System

Abstract

An object of the system according to the embodiment is to efficiently monitor the quality and safety of food and quickly detect an abnormality.SOLUTION: A system according to an embodiment includes an image collection unit, a data analysis unit, an abnormality detection unit, and a notification unit. The image collection unit collects images and data of food. The data analyzer analyzes the image and data of the food collected by the image collector. The abnormality detection unit detects an abnormality based on a result of the analysis by the data analysis unit. The notification unit notifies the abnormality detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

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

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

[0006] The system according to the embodiment includes an image collection unit, a data analysis unit, an anomaly detection unit, and a notification unit. The image collection unit collects food images and data. The data analysis unit analyzes the food images and data collected by the image collection unit. The anomaly detection unit detects anomalies based on the results of the analysis by the data analysis unit. The notification unit notifies of an anomaly detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor the quality and safety of food and quickly detect any 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 AI monitoring system according to an embodiment of the present invention is a system that uses AI to monitor the quality and safety of fresh foods and processed foods, and detects and notifies abnormalities. This enables the food safety AI monitoring system to maintain a high level of food quality and safety.

[0029] The food safety AI monitoring system according to the embodiment includes an image collection unit, a data analysis unit, an anomaly detection unit, and a notification unit. The image collection unit collects food images and data. For example, it uses cameras and sensors in supermarkets and food factories to capture images of fresh foods and processed products and collect the data. The image collection unit can also collect environmental data using temperature and humidity sensors. For example, it can monitor the temperature and humidity inside a refrigerator in real time to obtain a detailed understanding of food storage conditions. The data analysis unit uses a generation AI to analyze the collected food images and data. For example, the generation AI can analyze the color, shape, and surface condition of food to detect abnormalities such as spoilage, discoloration, and foreign matter contamination. The data analysis unit can also predict microbial growth status and evaluate the risk of food spoilage. For example, it can evaluate the risk of microbial growth based on data on the surface condition and temperature of food. The anomaly detection unit detects anomalies based on the results of analysis by the data analysis unit. For example, if the generation AI determines that there is a problem with the quality or safety of food, it detects an anomaly. The anomaly detection unit can also identify the cause of the anomaly and propose specific countermeasures to address the cause. For example, it analyzes the causes of food spoilage or discoloration and proposes specific countermeasures. The notification unit notifies of abnormalities detected by the anomaly detection unit. For example, if the generation AI detects spoilage or discoloration, it issues a warning to consumers and businesses. Notifications are sent via smartphone apps, email, or messaging services, allowing consumers and businesses to respond quickly. As a result, the food safety AI monitoring system according to the embodiment can maintain a high level of food quality and safety. For example, consumers can purchase and consume food with peace of mind, and businesses can improve the efficiency of quality control. In addition, food safety can be ensured by responding quickly when an abnormality occurs.

[0030] When collecting food images and data, the image collection unit can simultaneously collect environmental data such as temperature and humidity. The image collection unit can also use temperature and humidity sensors, for example, to simultaneously collect environmental data. For example, the image collection unit can monitor the temperature and humidity inside a refrigerator in real time to obtain a detailed understanding of the food storage conditions. This allows for comprehensive monitoring of factors that affect the condition of food.

[0031] The image collection unit can add information about the food's production history and distribution route to images and data of the food. The image collection unit, for example, adds production history information to images and data of the food. For example, information about the producer, production date, and information about the pesticides and fertilizers used can be registered in a database to enhance traceability. The image collection unit can also add distribution route information. For example, information such as the delivery route, delivery company, and delivery date and time can be registered in a database to track the food's distribution route. This enhances traceability.

[0032] When collecting food images and data, the image collection unit simultaneously collects audio data, and can detect changes in quality from the sounds of food packaging and handling. For example, when collecting food images and data, the image collection unit simultaneously collects audio data. For example, the sounds of food packaging and handling are recorded with a microphone to detect changes in quality. The image collection unit can also analyze the collected audio data to detect changes in food quality. For example, changes in packaging sounds and handling sounds are analyzed to detect quality declines. This makes it possible to detect changes in food quality from the audio data.

[0033] The image collection unit can add nutritional information about food to images and data of food and provide it to consumers in cooperation with a health management app. The image collection unit, for example, adds nutritional information to images and data of food. For example, the image collection unit registers the food's ingredient list and nutritional value in a database and provides it to consumers. The image collection unit can also provide nutritional information to consumers in cooperation with a health management app. For example, the nutritional information about food can be linked to a health management app and used by consumers when recording their meals and calculating nutrition. This makes it possible to provide nutritional information to consumers and support health management.

[0034] When analyzing food images and data, the data analysis unit can predict the growth status of microorganisms and assess the risk of food spoilage. The data analysis unit, for example, uses generative AI to analyze food images and data and predict the growth status of microorganisms. For example, it assesses the risk of microbial growth based on the condition of the food's surface and temperature data. The data analysis unit can also assess the risk of food spoilage based on collected data. For example, it analyzes changes in temperature and humidity to assess the risk of spoilage. This makes it possible to predict the risk of food spoilage and strengthen quality control.

[0035] When analyzing food images and data, the data analysis unit adds information about the chemical composition of the food, allowing it to detect allergens and evaluate nutritional value. For example, the data analysis unit adds information about the chemical composition of the food to the data analyzed by the generative AI. For example, it registers the food's ingredient list and allergen information in a database and detects allergens. The data analysis unit can also evaluate the nutritional value of food based on the collected data. For example, it analyzes nutritional components such as calories, vitamins, and minerals and evaluates nutritional value. This makes it possible to detect allergens and evaluate nutritional value.

[0036] The data analysis unit adds food aroma data when analyzing food images and data, and can detect quality degradation from changes in aroma. The data analysis unit adds aroma data when analyzing food images and data, for example, using generative AI. For example, the data analysis unit detects food aromas with a sensor and analyzes changes in quality. The data analysis unit can also detect quality degradation based on the collected aroma data. For example, it analyzes changes in aroma, color, and texture to detect quality degradation. This makes it possible to detect quality degradation from changes in aroma.

[0037] When analyzing food images and data, the data analysis unit adds information on food storage and cooking methods, making it possible to suggest optimal storage and cooking methods. For example, the data analysis unit adds information on food storage and cooking methods to the data analyzed by the generative AI. For example, it registers food storage temperatures and cooking times in a database and suggests optimal methods. The data analysis unit can also suggest optimal storage and cooking methods based on the collected data. For example, it suggests storage methods such as refrigerating, freezing, and storing at room temperature, as well as cooking methods such as baking, boiling, and steaming. This makes it possible to suggest optimal storage and cooking methods.

[0038] When an anomaly is detected, the anomaly detection unit can identify the cause of the anomaly and propose specific countermeasures to address that cause. When an anomaly is detected, the anomaly detection unit can, for example, use generative AI to identify the cause of the anomaly. For example, it can analyze the cause of food spoilage or discoloration and propose specific countermeasures. The anomaly detection unit can also propose specific countermeasures to address the cause of the anomaly based on the collected data. For example, it can propose process improvements or equipment installation. This makes it possible to identify the cause of the anomaly and propose specific countermeasures.

[0039] When an anomaly is detected, the anomaly detection unit can evaluate the severity of the anomaly and automatically propose countermeasures according to the severity. For example, when the generation AI detects an anomaly, the anomaly detection unit builds a system that evaluates the severity of the anomaly. For example, it analyzes the degree of decay or discoloration and evaluates the severity. The anomaly detection unit can also automatically propose countermeasures according to the severity based on the collected data. For example, it proposes emergency responses and long-term measures. This makes it possible to automatically propose countermeasures according to the severity of the anomaly.

[0040] When detecting an anomaly, the anomaly detection unit can analyze the frequency and patterns of an anomaly and propose preventive measures. The anomaly detection unit, for example, uses generative AI to build a system that analyzes the frequency and patterns of an anomaly. For example, it analyzes the tendency of an anomaly to occur based on past data. The anomaly detection unit can also propose preventive measures based on the collected data. For example, it makes suggestions for process improvements or equipment installation. This makes it possible to analyze the frequency and patterns of an anomaly and propose preventive measures.

[0041] When an anomaly is detected, the anomaly detection unit can evaluate the extent of the anomaly's impact and propose measures to minimize the impact. For example, when the generation AI detects an anomaly, the anomaly detection unit builds a system to evaluate the extent of the anomaly's impact. For example, it analyzes whether the food around the food in which the anomaly occurred has been affected. The anomaly detection unit can also propose measures to minimize the impact based on the collected data. For example, it makes suggestions for process improvements or equipment installation. This makes it possible to evaluate the extent of the anomaly's impact and propose measures to minimize the impact.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The food safety AI monitoring system can further include a purchase history analysis unit that analyzes consumer purchasing history. The purchase history analysis unit, for example, collects data on foods purchased in the past by consumers and analyzes their preferences and purchasing patterns. This makes it possible to provide personalized food recommendations to consumers. For example, it can identify the types and brands of food frequently purchased by a particular consumer and notify that consumer of related new products and special sales information. The purchase history analysis unit can also collect feedback on food quality and safety based on the consumer's purchasing history, which can be used to improve the system's accuracy. This can improve the consumer's purchasing experience.

[0044] The food safety AI monitoring system can further include a production location information collection unit that collects information on the origin of food. The production location information collection unit, for example, collects data on the origin of food and provides it to consumers. This allows consumers to confirm the origin of food and purchase it with peace of mind. For example, providing information on the quality and safety of food produced in a specific region can gain consumer trust. The production location information collection unit can also strengthen food traceability based on the collected data. This makes it possible to strengthen quality control based on information on the origin of food.

[0045] The food safety AI monitoring system can further include a health monitoring unit that monitors the consumer's health condition. The health monitoring unit, for example, collects consumer health data and uses it to make food recommendations. This makes it possible to make food recommendations based on the consumer's health condition. For example, it can recommend foods and nutrients suitable for specific health conditions, supporting the consumer's health management. The health monitoring unit can also evaluate the consumer's health condition based on the collected health data and make appropriate food recommendations. This makes it possible to make food recommendations based on the consumer's health condition.

[0046] The food safety AI monitoring system can further include a promotion proposal unit to increase consumer purchasing motivation. The promotion proposal unit, for example, analyzes consumers' purchasing history and preferences to propose effective promotion campaigns. This can increase consumer purchasing motivation. For example, it can provide discount coupons or special offers to specific consumers to stimulate their purchasing motivation. The promotion proposal unit can also propose promotion strategies tailored to consumer preferences based on the collected data. This enables effective promotions to increase consumer purchasing motivation.

[0047] The food safety AI monitoring system can further include a meal record analysis unit that analyzes consumers' meal records. For example, the meal record analysis unit collects data from consumers' daily meal records and analyzes nutritional balance and eating trends. This can support consumers' health management. For example, if a consumer is lacking in a particular nutrient, it can suggest foods containing that nutrient. The meal record analysis unit can also evaluate consumers' eating habits based on the collected data and suggest improvement measures. This enables effective suggestions to support consumers' health management.

[0048] The food safety AI monitoring system can further include a purchasing behavior prediction unit that predicts consumer purchasing behavior. The purchasing behavior prediction unit, for example, analyzes consumers' purchasing history and preferences to predict future purchasing behavior. This makes it possible to make food recommendations to consumers at the appropriate time. For example, if a particular consumer tends to purchase certain foods at a particular time of year, related foods can be recommended for that time of year. The purchasing behavior prediction unit can also analyze consumer purchasing patterns based on the collected data and propose effective marketing strategies. This makes it possible to predict consumer purchasing behavior and make appropriate recommendations.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The image collection unit collects food images and data. For example, cameras and sensors are used to take images of fresh food and processed products in supermarkets and food factories, and the data is collected. Temperature and humidity sensors can also be used to collect environmental data. For example, the temperature and humidity inside a refrigerator can be monitored in real time to obtain a detailed understanding of the food storage conditions. Step 2: The data analysis unit uses AI to analyze the collected food images and data. For example, it analyzes the color, shape, and surface condition of the food to detect abnormalities such as spoilage, discoloration, and the presence of foreign matter. It can also predict the growth of microorganisms and assess the risk of food spoilage. For example, it evaluates the risk of microbial growth based on the surface condition and temperature data of the food. Step 3: The anomaly detection unit detects anomalies based on the results of analysis by the data analysis unit. For example, if the generation AI determines that there is a problem with the quality or safety of food, it will detect an anomaly. It can also identify the cause of the anomaly and propose specific countermeasures for that cause. For example, it can analyze the cause of food spoilage or discoloration and propose specific countermeasures. Step 4: The notification unit notifies consumers and businesses of any abnormalities detected by the anomaly detection unit. For example, if the generation AI detects spoilage or discoloration, it will issue a warning to consumers and businesses. Notifications are sent via smartphone apps, email, or messaging services, allowing consumers and businesses to respond quickly.

[0051] (Example 2) The food safety AI monitoring system according to an embodiment of the present invention is a system that uses AI to monitor the quality and safety of fresh foods and processed foods, and detects and notifies abnormalities. This enables the food safety AI monitoring system to maintain a high level of food quality and safety.

[0052] The food safety AI monitoring system according to the embodiment includes an image collection unit, a data analysis unit, an anomaly detection unit, and a notification unit. The image collection unit collects food images and data. For example, it uses cameras and sensors in supermarkets and food factories to capture images of fresh foods and processed products and collect the data. The image collection unit can also collect environmental data using temperature and humidity sensors. For example, it can monitor the temperature and humidity inside a refrigerator in real time to obtain a detailed understanding of food storage conditions. The data analysis unit uses a generation AI to analyze the collected food images and data. For example, the generation AI can analyze the color, shape, and surface condition of food to detect abnormalities such as spoilage, discoloration, and foreign matter contamination. The data analysis unit can also predict microbial growth status and evaluate the risk of food spoilage. For example, it can evaluate the risk of microbial growth based on data on the surface condition and temperature of food. The anomaly detection unit detects anomalies based on the results of analysis by the data analysis unit. For example, if the generation AI determines that there is a problem with the quality or safety of food, it detects an anomaly. The anomaly detection unit can also identify the cause of the anomaly and propose specific countermeasures to address the cause. For example, it analyzes the causes of food spoilage or discoloration and proposes specific countermeasures. The notification unit notifies of abnormalities detected by the anomaly detection unit. For example, if the generation AI detects spoilage or discoloration, it issues a warning to consumers and businesses. Notifications are sent via smartphone apps, email, or messaging services, allowing consumers and businesses to respond quickly. As a result, the food safety AI monitoring system according to the embodiment can maintain a high level of food quality and safety. For example, consumers can purchase and consume food with peace of mind, and businesses can improve the efficiency of quality control. In addition, food safety can be ensured by responding quickly when an abnormality occurs.

[0053] When collecting food images and data, the image collection unit can simultaneously collect environmental data such as temperature and humidity. The image collection unit can also use temperature and humidity sensors, for example, to simultaneously collect environmental data. For example, the image collection unit can monitor the temperature and humidity inside a refrigerator in real time to obtain a detailed understanding of the food storage conditions. This allows for comprehensive monitoring of factors that affect the condition of food.

[0054] The image collection unit can add information about the food's production history and distribution route to images and data of the food. The image collection unit, for example, adds production history information to images and data of the food. For example, information about the producer, production date, and information about the pesticides and fertilizers used can be registered in a database to enhance traceability. The image collection unit can also add distribution route information. For example, information such as the delivery route, delivery company, and delivery date and time can be registered in a database to track the food's distribution route. This enhances traceability.

[0055] The image collection unit can collect emotions of consumers when selecting food in real time and analyze consumer preferences based on that data. The image collection unit, for example, uses an emotion estimation function to analyze facial expressions and voices of consumers when selecting food and collect emotion data. For example, a camera or microphone can be used to analyze emotions such as joy or surprise of consumers in real time. The image collection unit can also analyze consumer preferences based on the collected emotion data. For example, the image collection unit can identify consumer preferences based on past purchase history and survey results and make personalized food suggestions. This makes it possible to analyze consumer preferences and make personalized food suggestions.

[0056] When collecting food images and data, the image collection unit simultaneously collects audio data, and can detect changes in quality from the sounds of food packaging and handling. For example, when collecting food images and data, the image collection unit simultaneously collects audio data. For example, the sounds of food packaging and handling are recorded with a microphone to detect changes in quality. The image collection unit can also analyze the collected audio data to detect changes in food quality. For example, changes in packaging sounds and handling sounds are analyzed to detect quality declines. This makes it possible to detect changes in food quality from the audio data.

[0057] The image collection unit can add nutritional information about food to images and data of food and provide it to consumers in cooperation with a health management app. The image collection unit, for example, adds nutritional information to images and data of food. For example, the image collection unit registers the food's ingredient list and nutritional value in a database and provides it to consumers. The image collection unit can also provide nutritional information to consumers in cooperation with a health management app. For example, the nutritional information about food can be linked to a health management app and used by consumers when recording their meals and calculating nutrition. This makes it possible to provide nutritional information to consumers and support health management.

[0058] The image collection unit can analyze consumers' emotional responses to the collected food data and propose a marketing strategy to elicit positive emotions. The image collection unit, for example, uses an emotion estimation function to analyze consumers' emotional responses to the collected food data in real time. For example, it analyzes the facial expressions and voices of consumers when they look at food to collect emotional data. Furthermore, the image collection unit can propose a marketing strategy to elicit positive emotions based on the collected emotional data. For example, it can propose a promotional campaign or advertising strategy to increase consumers' purchasing motivation. This makes it possible to propose a marketing strategy based on consumer emotions.

[0059] When analyzing food images and data, the data analysis unit can predict the growth status of microorganisms and assess the risk of food spoilage. The data analysis unit, for example, uses generative AI to analyze food images and data and predict the growth status of microorganisms. For example, it assesses the risk of microbial growth based on the condition of the food's surface and temperature data. The data analysis unit can also assess the risk of food spoilage based on collected data. For example, it analyzes changes in temperature and humidity to assess the risk of spoilage. This makes it possible to predict the risk of food spoilage and strengthen quality control.

[0060] When analyzing food images and data, the data analysis unit adds information about the chemical composition of the food, allowing it to detect allergens and evaluate nutritional value. For example, the data analysis unit adds information about the chemical composition of the food to the data analyzed by the generative AI. For example, it registers the food's ingredient list and allergen information in a database and detects allergens. The data analysis unit can also evaluate the nutritional value of food based on the collected data. For example, it analyzes nutritional components such as calories, vitamins, and minerals and evaluates nutritional value. This makes it possible to detect allergens and evaluate nutritional value.

[0061] The data analysis unit can analyze the emotions consumers have toward food quality and adjust the quality evaluation criteria based on those emotions. The data analysis unit, for example, uses an emotion estimation function to analyze the emotions consumers have toward food quality in real time. For example, it analyzes the facial expressions and voices of consumers when they view food and collects emotion data. The data analysis unit can also adjust the quality evaluation criteria based on the collected emotion data. For example, it sets the quality evaluation criteria based on sensory evaluation or mechanical evaluation. This makes it possible to set quality evaluation criteria based on consumer emotions.

[0062] The data analysis unit adds food aroma data when analyzing food images and data, and can detect quality degradation from changes in aroma. The data analysis unit adds aroma data when analyzing food images and data, for example, using generative AI. For example, the data analysis unit detects food aromas with a sensor and analyzes changes in quality. The data analysis unit can also detect quality degradation based on the collected aroma data. For example, it analyzes changes in aroma, color, and texture to detect quality degradation. This makes it possible to detect quality degradation from changes in aroma.

[0063] When analyzing food images and data, the data analysis unit adds information on food storage and cooking methods, making it possible to suggest optimal storage and cooking methods. For example, the data analysis unit adds information on food storage and cooking methods to the data analyzed by the generative AI. For example, it registers food storage temperatures and cooking times in a database and suggests optimal methods. The data analysis unit can also suggest optimal storage and cooking methods based on the collected data. For example, it suggests storage methods such as refrigerating, freezing, and storing at room temperature, as well as cooking methods such as baking, boiling, and steaming. This makes it possible to suggest optimal storage and cooking methods.

[0064] The data analysis unit can collect consumers' emotional responses to the food analysis results and, based on that data, propose improvement measures to increase consumer satisfaction. The data analysis unit, for example, uses an emotion estimation function to collect consumers' emotional responses to the food analysis results in real time. For example, it analyzes the facial expressions and voices of consumers when they view the analysis results to collect emotional data. The data analysis unit can also propose improvement measures to increase consumer satisfaction based on the collected emotional data. For example, it makes suggestions for product improvements or service improvements. This makes it possible to propose improvement measures to increase consumer satisfaction.

[0065] When an anomaly is detected, the anomaly detection unit can identify the cause of the anomaly and propose specific countermeasures to address that cause. When an anomaly is detected, the anomaly detection unit can, for example, use generative AI to identify the cause of the anomaly. For example, it can analyze the cause of food spoilage or discoloration and propose specific countermeasures. The anomaly detection unit can also propose specific countermeasures to address the cause of the anomaly based on the collected data. For example, it can propose process improvements or equipment installation. This makes it possible to identify the cause of the anomaly and propose specific countermeasures.

[0066] When an anomaly is detected, the anomaly detection unit can evaluate the severity of the anomaly and automatically propose countermeasures according to the severity. For example, when the generation AI detects an anomaly, the anomaly detection unit builds a system that evaluates the severity of the anomaly. For example, it analyzes the degree of decay or discoloration and evaluates the severity. The anomaly detection unit can also automatically propose countermeasures according to the severity based on the collected data. For example, it proposes emergency responses and long-term measures. This makes it possible to automatically propose countermeasures according to the severity of the anomaly.

[0067] The anomaly detection unit can analyze the emotional reaction of a consumer who receives an anomaly notification and select a notification method that takes those emotions into consideration. The anomaly detection unit, for example, uses an emotion estimation function to analyze the emotional reaction of a consumer who receives an anomaly notification in real time. For example, it analyzes facial expressions and voice when the notification is received and collects emotional data. Furthermore, the anomaly detection unit can select a notification method that takes those emotions into consideration based on the collected emotional data. For example, it selects notification methods such as email notification, app notification, and alert sound. This makes it possible to select a notification method that takes the consumer's emotions into consideration.

[0068] When detecting an anomaly, the anomaly detection unit can analyze the frequency and patterns of an anomaly and propose preventive measures. The anomaly detection unit, for example, uses generative AI to build a system that analyzes the frequency and patterns of an anomaly. For example, it analyzes the tendency of an anomaly to occur based on past data. The anomaly detection unit can also propose preventive measures based on the collected data. For example, it makes suggestions for process improvements or equipment installation. This makes it possible to analyze the frequency and patterns of an anomaly and propose preventive measures.

[0069] When an anomaly is detected, the anomaly detection unit can evaluate the extent of the anomaly's impact and propose measures to minimize the impact. For example, when the generation AI detects an anomaly, the anomaly detection unit builds a system to evaluate the extent of the anomaly's impact. For example, it analyzes whether the food around the food in which the anomaly occurred has been affected. The anomaly detection unit can also propose measures to minimize the impact based on the collected data. For example, it makes suggestions for process improvements or equipment installation. This makes it possible to evaluate the extent of the anomaly's impact and propose measures to minimize the impact.

[0070] The anomaly detection unit collects consumers' emotional responses to the anomaly notification and can improve the content and method of the notification based on that data. The anomaly detection unit, for example, uses an emotion estimation function to collect consumers' emotional responses to the anomaly notification in real time. For example, it analyzes facial expressions and voices when receiving the notification to collect emotional data. The anomaly detection unit can also improve the content and method of the notification based on the collected emotional data. For example, it can adjust the timing and level of detail of the notification. This makes it possible to improve the content and method of the notification based on the consumer's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The food safety AI monitoring system can further include a purchase history analysis unit that analyzes consumer purchasing history. The purchase history analysis unit, for example, collects data on foods purchased in the past by consumers and analyzes their preferences and purchasing patterns. This makes it possible to provide personalized food recommendations to consumers. For example, it can identify the types and brands of food frequently purchased by a particular consumer and notify that consumer of related new products and special sales information. The purchase history analysis unit can also collect feedback on food quality and safety based on the consumer's purchasing history, which can be used to improve the system's accuracy. This can improve the consumer's purchasing experience.

[0073] The food safety AI monitoring system can further include a production location information collection unit that collects information on the origin of food. The production location information collection unit, for example, collects data on the origin of food and provides it to consumers. This allows consumers to confirm the origin of food and purchase it with peace of mind. For example, providing information on the quality and safety of food produced in a specific region can gain consumer trust. The production location information collection unit can also strengthen food traceability based on the collected data. This makes it possible to strengthen quality control based on information on the origin of food.

[0074] The food safety AI monitoring system can further include an emotion estimation and suggestion unit that estimates consumer emotions and makes food suggestions based on the estimated emotions. The emotion estimation and suggestion unit, for example, analyzes facial expressions and voices of consumers when selecting foods to collect emotion data. This makes it possible to make personalized food suggestions based on the consumer's emotions. For example, it can identify foods that consumers find delightful or surprising and suggest related foods to the consumer. The emotion estimation and suggestion unit can also analyze consumer preferences based on the collected emotion data to make more appropriate food suggestions. This makes it possible to make food suggestions based on the consumer's emotions.

[0075] The food safety AI monitoring system can further include a health monitoring unit that monitors the consumer's health condition. The health monitoring unit, for example, collects consumer health data and uses it to make food recommendations. This makes it possible to make food recommendations based on the consumer's health condition. For example, it can recommend foods and nutrients suitable for specific health conditions, supporting the consumer's health management. The health monitoring unit can also evaluate the consumer's health condition based on the collected health data and make appropriate food recommendations. This makes it possible to make food recommendations based on the consumer's health condition.

[0076] The food safety AI monitoring system can further include an emotional quality evaluation unit that estimates consumer emotions and evaluates food quality based on the estimated emotions. The emotional quality evaluation unit, for example, analyzes the facial expressions and voices of consumers when they view food and collects emotional data. This makes it possible to evaluate food quality based on consumer emotions. For example, it can highly evaluate the quality of food that makes consumers feel happy or surprised, and perform quality control based on that evaluation. The emotional quality evaluation unit can also adjust quality evaluation standards based on the collected emotional data. This makes it possible to evaluate quality based on consumer emotions.

[0077] The food safety AI monitoring system can further include a promotion proposal unit to increase consumer purchasing motivation. The promotion proposal unit, for example, analyzes consumers' purchasing history and preferences to propose effective promotion campaigns. This can increase consumer purchasing motivation. For example, it can provide discount coupons or special offers to specific consumers to stimulate their purchasing motivation. The promotion proposal unit can also propose promotion strategies tailored to consumer preferences based on the collected data. This enables effective promotions to increase consumer purchasing motivation.

[0078] The food safety AI monitoring system can further include an emotional marketing proposal unit that estimates consumer emotions and proposes marketing strategies based on the estimated emotions. The emotional marketing proposal unit, for example, analyzes the facial expressions and voices of consumers when they look at food and collects emotional data. This makes it possible to propose effective marketing strategies based on consumer emotions. For example, it can propose promotional campaigns related to foods that make consumers feel happy or surprised, thereby increasing their desire to purchase. The emotional marketing proposal unit can also analyze consumer preferences based on the collected emotional data and propose more appropriate marketing strategies. This makes it possible to implement marketing strategies based on consumer emotions.

[0079] The food safety AI monitoring system can further include a meal record analysis unit that analyzes consumers' meal records. For example, the meal record analysis unit collects data from consumers' daily meal records and analyzes nutritional balance and eating trends. This can support consumers' health management. For example, if a consumer is lacking in a particular nutrient, it can suggest foods containing that nutrient. The meal record analysis unit can also evaluate consumers' eating habits based on the collected data and suggest improvement measures. This enables effective suggestions to support consumers' health management.

[0080] The food safety AI monitoring system can further include an emotion preservation suggestion unit that estimates consumer emotions and suggests food storage methods based on the estimated emotions. The emotion preservation suggestion unit, for example, analyzes facial expressions and voices of consumers when storing food to collect emotion data. This makes it possible to suggest optimal storage methods based on the consumer's emotions. For example, if a consumer feels anxious or worried, the unit can suggest a storage method that takes those emotions into consideration. The emotion preservation suggestion unit can also analyze the consumer's preferences and storage habits based on the collected emotion data to suggest more appropriate storage methods. This makes it possible to suggest storage methods based on the consumer's emotions.

[0081] The food safety AI monitoring system can further include a purchasing behavior prediction unit that predicts consumer purchasing behavior. The purchasing behavior prediction unit, for example, analyzes consumers' purchasing history and preferences to predict future purchasing behavior. This makes it possible to make food recommendations to consumers at the appropriate time. For example, if a particular consumer tends to purchase certain foods at a particular time of year, related foods can be recommended for that time of year. The purchasing behavior prediction unit can also analyze consumer purchasing patterns based on the collected data and propose effective marketing strategies. This makes it possible to predict consumer purchasing behavior and make appropriate recommendations.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The image collection unit collects food images and data. For example, cameras and sensors are used to take images of fresh food and processed products in supermarkets and food factories, and the data is collected. Temperature and humidity sensors can also be used to collect environmental data. For example, the temperature and humidity inside a refrigerator can be monitored in real time to obtain a detailed understanding of the food storage conditions. Step 2: The data analysis unit uses AI to analyze the collected food images and data. For example, it analyzes the color, shape, and surface condition of the food to detect abnormalities such as spoilage, discoloration, and the presence of foreign matter. It can also predict the growth of microorganisms and assess the risk of food spoilage. For example, it evaluates the risk of microbial growth based on the surface condition and temperature data of the food. Step 3: The anomaly detection unit detects anomalies based on the results of analysis by the data analysis unit. For example, if the generation AI determines that there is a problem with the quality or safety of food, it will detect an anomaly. It can also identify the cause of the anomaly and propose specific countermeasures for that cause. For example, it can analyze the cause of food spoilage or discoloration and propose specific countermeasures. Step 4: The notification unit notifies consumers and businesses of any abnormalities detected by the anomaly detection unit. For example, if the generation AI detects spoilage or discoloration, it will issue a warning to consumers and businesses. Notifications are sent via smartphone apps, email, or messaging services, allowing consumers and businesses to respond quickly.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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, in order to avoid confusion and to 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.

[0150] 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]

[0151] 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 collection unit that collects images and data of food; a data analysis unit that analyzes the food images and data collected by the image collection unit; an anomaly detection unit that detects an anomaly based on the results of the analysis by the data analysis unit; a notification unit that notifies the user of an abnormality detected by the abnormality detection unit. A system characterized by:

2. The image acquisition unit Collecting real-time data on consumers' emotions when choosing food and analyzing their preferences based on that data 2. The system of claim 1.

3. The image acquisition unit When collecting food images and data, audio data is also collected at the same time, and quality changes can be detected from the sounds of food packaging and handling.

2. The system of claim 1.

4. The data analysis unit When analyzing food images and data, predict the growth of microorganisms and assess the risk of food spoilage.

2. The system of claim 1.

5. The abnormality detection unit When an anomaly is detected, the cause of the anomaly is identified and specific measures are proposed to address the cause.

2. The system of claim 1.

6. The data analysis unit Analyze consumer sentiment toward food quality and adjust quality assessment criteria based on that sentiment 2. The system of claim 1.

7. The abnormality detection unit Analyze the emotional reactions of consumers who receive abnormality notifications and select a notification method that takes their emotions into consideration.

2. The system of claim 1.

8. The abnormality detection unit Collecting consumers' emotional responses to abnormal notifications and using that data to improve the content and method of notifications 2. The system of claim 1.

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