selection unit, and a coating unit. The imaging unit photographs the surface of the food. The imaging unit can, for example, use a high-precision camera to photograph the surface of the food. The imaging unit can also use light of different wavelengths to make it easier to identify types of bacteria. For example, ultraviolet light is used to highlight specific bacteria, and AI identifies their types. The analysis unit analyzes the images captured by the imaging unit. The analysis unit analyzes the captured images using, for example, AI to identify the types of bacteria and their growth status. The identification unit identifies the types of bacteria and their growth status based on the images analyzed by the analysis unit. The identification unit identifies the types of bacteria and their growth status using, for example, AI. The selection unit selects an appropriate chemical based on the bacteria identified by the identification unit. The selection unit selects an appropriate chemical using, for example, AI. The coating unit automatically applies the chemical selected by the selection unit. The coating unit automatically applies the chemical selected using, for example, AI. As a result, the food shelf life extension system according to this embodiment can extend the shelf life of food.

[0073] The imaging unit photographs the surface of food. For example, it can use a high-precision camera to photograph the food surface. Specifically, the camera acquires high-resolution images, allowing for detailed capture of minute bacteria and the surface condition of the food. The imaging unit can also use different wavelengths of light to facilitate the identification of bacterial species. For example, ultraviolet light can be used to highlight specific bacteria, and AI can identify their species. Ultraviolet light enhances the fluorescence emitted by certain bacteria, improving the accuracy of bacterial detection. Furthermore, by combining different wavelengths of light, such as infrared and visible light, the imaging unit can more accurately determine the type and growth status of bacteria. This allows the imaging unit to capture the surface condition of food from multiple angles and collect high-quality data to provide to the analysis unit.

[0074] The analysis unit analyzes images captured by the imaging unit. For example, the analysis unit uses AI to analyze the captured images and identify the type and growth status of bacteria. Specifically, the AI ​​uses image recognition technology to analyze the shape, color, and pattern of bacteria from the captured images to identify the type of bacteria. Furthermore, the AI ​​can evaluate the growth status and progression of bacteria by comparing it with a past database. For example, the AI ​​analyzes the density and distribution of bacteria in the image to predict the rate and extent of bacterial growth. The AI ​​can also use anomaly detection algorithms to detect unusual bacterial patterns or abnormal growth conditions. This allows the analysis unit to quickly and accurately analyze captured images and understand the type and growth status of bacteria in real time. Finally, the analysis unit provides the analysis results to the identification unit to support processing in the next step.

[0075] The identification unit identifies the type and growth status of bacteria based on the images analyzed by the analysis unit. The identification unit uses AI, for example, to identify the type and growth status of bacteria. Specifically, the AI ​​identifies the type of bacteria and evaluates its growth status based on the data provided by the analysis unit. The AI ​​analyzes the shape, color, and pattern of the bacteria in detail to identify the characteristics of specific bacteria. In addition, the AI ​​can more accurately identify the type and growth status of bacteria by comparing it with past databases. For example, the AI ​​analyzes the environments in which a particular bacterium has grown in the past and compares it with the current situation to predict the growth rate and range of the bacteria. As a result, the identification unit can quickly and accurately identify the type and growth status of bacteria and generate information to provide to the selection unit.

[0076] The selection unit selects the appropriate drug based on the bacteria identified by the identification unit. The selection unit uses AI, for example, to select the appropriate drug. Specifically, the AI ​​selects the optimal drug based on data on the type and growth status of the bacteria provided by the identification unit. The AI ​​utilizes past databases and expertise to identify drugs that are effective against specific bacteria. The AI ​​can also comprehensively evaluate the effects, side effects, and usage conditions of drugs to select the optimal drug. For example, the AI ​​generates a list of drugs that are effective against a specific bacteria and selects the most suitable drug from among them. This allows the selection unit to quickly and accurately select the optimal drug according to the type and growth status of the bacteria and generate information to provide to the application unit.

[0077] The application unit automatically applies the chemical selected by the selection unit. For example, the application unit automatically applies the chemical selected using AI. Specifically, the application unit applies the chemical in the appropriate amount and method based on the chemical information provided by the selection unit. The AI ​​optimizes the timing, amount, and area of ​​application, enabling effective chemical application. For example, the AI ​​monitors the bacterial growth status and the surface condition of the food in real time and applies the chemical at the optimal time. Furthermore, the application unit can apply the chemical uniformly using automated machinery. As a result, the application unit can apply the selected chemical efficiently and effectively, extending the shelf life of the food. In addition, the application unit can monitor the effect after application and perform additional application or adjustments as needed. As a result, the application unit can maximize the shelf life of the food and maintain its quality.

[0078] The monitoring unit can monitor bacterial growth in real time. The monitoring unit can, for example, use AI to monitor bacterial growth in real time. The monitoring unit can, for example, adjust the monitoring frequency to monitor the bacterial growth status in real time. The monitoring unit can, for example, select the type of sensor to use to monitor the bacterial growth status in real time. The monitoring unit can, for example, adjust the data update interval to monitor the bacterial growth status in real time. This allows for the application of chemicals at the appropriate time by monitoring bacterial growth in real time.

[0079] The calculation unit can automatically calculate the required amount of chemicals. For example, the calculation unit can automatically calculate the required amount of chemicals using AI. For example, the calculation unit can select the algorithm to be used and automatically calculate the required amount of chemicals. For example, the calculation unit can adjust the accuracy of the calculation and automatically calculate the required amount of chemicals. For example, the calculation unit can adjust the calculation time and automatically calculate the required amount of chemicals. This allows for the automatic calculation of the required amount of chemicals, preventing the wasteful use of chemicals.

[0080] The analysis unit can determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can use AI to determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can select a dataset to use and determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can select a learning algorithm and determine the appearance and reproductive capacity of bacteria based on pre-trained data. For example, the analysis unit can adjust the learning period and determine the appearance and reproductive capacity of bacteria based on pre-trained data. This improves the accuracy of the analysis by determining the appearance and reproductive capacity of bacteria based on pre-trained data.

[0081] The coating unit can automatically apply selected chemicals. For example, the coating unit can automatically apply chemicals selected using AI. For example, the coating unit can select the type of coating device and automatically apply the selected chemicals. For example, the coating unit can adjust the application amount and automatically apply the selected chemicals. For example, the coating unit can adjust the application range and automatically apply the selected chemicals. This allows for the automatic application of selected chemicals, thereby suppressing bacterial growth and extending the shelf life of food.

[0082] The camera unit can estimate the user's emotions and adjust the timing of the shot based on those emotions. For example, the camera unit can use AI to estimate the user's emotions and adjust the timing of the shot based on those emotions. For example, if the user is stressed, the AI ​​will automatically adjust the timing of the shot to reduce the user's burden. For example, if the user is relaxed, the AI ​​will flexibly adjust the timing of the shot to capture the optimal moment. For example, if the user is in a hurry, the AI ​​will quickly take the shot and instantly obtain the necessary information. This reduces the user's burden by adjusting the timing of the shot based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The imaging unit can simultaneously measure the surface temperature of food during imaging and adjust the imaging frequency based on temperature changes. For example, the imaging unit can use AI to simultaneously measure the surface temperature of food during imaging and adjust the imaging frequency based on temperature changes. For example, if the surface temperature of food changes rapidly, the imaging unit's AI will increase the imaging frequency to acquire detailed data. For example, if the temperature is stable, the imaging unit's AI will set a lower imaging frequency to avoid acquiring unnecessary data. For example, if the temperature change is gradual, the imaging unit's AI will take pictures at appropriate intervals to efficiently collect the necessary information. In this way, by adjusting the imaging frequency based on the surface temperature of food, detailed data can be efficiently acquired.

[0084] The imaging unit can make it easier to identify types of bacteria by using light of different wavelengths during imaging. For example, the imaging unit can use AI to make it easier to identify types of bacteria by using light of different wavelengths during imaging. For example, the imaging unit can use ultraviolet light to highlight specific bacteria, and AI can identify their type. For example, the imaging unit can use infrared light to understand the bacterial growth status in detail, and AI can analyze it. For example, the imaging unit can combine visible light with light of different wavelengths to identify types of bacteria from multiple angles. In this way, it is possible to make it easier to identify types of bacteria by using light of different wavelengths.

[0085] The camera unit can estimate the user's emotions and determine the priority of food to photograph based on those emotions. For example, the camera unit can use AI to estimate the user's emotions and determine the priority of food to photograph based on those emotions. For example, if the user is stressed, the AI ​​will prioritize photographing important food items. For example, if the user is relaxed, the AI ​​will photograph all food items equally. For example, if the user is in a hurry, the AI ​​will prioritize photographing food items that spoil quickly. This allows for prioritizing the photographing of important food items by determining the priority of food to photograph based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The camera unit can automatically adjust the camera position and angle according to the shape and size of the food during shooting. For example, the camera unit can use AI to automatically adjust the camera position and angle according to the shape and size of the food during shooting. For example, for large food items, the camera unit's AI will set the camera position high to capture the whole thing. For example, for small food items, the camera unit's AI will set the camera position low to capture details. For example, for irregularly shaped food items, the camera unit's AI will automatically adjust to the optimal angle to capture the whole thing. In this way, optimal shooting is possible by automatically adjusting the camera position and angle according to the shape and size of the food.

[0087] The photography unit can select the optimal shooting method while considering the packaging condition of the food. For example, the photography unit can use AI to select the optimal shooting method while considering the packaging condition of the food. For example, if the packaging is transparent, the photography unit will take the picture as is using AI. For example, if the packaging is opaque, the photography unit will open the packaging before taking the picture using AI. For example, if the food is partially packaged, the photography unit will take the picture at an angle that minimizes the impact of the packaging. By selecting the optimal shooting method while considering the packaging condition of the food, accurate data can be obtained.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can use AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can use AI to optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can use AI to automatically adjust the analysis algorithm based on past data. For example, the analysis unit can use AI to improve accuracy by referring to past analysis results. For example, the analysis unit can use AI to select the optimal analysis method by analyzing past data. This improves the accuracy of the analysis by optimizing the analysis algorithm by referring to past analysis data.

[0090] The analysis unit can apply different analysis methods to different types of food during analysis. For example, the analysis unit can use AI to apply different analysis methods to different types of food during analysis. For example, in the case of meat, the analysis unit can apply an analysis method in which the AI ​​focuses on specific bacteria. For example, in the case of vegetables, the analysis unit can apply an analysis method in which the AI ​​analyzes the growth status of different bacteria. For example, in the case of seafood, the analysis unit can apply an analysis method in which the AI ​​operates under specific temperature conditions. By applying different analysis methods to different types of food, the accuracy of the analysis is improved.

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can use AI to estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize important analyses. If the user is relaxed, the AI ​​will perform all analyses equally. If the user is in a hurry, the AI ​​will quickly perform the necessary analyses. This allows for prioritizing important analyses by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The analysis unit can improve the accuracy of its analysis by referring to food storage environment data during the analysis. For example, the analysis unit can use AI to improve the accuracy of its analysis by referring to food storage environment data during the analysis. For example, the AI ​​improves the accuracy of the analysis based on storage temperature data. For example, the AI ​​corrects the analysis results by referring to storage humidity data. For example, the AI ​​adjusts the analysis method by considering storage period data. This allows for more accurate analysis by improving the accuracy of the analysis by referring to food storage environment data.

[0093] The analysis unit can perform analysis while considering the food's manufacturing date and expiration date information. For example, the analysis unit can use AI to perform analysis while considering the food's manufacturing date and expiration date information. For example, the analysis unit can use AI to adjust the analysis method based on manufacturing date data. For example, the analysis unit can use AI to correct the analysis results by referring to expiration date information. For example, the analysis unit can use AI to select the optimal analysis method by considering both the manufacturing date and expiration date. As a result, the accuracy of the analysis is improved by performing analysis while considering the food's manufacturing date and expiration date information.

[0094] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, the identification unit can use AI to estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit provides a simple and highly visible display method. For example, if the user is relaxed, the identification unit provides a display method that includes detailed information. For example, if the user is in a hurry, the identification unit provides a display method that gets straight to the point. By adjusting the display method of the identification results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The identification unit can predict the growth rate of bacteria at a specific time and identify future reproduction conditions. The identification unit can, for example, use AI to predict the growth rate of bacteria at a specific time and identify future reproduction conditions. The identification unit can, for example, use AI to predict future reproduction conditions based on the current growth rate of bacteria. The identification unit can, for example, refer to past data and use AI to identify the growth pattern of bacteria. The identification unit can, for example, consider changes in temperature and humidity and use AI to predict future reproduction conditions of bacteria. By predicting the growth rate of bacteria and identifying future reproduction conditions, appropriate countermeasures can be taken.

[0096] The identification unit can improve the accuracy of identification by analyzing the genetic information of the fungus at the time of identification. The identification unit can improve the accuracy of identification by analyzing the genetic information of the fungus at the time of identification, for example, using AI. The identification unit can improve the accuracy of identification by using AI based on the genetic information of the fungus. The identification unit can improve the accuracy of identification by using AI to analyze the genetic information and use AI to identify the type of fungus in detail. The identification unit can improve the accuracy of identification by using AI to combine the genetic information and reproduction status, for example. As a result, by analyzing the genetic information of the fungus and improving the accuracy of identification, more accurate identification becomes possible.

[0097] The identification unit can estimate the user's emotions and determine specific priorities based on the estimated emotions. For example, the identification unit can use AI to estimate the user's emotions and determine specific priorities based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize important identifications. For example, if the user is relaxed, the AI ​​will perform all identifications equally. For example, if the user is in a hurry, the AI ​​will quickly perform the necessary identifications. This allows for prioritizing important identifications by determining specific priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The identification unit can improve the accuracy of identification by referring to food storage environment data at the time of identification. The identification unit can improve the accuracy of identification by referring to food storage environment data at the time of identification, for example, using AI. The identification unit can improve the accuracy of identification by using AI based on storage temperature data, for example. The identification unit can correct the identification results by referring to storage humidity data, for example. The identification unit can adjust the identification method by using AI considering storage period data, for example. As a result, more accurate identification becomes possible by improving the accuracy of identification by referring to food storage environment data.

[0099] The identification unit can perform identification by referring to the fungus's past reproduction data at the time of identification. The identification unit can, for example, use AI to refer to the fungus's past reproduction data at the time of identification. The identification unit can, for example, adjust the identification method based on past reproduction data using AI. The identification unit can, for example, refer to past data and the AI ​​can identify the type of fungus. The identification unit can, for example, combine reproduction data with the current situation to improve the accuracy of identification using AI. As a result, the accuracy of identification is improved by referring to the fungus's past reproduction data.

[0100] The selection unit can estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. For example, the selection unit can use AI to estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. For example, if the user is nervous, the selection unit provides a simple and highly visible display method. For example, if the user is relaxed, the selection unit provides a display method that includes detailed information. For example, if the user is in a hurry, the selection unit provides a display method that gets straight to the point. By adjusting the display method of the selection results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The selection unit can optimize its selection algorithm by referring to past selection data during the selection process. For example, the selection unit can use AI to optimize its selection algorithm by referring to past selection data during the selection process. For example, the selection unit can use AI to automatically adjust the selection algorithm based on past data. For example, the selection unit can use AI to improve accuracy by referring to past selection results. For example, the selection unit can analyze past data and use AI to select the optimal selection method. This improves selection accuracy by optimizing the selection algorithm by referring to past selection data.

[0102] The selection unit can select the optimal drug by considering bacterial resistance information during the selection process. For example, the selection unit can use AI to select the optimal drug by considering bacterial resistance information during the selection process. For example, the selection unit uses AI to select the optimal drug based on bacterial resistance information. For example, the selection unit analyzes resistance information and uses AI to select an effective drug against the bacteria. For example, the selection unit combines resistance information with the reproduction status and uses AI to select the optimal drug. This allows for the selection of an effective drug by considering bacterial resistance information.

[0103] The selection unit can estimate the user's emotions and determine selection priorities based on the estimated emotions. For example, the selection unit can use AI to estimate the user's emotions and determine selection priorities based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize important selections. For example, if the user is relaxed, the AI ​​will make all selections equally. For example, if the user is in a hurry, the AI ​​will quickly make the necessary selections. This allows for prioritizing important selections by determining selection priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The selection unit can improve the accuracy of its selection process by referring to food storage environment data during the selection process. For example, the selection unit can use AI to improve the accuracy of its selection process by referring to food storage environment data during the selection process. For example, the selection unit can use AI to improve selection accuracy based on storage temperature data. For example, the selection unit can use AI to correct selection results by referring to storage humidity data. For example, the selection unit can use AI to adjust the selection method by considering storage period data. This allows for more accurate selection by improving the accuracy of the selection process by referring to food storage environment data.

[0105] The selection unit can make selections while considering drug inventory information. The selection unit can, for example, use AI to make selections while considering drug inventory information. The selection unit can, for example, use AI to select the optimal drug based on inventory information. The selection unit can, for example, use AI to suggest an alternative drug if inventory is low. The selection unit can, for example, use AI to select the optimal drug by combining inventory information and bacterial resistance information. As a result, inventory management becomes more efficient by making selections while considering drug inventory information.

[0106] The application unit can estimate the user's emotions and adjust the application timing based on those emotions. For example, the application unit can use AI to estimate the user's emotions and adjust the application timing based on those emotions. For example, if the user is stressed, the AI ​​will automatically adjust the application timing to reduce the user's burden. For example, if the user is relaxed, the AI ​​will flexibly adjust the application timing to capture the optimal moment. For example, if the user is in a hurry, the AI ​​will quickly apply the product and instantly obtain the necessary information. This reduces the user's burden by adjusting the application timing based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The coating unit can monitor the surface condition of the food in real time during coating and adjust the optimal coating amount. For example, the coating unit can use AI to monitor the surface condition of the food in real time during coating and adjust the optimal coating amount. For example, if the surface condition of the food changes, the AI ​​will automatically adjust the coating amount. For example, if the surface is dry, the AI ​​will increase the coating amount. For example, if the surface is wet, the AI ​​will decrease the coating amount. In this way, by monitoring the surface condition of the food in real time and adjusting the optimal coating amount, it is possible to prevent the unnecessary use of chemicals.

[0108] The coating unit can maximize its effect by simultaneously applying different chemicals during application. For example, the coating unit can maximize its effect by simultaneously applying different chemicals using AI. For example, the coating unit can apply multiple chemicals simultaneously, with AI maximizing the effect. For example, the coating unit can apply different chemicals sequentially, with AI maximizing the effect. For example, the coating unit can mix and apply multiple chemicals, with AI maximizing the effect. In this way, the effect can be maximized by simultaneously applying different chemicals.

[0109] The application unit can estimate the user's emotions and determine the priority of application based on the estimated emotions. For example, the application unit can use AI to estimate the user's emotions and determine the priority of application based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize important applications. For example, if the user is relaxed, the AI ​​will apply all applications evenly. For example, if the user is in a hurry, the AI ​​will quickly apply the necessary applications. This allows for prioritizing important applications by determining the priority of application based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The coating unit can automatically adjust the position and angle of the coating device according to the shape and size of the food during coating. For example, the coating unit can use AI to automatically adjust the position and angle of the coating device according to the shape and size of the food during coating. For example, in the case of a large food item, the AI ​​will set the position of the coating device higher and coat the entire item. For example, in the case of a small food item, the AI ​​will set the position of the coating device lower and coat the details. For example, in the case of an irregularly shaped food item, the AI ​​will automatically adjust the optimal angle and coat the entire item. In this way, optimal coating is possible by automatically adjusting the position and angle of the coating device according to the shape and size of the food item.

[0111] The coating unit can apply different coating methods depending on the type of chemical during coating. For example, the coating unit can use AI to apply different coating methods depending on the type of chemical during coating. For example, for a specific chemical, the AI ​​can apply spray coating. For example, for a different chemical, the AI ​​can apply brush coating. For example, the AI ​​can select the optimal coating method according to the characteristics of the chemical. This enables effective coating by applying different coating methods for each type of chemical.

[0112] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, the monitoring unit can use AI to estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit provides a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit provides a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit provides a display method that gets straight to the point. By adjusting the display method of the monitoring results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can use AI to optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can use AI to automatically adjust the monitoring algorithm based on past data. For example, the monitoring unit can use AI to improve accuracy by referring to past monitoring results. For example, the monitoring unit can use AI to select the optimal monitoring method by analyzing past data. As a result, monitoring accuracy is improved by optimizing the monitoring algorithm by referring to past monitoring data.

[0114] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, the monitoring unit can use AI to estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize important monitoring. For example, if the user is relaxed, the AI ​​will perform all monitoring equally. For example, if the user is in a hurry, the AI ​​will quickly perform necessary monitoring. This allows for prioritizing important monitoring by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The monitoring unit can improve the accuracy of monitoring by referring to food storage environment data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to food storage environment data during monitoring, for example, using AI. The monitoring unit can improve monitoring accuracy using AI based on storage temperature data, for example. The monitoring unit can correct monitoring results using AI by referring to storage humidity data, for example. The monitoring unit can adjust the monitoring method using AI by considering storage period data, for example. As a result, more accurate monitoring becomes possible by improving monitoring accuracy by referring to food storage environment data.

[0116] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated user emotions. For example, the calculation unit can use AI to estimate the user's emotions and adjust the display method of the calculation results based on the estimated user emotions. For example, if the user is nervous, the calculation unit provides a simple and highly visible display method. For example, if the user is relaxed, the calculation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the calculation unit provides a display method that gets straight to the point. By adjusting the display method of the calculation results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The calculation unit can optimize the calculation algorithm by referring to past calculation data during calculation. For example, the calculation unit can use AI to optimize the calculation algorithm by referring to past calculation data during calculation. For example, the calculation unit can use AI to automatically adjust the calculation algorithm based on past data. For example, the calculation unit can use AI to improve accuracy by referring to past calculation results. For example, the calculation unit can analyze past data and use AI to select the optimal calculation method. This improves calculation accuracy by optimizing the calculation algorithm by referring to past calculation data.

[0118] The computation unit can estimate the user's emotions and determine the priority of calculations based on the estimated emotions. For example, the computation unit can use AI to estimate the user's emotions and determine the priority of calculations based on the estimated emotions. For example, if the user is stressed, the computation unit will have the AI ​​prioritize important calculations. For example, if the user is relaxed, the computation unit will have the AI ​​perform all calculations equally. For example, if the user is in a hurry, the computation unit will have the AI ​​perform the necessary calculations quickly. In this way, by determining the priority of calculations based on the user's emotions, important calculations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The calculation unit can improve the accuracy of calculations by referring to food storage environment data during calculations. For example, the calculation unit can improve the accuracy of calculations by referring to food storage environment data during calculations using AI. For example, the calculation unit can improve the accuracy of calculations using AI based on storage temperature data. For example, the calculation unit can correct the calculation results using AI by referring to storage humidity data. For example, the calculation unit can adjust the calculation method using AI by considering storage period data. In this way, by improving the accuracy of calculations by referring to food storage environment data, more accurate calculations become possible.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The food shelf-life extension system can also be equipped with a temperature control unit. This unit can monitor the food's storage temperature in real time and adjust it to an optimal temperature. For example, it can control the cooling device to keep the food's storage temperature low. It can also control the heating device to raise the food's storage temperature appropriately. Furthermore, the temperature control unit can monitor the food's surface temperature using a temperature sensor and cool or heat it according to temperature changes. This allows for optimal storage of the food, suppressing bacterial growth and extending its shelf life.

[0122] The food shelf-life extension system can also be equipped with a humidity control unit. This unit can monitor the humidity of the food storage environment in real time and adjust it to an optimal level. For example, it can control a humidifier to maintain a high humidity level, or control a dehumidifier to maintain a low humidity level. Furthermore, the unit can monitor the humidity level using a humidity sensor and adjust the humidity level accordingly. By maintaining an optimal humidity level in the food storage environment, bacterial growth can be suppressed, extending the food's shelf life.

[0123] The food shelf-life extension system may also include a light adjustment unit. This unit can monitor the light intensity of the food storage environment in real time and adjust it to the optimal level. For example, it can control the lighting device to maintain an appropriate light intensity in the food storage environment. It can also control the light-blocking device to keep the light intensity low. Furthermore, the light adjustment unit can monitor the light intensity of the food storage environment using a light sensor and adjust lighting or shading in response to changes in light intensity. This allows for optimal light intensity in the food storage environment, suppressing bacterial growth and extending the food's shelf life.

[0124] The food shelf-life extension system can also be equipped with a vibration detection unit. This unit can monitor vibrations in the food storage environment in real time and issue a warning if vibrations occur. For example, the vibration detection unit can use a vibration sensor to monitor vibrations in the food storage environment and issue a warning if the vibration exceeds a certain threshold. Furthermore, the vibration detection unit can identify the source of the vibration and take appropriate countermeasures. In addition, the vibration detection unit can record vibration data for later analysis. This allows for monitoring vibrations in the food storage environment and preventing quality deterioration due to vibrations, thereby extending the food's shelf life.

[0125] The food shelf-life extension system can also be equipped with a gas detection unit. The gas detection unit can monitor the gas concentration in the food storage environment in real time and issue a warning if an abnormal gas concentration is detected. For example, the gas detection unit can use a gas sensor to monitor the gas concentration in the food storage environment and issue a warning if the abnormal gas concentration exceeds a certain threshold. Furthermore, the gas detection unit can identify the type of gas and take appropriate countermeasures. In addition, the gas detection unit can record gas data for later analysis. This allows for monitoring the gas concentration in the food storage environment and preventing quality deterioration due to gases, thereby extending the food's shelf life.

[0126] The food shelf-life extension system can also estimate the user's emotions and adjust the storage environment based on those emotions. For example, if the user is stressed, the system automatically adjusts the temperature and humidity of the storage environment to reduce the user's burden. If the user is relaxed, the system maintains the optimal storage environment to preserve the quality of the food. Furthermore, if the user is in a hurry, the system quickly adjusts the storage environment and provides the necessary information immediately. In this way, by adjusting the storage environment based on the user's emotions, the system can reduce the user's burden and extend the shelf life of food.

[0127] The food shelf-life extension system can also estimate the user's emotions and adjust the way warnings are displayed based on those emotions. For example, if the user is stressed, the system provides a simple and highly visible warning. If the user is relaxed, the system provides a warning with more detailed information. Furthermore, if the user is in a hurry, the system provides a concise warning. By adjusting the way warnings are displayed based on the user's emotions, the system can provide warnings that are easy for the user to see and enable quick responses.

[0128] The food shelf-life extension system can also estimate the user's emotions and adjust the monitoring frequency of the storage environment based on those emotions. For example, if the user is stressed, the system increases the monitoring frequency to acquire more detailed data. If the user is relaxed, the system lowers the monitoring frequency to avoid acquiring unnecessary data. Furthermore, if the user is in a hurry, the system monitors quickly and provides the necessary information immediately. This allows for efficient data acquisition by adjusting the monitoring frequency of the storage environment based on the user's emotions.

[0129] The food shelf-life extension system can also estimate the user's emotions and adjust the drug selection method based on those emotions. For example, if the user is stressed, the system provides a simple and easy-to-understand selection method. If the user is relaxed, the system provides a selection method that includes detailed information. Furthermore, if the user is in a hurry, the system provides a selection method that gets straight to the point. By adjusting the drug selection method based on the user's emotions, it becomes possible to provide a selection method that is easy for the user to understand and enables a quick response.

[0130] The food shelf-life extension system can also estimate the user's emotions and adjust the application method of the medication based on those emotions. For example, if the user is stressed, the system automatically adjusts the application method to reduce the user's burden. If the user is relaxed, the system flexibly adjusts the application method to capture the optimal moment. Furthermore, if the user is in a hurry, the system applies the medication quickly and provides the necessary information immediately. In this way, by adjusting the medication application method based on the user's emotions, the system can reduce the user's burden and extend the shelf life of food.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The imaging unit photographs the surface of the food. The imaging unit can photograph the surface of the food using a high-precision camera and can also use light of different wavelengths to make it easier to identify types of bacteria. For example, ultraviolet light is used to highlight specific bacteria, and AI identifies their type. Step 2: The analysis unit analyzes the images captured by the imaging unit. The analysis unit uses AI to analyze the captured images and identify the type of fungus and its growth stage. Step 3: The identification unit identifies the type of bacteria and their growth status based on the images analyzed by the analysis unit. The identification unit uses AI to identify the type of bacteria and their growth status. Step 4: The selection unit selects the appropriate drug based on the bacteria identified by the identification unit. The selection unit uses AI to select the appropriate drug. Step 5: The application unit automatically applies the chemical selected by the selection unit. The application unit automatically applies the chemical selected using AI.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the imaging unit, analysis unit, identification unit, selection unit, coating unit, monitoring unit, and calculation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart device 14 to photograph the surface of the food. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the captured image. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of bacteria and their growth status. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate chemical. The coating unit is implemented by the control unit 46A of the smart device 14 and automatically applies the selected chemical. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors bacterial growth in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically calculates the required amount of chemical. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the imaging unit, analysis unit, identification unit, selection unit, coating unit, monitoring unit, and calculation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the smart glasses 214 to photograph the surface of the food. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the captured image. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of bacteria and their growth status. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate chemical. The coating unit is implemented by the control unit 46A of the smart glasses 214 and automatically applies the selected chemical. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors bacterial growth in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically calculates the required amount of chemical. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the imaging unit, analysis unit, identification unit, selection unit, coating unit, monitoring unit, and calculation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the headset terminal 314 to photograph the surface of the food. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the captured image. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of bacteria and their growth status. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate chemical. The coating unit is implemented by the control unit 46A of the headset terminal 314 and automatically applies the selected chemical. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors bacterial growth in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically calculates the required amount of chemical. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the imaging unit, analysis unit, identification unit, selection unit, coating unit, monitoring unit, and calculation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the imaging unit uses the camera 42 of the robot 414 to photograph the surface of the food. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the captured image. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of bacteria and their growth status. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate chemical. The coating unit is implemented by the control unit 46A of the robot 414 and automatically applies the selected chemical. The monitoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and monitors bacterial growth in real time. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically calculates the required amount of chemical. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The camera unit photographs the surface of the food, An analysis unit that analyzes the images captured by the aforementioned imaging unit, An identification unit identifies the type of bacteria and its growth status based on the image analyzed by the aforementioned analysis unit, A selection unit that selects an appropriate drug based on the bacteria identified by the aforementioned identification unit, The system includes a dispensing unit that automatically applies the chemical selected by the selection unit. A system characterized by the following features. (Note 2) It is equipped with a monitoring unit that monitors bacterial growth in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a calculation unit that automatically calculates the required amount of chemicals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The appearance and reproductive capacity of bacteria are determined based on pre-trained data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The coating portion is The selected chemical is applied automatically. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is During shooting, the surface temperature of the food is measured simultaneously, and the frequency of shooting is adjusted based on the temperature changes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is During imaging, different wavelengths of light are used to make it easier to identify the type of bacteria. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of food items to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is During shooting, the camera position and angle are automatically adjusted according to the shape and size of the food. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is When taking photos, the optimal shooting method is selected considering the packaging condition of the food. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, different analytical methods are applied to each type of food. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to food storage environment data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the manufacturing date and expiration date information of the food products will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, At specific points in time, predict the growth rate of the fungus and identify its future reproduction status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, the genetic information of the bacteria is analyzed to improve the accuracy of identification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, It estimates the user's emotions and determines specific priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, At specific times, food storage environment data is referenced to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, During identification, the bacteria's past reproduction data is referenced. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is The system estimates the user's emotions and adjusts how the selection results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is During the selection process, the selection algorithm is optimized by referring to past selection data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is When selecting a drug, consider bacterial resistance information to choose the most suitable chemical. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is It estimates the user's emotions and determines selection priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is During the selection process, we refer to food storage environment data to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is When making a selection, we will take into account the availability of the medications. The system described in Appendix 1, characterized by the features described herein. (Note 30) The coating portion is It estimates the user's emotions and adjusts the application timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The coating portion is During application, the surface condition of the food is monitored in real time, and the optimal amount of coating is adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The coating portion is The effect is maximized by applying different chemicals simultaneously during application. The system described in Appendix 1, characterized by the features described herein. (Note 33) The coating portion is It estimates the user's emotions and determines the application priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The coating portion is During application, the position and angle of the application device are automatically adjusted according to the shape and size of the food. The system described in Appendix 1, characterized by the features described herein. (Note 35) The coating portion is When applying the chemical, different application methods are applied depending on the type of chemical. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to past monitoring data. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to food storage environment data. The system described in Appendix 2, characterized by the features described herein. (Note 40) The calculation unit, It estimates the user's emotions and adjusts how the calculation results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The calculation unit, During calculations, the calculation algorithm is optimized by referring to past calculation data. The system described in Appendix 3, characterized by the features described herein. (Note 42) The calculation unit, The system estimates the user's emotions and determines the priority of calculations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The calculation unit, During calculations, the system references food storage environment data to improve calculation accuracy. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The camera unit photographs the surface of the food, An analysis unit that analyzes the images captured by the aforementioned imaging unit, An identification unit identifies the type of bacteria and its growth status based on the image analyzed by the aforementioned analysis unit, A selection unit that selects an appropriate drug based on the bacteria identified by the aforementioned identification unit, The system includes a dispensing unit that automatically applies the chemical selected by the selection unit. A system characterized by the following features.

2. It is equipped with a monitoring unit that monitors bacterial growth in real time. The system according to feature 1.

3. It includes a calculation unit that automatically calculates the required amount of chemicals. The system according to feature 1.

4. The aforementioned analysis unit, The appearance and reproductive capacity of bacteria are determined based on pre-trained data. The system according to feature 1.

5. The coating portion is The selected chemical is applied automatically. The system according to feature 1.

6. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

7. The aforementioned imaging unit is During shooting, the surface temperature of the food is measured simultaneously, and the frequency of shooting is adjusted based on the temperature changes. The system according to feature 1.

8. The aforementioned imaging unit is During imaging, different wavelengths of light are used to make it easier to identify the type of bacteria. The system according to feature 1.

9. The aforementioned imaging unit is It estimates the user's emotions and determines the priority of food items to photograph based on those estimated emotions. The system according to feature 1.

10. The aforementioned imaging unit is During shooting, the camera position and angle are automatically adjusted according to the shape and size of the food. The system according to feature 1.

Images