System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine a rotten state of food.SOLUTION: A system according to an embodiment includes an odor sensor, a generation AI, and a display unit. The odor sensor is attached to the mobile phone. The generation AI analyzes the odor detected by the odor sensor. The display unit displays a result analyzed by the generation AI.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 techniques have had the problem of making it difficult to quickly and accurately determine the state of spoilage of food.

[0005] The system according to the embodiment aims to quickly and accurately determine the spoilage state of food. [Means for solving the problem]

[0006] The system according to the embodiment includes an odor sensor, a generation AI, and a display unit. The odor sensor is attached to a mobile phone. The generation AI analyzes the odor data detected by the odor sensor. The display unit displays the results of the analysis by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine the spoilage state of food. [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) In the food spoilage determination system according to an embodiment of the present invention, an odor sensor attached to a mobile phone detects the odor of food, a generation AI analyzes the data, determines the state of spoilage, and a display unit displays the determination result. This allows consumers to easily check the state of spoilage of food.

[0029] A food spoilage determination system according to an embodiment includes an odor sensor, a generation AI, and a display unit. The odor sensor detects the odor of food. For example, the odor sensor may detect volatile organic compounds (VOCs) using a gas sensor. The odor sensor may also detect specific odor components using electronic nose technology. The odor sensor may also detect specific odor components emitted from foods such as meat, fish, and vegetables. The generation AI analyzes the odor data detected by the odor sensor. For example, the generation AI may analyze the odor data using a neural network to determine the spoilage state. The generation AI may also analyze the odor data using a machine learning algorithm. The generation AI may output a result, such as "This food is spoiled" or "This food is still fresh." The display unit displays the analysis results of the generation AI. For example, the display unit may display the determination result using an LCD display. The display unit may also display the determination result using an LED display. The display unit may display a red warning if the food is spoiled and a green warning if the food is fresh. This allows consumers to easily check the spoilage state of food. For example, consumers can check the spoilage status of food on their mobile phone screen, check the safety of food purchased at the supermarket, and regularly check the condition of food stored at home.

[0030] The odor sensor can detect specific odor components emitted from at least one of meat, fish, and vegetables. The odor sensor can detect specific odor components emitted from foods such as meat, fish, and vegetables. For example, the odor sensor can detect amines. The odor sensor can also detect hydrogen sulfide. The odor sensor can also detect volatile organic compounds (VOCs), for example. This allows the odor components of specific foods to be detected.

[0031] The generative AI can analyze smell data and output results such as "This food is spoiled" or "This food is still fresh." For example, the generative AI analyzes smell data and outputs a result such as "This food is spoiled." For example, the generative AI determines spoilage based on the concentration of smell components. The generative AI can also analyze smell data and output a result such as "This food is still fresh." For example, the generative AI determines freshness based on detected chemicals. The generative AI can also analyze smell data using, for example, a neural network to determine the state of spoilage. This allows for a clear determination of the state of spoilage of food.

[0032] The display unit can display a red warning if the food is spoiled, and a green warning if the food is fresh. For example, the display unit displays a red warning if the food is spoiled. For example, the display unit can flash a red display. Furthermore, the display unit displays a green display if the food is fresh. For example, the display unit can light up a green display. Furthermore, the display unit can display the judgment result using, for example, an LCD display. This makes it possible to visually indicate the spoilage state in an easy-to-understand manner.

[0033] The generation AI can set different judgment criteria for at least one of meat, fish, vegetables, and fruit. The generation AI, for example, sets different judgment criteria for meat. For example, the generation AI sets the judgment criteria based on the speed at which meat spoils. The generation AI also sets different judgment criteria for fish. For example, the generation AI sets the judgment criteria based on the threshold for the odor components of fish. The generation AI also sets different judgment criteria for vegetables. For example, the generation AI sets the judgment criteria based on the speed at which vegetables spoil. The generation AI also sets different judgment criteria for fruit. For example, the generation AI sets the judgment criteria based on the threshold for the odor components of fruit. This enables more accurate judgment of the spoilage state of each food item.

[0034] The generation AI can predict the state of spoilage several days from now based on current smell data and notify consumers. The generation AI can, for example, predict the state of spoilage several days from now based on current smell data. For example, the generation AI predicts changes in smell components. The generation AI can also predict the state of spoilage by taking into account the effects of temperature and humidity. The generation AI can also notify consumers, for example. For example, the generation AI can notify a smartphone. The generation AI can also notify by an alarm sound. The generation AI can also notify by email, for example. This makes it possible to predict the state of spoilage in the future and notify consumers.

[0035] The odor sensor can allow the user to customize the installation position, enabling odor detection in the optimal position. The odor sensor can, for example, allow the user to customize the installation position. For example, the odor sensor can be provided with multiple installation points on the back or side of a mobile phone. The odor sensor can also allow the user to freely select the installation position. The odor sensor can also adjust the installation position, for example, taking into account the diffusion range of the odor. This allows the user to detect odors in the optimal position.

[0036] The smell sensor can add a function that allows the sensitivity to be adjusted in real time, enabling optimal detection according to the environment. The smell sensor can add a function that allows the sensitivity to be adjusted in real time. For example, the smell sensor can allow the sensitivity to be adjusted through a dedicated app. The smell sensor can also allow the user to change the sensitivity by operating a slider within the app. The smell sensor can also adjust the sensitivity taking into account environmental factors, for example. This enables optimal smell detection according to the environment.

[0037] The smell sensor can be made detachable so that it can be attached to other devices. For example, the smell sensor can be designed to be detachable. For example, by providing a dedicated attachment, the smell sensor can be easily attached to other devices such as tablets and smart watches. The smell sensor can also be designed to simplify the removal procedure. Furthermore, the smell sensor can be designed with attachment compatibility in mind, for example. This allows the smell sensor to be used with other devices.

[0038] Multiple odor sensors can be attached to simultaneously collect odor data from different locations, enabling highly accurate determinations. For example, multiple odor sensors can be attached. For example, sensors can be placed on the top, bottom, left, and right of a mobile phone. Furthermore, odor sensors can simultaneously collect odor data from different locations. For example, odor sensors can simultaneously collect odor data from multiple locations, enabling more accurate determinations. Furthermore, for example, multiple odor sensors can be used to identify the source of an odor. This allows data to be collected from multiple locations, improving the accuracy of determinations.

[0039] When analyzing odor data, the generation AI can add a function to detect anomalies by comparing it with past data. For example, when analyzing odor data, the generation AI can add a function to detect anomalies by comparing it with a past database. For example, the generation AI can detect abnormal odor components by comparing it with past data on fresh food. The generation AI can also detect anomalies based on past storage periods. The generation AI can also introduce, for example, an algorithm to identify abnormal patterns. This allows it to detect anomalies by comparing it with past data.

[0040] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0041] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0042] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0043] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0044] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0045] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0046] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0047] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0048] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0049] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0050] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0051] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0052] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0053] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0054] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0055] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0056] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0057] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0058] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0059] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0060] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0061] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0062] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0063] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The food spoilage detection system can also be equipped with a voice assistant unit, which allows users to operate the system by voice. For example, when a user says, "Tell me the condition of this food," the voice assistant unit activates the odor sensor and responds by voice with the results of the analysis by the generation AI. The voice assistant unit can also provide the user with advice on food storage methods and expiration dates. This allows users to operate the system without using their hands, improving convenience.

[0066] The food spoilage detection system can further include a temperature adjustment unit. The temperature adjustment unit has the function of automatically adjusting the food storage temperature. For example, if the odor sensor detects food spoilage, the temperature adjustment unit will slow the spoilage process by lowering the refrigerator temperature. The temperature adjustment unit can also set the optimal storage temperature depending on the type of food. This can extend the food's shelf life.

[0067] The food spoilage determination system can further include a recipe suggestion unit. The recipe suggestion unit has the function of suggesting recipes using spoiled food. For example, if the odor sensor detects spoilage of vegetables, the recipe suggestion unit will display recipes for soups or stir-fries using those vegetables. The recipe suggestion unit can also customize recipes based on the user's preferences and past cooking history. This reduces food waste and allows for efficient consumption.

[0068] The food spoilage determination system can further include a nutritional information providing unit. The nutritional information providing unit has the function of providing information about the nutritional value of food. For example, it displays the nutritional value of food detected by an odor sensor. The nutritional information providing unit can also provide nutritional advice based on the user's health condition and dietary restrictions. This allows the user to maintain a healthy diet.

[0069] The food spoilage assessment system can also add a function for managing food purchase history. The purchase history management unit records the history of food purchased by the user and provides information on expiration dates and storage methods. For example, the system will notify the user when the expiration date of food purchased by the user is approaching. The purchase history management unit can also suggest the next purchase list based on past purchase history. This allows for efficient food management.

[0070] The food spoilage assessment system can further include a food waste management unit. The waste management unit records the amount and type of food discarded by the user and provides advice on how to reduce waste. For example, based on data on the amount of food discarded by the user, the unit can suggest an appropriate amount to purchase the next time. The waste management unit can also suggest storage methods and recipes to reduce waste. This reduces food waste and enables environmentally friendly consumption.

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

[0072] Step 1: The odor sensor detects the odor of food. For example, the odor sensor uses a gas sensor to detect volatile organic compounds (VOCs). The odor sensor can also detect specific odor components using electronic nose technology. Furthermore, the odor sensor can detect specific odor components emitted from foods such as meat, fish, and vegetables. Step 2: The generation AI analyzes the odor data detected by the odor sensor. For example, the generation AI can analyze the odor data using a neural network to determine the state of spoilage. The generation AI can also analyze the odor data using a machine learning algorithm. The generation AI outputs a result such as "This food is spoiled" or "This food is still fresh." Step 3: The display unit displays the results of the analysis by the generation AI. For example, the display unit displays the judgment result using an LCD display. The display unit can also display the judgment result using an LED display. Furthermore, the display unit displays a red warning if the product is spoiled and a green warning if the product is fresh.

[0073] (Example 2) In the food spoilage determination system according to an embodiment of the present invention, an odor sensor attached to a mobile phone detects the odor of food, a generation AI analyzes the data, determines the state of spoilage, and a display unit displays the determination result. This allows consumers to easily check the state of spoilage of food.

[0074] A food spoilage determination system according to an embodiment includes an odor sensor, a generation AI, and a display unit. The odor sensor detects the odor of food. For example, the odor sensor may detect volatile organic compounds (VOCs) using a gas sensor. The odor sensor may also detect specific odor components using electronic nose technology. The odor sensor may also detect specific odor components emitted from foods such as meat, fish, and vegetables. The generation AI analyzes the odor data detected by the odor sensor. For example, the generation AI may analyze the odor data using a neural network to determine the spoilage state. The generation AI may also analyze the odor data using a machine learning algorithm. The generation AI may output a result, such as "This food is spoiled" or "This food is still fresh." The display unit displays the analysis results of the generation AI. For example, the display unit may display the determination result using an LCD display. The display unit may also display the determination result using an LED display. The display unit may display a red warning if the food is spoiled and a green warning if the food is fresh. This allows consumers to easily check the spoilage state of food. For example, consumers can check the spoilage status of food on their mobile phone screen, check the safety of food purchased at the supermarket, and regularly check the condition of food stored at home.

[0075] The odor sensor can detect specific odor components emitted from at least one of meat, fish, and vegetables. The odor sensor can detect specific odor components emitted from foods such as meat, fish, and vegetables. For example, the odor sensor can detect amines. The odor sensor can also detect hydrogen sulfide. The odor sensor can also detect volatile organic compounds (VOCs), for example. This allows the odor components of specific foods to be detected.

[0076] The generative AI can analyze smell data and output results such as "This food is spoiled" or "This food is still fresh." For example, the generative AI analyzes smell data and outputs a result such as "This food is spoiled." For example, the generative AI determines spoilage based on the concentration of smell components. The generative AI can also analyze smell data and output a result such as "This food is still fresh." For example, the generative AI determines freshness based on detected chemicals. The generative AI can also analyze smell data using, for example, a neural network to determine the state of spoilage. This allows for a clear determination of the state of spoilage of food.

[0077] The display unit can display a red warning if the food is spoiled, and a green warning if the food is fresh. For example, the display unit displays a red warning if the food is spoiled. For example, the display unit can flash a red display. Furthermore, the display unit displays a green display if the food is fresh. For example, the display unit can light up a green display. Furthermore, the display unit can display the judgment result using, for example, an LCD display. This makes it possible to visually indicate the spoilage state in an easy-to-understand manner.

[0078] The generation AI can set different judgment criteria for at least one of meat, fish, vegetables, and fruit. The generation AI, for example, sets different judgment criteria for meat. For example, the generation AI sets the judgment criteria based on the speed at which meat spoils. The generation AI also sets different judgment criteria for fish. For example, the generation AI sets the judgment criteria based on the threshold for the odor components of fish. The generation AI also sets different judgment criteria for vegetables. For example, the generation AI sets the judgment criteria based on the speed at which vegetables spoil. The generation AI also sets different judgment criteria for fruit. For example, the generation AI sets the judgment criteria based on the threshold for the odor components of fruit. This enables more accurate judgment of the spoilage state of each food item.

[0079] The generation AI can predict the state of spoilage several days from now based on current smell data and notify consumers. The generation AI can, for example, predict the state of spoilage several days from now based on current smell data. For example, the generation AI predicts changes in smell components. The generation AI can also predict the state of spoilage by taking into account the effects of temperature and humidity. The generation AI can also notify consumers, for example. For example, the generation AI can notify a smartphone. The generation AI can also notify by an alarm sound. The generation AI can also notify by email, for example. This makes it possible to predict the state of spoilage in the future and notify consumers.

[0080] The odor sensor can allow the user to customize the installation position, enabling odor detection in the optimal position. The odor sensor can, for example, allow the user to customize the installation position. For example, the odor sensor can be provided with multiple installation points on the back or side of a mobile phone. The odor sensor can also allow the user to freely select the installation position. The odor sensor can also adjust the installation position, for example, taking into account the diffusion range of the odor. This allows the user to detect odors in the optimal position.

[0081] The smell sensor can add a function that allows the sensitivity to be adjusted in real time, enabling optimal detection according to the environment. The smell sensor can add a function that allows the sensitivity to be adjusted in real time. For example, the smell sensor can allow the sensitivity to be adjusted through a dedicated app. The smell sensor can also allow the user to change the sensitivity by operating a slider within the app. The smell sensor can also adjust the sensitivity taking into account environmental factors, for example. This enables optimal smell detection according to the environment.

[0082] The emotion estimation function can provide guidance to alleviate any anxiety or doubts the user may have about installing the odor sensor. The emotion estimation function, for example, detects in real time any anxiety the user may have about installing the odor sensor. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time any doubt the user may have about installing the odor sensor. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, appropriate guidance. For example, the emotion estimation function displays detailed instructions on how to install the odor sensor. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt, allowing the odor sensor to be installed smoothly.

[0083] The smell sensor can be made detachable so that it can be attached to other devices. For example, the smell sensor can be designed to be detachable. For example, by providing a dedicated attachment, the smell sensor can be easily attached to other devices such as tablets and smart watches. The smell sensor can also be designed to simplify the removal procedure. Furthermore, the smell sensor can be designed with attachment compatibility in mind, for example. This allows the smell sensor to be used with other devices.

[0084] Multiple odor sensors can be attached to simultaneously collect odor data from different locations, enabling highly accurate determinations. For example, multiple odor sensors can be attached. For example, sensors can be placed on the top, bottom, left, and right of a mobile phone. Furthermore, odor sensors can simultaneously collect odor data from different locations. For example, odor sensors can simultaneously collect odor data from multiple locations, enabling more accurate determinations. Furthermore, for example, multiple odor sensors can be used to identify the source of an odor. This allows data to be collected from multiple locations, improving the accuracy of determinations.

[0085] When analyzing odor data, the generation AI can add a function to detect anomalies by comparing it with past data. For example, when analyzing odor data, the generation AI can add a function to detect anomalies by comparing it with a past database. For example, the generation AI can detect abnormal odor components by comparing it with past data on fresh food. The generation AI can also detect anomalies based on past storage periods. The generation AI can also introduce, for example, an algorithm to identify abnormal patterns. This allows it to detect anomalies by comparing it with past data.

[0086] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0087] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0088] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0089] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0090] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0091] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0092] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0093] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0094] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0095] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0096] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0097] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0098] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0099] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0100] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0101] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0102] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0103] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0104] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0105] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0106] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0107] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0108] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0109] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0110] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0111] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0112] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0113] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0114] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0115] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0116] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0117] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0118] The emotion estimation function can provide an explanation to alleviate any anxiety or doubt a user may have about the judgment result of the generation AI. The emotion estimation function, for example, detects in real time the anxiety a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects anxiety by analyzing the user's facial expression. The emotion estimation function also detects in real time the doubt a user feels about the judgment result of the generation AI. For example, the emotion estimation function detects doubt by analyzing the user's voice. The emotion estimation function also provides, for example, an appropriate explanation. For example, the emotion estimation function displays a detailed explanation of the judgment result. The emotion estimation function can also provide advice to alleviate the user's anxiety or doubt. This reduces the user's anxiety or doubt and improves the reliability of the judgment result.

[0119] When analyzing odor data, the generative AI can also integrate and analyze data from other sensors. For example, when analyzing odor data, the generative AI can also integrate and analyze data from temperature and humidity sensors. For example, the generative AI collects temperature and humidity data in real time and reflects it in the analysis. The generative AI can also introduce algorithms that integrate data from other sensors. The generative AI can also correct odor data based on data from temperature and humidity sensors, for example. This allows data from other sensors to be integrated and analyzed.

[0120] When analyzing odor data, the generative AI can analyze the odor data of different foods simultaneously and determine the spoilage state of multiple foods at once. The generative AI, for example, analyzes the odor data of different foods simultaneously. For example, the generative AI analyzes the odor data of meat and vegetables simultaneously. The generative AI can also determine the spoilage state of multiple foods at once. For example, the generative AI can analyze the odor data of multiple foods using a parallel processing algorithm. The generative AI can also develop an algorithm for determining the spoilage state of multiple foods at once. This makes it possible to determine the spoilage state of multiple foods at once.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The food spoilage detection system can also be equipped with a voice assistant unit, which allows users to operate the system by voice. For example, when a user says, "Tell me the condition of this food," the voice assistant unit activates the odor sensor and responds by voice with the results of the analysis by the generation AI. The voice assistant unit can also provide the user with advice on food storage methods and expiration dates. This allows users to operate the system without using their hands, improving convenience.

[0123] The food spoilage detection system can further include a temperature adjustment unit. The temperature adjustment unit has the function of automatically adjusting the food storage temperature. For example, if the odor sensor detects food spoilage, the temperature adjustment unit will slow the spoilage process by lowering the refrigerator temperature. The temperature adjustment unit can also set the optimal storage temperature depending on the type of food. This can extend the food's shelf life.

[0124] The food spoilage detection system can also use an emotion estimation function to change the notification method based on the user's emotions. For example, if the user is feeling anxious, the system can notify them in a gentle voice. If the user is in a hurry, the system can notify them quickly with a short message. This allows for flexible responses according to the user's emotions.

[0125] The food spoilage determination system can further include a recipe suggestion unit. The recipe suggestion unit has the function of suggesting recipes using spoiled food. For example, if the odor sensor detects spoilage of vegetables, the recipe suggestion unit will display recipes for soups or stir-fries using those vegetables. The recipe suggestion unit can also customize recipes based on the user's preferences and past cooking history. This reduces food waste and allows for efficient consumption.

[0126] The food spoilage detection system can also use an emotion estimation function to suggest storage methods based on the user's emotions. For example, if the user is feeling anxious, the system can provide detailed instructions on how to store food. If the user is feeling relaxed, the system can suggest simple storage methods. This allows the system to provide appropriate advice based on the user's emotions.

[0127] The food spoilage determination system can further include a nutritional information providing unit. The nutritional information providing unit has the function of providing information about the nutritional value of food. For example, it displays the nutritional value of food detected by an odor sensor. The nutritional information providing unit can also provide nutritional advice based on the user's health condition and dietary restrictions. This allows the user to maintain a healthy diet.

[0128] The food spoilage detection system can also use an emotion estimation function to remind the user of food expiration dates based on their emotions. For example, if the user is feeling stressed, the system can remind them with a gentle voice. If the user is busy, the system can quickly remind them with a short message. This allows for flexible reminders that respond to the user's emotions.

[0129] The food spoilage assessment system can also add a function for managing food purchase history. The purchase history management unit records the history of food purchased by the user and provides information on expiration dates and storage methods. For example, the system will notify the user when the expiration date of food purchased by the user is approaching. The purchase history management unit can also suggest the next purchase list based on past purchase history. This allows for efficient food management.

[0130] The food spoilage detection system can also use an emotion estimation function to suggest food storage locations based on the user's emotions. For example, if the user is feeling anxious, the system can provide detailed storage locations. If the user is feeling relaxed, the system can suggest simple storage locations. This allows the system to provide appropriate storage location advice based on the user's emotions.

[0131] The food spoilage assessment system can further include a food waste management unit. The waste management unit records the amount and type of food discarded by the user and provides advice on how to reduce waste. For example, based on data on the amount of food discarded by the user, the unit can suggest an appropriate amount to purchase the next time. The waste management unit can also suggest storage methods and recipes to reduce waste. This reduces food waste and enables environmentally friendly consumption.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The odor sensor detects the odor of food. For example, the odor sensor uses a gas sensor to detect volatile organic compounds (VOCs). The odor sensor can also detect specific odor components using electronic nose technology. Furthermore, the odor sensor can detect specific odor components emitted from foods such as meat, fish, and vegetables. Step 2: The generation AI analyzes the odor data detected by the odor sensor. For example, the generation AI can analyze the odor data using a neural network to determine the state of spoilage. The generation AI can also analyze the odor data using a machine learning algorithm. The generation AI outputs a result such as "This food is spoiled" or "This food is still fresh." Step 3: The display unit displays the results of the analysis by the generation AI. For example, the display unit displays the judgment result using an LCD display. The display unit can also display the judgment result using an LED display. Furthermore, the display unit displays a red warning if the product is spoiled and a green warning if the product is fresh.

[0134] 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.

[0135] 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.

[0136] 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.

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

[0138] 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.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

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

[0150] 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.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] 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]

[0201] 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 odor sensor attached to a mobile phone, A generation AI that analyzes odor data detected by the odor sensor; A display unit that displays the results of the analysis performed by the generation AI. A system characterized by:

2. The generated AI is The smell data is analyzed and the results are output in the form of "This food is spoiled" or "This food is still fresh" 2. The system of claim 1.

3. The generated AI is Set different standards for at least one of meat, fish, vegetables, and fruits.

2. The system of claim 1.

4. The generated AI is When analyzing the odor data, add a function to compare it with past data to detect abnormalities.

2. The system of claim 1.

5. The emotion estimation function is Providing guidance to alleviate any anxiety or doubts a user may have about installing the odor sensor.

2. The system of claim 1.

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