System

Abstract

An object of the system according to the embodiment is to monitor the quality and safety of food in real time and to quickly detect and notify an abnormality.SOLUTION: A system includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires an image of a food product. The analyzer analyzes the image of the food acquired by the image acquirer. The notification unit detects an abnormality based on the result analyzed by the analysis unit, and notifies the user of the abnormality.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to monitor food quality and safety in real time and quickly detect abnormalities.

[0005] The system according to the embodiment aims to monitor the quality and safety of food in real time and quickly detect and notify abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires an image of the food. The analysis unit analyzes the image of the food acquired by the image acquisition unit. The notification unit detects an abnormality based on the analysis result by the analysis unit and notifies the user. [Effects of the Invention]

[0007] The system according to the embodiment can monitor food quality and safety in real time and quickly detect and notify abnormalities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The food safety system according to the embodiment of the present invention uses AI to monitor the quality and safety of fresh foods and processed foods, and detects and notifies customers of abnormalities. This allows consumers to purchase safe food ingredients with peace of mind.

[0029] A food safety system according to an embodiment includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh food lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. The image acquisition unit can also acquire food images using a smartphone camera. For example, the camera takes high-resolution images of food and acquires image data in real time. The sensor detects the surface condition of the food and acquires it as image data. The smartphone camera allows users to easily acquire food images. The analysis unit analyzes the food images acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to detect abnormalities in the freshness and appearance of food. The generation AI can also analyze changes in food color and the presence or absence of scratches. The generation AI can also analyze abnormalities on the surface of food. For example, the generation AI analyzes the condition of food using a text generation AI (e.g., LLM). The generation AI evaluates the freshness of food based on image data and detects abnormalities. The generation AI detects changes in food color and analyzes abnormalities. The generation AI analyzes whether there are scratches on the surface of the food and detects abnormalities. The notification unit detects abnormalities based on the results of the analysis by the analysis unit and notifies the user. For example, the notification may be sent to the user via a smartphone app. The notification may also be sent to the user via email. The notification unit may also send the notification to the user via a web application. For example, the smartphone app may notify the user of abnormalities in real time. The email may notify the user of details of the abnormality. The web application may allow the user to check information about the abnormality. As a result, the food safety system according to the embodiment monitors the quality and safety of food in real time and notifies the user if an abnormality is detected, thereby enabling consumers to purchase safe food with peace of mind.

[0030] The analysis unit can non-destructively analyze the internal structure of food and detect internal abnormalities. The analysis unit, for example, uses generative AI to develop technology for non-destructively analyzing the internal structure of food. For example, X-rays or ultrasound can be used to detect internal abnormalities and evaluate not only the appearance but also the internal quality. The analysis unit can also use CT scanning technology to non-destructively analyze the internal structure of food. For example, CT scanning technology can be used to analyze the inside of food in detail and detect abnormalities. The analysis unit can also use infrared technology to non-destructively analyze the internal structure of food. For example, infrared technology can be used to analyze the temperature distribution inside food and detect abnormalities. This enables more accurate quality monitoring by non-destructively analyzing the internal structure of food and detecting internal abnormalities.

[0031] When analyzing the condition of food, the analysis unit can simultaneously take into account environmental data such as temperature and humidity, enabling more accurate anomaly detection. For example, when the generative AI analyzes the condition of food, the analysis unit can simultaneously acquire environmental data such as temperature and humidity and incorporate it into the analysis, building a system. For example, freshness can be evaluated taking into account temperature changes inside a refrigerator. The analysis unit can also acquire environmental data using temperature and humidity sensors and incorporate it into the analysis. For example, the temperature inside a refrigerator can be monitored in real time using a temperature sensor and incorporated into the analysis. The humidity inside a refrigerator can be monitored in real time using a humidity sensor and incorporated into the analysis. The analysis unit can also use a database to analyze the condition of food based on environmental data. For example, the freshness of food can be evaluated based on past environmental data and anomalies can be detected. This enables more accurate anomaly detection by taking into account environmental data such as temperature and humidity.

[0032] The image acquisition unit can be applied not only to food but also to the quality monitoring of pharmaceuticals or cosmetics. The image acquisition unit, for example, uses image recognition technology to build a pharmaceutical quality monitoring system. For example, it detects abnormalities or chips on the surface of tablets. The image acquisition unit can also build a cosmetics quality monitoring system. For example, it detects damage or dirt on cosmetic packaging. The image acquisition unit can also use dedicated cameras and sensors for the quality monitoring of pharmaceuticals and cosmetics. For example, to monitor the quality of pharmaceuticals, a high-resolution camera is used to capture detailed images of the surface of a tablet. To monitor the quality of cosmetics, a sensor is used to detect the condition of the packaging. This makes it possible to apply the system to the quality monitoring of pharmaceuticals and cosmetics as well as food, enabling a wide range of quality monitoring.

[0033] The image acquisition unit can simultaneously monitor not only the condition of the food, but also the condition of the packaging or the accuracy of the label. The image acquisition unit, for example, uses generative AI to build a system that monitors the condition of food packaging. For example, it detects tears or stains on the packaging. The image acquisition unit can also build a system that monitors the accuracy of the label. For example, it detects printing errors or missing information on the label. The image acquisition unit can also use dedicated cameras and sensors to monitor the condition of the packaging and the accuracy of the label. For example, to monitor the condition of the packaging, a high-resolution camera is used to capture detailed images of tears or stains on the packaging. To monitor the accuracy of the label, a sensor is used to detect the printing on the label. This enables more comprehensive quality monitoring by simultaneously monitoring not only the condition of the food, but also the condition of the packaging and the accuracy of the label.

[0034] The food safety system further includes an anomaly detection unit that detects anomalies, and a countermeasure proposal unit that analyzes the cause of the anomaly detected by the anomaly detection unit and proposes specific countermeasures to the user. For example, when the generation AI detects an anomaly, the anomaly detection unit analyzes the cause and builds a system that proposes specific countermeasures to the user. For example, it proposes food storage and cooking methods. The anomaly detection unit can also use a database to analyze the cause of the anomaly. For example, it can identify the cause based on past anomaly data and propose countermeasures. The countermeasure proposal unit can also use a notification system to propose specific countermeasures to the user. For example, it can propose countermeasures via a smartphone app. This allows the user to take appropriate action by analyzing the cause of the anomaly and proposing specific countermeasures.

[0035] The anomaly detection unit can personalize the anomaly detection results based on the user's purchasing history or preferences, and provide more appropriate notifications. The anomaly detection unit, for example, builds a system that personalizes the anomaly detection results based on the user's purchasing history or preferences. For example, notifications are given priority for foods that the user frequently purchases. The anomaly detection unit can also use a machine learning algorithm to optimize notifications based on the user's purchasing history and preferences. For example, the machine learning algorithm can be used to learn the user's purchasing history and preferences and optimize the notifications. The anomaly detection unit can also use a database to personalize notifications based on the user's purchasing history and preferences. For example, notifications can be optimized based on past purchasing data. This allows for more appropriate notifications by personalizing notifications based on the user's purchasing history and preferences.

[0036] Furthermore, the food safety system can support food management within the home by incorporating an anomaly detection unit that detects anomalies and anomalies detected by the anomaly detection unit into a home refrigerator or pantry. The anomaly detection unit, for example, can be incorporated into a home refrigerator to build a system that supports food management within the home. For example, the anomaly detection unit can monitor the freshness of food in the refrigerator in real time and notify the user if an anomaly is detected. The anomaly detection unit can also be incorporated into a pantry to support food management within the home. For example, the anomaly detection unit can monitor the status of food in the pantry and notify the user if an anomaly is detected. The anomaly detection unit can also use dedicated sensors or cameras to incorporate the anomaly detection and notification functions into a home refrigerator or pantry. For example, to monitor the status of food in the refrigerator, a sensor can be used to monitor the temperature and humidity in real time. To monitor the status of food in the pantry, a camera can be used to capture and analyze images of food. In this way, by incorporating the anomaly detection and notification functions into a home refrigerator or pantry, food management within the home can be supported.

[0037] Furthermore, the food safety system can promote information sharing among consumers by using an anomaly detection unit that detects anomalies and a generation AI to share the results of anomalies detected by the anomaly detection unit on social media and community platforms. The anomaly detection unit, for example, uses a generation AI to build a system for sharing the results of anomaly detection on social media. For example, information about foods in which anomalies have been detected is shared on social media, promoting information sharing among consumers. The anomaly detection unit can also use a generation AI to build a system for sharing the results of anomaly detection on community platforms. For example, information about foods in which anomalies have been detected is shared on online forums, promoting information sharing among consumers. The anomaly detection unit can also use a dedicated application to share the results of anomaly detection on social media and community platforms. For example, information about anomalies is shared on social media and online forums through a dedicated app. In this way, sharing the results of anomaly detection on social media and community platforms can promote information sharing among consumers.

[0038] Furthermore, the food safety system can use the generation AI to track the quality history of food purchased by consumers and provide reports to increase their sense of security. For example, the generation AI builds a system that tracks the quality history of food purchased by consumers. For example, it provides quality information from food production to consumption as a report based on the purchase history. The generation AI can also use a database to track the quality history. For example, it tracks the quality history of food based on past quality data. The report can be provided in PDF format, for example. The report can also be sent by email. For example, a report detailing the quality history is provided to consumers in PDF format. The report is sent by email so that consumers can check the quality history. In this way, the quality history of food purchased by consumers can be tracked and reports to increase their sense of security are provided, thereby improving consumer security.

[0039] Generative AI can increase transparency by providing information about producers or places of production when consumers purchase food. For example, generative AI can build a system that provides information about producers and places of production when consumers purchase food. For example, producer information can be displayed by scanning a QR code. Generative AI can also use a database to provide information about producers and places of production. For example, it can retrieve detailed information about producers and places of production from a database and provide it to consumers. Generative AI can also use a dedicated application to provide information about producers and places of production. For example, it can display producer information and place of production information through a dedicated app. This can increase transparency by providing information about producers and places of production when consumers purchase food.

[0040] The generation AI can provide nutritional value or allergen information for foods purchased by consumers. For example, the generation AI can build a system that provides nutritional value and allergen information for foods purchased by consumers. For example, the generation AI can analyze food ingredient labels and display nutritional value and allergen information. The generation AI can also use a database to provide nutritional value and allergen information. For example, the generation AI can retrieve food ingredient data from a database and provide nutritional value and allergen information. The generation AI can also use a dedicated application to provide nutritional value and allergen information. For example, the generation AI can display nutritional value and allergen information through a dedicated app. This can increase consumer peace of mind by providing nutritional value and allergen information for the foods they purchase.

[0041] Generative AI can suggest cooking methods or recipes for food purchased by consumers. For example, generative AI can build a system that suggests cooking methods and recipes for food purchased by consumers. For example, it can automatically generate recipes based on purchased ingredients. Generative AI can also use a database to suggest cooking methods and recipes. For example, it can suggest cooking methods and recipes based on past recipe data. Generative AI can also use a dedicated application to suggest cooking methods and recipes. For example, it can display cooking methods and recipes through a dedicated app. This can increase consumer peace of mind by suggesting cooking methods and recipes for food purchased by consumers.

[0042] Generative AI can monitor a food manufacturer's entire production line and propose preventive measures before an abnormality occurs. For example, generative AI builds a system that monitors a food manufacturer's entire production line. For example, sensors are used to collect production line data and propose preventive measures before an abnormality occurs. Generative AI can also analyze production line data in real time and propose preventive measures before an abnormality occurs. For example, it monitors changes in temperature and humidity and proposes measures before an abnormality occurs. Generative AI can also use machine learning algorithms to propose preventive measures. For example, a machine learning algorithm is used to learn production line data and propose measures before an abnormality occurs. This makes it possible to monitor a food manufacturer's entire production line and propose preventive measures before an abnormality occurs, thereby strengthening quality control.

[0043] Generative AI can enable food businesses to analyze quality control data in real time and implement improvement measures immediately. For example, generative AI can build a system that allows food businesses to analyze quality control data in real time. For example, it can collect data using sensors and analyze it in real time. Generative AI can also use a database to analyze quality control data in real time and implement improvement measures immediately. For example, it can suggest quality control improvement measures based on past data. Generative AI can also use machine learning algorithms to analyze quality control data in real time and implement improvement measures immediately. For example, it can use machine learning algorithms to learn quality control data and suggest improvement measures. This allows food businesses to analyze quality control data in real time and implement improvement measures immediately, thereby improving the efficiency of quality control.

[0044] Generative AI can monitor the entire supply chain of a food business and strengthen quality control. For example, generative AI builds a system to monitor the entire supply chain of a food business. For example, it collects data at each stage of the supply chain and strengthens quality control. Generative AI can also use a database to monitor the entire supply chain and strengthen quality control. For example, it strengthens quality control based on data collected at each stage of the supply chain. Generative AI can also use a machine learning algorithm to monitor the entire supply chain and strengthen quality control. For example, it uses a machine learning algorithm to learn supply chain data and strengthen quality control. In this way, the efficiency of quality control can be improved by monitoring the entire supply chain of a food business and strengthening quality control.

[0045] Generative AI enables food businesses to collect consumer feedback in real time and reflect it in quality control. For example, generative AI can build a system that allows food businesses to collect consumer feedback in real time. For example, feedback can be collected through an online platform. Generative AI can also use a database to collect consumer feedback in real time and reflect it in quality control. For example, it can improve quality control based on the collected feedback data. Generative AI can also use machine learning algorithms to collect consumer feedback in real time and reflect it in quality control. For example, it can use machine learning algorithms to learn from the feedback data and improve quality control. This allows food businesses to collect consumer feedback in real time and reflect it in quality control, thereby improving the efficiency of quality control.

[0046] Generative AI can use blockchain technology to record the entire process from food production to consumption, ensuring traceability. For example, Generative AI could build a system that uses blockchain technology to record the entire process from food production to consumption. For example, it could record data at each stage, including production location, processing, transportation, and sales, to ensure traceability. Generative AI could also use blockchain technology to achieve decentralized data management and tamper-proofing. For example, it could use blockchain technology to decentralize data management at each stage, preventing tampering. Generative AI could also use a database to ensure traceability. For example, it could record data at each stage in a database to ensure traceability. In this way, the entire process from food production to consumption could be recorded using blockchain technology to ensure traceability, thereby improving food safety and reliability.

[0047] The generation AI can enable consumers to check food traceability information on their smartphones. For example, the generation AI can build a system that allows consumers to check food traceability information on their smartphones. For example, the traceability information can be displayed by scanning a QR code. The generation AI can also use a dedicated application to check the traceability information. For example, the traceability information can be displayed through a dedicated app. The generation AI can also use a database to check the traceability information. For example, the generation AI can retrieve traceability information from a database and display it on a smartphone. This allows consumers to check food traceability information on their smartphones, thereby increasing their sense of security.

[0048] Generative AI can apply food traceability information to other consumer goods. For example, generative AI can build a system that applies food traceability information to other consumer goods. For example, it can record the process of clothing from production to consumption to ensure traceability. Generative AI can also use a database to record traceability information for other consumer goods. For example, it can record data on clothing and pharmaceuticals from production to consumption in a database to ensure traceability. Generative AI can also use blockchain technology to record traceability information for other consumer goods. For example, blockchain technology can be used to record traceability information for clothing and pharmaceuticals, preventing tampering. In this way, by applying food traceability information to other consumer goods, traceability can be ensured in a wide range of fields.

[0049] Generative AI can increase transparency by enabling consumers to share food traceability information on social media. For example, generative AI can build a system that allows consumers to share food traceability information on social media. For example, it can provide a function that allows traceability information to be easily shared on social media. Generative AI can also use a dedicated application to share traceability information. For example, it can share traceability information on social media through a dedicated app. Generative AI can also use a database to share traceability information. For example, it can retrieve traceability information from a database and share it on social media. This can increase transparency by allowing consumers to share food traceability information on social media.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing the condition of food, the analysis unit simultaneously acquires data on the food's aroma and sound, enabling more accurate anomaly detection. For example, an aroma sensor can be used to monitor the food's aroma in real time to detect anomalies. A sound sensor can also be used to monitor the food's sound in real time to detect anomalies. This allows for more accurate anomaly detection by taking into account the food's aroma and sound data.

[0052] The analysis unit can analyze the ingredients of food while non-destructively analyzing the internal structure of food. For example, near-infrared spectroscopy can be used to analyze the ingredients of food. Raman spectroscopy can also be used to analyze the ingredients of food. This allows for more accurate quality monitoring by simultaneously analyzing the internal structure and ingredients of food.

[0053] When analyzing the condition of food, the analysis unit can simultaneously consider data on the food's storage period and distribution route, enabling more accurate anomaly detection. For example, freshness can be evaluated taking storage period into account. The condition of food can also be analyzed based on distribution route data. This allows for more accurate anomaly detection by taking storage period and distribution route data into account.

[0054] The image acquisition unit can be applied to quality monitoring of not only food but also clothing or furniture. For example, a clothing quality monitoring system can be constructed. For example, abnormalities or stains on the surface of clothing can be detected. It can also be used to construct a furniture quality monitoring system. For example, scratches or damage on the surface of furniture can be detected. This allows for a wide range of quality monitoring to be performed by applying the image acquisition unit to the quality monitoring of not only food but also clothing and furniture.

[0055] The image acquisition unit can simultaneously monitor not only the state of the food, but also changes in the temperature and humidity of the food. For example, a temperature sensor can be used to monitor the temperature of the food in real time to detect any abnormalities. A humidity sensor can also be used to monitor the humidity of the food in real time to detect any abnormalities. This allows for more accurate abnormality detection by taking into account changes in the temperature and humidity of the food.

[0056] Furthermore, the food safety system includes an anomaly detection unit that detects anomalies, and a countermeasure suggestion unit that analyzes the cause of the anomaly detected by the anomaly detection unit and proposes specific countermeasures to the user. For example, the countermeasure suggestion unit proposes methods for storing and cooking food. The anomaly detection unit can also use a database to analyze the cause of the anomaly. This allows the user to take appropriate action by analyzing the cause of the anomaly and proposing specific countermeasures.

[0057] Furthermore, the food safety system can incorporate an anomaly detection unit that detects anomalies and the anomalies detected by the anomaly detection unit into a home refrigerator or pantry to support food management within the home. For example, it can monitor the freshness of food in the refrigerator in real time and notify users if an anomaly is detected. The anomaly detection unit can also incorporate anomaly detection and notification functions into a pantry to support food management within the home. In this way, by incorporating anomaly detection and notification functions into a home refrigerator or pantry, it can support food management within the home.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh food lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. Furthermore, the image acquisition unit can also acquire food images using a smartphone camera. For example, the camera takes high-resolution images of food and acquires image data in real time. The sensor detects the surface condition of the food and acquires it as image data. The smartphone camera allows users to easily acquire food images. Step 2: The analysis unit analyzes the food images acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to detect abnormalities in the freshness and appearance of the food. The generation AI can also analyze changes in the color of the food and the presence or absence of scratches. The generation AI evaluates the freshness of the food based on the image data and detects abnormalities. The generation AI detects changes in the color of the food and analyzes abnormalities. The generation AI analyzes whether there are scratches on the surface of the food and detects abnormalities. Step 3: The notification unit detects an anomaly based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent to the user via a smartphone app. It can also be sent to the user via email. It can also be sent to the user via a web application. The smartphone app notifies the user of the anomaly in real time. The email notifies the user of details of the anomaly. The web application allows the user to check information about the anomaly.

[0060] (Example 2) The food safety system according to the embodiment of the present invention uses AI to monitor the quality and safety of fresh foods and processed foods, and detects and notifies customers of abnormalities. This allows consumers to purchase safe food ingredients with peace of mind.

[0061] A food safety system according to an embodiment includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh food lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. The image acquisition unit can also acquire food images using a smartphone camera. For example, the camera takes high-resolution images of food and acquires image data in real time. The sensor detects the surface condition of the food and acquires it as image data. The smartphone camera allows users to easily acquire food images. The analysis unit analyzes the food images acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to detect abnormalities in the freshness and appearance of food. The generation AI can also analyze changes in food color and the presence or absence of scratches. The generation AI can also analyze abnormalities on the surface of food. For example, the generation AI analyzes the condition of food using a text generation AI (e.g., LLM). The generation AI evaluates the freshness of food based on image data and detects abnormalities. The generation AI detects changes in food color and analyzes abnormalities. The generation AI analyzes whether there are scratches on the surface of the food and detects abnormalities. The notification unit detects abnormalities based on the results of the analysis by the analysis unit and notifies the user. For example, the notification may be sent to the user via a smartphone app. The notification may also be sent to the user via email. The notification unit may also send the notification to the user via a web application. For example, the smartphone app may notify the user of abnormalities in real time. The email may notify the user of details of the abnormality. The web application may allow the user to check information about the abnormality. As a result, the food safety system according to the embodiment monitors the quality and safety of food in real time and notifies the user if an abnormality is detected, thereby enabling consumers to purchase safe food with peace of mind.

[0062] The analysis unit can non-destructively analyze the internal structure of food and detect internal abnormalities. The analysis unit, for example, uses generative AI to develop technology for non-destructively analyzing the internal structure of food. For example, X-rays or ultrasound can be used to detect internal abnormalities and evaluate not only the appearance but also the internal quality. The analysis unit can also use CT scanning technology to non-destructively analyze the internal structure of food. For example, CT scanning technology can be used to analyze the inside of food in detail and detect abnormalities. The analysis unit can also use infrared technology to non-destructively analyze the internal structure of food. For example, infrared technology can be used to analyze the temperature distribution inside food and detect abnormalities. This enables more accurate quality monitoring by non-destructively analyzing the internal structure of food and detecting internal abnormalities.

[0063] When analyzing the condition of food, the analysis unit can simultaneously take into account environmental data such as temperature and humidity, enabling more accurate anomaly detection. For example, when the generative AI analyzes the condition of food, the analysis unit can simultaneously acquire environmental data such as temperature and humidity and incorporate it into the analysis, building a system. For example, freshness can be evaluated taking into account temperature changes inside a refrigerator. The analysis unit can also acquire environmental data using temperature and humidity sensors and incorporate it into the analysis. For example, the temperature inside a refrigerator can be monitored in real time using a temperature sensor and incorporated into the analysis. The humidity inside a refrigerator can be monitored in real time using a humidity sensor and incorporated into the analysis. The analysis unit can also use a database to analyze the condition of food based on environmental data. For example, the freshness of food can be evaluated based on past environmental data and anomalies can be detected. This enables more accurate anomaly detection by taking into account environmental data such as temperature and humidity.

[0064] The analysis unit can use the emotion estimation function to analyze the emotions consumers have regarding the condition of food and adjust the anomaly detection criteria based on the emotions. For example, the analysis unit uses the emotion estimation function to build a system that analyzes the emotions consumers have regarding the condition of food. For example, the analysis unit identifies food conditions that cause anxiety to consumers and reflects the criteria in anomaly detection. The analysis unit can also use the emotion estimation function to collect consumer emotion data and reflect it in the analysis. For example, the analysis unit can collect consumer facial expression data and analyze emotions. The analysis unit can also collect consumer voice data and analyze emotions. The analysis unit can also use the emotion estimation function to use a machine learning algorithm to adjust the anomaly detection criteria based on the consumer's emotions. For example, the analysis unit can use a machine learning algorithm to learn consumer emotion data and optimize the anomaly detection criteria. By adjusting the anomaly detection criteria based on the consumer's emotions, anomaly detection that is more suitable for consumers can be achieved.

[0065] The image acquisition unit can be applied not only to food but also to the quality monitoring of pharmaceuticals or cosmetics. The image acquisition unit, for example, uses image recognition technology to build a pharmaceutical quality monitoring system. For example, it detects abnormalities or chips on the surface of tablets. The image acquisition unit can also build a cosmetics quality monitoring system. For example, it detects damage or dirt on cosmetic packaging. The image acquisition unit can also use dedicated cameras and sensors for the quality monitoring of pharmaceuticals and cosmetics. For example, to monitor the quality of pharmaceuticals, a high-resolution camera is used to capture detailed images of the surface of a tablet. To monitor the quality of cosmetics, a sensor is used to detect the condition of the packaging. This makes it possible to apply the system to the quality monitoring of pharmaceuticals and cosmetics as well as food, enabling a wide range of quality monitoring.

[0066] The image acquisition unit can simultaneously monitor not only the condition of the food, but also the condition of the packaging or the accuracy of the label. The image acquisition unit, for example, uses generative AI to build a system that monitors the condition of food packaging. For example, it detects tears or stains on the packaging. The image acquisition unit can also build a system that monitors the accuracy of the label. For example, it detects printing errors or missing information on the label. The image acquisition unit can also use dedicated cameras and sensors to monitor the condition of the packaging and the accuracy of the label. For example, to monitor the condition of the packaging, a high-resolution camera is used to capture detailed images of tears or stains on the packaging. To monitor the accuracy of the label, a sensor is used to detect the printing on the label. This enables more comprehensive quality monitoring by simultaneously monitoring not only the condition of the food, but also the condition of the packaging and the accuracy of the label.

[0067] The analysis unit can use the emotion estimation function to provide real-time feedback on the emotions consumers have regarding the condition of food, thereby increasing the consumer's sense of security. For example, the analysis unit uses the emotion estimation function to analyze the emotions consumers have regarding the condition of food in real time and build a system that provides feedback based on the results. For example, the analysis unit can display the food condition that gives the consumer a sense of security. The analysis unit can also use the emotion estimation function to collect consumer emotion data in real time and reflect this in the feedback. For example, the analysis unit can collect consumer facial expression data in real time and analyze their emotions. The analysis unit can also use the emotion estimation function to use a machine learning algorithm to provide feedback based on the consumer's emotions. For example, the analysis unit can use a machine learning algorithm to learn the consumer's emotion data and optimize the feedback. This can increase the consumer's sense of security by providing real-time feedback based on the consumer's emotions.

[0068] The food safety system further includes an anomaly detection unit that detects anomalies, and a countermeasure proposal unit that analyzes the cause of the anomaly detected by the anomaly detection unit and proposes specific countermeasures to the user. For example, when the generation AI detects an anomaly, the anomaly detection unit analyzes the cause and builds a system that proposes specific countermeasures to the user. For example, it proposes food storage and cooking methods. The anomaly detection unit can also use a database to analyze the cause of the anomaly. For example, it can identify the cause based on past anomaly data and propose countermeasures. The countermeasure proposal unit can also use a notification system to propose specific countermeasures to the user. For example, it can propose countermeasures via a smartphone app. This allows the user to take appropriate action by analyzing the cause of the anomaly and proposing specific countermeasures.

[0069] The anomaly detection unit can personalize the anomaly detection results based on the user's purchasing history or preferences, and provide more appropriate notifications. The anomaly detection unit, for example, builds a system that personalizes the anomaly detection results based on the user's purchasing history or preferences. For example, notifications are given priority for foods that the user frequently purchases. The anomaly detection unit can also use a machine learning algorithm to optimize notifications based on the user's purchasing history and preferences. For example, the machine learning algorithm can be used to learn the user's purchasing history and preferences and optimize the notifications. The anomaly detection unit can also use a database to personalize notifications based on the user's purchasing history and preferences. For example, notifications can be optimized based on past purchasing data. This allows for more appropriate notifications by personalizing notifications based on the user's purchasing history and preferences.

[0070] The anomaly detection unit can use the emotion estimation function to analyze the emotion of the user when receiving an abnormality notification and optimize the content and timing of the notification. For example, the anomaly detection unit can use the emotion estimation function to analyze the emotion of the user when receiving an abnormality notification and build a system that optimizes the content and timing of the notification based on the results. For example, the notification can be sent at a time that avoids a time when the user feels anxious. The anomaly detection unit can also use the emotion estimation function to collect user emotion data and optimize the content and timing of the notification. For example, the anomaly detection unit can collect user facial expression data and analyze the emotion. The anomaly detection unit can also use the emotion estimation function to use a machine learning algorithm to optimize the content and timing of the notification based on the user's emotion. For example, the machine learning algorithm can learn the user's emotion data and optimize the content and timing of the notification. This allows the content and timing of the notification to be optimized based on the user's emotion, thereby enabling more effective notifications.

[0071] Furthermore, the food safety system can support food management within the home by incorporating an anomaly detection unit that detects anomalies and anomalies detected by the anomaly detection unit into a home refrigerator or pantry. The anomaly detection unit, for example, can be incorporated into a home refrigerator to build a system that supports food management within the home. For example, the anomaly detection unit can monitor the freshness of food in the refrigerator in real time and notify the user if an anomaly is detected. The anomaly detection unit can also be incorporated into a pantry to support food management within the home. For example, the anomaly detection unit can monitor the status of food in the pantry and notify the user if an anomaly is detected. The anomaly detection unit can also use dedicated sensors or cameras to incorporate the anomaly detection and notification functions into a home refrigerator or pantry. For example, to monitor the status of food in the refrigerator, a sensor can be used to monitor the temperature and humidity in real time. To monitor the status of food in the pantry, a camera can be used to capture and analyze images of food. In this way, by incorporating the anomaly detection and notification functions into a home refrigerator or pantry, food management within the home can be supported.

[0072] Furthermore, the food safety system can promote information sharing among consumers by using an anomaly detection unit that detects anomalies and a generation AI to share the results of anomalies detected by the anomaly detection unit on social media and community platforms. The anomaly detection unit, for example, uses a generation AI to build a system for sharing the results of anomaly detection on social media. For example, information about foods in which anomalies have been detected is shared on social media, promoting information sharing among consumers. The anomaly detection unit can also use a generation AI to build a system for sharing the results of anomaly detection on community platforms. For example, information about foods in which anomalies have been detected is shared on online forums, promoting information sharing among consumers. The anomaly detection unit can also use a dedicated application to share the results of anomaly detection on social media and community platforms. For example, information about anomalies is shared on social media and online forums through a dedicated app. In this way, sharing the results of anomaly detection on social media and community platforms can promote information sharing among consumers.

[0073] Furthermore, the food safety system includes an anomaly detection unit that detects anomalies, and an emotion estimation function that analyzes the emotion of the user when receiving the anomaly notification detected by the anomaly detection unit and customizes the content of the notification based on the emotion. The anomaly detection unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving the anomaly notification, and builds a system that customizes the content of the notification based on the result. For example, if the user feels anxious, the content is changed to one that gives a sense of security. The anomaly detection unit can also use the emotion estimation function to collect user emotion data and customize the content of the notification. For example, it can collect user facial expression data and analyze the emotion. It can also collect user voice data and analyze the emotion. The anomaly detection unit can also use a machine learning algorithm to customize the content of the notification based on the user's emotion using the emotion estimation function. For example, it can use a machine learning algorithm to learn the user's emotion data and optimize the content of the notification. This allows the content of the notification to be customized based on the user's emotion, enabling more effective notifications.

[0074] Furthermore, the food safety system can use the generation AI to track the quality history of food purchased by consumers and provide reports to increase their sense of security. For example, the generation AI builds a system that tracks the quality history of food purchased by consumers. For example, it provides quality information from food production to consumption as a report based on the purchase history. The generation AI can also use a database to track the quality history. For example, it tracks the quality history of food based on past quality data. The report can be provided in PDF format, for example. The report can also be sent by email. For example, a report detailing the quality history is provided to consumers in PDF format. The report is sent by email so that consumers can check the quality history. In this way, the quality history of food purchased by consumers can be tracked and reports to increase their sense of security are provided, thereby improving consumer security.

[0075] Generative AI can increase transparency by providing information about producers or places of production when consumers purchase food. For example, generative AI can build a system that provides information about producers and places of production when consumers purchase food. For example, producer information can be displayed by scanning a QR code. Generative AI can also use a database to provide information about producers and places of production. For example, it can retrieve detailed information about producers and places of production from a database and provide it to consumers. Generative AI can also use a dedicated application to provide information about producers and places of production. For example, it can display producer information and place of production information through a dedicated app. This can increase transparency by providing information about producers and places of production when consumers purchase food.

[0076] The generative AI can analyze the emotions consumers have when purchasing food products and make suggestions to support the purchase based on those emotions. For example, the generative AI can use an emotion estimation function to analyze the emotions consumers have when purchasing food products and build a system that makes suggestions to support the purchase based on those emotions. For example, if the consumer feels anxious, the generative AI can make suggestions that give a sense of security. The generative AI can also collect consumer emotional data and make suggestions to support the purchase. For example, it can collect consumer facial expression data and analyze their emotions. It can also collect consumer voice data and analyze their emotions. The generative AI can also use a machine learning algorithm to make suggestions to support the purchase based on the consumer's emotions. For example, it can use a machine learning algorithm to learn consumer emotional data and optimize the suggestions. This can improve the consumer's sense of security by making suggestions to support the purchase based on the consumer's emotions.

[0077] The generation AI can provide nutritional value or allergen information for foods purchased by consumers. For example, the generation AI can build a system that provides nutritional value and allergen information for foods purchased by consumers. For example, the generation AI can analyze food ingredient labels and display nutritional value and allergen information. The generation AI can also use a database to provide nutritional value and allergen information. For example, the generation AI can retrieve food ingredient data from a database and provide nutritional value and allergen information. The generation AI can also use a dedicated application to provide nutritional value and allergen information. For example, the generation AI can display nutritional value and allergen information through a dedicated app. This can increase consumer peace of mind by providing nutritional value and allergen information for the foods they purchase.

[0078] Generative AI can suggest cooking methods or recipes for food purchased by consumers. For example, generative AI can build a system that suggests cooking methods and recipes for food purchased by consumers. For example, it can automatically generate recipes based on purchased ingredients. Generative AI can also use a database to suggest cooking methods and recipes. For example, it can suggest cooking methods and recipes based on past recipe data. Generative AI can also use a dedicated application to suggest cooking methods and recipes. For example, it can display cooking methods and recipes through a dedicated app. This can increase consumer peace of mind by suggesting cooking methods and recipes for food purchased by consumers.

[0079] Generative AI can monitor a food manufacturer's entire production line and propose preventive measures before an abnormality occurs. For example, generative AI builds a system that monitors a food manufacturer's entire production line. For example, sensors are used to collect production line data and propose preventive measures before an abnormality occurs. Generative AI can also analyze production line data in real time and propose preventive measures before an abnormality occurs. For example, it monitors changes in temperature and humidity and proposes measures before an abnormality occurs. Generative AI can also use machine learning algorithms to propose preventive measures. For example, a machine learning algorithm is used to learn production line data and propose measures before an abnormality occurs. This makes it possible to monitor a food manufacturer's entire production line and propose preventive measures before an abnormality occurs, thereby strengthening quality control.

[0080] Generative AI can enable food businesses to analyze quality control data in real time and implement improvement measures immediately. For example, generative AI can build a system that allows food businesses to analyze quality control data in real time. For example, it can collect data using sensors and analyze it in real time. Generative AI can also use a database to analyze quality control data in real time and implement improvement measures immediately. For example, it can suggest quality control improvement measures based on past data. Generative AI can also use machine learning algorithms to analyze quality control data in real time and implement improvement measures immediately. For example, it can use machine learning algorithms to learn quality control data and suggest improvement measures. This allows food businesses to analyze quality control data in real time and implement improvement measures immediately, thereby improving the efficiency of quality control.

[0081] The generative AI can analyze employee emotions and propose measures to improve quality control efficiency. For example, the generative AI can use an emotion estimation function to analyze employee emotions and build a system that proposes measures to improve quality control efficiency based on the results. For example, measures to reduce employee stress can be proposed. The generative AI can also collect employee emotion data and propose measures to improve quality control efficiency. For example, it can collect employee facial expression data and analyze their emotions. It can also collect employee voice data and analyze their emotions. The generative AI can also use a machine learning algorithm to propose measures to improve quality control efficiency based on employee emotions. For example, it can use a machine learning algorithm to learn employee emotion data and propose measures. In this way, the efficiency of quality control can be improved by analyzing employee emotions and proposing measures to improve quality control efficiency.

[0082] Generative AI can monitor the entire supply chain of a food business and strengthen quality control. For example, generative AI builds a system to monitor the entire supply chain of a food business. For example, it collects data at each stage of the supply chain and strengthens quality control. Generative AI can also use a database to monitor the entire supply chain and strengthen quality control. For example, it strengthens quality control based on data collected at each stage of the supply chain. Generative AI can also use a machine learning algorithm to monitor the entire supply chain and strengthen quality control. For example, it uses a machine learning algorithm to learn supply chain data and strengthen quality control. In this way, the efficiency of quality control can be improved by monitoring the entire supply chain of a food business and strengthening quality control.

[0083] Generative AI enables food businesses to collect consumer feedback in real time and reflect it in quality control. For example, generative AI can build a system that allows food businesses to collect consumer feedback in real time. For example, feedback can be collected through an online platform. Generative AI can also use a database to collect consumer feedback in real time and reflect it in quality control. For example, it can improve quality control based on the collected feedback data. Generative AI can also use machine learning algorithms to collect consumer feedback in real time and reflect it in quality control. For example, it can use machine learning algorithms to learn from the feedback data and improve quality control. This allows food businesses to collect consumer feedback in real time and reflect it in quality control, thereby improving the efficiency of quality control.

[0084] The generative AI can analyze employee emotions and propose measures to improve quality control efficiency. For example, the generative AI can use an emotion estimation function to analyze employee emotions and build a system that proposes measures to improve quality control efficiency based on the results. For example, measures to reduce employee stress can be proposed. The generative AI can also collect employee emotion data and propose measures to improve quality control efficiency. For example, it can collect employee facial expression data and analyze their emotions. It can also collect employee voice data and analyze their emotions. The generative AI can also use a machine learning algorithm to propose measures to improve quality control efficiency based on employee emotions. For example, it can use a machine learning algorithm to learn employee emotion data and propose measures. In this way, the efficiency of quality control can be improved by analyzing employee emotions and proposing measures to improve quality control efficiency.

[0085] Generative AI can use blockchain technology to record the entire process from food production to consumption, ensuring traceability. For example, Generative AI could build a system that uses blockchain technology to record the entire process from food production to consumption. For example, it could record data at each stage, including production location, processing, transportation, and sales, to ensure traceability. Generative AI could also use blockchain technology to achieve decentralized data management and tamper-proofing. For example, it could use blockchain technology to decentralize data management at each stage, preventing tampering. Generative AI could also use a database to ensure traceability. For example, it could record data at each stage in a database to ensure traceability. In this way, the entire process from food production to consumption could be recorded using blockchain technology to ensure traceability, thereby improving food safety and reliability.

[0086] The generation AI can enable consumers to check food traceability information on their smartphones. For example, the generation AI can build a system that allows consumers to check food traceability information on their smartphones. For example, the traceability information can be displayed by scanning a QR code. The generation AI can also use a dedicated application to check the traceability information. For example, the traceability information can be displayed through a dedicated app. The generation AI can also use a database to check the traceability information. For example, the generation AI can retrieve traceability information from a database and display it on a smartphone. This allows consumers to check food traceability information on their smartphones, thereby increasing their sense of security.

[0087] The generative AI can analyze the emotions felt by consumers when they check traceability information and improve the method of providing information based on those emotions. For example, the generative AI can use an emotion estimation function to analyze the emotions felt by consumers when they check traceability information and build a system that improves the method of providing information based on the results. For example, it can provide information that gives consumers a sense of security. The generative AI can also collect consumer emotion data and improve the method of providing information. For example, it can collect consumer facial expression data and analyze their emotions. It can also collect consumer voice data and analyze their emotions. The generative AI can also use a machine learning algorithm to improve the method of providing information based on consumer emotions. For example, it can use a machine learning algorithm to learn consumer emotion data and optimize the method of providing information. In this way, it can improve the method of providing information based on consumer emotions, thereby increasing consumer security.

[0088] Generative AI can apply food traceability information to other consumer goods. For example, generative AI can build a system that applies food traceability information to other consumer goods. For example, it can record the process of clothing from production to consumption to ensure traceability. Generative AI can also use a database to record traceability information for other consumer goods. For example, it can record data on clothing and pharmaceuticals from production to consumption in a database to ensure traceability. Generative AI can also use blockchain technology to record traceability information for other consumer goods. For example, blockchain technology can be used to record traceability information for clothing and pharmaceuticals, preventing tampering. In this way, by applying food traceability information to other consumer goods, traceability can be ensured in a wide range of fields.

[0089] Generative AI can increase transparency by enabling consumers to share food traceability information on social media. For example, generative AI can build a system that allows consumers to share food traceability information on social media. For example, it can provide a function that allows traceability information to be easily shared on social media. Generative AI can also use a dedicated application to share traceability information. For example, it can share traceability information on social media through a dedicated app. Generative AI can also use a database to share traceability information. For example, it can retrieve traceability information from a database and share it on social media. This can increase transparency by allowing consumers to share food traceability information on social media.

[0090] The generative AI can analyze the emotions felt by consumers when they check traceability information and improve the method of providing information based on those emotions. For example, the generative AI can use an emotion estimation function to analyze the emotions felt by consumers when they check traceability information and build a system that improves the method of providing information based on the results. For example, it can provide information that gives consumers a sense of security. The generative AI can also collect consumer emotion data and improve the method of providing information. For example, it can collect consumer facial expression data and analyze their emotions. It can also collect consumer voice data and analyze their emotions. The generative AI can also use a machine learning algorithm to improve the method of providing information based on consumer emotions. For example, it can use a machine learning algorithm to learn consumer emotion data and optimize the method of providing information. In this way, it can improve the method of providing information based on consumer emotions, thereby increasing consumer security.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing the condition of food, the analysis unit simultaneously acquires data on the food's aroma and sound, enabling more accurate anomaly detection. For example, an aroma sensor can be used to monitor the food's aroma in real time to detect anomalies. A sound sensor can also be used to monitor the food's sound in real time to detect anomalies. This allows for more accurate anomaly detection by taking into account the food's aroma and sound data.

[0093] The analysis unit can analyze the ingredients of food while non-destructively analyzing the internal structure of food. For example, near-infrared spectroscopy can be used to analyze the ingredients of food. Raman spectroscopy can also be used to analyze the ingredients of food. This allows for more accurate quality monitoring by simultaneously analyzing the internal structure and ingredients of food.

[0094] When analyzing the condition of food, the analysis unit can simultaneously consider data on the food's storage period and distribution route, enabling more accurate anomaly detection. For example, freshness can be evaluated taking storage period into account. The condition of food can also be analyzed based on distribution route data. This allows for more accurate anomaly detection by taking storage period and distribution route data into account.

[0095] The analysis unit can use the emotion estimation function to analyze the emotions consumers have about the condition of the food and adjust the way the food is displayed based on those emotions. For example, it can identify a display method that gives consumers a sense of security and reflect that standard in the display. The analysis unit can also use the emotion estimation function to collect consumer emotion data and reflect that in the display. This allows the display to be more suitable for consumers by adjusting the display method based on the consumer's emotions.

[0096] The image acquisition unit can be applied to quality monitoring of not only food but also clothing or furniture. For example, a clothing quality monitoring system can be constructed. For example, abnormalities or stains on the surface of clothing can be detected. It can also be used to construct a furniture quality monitoring system. For example, scratches or damage on the surface of furniture can be detected. This allows for a wide range of quality monitoring to be performed by applying the image acquisition unit to the quality monitoring of not only food but also clothing and furniture.

[0097] The image acquisition unit can simultaneously monitor not only the state of the food, but also changes in the temperature and humidity of the food. For example, a temperature sensor can be used to monitor the temperature of the food in real time to detect any abnormalities. A humidity sensor can also be used to monitor the humidity of the food in real time to detect any abnormalities. This allows for more accurate abnormality detection by taking into account changes in the temperature and humidity of the food.

[0098] The analysis unit can use the emotion estimation function to provide real-time feedback on the emotions consumers have regarding the condition of food products, thereby supporting their purchasing behavior. For example, it can display the condition of food products that gives the consumer a sense of security. The analysis unit can also use the emotion estimation function to collect consumer emotion data in real time and reflect this in the feedback. This makes it possible to support consumer purchasing behavior by providing real-time feedback based on the consumer's emotions.

[0099] Furthermore, the food safety system includes an anomaly detection unit that detects anomalies, and a countermeasure suggestion unit that analyzes the cause of the anomaly detected by the anomaly detection unit and proposes specific countermeasures to the user. For example, the countermeasure suggestion unit proposes methods for storing and cooking food. The anomaly detection unit can also use a database to analyze the cause of the anomaly. This allows the user to take appropriate action by analyzing the cause of the anomaly and proposing specific countermeasures.

[0100] The anomaly detection unit can use the emotion estimation function to analyze the user's emotions when receiving an anomaly notification and optimize the content and timing of the notification. For example, the notification can be sent at a time that avoids timing when the user feels anxious. The anomaly detection unit can also use the emotion estimation function to collect user emotion data and optimize the content and timing of the notification. This allows for more effective notification by optimizing the content and timing of the notification based on the user's emotions.

[0101] Furthermore, the food safety system can incorporate an anomaly detection unit that detects anomalies and the anomalies detected by the anomaly detection unit into a home refrigerator or pantry to support food management within the home. For example, it can monitor the freshness of food in the refrigerator in real time and notify users if an anomaly is detected. The anomaly detection unit can also incorporate anomaly detection and notification functions into a pantry to support food management within the home. In this way, by incorporating anomaly detection and notification functions into a home refrigerator or pantry, it can support food management within the home.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh food lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. Furthermore, the image acquisition unit can also acquire food images using a smartphone camera. For example, the camera takes high-resolution images of food and acquires image data in real time. The sensor detects the surface condition of the food and acquires it as image data. The smartphone camera allows users to easily acquire food images. Step 2: The analysis unit analyzes the food images acquired by the image acquisition unit. For example, the generation AI uses image recognition technology to detect abnormalities in the freshness and appearance of the food. The generation AI can also analyze changes in the color of the food and the presence or absence of scratches. The generation AI evaluates the freshness of the food based on the image data and detects abnormalities. The generation AI detects changes in the color of the food and analyzes abnormalities. The generation AI analyzes whether there are scratches on the surface of the food and detects abnormalities. Step 3: The notification unit detects an anomaly based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent to the user via a smartphone app. It can also be sent to the user via email. It can also be sent to the user via a web application. The smartphone app notifies the user of the anomaly in real time. The email notifies the user of details of the anomaly. The web application allows the user to check information about the anomaly.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

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

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires an image of the food; an analysis unit that analyzes the image of the food acquired by the image acquisition unit; a notification unit that detects an abnormality based on the result of the analysis by the analysis unit and notifies the user. A system characterized by:

2. The image acquisition unit It can be applied to the quality monitoring of not only food but also medicines and cosmetics.

2. The system of claim 1.

3. an abnormality detection unit that detects an abnormality; a countermeasure suggestion unit that analyzes the cause of the abnormality detected by the abnormality detection unit and suggests specific countermeasures to a user. A system characterized by:

4. The generating AI is Providing reports to help consumers track the quality history of their food purchases and increase their peace of mind A system characterized by:

5. The generating AI is The entire food process from production to consumption will be recorded using blockchain technology to ensure traceability. A system characterized by:

Images