system

Abstract

The system according to this embodiment aims to detect mislabeling of origin and to perform quality control and fraud prevention. [Solution] The system according to the embodiment comprises an acquisition unit, an identification unit, a detection unit, a quality control unit, and a fraud prevention unit. The acquisition unit acquires images of agricultural products and marine products. The identification unit analyzes the images acquired by the acquisition unit and identifies characteristics specific to the place of origin. The detection unit detects misrepresentation of the place of origin based on the characteristics identified by the identification unit. The quality control unit performs quality control based on the results detected by the detection unit. The fraud prevention unit takes fraud prevention measures based on the quality controlled by the quality control unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to detect origin forgery, and there is room for improvement in quality control and fraud prevention.

[0005] The system according to the embodiment aims to detect origin forgery and perform quality control and fraud prevention.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an identification unit, a detection unit, a quality control unit, and a fraud prevention unit. The acquisition unit acquires images of agricultural products and marine products. The identification unit analyzes the images acquired by the acquisition unit and identifies characteristics specific to the place of origin. The detection unit detects fraudulent origin based on the characteristics identified by the identification unit. The quality control unit performs quality control based on the results detected by the detection unit. The fraud prevention unit implements fraud prevention measures based on the quality controlled by the quality control unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect mislabeling of origin and perform quality control and fraud prevention. [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 numbered 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 applicable 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 origin mislabeling detection system according to an embodiment of the present invention is a system that detects origin mislabeling of agricultural and marine products using a generating AI and image analysis technology. This origin mislabeling detection system acquires images of agricultural and marine products and inputs them into the generating AI. The generating AI uses image analysis technology to identify characteristics specific to the production area and detects origin mislabeling. Next, quality control is performed based on the detection results, and fraud prevention measures are taken. This realizes highly reliable quality control and fraud prevention. For example, the origin mislabeling detection system uses cameras and sensors to acquire images of agricultural and marine products. For example, images are taken with a camera at the time of harvesting agricultural products, and these images are input into the generating AI. The generating AI uses image analysis technology to identify characteristics specific to the production area. For example, agricultural products grown in a particular region have characteristics of color and shape specific to that region. The generating AI identifies these characteristics and detects origin mislabeling. Next, quality control is performed based on the results detected by the generating AI. For example, if mislabeling of origin is detected, the agricultural or marine product in question will be excluded, and only high-quality products will be offered to the market. Furthermore, based on the detection results, measures to prevent fraud will be implemented. For instance, strict monitoring will be conducted in areas or businesses where mislabeling of origin is frequent to prevent fraudulent activity. In this way, the mislabeling of origin detection system contributes to improving the quality of agricultural and marine products and increasing operational efficiency. For example, compared to conventional manual detection of mislabeling of origin, using generated AI allows for faster and more accurate detection. Furthermore, automation of quality control is expected to improve operational efficiency and reduce costs. Moreover, reliable quality control can earn consumer trust. This technology is expected to bring about significant advancements in the food distribution industry. For example, it can promote the sale of local agricultural products and revitalize the local economy. It can also improve transparency throughout the food industry by providing consumers with accurate origin information. Thus, the mislabeling of origin detection system can efficiently detect mislabeling of agricultural and marine products, achieving quality control and fraud prevention.

[0029] The origin mislabeling detection system according to this embodiment comprises an acquisition unit, an identification unit, a detection unit, a quality control unit, and a fraud prevention unit. The acquisition unit acquires images of agricultural products and marine products. The acquisition unit can acquire images of agricultural products and marine products using, for example, a camera or sensors. For example, the acquisition unit takes pictures with a camera when agricultural products are harvested and inputs those images into a generation AI. The identification unit uses the generation AI to analyze the images acquired by the acquisition unit and identify characteristics specific to the origin. The identification unit can use, for example, the generation AI to identify characteristics of color and shape specific to the origin. For example, the identification unit identifies that agricultural products grown in a particular region have characteristics of color and shape specific to that region. The detection unit detects origin mislabeling based on the characteristics identified by the identification unit. The detection unit can detect origin mislabeling based on the identified characteristics. For example, the detection unit detects origin mislabeling when the identified characteristics do not match the characteristics of a particular region. The quality control unit performs quality control based on the results detected by the detection unit. The Quality Control Department can, for example, provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude the agricultural or marine product in question and provide only high-quality products to the market. The Fraud Prevention Department implements fraud prevention measures based on the quality managed by the Quality Control Department. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where mislabeling of origin is frequent. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where mislabeling of origin is frequent. As a result, the mislabeling of origin detection system according to this embodiment can efficiently detect mislabeling of the origin of agricultural and marine products, achieving quality control and fraud prevention.

[0030] The acquisition unit acquires images of agricultural and marine products. For example, it can acquire images of agricultural and marine products using cameras and sensors. Specifically, it can capture detailed images of agricultural products using a high-resolution camera during harvest and input these images into the generating AI. The camera can capture fine details such as the color, shape, and surface texture of the agricultural products. For marine products, underwater cameras or underwater drones can be used to record the freshness and appearance in detail at the time of catch. This image data is uploaded to a cloud server in real time, making it accessible to the generating AI. Furthermore, the acquisition unit can also acquire environmental data at the time of harvest or catch by using temperature and humidity sensors. This allows for more accurate origin identification by combining image data and environmental data. For example, agricultural products grown in a specific region grow under specific temperature and humidity conditions, so this environmental data is also an important factor in origin identification. The acquisition unit centrally manages this data and can collaborate with other systems and departments as needed. For example, acquired data is stored on a cloud server, making it accessible to the identification and detection units. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The identification unit uses generative AI to analyze images acquired by the acquisition unit and identify characteristics specific to the production area. For example, the identification unit can use generative AI to identify color and shape characteristics specific to a particular production area. Specifically, the generative AI uses a deep learning algorithm to learn from a vast amount of image data and extract characteristics specific to each production area. For example, it can identify that crops grown in a particular region have color and shape characteristics specific to that region. To identify these characteristics with high accuracy, the generative AI analyzes image data using multiple neural network layers. Furthermore, the generative AI can utilize historical data and statistical information to learn long-term trends and patterns, enabling more accurate identification. For example, it can learn changes in the color and shape of crops under specific seasons and climatic conditions and use that information to identify the production area. In addition, the identification unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the identification unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The detection unit detects origin mislabeling based on features identified by the identification unit. For example, the detection unit can detect origin mislabeling based on identified features. Specifically, it detects origin mislabeling when the identified features do not match the features of a particular region. The detection unit compares the features identified by the generating AI with legitimate origin information registered in the database and issues an alert if they do not match. For example, if the color or shape of agricultural products grown in a particular region matches the features of other regions, it determines that there is a high possibility of origin mislabeling. Furthermore, the detection unit can analyze patterns and trends of origin mislabeling using past data and statistical information to perform more accurate detection. For example, it can learn patterns of origin mislabeling that frequently occur in specific businesses or regions and detect them early based on that. In addition, the detection unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The Quality Control Department performs quality control based on the results detected by the Detection Department. For example, the Quality Control Department can provide only high-quality products to the market based on the detected results. Specifically, if the Detection Department detects mislabeling of origin, it will exclude the agricultural or marine products in question and provide only high-quality products to the market. Based on the data from the Detection Department, the Quality Control Department selects products that meet quality standards and applies appropriate labeling and packaging. The Quality Control Department also conducts regular quality checks and takes measures to maintain product quality. For example, it conducts regular sample inspections and quality evaluations by third-party organizations to remove products that do not meet quality standards from the market. Furthermore, the Quality Control Department can collect consumer feedback and use it to improve quality control. For example, it can review the quality control process and implement improvement measures based on consumer complaints and opinions. In this way, the Quality Control Department can consistently provide high-quality products to the market and maintain consumer trust.

[0034] The Fraud Prevention Department implements fraud prevention measures based on the quality controlled by the Quality Control Department. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Specifically, the Fraud Prevention Department identifies regions and businesses with a high risk of origin misrepresentation based on past data and statistical information, and conducts focused monitoring. The Fraud Prevention Department also conducts regular on-site inspections and sample tests to detect signs of fraudulent activity early. Furthermore, the Fraud Prevention Department conducts educational and awareness-raising activities for businesses and producers to implement measures to reduce the risk of origin misrepresentation. For example, it holds seminars and workshops on the risks and impacts of origin misrepresentation to provide businesses and producers with accurate information. The Fraud Prevention Department can also establish a contact point for receiving information from consumers, which can be used to help detect fraudulent activities early. In this way, the Fraud Prevention Department can reduce the risk of origin misrepresentation and maintain consumer trust. Furthermore, the Fraud Prevention Department can improve the accuracy of fraud detection by utilizing the latest technologies and methods. For example, blockchain technology can be used to make the distribution history of agricultural and marine products transparent, making it easier to detect fraudulent activity. This allows the fraud prevention department to implement highly accurate fraud prevention measures based on the latest information and technology.

[0035] The acquisition unit can acquire images of agricultural products and marine products using cameras and sensors. For example, the acquisition unit can acquire images of agricultural products and marine products using cameras and sensors. For example, the acquisition unit can take pictures with a camera when harvesting agricultural products and input those images into a generation AI. The acquisition unit can acquire optimal images by adjusting the camera resolution and sensor sensitivity, for example. For example, the acquisition unit can acquire detailed images of agricultural products using a high-resolution camera. The acquisition unit can also acquire clear images even in dark environments by adjusting the sensor sensitivity. In this way, the acquisition unit can efficiently acquire images of agricultural products and marine products using cameras and sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data acquired by cameras and sensors into a generation AI and have the generation AI perform analysis of the image data.

[0036] The identification unit can use generative AI to identify color and shape characteristics specific to a particular region. For example, the identification unit can use generative AI to identify color and shape characteristics specific to a particular region. For example, the identification unit can identify that crops grown in a specific region have color and shape characteristics specific to that region. For example, the identification unit can use generative AI to analyze color patterns and shape characteristics and identify region-specific characteristics. For example, the identification unit can use generative AI to identify color patterns and shape characteristics of crops grown in a specific region. The identification unit can also use generative AI to identify characteristics such as patterns and textures of crops. For example, the identification unit can use generative AI to analyze patterns and textures of crops and identify region-specific characteristics. In this way, the identification unit can accurately identify region-specific characteristics by using generative AI. Some or all of the above-described processes in the identification unit may be performed using generative AI, or they may not be performed using generative AI. For example, the identification unit can input image data into the generative AI and have the generative AI perform the identification of region-specific characteristics.

[0037] The detection unit can detect origin misrepresentation based on identified features. The detection unit can, for example, detect origin misrepresentation based on identified features. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. The detection unit can, for example, detect origin misrepresentation if the identified features do not match the features of a specific region. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. The detection unit can also detect origin misrepresentation if the identified features do not match the features of a specific region. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. This allows the detection unit to accurately detect origin misrepresentation based on identified features. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the identified features into a generating AI and have the generating AI perform origin misrepresentation detection.

[0038] The Quality Control Department can provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. The Quality Control Department can provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. The Quality Control Department can also provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. In this way, the Quality Control Department can gain consumer trust by providing only high-quality products to the market. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the quality control department can input the detected results into a generating AI, which can then perform the task of selecting high-quality products.

[0039] The fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. The fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Furthermore, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Thus, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring. Some or all of the above-described processes in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input monitoring data into a generating AI and have the generating AI perform fraud detection.

[0040] The acquisition unit can select the optimal image acquisition method according to the growth stage of agricultural or marine products. For example, in the early growth stage, the acquisition unit uses a wide-angle lens to grasp the overall shape. In the mid-growth stage, for example, the acquisition unit uses a macro lens to capture color changes in detail. In the late growth stage, for example, the acquisition unit acquires high-resolution images to determine the timing of harvest. In this way, the acquisition unit can acquire more accurate images by selecting the optimal image acquisition method according to the growth stage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the selection of an image acquisition method according to the growth stage.

[0041] The acquisition unit can optimize image quality by considering weather and environmental conditions. For example, in rainy weather, the acquisition unit uses a waterproof camera to acquire images. For example, in sunny weather, the acquisition unit uses a polarizing filter to suppress light reflection. For example, in windy conditions, the acquisition unit uses a tripod to acquire images to prevent blurring. In this way, the acquisition unit can optimize image quality by considering weather and environmental conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the optimization of image acquisition considering weather and environmental conditions.

[0042] The acquisition unit can prioritize the acquisition of highly relevant images by considering the geographical location information of agricultural products and marine products. For example, the acquisition unit prioritizes the acquisition of images of agricultural products grown in a specific region. For example, the acquisition unit prioritizes the acquisition of images of marine products harvested in a geographically nearby location. For example, the acquisition unit acquires highly relevant images by considering the characteristics of each region. In this way, the acquisition unit can prioritize the acquisition of highly relevant images by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant images.

[0043] The acquisition unit can analyze social media activity related to agricultural and marine products and acquire relevant images. For example, the acquisition unit can prioritize acquiring images of agricultural products that are trending on social media. For example, the acquisition unit can prioritize acquiring images of marine products that are popular on social media. For example, the acquisition unit can analyze social media trends and acquire relevant images. In this way, the acquisition unit can efficiently acquire relevant images by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input social media activity data into a generating AI and have the generating AI acquire relevant images.

[0044] The identification unit can adjust the level of detail of identification based on the importance of agricultural and marine products. For example, the identification unit displays detailed identification results for important agricultural products. For example, the identification unit displays concise identification results for less important marine products. The identification unit adjusts the level of detail of the identification results according to the importance. In this way, the identification unit can appropriately provide the necessary information by adjusting the level of detail of identification based on importance. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input importance data of agricultural and marine products into a generating AI and have the generating AI perform the adjustment of the level of detail of identification.

[0045] The identification unit can apply different identification algorithms depending on the category of agricultural or marine products. For example, the identification unit can apply an identification algorithm that emphasizes color and shape characteristics to fruits. For example, the identification unit can apply an identification algorithm that emphasizes leaf shape and color characteristics to vegetables. For example, the identification unit can apply an identification algorithm that emphasizes fish species and size characteristics to marine products. In this way, the identification unit can improve its identification accuracy by applying an identification algorithm according to the category. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the identification unit can input category data of agricultural or marine products into a generative AI and have the generative AI execute the application of different identification algorithms.

[0046] The identification unit can determine the priority of identification based on the harvest time of agricultural and marine products. For example, the identification unit prioritizes the identification of agricultural products whose harvest time is approaching. For example, the identification unit postpones the identification of marine products whose harvest time has passed. For example, the identification unit adjusts the priority of identification according to the harvest time. In this way, the identification unit can prioritize the provision of important information by determining the priority of identification based on the harvest time. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input harvest time data of agricultural and marine products into a generating AI and have the generating AI perform the determination of the priority of identification.

[0047] The identification unit can adjust the order of identification based on the relevance of agricultural and marine products. For example, the identification unit prioritizes identifying highly relevant agricultural products. For example, the identification unit postpones identifying less relevant marine products. For example, the identification unit adjusts the order of identification according to relevance. In this way, the identification unit can efficiently provide identification results by adjusting the order of identification based on relevance. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input relevance data of agricultural and marine products into a generative AI and have the generative AI perform the adjustment of the order of identification.

[0048] The detection unit can improve detection accuracy by considering the interrelationships between crops and marine products. For example, the detection unit performs detection considering the interrelationships according to the growth stage of crops. For example, the detection unit performs detection based on interrelationships considering the ecosystem of marine products. For example, the detection unit analyzes the interrelationships between crops and marine products to improve detection accuracy. In this way, the detection unit can improve detection accuracy by considering the interrelationships between crops and marine products. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the interrelationships between crops and marine products into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0049] The detection unit can perform detection while considering the attribute information of producers of agricultural and marine products. The detection unit can improve detection accuracy by considering, for example, the producer's past performance. The detection unit can adjust detection criteria based on, for example, the producer's regional information. The detection unit can improve detection accuracy by analyzing, for example, the producer's attribute information. In this way, the detection unit can improve detection accuracy by considering the producer's attribute information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without using AI. For example, the detection unit can input producer attribute information into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0050] The detection unit can perform detection while considering the geographical distribution of agricultural products and marine products. For example, the detection unit prioritizes detecting agricultural products harvested in geographically close locations. For example, the detection unit efficiently detects marine products that are geographically dispersed. For example, the detection unit improves the accuracy of detection by considering geographical distribution. In this way, the detection unit can improve the accuracy of detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical distribution data of agricultural products and marine products into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0051] The detection unit can improve detection accuracy by referring to relevant literature on agricultural and marine products. For example, the detection unit can perform detection by understanding the characteristics of agricultural products in detail based on relevant literature. For example, the detection unit can perform detection by accurately understanding the characteristics of marine products by referring to relevant literature. For example, the detection unit can improve detection accuracy by analyzing relevant literature. In this way, the detection unit can improve detection accuracy by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input relevant literature data on agricultural and marine products into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0052] The quality control department can predict current quality by referring to past quality data. For example, the quality control department predicts current quality based on past quality data. For example, the quality control department analyzes past quality data to understand quality fluctuation patterns and predict current quality. For example, the quality control department predicts current quality by combining past quality data with current environmental conditions. In this way, the quality control department can accurately predict current quality by referring to past quality data. Some or all of the above processes in the quality control department may be performed using AI, for example, or without AI. For example, the quality control department can input past quality data into a generating AI and have the generating AI perform a prediction of current quality.

[0053] The Quality Control Department can apply different quality control methods to each category of agricultural and marine products. For example, the Quality Control Department might apply a quality control method that emphasizes changes in color and shape to fruits. For example, the Quality Control Department might apply a quality control method that emphasizes freshness and nutritional value to vegetables. For example, the Quality Control Department might apply a quality control method that emphasizes freshness and storage conditions to marine products. In this way, the Quality Control Department can improve the accuracy of quality control by applying different quality control methods to each category. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input category data of agricultural and marine products into a generating AI and have the generating AI execute the application of different quality control methods.

[0054] The Quality Control Department can analyze changes in quality based on the harvest time of agricultural and marine products. For example, the Quality Control Department can focus on managing the quality of agricultural products that are nearing their harvest time. For example, the Quality Control Department can analyze the quality of marine products that have passed their harvest time and apply appropriate management methods. For example, the Quality Control Department can adjust its quality control methods according to the harvest time. In this way, the Quality Control Department can improve the accuracy of quality control by analyzing changes in quality based on the harvest time. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not using AI. For example, the Quality Control Department can input harvest time data for agricultural and marine products into a generating AI and have the generating AI perform an analysis of changes in quality.

[0055] The Quality Control Department can analyze the quality of agricultural and marine products by referring to relevant market data. For example, the Quality Control Department can analyze the quality of agricultural products based on market data. For example, the Quality Control Department can evaluate the quality of marine products by referring to market data. For example, the Quality Control Department can analyze quality fluctuations by combining market data and quality data. This allows the Quality Control Department to accurately analyze quality fluctuations by referring to relevant market data. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input market data for agricultural and marine products into a generating AI and have the generating AI perform the quality analysis.

[0056] The fraud prevention unit can predict current fraud by referring to past fraud data. For example, the fraud prevention unit predicts current fraud based on past fraud data. For example, the fraud prevention unit analyzes past fraud data to understand fraud occurrence patterns and predict current fraud. For example, the fraud prevention unit combines past fraud data with the current situation to predict the occurrence of fraud. In this way, the fraud prevention unit can accurately predict current fraud by referring to past fraud data. Some or all of the above processes in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input past fraud data into a generating AI and have the generating AI perform a prediction of current fraud.

[0057] The fraud prevention unit can implement fraud prevention measures by considering the attribute information of producers of agricultural and marine products. For example, the fraud prevention unit may implement fraud prevention measures by considering the producer's past performance. For example, the fraud prevention unit may adjust fraud prevention measures based on the producer's regional information. For example, the fraud prevention unit may analyze the producer's attribute information and implement fraud prevention measures. As a result, the fraud prevention unit can implement more effective fraud prevention measures by considering the producer's attribute information. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit may input the producer's attribute information into a generating AI and have the generating AI execute the fraud prevention measures.

[0058] The fraud prevention unit can implement fraud prevention measures considering the geographical distribution of agricultural and marine products. For example, the fraud prevention unit can implement fraud prevention measures for agricultural products harvested in geographically close locations. For example, the fraud prevention unit can implement efficient fraud prevention measures for geographically dispersed marine products. For example, the fraud prevention unit can adjust fraud prevention measures considering geographical distribution. In this way, the fraud prevention unit can implement more effective fraud prevention measures by considering geographical distribution. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input geographical distribution data of agricultural and marine products into a generating AI and have the generating AI execute fraud prevention measures.

[0059] The fraud prevention unit can improve the accuracy of fraud prevention measures by referring to relevant literature on agricultural and marine products. For example, the fraud prevention unit can understand and implement detailed fraud prevention measures for agricultural products based on relevant literature. For example, the fraud prevention unit can accurately understand and implement fraud prevention measures for marine products by referring to relevant literature. For example, the fraud prevention unit can improve the accuracy of fraud prevention measures by analyzing relevant literature. In this way, the fraud prevention unit can improve the accuracy of fraud prevention measures by referring to relevant literature. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input relevant literature data on agricultural and marine products into a generating AI and have the generating AI perform the improvement of the accuracy of fraud prevention measures.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The image acquisition unit can acquire metadata simultaneously when acquiring images of agricultural products and marine products. For example, the acquisition unit can acquire metadata such as the date and time the image was taken, the location where it was taken, and the settings of the camera used. This allows the acquisition unit to perform more detailed analysis by utilizing the image metadata. For example, it can estimate the growth stage of agricultural products based on the date and time the image was taken and perform analysis according to that growth stage. It can also perform analysis that takes into account region-specific characteristics based on the information of the location where the image was taken. Furthermore, it can evaluate the image quality based on the camera settings and improve the accuracy of the analysis results. In this way, the acquisition unit can perform more accurate analysis by utilizing the image metadata.

[0062] The identification unit can analyze images of crops and marine products by combining multiple images. For example, it can combine and analyze images of the same crop taken from different angles. This allows the identification unit to grasp the overall picture of the crop and identify its characteristics more accurately. The identification unit can also combine and analyze images taken at different times. For example, it can combine and analyze images from the early, middle, and late stages of growth to identify changes in characteristics during the growth process. Furthermore, the identification unit can combine and analyze images taken under different environmental conditions. For example, it can combine and analyze images taken in sunny and rainy weather to identify changes in characteristics due to environmental conditions. In this way, the identification unit can identify characteristics more accurately by combining and analyzing multiple images.

[0063] The detection unit can improve detection accuracy by referring to past data when analyzing images of agricultural and marine products. For example, the detection unit can refer to data on previously detected cases of origin mislabeling and detect agricultural and marine products with similar characteristics. This allows the detection unit to improve the accuracy of origin mislabeling detection by utilizing past data. Furthermore, the detection unit can learn patterns of origin mislabeling based on past data and respond to new methods of origin mislabeling. In addition, the detection unit can identify regions and businesses with a high risk of origin mislabeling based on past data and monitor them intensively. This allows the detection unit to improve the accuracy of origin mislabeling detection by utilizing past data.

[0064] The Quality Control Department can utilize consumer feedback when evaluating the quality of agricultural and marine products. For example, the Quality Control Department can collect ratings and reviews from consumers and incorporate them into quality evaluations. This allows the Quality Control Department to conduct quality evaluations that incorporate consumer opinions. Furthermore, the Quality Control Department can revise its quality control standards based on consumer feedback. For example, if consumer ratings are low, it can analyze the causes and improve the quality control standards. In addition, the Quality Control Department can introduce new quality control methods based on consumer feedback. For example, it can focus on managing specific quality items in response to consumer requests. In this way, the Quality Control Department can implement more appropriate quality control by utilizing consumer feedback.

[0065] The fraud prevention department can utilize blockchain technology to prevent fraudulent activities involving agricultural and marine products. For example, the fraud prevention department can record information from the production to the distribution of agricultural and marine products on the blockchain to prevent tampering. This allows the fraud prevention department to ensure transparency of information and proactively prevent fraudulent activities. Furthermore, the fraud prevention department can detect signs of fraudulent activity based on the information recorded on the blockchain. For example, it can analyze distribution routes and transaction histories to identify transactions with a high risk of fraud. In addition, the fraud prevention department can use blockchain technology to track fraudulent activities. For example, if fraud occurs, it can track the transaction history and identify the businesses involved. In this way, the fraud prevention department can effectively prevent and track fraudulent activities by utilizing blockchain technology.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires images of agricultural products and marine products. The acquisition unit can acquire images of agricultural products and marine products using, for example, a camera or sensors. For example, the acquisition unit can take pictures with a camera when harvesting agricultural products and input those images into the generation AI. Step 2: The identification unit uses generational AI to analyze the images acquired by the acquisition unit and identify characteristics specific to the production area. For example, the identification unit can use generational AI to identify characteristics of color and shape specific to the production area. For example, the identification unit can identify that crops grown in a particular region have characteristics of color and shape specific to that region. Step 3: The detection unit detects origin misrepresentation based on the characteristics identified by the identification unit. For example, the detection unit can detect origin misrepresentation based on the identified characteristics. For example, the detection unit detects origin misrepresentation if the identified characteristics do not match the characteristics of a particular region. Step 4: The Quality Control Department performs quality control based on the results detected by the Detection Department. For example, the Quality Control Department can provide only high-quality products to the market based on the detected results. For instance, if the Quality Control Department detects mislabeling of origin, it can exclude the agricultural or marine products in question and provide only high-quality products to the market. Step 5: The fraud prevention department implements fraud prevention measures based on the quality controlled by the quality control department. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent.

[0068] (Example of form 2) The origin mislabeling detection system according to an embodiment of the present invention is a system that detects origin mislabeling of agricultural and marine products using a generating AI and image analysis technology. This origin mislabeling detection system acquires images of agricultural and marine products and inputs them into the generating AI. The generating AI uses image analysis technology to identify characteristics specific to the production area and detects origin mislabeling. Next, quality control is performed based on the detection results, and fraud prevention measures are taken. This realizes highly reliable quality control and fraud prevention. For example, the origin mislabeling detection system uses cameras and sensors to acquire images of agricultural and marine products. For example, images are taken with a camera at the time of harvesting agricultural products, and these images are input into the generating AI. The generating AI uses image analysis technology to identify characteristics specific to the production area. For example, agricultural products grown in a particular region have characteristics of color and shape specific to that region. The generating AI identifies these characteristics and detects origin mislabeling. Next, quality control is performed based on the results detected by the generating AI. For example, if mislabeling of origin is detected, the agricultural or marine product in question will be excluded, and only high-quality products will be offered to the market. Furthermore, based on the detection results, measures to prevent fraud will be implemented. For instance, strict monitoring will be conducted in areas or businesses where mislabeling of origin is frequent to prevent fraudulent activity. In this way, the mislabeling of origin detection system contributes to improving the quality of agricultural and marine products and increasing operational efficiency. For example, compared to conventional manual detection of mislabeling of origin, using generated AI allows for faster and more accurate detection. Furthermore, automation of quality control is expected to improve operational efficiency and reduce costs. Moreover, reliable quality control can earn consumer trust. This technology is expected to bring about significant advancements in the food distribution industry. For example, it can promote the sale of local agricultural products and revitalize the local economy. It can also improve transparency throughout the food industry by providing consumers with accurate origin information. Thus, the mislabeling of origin detection system can efficiently detect mislabeling of agricultural and marine products, achieving quality control and fraud prevention.

[0069] The origin mislabeling detection system according to this embodiment comprises an acquisition unit, an identification unit, a detection unit, a quality control unit, and a fraud prevention unit. The acquisition unit acquires images of agricultural products and marine products. The acquisition unit can acquire images of agricultural products and marine products using, for example, a camera or sensors. For example, the acquisition unit takes pictures with a camera when agricultural products are harvested and inputs those images into a generation AI. The identification unit uses the generation AI to analyze the images acquired by the acquisition unit and identify characteristics specific to the origin. The identification unit can use, for example, the generation AI to identify characteristics of color and shape specific to the origin. For example, the identification unit identifies that agricultural products grown in a particular region have characteristics of color and shape specific to that region. The detection unit detects origin mislabeling based on the characteristics identified by the identification unit. The detection unit can detect origin mislabeling based on the identified characteristics. For example, the detection unit detects origin mislabeling when the identified characteristics do not match the characteristics of a particular region. The quality control unit performs quality control based on the results detected by the detection unit. The Quality Control Department can, for example, provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude the agricultural or marine product in question and provide only high-quality products to the market. The Fraud Prevention Department implements fraud prevention measures based on the quality managed by the Quality Control Department. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where mislabeling of origin is frequent. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where mislabeling of origin is frequent. As a result, the mislabeling of origin detection system according to this embodiment can efficiently detect mislabeling of the origin of agricultural and marine products, achieving quality control and fraud prevention.

[0070] The acquisition unit acquires images of agricultural and marine products. For example, it can acquire images of agricultural and marine products using cameras and sensors. Specifically, it can capture detailed images of agricultural products using a high-resolution camera during harvest and input these images into the generating AI. The camera can capture fine details such as the color, shape, and surface texture of the agricultural products. For marine products, underwater cameras or underwater drones can be used to record the freshness and appearance in detail at the time of catch. This image data is uploaded to a cloud server in real time, making it accessible to the generating AI. Furthermore, the acquisition unit can also acquire environmental data at the time of harvest or catch by using temperature and humidity sensors. This allows for more accurate origin identification by combining image data and environmental data. For example, agricultural products grown in a specific region grow under specific temperature and humidity conditions, so this environmental data is also an important factor in origin identification. The acquisition unit centrally manages this data and can collaborate with other systems and departments as needed. For example, acquired data is stored on a cloud server, making it accessible to the identification and detection units. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The identification unit uses generative AI to analyze images acquired by the acquisition unit and identify characteristics specific to the production area. For example, the identification unit can use generative AI to identify color and shape characteristics specific to a particular production area. Specifically, the generative AI uses a deep learning algorithm to learn from a vast amount of image data and extract characteristics specific to each production area. For example, it can identify that crops grown in a particular region have color and shape characteristics specific to that region. To identify these characteristics with high accuracy, the generative AI analyzes image data using multiple neural network layers. Furthermore, the generative AI can utilize historical data and statistical information to learn long-term trends and patterns, enabling more accurate identification. For example, it can learn changes in the color and shape of crops under specific seasons and climatic conditions and use that information to identify the production area. In addition, the identification unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the identification unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The detection unit detects origin mislabeling based on features identified by the identification unit. For example, the detection unit can detect origin mislabeling based on identified features. Specifically, it detects origin mislabeling when the identified features do not match the features of a particular region. The detection unit compares the features identified by the generating AI with legitimate origin information registered in the database and issues an alert if they do not match. For example, if the color or shape of agricultural products grown in a particular region matches the features of other regions, it determines that there is a high possibility of origin mislabeling. Furthermore, the detection unit can analyze patterns and trends of origin mislabeling using past data and statistical information to perform more accurate detection. For example, it can learn patterns of origin mislabeling that frequently occur in specific businesses or regions and detect them early based on that. In addition, the detection unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0073] The Quality Control Department performs quality control based on the results detected by the Detection Department. For example, the Quality Control Department can provide only high-quality products to the market based on the detected results. Specifically, if the Detection Department detects mislabeling of origin, it will exclude the agricultural or marine products in question and provide only high-quality products to the market. Based on the data from the Detection Department, the Quality Control Department selects products that meet quality standards and applies appropriate labeling and packaging. The Quality Control Department also conducts regular quality checks and takes measures to maintain product quality. For example, it conducts regular sample inspections and quality evaluations by third-party organizations to remove products that do not meet quality standards from the market. Furthermore, the Quality Control Department can collect consumer feedback and use it to improve quality control. For example, it can review the quality control process and implement improvement measures based on consumer complaints and opinions. In this way, the Quality Control Department can consistently provide high-quality products to the market and maintain consumer trust.

[0074] The Fraud Prevention Department implements fraud prevention measures based on the quality controlled by the Quality Control Department. For example, the Fraud Prevention Department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Specifically, the Fraud Prevention Department identifies regions and businesses with a high risk of origin misrepresentation based on past data and statistical information, and conducts focused monitoring. The Fraud Prevention Department also conducts regular on-site inspections and sample tests to detect signs of fraudulent activity early. Furthermore, the Fraud Prevention Department conducts educational and awareness-raising activities for businesses and producers to implement measures to reduce the risk of origin misrepresentation. For example, it holds seminars and workshops on the risks and impacts of origin misrepresentation to provide businesses and producers with accurate information. The Fraud Prevention Department can also establish a contact point for receiving information from consumers, which can be used to help detect fraudulent activities early. In this way, the Fraud Prevention Department can reduce the risk of origin misrepresentation and maintain consumer trust. Furthermore, the Fraud Prevention Department can improve the accuracy of fraud detection by utilizing the latest technologies and methods. For example, blockchain technology can be used to make the distribution history of agricultural and marine products transparent, making it easier to detect fraudulent activity. This allows the fraud prevention department to implement highly accurate fraud prevention measures based on the latest information and technology.

[0075] The acquisition unit can acquire images of agricultural products and marine products using cameras and sensors. For example, the acquisition unit can acquire images of agricultural products and marine products using cameras and sensors. For example, the acquisition unit can take pictures with a camera when harvesting agricultural products and input those images into a generation AI. The acquisition unit can acquire optimal images by adjusting the camera resolution and sensor sensitivity, for example. For example, the acquisition unit can acquire detailed images of agricultural products using a high-resolution camera. The acquisition unit can also acquire clear images even in dark environments by adjusting the sensor sensitivity. In this way, the acquisition unit can efficiently acquire images of agricultural products and marine products using cameras and sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input image data acquired by cameras and sensors into a generation AI and have the generation AI perform analysis of the image data.

[0076] The identification unit can use generative AI to identify color and shape characteristics specific to a particular region. For example, the identification unit can use generative AI to identify color and shape characteristics specific to a particular region. For example, the identification unit can identify that crops grown in a specific region have color and shape characteristics specific to that region. For example, the identification unit can use generative AI to analyze color patterns and shape characteristics and identify region-specific characteristics. For example, the identification unit can use generative AI to identify color patterns and shape characteristics of crops grown in a specific region. The identification unit can also use generative AI to identify characteristics such as patterns and textures of crops. For example, the identification unit can use generative AI to analyze patterns and textures of crops and identify region-specific characteristics. In this way, the identification unit can accurately identify region-specific characteristics by using generative AI. Some or all of the above-described processes in the identification unit may be performed using generative AI, or they may not be performed using generative AI. For example, the identification unit can input image data into the generative AI and have the generative AI perform the identification of region-specific characteristics.

[0077] The detection unit can detect origin misrepresentation based on identified features. The detection unit can, for example, detect origin misrepresentation based on identified features. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. The detection unit can, for example, detect origin misrepresentation if the identified features do not match the features of a specific region. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. The detection unit can also detect origin misrepresentation if the identified features do not match the features of a specific region. For example, the detection unit can detect origin misrepresentation if the identified features do not match the features of a specific region. This allows the detection unit to accurately detect origin misrepresentation based on identified features. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the identified features into a generating AI and have the generating AI perform origin misrepresentation detection.

[0078] The Quality Control Department can provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. The Quality Control Department can provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. The Quality Control Department can also provide only high-quality products to the market based on the detected results. For example, if the Quality Control Department detects mislabeling of origin, it can exclude those agricultural or marine products and provide only high-quality products to the market. In this way, the Quality Control Department can gain consumer trust by providing only high-quality products to the market. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the quality control department can input the detected results into a generating AI, which can then perform the task of selecting high-quality products.

[0079] The fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. The fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Furthermore, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent. Thus, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring. Some or all of the above-described processes in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input monitoring data into a generating AI and have the generating AI perform fraud detection.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit reduces the frequency of image acquisition and increases it if the user is relaxed. For example, if the user is in a hurry, the acquisition unit acquires images quickly and acquires detailed images if the user is calm. For example, if the user is excited, the acquisition unit adjusts the timing of image acquisition to avoid missing important moments. In this way, the acquisition unit can acquire more appropriate images by adjusting the timing of image acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The acquisition unit can select the optimal image acquisition method according to the growth stage of agricultural or marine products. For example, in the early growth stage, the acquisition unit uses a wide-angle lens to grasp the overall shape. In the mid-growth stage, for example, the acquisition unit uses a macro lens to capture color changes in detail. In the late growth stage, for example, the acquisition unit acquires high-resolution images to determine the timing of harvest. In this way, the acquisition unit can acquire more accurate images by selecting the optimal image acquisition method according to the growth stage. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the selection of an image acquisition method according to the growth stage.

[0082] The acquisition unit can optimize image quality by considering weather and environmental conditions. For example, in rainy weather, the acquisition unit uses a waterproof camera to acquire images. For example, in sunny weather, the acquisition unit uses a polarizing filter to suppress light reflection. For example, in windy conditions, the acquisition unit uses a tripod to acquire images to prevent blurring. In this way, the acquisition unit can optimize image quality by considering weather and environmental conditions. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the optimization of image acquisition considering weather and environmental conditions.

[0083] The acquisition unit can estimate the user's emotions and determine the priority of images to acquire based on the estimated user emotions. For example, if the user is excited, the acquisition unit prioritizes acquiring images of important moments. If the user is relaxed, the acquisition unit acquires a wide range of images to grasp the overall situation. If the user is stressed, the acquisition unit acquires only the minimum necessary images. In this way, the acquisition unit can prioritize the acquisition of important images by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform the determination of image priority.

[0084] The acquisition unit can prioritize the acquisition of highly relevant images by considering the geographical location information of agricultural products and marine products. For example, the acquisition unit prioritizes the acquisition of images of agricultural products grown in a specific region. For example, the acquisition unit prioritizes the acquisition of images of marine products harvested in a geographically nearby location. For example, the acquisition unit acquires highly relevant images by considering the characteristics of each region. In this way, the acquisition unit can prioritize the acquisition of highly relevant images by considering geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant images.

[0085] The acquisition unit can analyze social media activity related to agricultural and marine products and acquire relevant images. For example, the acquisition unit can prioritize acquiring images of agricultural products that are trending on social media. For example, the acquisition unit can prioritize acquiring images of marine products that are popular on social media. For example, the acquisition unit can analyze social media trends and acquire relevant images. In this way, the acquisition unit can efficiently acquire relevant images by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input social media activity data into a generating AI and have the generating AI acquire relevant images.

[0086] The identification unit can estimate the user's emotions and adjust the way the identification is expressed based on the estimated emotions. For example, if the user is relaxed, the identification unit displays an identification result that includes a detailed explanation. For example, if the user is in a hurry, the identification unit displays a concise identification result. For example, if the user is excited, the identification unit displays a visually appealing identification result. In this way, the identification unit can provide more appropriate identification results by adjusting the way the identification is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI adjust the way the identification is expressed.

[0087] The identification unit can adjust the level of detail of identification based on the importance of agricultural and marine products. For example, the identification unit displays detailed identification results for important agricultural products. For example, the identification unit displays concise identification results for less important marine products. The identification unit adjusts the level of detail of the identification results according to the importance. In this way, the identification unit can appropriately provide the necessary information by adjusting the level of detail of identification based on importance. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input importance data of agricultural and marine products into a generating AI and have the generating AI perform the adjustment of the level of detail of identification.

[0088] The identification unit can apply different identification algorithms depending on the category of agricultural or marine products. For example, the identification unit can apply an identification algorithm that emphasizes color and shape characteristics to fruits. For example, the identification unit can apply an identification algorithm that emphasizes leaf shape and color characteristics to vegetables. For example, the identification unit can apply an identification algorithm that emphasizes fish species and size characteristics to marine products. In this way, the identification unit can improve its identification accuracy by applying an identification algorithm according to the category. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the identification unit can input category data of agricultural or marine products into a generative AI and have the generative AI execute the application of different identification algorithms.

[0089] The identification unit can estimate the user's emotions and adjust the length of the identification based on the estimated emotions. For example, if the user is in a hurry, the identification unit displays a short, concise identification result. If the user is relaxed, the identification unit displays a longer identification result with a detailed explanation. If the user is excited, the identification unit displays an identification result with a visually stimulating effect. In this way, the identification unit can provide more appropriate identification results by adjusting the length of the identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using a generative AI, or not using a generative AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI adjust the length of the identification.

[0090] The identification unit can determine the priority of identification based on the harvest time of agricultural and marine products. For example, the identification unit prioritizes the identification of agricultural products whose harvest time is approaching. For example, the identification unit postpones the identification of marine products whose harvest time has passed. For example, the identification unit adjusts the priority of identification according to the harvest time. In this way, the identification unit can prioritize the provision of important information by determining the priority of identification based on the harvest time. Some or all of the above processing in the identification unit may be performed using, for example, a generating AI, or without a generating AI. For example, the identification unit can input harvest time data of agricultural and marine products into a generating AI and have the generating AI perform the determination of the priority of identification.

[0091] The identification unit can adjust the order of identification based on the relevance of agricultural and marine products. For example, the identification unit prioritizes identifying highly relevant agricultural products. For example, the identification unit postpones identifying less relevant marine products. For example, the identification unit adjusts the order of identification according to relevance. In this way, the identification unit can efficiently provide identification results by adjusting the order of identification based on relevance. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can input relevance data of agricultural and marine products into a generative AI and have the generative AI perform the adjustment of the order of identification.

[0092] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, if the user is relaxed, the detection unit will use strict criteria for detection. If the user is in a hurry, the detection unit will use lenient criteria for detection. If the user is excited, the detection unit will highlight important points for detection. In this way, the detection unit can provide more appropriate detection results by adjusting the detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection criteria.

[0093] The detection unit can improve detection accuracy by considering the interrelationships between crops and marine products. For example, the detection unit performs detection considering the interrelationships according to the growth stage of crops. For example, the detection unit performs detection based on interrelationships considering the ecosystem of marine products. For example, the detection unit analyzes the interrelationships between crops and marine products to improve detection accuracy. In this way, the detection unit can improve detection accuracy by considering the interrelationships between crops and marine products. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data on the interrelationships between crops and marine products into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0094] The detection unit can perform detection while considering the attribute information of producers of agricultural and marine products. The detection unit can improve detection accuracy by considering, for example, the producer's past performance. The detection unit can adjust detection criteria based on, for example, the producer's regional information. The detection unit can improve detection accuracy by analyzing, for example, the producer's attribute information. In this way, the detection unit can improve detection accuracy by considering the producer's attribute information. Some or all of the above processing in the detection unit may be performed using, for example, AI, or without using AI. For example, the detection unit can input producer attribute information into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0095] The detection unit can estimate the user's emotions and adjust the order in which the detection results are displayed based on the estimated emotions. For example, if the user is in a hurry, the detection unit will prioritize displaying important results. For example, if the user is relaxed, the detection unit will display detailed results in a sequential manner. For example, if the user is excited, the detection unit will prioritize displaying visually appealing results. In this way, the detection unit can prioritize displaying important results by adjusting the order in which the detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the detection results.

[0096] The detection unit can perform detection while considering the geographical distribution of agricultural products and marine products. For example, the detection unit prioritizes detecting agricultural products harvested in geographically close locations. For example, the detection unit efficiently detects marine products that are geographically dispersed. For example, the detection unit improves the accuracy of detection by considering geographical distribution. In this way, the detection unit can improve the accuracy of detection by considering geographical distribution. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical distribution data of agricultural products and marine products into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0097] The detection unit can improve detection accuracy by referring to relevant literature on agricultural and marine products. For example, the detection unit can perform detection by understanding the characteristics of agricultural products in detail based on relevant literature. For example, the detection unit can perform detection by accurately understanding the characteristics of marine products by referring to relevant literature. For example, the detection unit can improve detection accuracy by analyzing relevant literature. In this way, the detection unit can improve detection accuracy by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input relevant literature data on agricultural and marine products into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0098] The Quality Control Department can estimate the user's emotions and adjust its quality control methods based on those emotions. For example, if the user is relaxed, the Quality Control Department can perform detailed quality control. If the user is in a hurry, the Quality Control Department can perform simplified quality control. If the user is excited, the Quality Control Department can perform quality control by highlighting key points. In this way, the Quality Control Department can perform more appropriate quality control by adjusting its methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Quality Control Department may be performed using AI or not. For example, the Quality Control Department can input user emotion data into a generative AI and have the generative AI adjust its quality control methods.

[0099] The quality control department can predict current quality by referring to past quality data. For example, the quality control department predicts current quality based on past quality data. For example, the quality control department analyzes past quality data to understand quality fluctuation patterns and predict current quality. For example, the quality control department predicts current quality by combining past quality data with current environmental conditions. In this way, the quality control department can accurately predict current quality by referring to past quality data. Some or all of the above processes in the quality control department may be performed using AI, for example, or without AI. For example, the quality control department can input past quality data into a generating AI and have the generating AI perform a prediction of current quality.

[0100] The Quality Control Department can apply different quality control methods to each category of agricultural and marine products. For example, the Quality Control Department might apply a quality control method that emphasizes changes in color and shape to fruits. For example, the Quality Control Department might apply a quality control method that emphasizes freshness and nutritional value to vegetables. For example, the Quality Control Department might apply a quality control method that emphasizes freshness and storage conditions to marine products. In this way, the Quality Control Department can improve the accuracy of quality control by applying different quality control methods to each category. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input category data of agricultural and marine products into a generating AI and have the generating AI execute the application of different quality control methods.

[0101] The Quality Control Department can estimate the user's emotions and adjust the importance of quality control based on the estimated emotions. For example, if the user is relaxed, the Quality Control Department can perform detailed quality control. If the user is in a hurry, the Quality Control Department can perform simple quality control. If the user is excited, the Quality Control Department can perform quality control by highlighting key points. In this way, the Quality Control Department can perform more appropriate quality control by adjusting the importance of quality control according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Quality Control Department may be performed using AI or not using AI. For example, the Quality Control Department can input user emotion data into a generative AI and have the generative AI adjust the importance of quality control.

[0102] The Quality Control Department can analyze changes in quality based on the harvest time of agricultural and marine products. For example, the Quality Control Department can focus on managing the quality of agricultural products that are nearing their harvest time. For example, the Quality Control Department can analyze the quality of marine products that have passed their harvest time and apply appropriate management methods. For example, the Quality Control Department can adjust its quality control methods according to the harvest time. In this way, the Quality Control Department can improve the accuracy of quality control by analyzing changes in quality based on the harvest time. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or not using AI. For example, the Quality Control Department can input harvest time data for agricultural and marine products into a generating AI and have the generating AI perform an analysis of changes in quality.

[0103] The Quality Control Department can analyze the quality of agricultural and marine products by referring to relevant market data. For example, the Quality Control Department can analyze the quality of agricultural products based on market data. For example, the Quality Control Department can evaluate the quality of marine products by referring to market data. For example, the Quality Control Department can analyze quality fluctuations by combining market data and quality data. This allows the Quality Control Department to accurately analyze quality fluctuations by referring to relevant market data. Some or all of the above processes in the Quality Control Department may be performed using AI, for example, or without AI. For example, the Quality Control Department can input market data for agricultural and marine products into a generating AI and have the generating AI perform the quality analysis.

[0104] The fraud prevention unit can estimate the user's emotions and determine the priority of fraud prevention measures based on the estimated emotions. For example, if the user is relaxed, the fraud prevention unit will prioritize detailed fraud prevention measures. If the user is in a hurry, the fraud prevention unit will prioritize simple fraud prevention measures. If the user is excited, the fraud prevention unit will prioritize important fraud prevention measures. This allows the fraud prevention unit to implement more effective fraud prevention measures by determining the priority of fraud prevention measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fraud prevention unit may be performed using AI or not using AI. For example, the fraud prevention unit can input user emotion data into a generative AI and have the generative AI determine the priority of fraud prevention measures.

[0105] The fraud prevention unit can predict current fraud by referring to past fraud data. For example, the fraud prevention unit predicts current fraud based on past fraud data. For example, the fraud prevention unit analyzes past fraud data to understand fraud occurrence patterns and predict current fraud. For example, the fraud prevention unit combines past fraud data with the current situation to predict the occurrence of fraud. In this way, the fraud prevention unit can accurately predict current fraud by referring to past fraud data. Some or all of the above processes in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input past fraud data into a generating AI and have the generating AI perform a prediction of current fraud.

[0106] The fraud prevention unit can implement fraud prevention measures by considering the attribute information of producers of agricultural and marine products. For example, the fraud prevention unit may implement fraud prevention measures by considering the producer's past performance. For example, the fraud prevention unit may adjust fraud prevention measures based on the producer's regional information. For example, the fraud prevention unit may analyze the producer's attribute information and implement fraud prevention measures. As a result, the fraud prevention unit can implement more effective fraud prevention measures by considering the producer's attribute information. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit may input the producer's attribute information into a generating AI and have the generating AI execute the fraud prevention measures.

[0107] The fraud prevention unit can estimate the user's emotions and adjust the display method of fraud prevention measures based on the estimated user emotions. For example, if the user is relaxed, the fraud prevention unit will display detailed fraud prevention measures. For example, if the user is in a hurry, the fraud prevention unit will display simple fraud prevention measures. For example, if the user is excited, the fraud prevention unit will highlight important fraud prevention measures. In this way, the fraud prevention unit can provide more effective fraud prevention measures by adjusting the display method of fraud prevention measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the fraud prevention unit may be performed using AI or not using AI. For example, the fraud prevention unit can input user emotion data into a generative AI and have the generative AI adjust the display method of fraud prevention measures.

[0108] The fraud prevention unit can implement fraud prevention measures considering the geographical distribution of agricultural and marine products. For example, the fraud prevention unit can implement fraud prevention measures for agricultural products harvested in geographically close locations. For example, the fraud prevention unit can implement efficient fraud prevention measures for geographically dispersed marine products. For example, the fraud prevention unit can adjust fraud prevention measures considering geographical distribution. In this way, the fraud prevention unit can implement more effective fraud prevention measures by considering geographical distribution. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input geographical distribution data of agricultural and marine products into a generating AI and have the generating AI execute fraud prevention measures.

[0109] The fraud prevention unit can improve the accuracy of fraud prevention measures by referring to relevant literature on agricultural and marine products. For example, the fraud prevention unit can understand and implement detailed fraud prevention measures for agricultural products based on relevant literature. For example, the fraud prevention unit can accurately understand and implement fraud prevention measures for marine products by referring to relevant literature. For example, the fraud prevention unit can improve the accuracy of fraud prevention measures by analyzing relevant literature. In this way, the fraud prevention unit can improve the accuracy of fraud prevention measures by referring to relevant literature. Some or all of the above processing in the fraud prevention unit may be performed using AI, for example, or without AI. For example, the fraud prevention unit can input relevant literature data on agricultural and marine products into a generating AI and have the generating AI perform the improvement of the accuracy of fraud prevention measures.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The image acquisition unit can acquire metadata simultaneously when acquiring images of agricultural products and marine products. For example, the acquisition unit can acquire metadata such as the date and time the image was taken, the location where it was taken, and the settings of the camera used. This allows the acquisition unit to perform more detailed analysis by utilizing the image metadata. For example, it can estimate the growth stage of agricultural products based on the date and time the image was taken and perform analysis according to that growth stage. It can also perform analysis that takes into account region-specific characteristics based on the information of the location where the image was taken. Furthermore, it can evaluate the image quality based on the camera settings and improve the accuracy of the analysis results. In this way, the acquisition unit can perform more accurate analysis by utilizing the image metadata.

[0112] The identification unit can analyze images of crops and marine products by combining multiple images. For example, it can combine and analyze images of the same crop taken from different angles. This allows the identification unit to grasp the overall picture of the crop and identify its characteristics more accurately. The identification unit can also combine and analyze images taken at different times. For example, it can combine and analyze images from the early, middle, and late stages of growth to identify changes in characteristics during the growth process. Furthermore, the identification unit can combine and analyze images taken under different environmental conditions. For example, it can combine and analyze images taken in sunny and rainy weather to identify changes in characteristics due to environmental conditions. In this way, the identification unit can identify characteristics more accurately by combining and analyzing multiple images.

[0113] The detection unit can improve detection accuracy by referring to past data when analyzing images of agricultural and marine products. For example, the detection unit can refer to data on previously detected cases of origin mislabeling and detect agricultural and marine products with similar characteristics. This allows the detection unit to improve the accuracy of origin mislabeling detection by utilizing past data. Furthermore, the detection unit can learn patterns of origin mislabeling based on past data and respond to new methods of origin mislabeling. In addition, the detection unit can identify regions and businesses with a high risk of origin mislabeling based on past data and monitor them intensively. This allows the detection unit to improve the accuracy of origin mislabeling detection by utilizing past data.

[0114] The Quality Control Department can utilize consumer feedback when evaluating the quality of agricultural and marine products. For example, the Quality Control Department can collect ratings and reviews from consumers and incorporate them into quality evaluations. This allows the Quality Control Department to conduct quality evaluations that incorporate consumer opinions. Furthermore, the Quality Control Department can revise its quality control standards based on consumer feedback. For example, if consumer ratings are low, it can analyze the causes and improve the quality control standards. In addition, the Quality Control Department can introduce new quality control methods based on consumer feedback. For example, it can focus on managing specific quality items in response to consumer requests. In this way, the Quality Control Department can implement more appropriate quality control by utilizing consumer feedback.

[0115] The fraud prevention department can utilize blockchain technology to prevent fraudulent activities involving agricultural and marine products. For example, the fraud prevention department can record information from the production to the distribution of agricultural and marine products on the blockchain to prevent tampering. This allows the fraud prevention department to ensure transparency of information and proactively prevent fraudulent activities. Furthermore, the fraud prevention department can detect signs of fraudulent activity based on the information recorded on the blockchain. For example, it can analyze distribution routes and transaction histories to identify transactions with a high risk of fraud. In addition, the fraud prevention department can use blockchain technology to track fraudulent activities. For example, if fraud occurs, it can track the transaction history and identify the businesses involved. In this way, the fraud prevention department can effectively prevent and track fraudulent activities by utilizing blockchain technology.

[0116] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit reduces the frequency of image acquisition and increases it if the user is relaxed. For example, if the user is in a hurry, the acquisition unit acquires images quickly and acquires detailed images if the user is calm. For example, if the user is excited, the acquisition unit adjusts the timing of image acquisition to avoid missing important moments. In this way, the acquisition unit can acquire more appropriate images by adjusting the timing of image acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The identification unit can estimate the user's emotions and adjust the way the identification is expressed based on the estimated emotions. For example, if the user is relaxed, the identification unit displays an identification result that includes a detailed explanation. For example, if the user is in a hurry, the identification unit displays a concise identification result. For example, if the user is excited, the identification unit displays a visually appealing identification result. In this way, the identification unit can provide more appropriate identification results by adjusting the way the identification is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI adjust the way the identification is expressed.

[0118] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, if the user is relaxed, the detection unit will use strict criteria for detection. If the user is in a hurry, the detection unit will use lenient criteria for detection. If the user is excited, the detection unit will highlight important points for detection. In this way, the detection unit can provide more appropriate detection results by adjusting the detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or not using AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection criteria.

[0119] The Quality Control Department can estimate the user's emotions and adjust its quality control methods based on those emotions. For example, if the user is relaxed, the Quality Control Department can perform detailed quality control. If the user is in a hurry, the Quality Control Department can perform simplified quality control. If the user is excited, the Quality Control Department can perform quality control by highlighting key points. In this way, the Quality Control Department can perform more appropriate quality control by adjusting its methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Quality Control Department may be performed using AI or not. For example, the Quality Control Department can input user emotion data into a generative AI and have the generative AI adjust its quality control methods.

[0120] The fraud prevention unit can estimate the user's emotions and determine the priority of fraud prevention measures based on the estimated emotions. For example, if the user is relaxed, the fraud prevention unit will prioritize detailed fraud prevention measures. If the user is in a hurry, the fraud prevention unit will prioritize simple fraud prevention measures. If the user is excited, the fraud prevention unit will prioritize important fraud prevention measures. This allows the fraud prevention unit to implement more effective fraud prevention measures by determining the priority of fraud prevention measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fraud prevention unit may be performed using AI or not using AI. For example, the fraud prevention unit can input user emotion data into a generative AI and have the generative AI determine the priority of fraud prevention measures.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The acquisition unit acquires images of agricultural products and marine products. The acquisition unit can acquire images of agricultural products and marine products using, for example, a camera or sensors. For example, the acquisition unit can take pictures with a camera when harvesting agricultural products and input those images into the generation AI. Step 2: The identification unit uses generational AI to analyze the images acquired by the acquisition unit and identify characteristics specific to the production area. For example, the identification unit can use generational AI to identify characteristics of color and shape specific to the production area. For example, the identification unit can identify that crops grown in a particular region have characteristics of color and shape specific to that region. Step 3: The detection unit detects origin misrepresentation based on the characteristics identified by the identification unit. For example, the detection unit can detect origin misrepresentation based on the identified characteristics. For example, the detection unit detects origin misrepresentation if the identified characteristics do not match the characteristics of a particular region. Step 4: The Quality Control Department performs quality control based on the results detected by the Detection Department. For example, the Quality Control Department can provide only high-quality products to the market based on the detected results. For instance, if the Quality Control Department detects mislabeling of origin, it can exclude the agricultural or marine products in question and provide only high-quality products to the market. Step 5: The fraud prevention department implements fraud prevention measures based on the quality controlled by the quality control department. For example, the fraud prevention department can prevent fraudulent activities by conducting strict monitoring of regions and businesses where origin misrepresentation is frequent.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the acquisition unit, identification unit, detection unit, quality control unit, and fraud prevention unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires images of agricultural products or marine products using the camera 42 and sensors of the smart device 14. The identification unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the acquired images using generating AI and identifies characteristics specific to the place of origin. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, which detects fraudulent origin based on the identified characteristics. The quality control unit is implemented in the specific processing unit 290 of the data processing unit 12, which performs quality control based on the detection results. The fraud prevention unit is implemented in the specific processing unit 290 of the data processing unit 12, which takes fraud prevention measures based on the quality control results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the acquisition unit, identification unit, detection unit, quality control unit, and fraud prevention unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images of agricultural products or marine products using the camera 42 and sensors of the smart glasses 214. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images using generating AI and identifies characteristics specific to the place of origin. The detection unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which detects fraudulent origin based on the identified characteristics. The quality control unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which performs quality control based on the detection results. The fraud prevention unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which takes fraud prevention measures based on the quality control results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the acquisition unit, identification unit, detection unit, quality control unit, and fraud prevention unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images of agricultural products and marine products using the camera 42 and sensors of the headset terminal 314. The identification unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which analyzes the acquired images using generating AI and identifies characteristics specific to the place of origin. The detection unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which detects fraudulent origin based on the identified characteristics. The quality control unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which performs quality control based on the detection results. The fraud prevention unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which takes fraud prevention measures based on the quality control results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the acquisition unit, identification unit, detection unit, quality control unit, and fraud prevention unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images of agricultural products and marine products using the camera 42 and sensors of the robot 414. The identification unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which analyzes the acquired images using generating AI and identifies characteristics specific to the place of origin. The detection unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which detects fraudulent origin based on the identified characteristics. The quality control unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which performs quality control based on the detection results. The fraud prevention unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which takes fraud prevention measures based on the quality control results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) An acquisition unit that acquires images of agricultural products and marine products, An identification unit analyzes the image acquired by the acquisition unit and identifies characteristics specific to the production area, A detection unit that detects mislabeling of origin based on the characteristics identified by the aforementioned identification unit, A quality control unit that performs quality control based on the results detected by the aforementioned detection unit, The system includes a fraud prevention unit that implements fraud prevention measures based on the quality managed by the aforementioned quality control unit. A system characterized by the following features. (Note 2) The acquisition unit is, Cameras and sensors are used to acquire images of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned identification unit is Using generative AI, we identify the unique color and shape characteristics specific to a particular region. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit, Detecting origin mislabeling based on identified characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Quality Control Department Based on the detected results, only high-quality products will be offered to the market. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned fraud prevention unit is We will implement strict monitoring of regions and businesses where mislabeling of origin is frequent, and prevent fraudulent activities from occurring. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Select the optimal image acquisition method according to the growth stage of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Optimize image quality by considering weather and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of images to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Prioritize the acquisition of highly relevant images, taking into account the geographical location of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, Analyze social media activity related to agricultural and marine products and retrieve relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned identification unit is It estimates the user's emotions and adjusts the way identification is represented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned identification unit is Adjust the level of detail in identification based on the importance of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned identification unit is Apply different identification algorithms depending on the category of agricultural or marine products. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned identification unit is It estimates the user's sentiment and adjusts the length of the identification based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned identification unit is Prioritize identification based on the harvest time of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned identification unit is The order of identification is adjusted based on the relationship between agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit, Improving detection accuracy by considering the interrelationships between agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit, Detection is performed by taking into account the attribute information of producers of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit, It estimates the user's emotions and adjusts the order in which detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit, Detection is performed considering the geographical distribution of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit, Improve detection accuracy by referring to relevant literature on agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Quality Control Department We estimate user sentiment and adjust quality control methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Quality Control Department Predicting current quality by referring to past quality data The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Quality Control Department Different quality control methods are applied to each category of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Quality Control Department We estimate user sentiment and adjust the importance of quality control based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Quality Control Department Analyze changes in quality based on the harvest time of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Quality Control Department Analyze quality by referring to relevant market data for agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned fraud prevention unit is The system estimates user sentiment and prioritizes fraud prevention measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned fraud prevention unit is Predicting current fraud by referencing past fraud data The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned fraud prevention unit is Implement fraud prevention measures by taking into account the attribute information of producers of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned fraud prevention unit is The system estimates user sentiment and adjusts how fraud prevention measures are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned fraud prevention unit is Measures to prevent fraud will be taken into consideration, taking into account the geographical distribution of agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned fraud prevention unit is We will improve the accuracy of fraud prevention measures by referring to relevant literature on agricultural and marine products. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. An acquisition unit that acquires images of agricultural products and marine products, An identification unit analyzes the image acquired by the acquisition unit and identifies characteristics specific to the production area, A detection unit that detects mislabeling of origin based on the characteristics identified by the aforementioned identification unit, A quality control unit that performs quality control based on the results detected by the aforementioned detection unit, The system includes a fraud prevention unit that implements fraud prevention measures based on the quality managed by the aforementioned quality control unit. A system characterized by the following features.

2. The acquisition unit is, Cameras and sensors are used to acquire images of agricultural and marine products. The system according to feature 1.

3. The aforementioned identification unit is Using generative AI, we identify the unique color and shape characteristics specific to a particular region. The system according to feature 1.

4. The detection unit, Detecting origin mislabeling based on identified characteristics. The system according to feature 1.

5. The aforementioned Quality Control Department Based on the detected results, only high-quality products will be offered to the market. The system according to feature 1.

6. The aforementioned fraud prevention unit is We will implement strict monitoring of regions and businesses where mislabeling of origin is frequent, and prevent fraudulent activities from occurring. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition based on those emotions. The system according to feature 1.

8. The acquisition unit is, Select the optimal image acquisition method according to the growth stage of agricultural and marine products. The system according to feature 1.

Images