System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining image data of a food product; means for obtaining environmental data of the food product; means for transmitting the obtained image data and environmental data to a server; means for analyzing the image data and the environmental data and determining safety of the food product at the server; and means for receiving and displaying an analysis result to a user.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] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] ---

[0006] Conventional methods for ensuring food safety have mainly relied on visual and olfactory evaluation, but these methods have limited accuracy and are prone to misjudgments. Furthermore, because they require specialized knowledge and experience, it is difficult for ordinary consumers and non-experts to properly assess food safety. Therefore, there is a need for a system that can easily and accurately assess food safety in homes, restaurants, food processing plants, and other settings. [Means for solving the problem]

[0007] To solve this problem, the present invention provides the following means: a system including means for acquiring image data of food, means for acquiring environmental data of the food, means for transmitting the acquired image data and environmental data to a server, means for analyzing the image data and environmental data in the server and determining the safety of the food, and means for receiving and displaying the analysis results to a user. This allows even ordinary consumers and non-experts to easily and accurately evaluate the safety of food.

[0008] ---

[0009] "Food image data" is data that indicates the appearance and color of food.

[0010] "Food environmental data" refers to data indicating the temperature, humidity, and concentration of volatile organic compounds around the food.

[0011] The "acquisition means" refers to a device or method for acquiring food image data and environmental data using a camera and a sensor.

[0012] The "transmitting means" refers to a device or method for transmitting the acquired data to the server.

[0013] A "server" is a computer system that analyzes received data and generates a result.

[0014] "Means for analysis" refers to devices and methods for analyzing food image data and environmental data obtained using generative AI and databases.

[0015] "Means for judgment" refers to devices and methods for evaluating food safety based on the analysis results.

[0016] The "receiving means" refers to a device or method for receiving the analysis results sent from the server.

[0017] The "display means" refers to a device or method for visually presenting the received analysis results to the user. [Brief explanation of the drawings]

[0018] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] ---

[0040] This invention is a system for evaluating food safety that can be used in a variety of environments, including homes, restaurants, food processing factories, etc. Food safety is evaluated with high accuracy by acquiring food image data and environmental data, sending them to a server for analysis, and displaying the results to the user.

[0041] Program processing overview

[0042] 1. Food Preparation

[0043] The user places the food in front of the device's camera and sensor.

[0044] The user launches the dedicated app and taps the start scan button.

[0045] 2. Acquisition of food data

[0046] The device uses a camera to capture image data of the food.

[0047] The device collects environmental data about the food using built-in sensors (temperature sensor, humidity sensor, gas sensor).

[0048] 3. Data transmission

[0049] The image data and environmental data acquired by the terminal are encrypted and transmitted to the server using a secure protocol.

[0050] 4. Data Analysis

[0051] The server parses the received data.

[0052] The image data is analyzed for changes in the food's appearance and color and compared with an existing database of spoiled food.

[0053] Environmental data is analyzed to detect abnormal values, including temperature, humidity, and concentration of volatile organic compounds.

[0054] The generative AI will comprehensively evaluate this data and determine the degree of food spoilage.

[0055] 5. Sending and displaying analysis results

[0056] The server encodes the analysis results and sends them to the device.

[0057] The terminal receives the analysis results and updates the user interface to display them to the user.

[0058] The user checks the displayed results and determines whether the food is suitable for use.

[0059] Specific examples

[0060] Example 1: Home use

[0061] 1. A user wants to assess the safety of raw fish taken out of the refrigerator:

[0062] The user places a raw fish in front of the device's sensor and camera.

[0063] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0064] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0065] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[0066] Example 2: Use at a restaurant

[0067] 1. A user (chef) wants to check the quality of the meat he received:

[0068] The user places the meat in front of the device's sensor and camera.

[0069] The user launches the app and taps the scan button.

[0070] The device captures images of the meat and environmental data and sends them to the server.

[0071] The server analyzes the data and determines that the meat is fresh.

[0072] The terminal displays the results, and the user decides to use the meat for cooking.

[0073] This allows for easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. The present invention is implemented as a specific means for checking food safety and has a wide range of applications. By using this system, food hygiene can be effectively managed in ordinary homes and industries, even without advanced specialized knowledge.

[0074] The processing flow will be explained below.

[0075] ---

[0076] Step 1:

[0077] The user places the food in front of the device's camera and sensor.

[0078] The user launches the dedicated app on their smartphone and taps the start scan button.

[0079] Step 2:

[0080] The device activates the camera and captures image data of the food.

[0081] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0082] Step 3:

[0083] The image data and environmental data acquired by the terminal are packaged.

[0084] Step 4:

[0085] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0086] Step 5:

[0087] The server decrypts the received data and prepares it for analysis.

[0088] Step 6:

[0089] The server launches the generative AI and analyzes the food image data.

[0090] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0091] Step 7:

[0092] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0093] Step 8:

[0094] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[0095] Step 9:

[0096] The server generates the analysis results and outputs the results in text format (e.g. "fresh", "slight signs of spoilage", "advanced spoilage").

[0097] Step 10:

[0098] The server encodes the analysis results and sends them to the device.

[0099] Step 11:

[0100] The terminal decodes the analysis results received.

[0101] Step 12:

[0102] The device updates the user interface and displays the analysis results to the user (e.g., "This food is fresh").

[0103] Step 13:

[0104] The user checks the displayed results and decides whether to use the food.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] Current food safety assessment systems require specialized knowledge and time, and are often subject to human error. A simple and highly accurate method for assessing food safety in various environments, such as homes, restaurants, and food processing plants, is needed.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes means for receiving, decoding, and analyzing image data and environmental data, and determining food safety using a generative AI model, means for encoding and transmitting the analysis results, and means for displaying the results to the user, thereby enabling the acquired data to be analyzed with high accuracy and the results to be provided to the user quickly.

[0110] "Food image data" is data that indicates the appearance and color of food.

[0111] "Food environmental data" refers to data including the temperature, humidity, and concentration of volatile organic compounds (VOCs) surrounding the food.

[0112] "Encryption" is the process of converting data into a format that cannot be deciphered by third parties in order to transmit it securely.

[0113] "Server" refers to a device or system that receives and analyzes image data and environmental data.

[0114] A "generative AI model" is a collection of algorithms that use machine learning and deep learning techniques to assess food safety.

[0115] "Analysis" refers to a series of processes that process received image data and environmental data to assess food safety.

[0116] "Encoding" is the process of converting the analysis results into an appropriate format and preparing them for notification to the user.

[0117] The "means for displaying to the user" is an interface for visually presenting the analysis results to the user.

[0118] MODE FOR CARRYING OUT THE INVENTION

[0119] The present invention is a system for assessing food safety that can be used in environments such as homes, restaurants, food processing factories, etc. This system allows users to acquire food image data and environmental data using a terminal, transmit the data to a server for analysis, and display the results to the user, thereby assessing food safety with high accuracy.

[0120] Hardware and software used

[0121] Terminal: A device such as a smartphone or tablet that has a built-in camera, temperature sensor, humidity sensor, and gas sensor.

[0122] Server: A central server for analyzing data, processing image data and environmental data using generative AI models.

[0123] Dedicated app: An application that runs on the device and is used to acquire, encrypt, transmit, and display analysis results of data.

[0124] Data processing and calculation

[0125] 1. Food data acquisition: The device's camera acquires image data of the food, and the temperature sensor, humidity sensor, and gas sensor acquire environmental data of the food.

[0126] 2. Data encryption and transmission: The acquired data is encrypted using a secure encryption method such as AES-256 and transmitted to the server using the SSL / TLS protocol.

[0127] 3. Data analysis: The server decrypts the received data, and the image data is analyzed using a machine learning algorithm, while the environmental data is analyzed for abnormalities. The generative AI model then performs a comprehensive evaluation and determines safety.

[0128] 4. Feedback of analysis results: The analysis results are encoded in JSON format or similar and sent back to the terminal via SSL / TLS. The terminal receives the results and displays them to the user in an appropriate format.

[0129] Specific examples

[0130] Example 1: Home use

[0131] If a user wants to assess the safety of raw fish taken out of the refrigerator:

[0132] The user places a raw fish in front of the device's camera and sensor.

[0133] The user launches the app and taps the start scan button. The device captures images of the raw fish and environmental data, and sends them to the server.

[0134] The server analyzes the data and determines, "This fish is beginning to lose its freshness."

[0135] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[0136] Example 2: Use at a restaurant

[0137] If a user (chef) wants to check the quality of the meat he receives:

[0138] The user places the meat in front of the device's camera and sensor.

[0139] The user launches the app and taps the start scan button.

[0140] The device captures images of the meat and environmental data and sends them to the server.

[0141] The server analyzes the data and determines that the meat is fresh.

[0142] The terminal displays the results, and the user decides to use the meat for cooking.

[0143] Prompt Sentence Examples

[0144] "Please assess the safety of raw fish. We will provide image data as well as data on temperature, humidity, and volatile organic compounds. Please use this information to determine the state of spoilage."

[0145] This system allows users to easily and accurately evaluate food safety, and provides a means for effective food hygiene management in households and industries without requiring advanced expertise.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The user places the food to be evaluated in front of the device's camera and sensor, launches the dedicated app, and taps the "Start Scan" button, which activates the device's camera and begins capturing food images. The inputs are user actions and food placement, and the output is camera activation.

[0149] Step 2:

[0150] The device's camera continuously captures food image data from multiple angles. Specifically, the camera takes still images or videos at a set resolution and frame rate. The input is the camera activation, and the output is food image data.

[0151] Step 3:

[0152] The device acquires environmental data about the food using its built-in temperature, humidity, and gas sensors. Specifically, the temperature sensor measures the ambient temperature, the humidity sensor acquires relative humidity data, and the gas sensor records the concentration of volatile organic compounds (VOCs). The input is sensor activation, and the output is environmental data about temperature, humidity, and VOCs.

[0153] Step 4:

[0154] The image data and environmental data acquired by the terminal are encrypted using a secure encryption method such as AES-256. The input is raw image data and environmental data, and the output is encrypted data.

[0155] Step 5:

[0156] The terminal sends encrypted data to the server over the Internet using the SSL / TLS protocol. The input is the encrypted data, and the output is the data transmission to the server.

[0157] Step 6:

[0158] The server receives encrypted data sent from the terminal using the SSL / TLS protocol. The input is the encrypted data, and the output is the encrypted data stored on the server.

[0159] Step 7:

[0160] The server decrypts the data it receives and converts it into a parsable format. The input is the encrypted data and the output is the decrypted data.

[0161] Step 8:

[0162] The server's image analysis module uses machine learning algorithms to analyze the appearance and color of food. Specifically, it identifies patterns characteristic of spoilage from the image data. The input is the decoded image data, and the output is the analyzed image data.

[0163] Step 9:

[0164] The server's data analysis module analyzes the temperature, humidity, and VOC data to detect abnormal values ​​that exceed certain thresholds. The input is the decoded environmental data, and the output is the analyzed environmental data.

[0165] Step 10:

[0166] The generative AI integrates image data and environmental data and evaluates food safety by comparing it with accumulated data on spoiled food. The input is the analyzed image data and environmental data, and the output is a comprehensive evaluation result.

[0167] Step 11:

[0168] The server encodes the parsed results into a standard format such as JSON. The input is the evaluation result, and the output is the encoded result.

[0169] Step 12:

[0170] The server then sends the encoded parsed result to the terminal using the SSL / TLS protocol. The input is the encoded result, and the output is the data sent to the terminal.

[0171] Step 13:

[0172] The terminal decodes the analysis results it receives, updates the user interface, and displays them to the user. Specifically, it displays a message such as "This fish is starting to lose its freshness." The input is the encoded result, and the output is the updated user interface.

[0173] Step 14:

[0174] The user checks the displayed analysis results and determines whether the food is suitable for use. The input is the display of the analysis results, and the output is the determination of suitability for use.

[0175] (Application example 1)

[0176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0177] Currently, food processing plants face the problem of requiring a great deal of time and effort to accurately assess food safety. Manual assessments, in particular, have limitations, resulting in a high risk of oversights and misjudgments. Furthermore, existing automated systems often lack sufficient performance, making it difficult to comprehensively assess food appearance and environmental data. This can potentially increase consumer health risks. Therefore, there is a need for a system that can efficiently and accurately assess food safety.

[0178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0179] In this invention, the server includes means for analyzing image data to evaluate the appearance and color of food, means for analyzing environmental data to evaluate temperature, humidity, and concentrations of volatile organic compounds, and means for evaluating food safety by comprehensively assessing the analysis results. This enables highly accurate and efficient food safety evaluation through a system installed in a robot used in a food processing factory.

[0180] "Food image data" is data that includes visual information about the appearance and color of food.

[0181] "Food environmental data" means data that includes physical environmental information such as temperature, humidity, and concentrations of volatile organic compounds related to the storage and handling of food.

[0182] The "server" is a computer system that analyzes the acquired image data and environmental data and performs calculations to determine the safety of food.

[0183] The "means for receiving the analysis results and displaying them to the user" is a combination of a device and software for receiving the analysis results sent from the server and visually presenting them to the user.

[0184] "Means installed on a robot and used in a food processing factory" refers to an automated machine equipped with a food safety evaluation system and used in a food processing factory.

[0185] This invention provides a specific method for constructing a system for efficiently and accurately evaluating food safety in a food processing factory, as will be described in detail below.

[0186] Overall system configuration

[0187] This system consists of a robot, a camera, various sensors, a server, and a user interface. We will explain the function and interaction of each component.

[0188] Robot and sensor installation

[0189] The robot is equipped with a camera and sensors for temperature, humidity, and volatile organic compounds. The camera is used to capture images of the food in real time, while the sensors collect environmental data around the food.

[0190] Server Roles

[0191] The server receives the image data and environmental data sent from the robot and analyzes them. Specifically, the server performs the following processes:

[0192] Image data analysis: Image analysis algorithms are used to evaluate the appearance and color of food.

[0193] Environmental data analysis: Check temperature, humidity, and volatile organic compound concentrations and assess whether each value is within a safe range.

[0194] Comprehensive assessment: Using a generative AI model, this data is comprehensively assessed to determine food safety.

[0195] User Interface

[0196] The analysis results are displayed to the user via a user interface, which is a display installed on the robot and can be easily seen by people working at the processing site.

[0197] Technology and hardware used

[0198] Camera: Image capture device, for example "Logitech HD Webcam C270"

[0199] Temperature and humidity sensors: Data collection devices, for example, the Adafruit DHT22

[0200] Server: A computer system for data analysis, analytical models using generative AI

[0201] User interface: Display mounted on the robot

[0202] Specific examples

[0203] A concrete example would be checking the quality of meat at a food processing plant. A robot would take a picture of newly arrived meat with a camera and simultaneously collect environmental data. The data would be sent to a server, which would then analyze it. The safety assessment results would be displayed on the robot's display, and workers would then decide whether or not to use the meat based on the results.

[0204] Prompt Sentence Examples

[0205] When using a generative AI model, use the following prompt:

[0206] "Develop an application to transmit image data and environmental data to evaluate food safety. Food images will be captured by a camera, and environmental data (temperature, humidity) will be acquired by sensors. The data will be transmitted to a secure server. Include a function to display the analysis results on the server side."

[0207] This will enable the creation of a system that can evaluate food safety with high accuracy and efficiency.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] The terminal (robot) uses a camera to acquire image data of food. Specifically, the food is placed in front of the camera and the image is captured by pressing the capture button. The input is the physical arrangement of the food, and the output is the captured image data.

[0211] Step 2:

[0212] The terminal (robot) acquires environmental data about food using temperature, humidity, and volatile organic compound sensors. Specifically, the sensors measure the temperature, humidity, and volatile organic compound values ​​around the food. The input is the measurement data acquired from each sensor, and the output is environmental data (temperature, humidity, and volatile organic compound concentrations).

[0213] Step 3:

[0214] The device encrypts the image and environmental data it acquires and sends it to a server using a secure protocol. Specifically, the data is converted into a secure format and uploaded to the server via the Internet. The input is the acquired image and environmental data, and the output is the encrypted data sent to the server.

[0215] Step 4:

[0216] The server analyzes the received image data. Specifically, it evaluates the appearance and color of the food using an image analysis algorithm. The input is the image data sent to the server, and the output is the analyzed appearance and color evaluation results.

[0217] Step 5:

[0218] The server analyzes the environmental data it receives. Specifically, it evaluates whether each piece of environmental data (temperature, humidity, and concentration of volatile organic compounds) is within a safe range. The input is the environmental data sent to the server, and the output is the evaluation result of each environmental element.

[0219] Step 6:

[0220] The server comprehensively evaluates the results of image data analysis and environmental data analysis. Specifically, it uses a generative AI model to integrate these data and determine food safety. The input is the analysis results of the images and environmental data, and the output is the overall safety assessment result.

[0221] Step 7:

[0222] The server encodes the overall evaluation result and sends it to the terminal. Specifically, it converts the evaluation result into a format that is easy for the user to understand and sends it to the terminal. The input is the overall evaluation result, and the output is the evaluation result data sent to the terminal.

[0223] Step 8:

[0224] The evaluation results received by the terminal are displayed on the user interface. Specifically, the evaluation results are displayed on the terminal display so that the worker can check them. The input is the evaluation result data received from the server, and the output is the displayed evaluation result.

[0225] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0226] ---

[0227] This invention combines a system for evaluating food safety with an emotion engine that recognizes the user's emotions, allowing the displayed results and advice to be appropriately adjusted according to the user's emotional state, providing a better user experience.

[0228] Program processing overview

[0229] 1. Food Preparation

[0230] The user places the food in front of the device's camera and sensor.

[0231] The user launches the dedicated app on their smartphone and taps the start scan button.

[0232] 2. Acquisition of food data

[0233] The device activates the camera and captures image data of the food.

[0234] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0235] 3. Data transmission

[0236] The image data and environmental data acquired by the terminal are packaged.

[0237] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0238] 4. Data Analysis

[0239] The server decrypts the received data and prepares it for analysis.

[0240] The server launches the generative AI and analyzes the food image data.

[0241] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0242] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0243] Generative AI integrates and evaluates this data to determine the level of food spoilage.

[0244] 5. Sending analysis results

[0245] The server generates the analysis results, encodes them and sends them to the device.

[0246] 6. Displaying the results

[0247] The device receives and decodes the analysis results.

[0248] 7. Emotional awareness and outcome regulation

[0249] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0250] The device adjusts the content and method of displaying the analysis results based on the user's emotional data obtained by the emotion engine.

[0251] The device displays the analysis results to the user and provides advice or warnings as needed.

[0252] 8. User discretion

[0253] The user checks the displayed results and advice and decides whether to use the food.

[0254] Specific examples

[0255] Example 1: Home use

[0256] 1. A user wants to check the safety of raw fish they just took out of the refrigerator:

[0257] The user places a raw fish in front of the device's sensor and camera.

[0258] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0259] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0260] The device receives the results and analyzes the user's facial expressions and tone of voice.

[0261] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[0262] The user checks this advice and decides to cook the raw fish immediately.

[0263] Example 2: Use at a restaurant

[0264] 1. A user (chef) wants to check the quality of the meat he received:

[0265] The user places the meat in front of the device's sensor and camera.

[0266] The user launches the app and taps the scan button.

[0267] The device acquires images of the meat and environmental data and sends them to the server.

[0268] The server analyzes the data and determines that the meat is fresh.

[0269] The terminal receives the result and analyzes the user's tone of voice.

[0270] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[0271] The user decides to use the meat in a dish.

[0272] This enables easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. Furthermore, by taking user emotions into consideration, a better user experience can be provided, and appropriate advice and warnings can be given. This invention has a wide range of applications and will contribute to the advancement of food hygiene management.

[0273] The processing flow will be explained below.

[0274] ---

[0275] Step 1:

[0276] The user places the food in front of the device's camera and sensor.

[0277] The user launches the dedicated app on their smartphone and taps the start scan button.

[0278] Step 2:

[0279] The device activates the camera and captures image data of the food.

[0280] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0281] Step 3:

[0282] The image data and environmental data acquired by the terminal are packaged.

[0283] Step 4:

[0284] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0285] Step 5:

[0286] The server decrypts the received data and prepares it for analysis.

[0287] Step 6:

[0288] The server launches the generative AI and analyzes the food image data.

[0289] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0290] Step 7:

[0291] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0292] Step 8:

[0293] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[0294] Step 9:

[0295] The server generates the analysis results, encodes them and sends them to the device.

[0296] Step 10:

[0297] The terminal decodes the analysis results received.

[0298] Step 11:

[0299] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0300] The terminal acquires the user's emotion data.

[0301] Step 12:

[0302] The device adjusts the analysis results based on the user's emotions and changes the content and method of display.

[0303] For example, if the user looks worried, add detailed advice or warnings.

[0304] Step 13:

[0305] The terminal displays the final analysis results and advice to the user.

[0306] It provides users with appropriate information and helps them make decisions about whether or not to use food.

[0307] Step 14:

[0308] The user checks the displayed results and advice and decides whether to use the food.

[0309] As a specific example, when checking the safety of raw fish taken out of the refrigerator at home, the process would be as follows:

[0310] 1. The user places a raw fish in front of the device's sensor and camera.

[0311] 2. The user launches the dedicated app and taps the scan button.

[0312] 3. The device acquires images of the raw fish and environmental data and sends them to the server.

[0313] 4. The server analyzes the data and determines that the fish is beginning to lose its freshness.

[0314] 5. The device receives the analysis results and analyzes the user's facial expressions and tone of voice to recognize emotions.

[0315] 6. The device recognizes that the user looks worried and displays the message, "The fish is starting to lose its freshness. We recommend cooking it as soon as possible."

[0316] 7. The user reviews this advice and decides to cook the raw fish immediately.

[0317] In this way, the present invention is a system that not only enables simple and highly accurate evaluation of food safety in homes, restaurants, food processing factories, etc., but also provides more appropriate information and advice by taking into account the user's emotions.

[0318] Example 2

[0319] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0320] Ensuring food safety is important in modern society, but a lack of specialized knowledge and equipment, particularly in households and small restaurants, makes it difficult to determine whether food has deteriorated or spoiled. Furthermore, a lack of appropriate advice or warnings based on the user's emotional state can make it difficult to make judgments in actual situations. This can lead to food waste and the risk of health hazards.

[0321] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0322] In this invention, the server includes means for acquiring image data of food, means for acquiring environmental data of food, means for transmitting the acquired image data and environmental data to the server, means for analyzing the image data and environmental data in the server and determining the safety of the food, means for receiving and displaying the analysis results to the user, means for recognizing the emotional state of the user, and means for adjusting the content and manner of displaying the analysis results based on the recognized emotional state of the user. This makes it possible to evaluate the safety of food with high accuracy and display appropriate advice and warnings according to the emotional state of the user.

[0323] "Food image data" refers to image information that shows the appearance and color of food.

[0324] "Food environmental data" means data that indicates environmental information such as the temperature, humidity, and concentration of volatile organic compounds around the food.

[0325] "Means of acquisition" means means that have the function of collecting data using devices such as cameras and sensors.

[0326] "Means for transmitting to a server" means means having the function of transmitting collected data to a server via the Internet or other communication protocols.

[0327] "Means for analysis on the server" means means that has the functionality to analyze received data using software or algorithms on the server.

[0328] "Means for determining food safety" means means that have the function of analyzing acquired data and assessing whether food is safe.

[0329] "Means for receiving the analysis results and displaying them to the user" means means having a function for receiving the analysis results and displaying them on a user interface.

[0330] "Means for recognizing the emotional state of the user" means means having a function of analyzing the user's facial expressions and tone of voice using a camera or microphone and recognizing the emotional state of the user.

[0331] "Means for adjusting the content and manner of displaying the analysis results" means means having a function for changing the content and manner of displaying the analysis results based on the emotional state of the user.

[0332] The present invention is a food safety evaluation system that can recognize the user's emotional state and adjust the results accordingly. This system provides appropriate advice according to the user's emotions, enabling more accurate food safety evaluation.

[0333] Program processing overview

[0334] The system is implemented using the following hardware and software.

[0335] Hardware: Smartphone (equipped with camera, temperature sensor, humidity sensor, and gas sensor)

[0336] Software: Dedicated application, generative AI model, emotion recognition engine

[0337] Communication protocol: HTTPS (secure communication)

[0338] Specific explanation of the process

[0339] 1. Food Preparation

[0340] Users place the food they want to evaluate in front of the smartphone camera and sensor, launch the dedicated app, and tap the "Start Scan" button in the app to begin data collection.

[0341] 2. Acquisition of food data

[0342] The device activates the camera to capture image data of the food, and also captures environmental data using the built-in temperature, humidity, and gas sensors.

[0343] 3. Data transmission

[0344] The device packages and encrypts the acquired image and environmental data, and then transmits it to the server using a secure protocol (HTTPS).

[0345] 4. Data Analysis

[0346] The server decodes the received data and activates a generative AI model to analyze the image data. The analysis results are compared with an existing spoiled food database. At the same time, environmental data is analyzed to detect anomalies. These data are then integrated to assess the degree of spoilage of the food.

[0347] 5. Sending analysis results

[0348] The server generates and encodes the analysis results and transmits them to the terminal.

[0349] 6. Emotional awareness and outcome regulation

[0350] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. Based on the acquired emotional data, the device adjusts the display of the analysis results and provides appropriate advice or warnings.

[0351] 7. Displaying the results

[0352] The terminal displays the final analysis results to the user.

[0353] 8. User discretion

[0354] The user checks the displayed analysis results and advice and decides whether or not to use the food.

[0355] Specific examples

[0356] Example 1: Home use

[0357] If a user wants to check the safety of raw fish they just took out of the refrigerator:

[0358] The user places a raw fish in front of the device's sensor and camera.

[0359] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0360] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0361] The device receives the results and analyzes the user's facial expressions and tone of voice.

[0362] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[0363] The user checks this advice and decides to cook the raw fish immediately.

[0364] Example 2: Use at a restaurant

[0365] If a user (chef) wants to check the quality of the meat he receives:

[0366] The user places the meat in front of the device's sensor and camera.

[0367] The user launches the app and taps the scan button.

[0368] The device acquires images of the meat and environmental data and sends them to the server.

[0369] The server analyzes the data and determines that the meat is fresh.

[0370] The terminal receives the result and analyzes the user's tone of voice.

[0371] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[0372] The user decides to use the meat in a dish.

[0373] Examples of prompt statements

[0374] An example of a prompt sentence to input to the generative AI model is as follows:

[0375] Prompt Sentence Example 1

[0376] "The user takes raw fish from the refrigerator and places it in front of the device's sensor and camera, then launches the dedicated app and taps the scan button. The device acquires the data and sends it to the server, which then analyzes it and returns a judgment on the fish's freshness."

[0377] Prompt Sentence Example 2

[0378] "To check the quality of the meat that a restaurant chef receives, they place the meat in front of the device's sensor and camera, launch a dedicated app, and tap the scan button. The device acquires the data and sends it to the server, which then returns the analysis results."

[0379] The above is a specific embodiment of a system for evaluating food safety. This system is expected to be widely used in homes, restaurants, food processing plants, etc. Feedback based on the user's emotional state can provide more appropriate advice, thereby reducing food waste and health risks.

[0380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] Food preparation

[0383] The user places the food they want to evaluate in front of the smartphone's camera and sensor.

[0384] Specific operation: Place food such as raw fish or meat in front of the smartphone's camera and sensors (temperature sensor, humidity sensor, gas sensor).

[0385] The user launches the dedicated app on their smartphone and taps the start scan button.

[0386] Specific operation: Tap the application icon to launch the app and press the "Start Scan" button that appears on the screen.

[0387] Input: Food

[0388] Output: Scan start command

[0389] Step 2:

[0390] Acquiring food data

[0391] The device activates the camera and captures image data of the food.

[0392] What it does: The camera automatically starts and captures an image of the food.

[0393] The device collects environmental data about the food using temperature, humidity, and gas sensors.

[0394] Specific operation: The temperature sensor measures the surface temperature of the food, the humidity sensor measures the ambient humidity, and the gas sensor detects volatile organic compounds (e.g., ethylene gas).

[0395] Input: Scan start command

[0396] Output: Image data, environmental data (temperature, humidity, gas concentration)

[0397] Step 3:

[0398] Sending data

[0399] The image data and environmental data acquired by the terminal are packaged.

[0400] Specific operation: Image data and each sensor data are combined into one data packet.

[0401] The device encrypts the packaged data and sends it to the server using a secure protocol (HTTPS).

[0402] Specific operation: Encrypts data using a data encryption library and sends it to the server using a secure communication protocol.

[0403] Input: Image data, environmental data

[0404] Output: Encrypted data packet

[0405] Step 4:

[0406] Data analysis

[0407] The server decrypts the received data packets.

[0408] Specific operation: Decrypts encrypted data packets and breaks them down into image data and environmental data.

[0409] The server launches the generative AI model and analyzes the food image data.

[0410] Specific operation: Image data of food is input into the generative AI model and analysis processing is performed.

[0411] The server compares the analysis results with an existing database of spoiled food.

[0412] Specific operation: Image data is compared with a database of spoiled food to detect changes in the appearance and color of the food.

[0413] The server analyzes the environmental data and detects abnormal values.

[0414] Specific operation: Environmental data is run through an analytical algorithm to identify outliers.

[0415] Generative AI integrates this data to determine the level of food spoilage.

[0416] Specific operation: Integrates image data and environmental data to calculate food spoilage scores.

[0417] Input: Encrypted data packet

[0418] Output: Food spoilage evaluation results

[0419] Step 5:

[0420] Sending analysis results

[0421] The server encodes the analysis results and sends them to the device.

[0422] Specific operation: The analysis results are generated in text or numerical format, encoded, and sent to the terminal.

[0423] Input: Food spoilage evaluation results

[0424] Output: The encoded parsed result

[0425] Step 6:

[0426] Displaying the results

[0427] The device receives and decodes the analysis results.

[0428] Specific operation: The received analysis results are decoded and converted into a format that can be displayed on the user interface.

[0429] The terminal displays the analysis results to the user.

[0430] Specific operation: The analysis results are displayed on the screen and notified to the user.

[0431] Input: The encoded parsed result

[0432] Output: Display of analysis results

[0433] Step 7:

[0434] Emotion recognition and outcome regulation

[0435] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0436] Specific operation: The camera and microphone are used to acquire the user's emotional data, which is then analyzed by the emotion recognition engine.

[0437] The device adjusts the content and manner of displaying the analysis results based on the emotion recognition data.

[0438] Specific operation: The displayed analysis results are changed to match the user's emotional state.

[0439] Input: User emotion data

[0440] Output: Adjusted analysis results display

[0441] Step 8:

[0442] User decision

[0443] The user checks the displayed analysis results and advice and decides whether or not to consume the food.

[0444] Specific operation: The user decides whether to cook or discard food based on the displayed information.

[0445] Input: Adjusted analysis result display content

[0446] Output: User decision

[0447] (Application example 2)

[0448] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0449] Current food safety assessment systems analyze food image data and environmental data to determine safety, but do not provide feedback based on the user's emotional state. This can lead to users being unable to respond appropriately to the results. Furthermore, because these systems are not designed for use in autonomous vehicles, such as while traveling or on the move, it is difficult to achieve both safety assessment and a sense of security. Therefore, the present invention proposes a system that solves these issues and can provide food safety assessment and feedback based on the user's emotional state.

[0450] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing food image data and environmental data to determine food safety, means for recognizing user emotion data, and means for adjusting the display content of the analysis results based on the recognized user emotion data. This enables appropriate feedback according to the user's emotional state, realizing a food safety evaluation system that can be used safely even in an autonomous vehicle while traveling or on the move.

[0451] "Food image data" is digital image data that shows the appearance and color of food.

[0452] "Food environmental data" refers to data on food storage conditions, such as temperature, humidity, and concentration of volatile organic compounds.

[0453] "User emotion data" refers to data on the user's emotional state recognized based on their facial expression or tone of voice.

[0454] "Analysis results" are the results of an evaluation of food safety obtained by analyzing food image data and environmental data.

[0455] The "recognition means" refers to a means having a function of recognizing the emotional state of the user using a camera or a microphone.

[0456] The "means for adjusting the display content" is a means for changing the display method and content of the analysis results based on the user's emotion data.

[0457] This invention adds a function to recognize user emotional data to a food safety evaluation system, providing feedback based on the user's emotional state. Specifically, food image data and environmental data are acquired, sent to a server for analysis, and the analysis results are then displayed to the user. The system also recognizes emotional data, such as the user's facial expression and tone of voice, and adjusts the display of the analysis results accordingly. This system is particularly intended for use in autonomous vehicles, providing food safety evaluations and a sense of security while on the move.

[0458] Hardware used:

[0459] Camera: A device for capturing food image data and user facial expression data (e.g., a typical webcam)

[0460] Sensors: Devices for acquiring environmental data such as temperature, humidity, and concentration of volatile organic compounds (e.g., DHT22 sensor, MQ-135 gas sensor)

[0461] Software used:

[0462] cv2 (OpenCV): A library for acquiring and analyzing food image data and user facial expression data.

[0463] requests: An HTTP client library for sending retrieved data to a server.

[0464] json: A library for serializing and deserializing data.

[0465] The specific operation of the system will now be described.

[0466] The user places the food on the device and launches the dedicated app to begin scanning. The device then uses its camera to capture image data of the food and sensors to acquire environmental data such as temperature, humidity, and gas concentration. This data is then packaged and sent to the server via a secure protocol.

[0467] The server analyzes the received data. The image data is compared against an existing database of spoiled food to identify changes in appearance and color. Environmental data is also analyzed to determine if any outliers are detected. A generative AI model on the server integrates this data and assesses the food's safety.

[0468] Once the analysis results are sent to the device, the device activates an emotion engine that analyzes the user's facial expressions and tone of voice. Based on this emotional data, the device adjusts the content and display of the analysis results. For example, if the user looks worried, the device may add advice such as "We recommend you consume the product as soon as possible."

[0469] Specific examples are shown below.

[0470] Example 1: Use in autonomous vehicles

[0471] If a user wants to evaluate the safety of a sandwich purchased from an in-train vendor, they place the sandwich in front of the device's camera and sensors. When they launch the app and tap the Start Scan button, the camera captures image data of the sandwich, and the sensors acquire temperature, humidity, and gas concentration data, which they then send to the server. If the analysis returns "This sandwich is safe," and the emotion engine recognizes anxiety from the user's facial expression, the screen will display "It is safe, but we recommend consuming it as soon as possible."

[0472] Example prompts for generative AI models

[0473] "Please rate the safety of the following food items. The data includes images of the food items and measurements of temperature, humidity, and gas concentrations. Based on your results, please rate whether the food items are safe or should not be consumed, and explain why."

[0474] As described above, the present invention realizes a system that can provide users with a sense of security by combining food safety evaluation with feedback based on the user's emotions.

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Step 1:

[0477] The user places the food in front of the device's camera and sensor, launches the dedicated app, and taps the start scan button. The input is the user's operation and the placement of the food, and the output is the device activating the camera and sensor.

[0478] Step 2:

[0479] The device uses a camera to capture image data of food. The input is the camera image, and the output is image data of the food. Specifically, the camera takes an image of the food and stores the data in temporary memory.

[0480] Step 3:

[0481] The device acquires food environment data using temperature, humidity, and gas sensors. The input is sensor information, and the output is temperature, humidity, and gas concentration data. Each sensor measures data in real time and stores it in an internal buffer.

[0482] Step 4:

[0483] The image data and environmental data acquired by the terminal are packaged, encrypted, and sent to the server using a secure protocol (e.g., HTTPS). The input is image data and environmental data, and an encrypted data package is generated as the output. Specifically, the data is encrypted through the encryption module and sent to the specified server address.

[0484] Step 5:

[0485] The server decrypts the received data and prepares it for analysis. The input is an encrypted data package, and the output is decrypted data. Upon receiving the request, the server decrypts the data and separates the image data from the environmental data.

[0486] Step 6:

[0487] The server launches the generative AI to analyze the food image data. The input is the decoded image data, and the output is an evaluation of the food's appearance and color. The AI ​​model compares the image data with an existing database of spoiled food to detect abnormalities in color and shape.

[0488] Step 7:

[0489] The server analyzes environmental data (temperature, humidity, gas concentration) and detects abnormal values. The input is the decoded environmental data, and the output is the abnormality analysis result. An algorithm is used to compare each item of environmental data with a reference value and detect abnormal values.

[0490] Step 8:

[0491] The generative AI integrates the image data and environmental data evaluation to make a final judgment on food safety. The inputs are appearance and color evaluation and anomaly analysis results of environmental data, and the output is a final safety evaluation. The AI ​​model performs a comprehensive evaluation and outputs a safety judgment.

[0492] Step 9:

[0493] The server generates the analysis results, encodes them, and sends them to the terminal. The input is the final security assessment result, and encrypted analysis result data is generated as the output. The process of encoding and encrypting the data and sending it to the terminal is executed.

[0494] Step 10:

[0495] The terminal receives and decodes the analysis results. The input is the encrypted analysis result data, and the output is the decrypted analysis result. The terminal that receives the request decrypts the data and prepares it for display.

[0496] Step 11:

[0497] The device activates the emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the user's emotional data. The emotion analysis algorithm analyzes the user's facial expressions and tone of voice in real time to determine their emotions.

[0498] Step 12:

[0499] The device adjusts the content and display method of the analysis results based on the user's recognized emotional data. The input is the analysis result data and the user's emotional data, and the adjusted analysis results are generated as the output. The tone and level of detail of the displayed content are adjusted based on the emotional data, and the final display content is determined.

[0500] Step 13:

[0501] The terminal displays the analysis results to the user, adding advice and warnings as needed. The input is the adjusted analysis results, and the output is the analysis content displayed to the user. The terminal displays the analysis results on the screen, adding additional explanations and advice as needed.

[0502] 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.

[0503] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0504] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0505] [Second embodiment]

[0506] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0507] 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.

[0508] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0509] 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.

[0510] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0511] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0512] 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.

[0513] 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.

[0514] 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 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.

[0515] 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.

[0516] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0517] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0518] ---

[0519] This invention is a system for evaluating food safety that can be used in a variety of environments, including homes, restaurants, food processing factories, etc. Food safety is evaluated with high accuracy by acquiring food image data and environmental data, sending them to a server for analysis, and displaying the results to the user.

[0520] Program processing overview

[0521] 1. Food Preparation

[0522] The user places the food in front of the device's camera and sensor.

[0523] The user launches the dedicated app and taps the start scan button.

[0524] 2. Acquisition of food data

[0525] The device uses a camera to capture image data of the food.

[0526] The device collects environmental data about the food using built-in sensors (temperature sensor, humidity sensor, gas sensor).

[0527] 3. Data transmission

[0528] The image data and environmental data acquired by the terminal are encrypted and transmitted to the server using a secure protocol.

[0529] 4. Data Analysis

[0530] The server parses the received data.

[0531] The image data is analyzed for changes in the food's appearance and color and compared with an existing database of spoiled food.

[0532] Environmental data is analyzed to detect abnormal values, including temperature, humidity, and concentration of volatile organic compounds.

[0533] The generative AI will comprehensively evaluate this data and determine the degree of food spoilage.

[0534] 5. Sending and displaying analysis results

[0535] The server encodes the analysis results and sends them to the device.

[0536] The terminal receives the analysis results and updates the user interface to display them to the user.

[0537] The user checks the displayed results and determines whether the food is suitable for use.

[0538] Specific examples

[0539] Example 1: Home use

[0540] 1. A user wants to assess the safety of raw fish taken out of the refrigerator:

[0541] The user places a raw fish in front of the device's sensor and camera.

[0542] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0543] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0544] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[0545] Example 2: Use at a restaurant

[0546] 1. A user (chef) wants to check the quality of the meat he received:

[0547] The user places the meat in front of the device's sensor and camera.

[0548] The user launches the app and taps the scan button.

[0549] The device captures images of the meat and environmental data and sends them to the server.

[0550] The server analyzes the data and determines that the meat is fresh.

[0551] The terminal displays the results, and the user decides to use the meat for cooking.

[0552] This allows for easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. The present invention is implemented as a specific means for checking food safety and has a wide range of applications. By using this system, food hygiene can be effectively managed in ordinary homes and industries, even without advanced specialized knowledge.

[0553] The processing flow will be explained below.

[0554] ---

[0555] Step 1:

[0556] The user places the food in front of the device's camera and sensor.

[0557] The user launches the dedicated app on their smartphone and taps the start scan button.

[0558] Step 2:

[0559] The device activates the camera and captures image data of the food.

[0560] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0561] Step 3:

[0562] The image data and environmental data acquired by the terminal are packaged.

[0563] Step 4:

[0564] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0565] Step 5:

[0566] The server decrypts the received data and prepares it for analysis.

[0567] Step 6:

[0568] The server launches the generative AI and analyzes the food image data.

[0569] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0570] Step 7:

[0571] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0572] Step 8:

[0573] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[0574] Step 9:

[0575] The server generates the analysis results and outputs the results in text format (e.g. "fresh", "slight signs of spoilage", "advanced spoilage").

[0576] Step 10:

[0577] The server encodes the analysis results and sends them to the device.

[0578] Step 11:

[0579] The terminal decodes the analysis results received.

[0580] Step 12:

[0581] The device updates the user interface and displays the analysis results to the user (e.g., "This food is fresh").

[0582] Step 13:

[0583] The user checks the displayed results and decides whether to use the food.

[0584] Example 1

[0585] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0586] Current food safety assessment systems require specialized knowledge and time, and are often subject to human error. A simple and highly accurate method for assessing food safety in various environments, such as homes, restaurants, and food processing plants, is needed.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0588] In this invention, the server includes means for receiving, decoding, and analyzing image data and environmental data, and determining food safety using a generative AI model, means for encoding and transmitting the analysis results, and means for displaying the results to the user, thereby enabling the acquired data to be analyzed with high accuracy and the results to be provided to the user quickly.

[0589] "Food image data" is data that indicates the appearance and color of food.

[0590] "Food environmental data" refers to data including the temperature, humidity, and concentration of volatile organic compounds (VOCs) surrounding the food.

[0591] "Encryption" is the process of converting data into a format that cannot be deciphered by third parties in order to transmit it securely.

[0592] "Server" refers to a device or system that receives and analyzes image data and environmental data.

[0593] A "generative AI model" is a collection of algorithms that use machine learning and deep learning techniques to assess food safety.

[0594] "Analysis" refers to a series of processes that process received image data and environmental data to assess food safety.

[0595] "Encoding" is the process of converting the analysis results into an appropriate format and preparing them for notification to the user.

[0596] The "means for displaying to the user" is an interface for visually presenting the analysis results to the user.

[0597] MODE FOR CARRYING OUT THE INVENTION

[0598] The present invention is a system for assessing food safety that can be used in environments such as homes, restaurants, food processing factories, etc. This system allows users to acquire food image data and environmental data using a terminal, transmit the data to a server for analysis, and display the results to the user, thereby assessing food safety with high accuracy.

[0599] Hardware and software used

[0600] Terminal: A device such as a smartphone or tablet that has a built-in camera, temperature sensor, humidity sensor, and gas sensor.

[0601] Server: A central server for analyzing data, processing image data and environmental data using generative AI models.

[0602] Dedicated app: An application that runs on the device and is used to acquire, encrypt, transmit, and display analysis results of data.

[0603] Data processing and calculation

[0604] 1. Food data acquisition: The device's camera acquires image data of the food, and the temperature sensor, humidity sensor, and gas sensor acquire environmental data of the food.

[0605] 2. Data encryption and transmission: The acquired data is encrypted using a secure encryption method such as AES-256 and transmitted to the server using the SSL / TLS protocol.

[0606] 3. Data analysis: The server decrypts the received data, and the image data is analyzed using a machine learning algorithm, while the environmental data is analyzed for abnormalities. The generative AI model then performs a comprehensive evaluation and determines safety.

[0607] 4. Feedback of analysis results: The analysis results are encoded in JSON format or similar and sent back to the terminal via SSL / TLS. The terminal receives the results and displays them to the user in an appropriate format.

[0608] Specific examples

[0609] Example 1: Home use

[0610] If a user wants to assess the safety of raw fish taken out of the refrigerator:

[0611] The user places a raw fish in front of the device's camera and sensor.

[0612] The user launches the app and taps the start scan button. The device captures images of the raw fish and environmental data, and sends them to the server.

[0613] The server analyzes the data and determines, "This fish is beginning to lose its freshness."

[0614] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[0615] Example 2: Use at a restaurant

[0616] If a user (chef) wants to check the quality of the meat he receives:

[0617] The user places the meat in front of the device's camera and sensor.

[0618] The user launches the app and taps the start scan button.

[0619] The device captures images of the meat and environmental data and sends them to the server.

[0620] The server analyzes the data and determines that the meat is fresh.

[0621] The terminal displays the results, and the user decides to use the meat for cooking.

[0622] Prompt Sentence Examples

[0623] "Please assess the safety of raw fish. We will provide image data as well as data on temperature, humidity, and volatile organic compounds. Please use this information to determine the state of spoilage."

[0624] This system allows users to easily and accurately evaluate food safety, and provides a means for effective food hygiene management in households and industries without requiring advanced expertise.

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] The user places the food to be evaluated in front of the device's camera and sensor, launches the dedicated app, and taps the "Start Scan" button, which activates the device's camera and begins capturing food images. The inputs are user actions and food placement, and the output is camera activation.

[0628] Step 2:

[0629] The device's camera continuously captures food image data from multiple angles. Specifically, the camera takes still images or videos at a set resolution and frame rate. The input is the camera activation, and the output is food image data.

[0630] Step 3:

[0631] The device acquires environmental data about the food using its built-in temperature, humidity, and gas sensors. Specifically, the temperature sensor measures the ambient temperature, the humidity sensor acquires relative humidity data, and the gas sensor records the concentration of volatile organic compounds (VOCs). The input is sensor activation, and the output is environmental data about temperature, humidity, and VOCs.

[0632] Step 4:

[0633] The image data and environmental data acquired by the terminal are encrypted using a secure encryption method such as AES-256. The input is raw image data and environmental data, and the output is encrypted data.

[0634] Step 5:

[0635] The terminal sends encrypted data to the server over the Internet using the SSL / TLS protocol. The input is the encrypted data, and the output is the data transmission to the server.

[0636] Step 6:

[0637] The server receives encrypted data sent from the terminal using the SSL / TLS protocol. The input is the encrypted data, and the output is the encrypted data stored on the server.

[0638] Step 7:

[0639] The server decrypts the data it receives and converts it into a parsable format. The input is the encrypted data and the output is the decrypted data.

[0640] Step 8:

[0641] The server's image analysis module uses machine learning algorithms to analyze the appearance and color of food. Specifically, it identifies patterns characteristic of spoilage from the image data. The input is the decoded image data, and the output is the analyzed image data.

[0642] Step 9:

[0643] The server's data analysis module analyzes the temperature, humidity, and VOC data to detect abnormal values ​​that exceed certain thresholds. The input is the decoded environmental data, and the output is the analyzed environmental data.

[0644] Step 10:

[0645] The generative AI integrates image data and environmental data and evaluates food safety by comparing it with accumulated data on spoiled food. The input is the analyzed image data and environmental data, and the output is a comprehensive evaluation result.

[0646] Step 11:

[0647] The server encodes the parsed results into a standard format such as JSON. The input is the evaluation result, and the output is the encoded result.

[0648] Step 12:

[0649] The server then sends the encoded parsed result to the terminal using the SSL / TLS protocol. The input is the encoded result, and the output is the data sent to the terminal.

[0650] Step 13:

[0651] The terminal decodes the analysis results it receives, updates the user interface, and displays them to the user. Specifically, it displays a message such as "This fish is starting to lose its freshness." The input is the encoded result, and the output is the updated user interface.

[0652] Step 14:

[0653] The user checks the displayed analysis results and determines whether the food is suitable for use. The input is the display of the analysis results, and the output is the determination of suitability for use.

[0654] (Application example 1)

[0655] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] Currently, food processing plants face the problem of requiring a great deal of time and effort to accurately assess food safety. Manual assessments, in particular, have limitations, resulting in a high risk of oversights and misjudgments. Furthermore, existing automated systems often lack sufficient performance, making it difficult to comprehensively assess food appearance and environmental data. This can potentially increase consumer health risks. Therefore, there is a need for a system that can efficiently and accurately assess food safety.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0658] In this invention, the server includes means for analyzing image data to evaluate the appearance and color of food, means for analyzing environmental data to evaluate temperature, humidity, and concentrations of volatile organic compounds, and means for evaluating food safety by comprehensively assessing the analysis results. This enables highly accurate and efficient food safety evaluation through a system installed in a robot used in a food processing factory.

[0659] "Food image data" is data that includes visual information about the appearance and color of food.

[0660] "Food environmental data" means data that includes physical environmental information such as temperature, humidity, and concentrations of volatile organic compounds related to the storage and handling of food.

[0661] The "server" is a computer system that analyzes the acquired image data and environmental data and performs calculations to determine the safety of food.

[0662] The "means for receiving the analysis results and displaying them to the user" is a combination of a device and software for receiving the analysis results sent from the server and visually presenting them to the user.

[0663] "Means installed on a robot and used in a food processing factory" refers to an automated machine equipped with a food safety evaluation system and used in a food processing factory.

[0664] This invention provides a specific method for constructing a system for efficiently and accurately evaluating food safety in a food processing factory, as will be described in detail below.

[0665] Overall system configuration

[0666] This system consists of a robot, a camera, various sensors, a server, and a user interface. We will explain the function and interaction of each component.

[0667] Robot and sensor installation

[0668] The robot is equipped with a camera and sensors for temperature, humidity, and volatile organic compounds. The camera is used to capture images of the food in real time, while the sensors collect environmental data around the food.

[0669] Server Roles

[0670] The server receives the image data and environmental data sent from the robot and analyzes them. Specifically, the server performs the following processes:

[0671] Image data analysis: Image analysis algorithms are used to evaluate the appearance and color of food.

[0672] Environmental data analysis: Check temperature, humidity, and volatile organic compound concentrations and assess whether each value is within a safe range.

[0673] Comprehensive assessment: Using a generative AI model, this data is comprehensively assessed to determine food safety.

[0674] User Interface

[0675] The analysis results are displayed to the user via a user interface, which is a display installed on the robot and can be easily seen by people working at the processing site.

[0676] Technology and hardware used

[0677] Camera: Image capture device, for example "Logitech HD Webcam C270"

[0678] Temperature and humidity sensors: Data collection devices, for example, the Adafruit DHT22

[0679] Server: A computer system for data analysis, analytical models using generative AI

[0680] User interface: Display mounted on the robot

[0681] Specific examples

[0682] A concrete example would be checking the quality of meat at a food processing plant. A robot would take a picture of newly arrived meat with a camera and simultaneously collect environmental data. The data would be sent to a server, which would then analyze it. The safety assessment results would be displayed on the robot's display, and workers would then decide whether or not to use the meat based on the results.

[0683] Prompt Sentence Examples

[0684] When using a generative AI model, use the following prompt:

[0685] "Develop an application to transmit image data and environmental data to evaluate food safety. Food images will be captured by a camera, and environmental data (temperature, humidity) will be acquired by sensors. The data will be transmitted to a secure server. Include a function to display the analysis results on the server side."

[0686] This will enable the creation of a system that can evaluate food safety with high accuracy and efficiency.

[0687] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0688] Step 1:

[0689] The terminal (robot) uses a camera to acquire image data of food. Specifically, the food is placed in front of the camera and the image is captured by pressing the capture button. The input is the physical arrangement of the food, and the output is the captured image data.

[0690] Step 2:

[0691] The terminal (robot) acquires environmental data about food using temperature, humidity, and volatile organic compound sensors. Specifically, the sensors measure the temperature, humidity, and volatile organic compound values ​​around the food. The input is the measurement data acquired from each sensor, and the output is environmental data (temperature, humidity, and volatile organic compound concentrations).

[0692] Step 3:

[0693] The device encrypts the image and environmental data it acquires and sends it to a server using a secure protocol. Specifically, the data is converted into a secure format and uploaded to the server via the Internet. The input is the acquired image and environmental data, and the output is the encrypted data sent to the server.

[0694] Step 4:

[0695] The server analyzes the received image data. Specifically, it evaluates the appearance and color of the food using an image analysis algorithm. The input is the image data sent to the server, and the output is the analyzed appearance and color evaluation results.

[0696] Step 5:

[0697] The server analyzes the environmental data it receives. Specifically, it evaluates whether each piece of environmental data (temperature, humidity, and concentration of volatile organic compounds) is within a safe range. The input is the environmental data sent to the server, and the output is the evaluation result of each environmental element.

[0698] Step 6:

[0699] The server comprehensively evaluates the results of image data analysis and environmental data analysis. Specifically, it uses a generative AI model to integrate these data and determine food safety. The input is the analysis results of the images and environmental data, and the output is the overall safety assessment result.

[0700] Step 7:

[0701] The server encodes the overall evaluation result and sends it to the terminal. Specifically, it converts the evaluation result into a format that is easy for the user to understand and sends it to the terminal. The input is the overall evaluation result, and the output is the evaluation result data sent to the terminal.

[0702] Step 8:

[0703] The evaluation results received by the terminal are displayed on the user interface. Specifically, the evaluation results are displayed on the terminal display so that the worker can check them. The input is the evaluation result data received from the server, and the output is the displayed evaluation result.

[0704] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0705] ---

[0706] This invention combines a system for evaluating food safety with an emotion engine that recognizes the user's emotions, allowing the displayed results and advice to be appropriately adjusted according to the user's emotional state, providing a better user experience.

[0707] Program processing overview

[0708] 1. Food Preparation

[0709] The user places the food in front of the device's camera and sensor.

[0710] The user launches the dedicated app on their smartphone and taps the start scan button.

[0711] 2. Acquisition of food data

[0712] The device activates the camera and captures image data of the food.

[0713] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0714] 3. Data transmission

[0715] The image data and environmental data acquired by the terminal are packaged.

[0716] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0717] 4. Data Analysis

[0718] The server decrypts the received data and prepares it for analysis.

[0719] The server launches the generative AI and analyzes the food image data.

[0720] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0721] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0722] Generative AI integrates and evaluates this data to determine the level of food spoilage.

[0723] 5. Sending analysis results

[0724] The server generates the analysis results, encodes them and sends them to the device.

[0725] 6. Displaying the results

[0726] The device receives and decodes the analysis results.

[0727] 7. Emotional awareness and outcome regulation

[0728] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0729] The device adjusts the content and method of displaying the analysis results based on the user's emotional data obtained by the emotion engine.

[0730] The device displays the analysis results to the user and provides advice or warnings as needed.

[0731] 8. User discretion

[0732] The user checks the displayed results and advice and decides whether to use the food.

[0733] Specific examples

[0734] Example 1: Home use

[0735] 1. A user wants to check the safety of raw fish they just took out of the refrigerator:

[0736] The user places a raw fish in front of the device's sensor and camera.

[0737] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0738] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0739] The device receives the results and analyzes the user's facial expressions and tone of voice.

[0740] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[0741] The user checks this advice and decides to cook the raw fish immediately.

[0742] Example 2: Use at a restaurant

[0743] 1. A user (chef) wants to check the quality of the meat he received:

[0744] The user places the meat in front of the device's sensor and camera.

[0745] The user launches the app and taps the scan button.

[0746] The device acquires images of the meat and environmental data and sends them to the server.

[0747] The server analyzes the data and determines that the meat is fresh.

[0748] The terminal receives the result and analyzes the user's tone of voice.

[0749] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[0750] The user decides to use the meat in a dish.

[0751] This enables easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. Furthermore, by taking user emotions into consideration, a better user experience can be provided, and appropriate advice and warnings can be given. This invention has a wide range of applications and will contribute to the advancement of food hygiene management.

[0752] The processing flow will be explained below.

[0753] ---

[0754] Step 1:

[0755] The user places the food in front of the device's camera and sensor.

[0756] The user launches the dedicated app on their smartphone and taps the start scan button.

[0757] Step 2:

[0758] The device activates the camera and captures image data of the food.

[0759] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[0760] Step 3:

[0761] The image data and environmental data acquired by the terminal are packaged.

[0762] Step 4:

[0763] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[0764] Step 5:

[0765] The server decrypts the received data and prepares it for analysis.

[0766] Step 6:

[0767] The server launches the generative AI and analyzes the food image data.

[0768] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[0769] Step 7:

[0770] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[0771] Step 8:

[0772] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[0773] Step 9:

[0774] The server generates the analysis results, encodes them and sends them to the device.

[0775] Step 10:

[0776] The terminal decodes the analysis results received.

[0777] Step 11:

[0778] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0779] The terminal acquires the user's emotion data.

[0780] Step 12:

[0781] The device adjusts the analysis results based on the user's emotions and changes the content and method of display.

[0782] For example, if the user looks worried, add detailed advice or warnings.

[0783] Step 13:

[0784] The terminal displays the final analysis results and advice to the user.

[0785] It provides users with appropriate information and helps them make decisions about whether or not to use food.

[0786] Step 14:

[0787] The user checks the displayed results and advice and decides whether to use the food.

[0788] As a specific example, when checking the safety of raw fish taken out of the refrigerator at home, the process would be as follows:

[0789] 1. The user places a raw fish in front of the device's sensor and camera.

[0790] 2. The user launches the dedicated app and taps the scan button.

[0791] 3. The device acquires images of the raw fish and environmental data and sends them to the server.

[0792] 4. The server analyzes the data and determines that the fish is beginning to lose its freshness.

[0793] 5. The device receives the analysis results and analyzes the user's facial expressions and tone of voice to recognize emotions.

[0794] 6. The device recognizes that the user looks worried and displays the message, "The fish is starting to lose its freshness. We recommend cooking it as soon as possible."

[0795] 7. The user reviews this advice and decides to cook the raw fish immediately.

[0796] In this way, the present invention is a system that not only enables simple and highly accurate evaluation of food safety in homes, restaurants, food processing factories, etc., but also provides more appropriate information and advice by taking into account the user's emotions.

[0797] Example 2

[0798] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0799] Ensuring food safety is important in modern society, but a lack of specialized knowledge and equipment, particularly in households and small restaurants, makes it difficult to determine whether food has deteriorated or spoiled. Furthermore, a lack of appropriate advice or warnings based on the user's emotional state can make it difficult to make judgments in actual situations. This can lead to food waste and the risk of health hazards.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0801] In this invention, the server includes means for acquiring image data of food, means for acquiring environmental data of food, means for transmitting the acquired image data and environmental data to the server, means for analyzing the image data and environmental data in the server and determining the safety of the food, means for receiving and displaying the analysis results to the user, means for recognizing the emotional state of the user, and means for adjusting the content and manner of displaying the analysis results based on the recognized emotional state of the user. This makes it possible to evaluate the safety of food with high accuracy and display appropriate advice and warnings according to the emotional state of the user.

[0802] "Food image data" refers to image information that shows the appearance and color of food.

[0803] "Food environmental data" means data that indicates environmental information such as the temperature, humidity, and concentration of volatile organic compounds around the food.

[0804] "Means of acquisition" means means that have the function of collecting data using devices such as cameras and sensors.

[0805] "Means for transmitting to a server" means means having the function of transmitting collected data to a server via the Internet or other communication protocols.

[0806] "Means for analysis on the server" means means that has the functionality to analyze received data using software or algorithms on the server.

[0807] "Means for determining food safety" means means that have the function of analyzing acquired data and assessing whether food is safe.

[0808] "Means for receiving the analysis results and displaying them to the user" means means having a function for receiving the analysis results and displaying them on a user interface.

[0809] "Means for recognizing the emotional state of the user" means means having a function of analyzing the user's facial expressions and tone of voice using a camera or microphone and recognizing the emotional state of the user.

[0810] "Means for adjusting the content and manner of displaying the analysis results" means means having a function for changing the content and manner of displaying the analysis results based on the emotional state of the user.

[0811] The present invention is a food safety evaluation system that can recognize the user's emotional state and adjust the results accordingly. This system provides appropriate advice according to the user's emotions, enabling more accurate food safety evaluation.

[0812] Program processing overview

[0813] The system is implemented using the following hardware and software.

[0814] Hardware: Smartphone (equipped with camera, temperature sensor, humidity sensor, and gas sensor)

[0815] Software: Dedicated application, generative AI model, emotion recognition engine

[0816] Communication protocol: HTTPS (secure communication)

[0817] Specific explanation of the process

[0818] 1. Food Preparation

[0819] Users place the food they want to evaluate in front of the smartphone camera and sensor, launch the dedicated app, and tap the "Start Scan" button in the app to begin data collection.

[0820] 2. Acquisition of food data

[0821] The device activates the camera to capture image data of the food, and also captures environmental data using the built-in temperature, humidity, and gas sensors.

[0822] 3. Data transmission

[0823] The device packages and encrypts the acquired image and environmental data, and then transmits it to the server using a secure protocol (HTTPS).

[0824] 4. Data Analysis

[0825] The server decodes the received data and activates a generative AI model to analyze the image data. The analysis results are compared with an existing spoiled food database. At the same time, environmental data is analyzed to detect anomalies. These data are then integrated to assess the degree of spoilage of the food.

[0826] 5. Sending analysis results

[0827] The server generates and encodes the analysis results and transmits them to the terminal.

[0828] 6. Emotional awareness and outcome regulation

[0829] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. Based on the acquired emotional data, the device adjusts the display of the analysis results and provides appropriate advice or warnings.

[0830] 7. Displaying the results

[0831] The terminal displays the final analysis results to the user.

[0832] 8. User discretion

[0833] The user checks the displayed analysis results and advice and decides whether or not to use the food.

[0834] Specific examples

[0835] Example 1: Home use

[0836] If a user wants to check the safety of raw fish they just took out of the refrigerator:

[0837] The user places a raw fish in front of the device's sensor and camera.

[0838] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[0839] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[0840] The device receives the results and analyzes the user's facial expressions and tone of voice.

[0841] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[0842] The user checks this advice and decides to cook the raw fish immediately.

[0843] Example 2: Use at a restaurant

[0844] If a user (chef) wants to check the quality of the meat he receives:

[0845] The user places the meat in front of the device's sensor and camera.

[0846] The user launches the app and taps the scan button.

[0847] The device acquires images of the meat and environmental data and sends them to the server.

[0848] The server analyzes the data and determines that the meat is fresh.

[0849] The terminal receives the result and analyzes the user's tone of voice.

[0850] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[0851] The user decides to use the meat in a dish.

[0852] Examples of prompt statements

[0853] An example of a prompt sentence to input to the generative AI model is as follows:

[0854] Prompt Sentence Example 1

[0855] "The user takes raw fish from the refrigerator and places it in front of the device's sensor and camera, then launches the dedicated app and taps the scan button. The device acquires the data and sends it to the server, which then analyzes it and returns a judgment on the fish's freshness."

[0856] Prompt Sentence Example 2

[0857] "To check the quality of the meat that a restaurant chef receives, they place the meat in front of the device's sensor and camera, launch a dedicated app, and tap the scan button. The device acquires the data and sends it to the server, which then returns the analysis results."

[0858] The above is a specific embodiment of a system for evaluating food safety. This system is expected to be widely used in homes, restaurants, food processing plants, etc. Feedback based on the user's emotional state can provide more appropriate advice, thereby reducing food waste and health risks.

[0859] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0860] Step 1:

[0861] Food preparation

[0862] The user places the food they want to evaluate in front of the smartphone's camera and sensor.

[0863] Specific operation: Place food such as raw fish or meat in front of the smartphone's camera and sensors (temperature sensor, humidity sensor, gas sensor).

[0864] The user launches the dedicated app on their smartphone and taps the start scan button.

[0865] Specific operation: Tap the application icon to launch the app and press the "Start Scan" button that appears on the screen.

[0866] Input: Food

[0867] Output: Scan start command

[0868] Step 2:

[0869] Acquiring food data

[0870] The device activates the camera and captures image data of the food.

[0871] What it does: The camera automatically starts and captures an image of the food.

[0872] The device collects environmental data about the food using temperature, humidity, and gas sensors.

[0873] Specific operation: The temperature sensor measures the surface temperature of the food, the humidity sensor measures the ambient humidity, and the gas sensor detects volatile organic compounds (e.g., ethylene gas).

[0874] Input: Scan start command

[0875] Output: Image data, environmental data (temperature, humidity, gas concentration)

[0876] Step 3:

[0877] Sending data

[0878] The image data and environmental data acquired by the terminal are packaged.

[0879] Specific operation: Image data and each sensor data are combined into one data packet.

[0880] The device encrypts the packaged data and sends it to the server using a secure protocol (HTTPS).

[0881] Specific operation: Encrypts data using a data encryption library and sends it to the server using a secure communication protocol.

[0882] Input: Image data, environmental data

[0883] Output: Encrypted data packet

[0884] Step 4:

[0885] Data analysis

[0886] The server decrypts the received data packets.

[0887] Specific operation: Decrypts encrypted data packets and breaks them down into image data and environmental data.

[0888] The server launches the generative AI model and analyzes the food image data.

[0889] Specific operation: Image data of food is input into the generative AI model and analysis processing is performed.

[0890] The server compares the analysis results with an existing database of spoiled food.

[0891] Specific operation: Image data is compared with a database of spoiled food to detect changes in the appearance and color of the food.

[0892] The server analyzes the environmental data and detects abnormal values.

[0893] Specific operation: Environmental data is run through an analytical algorithm to identify outliers.

[0894] Generative AI integrates this data to determine the level of food spoilage.

[0895] Specific operation: Integrates image data and environmental data to calculate food spoilage scores.

[0896] Input: Encrypted data packet

[0897] Output: Food spoilage evaluation results

[0898] Step 5:

[0899] Sending analysis results

[0900] The server encodes the analysis results and sends them to the device.

[0901] Specific operation: The analysis results are generated in text or numerical format, encoded, and sent to the terminal.

[0902] Input: Food spoilage evaluation results

[0903] Output: The encoded parsed result

[0904] Step 6:

[0905] Displaying the results

[0906] The device receives and decodes the analysis results.

[0907] Specific operation: The received analysis results are decoded and converted into a format that can be displayed on the user interface.

[0908] The terminal displays the analysis results to the user.

[0909] Specific operation: The analysis results are displayed on the screen and notified to the user.

[0910] Input: The encoded parsed result

[0911] Output: Display of analysis results

[0912] Step 7:

[0913] Emotion recognition and outcome regulation

[0914] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[0915] Specific operation: The camera and microphone are used to acquire the user's emotional data, which is then analyzed by the emotion recognition engine.

[0916] The device adjusts the content and manner of displaying the analysis results based on the emotion recognition data.

[0917] Specific operation: The displayed analysis results are changed to match the user's emotional state.

[0918] Input: User emotion data

[0919] Output: Adjusted analysis results display

[0920] Step 8:

[0921] User decision

[0922] The user checks the displayed analysis results and advice and decides whether or not to consume the food.

[0923] Specific operation: The user decides whether to cook or discard food based on the displayed information.

[0924] Input: Adjusted analysis result display content

[0925] Output: User decision

[0926] (Application example 2)

[0927] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0928] Current food safety assessment systems analyze food image data and environmental data to determine safety, but do not provide feedback based on the user's emotional state. This can lead to users being unable to respond appropriately to the results. Furthermore, because these systems are not designed for use in autonomous vehicles, such as while traveling or on the move, it is difficult to achieve both safety assessment and a sense of security. Therefore, the present invention proposes a system that solves these issues and can provide food safety assessment and feedback based on the user's emotional state.

[0929] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing food image data and environmental data to determine food safety, means for recognizing user emotion data, and means for adjusting the display content of the analysis results based on the recognized user emotion data. This enables appropriate feedback according to the user's emotional state, realizing a food safety evaluation system that can be used safely even in an autonomous vehicle while traveling or on the move.

[0930] "Food image data" is digital image data that shows the appearance and color of food.

[0931] "Food environmental data" refers to data on food storage conditions, such as temperature, humidity, and concentration of volatile organic compounds.

[0932] "User emotion data" refers to data on the user's emotional state recognized based on their facial expression or tone of voice.

[0933] "Analysis results" are the results of an evaluation of food safety obtained by analyzing food image data and environmental data.

[0934] The "recognition means" refers to a means having a function of recognizing the emotional state of the user using a camera or a microphone.

[0935] The "means for adjusting the display content" is a means for changing the display method and content of the analysis results based on the user's emotion data.

[0936] This invention adds a function to recognize user emotional data to a food safety evaluation system, providing feedback based on the user's emotional state. Specifically, food image data and environmental data are acquired, sent to a server for analysis, and the analysis results are then displayed to the user. The system also recognizes emotional data, such as the user's facial expression and tone of voice, and adjusts the display of the analysis results accordingly. This system is particularly intended for use in autonomous vehicles, providing food safety evaluations and a sense of security while on the move.

[0937] Hardware used:

[0938] Camera: A device for capturing food image data and user facial expression data (e.g., a typical webcam)

[0939] Sensors: Devices for acquiring environmental data such as temperature, humidity, and concentration of volatile organic compounds (e.g., DHT22 sensor, MQ-135 gas sensor)

[0940] Software used:

[0941] cv2 (OpenCV): A library for acquiring and analyzing food image data and user facial expression data.

[0942] requests: An HTTP client library for sending retrieved data to a server.

[0943] json: A library for serializing and deserializing data.

[0944] The specific operation of the system will now be described.

[0945] The user places the food on the device and launches the dedicated app to begin scanning. The device then uses its camera to capture image data of the food and sensors to acquire environmental data such as temperature, humidity, and gas concentration. This data is then packaged and sent to the server via a secure protocol.

[0946] The server analyzes the received data. The image data is compared against an existing database of spoiled food to identify changes in appearance and color. Environmental data is also analyzed to determine if any outliers are detected. A generative AI model on the server integrates this data and assesses the food's safety.

[0947] Once the analysis results are sent to the device, the device activates an emotion engine that analyzes the user's facial expressions and tone of voice. Based on this emotional data, the device adjusts the content and display of the analysis results. For example, if the user looks worried, the device may add advice such as "We recommend you consume the product as soon as possible."

[0948] Specific examples are shown below.

[0949] Example 1: Use in autonomous vehicles

[0950] If a user wants to evaluate the safety of a sandwich purchased from an in-train vendor, they place the sandwich in front of the device's camera and sensors. When they launch the app and tap the Start Scan button, the camera captures image data of the sandwich, and the sensors acquire temperature, humidity, and gas concentration data, which they then send to the server. If the analysis returns "This sandwich is safe," and the emotion engine recognizes anxiety from the user's facial expression, the screen will display "It is safe, but we recommend consuming it as soon as possible."

[0951] Example prompts for generative AI models

[0952] "Please rate the safety of the following food items. The data includes images of the food items and measurements of temperature, humidity, and gas concentrations. Based on your results, please rate whether the food items are safe or should not be consumed, and explain why."

[0953] As described above, the present invention realizes a system that can provide users with a sense of security by combining food safety evaluation with feedback based on the user's emotions.

[0954] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0955] Step 1:

[0956] The user places the food in front of the device's camera and sensor, launches the dedicated app, and taps the start scan button. The input is the user's operation and the placement of the food, and the output is the device activating the camera and sensor.

[0957] Step 2:

[0958] The device uses a camera to capture image data of food. The input is the camera image, and the output is image data of the food. Specifically, the camera takes an image of the food and stores the data in temporary memory.

[0959] Step 3:

[0960] The device acquires food environment data using temperature, humidity, and gas sensors. The input is sensor information, and the output is temperature, humidity, and gas concentration data. Each sensor measures data in real time and stores it in an internal buffer.

[0961] Step 4:

[0962] The image data and environmental data acquired by the terminal are packaged, encrypted, and sent to the server using a secure protocol (e.g., HTTPS). The input is image data and environmental data, and an encrypted data package is generated as the output. Specifically, the data is encrypted through the encryption module and sent to the specified server address.

[0963] Step 5:

[0964] The server decrypts the received data and prepares it for analysis. The input is an encrypted data package, and the output is decrypted data. Upon receiving the request, the server decrypts the data and separates the image data from the environmental data.

[0965] Step 6:

[0966] The server launches the generative AI to analyze the food image data. The input is the decoded image data, and the output is an evaluation of the food's appearance and color. The AI ​​model compares the image data with an existing database of spoiled food to detect abnormalities in color and shape.

[0967] Step 7:

[0968] The server analyzes environmental data (temperature, humidity, gas concentration) and detects abnormal values. The input is the decoded environmental data, and the output is the abnormality analysis result. An algorithm is used to compare each item of environmental data with a reference value and detect abnormal values.

[0969] Step 8:

[0970] The generative AI integrates the image data and environmental data evaluation to make a final judgment on food safety. The inputs are appearance and color evaluation and anomaly analysis results of environmental data, and the output is a final safety evaluation. The AI ​​model performs a comprehensive evaluation and outputs a safety judgment.

[0971] Step 9:

[0972] The server generates the analysis results, encodes them, and sends them to the terminal. The input is the final security assessment result, and encrypted analysis result data is generated as the output. The process of encoding and encrypting the data and sending it to the terminal is executed.

[0973] Step 10:

[0974] The terminal receives and decodes the analysis results. The input is the encrypted analysis result data, and the output is the decrypted analysis result. The terminal that receives the request decrypts the data and prepares it for display.

[0975] Step 11:

[0976] The device activates the emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the user's emotional data. The emotion analysis algorithm analyzes the user's facial expressions and tone of voice in real time to determine their emotions.

[0977] Step 12:

[0978] The device adjusts the content and display method of the analysis results based on the user's recognized emotional data. The input is the analysis result data and the user's emotional data, and the adjusted analysis results are generated as the output. The tone and level of detail of the displayed content are adjusted based on the emotional data, and the final display content is determined.

[0979] Step 13:

[0980] The terminal displays the analysis results to the user, adding advice and warnings as needed. The input is the adjusted analysis results, and the output is the analysis content displayed to the user. The terminal displays the analysis results on the screen, adding additional explanations and advice as needed.

[0981] 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.

[0982] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0983] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0984] [Third embodiment]

[0985] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0986] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0987] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0988] 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.

[0989] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0990] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0991] 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.

[0992] 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.

[0993] 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 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.

[0994] 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.

[0995] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0996] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0997] ---

[0998] This invention is a system for evaluating food safety that can be used in a variety of environments, including homes, restaurants, food processing factories, etc. Food safety is evaluated with high accuracy by acquiring food image data and environmental data, sending them to a server for analysis, and displaying the results to the user.

[0999] Program processing overview

[1000] 1. Food Preparation

[1001] The user places the food in front of the device's camera and sensor.

[1002] The user launches the dedicated app and taps the start scan button.

[1003] 2. Acquisition of food data

[1004] The device uses a camera to capture image data of the food.

[1005] The device collects environmental data about the food using built-in sensors (temperature sensor, humidity sensor, gas sensor).

[1006] 3. Data transmission

[1007] The image data and environmental data acquired by the terminal are encrypted and transmitted to the server using a secure protocol.

[1008] 4. Data Analysis

[1009] The server parses the received data.

[1010] The image data is analyzed for changes in the food's appearance and color and compared with an existing database of spoiled food.

[1011] Environmental data is analyzed to detect abnormal values, including temperature, humidity, and concentration of volatile organic compounds.

[1012] The generative AI will comprehensively evaluate this data and determine the degree of food spoilage.

[1013] 5. Sending and displaying analysis results

[1014] The server encodes the analysis results and sends them to the device.

[1015] The terminal receives the analysis results and updates the user interface to display them to the user.

[1016] The user checks the displayed results and determines whether the food is suitable for use.

[1017] Specific examples

[1018] Example 1: Home use

[1019] 1. A user wants to assess the safety of raw fish taken out of the refrigerator:

[1020] The user places a raw fish in front of the device's sensor and camera.

[1021] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1022] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1023] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[1024] Example 2: Use at a restaurant

[1025] 1. A user (chef) wants to check the quality of the meat he received:

[1026] The user places the meat in front of the device's sensor and camera.

[1027] The user launches the app and taps the scan button.

[1028] The device captures images of the meat and environmental data and sends them to the server.

[1029] The server analyzes the data and determines that the meat is fresh.

[1030] The terminal displays the results, and the user decides to use the meat for cooking.

[1031] This allows for easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. The present invention is implemented as a specific means for checking food safety and has a wide range of applications. By using this system, food hygiene can be effectively managed in ordinary homes and industries, even without advanced specialized knowledge.

[1032] The processing flow will be explained below.

[1033] ---

[1034] Step 1:

[1035] The user places the food in front of the device's camera and sensor.

[1036] The user launches the dedicated app on their smartphone and taps the start scan button.

[1037] Step 2:

[1038] The device activates the camera and captures image data of the food.

[1039] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1040] Step 3:

[1041] The image data and environmental data acquired by the terminal are packaged.

[1042] Step 4:

[1043] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1044] Step 5:

[1045] The server decrypts the received data and prepares it for analysis.

[1046] Step 6:

[1047] The server launches the generative AI and analyzes the food image data.

[1048] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1049] Step 7:

[1050] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1051] Step 8:

[1052] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[1053] Step 9:

[1054] The server generates the analysis results and outputs the results in text format (e.g. "fresh", "slight signs of spoilage", "advanced spoilage").

[1055] Step 10:

[1056] The server encodes the analysis results and sends them to the device.

[1057] Step 11:

[1058] The terminal decodes the analysis results received.

[1059] Step 12:

[1060] The device updates the user interface and displays the analysis results to the user (e.g., "This food is fresh").

[1061] Step 13:

[1062] The user checks the displayed results and decides whether to use the food.

[1063] Example 1

[1064] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1065] Current food safety assessment systems require specialized knowledge and time, and are often subject to human error. A simple and highly accurate method for assessing food safety in various environments, such as homes, restaurants, and food processing plants, is needed.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1067] In this invention, the server includes means for receiving, decoding, and analyzing image data and environmental data, and determining food safety using a generative AI model, means for encoding and transmitting the analysis results, and means for displaying the results to the user, thereby enabling the acquired data to be analyzed with high accuracy and the results to be provided to the user quickly.

[1068] "Food image data" is data that indicates the appearance and color of food.

[1069] "Food environmental data" refers to data including the temperature, humidity, and concentration of volatile organic compounds (VOCs) surrounding the food.

[1070] "Encryption" is the process of converting data into a format that cannot be deciphered by third parties in order to transmit it securely.

[1071] "Server" refers to a device or system that receives and analyzes image data and environmental data.

[1072] A "generative AI model" is a collection of algorithms that use machine learning and deep learning techniques to assess food safety.

[1073] "Analysis" refers to a series of processes that process received image data and environmental data to assess food safety.

[1074] "Encoding" is the process of converting the analysis results into an appropriate format and preparing them for notification to the user.

[1075] The "means for displaying to the user" is an interface for visually presenting the analysis results to the user.

[1076] MODE FOR CARRYING OUT THE INVENTION

[1077] The present invention is a system for assessing food safety that can be used in environments such as homes, restaurants, food processing factories, etc. This system allows users to acquire food image data and environmental data using a terminal, transmit the data to a server for analysis, and display the results to the user, thereby assessing food safety with high accuracy.

[1078] Hardware and software used

[1079] Terminal: A device such as a smartphone or tablet that has a built-in camera, temperature sensor, humidity sensor, and gas sensor.

[1080] Server: A central server for analyzing data, processing image data and environmental data using generative AI models.

[1081] Dedicated app: An application that runs on the device and is used to acquire, encrypt, transmit, and display analysis results of data.

[1082] Data processing and calculation

[1083] 1. Food data acquisition: The device's camera acquires image data of the food, and the temperature sensor, humidity sensor, and gas sensor acquire environmental data of the food.

[1084] 2. Data encryption and transmission: The acquired data is encrypted using a secure encryption method such as AES-256 and transmitted to the server using the SSL / TLS protocol.

[1085] 3. Data analysis: The server decrypts the received data, and the image data is analyzed using a machine learning algorithm, while the environmental data is analyzed for abnormalities. The generative AI model then performs a comprehensive evaluation and determines safety.

[1086] 4. Feedback of analysis results: The analysis results are encoded in JSON format or similar and sent back to the terminal via SSL / TLS. The terminal receives the results and displays them to the user in an appropriate format.

[1087] Specific examples

[1088] Example 1: Home use

[1089] If a user wants to assess the safety of raw fish taken out of the refrigerator:

[1090] The user places a raw fish in front of the device's camera and sensor.

[1091] The user launches the app and taps the start scan button. The device captures images of the raw fish and environmental data, and sends them to the server.

[1092] The server analyzes the data and determines, "This fish is beginning to lose its freshness."

[1093] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[1094] Example 2: Use at a restaurant

[1095] If a user (chef) wants to check the quality of the meat he receives:

[1096] The user places the meat in front of the device's camera and sensor.

[1097] The user launches the app and taps the start scan button.

[1098] The device captures images of the meat and environmental data and sends them to the server.

[1099] The server analyzes the data and determines that the meat is fresh.

[1100] The terminal displays the results, and the user decides to use the meat for cooking.

[1101] Prompt Sentence Examples

[1102] "Please assess the safety of raw fish. We will provide image data as well as data on temperature, humidity, and volatile organic compounds. Please use this information to determine the state of spoilage."

[1103] This system allows users to easily and accurately evaluate food safety, and provides a means for effective food hygiene management in households and industries without requiring advanced expertise.

[1104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1105] Step 1:

[1106] The user places the food to be evaluated in front of the device's camera and sensor, launches the dedicated app, and taps the "Start Scan" button, which activates the device's camera and begins capturing food images. The inputs are user actions and food placement, and the output is camera activation.

[1107] Step 2:

[1108] The device's camera continuously captures food image data from multiple angles. Specifically, the camera takes still images or videos at a set resolution and frame rate. The input is the camera activation, and the output is food image data.

[1109] Step 3:

[1110] The device acquires environmental data about the food using its built-in temperature, humidity, and gas sensors. Specifically, the temperature sensor measures the ambient temperature, the humidity sensor acquires relative humidity data, and the gas sensor records the concentration of volatile organic compounds (VOCs). The input is sensor activation, and the output is environmental data about temperature, humidity, and VOCs.

[1111] Step 4:

[1112] The image data and environmental data acquired by the terminal are encrypted using a secure encryption method such as AES-256. The input is raw image data and environmental data, and the output is encrypted data.

[1113] Step 5:

[1114] The terminal sends encrypted data to the server over the Internet using the SSL / TLS protocol. The input is the encrypted data, and the output is the data transmission to the server.

[1115] Step 6:

[1116] The server receives encrypted data sent from the terminal using the SSL / TLS protocol. The input is the encrypted data, and the output is the encrypted data stored on the server.

[1117] Step 7:

[1118] The server decrypts the data it receives and converts it into a parsable format. The input is the encrypted data and the output is the decrypted data.

[1119] Step 8:

[1120] The server's image analysis module uses machine learning algorithms to analyze the appearance and color of food. Specifically, it identifies patterns characteristic of spoilage from the image data. The input is the decoded image data, and the output is the analyzed image data.

[1121] Step 9:

[1122] The server's data analysis module analyzes the temperature, humidity, and VOC data to detect abnormal values ​​that exceed certain thresholds. The input is the decoded environmental data, and the output is the analyzed environmental data.

[1123] Step 10:

[1124] The generative AI integrates image data and environmental data and evaluates food safety by comparing it with accumulated data on spoiled food. The input is the analyzed image data and environmental data, and the output is a comprehensive evaluation result.

[1125] Step 11:

[1126] The server encodes the parsed results into a standard format such as JSON. The input is the evaluation result, and the output is the encoded result.

[1127] Step 12:

[1128] The server then sends the encoded parsed result to the terminal using the SSL / TLS protocol. The input is the encoded result, and the output is the data sent to the terminal.

[1129] Step 13:

[1130] The terminal decodes the analysis results it receives, updates the user interface, and displays them to the user. Specifically, it displays a message such as "This fish is starting to lose its freshness." The input is the encoded result, and the output is the updated user interface.

[1131] Step 14:

[1132] The user checks the displayed analysis results and determines whether the food is suitable for use. The input is the display of the analysis results, and the output is the determination of suitability for use.

[1133] (Application example 1)

[1134] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1135] Currently, food processing plants face the problem of requiring a great deal of time and effort to accurately assess food safety. Manual assessments, in particular, have limitations, resulting in a high risk of oversights and misjudgments. Furthermore, existing automated systems often lack sufficient performance, making it difficult to comprehensively assess food appearance and environmental data. This can potentially increase consumer health risks. Therefore, there is a need for a system that can efficiently and accurately assess food safety.

[1136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1137] In this invention, the server includes means for analyzing image data to evaluate the appearance and color of food, means for analyzing environmental data to evaluate temperature, humidity, and concentrations of volatile organic compounds, and means for evaluating food safety by comprehensively assessing the analysis results. This enables highly accurate and efficient food safety evaluation through a system installed in a robot used in a food processing factory.

[1138] "Food image data" is data that includes visual information about the appearance and color of food.

[1139] "Food environmental data" means data that includes physical environmental information such as temperature, humidity, and concentrations of volatile organic compounds related to the storage and handling of food.

[1140] The "server" is a computer system that analyzes the acquired image data and environmental data and performs calculations to determine the safety of food.

[1141] The "means for receiving the analysis results and displaying them to the user" is a combination of a device and software for receiving the analysis results sent from the server and visually presenting them to the user.

[1142] "Means installed on a robot and used in a food processing factory" refers to an automated machine equipped with a food safety evaluation system and used in a food processing factory.

[1143] This invention provides a specific method for constructing a system for efficiently and accurately evaluating food safety in a food processing factory, as will be described in detail below.

[1144] Overall system configuration

[1145] This system consists of a robot, a camera, various sensors, a server, and a user interface. We will explain the function and interaction of each component.

[1146] Robot and sensor installation

[1147] The robot is equipped with a camera and sensors for temperature, humidity, and volatile organic compounds. The camera is used to capture images of the food in real time, while the sensors collect environmental data around the food.

[1148] Server Roles

[1149] The server receives the image data and environmental data sent from the robot and analyzes them. Specifically, the server performs the following processes:

[1150] Image data analysis: Image analysis algorithms are used to evaluate the appearance and color of food.

[1151] Environmental data analysis: Check temperature, humidity, and volatile organic compound concentrations and assess whether each value is within a safe range.

[1152] Comprehensive assessment: Using a generative AI model, this data is comprehensively assessed to determine food safety.

[1153] User Interface

[1154] The analysis results are displayed to the user via a user interface, which is a display installed on the robot and can be easily seen by people working at the processing site.

[1155] Technology and hardware used

[1156] Camera: Image capture device, for example "Logitech HD Webcam C270"

[1157] Temperature and humidity sensors: Data collection devices, for example, the Adafruit DHT22

[1158] Server: A computer system for data analysis, analytical models using generative AI

[1159] User interface: Display mounted on the robot

[1160] Specific examples

[1161] A concrete example would be checking the quality of meat at a food processing plant. A robot would take a picture of newly arrived meat with a camera and simultaneously collect environmental data. The data would be sent to a server, which would then analyze it. The safety assessment results would be displayed on the robot's display, and workers would then decide whether or not to use the meat based on the results.

[1162] Prompt Sentence Examples

[1163] When using a generative AI model, use the following prompt:

[1164] "Develop an application to transmit image data and environmental data to evaluate food safety. Food images will be captured by a camera, and environmental data (temperature, humidity) will be acquired by sensors. The data will be transmitted to a secure server. Include a function to display the analysis results on the server side."

[1165] This will enable the creation of a system that can evaluate food safety with high accuracy and efficiency.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The terminal (robot) uses a camera to acquire image data of food. Specifically, the food is placed in front of the camera and the image is captured by pressing the capture button. The input is the physical arrangement of the food, and the output is the captured image data.

[1169] Step 2:

[1170] The terminal (robot) acquires environmental data about food using temperature, humidity, and volatile organic compound sensors. Specifically, the sensors measure the temperature, humidity, and volatile organic compound values ​​around the food. The input is the measurement data acquired from each sensor, and the output is environmental data (temperature, humidity, and volatile organic compound concentrations).

[1171] Step 3:

[1172] The device encrypts the image and environmental data it acquires and sends it to a server using a secure protocol. Specifically, the data is converted into a secure format and uploaded to the server via the Internet. The input is the acquired image and environmental data, and the output is the encrypted data sent to the server.

[1173] Step 4:

[1174] The server analyzes the received image data. Specifically, it evaluates the appearance and color of the food using an image analysis algorithm. The input is the image data sent to the server, and the output is the analyzed appearance and color evaluation results.

[1175] Step 5:

[1176] The server analyzes the environmental data it receives. Specifically, it evaluates whether each piece of environmental data (temperature, humidity, and concentration of volatile organic compounds) is within a safe range. The input is the environmental data sent to the server, and the output is the evaluation result of each environmental element.

[1177] Step 6:

[1178] The server comprehensively evaluates the results of image data analysis and environmental data analysis. Specifically, it uses a generative AI model to integrate these data and determine food safety. The input is the analysis results of the images and environmental data, and the output is the overall safety assessment result.

[1179] Step 7:

[1180] The server encodes the overall evaluation result and sends it to the terminal. Specifically, it converts the evaluation result into a format that is easy for the user to understand and sends it to the terminal. The input is the overall evaluation result, and the output is the evaluation result data sent to the terminal.

[1181] Step 8:

[1182] The evaluation results received by the terminal are displayed on the user interface. Specifically, the evaluation results are displayed on the terminal display so that the worker can check them. The input is the evaluation result data received from the server, and the output is the displayed evaluation result.

[1183] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1184] ---

[1185] This invention combines a system for evaluating food safety with an emotion engine that recognizes the user's emotions, allowing the displayed results and advice to be appropriately adjusted according to the user's emotional state, providing a better user experience.

[1186] Program processing overview

[1187] 1. Food Preparation

[1188] The user places the food in front of the device's camera and sensor.

[1189] The user launches the dedicated app on their smartphone and taps the start scan button.

[1190] 2. Acquisition of food data

[1191] The device activates the camera and captures image data of the food.

[1192] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1193] 3. Data transmission

[1194] The image data and environmental data acquired by the terminal are packaged.

[1195] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1196] 4. Data Analysis

[1197] The server decrypts the received data and prepares it for analysis.

[1198] The server launches the generative AI and analyzes the food image data.

[1199] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1200] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1201] Generative AI integrates and evaluates this data to determine the level of food spoilage.

[1202] 5. Sending analysis results

[1203] The server generates the analysis results, encodes them and sends them to the device.

[1204] 6. Displaying the results

[1205] The device receives and decodes the analysis results.

[1206] 7. Emotional awareness and outcome regulation

[1207] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1208] The device adjusts the content and method of displaying the analysis results based on the user's emotional data obtained by the emotion engine.

[1209] The device displays the analysis results to the user and provides advice or warnings as needed.

[1210] 8. User discretion

[1211] The user checks the displayed results and advice and decides whether to use the food.

[1212] Specific examples

[1213] Example 1: Home use

[1214] 1. A user wants to check the safety of raw fish they just took out of the refrigerator:

[1215] The user places a raw fish in front of the device's sensor and camera.

[1216] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1217] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1218] The device receives the results and analyzes the user's facial expressions and tone of voice.

[1219] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[1220] The user checks this advice and decides to cook the raw fish immediately.

[1221] Example 2: Use at a restaurant

[1222] 1. A user (chef) wants to check the quality of the meat he received:

[1223] The user places the meat in front of the device's sensor and camera.

[1224] The user launches the app and taps the scan button.

[1225] The device acquires images of the meat and environmental data and sends them to the server.

[1226] The server analyzes the data and determines that the meat is fresh.

[1227] The terminal receives the result and analyzes the user's tone of voice.

[1228] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[1229] The user decides to use the meat in a dish.

[1230] This enables easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. Furthermore, by taking user emotions into consideration, a better user experience can be provided, and appropriate advice and warnings can be given. This invention has a wide range of applications and will contribute to the advancement of food hygiene management.

[1231] The processing flow will be explained below.

[1232] ---

[1233] Step 1:

[1234] The user places the food in front of the device's camera and sensor.

[1235] The user launches the dedicated app on their smartphone and taps the start scan button.

[1236] Step 2:

[1237] The device activates the camera and captures image data of the food.

[1238] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1239] Step 3:

[1240] The image data and environmental data acquired by the terminal are packaged.

[1241] Step 4:

[1242] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1243] Step 5:

[1244] The server decrypts the received data and prepares it for analysis.

[1245] Step 6:

[1246] The server launches the generative AI and analyzes the food image data.

[1247] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1248] Step 7:

[1249] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1250] Step 8:

[1251] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[1252] Step 9:

[1253] The server generates the analysis results, encodes them and sends them to the device.

[1254] Step 10:

[1255] The terminal decodes the analysis results received.

[1256] Step 11:

[1257] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1258] The terminal acquires the user's emotion data.

[1259] Step 12:

[1260] The device adjusts the analysis results based on the user's emotions and changes the content and method of display.

[1261] For example, if the user looks worried, add detailed advice or warnings.

[1262] Step 13:

[1263] The terminal displays the final analysis results and advice to the user.

[1264] It provides users with appropriate information and helps them make decisions about whether or not to use food.

[1265] Step 14:

[1266] The user checks the displayed results and advice and decides whether to use the food.

[1267] As a specific example, when checking the safety of raw fish taken out of the refrigerator at home, the process would be as follows:

[1268] 1. The user places a raw fish in front of the device's sensor and camera.

[1269] 2. The user launches the dedicated app and taps the scan button.

[1270] 3. The device acquires images of the raw fish and environmental data and sends them to the server.

[1271] 4. The server analyzes the data and determines that the fish is beginning to lose its freshness.

[1272] 5. The device receives the analysis results and analyzes the user's facial expressions and tone of voice to recognize emotions.

[1273] 6. The device recognizes that the user looks worried and displays the message, "The fish is starting to lose its freshness. We recommend cooking it as soon as possible."

[1274] 7. The user reviews this advice and decides to cook the raw fish immediately.

[1275] In this way, the present invention is a system that not only enables simple and highly accurate evaluation of food safety in homes, restaurants, food processing factories, etc., but also provides more appropriate information and advice by taking into account the user's emotions.

[1276] Example 2

[1277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1278] Ensuring food safety is important in modern society, but a lack of specialized knowledge and equipment, particularly in households and small restaurants, makes it difficult to determine whether food has deteriorated or spoiled. Furthermore, a lack of appropriate advice or warnings based on the user's emotional state can make it difficult to make judgments in actual situations. This can lead to food waste and the risk of health hazards.

[1279] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1280] In this invention, the server includes means for acquiring image data of food, means for acquiring environmental data of food, means for transmitting the acquired image data and environmental data to the server, means for analyzing the image data and environmental data in the server and determining the safety of the food, means for receiving and displaying the analysis results to the user, means for recognizing the emotional state of the user, and means for adjusting the content and manner of displaying the analysis results based on the recognized emotional state of the user. This makes it possible to evaluate the safety of food with high accuracy and display appropriate advice and warnings according to the emotional state of the user.

[1281] "Food image data" refers to image information that shows the appearance and color of food.

[1282] "Food environmental data" means data that indicates environmental information such as the temperature, humidity, and concentration of volatile organic compounds around the food.

[1283] "Means of acquisition" means means that have the function of collecting data using devices such as cameras and sensors.

[1284] "Means for transmitting to a server" means means having the function of transmitting collected data to a server via the Internet or other communication protocols.

[1285] "Means for analysis on the server" means means that has the functionality to analyze received data using software or algorithms on the server.

[1286] "Means for determining food safety" means means that have the function of analyzing acquired data and assessing whether food is safe.

[1287] "Means for receiving the analysis results and displaying them to the user" means means having a function for receiving the analysis results and displaying them on a user interface.

[1288] "Means for recognizing the emotional state of the user" means means having a function of analyzing the user's facial expressions and tone of voice using a camera or microphone and recognizing the emotional state of the user.

[1289] "Means for adjusting the content and manner of displaying the analysis results" means means having a function for changing the content and manner of displaying the analysis results based on the emotional state of the user.

[1290] The present invention is a food safety evaluation system that can recognize the user's emotional state and adjust the results accordingly. This system provides appropriate advice according to the user's emotions, enabling more accurate food safety evaluation.

[1291] Program processing overview

[1292] The system is implemented using the following hardware and software.

[1293] Hardware: Smartphone (equipped with camera, temperature sensor, humidity sensor, and gas sensor)

[1294] Software: Dedicated application, generative AI model, emotion recognition engine

[1295] Communication protocol: HTTPS (secure communication)

[1296] Specific explanation of the process

[1297] 1. Food Preparation

[1298] Users place the food they want to evaluate in front of the smartphone camera and sensor, launch the dedicated app, and tap the "Start Scan" button in the app to begin data collection.

[1299] 2. Acquisition of food data

[1300] The device activates the camera to capture image data of the food, and also captures environmental data using the built-in temperature, humidity, and gas sensors.

[1301] 3. Data transmission

[1302] The device packages and encrypts the acquired image and environmental data, and then transmits it to the server using a secure protocol (HTTPS).

[1303] 4. Data Analysis

[1304] The server decodes the received data and activates a generative AI model to analyze the image data. The analysis results are compared with an existing spoiled food database. At the same time, environmental data is analyzed to detect anomalies. These data are then integrated to assess the degree of spoilage of the food.

[1305] 5. Sending analysis results

[1306] The server generates and encodes the analysis results and transmits them to the terminal.

[1307] 6. Emotional awareness and outcome regulation

[1308] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. Based on the acquired emotional data, the device adjusts the display of the analysis results and provides appropriate advice or warnings.

[1309] 7. Displaying the results

[1310] The terminal displays the final analysis results to the user.

[1311] 8. User discretion

[1312] The user checks the displayed analysis results and advice and decides whether or not to use the food.

[1313] Specific examples

[1314] Example 1: Home use

[1315] If a user wants to check the safety of raw fish they just took out of the refrigerator:

[1316] The user places a raw fish in front of the device's sensor and camera.

[1317] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1318] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1319] The device receives the results and analyzes the user's facial expressions and tone of voice.

[1320] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[1321] The user checks this advice and decides to cook the raw fish immediately.

[1322] Example 2: Use at a restaurant

[1323] If a user (chef) wants to check the quality of the meat he receives:

[1324] The user places the meat in front of the device's sensor and camera.

[1325] The user launches the app and taps the scan button.

[1326] The device acquires images of the meat and environmental data and sends them to the server.

[1327] The server analyzes the data and determines that the meat is fresh.

[1328] The terminal receives the result and analyzes the user's tone of voice.

[1329] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[1330] The user decides to use the meat in a dish.

[1331] Examples of prompt statements

[1332] An example of a prompt sentence to input to the generative AI model is as follows:

[1333] Prompt Sentence Example 1

[1334] "The user takes raw fish from the refrigerator and places it in front of the device's sensor and camera, then launches the dedicated app and taps the scan button. The device acquires the data and sends it to the server, which then analyzes it and returns a judgment on the fish's freshness."

[1335] Prompt Sentence Example 2

[1336] "To check the quality of the meat that a restaurant chef receives, they place the meat in front of the device's sensor and camera, launch a dedicated app, and tap the scan button. The device acquires the data and sends it to the server, which then returns the analysis results."

[1337] The above is a specific embodiment of a system for evaluating food safety. This system is expected to be widely used in homes, restaurants, food processing plants, etc. Feedback based on the user's emotional state can provide more appropriate advice, thereby reducing food waste and health risks.

[1338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1339] Step 1:

[1340] Food preparation

[1341] The user places the food they want to evaluate in front of the smartphone's camera and sensor.

[1342] Specific operation: Place food such as raw fish or meat in front of the smartphone's camera and sensors (temperature sensor, humidity sensor, gas sensor).

[1343] The user launches the dedicated app on their smartphone and taps the start scan button.

[1344] Specific operation: Tap the application icon to launch the app and press the "Start Scan" button that appears on the screen.

[1345] Input: Food

[1346] Output: Scan start command

[1347] Step 2:

[1348] Acquiring food data

[1349] The device activates the camera and captures image data of the food.

[1350] What it does: The camera automatically starts and captures an image of the food.

[1351] The device collects environmental data about the food using temperature, humidity, and gas sensors.

[1352] Specific operation: The temperature sensor measures the surface temperature of the food, the humidity sensor measures the ambient humidity, and the gas sensor detects volatile organic compounds (e.g., ethylene gas).

[1353] Input: Scan start command

[1354] Output: Image data, environmental data (temperature, humidity, gas concentration)

[1355] Step 3:

[1356] Sending data

[1357] The image data and environmental data acquired by the terminal are packaged.

[1358] Specific operation: Image data and each sensor data are combined into one data packet.

[1359] The device encrypts the packaged data and sends it to the server using a secure protocol (HTTPS).

[1360] Specific operation: Encrypts data using a data encryption library and sends it to the server using a secure communication protocol.

[1361] Input: Image data, environmental data

[1362] Output: Encrypted data packet

[1363] Step 4:

[1364] Data analysis

[1365] The server decrypts the received data packets.

[1366] Specific operation: Decrypts encrypted data packets and breaks them down into image data and environmental data.

[1367] The server launches the generative AI model and analyzes the food image data.

[1368] Specific operation: Image data of food is input into the generative AI model and analysis processing is performed.

[1369] The server compares the analysis results with an existing database of spoiled food.

[1370] Specific operation: Image data is compared with a database of spoiled food to detect changes in the appearance and color of the food.

[1371] The server analyzes the environmental data and detects abnormal values.

[1372] Specific operation: Environmental data is run through an analytical algorithm to identify outliers.

[1373] Generative AI integrates this data to determine the level of food spoilage.

[1374] Specific operation: Integrates image data and environmental data to calculate food spoilage scores.

[1375] Input: Encrypted data packet

[1376] Output: Food spoilage evaluation results

[1377] Step 5:

[1378] Sending analysis results

[1379] The server encodes the analysis results and sends them to the device.

[1380] Specific operation: The analysis results are generated in text or numerical format, encoded, and sent to the terminal.

[1381] Input: Food spoilage evaluation results

[1382] Output: The encoded parsed result

[1383] Step 6:

[1384] Displaying the results

[1385] The device receives and decodes the analysis results.

[1386] Specific operation: The received analysis results are decoded and converted into a format that can be displayed on the user interface.

[1387] The terminal displays the analysis results to the user.

[1388] Specific operation: The analysis results are displayed on the screen and notified to the user.

[1389] Input: The encoded parsed result

[1390] Output: Display of analysis results

[1391] Step 7:

[1392] Emotion recognition and outcome regulation

[1393] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1394] Specific operation: The camera and microphone are used to acquire the user's emotional data, which is then analyzed by the emotion recognition engine.

[1395] The device adjusts the content and manner of displaying the analysis results based on the emotion recognition data.

[1396] Specific operation: The displayed analysis results are changed to match the user's emotional state.

[1397] Input: User emotion data

[1398] Output: Adjusted analysis results display

[1399] Step 8:

[1400] User decision

[1401] The user checks the displayed analysis results and advice and decides whether or not to consume the food.

[1402] Specific operation: The user decides whether to cook or discard food based on the displayed information.

[1403] Input: Adjusted analysis result display content

[1404] Output: User decision

[1405] (Application example 2)

[1406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1407] Current food safety assessment systems analyze food image data and environmental data to determine safety, but do not provide feedback based on the user's emotional state. This can lead to users being unable to respond appropriately to the results. Furthermore, because these systems are not designed for use in autonomous vehicles, such as while traveling or on the move, it is difficult to achieve both safety assessment and a sense of security. Therefore, the present invention proposes a system that solves these issues and can provide food safety assessment and feedback based on the user's emotional state.

[1408] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing food image data and environmental data to determine food safety, means for recognizing user emotion data, and means for adjusting the display content of the analysis results based on the recognized user emotion data. This enables appropriate feedback according to the user's emotional state, realizing a food safety evaluation system that can be used safely even in an autonomous vehicle while traveling or on the move.

[1409] "Food image data" is digital image data that shows the appearance and color of food.

[1410] "Food environmental data" refers to data on food storage conditions, such as temperature, humidity, and concentration of volatile organic compounds.

[1411] "User emotion data" refers to data on the user's emotional state recognized based on their facial expression or tone of voice.

[1412] "Analysis results" are the results of an evaluation of food safety obtained by analyzing food image data and environmental data.

[1413] The "recognition means" refers to a means having a function of recognizing the emotional state of the user using a camera or a microphone.

[1414] The "means for adjusting the display content" is a means for changing the display method and content of the analysis results based on the user's emotion data.

[1415] This invention adds a function to recognize user emotional data to a food safety evaluation system, providing feedback based on the user's emotional state. Specifically, food image data and environmental data are acquired, sent to a server for analysis, and the analysis results are then displayed to the user. The system also recognizes emotional data, such as the user's facial expression and tone of voice, and adjusts the display of the analysis results accordingly. This system is particularly intended for use in autonomous vehicles, providing food safety evaluations and a sense of security while on the move.

[1416] Hardware used:

[1417] Camera: A device for capturing food image data and user facial expression data (e.g., a typical webcam)

[1418] Sensors: Devices for acquiring environmental data such as temperature, humidity, and concentration of volatile organic compounds (e.g., DHT22 sensor, MQ-135 gas sensor)

[1419] Software used:

[1420] cv2 (OpenCV): A library for acquiring and analyzing food image data and user facial expression data.

[1421] requests: An HTTP client library for sending retrieved data to a server.

[1422] json: A library for serializing and deserializing data.

[1423] The specific operation of the system will now be described.

[1424] The user places the food on the device and launches the dedicated app to begin scanning. The device then uses its camera to capture image data of the food and sensors to acquire environmental data such as temperature, humidity, and gas concentration. This data is then packaged and sent to the server via a secure protocol.

[1425] The server analyzes the received data. The image data is compared against an existing database of spoiled food to identify changes in appearance and color. Environmental data is also analyzed to determine if any outliers are detected. A generative AI model on the server integrates this data and assesses the food's safety.

[1426] Once the analysis results are sent to the device, the device activates an emotion engine that analyzes the user's facial expressions and tone of voice. Based on this emotional data, the device adjusts the content and display of the analysis results. For example, if the user looks worried, the device may add advice such as "We recommend you consume the product as soon as possible."

[1427] Specific examples are shown below.

[1428] Example 1: Use in autonomous vehicles

[1429] If a user wants to evaluate the safety of a sandwich purchased from an in-train vendor, they place the sandwich in front of the device's camera and sensors. When they launch the app and tap the Start Scan button, the camera captures image data of the sandwich, and the sensors acquire temperature, humidity, and gas concentration data, which they then send to the server. If the analysis returns "This sandwich is safe," and the emotion engine recognizes anxiety from the user's facial expression, the screen will display "It is safe, but we recommend consuming it as soon as possible."

[1430] Example prompts for generative AI models

[1431] "Please rate the safety of the following food items. The data includes images of the food items and measurements of temperature, humidity, and gas concentrations. Based on your results, please rate whether the food items are safe or should not be consumed, and explain why."

[1432] As described above, the present invention realizes a system that can provide users with a sense of security by combining food safety evaluation with feedback based on the user's emotions.

[1433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1434] Step 1:

[1435] The user places the food in front of the device's camera and sensor, launches the dedicated app, and taps the start scan button. The input is the user's operation and the placement of the food, and the output is the device activating the camera and sensor.

[1436] Step 2:

[1437] The device uses a camera to capture image data of food. The input is the camera image, and the output is image data of the food. Specifically, the camera takes an image of the food and stores the data in temporary memory.

[1438] Step 3:

[1439] The device acquires food environment data using temperature, humidity, and gas sensors. The input is sensor information, and the output is temperature, humidity, and gas concentration data. Each sensor measures data in real time and stores it in an internal buffer.

[1440] Step 4:

[1441] The image data and environmental data acquired by the terminal are packaged, encrypted, and sent to the server using a secure protocol (e.g., HTTPS). The input is image data and environmental data, and an encrypted data package is generated as the output. Specifically, the data is encrypted through the encryption module and sent to the specified server address.

[1442] Step 5:

[1443] The server decrypts the received data and prepares it for analysis. The input is an encrypted data package, and the output is decrypted data. Upon receiving the request, the server decrypts the data and separates the image data from the environmental data.

[1444] Step 6:

[1445] The server launches the generative AI to analyze the food image data. The input is the decoded image data, and the output is an evaluation of the food's appearance and color. The AI ​​model compares the image data with an existing database of spoiled food to detect abnormalities in color and shape.

[1446] Step 7:

[1447] The server analyzes environmental data (temperature, humidity, gas concentration) and detects abnormal values. The input is the decoded environmental data, and the output is the abnormality analysis result. An algorithm is used to compare each item of environmental data with a reference value and detect abnormal values.

[1448] Step 8:

[1449] The generative AI integrates the image data and environmental data evaluation to make a final judgment on food safety. The inputs are appearance and color evaluation and anomaly analysis results of environmental data, and the output is a final safety evaluation. The AI ​​model performs a comprehensive evaluation and outputs a safety judgment.

[1450] Step 9:

[1451] The server generates the analysis results, encodes them, and sends them to the terminal. The input is the final security assessment result, and encrypted analysis result data is generated as the output. The process of encoding and encrypting the data and sending it to the terminal is executed.

[1452] Step 10:

[1453] The terminal receives and decodes the analysis results. The input is the encrypted analysis result data, and the output is the decrypted analysis result. The terminal that receives the request decrypts the data and prepares it for display.

[1454] Step 11:

[1455] The device activates the emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the user's emotional data. The emotion analysis algorithm analyzes the user's facial expressions and tone of voice in real time to determine their emotions.

[1456] Step 12:

[1457] The device adjusts the content and display method of the analysis results based on the user's recognized emotional data. The input is the analysis result data and the user's emotional data, and the adjusted analysis results are generated as the output. The tone and level of detail of the displayed content are adjusted based on the emotional data, and the final display content is determined.

[1458] Step 13:

[1459] The terminal displays the analysis results to the user, adding advice and warnings as needed. The input is the adjusted analysis results, and the output is the analysis content displayed to the user. The terminal displays the analysis results on the screen, adding additional explanations and advice as needed.

[1460] 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.

[1461] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1462] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1463] [Fourth embodiment]

[1464] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1465] 7, a 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.

[1466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1467] 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.

[1468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1469] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1470] 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.

[1471] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1472] 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.

[1473] 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 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.

[1474] 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.

[1475] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1476] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1477] ---

[1478] This invention is a system for evaluating food safety that can be used in a variety of environments, including homes, restaurants, food processing factories, etc. Food safety is evaluated with high accuracy by acquiring food image data and environmental data, sending them to a server for analysis, and displaying the results to the user.

[1479] Program processing overview

[1480] 1. Food Preparation

[1481] The user places the food in front of the device's camera and sensor.

[1482] The user launches the dedicated app and taps the start scan button.

[1483] 2. Acquisition of food data

[1484] The device uses a camera to capture image data of the food.

[1485] The device collects environmental data about the food using built-in sensors (temperature sensor, humidity sensor, gas sensor).

[1486] 3. Data transmission

[1487] The image data and environmental data acquired by the terminal are encrypted and transmitted to the server using a secure protocol.

[1488] 4. Data Analysis

[1489] The server parses the received data.

[1490] The image data is analyzed for changes in the food's appearance and color and compared with an existing database of spoiled food.

[1491] Environmental data is analyzed to detect abnormal values, including temperature, humidity, and concentration of volatile organic compounds.

[1492] The generative AI will comprehensively evaluate this data and determine the degree of food spoilage.

[1493] 5. Sending and displaying analysis results

[1494] The server encodes the analysis results and sends them to the device.

[1495] The terminal receives the analysis results and updates the user interface to display them to the user.

[1496] The user checks the displayed results and determines whether the food is suitable for use.

[1497] Specific examples

[1498] Example 1: Home use

[1499] 1. A user wants to assess the safety of raw fish taken out of the refrigerator:

[1500] The user places a raw fish in front of the device's sensor and camera.

[1501] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1502] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1503] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[1504] Example 2: Use at a restaurant

[1505] 1. A user (chef) wants to check the quality of the meat he received:

[1506] The user places the meat in front of the device's sensor and camera.

[1507] The user launches the app and taps the scan button.

[1508] The device captures images of the meat and environmental data and sends them to the server.

[1509] The server analyzes the data and determines that the meat is fresh.

[1510] The terminal displays the results, and the user decides to use the meat for cooking.

[1511] This allows for easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. The present invention is implemented as a specific means for checking food safety and has a wide range of applications. By using this system, food hygiene can be effectively managed in ordinary homes and industries, even without advanced specialized knowledge.

[1512] The processing flow will be explained below.

[1513] ---

[1514] Step 1:

[1515] The user places the food in front of the device's camera and sensor.

[1516] The user launches the dedicated app on their smartphone and taps the start scan button.

[1517] Step 2:

[1518] The device activates the camera and captures image data of the food.

[1519] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1520] Step 3:

[1521] The image data and environmental data acquired by the terminal are packaged.

[1522] Step 4:

[1523] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1524] Step 5:

[1525] The server decrypts the received data and prepares it for analysis.

[1526] Step 6:

[1527] The server launches the generative AI and analyzes the food image data.

[1528] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1529] Step 7:

[1530] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1531] Step 8:

[1532] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[1533] Step 9:

[1534] The server generates the analysis results and outputs the results in text format (e.g. "fresh", "slight signs of spoilage", "advanced spoilage").

[1535] Step 10:

[1536] The server encodes the analysis results and sends them to the device.

[1537] Step 11:

[1538] The terminal decodes the analysis results received.

[1539] Step 12:

[1540] The device updates the user interface and displays the analysis results to the user (e.g., "This food is fresh").

[1541] Step 13:

[1542] The user checks the displayed results and decides whether to use the food.

[1543] Example 1

[1544] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] Current food safety assessment systems require specialized knowledge and time, and are often subject to human error. A simple and highly accurate method for assessing food safety in various environments, such as homes, restaurants, and food processing plants, is needed.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1547] In this invention, the server includes means for receiving, decoding, and analyzing image data and environmental data, and determining food safety using a generative AI model, means for encoding and transmitting the analysis results, and means for displaying the results to the user, thereby enabling the acquired data to be analyzed with high accuracy and the results to be provided to the user quickly.

[1548] "Food image data" is data that indicates the appearance and color of food.

[1549] "Food environmental data" refers to data including the temperature, humidity, and concentration of volatile organic compounds (VOCs) surrounding the food.

[1550] "Encryption" is the process of converting data into a format that cannot be deciphered by third parties in order to transmit it securely.

[1551] "Server" refers to a device or system that receives and analyzes image data and environmental data.

[1552] A "generative AI model" is a collection of algorithms that use machine learning and deep learning techniques to assess food safety.

[1553] "Analysis" refers to a series of processes that process received image data and environmental data to assess food safety.

[1554] "Encoding" is the process of converting the analysis results into an appropriate format and preparing them for notification to the user.

[1555] The "means for displaying to the user" is an interface for visually presenting the analysis results to the user.

[1556] MODE FOR CARRYING OUT THE INVENTION

[1557] The present invention is a system for assessing food safety that can be used in environments such as homes, restaurants, food processing factories, etc. This system allows users to acquire food image data and environmental data using a terminal, transmit the data to a server for analysis, and display the results to the user, thereby assessing food safety with high accuracy.

[1558] Hardware and software used

[1559] Terminal: A device such as a smartphone or tablet that has a built-in camera, temperature sensor, humidity sensor, and gas sensor.

[1560] Server: A central server for analyzing data, processing image data and environmental data using generative AI models.

[1561] Dedicated app: An application that runs on the device and is used to acquire, encrypt, transmit, and display analysis results of data.

[1562] Data processing and calculation

[1563] 1. Food data acquisition: The device's camera acquires image data of the food, and the temperature sensor, humidity sensor, and gas sensor acquire environmental data of the food.

[1564] 2. Data encryption and transmission: The acquired data is encrypted using a secure encryption method such as AES-256 and transmitted to the server using the SSL / TLS protocol.

[1565] 3. Data analysis: The server decrypts the received data, and the image data is analyzed using a machine learning algorithm, while the environmental data is analyzed for abnormalities. The generative AI model then performs a comprehensive evaluation and determines safety.

[1566] 4. Feedback of analysis results: The analysis results are encoded in JSON format or similar and sent back to the terminal via SSL / TLS. The terminal receives the results and displays them to the user in an appropriate format.

[1567] Specific examples

[1568] Example 1: Home use

[1569] If a user wants to assess the safety of raw fish taken out of the refrigerator:

[1570] The user places a raw fish in front of the device's camera and sensor.

[1571] The user launches the app and taps the start scan button. The device captures images of the raw fish and environmental data, and sends them to the server.

[1572] The server analyzes the data and determines, "This fish is beginning to lose its freshness."

[1573] The terminal displays the results to the user, who then decides to cook the raw fish immediately.

[1574] Example 2: Use at a restaurant

[1575] If a user (chef) wants to check the quality of the meat he receives:

[1576] The user places the meat in front of the device's camera and sensor.

[1577] The user launches the app and taps the start scan button.

[1578] The device captures images of the meat and environmental data and sends them to the server.

[1579] The server analyzes the data and determines that the meat is fresh.

[1580] The terminal displays the results, and the user decides to use the meat for cooking.

[1581] Prompt Sentence Examples

[1582] "Please assess the safety of raw fish. We will provide image data as well as data on temperature, humidity, and volatile organic compounds. Please use this information to determine the state of spoilage."

[1583] This system allows users to easily and accurately evaluate food safety, and provides a means for effective food hygiene management in households and industries without requiring advanced expertise.

[1584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1585] Step 1:

[1586] The user places the food to be evaluated in front of the device's camera and sensor, launches the dedicated app, and taps the "Start Scan" button, which activates the device's camera and begins capturing food images. The inputs are user actions and food placement, and the output is camera activation.

[1587] Step 2:

[1588] The device's camera continuously captures food image data from multiple angles. Specifically, the camera takes still images or videos at a set resolution and frame rate. The input is the camera activation, and the output is food image data.

[1589] Step 3:

[1590] The device acquires environmental data about the food using its built-in temperature, humidity, and gas sensors. Specifically, the temperature sensor measures the ambient temperature, the humidity sensor acquires relative humidity data, and the gas sensor records the concentration of volatile organic compounds (VOCs). The input is sensor activation, and the output is environmental data about temperature, humidity, and VOCs.

[1591] Step 4:

[1592] The image data and environmental data acquired by the terminal are encrypted using a secure encryption method such as AES-256. The input is raw image data and environmental data, and the output is encrypted data.

[1593] Step 5:

[1594] The terminal sends encrypted data to the server over the Internet using the SSL / TLS protocol. The input is the encrypted data, and the output is the data transmission to the server.

[1595] Step 6:

[1596] The server receives encrypted data sent from the terminal using the SSL / TLS protocol. The input is the encrypted data, and the output is the encrypted data stored on the server.

[1597] Step 7:

[1598] The server decrypts the data it receives and converts it into a parsable format. The input is the encrypted data and the output is the decrypted data.

[1599] Step 8:

[1600] The server's image analysis module uses machine learning algorithms to analyze the appearance and color of food. Specifically, it identifies patterns characteristic of spoilage from the image data. The input is the decoded image data, and the output is the analyzed image data.

[1601] Step 9:

[1602] The server's data analysis module analyzes the temperature, humidity, and VOC data to detect abnormal values ​​that exceed certain thresholds. The input is the decoded environmental data, and the output is the analyzed environmental data.

[1603] Step 10:

[1604] The generative AI integrates image data and environmental data and evaluates food safety by comparing it with accumulated data on spoiled food. The input is the analyzed image data and environmental data, and the output is a comprehensive evaluation result.

[1605] Step 11:

[1606] The server encodes the parsed results into a standard format such as JSON. The input is the evaluation result, and the output is the encoded result.

[1607] Step 12:

[1608] The server then sends the encoded parsed result to the terminal using the SSL / TLS protocol. The input is the encoded result, and the output is the data sent to the terminal.

[1609] Step 13:

[1610] The terminal decodes the analysis results it receives, updates the user interface, and displays them to the user. Specifically, it displays a message such as "This fish is starting to lose its freshness." The input is the encoded result, and the output is the updated user interface.

[1611] Step 14:

[1612] The user checks the displayed analysis results and determines whether the food is suitable for use. The input is the display of the analysis results, and the output is the determination of suitability for use.

[1613] (Application example 1)

[1614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1615] Currently, food processing plants face the problem of requiring a great deal of time and effort to accurately assess food safety. Manual assessments, in particular, have limitations, resulting in a high risk of oversights and misjudgments. Furthermore, existing automated systems often lack sufficient performance, making it difficult to comprehensively assess food appearance and environmental data. This can potentially increase consumer health risks. Therefore, there is a need for a system that can efficiently and accurately assess food safety.

[1616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1617] In this invention, the server includes means for analyzing image data to evaluate the appearance and color of food, means for analyzing environmental data to evaluate temperature, humidity, and concentrations of volatile organic compounds, and means for evaluating food safety by comprehensively assessing the analysis results. This enables highly accurate and efficient food safety evaluation through a system installed in a robot used in a food processing factory.

[1618] "Food image data" is data that includes visual information about the appearance and color of food.

[1619] "Food environmental data" means data that includes physical environmental information such as temperature, humidity, and concentrations of volatile organic compounds related to the storage and handling of food.

[1620] The "server" is a computer system that analyzes the acquired image data and environmental data and performs calculations to determine the safety of food.

[1621] The "means for receiving the analysis results and displaying them to the user" is a combination of a device and software for receiving the analysis results sent from the server and visually presenting them to the user.

[1622] "Means installed on a robot and used in a food processing factory" refers to an automated machine equipped with a food safety evaluation system and used in a food processing factory.

[1623] This invention provides a specific method for constructing a system for efficiently and accurately evaluating food safety in a food processing factory, as will be described in detail below.

[1624] Overall system configuration

[1625] This system consists of a robot, a camera, various sensors, a server, and a user interface. We will explain the function and interaction of each component.

[1626] Robot and sensor installation

[1627] The robot is equipped with a camera and sensors for temperature, humidity, and volatile organic compounds. The camera is used to capture images of the food in real time, while the sensors collect environmental data around the food.

[1628] Server Roles

[1629] The server receives the image data and environmental data sent from the robot and analyzes them. Specifically, the server performs the following processes:

[1630] Image data analysis: Image analysis algorithms are used to evaluate the appearance and color of food.

[1631] Environmental data analysis: Check temperature, humidity, and volatile organic compound concentrations and assess whether each value is within a safe range.

[1632] Comprehensive assessment: Using a generative AI model, this data is comprehensively assessed to determine food safety.

[1633] User Interface

[1634] The analysis results are displayed to the user via a user interface, which is a display installed on the robot and can be easily seen by people working at the processing site.

[1635] Technology and hardware used

[1636] Camera: Image capture device, for example "Logitech HD Webcam C270"

[1637] Temperature and humidity sensors: Data collection devices, for example, the Adafruit DHT22

[1638] Server: A computer system for data analysis, analytical models using generative AI

[1639] User interface: Display mounted on the robot

[1640] Specific examples

[1641] A concrete example would be checking the quality of meat at a food processing plant. A robot would take a picture of newly arrived meat with a camera and simultaneously collect environmental data. The data would be sent to a server, which would then analyze it. The safety assessment results would be displayed on the robot's display, and workers would then decide whether or not to use the meat based on the results.

[1642] Prompt Sentence Examples

[1643] When using a generative AI model, use the following prompt:

[1644] "Develop an application to transmit image data and environmental data to evaluate food safety. Food images will be captured by a camera, and environmental data (temperature, humidity) will be acquired by sensors. The data will be transmitted to a secure server. Include a function to display the analysis results on the server side."

[1645] This will enable the creation of a system that can evaluate food safety with high accuracy and efficiency.

[1646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1647] Step 1:

[1648] The terminal (robot) uses a camera to acquire image data of food. Specifically, the food is placed in front of the camera and the image is captured by pressing the capture button. The input is the physical arrangement of the food, and the output is the captured image data.

[1649] Step 2:

[1650] The terminal (robot) acquires environmental data about food using temperature, humidity, and volatile organic compound sensors. Specifically, the sensors measure the temperature, humidity, and volatile organic compound values ​​around the food. The input is the measurement data acquired from each sensor, and the output is environmental data (temperature, humidity, and volatile organic compound concentrations).

[1651] Step 3:

[1652] The device encrypts the image and environmental data it acquires and sends it to a server using a secure protocol. Specifically, the data is converted into a secure format and uploaded to the server via the Internet. The input is the acquired image and environmental data, and the output is the encrypted data sent to the server.

[1653] Step 4:

[1654] The server analyzes the received image data. Specifically, it evaluates the appearance and color of the food using an image analysis algorithm. The input is the image data sent to the server, and the output is the analyzed appearance and color evaluation results.

[1655] Step 5:

[1656] The server analyzes the environmental data it receives. Specifically, it evaluates whether each piece of environmental data (temperature, humidity, and concentration of volatile organic compounds) is within a safe range. The input is the environmental data sent to the server, and the output is the evaluation result of each environmental element.

[1657] Step 6:

[1658] The server comprehensively evaluates the results of image data analysis and environmental data analysis. Specifically, it uses a generative AI model to integrate these data and determine food safety. The input is the analysis results of the images and environmental data, and the output is the overall safety assessment result.

[1659] Step 7:

[1660] The server encodes the overall evaluation result and sends it to the terminal. Specifically, it converts the evaluation result into a format that is easy for the user to understand and sends it to the terminal. The input is the overall evaluation result, and the output is the evaluation result data sent to the terminal.

[1661] Step 8:

[1662] The evaluation results received by the terminal are displayed on the user interface. Specifically, the evaluation results are displayed on the terminal display so that the worker can check them. The input is the evaluation result data received from the server, and the output is the displayed evaluation result.

[1663] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1664] ---

[1665] This invention combines a system for evaluating food safety with an emotion engine that recognizes the user's emotions, allowing the displayed results and advice to be appropriately adjusted according to the user's emotional state, providing a better user experience.

[1666] Program processing overview

[1667] 1. Food Preparation

[1668] The user places the food in front of the device's camera and sensor.

[1669] The user launches the dedicated app on their smartphone and taps the start scan button.

[1670] 2. Acquisition of food data

[1671] The device activates the camera and captures image data of the food.

[1672] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1673] 3. Data transmission

[1674] The image data and environmental data acquired by the terminal are packaged.

[1675] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1676] 4. Data Analysis

[1677] The server decrypts the received data and prepares it for analysis.

[1678] The server launches the generative AI and analyzes the food image data.

[1679] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1680] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1681] Generative AI integrates and evaluates this data to determine the level of food spoilage.

[1682] 5. Sending analysis results

[1683] The server generates the analysis results, encodes them and sends them to the device.

[1684] 6. Displaying the results

[1685] The device receives and decodes the analysis results.

[1686] 7. Emotional awareness and outcome regulation

[1687] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1688] The device adjusts the content and method of displaying the analysis results based on the user's emotional data obtained by the emotion engine.

[1689] The device displays the analysis results to the user and provides advice or warnings as needed.

[1690] 8. User discretion

[1691] The user checks the displayed results and advice and decides whether to use the food.

[1692] Specific examples

[1693] Example 1: Home use

[1694] 1. A user wants to check the safety of raw fish they just took out of the refrigerator:

[1695] The user places a raw fish in front of the device's sensor and camera.

[1696] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1697] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1698] The device receives the results and analyzes the user's facial expressions and tone of voice.

[1699] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[1700] The user checks this advice and decides to cook the raw fish immediately.

[1701] Example 2: Use at a restaurant

[1702] 1. A user (chef) wants to check the quality of the meat he received:

[1703] The user places the meat in front of the device's sensor and camera.

[1704] The user launches the app and taps the scan button.

[1705] The device acquires images of the meat and environmental data and sends them to the server.

[1706] The server analyzes the data and determines that the meat is fresh.

[1707] The terminal receives the result and analyzes the user's tone of voice.

[1708] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[1709] The user decides to use the meat in a dish.

[1710] This enables easy and highly accurate evaluation of food safety in homes, restaurants, food processing plants, etc. Furthermore, by taking user emotions into consideration, a better user experience can be provided, and appropriate advice and warnings can be given. This invention has a wide range of applications and will contribute to the advancement of food hygiene management.

[1711] The processing flow will be explained below.

[1712] ---

[1713] Step 1:

[1714] The user places the food in front of the device's camera and sensor.

[1715] The user launches the dedicated app on their smartphone and taps the start scan button.

[1716] Step 2:

[1717] The device activates the camera and captures image data of the food.

[1718] The device collects environmental data about the food using its built-in temperature, humidity, and gas sensors.

[1719] Step 3:

[1720] The image data and environmental data acquired by the terminal are packaged.

[1721] Step 4:

[1722] The device encrypts the packaged data and sends it to the server using a secure protocol (e.g., HTTPS).

[1723] Step 5:

[1724] The server decrypts the received data and prepares it for analysis.

[1725] Step 6:

[1726] The server launches the generative AI and analyzes the food image data.

[1727] The server compares the image data with an existing database of spoiled food to look for changes in the food's appearance and color.

[1728] Step 7:

[1729] The server analyzes the food's environmental data (temperature, humidity, gas concentration) and detects abnormal values.

[1730] Step 8:

[1731] The server integrates the results of image data analysis and environmental data analysis to assess the degree of spoilage of food.

[1732] Step 9:

[1733] The server generates the analysis results, encodes them and sends them to the device.

[1734] Step 10:

[1735] The terminal decodes the analysis results received.

[1736] Step 11:

[1737] The device activates an emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1738] The terminal acquires the user's emotion data.

[1739] Step 12:

[1740] The device adjusts the analysis results based on the user's emotions and changes the content and method of display.

[1741] For example, if the user looks worried, add detailed advice or warnings.

[1742] Step 13:

[1743] The terminal displays the final analysis results and advice to the user.

[1744] It provides users with appropriate information and helps them make decisions about whether or not to use food.

[1745] Step 14:

[1746] The user checks the displayed results and advice and decides whether to use the food.

[1747] As a specific example, when checking the safety of raw fish taken out of the refrigerator at home, the process would be as follows:

[1748] 1. The user places a raw fish in front of the device's sensor and camera.

[1749] 2. The user launches the dedicated app and taps the scan button.

[1750] 3. The device acquires images of the raw fish and environmental data and sends them to the server.

[1751] 4. The server analyzes the data and determines that the fish is beginning to lose its freshness.

[1752] 5. The device receives the analysis results and analyzes the user's facial expressions and tone of voice to recognize emotions.

[1753] 6. The device recognizes that the user looks worried and displays the message, "The fish is starting to lose its freshness. We recommend cooking it as soon as possible."

[1754] 7. The user reviews this advice and decides to cook the raw fish immediately.

[1755] In this way, the present invention is a system that not only enables simple and highly accurate evaluation of food safety in homes, restaurants, food processing factories, etc., but also provides more appropriate information and advice by taking into account the user's emotions.

[1756] Example 2

[1757] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1758] Ensuring food safety is important in modern society, but a lack of specialized knowledge and equipment, particularly in households and small restaurants, makes it difficult to determine whether food has deteriorated or spoiled. Furthermore, a lack of appropriate advice or warnings based on the user's emotional state can make it difficult to make judgments in actual situations. This can lead to food waste and the risk of health hazards.

[1759] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1760] In this invention, the server includes means for acquiring image data of food, means for acquiring environmental data of food, means for transmitting the acquired image data and environmental data to the server, means for analyzing the image data and environmental data in the server and determining the safety of the food, means for receiving and displaying the analysis results to the user, means for recognizing the emotional state of the user, and means for adjusting the content and manner of displaying the analysis results based on the recognized emotional state of the user. This makes it possible to evaluate the safety of food with high accuracy and display appropriate advice and warnings according to the emotional state of the user.

[1761] "Food image data" refers to image information that shows the appearance and color of food.

[1762] "Food environmental data" means data that indicates environmental information such as the temperature, humidity, and concentration of volatile organic compounds around the food.

[1763] "Means of acquisition" means means that have the function of collecting data using devices such as cameras and sensors.

[1764] "Means for transmitting to a server" means means having the function of transmitting collected data to a server via the Internet or other communication protocols.

[1765] "Means for analysis on the server" means means that has the functionality to analyze received data using software or algorithms on the server.

[1766] "Means for determining food safety" means means that have the function of analyzing acquired data and assessing whether food is safe.

[1767] "Means for receiving the analysis results and displaying them to the user" means means having a function for receiving the analysis results and displaying them on a user interface.

[1768] "Means for recognizing the emotional state of the user" means means having a function of analyzing the user's facial expressions and tone of voice using a camera or microphone and recognizing the emotional state of the user.

[1769] "Means for adjusting the content and manner of displaying the analysis results" means means having a function for changing the content and manner of displaying the analysis results based on the emotional state of the user.

[1770] The present invention is a food safety evaluation system that can recognize the user's emotional state and adjust the results accordingly. This system provides appropriate advice according to the user's emotions, enabling more accurate food safety evaluation.

[1771] Program processing overview

[1772] The system is implemented using the following hardware and software.

[1773] Hardware: Smartphone (equipped with camera, temperature sensor, humidity sensor, and gas sensor)

[1774] Software: Dedicated application, generative AI model, emotion recognition engine

[1775] Communication protocol: HTTPS (secure communication)

[1776] Specific explanation of the process

[1777] 1. Food Preparation

[1778] Users place the food they want to evaluate in front of the smartphone camera and sensor, launch the dedicated app, and tap the "Start Scan" button in the app to begin data collection.

[1779] 2. Acquisition of food data

[1780] The device activates the camera to capture image data of the food, and also captures environmental data using the built-in temperature, humidity, and gas sensors.

[1781] 3. Data transmission

[1782] The device packages and encrypts the acquired image and environmental data, and then transmits it to the server using a secure protocol (HTTPS).

[1783] 4. Data Analysis

[1784] The server decodes the received data and activates a generative AI model to analyze the image data. The analysis results are compared with an existing spoiled food database. At the same time, environmental data is analyzed to detect anomalies. These data are then integrated to assess the degree of spoilage of the food.

[1785] 5. Sending analysis results

[1786] The server generates and encodes the analysis results and transmits them to the terminal.

[1787] 6. Emotional awareness and outcome regulation

[1788] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. Based on the acquired emotional data, the device adjusts the display of the analysis results and provides appropriate advice or warnings.

[1789] 7. Displaying the results

[1790] The terminal displays the final analysis results to the user.

[1791] 8. User discretion

[1792] The user checks the displayed analysis results and advice and decides whether or not to use the food.

[1793] Specific examples

[1794] Example 1: Home use

[1795] If a user wants to check the safety of raw fish they just took out of the refrigerator:

[1796] The user places a raw fish in front of the device's sensor and camera.

[1797] When a user launches the app and taps the scan button, the device captures an image of the raw fish and environmental data, which are then sent to the server.

[1798] The server analyzes the received data and determines that "this fish is beginning to lose its freshness."

[1799] The device receives the results and analyzes the user's facial expressions and tone of voice.

[1800] The device recognizes that the user looks worried and displays the message, "The fish is starting to lose some of its freshness. We recommend cooking it as soon as possible."

[1801] The user checks this advice and decides to cook the raw fish immediately.

[1802] Example 2: Use at a restaurant

[1803] If a user (chef) wants to check the quality of the meat he receives:

[1804] The user places the meat in front of the device's sensor and camera.

[1805] The user launches the app and taps the scan button.

[1806] The device acquires images of the meat and environmental data and sends them to the server.

[1807] The server analyzes the data and determines that the meat is fresh.

[1808] The terminal receives the result and analyzes the user's tone of voice.

[1809] The device recognizes the user's voice as a sign of relief and simply displays, "This meat is fresh."

[1810] The user decides to use the meat in a dish.

[1811] Examples of prompt statements

[1812] An example of a prompt sentence to input to the generative AI model is as follows:

[1813] Prompt Sentence Example 1

[1814] "The user takes raw fish from the refrigerator and places it in front of the device's sensor and camera, then launches the dedicated app and taps the scan button. The device acquires the data and sends it to the server, which then analyzes it and returns a judgment on the fish's freshness."

[1815] Prompt Sentence Example 2

[1816] "To check the quality of the meat that a restaurant chef receives, they place the meat in front of the device's sensor and camera, launch a dedicated app, and tap the scan button. The device acquires the data and sends it to the server, which then returns the analysis results."

[1817] The above is a specific embodiment of a system for evaluating food safety. This system is expected to be widely used in homes, restaurants, food processing plants, etc. Feedback based on the user's emotional state can provide more appropriate advice, thereby reducing food waste and health risks.

[1818] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1819] Step 1:

[1820] Food preparation

[1821] The user places the food they want to evaluate in front of the smartphone's camera and sensor.

[1822] Specific operation: Place food such as raw fish or meat in front of the smartphone's camera and sensors (temperature sensor, humidity sensor, gas sensor).

[1823] The user launches the dedicated app on their smartphone and taps the start scan button.

[1824] Specific operation: Tap the application icon to launch the app and press the "Start Scan" button that appears on the screen.

[1825] Input: Food

[1826] Output: Scan start command

[1827] Step 2:

[1828] Acquiring food data

[1829] The device activates the camera and captures image data of the food.

[1830] What it does: The camera automatically starts and captures an image of the food.

[1831] The device collects environmental data about the food using temperature, humidity, and gas sensors.

[1832] Specific operation: The temperature sensor measures the surface temperature of the food, the humidity sensor measures the ambient humidity, and the gas sensor detects volatile organic compounds (e.g., ethylene gas).

[1833] Input: Scan start command

[1834] Output: Image data, environmental data (temperature, humidity, gas concentration)

[1835] Step 3:

[1836] Sending data

[1837] The image data and environmental data acquired by the terminal are packaged.

[1838] Specific operation: Image data and each sensor data are combined into one data packet.

[1839] The device encrypts the packaged data and sends it to the server using a secure protocol (HTTPS).

[1840] Specific operation: Encrypts data using a data encryption library and sends it to the server using a secure communication protocol.

[1841] Input: Image data, environmental data

[1842] Output: Encrypted data packet

[1843] Step 4:

[1844] Data analysis

[1845] The server decrypts the received data packets.

[1846] Specific operation: Decrypts encrypted data packets and breaks them down into image data and environmental data.

[1847] The server launches the generative AI model and analyzes the food image data.

[1848] Specific operation: Image data of food is input into the generative AI model and analysis processing is performed.

[1849] The server compares the analysis results with an existing database of spoiled food.

[1850] Specific operation: Image data is compared with a database of spoiled food to detect changes in the appearance and color of the food.

[1851] The server analyzes the environmental data and detects abnormal values.

[1852] Specific operation: Environmental data is run through an analytical algorithm to identify outliers.

[1853] Generative AI integrates this data to determine the level of food spoilage.

[1854] Specific operation: Integrates image data and environmental data to calculate food spoilage scores.

[1855] Input: Encrypted data packet

[1856] Output: Food spoilage evaluation results

[1857] Step 5:

[1858] Sending analysis results

[1859] The server encodes the analysis results and sends them to the device.

[1860] Specific operation: The analysis results are generated in text or numerical format, encoded, and sent to the terminal.

[1861] Input: Food spoilage evaluation results

[1862] Output: The encoded parsed result

[1863] Step 6:

[1864] Displaying the results

[1865] The device receives and decodes the analysis results.

[1866] Specific operation: The received analysis results are decoded and converted into a format that can be displayed on the user interface.

[1867] The terminal displays the analysis results to the user.

[1868] Specific operation: The analysis results are displayed on the screen and notified to the user.

[1869] Input: The encoded parsed result

[1870] Output: Display of analysis results

[1871] Step 7:

[1872] Emotion recognition and outcome regulation

[1873] The device activates an emotion recognition engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice.

[1874] Specific operation: The camera and microphone are used to acquire the user's emotional data, which is then analyzed by the emotion recognition engine.

[1875] The device adjusts the content and manner of displaying the analysis results based on the emotion recognition data.

[1876] Specific operation: The displayed analysis results are changed to match the user's emotional state.

[1877] Input: User emotion data

[1878] Output: Adjusted analysis results display

[1879] Step 8:

[1880] User decision

[1881] The user checks the displayed analysis results and advice and decides whether or not to consume the food.

[1882] Specific operation: The user decides whether to cook or discard food based on the displayed information.

[1883] Input: Adjusted analysis result display content

[1884] Output: User decision

[1885] (Application example 2)

[1886] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1887] Current food safety assessment systems analyze food image data and environmental data to determine safety, but do not provide feedback based on the user's emotional state. This can lead to users being unable to respond appropriately to the results. Furthermore, because these systems are not designed for use in autonomous vehicles, such as while traveling or on the move, it is difficult to achieve both safety assessment and a sense of security. Therefore, the present invention proposes a system that solves these issues and can provide food safety assessment and feedback based on the user's emotional state.

[1888] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing food image data and environmental data to determine food safety, means for recognizing user emotion data, and means for adjusting the display content of the analysis results based on the recognized user emotion data. This enables appropriate feedback according to the user's emotional state, realizing a food safety evaluation system that can be used safely even in an autonomous vehicle while traveling or on the move.

[1889] "Food image data" is digital image data that shows the appearance and color of food.

[1890] "Food environmental data" refers to data on food storage conditions, such as temperature, humidity, and concentration of volatile organic compounds.

[1891] "User emotion data" refers to data on the user's emotional state recognized based on their facial expression or tone of voice.

[1892] "Analysis results" are the results of an evaluation of food safety obtained by analyzing food image data and environmental data.

[1893] The "recognition means" refers to a means having a function of recognizing the emotional state of the user using a camera or a microphone.

[1894] The "means for adjusting the display content" is a means for changing the display method and content of the analysis results based on the user's emotion data.

[1895] This invention adds a function to recognize user emotional data to a food safety evaluation system, providing feedback based on the user's emotional state. Specifically, food image data and environmental data are acquired, sent to a server for analysis, and the analysis results are then displayed to the user. The system also recognizes emotional data, such as the user's facial expression and tone of voice, and adjusts the display of the analysis results accordingly. This system is particularly intended for use in autonomous vehicles, providing food safety evaluations and a sense of security while on the move.

[1896] Hardware used:

[1897] Camera: A device for capturing food image data and user facial expression data (e.g., a typical webcam)

[1898] Sensors: Devices for acquiring environmental data such as temperature, humidity, and concentration of volatile organic compounds (e.g., DHT22 sensor, MQ-135 gas sensor)

[1899] Software used:

[1900] cv2 (OpenCV): A library for acquiring and analyzing food image data and user facial expression data.

[1901] requests: An HTTP client library for sending retrieved data to a server.

[1902] json: A library for serializing and deserializing data.

[1903] The specific operation of the system will now be described.

[1904] The user places the food on the device and launches the dedicated app to begin scanning. The device then uses its camera to capture image data of the food and sensors to acquire environmental data such as temperature, humidity, and gas concentration. This data is then packaged and sent to the server via a secure protocol.

[1905] The server analyzes the received data. The image data is compared against an existing database of spoiled food to identify changes in appearance and color. Environmental data is also analyzed to determine if any outliers are detected. A generative AI model on the server integrates this data and assesses the food's safety.

[1906] Once the analysis results are sent to the device, the device activates an emotion engine that analyzes the user's facial expressions and tone of voice. Based on this emotional data, the device adjusts the content and display of the analysis results. For example, if the user looks worried, the device may add advice such as "We recommend you consume the product as soon as possible."

[1907] Specific examples are shown below.

[1908] Example 1: Use in autonomous vehicles

[1909] If a user wants to evaluate the safety of a sandwich purchased from an in-train vendor, they place the sandwich in front of the device's camera and sensors. When they launch the app and tap the Start Scan button, the camera captures image data of the sandwich, and the sensors acquire temperature, humidity, and gas concentration data, which they then send to the server. If the analysis returns "This sandwich is safe," and the emotion engine recognizes anxiety from the user's facial expression, the screen will display "It is safe, but we recommend consuming it as soon as possible."

[1910] Example prompts for generative AI models

[1911] "Please rate the safety of the following food items. The data includes images of the food items and measurements of temperature, humidity, and gas concentrations. Based on your results, please rate whether the food items are safe or should not be consumed, and explain why."

[1912] As described above, the present invention realizes a system that can provide users with a sense of security by combining food safety evaluation with feedback based on the user's emotions.

[1913] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1914] Step 1:

[1915] The user places the food in front of the device's camera and sensor, launches the dedicated app, and taps the start scan button. The input is the user's operation and the placement of the food, and the output is the device activating the camera and sensor.

[1916] Step 2:

[1917] The device uses a camera to capture image data of food. The input is the camera image, and the output is image data of the food. Specifically, the camera takes an image of the food and stores the data in temporary memory.

[1918] Step 3:

[1919] The device acquires food environment data using temperature, humidity, and gas sensors. The input is sensor information, and the output is temperature, humidity, and gas concentration data. Each sensor measures data in real time and stores it in an internal buffer.

[1920] Step 4:

[1921] The image data and environmental data acquired by the terminal are packaged, encrypted, and sent to the server using a secure protocol (e.g., HTTPS). The input is image data and environmental data, and an encrypted data package is generated as the output. Specifically, the data is encrypted through the encryption module and sent to the specified server address.

[1922] Step 5:

[1923] The server decrypts the received data and prepares it for analysis. The input is an encrypted data package, and the output is decrypted data. Upon receiving the request, the server decrypts the data and separates the image data from the environmental data.

[1924] Step 6:

[1925] The server launches the generative AI to analyze the food image data. The input is the decoded image data, and the output is an evaluation of the food's appearance and color. The AI ​​model compares the image data with an existing database of spoiled food to detect abnormalities in color and shape.

[1926] Step 7:

[1927] The server analyzes environmental data (temperature, humidity, gas concentration) and detects abnormal values. The input is the decoded environmental data, and the output is the abnormality analysis result. An algorithm is used to compare each item of environmental data with a reference value and detect abnormal values.

[1928] Step 8:

[1929] The generative AI integrates the image data and environmental data evaluation to make a final judgment on food safety. The inputs are appearance and color evaluation and anomaly analysis results of environmental data, and the output is a final safety evaluation. The AI ​​model performs a comprehensive evaluation and outputs a safety judgment.

[1930] Step 9:

[1931] The server generates the analysis results, encodes them, and sends them to the terminal. The input is the final security assessment result, and encrypted analysis result data is generated as the output. The process of encoding and encrypting the data and sending it to the terminal is executed.

[1932] Step 10:

[1933] The terminal receives and decodes the analysis results. The input is the encrypted analysis result data, and the output is the decrypted analysis result. The terminal that receives the request decrypts the data and prepares it for display.

[1934] Step 11:

[1935] The device activates the emotion engine and uses the camera and microphone to analyze the user's facial expressions and tone of voice. The input is the user's facial expression and voice data, and the output is the user's emotional data. The emotion analysis algorithm analyzes the user's facial expressions and tone of voice in real time to determine their emotions.

[1936] Step 12:

[1937] The device adjusts the content and display method of the analysis results based on the user's recognized emotional data. The input is the analysis result data and the user's emotional data, and the adjusted analysis results are generated as the output. The tone and level of detail of the displayed content are adjusted based on the emotional data, and the final display content is determined.

[1938] Step 13:

[1939] The terminal displays the analysis results to the user, adding advice and warnings as needed. The input is the adjusted analysis results, and the output is the analysis content displayed to the user. The terminal displays the analysis results on the screen, adding additional explanations and advice as needed.

[1940] 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.

[1941] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1942] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1943] 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.

[1944] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1945] 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.

[1946] 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).

[1947] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1948] 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."

[1949] 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.

[1950] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1951] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1952] 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.

[1953] 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.

[1954] 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.

[1955] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1956] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1957] 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.

[1958] 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.

[1959] 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.

[1960] 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.

[1961] The following is further disclosed regarding the above embodiment.

[1962] ---

[1963] (Claim 1)

[1964] a means for acquiring image data of the food;

[1965] a means of obtaining environmental data on food;

[1966] means for transmitting the acquired image data and environmental data to a server;

[1967] a means for analyzing the image data and the environmental data in the server and determining the safety of the food;

[1968] means for receiving and displaying the analysis results to a user;

[1969] A system including:

[1970] (Claim 2)

[1971] 2. The system according to claim 1, wherein the image data of the food is data indicating the appearance and color of the food.

[1972] (Claim 3)

[1973] 2. The system according to claim 1, wherein the environmental data of the food includes temperature, humidity, and concentration of volatile organic compounds.

[1974] "Example 1"

[1975] (Claim 1)

[1976] a means for acquiring image data of the food;

[1977] a means of obtaining environmental data on food;

[1978] means for encrypting and transmitting the acquired image data and environmental data;

[1979] a means for receiving, decoding, and analyzing the image data and the environmental data in the server, and determining the safety of the food using the generative AI model;

[1980] a means for encoding and transmitting the analysis results;

[1981] means for receiving and displaying the analysis results to a user;

[1982] A system including:

[1983] (Claim 2)

[1984] 2. The system according to claim 1, wherein the image data of the food is data indicating the appearance and color of the food.

[1985] (Claim 3)

[1986] 2. The system according to claim 1, wherein the environmental data of the food includes temperature, humidity, and concentration of volatile organic compounds.

[1987] "Application Example 1"

[1988] (Claim 1)

[1989] a means for acquiring image data of the food;

[1990] a means of obtaining environmental data on food;

[1991] means for transmitting the acquired image data and environmental data to a server;

[1992] a means for analyzing the image data and the environmental data in the server and determining the safety of the food;

[1993] means for receiving and displaying the analysis results to a user;

[1994] means installed on the robot for use in a food processing plant;

[1995] A system including:

[1996] (Claim 2)

[1997] 2. The system according to claim 1, wherein the image data of the food is data indicating the appearance and color of the food.

[1998] (Claim 3)

[1999] 2. The system according to claim 1, wherein the environmental data of the food includes temperature, humidity, and concentration of volatile organic compounds.

[2000] "Example 2: Combining Emotion Engines"

[2001] (Claim 1)

[2002] a means for acquiring image data of the food;

[2003] a means of obtaining environmental data on food;

[2004] means for transmitting the acquired image data and environmental data to a server;

[2005] a means for analyzing the image data and the environmental data in the server and determining the safety of the food;

[2006] means for receiving and displaying the analysis results to a user;

[2007] means for recognizing the emotional state of a user;

[2008] means for adjusting the content and manner of displaying the analysis results based on the recognized emotional state of the user;

[2009] A system including:

[2010] (Claim 2)

[2011] 2. The system according to claim 1, wherein the image data of the food is data indicating the appearance and color of the food.

[2012] (Claim 3)

[2013] 2. The system according to claim 1, wherein the environmental data of the food includes temperature, humidity, and concentration of volatile organic compounds.

[2014] "Application example 2 when combining emotion engines"

[2015] (Claim 1)

[2016] a means for acquiring image data of the food;

[2017] a means of obtaining environmental data on food;

[2018] means for transmitting the acquired image data and environmental data to a server;

[2019] a means for analyzing the image data and the environmental data in the server and determining the safety of the food;

[2020] means for receiving and displaying the analysis results to a user;

[2021] means for recognizing user emotion data;

[2022] means for adjusting the display content of the analysis results based on the recognized user emotion data;

[2023] A system including:

[2024] (Claim 2)

[2025] 2. The system according to claim 1, wherein the image data of the food is data indicating the appearance and color of the food, and the analysis result indicates the freshness or degree of spoilage of the food.

[2026] (Claim 3)

[2027] 2. The system of claim 1, wherein the food environmental data includes temperature, humidity, and concentration of volatile organic compounds, and the user's emotional state is recognized based on facial expression or tone of voice. [Explanation of symbols]

[2028] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring image data of the food; a means of obtaining environmental data on food; means for transmitting the acquired image data and environmental data to a server; a means for analyzing the image data and the environmental data in the server and determining the safety of the food; means for receiving and displaying the analysis results to a user; A system including:

2. 2. The system according to claim 1, wherein the image data of the food is data representing the appearance and color of the food.

3. 2. The system according to claim 1, wherein the environmental data of the food includes data on temperature, humidity, and concentration of volatile organic compounds.

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