System

Abstract

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

Description

[Technical Field]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0029] A food safety system according to an embodiment includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires images of food. For example, it uses a camera to acquire images of fresh foods and processed foods lined up on supermarket shelves in real time. The image acquisition unit can also acquire images of food using a sensor. For example, it uses an infrared sensor to measure the temperature of food and acquire the images. The image acquisition unit can also acquire images of food over a wide area using a drone. For example, a drone flies over supermarket shelves and acquires images of food. The analysis unit analyzes the food images acquired by the image acquisition unit. For example, AI can use image recognition technology to analyze the color, shape, surface condition, etc. of food to determine whether there are any quality issues. The analysis unit can also detect abnormalities in food using AI deep learning. For example, AI can study a large number of food images and detect abnormal patterns. The analysis unit can also detect abnormalities in food using AI multimodal analysis. For example, AI can combine and analyze image and temperature data to detect abnormalities. The notification unit detects abnormalities based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent through a smartphone app. The notification unit can also send the notification via email. For example, a notification of an abnormality is sent to an email address registered by the user. The notification unit can also send the notification through a voice assistant. For example, a smart speaker notifies the user of the abnormality by voice. In this way, the food safety system according to the embodiment can monitor the quality and safety of food in real time, detect abnormalities, and notify the user. For example, by receiving the notification, consumers can avoid foods with quality issues. Furthermore, food businesses can respond immediately if an abnormality is detected. Furthermore, consumers can purchase food with peace of mind even when shopping online.

[0030] The image acquisition unit simultaneously acquires environmental data such as temperature and humidity, and can combine the environmental data to predict food deterioration. For example, when acquiring images of food with a camera, the image acquisition unit simultaneously collects data from a temperature sensor and a humidity sensor. For example, the image acquisition unit can monitor the temperature and humidity inside a refrigerator in real time to predict the rate of food deterioration. The image acquisition unit can also monitor the food storage environment using a temperature sensor and a humidity sensor. For example, if the temperature inside a refrigerator rises, it can predict that food deterioration will progress. Furthermore, the image acquisition unit can predict food deterioration based on the environmental data. For example, it can combine temperature and humidity data to predict the rate of food deterioration. In this way, by combining environmental data, the accuracy of food deterioration predictions can be improved.

[0031] The analysis unit uses an odor sensor to detect the smell of food and can determine that there is a quality problem if the smell is abnormal. For example, when the AI ​​analyzes an image of food, the analysis unit uses an odor sensor to detect the smell of food. For example, if a rotten or abnormal smell is detected, it determines that there is a quality problem. The analysis unit can also use an odor sensor to monitor the smell of food in real time. For example, the odor sensor can constantly monitor the smell of food and notify if an abnormality is detected. Furthermore, the analysis unit can use an odor sensor to collect the smell of food as data. For example, it can record the intensity and type of smell as data and detect abnormalities. In this way, the odor sensor can detect quality problems that cannot be determined by appearance alone.

[0032] The image acquisition unit simultaneously monitors the inventory status on shelves, making it possible to streamline inventory management based on the inventory status. For example, when AI analyzes images of food, the image acquisition unit simultaneously monitors the inventory status on shelves. For example, it counts the number of foods lined up on shelves and issues a notification when inventory is low. The image acquisition unit can also monitor the inventory status on shelves in real time. For example, a camera constantly monitors the inventory status on shelves and issues a notification when inventory is low. Furthermore, the image acquisition unit can collect inventory status as data. For example, it can record the number and type of inventory as data, making inventory management more efficient. This improves the efficiency of inventory management by monitoring the inventory status on shelves in real time.

[0033] The analysis unit can use AI models specialized for different types of food to perform optimal monitoring for each food. For example, the analysis unit develops AI models specialized for different types of food to perform optimal monitoring for each food. For example, it develops a model to evaluate the freshness of meat and a model to evaluate the freshness of fish. The analysis unit can also use specialized AI models to monitor food quality in real time. For example, a model to evaluate the freshness of meat monitors meat quality in real time. Furthermore, the analysis unit can also use specialized AI models to detect abnormalities in food. For example, a model to evaluate the freshness of fish detects abnormalities in fish. In this way, by using AI models specialized for different types of food, the accuracy of quality assessment is improved.

[0034] When an abnormality is detected, the analysis unit can automatically identify the cause of the abnormality and propose specific countermeasures. For example, when AI detects an abnormality, the analysis unit automatically identifies the cause of the abnormality. For example, it can analyze the cause of a change in food color and identify a problem with the storage environment. The analysis unit can also collect the cause of the abnormality as data. For example, it can record the cause of the abnormality as data and use it for future countermeasures. Furthermore, the analysis unit can propose specific countermeasures based on the cause of the abnormality. For example, it can propose countermeasures such as improving the storage environment or strengthening quality control. This makes it possible to automatically identify the cause of the abnormality and propose specific countermeasures, enabling a rapid response.

[0035] The analysis unit can introduce an algorithm that learns from past abnormal data and predicts abnormality patterns. For example, the analysis unit introduces an algorithm in which AI learns from past abnormal data and predicts abnormality patterns. For example, conditions under which abnormalities are likely to occur are identified based on past abnormal data. The analysis unit can also collect abnormality patterns as data. For example, abnormality patterns are recorded as data and used for future predictions. Furthermore, the analysis unit can predict abnormalities based on abnormality patterns. For example, an abnormality is predicted based on conditions under which anomalies are likely to occur. In this way, the accuracy of anomaly detection is improved by learning from past abnormal data.

[0036] The analysis unit can apply the anomaly detection and notification function not only to food products but also to other consumer goods such as medicines and cosmetics. For example, the analysis unit can apply the anomaly detection and notification function to medicines and notify the user if there is a quality problem. For example, if there is an abnormality in the color or shape of a medicine, the user will be notified. The analysis unit can also apply the anomaly detection and notification function to cosmetics. For example, if there is an abnormality in the packaging of a cosmetic product, the user will be notified. Furthermore, the analysis unit can also apply the anomaly detection and notification function to daily necessities. For example, if there is a quality problem with a daily necessities, the user will be notified. In this way, applying the anomaly detection and notification function to other consumer goods makes quality control possible in a wide range of fields.

[0037] When an abnormality is detected, the analysis unit can automatically refer to relevant laws, regulations, and guidelines and propose appropriate responses. For example, when AI detects an abnormality, the analysis unit automatically refers to relevant laws, regulations, and guidelines. For example, it refers to the Food Sanitation Act and the Consumer Protection Act and proposes appropriate responses. The analysis unit can also store laws, regulations, and guidelines in a database and automatically refer to them when an abnormality is detected. For example, it proposes appropriate responses based on the laws, regulations, and guidelines stored in the database. Furthermore, the analysis unit can automatically propose responses based on laws, regulations, and guidelines. For example, it proposes corrective procedures and preventive measures. This allows appropriate responses to be taken quickly by automatically proposing responses based on laws, regulations, and guidelines.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The analysis unit can analyze the nutritional value of food and provide health information to the user. For example, it can analyze the ingredients of food and evaluate the vitamin and mineral content. The analysis unit can also suggest appropriate foods based on the user's health condition. For example, if the user needs a specific nutrient, it can suggest foods that contain a lot of that nutrient. Furthermore, the analysis unit can collect data on the nutritional value of food and use it to help the user improve their diet. This allows the user to obtain information to maintain a healthy diet.

[0040] The analysis unit can analyze the production history of food and provide users with traceability information. For example, it can analyze which farm the food was produced on and what process it went through before it was put on the shelves of a store. The analysis unit can also evaluate the safety of food based on the production history. For example, it can check whether a specific pesticide was used. Furthermore, the analysis unit can collect production history data and provide users with transparent information. This allows users to understand the production process of food and make purchases with peace of mind.

[0041] The analysis unit can analyze the packaging condition of food and notify the user if there is a problem with the packaging. For example, it can notify the user if the packaging is torn or if the sealing is insufficient. The analysis unit can also evaluate the quality of food based on the packaging condition. For example, if the packaging is not appropriate, it can determine that there is a quality problem. Furthermore, the analysis unit can collect data on the packaging condition and use it to make future improvements. This allows the user to check the quality of food based on the packaging condition.

[0042] The analysis unit can analyze the expiration date of food products and suggest the optimal timing for consumption to the user. For example, it can suggest that food products with an approaching expiration date be consumed first. The analysis unit can also suggest a method for storing food products based on the expiration date. For example, it can suggest that foods with a long expiration date be frozen. Furthermore, the analysis unit can collect expiration dates as data and analyze the user's consumption patterns. This allows the user to consume food without waste.

[0043] The analysis unit can analyze food price fluctuations and suggest optimal purchasing times to users. For example, it can predict when prices will fall and suggest purchasing at that time. The analysis unit can also recommend specific food items to purchase based on price fluctuations. For example, it can suggest foods with stable prices. Furthermore, the analysis unit can collect price fluctuations as data and use it to predict future prices. This allows users to purchase food at the optimal time.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh foods and processed foods lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. For example, an infrared sensor is used to measure the temperature of the food and acquire the image. Furthermore, drones can be used to acquire images of a wide range of food. For example, a drone flies over the supermarket shelves and acquires images of food. Step 2: The analysis unit analyzes the food images captured by the image acquisition unit. For example, AI can use image recognition technology to analyze the color, shape, and surface condition of the food to determine whether there are any quality issues. AI can also use deep learning to detect abnormalities in food. Furthermore, AI can use multimodal analysis to combine and analyze image and temperature data to detect abnormalities. Step 3: The notification unit detects an abnormality based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent via a smartphone app, email, or voice assistant.

[0046] (Example 2) The food safety system according to the embodiment of the present invention uses AI to monitor the quality and safety of fresh foods and processed foods, and detects and notifies customers of abnormalities. This allows consumers to purchase safe food ingredients with peace of mind.

[0047] A food safety system according to an embodiment includes an image acquisition unit, an analysis unit, and a notification unit. The image acquisition unit acquires images of food. For example, it uses a camera to acquire images of fresh foods and processed foods lined up on supermarket shelves in real time. The image acquisition unit can also acquire images of food using a sensor. For example, it uses an infrared sensor to measure the temperature of food and acquire the images. The image acquisition unit can also acquire images of food over a wide area using a drone. For example, a drone flies over supermarket shelves and acquires images of food. The analysis unit analyzes the food images acquired by the image acquisition unit. For example, AI can use image recognition technology to analyze the color, shape, surface condition, etc. of food to determine whether there are any quality issues. The analysis unit can also detect abnormalities in food using AI deep learning. For example, AI can study a large number of food images and detect abnormal patterns. The analysis unit can also detect abnormalities in food using AI multimodal analysis. For example, AI can combine and analyze image and temperature data to detect abnormalities. The notification unit detects abnormalities based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent through a smartphone app. The notification unit can also send the notification via email. For example, a notification of an abnormality is sent to an email address registered by the user. The notification unit can also send the notification through a voice assistant. For example, a smart speaker notifies the user of the abnormality by voice. In this way, the food safety system according to the embodiment can monitor the quality and safety of food in real time, detect abnormalities, and notify the user. For example, by receiving the notification, consumers can avoid foods with quality issues. Furthermore, food businesses can respond immediately if an abnormality is detected. Furthermore, consumers can purchase food with peace of mind even when shopping online.

[0048] The image acquisition unit simultaneously acquires environmental data such as temperature and humidity, and can combine the environmental data to predict food deterioration. For example, when acquiring images of food with a camera, the image acquisition unit simultaneously collects data from a temperature sensor and a humidity sensor. For example, the image acquisition unit can monitor the temperature and humidity inside a refrigerator in real time to predict the rate of food deterioration. The image acquisition unit can also monitor the food storage environment using a temperature sensor and a humidity sensor. For example, if the temperature inside a refrigerator rises, it can predict that food deterioration will progress. Furthermore, the image acquisition unit can predict food deterioration based on the environmental data. For example, it can combine temperature and humidity data to predict the rate of food deterioration. In this way, by combining environmental data, the accuracy of food deterioration predictions can be improved.

[0049] The analysis unit uses an odor sensor to detect the smell of food and can determine that there is a quality problem if the smell is abnormal. For example, when the AI ​​analyzes an image of food, the analysis unit uses an odor sensor to detect the smell of food. For example, if a rotten or abnormal smell is detected, it determines that there is a quality problem. The analysis unit can also use an odor sensor to monitor the smell of food in real time. For example, the odor sensor can constantly monitor the smell of food and notify if an abnormality is detected. Furthermore, the analysis unit can use an odor sensor to collect the smell of food as data. For example, it can record the intensity and type of smell as data and detect abnormalities. In this way, the odor sensor can detect quality problems that cannot be determined by appearance alone.

[0050] The analysis unit can analyze the consumer's facial expression, estimate the consumer's emotions when they pick up the food, and evaluate the quality of the food based on those emotions. For example, the analysis unit uses AI to analyze the consumer's facial expression and estimate the emotions when they pick up the food. For example, if the consumer has an unpleasant expression, it determines that there is a problem with the quality of the food. The analysis unit can also analyze the consumer's facial expression in real time. For example, a camera can constantly monitor the consumer's facial expression and estimate their emotions. Furthermore, the analysis unit can evaluate the quality of food based on the consumer's emotional data. For example, it can analyze the consumer's emotional data and identify foods with quality issues. This allows for more accurate quality evaluation by evaluating food quality based on the consumer's emotions.

[0051] The image acquisition unit simultaneously monitors the inventory status on shelves, making it possible to streamline inventory management based on the inventory status. For example, when AI analyzes images of food, the image acquisition unit simultaneously monitors the inventory status on shelves. For example, it counts the number of foods lined up on shelves and issues a notification when inventory is low. The image acquisition unit can also monitor the inventory status on shelves in real time. For example, a camera constantly monitors the inventory status on shelves and issues a notification when inventory is low. Furthermore, the image acquisition unit can collect inventory status as data. For example, it can record the number and type of inventory as data, making inventory management more efficient. This improves the efficiency of inventory management by monitoring the inventory status on shelves in real time.

[0052] The analysis unit can use AI models specialized for different types of food to perform optimal monitoring for each food. For example, the analysis unit develops AI models specialized for different types of food to perform optimal monitoring for each food. For example, it develops a model to evaluate the freshness of meat and a model to evaluate the freshness of fish. The analysis unit can also use specialized AI models to monitor food quality in real time. For example, a model to evaluate the freshness of meat monitors meat quality in real time. Furthermore, the analysis unit can also use specialized AI models to detect abnormalities in food. For example, a model to evaluate the freshness of fish detects abnormalities in fish. In this way, by using AI models specialized for different types of food, the accuracy of quality assessment is improved.

[0053] The analysis unit can analyze consumer behavior patterns and make product suggestions that match the consumer's preferences. For example, AI can analyze consumer behavior patterns and make product suggestions that match the consumer's preferences. For example, it can analyze the food trends that consumers often pick up and suggest related products. The analysis unit can also analyze the consumer's purchasing history. For example, it can suggest related products based on data on food that the consumer has previously purchased. Furthermore, the analysis unit can analyze consumer behavior patterns in real time. For example, a camera can monitor the consumer's behavior and make product suggestions in real time. This makes it possible to analyze consumer behavior patterns and make personalized product suggestions.

[0054] When an abnormality is detected, the analysis unit can automatically identify the cause of the abnormality and propose specific countermeasures. For example, when AI detects an abnormality, the analysis unit automatically identifies the cause of the abnormality. For example, it can analyze the cause of a change in food color and identify a problem with the storage environment. The analysis unit can also collect the cause of the abnormality as data. For example, it can record the cause of the abnormality as data and use it for future countermeasures. Furthermore, the analysis unit can propose specific countermeasures based on the cause of the abnormality. For example, it can propose countermeasures such as improving the storage environment or strengthening quality control. This makes it possible to automatically identify the cause of the abnormality and propose specific countermeasures, enabling a rapid response.

[0055] The analysis unit can introduce an algorithm that learns from past abnormal data and predicts abnormality patterns. For example, the analysis unit introduces an algorithm in which AI learns from past abnormal data and predicts abnormality patterns. For example, conditions under which abnormalities are likely to occur are identified based on past abnormal data. The analysis unit can also collect abnormality patterns as data. For example, abnormality patterns are recorded as data and used for future predictions. Furthermore, the analysis unit can predict abnormalities based on abnormality patterns. For example, an abnormality is predicted based on conditions under which anomalies are likely to occur. In this way, the accuracy of anomaly detection is improved by learning from past abnormal data.

[0056] The analysis unit can analyze the user's emotional response and customize the notification content to match the user's emotions. For example, the analysis unit uses AI to analyze the user's emotional response and customize the notification content. For example, if the user expresses unpleasant emotions, the notification content is softened. The analysis unit can also customize the notification content based on the user's emotional data. For example, it analyzes the user's emotional data and generates appropriate notification content. Furthermore, the analysis unit can analyze the user's emotional response in real time. For example, a camera monitors the user's facial expressions and customizes the notification content in real time. This improves user satisfaction by providing notification content that matches the user's emotions.

[0057] The analysis unit can apply the anomaly detection and notification function not only to food products but also to other consumer goods such as medicines and cosmetics. For example, the analysis unit can apply the anomaly detection and notification function to medicines and notify the user if there is a quality problem. For example, if there is an abnormality in the color or shape of a medicine, the user will be notified. The analysis unit can also apply the anomaly detection and notification function to cosmetics. For example, if there is an abnormality in the packaging of a cosmetic product, the user will be notified. Furthermore, the analysis unit can also apply the anomaly detection and notification function to daily necessities. For example, if there is a quality problem with a daily necessities, the user will be notified. In this way, applying the anomaly detection and notification function to other consumer goods makes quality control possible in a wide range of fields.

[0058] When an abnormality is detected, the analysis unit can automatically refer to relevant laws, regulations, and guidelines and propose appropriate responses. For example, when AI detects an abnormality, the analysis unit automatically refers to relevant laws, regulations, and guidelines. For example, it refers to the Food Sanitation Act and the Consumer Protection Act and proposes appropriate responses. The analysis unit can also store laws, regulations, and guidelines in a database and automatically refer to them when an abnormality is detected. For example, it proposes appropriate responses based on the laws, regulations, and guidelines stored in the database. Furthermore, the analysis unit can automatically propose responses based on laws, regulations, and guidelines. For example, it proposes corrective procedures and preventive measures. This allows appropriate responses to be taken quickly by automatically proposing responses based on laws, regulations, and guidelines.

[0059] The analysis unit can monitor the emotions of a user who receives an abnormality notification in real time and send a follow-up notification as necessary. For example, the analysis unit uses AI to monitor the emotions of a user who receives an abnormality notification in real time. For example, if the user is feeling anxious, a follow-up notification is sent. The analysis unit can also send a follow-up notification based on the user's emotional data. For example, it can analyze the user's emotional data and generate an appropriate follow-up notification. Furthermore, the analysis unit can analyze the user's emotions in real time and send a follow-up notification as necessary. For example, a camera can monitor the user's facial expressions and send a follow-up notification in real time. This allows the user's emotions to be monitored in real time and follow-up notifications sent as necessary, improving the user's sense of security.

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

[0061] The analysis unit can analyze the nutritional value of food and provide health information to the user. For example, it can analyze the ingredients of food and evaluate the vitamin and mineral content. The analysis unit can also suggest appropriate foods based on the user's health condition. For example, if the user needs a specific nutrient, it can suggest foods that contain a lot of that nutrient. Furthermore, the analysis unit can collect data on the nutritional value of food and use it to help the user improve their diet. This allows the user to obtain information to maintain a healthy diet.

[0062] The analysis unit can estimate the user's emotions and suggest food recipes based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes using foods that have a relaxing effect. The analysis unit can also suggest recipes that match the season or event based on the user's emotional data. For example, if the user appears to be feeling happy, it can suggest recipes for a party. Furthermore, the analysis unit can analyze the user's emotions in real time and suggest recipes that match the user's mood at the time. This allows the user to enjoy meals that match their emotions.

[0063] The analysis unit can analyze the production history of food and provide users with traceability information. For example, it can analyze which farm the food was produced on and what process it went through before it was put on the shelves of a store. The analysis unit can also evaluate the safety of food based on the production history. For example, it can check whether a specific pesticide was used. Furthermore, the analysis unit can collect production history data and provide users with transparent information. This allows users to understand the production process of food and make purchases with peace of mind.

[0064] The analysis unit can estimate the user's emotions and suggest food storage methods based on the estimated emotions. For example, if the user feels busy, it can suggest an easy method of storage. The analysis unit can also provide advice on extending the food storage period based on the user's emotional data. For example, if the user feels anxious, it can provide detailed guidance on food storage. Furthermore, the analysis unit can analyze the user's emotions in real time and suggest storage methods that suit the situation at the time. This allows the user to know the appropriate storage method that suits their emotions.

[0065] The analysis unit can analyze the packaging condition of food and notify the user if there is a problem with the packaging. For example, it can notify the user if the packaging is torn or if the sealing is insufficient. The analysis unit can also evaluate the quality of food based on the packaging condition. For example, if the packaging is not appropriate, it can determine that there is a quality problem. Furthermore, the analysis unit can collect data on the packaging condition and use it to make future improvements. This allows the user to check the quality of food based on the packaging condition.

[0066] The analysis unit can estimate the user's emotions and suggest the best time to purchase food based on the estimated emotions. For example, if the user is tired, it can suggest purchasing online. The analysis unit can also suggest purchasing at a specific time based on the user's emotion data. For example, it can suggest purchasing at a time when the user is relaxed. Furthermore, the analysis unit can analyze the user's emotions in real time and suggest the best time to purchase based on the situation at the time. This allows the user to know the optimal time to purchase based on their emotions.

[0067] The analysis unit can analyze the expiration date of food products and suggest the optimal timing for consumption to the user. For example, it can suggest that food products with an approaching expiration date be consumed first. The analysis unit can also suggest a method for storing food products based on the expiration date. For example, it can suggest that foods with a long expiration date be frozen. Furthermore, the analysis unit can collect expiration dates as data and analyze the user's consumption patterns. This allows the user to consume food without waste.

[0068] The analysis unit can estimate the user's emotions and automatically generate a food shopping list based on the estimated emotions. For example, if the user is feeling stressed, foods with a relaxing effect can be added to the list. The analysis unit can also add foods containing specific nutrients to the list based on the user's emotional data. For example, if the user is tired, foods suitable for replenishing energy can be suggested. Furthermore, the analysis unit can analyze the user's emotions in real time and generate a shopping list tailored to the situation at that time. This allows the user to purchase foods that are optimally tailored to their emotions.

[0069] The analysis unit can analyze food price fluctuations and suggest optimal purchasing times to users. For example, it can predict when prices will fall and suggest purchasing at that time. The analysis unit can also recommend specific food items to purchase based on price fluctuations. For example, it can suggest foods with stable prices. Furthermore, the analysis unit can collect price fluctuations as data and use it to predict future prices. This allows users to purchase food at the optimal time.

[0070] The analysis unit can estimate the user's emotions and provide food reviews based on the estimated emotions. For example, if the user expresses positive emotions, it can provide highly rated reviews from other users. The analysis unit can also customize the content of the review based on the user's emotional data. For example, if the user expresses negative emotions, it can provide a detailed review. Furthermore, the analysis unit can analyze the user's emotions in real time and provide reviews tailored to the situation at the time. This allows the user to refer to reviews that match their emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The image acquisition unit acquires images of food. For example, a camera is used to acquire images of fresh foods and processed foods lined up on supermarket shelves in real time. Food images can also be acquired using a sensor. For example, an infrared sensor is used to measure the temperature of the food and acquire the image. Furthermore, drones can be used to acquire images of a wide range of food. For example, a drone flies over the supermarket shelves and acquires images of food. Step 2: The analysis unit analyzes the food images captured by the image acquisition unit. For example, AI can use image recognition technology to analyze the color, shape, and surface condition of the food to determine whether there are any quality issues. AI can also use deep learning to detect abnormalities in food. Furthermore, AI can use multimodal analysis to combine and analyze image and temperature data to detect abnormalities. Step 3: The notification unit detects an abnormality based on the results of the analysis by the analysis unit and notifies the user. For example, the notification can be sent via a smartphone app, email, or voice assistant.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

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

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

[0117] In the 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.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0121] The data processing system 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

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

2. The image acquisition unit Environmental data such as temperature and humidity are also collected at the same time, and food deterioration is predicted by combining this environmental data.

2. The system of claim 1.

3. The image acquisition unit The inventory status on the shelves is also monitored at the same time, and inventory management is streamlined based on the inventory status.

2. The system of claim 1.

4. The analysis unit When an abnormality is detected, the cause of the abnormality is automatically identified and specific countermeasures are proposed.

2. The system of claim 1.

5. The analysis unit Analyzing a consumer's facial expression, estimating the consumer's emotions when picking up a food item, and evaluating the quality of the food item based on the emotions.

2. The system of claim 1.

6. The analysis unit Using specialized AI models for different types of food, optimal monitoring is performed for each food.

2. The system of claim 1.

7. The analysis unit When an anomaly is detected, the system automatically refers to relevant laws, regulations, and guidelines and suggests appropriate actions to take.

2. The system of claim 1.

8. The analysis unit Analyzing the user's emotional response and customizing the notification content to match the user's emotions 2. The system of claim 1.

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