system

Abstract

We provide the system. [Solution] A means for receiving image information or text information entered by a user, Image analysis means for extracting text data from the aforementioned image information, A natural language processing means for identifying allergen components from the aforementioned text data, A comparative determination means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information, A means for presenting the results of the allergy risk assessment to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] For individuals with food allergies, they often worry about the possibility of triggering allergies when consuming unknown foods, especially in overseas restaurants. In such situations, it is difficult to safely select meals because the language of the menu and the ingredients used are unknown. Therefore, there is a need for a technology that can pre-judge the safety of meals and effectively identify the allergy risks for all foods.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system for processing image information or text information input by a user. This system includes an image analysis means for extracting text data from received image information and a natural language processing means for identifying allergen components based on that text data. It also includes a comparison and determination means for matching the allergen components with the user's registered allergy information, and by presenting the determination result to the user, allergy risks can be confirmed in advance. This allows the user to understand the allergy risk of each food and make safe food choices. The natural language processing means, equipped with multilingual support, is also useful overseas. Furthermore, it generates warning messages based on the allergy risk determination result to promote food safety.

[0006] A "user" refers to an individual who intends to use this system to determine if they have food allergies.

[0007] "Image information" refers to visual data related to food or menus that is provided by the user through photography or input.

[0008] "Text information" refers to string-formatted data that is either manually entered by the user or generated through image analysis.

[0009] "Image analysis means" refers to a function that includes algorithms and processes for extracting characters from received image information.

[0010] "Natural language processing means" refers to a technology that analyzes specific meanings and information from extracted text information to identify allergen components.

[0011] "Allergen components" refer to specific proteins or compounds found in food that can potentially trigger allergic reactions.

[0012] The "comparison and determination means" is a function that performs the process of determining allergy risk by comparing the user's allergy information stored in the database with allergen components.

[0013] "Multilingual support" refers to technology that can process and understand text in one or more different languages.

[0014] A "warning message" is a notification text generated to inform a user of an allergy risk. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The food allergy determination system according to the present invention helps users accurately determine the allergy risk of food. This system is realized through the collaborative operation of the user, terminal, and server.

[0037] Users can first take pictures of restaurant menus, food packaging, etc., using a smartphone or tablet. Users can also provide data by directly entering text information into their device.

[0038] The terminal is responsible for transmitting received image or text information to the server. The transmitted information is then analyzed on the server using advanced image analysis and natural language processing technologies. Specifically, the server extracts text data from the image information using image analysis tools. This is achieved through optical character recognition (OCR) technology.

[0039] Next, the server performs natural language processing on the extracted text data to identify allergen components. For example, it identifies words like "shrimp" listed on the menu and determines whether or not they are allergens.

[0040] Next, the server uses a comparison and determination mechanism to match the identified allergen component with the allergy information provided by the user. If an allergy risk is detected, the server generates a warning message to inform the user.

[0041] Users can receive this warning message through their device. For example, they might receive a notification stating, "This dish contains shrimp, so there is a risk of shellfish allergy." This allows users to make safer food choices.

[0042] Because it supports multiple languages, this system will function the same way even if the user encounters menus in a different language while abroad. Thus, the present invention aims to enable users to safely choose meals in any environment and to alleviate anxieties related to food allergies.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users take pictures of menus or food packaging using their smartphone or tablet camera, or they directly input the menu contents as text into their device.

[0046] Step 2:

[0047] The device sends captured image data or entered text information to the server. This transmission is securely performed via an internet connection.

[0048] Step 3:

[0049] The server receives image data transmitted from the terminal and extracts text data using image analysis tools. Specifically, it utilizes optical character recognition (OCR) technology to recognize characters within the image and converts them into a readable text format.

[0050] Step 4:

[0051] The server performs natural language processing (NLP) on the extracted text data. This identifies allergen components in the analyzed text. For example, the word "shrimp" is identified as an indicator of allergy risk.

[0052] Step 5:

[0053] The server compares the identified allergen components with the user's registered allergy information. This allows for a comparative assessment to evaluate the presence or absence of allergy risk.

[0054] Step 6:

[0055] Based on the assessment results, the server generates a warning message if an allergy risk is detected. For example, it might say, "This dish contains shrimp, therefore there is an allergy risk."

[0056] Step 7:

[0057] The terminal displays warning messages received from the server to the user. This allows the user to check the safety of the food and make appropriate food choices.

[0058] Through this process, users will be able to choose food with confidence and enjoy meals safely.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Modern consumers are required to quickly and accurately understand information about food ingredients, but many consumers do not have sufficient access to this information when traveling abroad or dining in different cultural environments, often facing allergies and health risks. This invention aims to solve this problem and provide a system that enables users to safely select food despite language barriers.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving digital information input from a user via a receiving terminal, analysis means utilizing optical character recognition technology for extracting character information from the digital information, and natural language processing means for identifying food components from the character information. This enables users to properly understand food information written in different languages ​​and compare it with their own health information, thereby preventing allergies and other health risks.

[0064] "Digital information" refers to image data and text data that users can input through their devices.

[0065] "Optical character recognition technology" is a technology for extracting character information from image data, and it performs the process of identifying text from digital images and converting it into a string of characters.

[0066] "Analysis means" refers to a set of technical functions for processing input digital information and extracting necessary text data.

[0067] "Natural language processing means" refers to methods for identifying and analyzing specific information from extracted text information, and is particularly a language understanding technology used to identify food components.

[0068] A "comparison and determination means" is a technical means that performs a process to determine health risks by comparing identified food components with the user's health information.

[0069] An "information presentation means" is a system component that has the function of notifying users of analysis results and information regarding risks.

[0070] "International language support" refers to the ability to recognize and process multiple languages, and to accurately understand and process text information written in different languages.

[0071] "Warning information" refers to advisory messages provided to users based on risk assessment results, intended to inform them of potential health risks.

[0072] This invention relates to a food information analysis system for evaluating a user's food allergies and health risks. This system is realized through the collaborative operation of a user, a terminal, and a server.

[0073] First, users can take pictures of food packaging or restaurant menus using devices such as smartphones or tablets. Users can also directly input text information into their devices. The device then sends the captured image data or the entered text data to the server.

[0074] Upon receiving the transmitted digital information, the server uses optical character recognition (OCR) technology to extract text information from the image data. This extraction provides information about the food and its ingredients in text format. Subsequently, the server uses natural language processing techniques on the extracted text information to identify the food ingredients. This process identifies specific keywords and phrases within the text and analyzes their meaning.

[0075] Specifically, the server identifies allergen-related words such as "shrimp" and "nuts" based on a list of food ingredients. It also appropriately processes information written in different languages ​​using its international language support function.

[0076] The server then compares the allergy and health information previously registered by the user with the identified food components to determine the health risk. Based on the determination, the server generates warning information and notifies the user. This warning information serves as an important guide for the user to make safe food choices.

[0077] For example, if a user has an image of a French menu taken overseas, the server will identify the word "crevettes" (shrimp) and warn of the risk of a shrimp allergy.

[0078] An example of a prompt message might be: "Identify the allergens in the French restaurant menu and compare them with the user's shellfish allergy information to determine the risk."

[0079] Thus, the system of the present invention helps users assess the risk of food allergies and make safe food choices, overcoming language barriers.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users take pictures of food packaging or restaurant menus using their smartphones or tablets. The captured images are either saved on the device or sent directly to the server via the application. At this stage, users can also manually input text information, which is then used for analysis in subsequent processing steps.

[0083] Step 2:

[0084] The terminal receives captured or inputted digital information and transmits it to the server using a configured network communication module. Specifically, the data is packaged according to a particular protocol and uploaded to the server's dedicated API via the internet. This output becomes the input data for analysis by the server.

[0085] Step 3:

[0086] The server acquires the received digital information and extracts character information from the image data using optical character recognition (OCR) technology. The image input to the OCR engine undergoes character identification and text conversion processes, and is output as a string of characters. This output character information is then passed on to the subsequent natural language processing step.

[0087] Step 4:

[0088] The server, upon receiving the textual information, uses natural language processing to identify specific food components from this information. Specifically, it utilizes a generative AI model to analyze specific keywords within the text, identify allergen components, and output them. This output is then used in the next comparison and judgment step.

[0089] Step 5:

[0090] The server compares the identified allergen component with the user's pre-registered allergy information. It searches the user's health database for relevant information and, if a matching allergen is found, identifies it as an allergy risk and outputs it. The results of this determination are used to generate a warning message.

[0091] Step 6:

[0092] The server that obtains the judgment results generates a warning message as needed. This message is intended to inform the user of potential health risks, and a specific example might be, "This dish contains certain allergens." This output is then notified to the user.

[0093] Step 7:

[0094] The user receives a warning message generated by the server through their device. This message pops up via the device's notification system, allowing the user to choose safer meals based on it. Risk information is displayed on the device's screen, serving as output for the user.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] When users with food allergies choose food products, there is a need to address the challenge of ensuring safe consumption by quickly and accurately identifying the allergens contained in the food and providing visual warnings. Furthermore, information must be available in multiple languages.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting text data from the image information, natural language processing means for identifying allergen components from the text data, comparison and determination means for determining allergy risk by comparing the allergen components with the user's allergy information, and output means for presenting the determination result of the allergy risk on a visual display device. This makes it possible for users to easily check allergen information and safely select food based on appropriate judgment.

[0100] A "user" refers to an individual who uses the system to check allergen information contained in food products.

[0101] "Image information" refers to visual data of food packaging and menus acquired by users using devices such as smart glasses.

[0102] "Text information" refers to written information about food products that users manually or automatically enter.

[0103] "Image analysis means" refers to a function that extracts text data from acquired image information using optical character recognition technology or the like.

[0104] "Natural language processing means" refers to techniques that analyze the meaning of text information in order to identify allergen components from text data.

[0105] The "comparison and determination means" refers to a function that compares extracted allergen components with the user's pre-registered allergy information to evaluate the allergy risk.

[0106] A "visual display device" refers to a device that uses smart glasses or other display devices to visually present information to the user.

[0107] "Output means" refers to a function that displays warnings or information to notify the user of the determined allergy risk.

[0108] This invention provides a system that quickly and accurately identifies allergen information contained in food and displays a warning on a visual display device used by the user. The system primarily utilizes the following hardware and software.

[0109] The user wears smart glasses and takes pictures of food packaging or restaurant menus with the camera. The acquired image information is sent to a server via the terminal. The server has OpenCV and Tesseract OCR installed for image analysis, and uses them to extract text data from the images. The extracted text data is analyzed using SpaCy, a natural language processing software, to identify allergen components.

[0110] The server is equipped with a comparison mechanism to match identified allergen components with the user's pre-registered allergy information. This comparison assesses the user's allergy risk, and if a risk exists, a warning message is immediately generated. The generated warning is displayed on the smart glasses' HUD, allowing the user to visually receive the information.

[0111] For example, if a user visits an Italian restaurant while out and about and takes a picture of a menu item called "Shrimp Risotto," the system will identify shrimp as an allergen and display a warning on the smart glasses saying, "You have a shrimp allergy. Please be careful." In this way, users can make food choices safely.

[0112] An example of a prompt message is: "There is a shrimp risotto on the menu. Identify the allergens and generate a risk warning."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user uses the camera on their smart glasses to take pictures of food packaging or restaurant menus. The camera captures the image information and sends that data to the device as input.

[0116] Step 2:

[0117] The terminal uploads the received image information to the cloud server. The server receives this image information as input and prepares to proceed to the next analysis step.

[0118] Step 3:

[0119] The server uses OpenCV and Tesseract OCR to extract text data from the received image information. At this stage, the input is image information, and the output is the corresponding text data. The text data includes the names of the food items and ingredient information listed on the menu.

[0120] Step 4:

[0121] The server analyzes the extracted text data using SpaCy, a natural language processing framework, to identify allergen components. The input is text data, and a list of allergens is output. In this process, the meaning and context of words are considered to determine whether a food component is an allergen.

[0122] Step 5:

[0123] The server matches the identified allergen components against the user's pre-registered allergy information. The input is a list of allergens and the user's allergy information, and the output is an assessment of the allergy risk. During the matching process, comparative calculations are performed to determine whether or not a risk exists.

[0124] Step 6:

[0125] The server generates a warning message based on the determined allergy risk and sends that message to the smart glasses via the terminal. The input is the result of the risk assessment, and the output is the warning message. The system visually informs the user of the risk.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] The food allergy detection system according to the present invention helps users evaluate the allergy risk of food and enjoy meals safely. This system consists of a user, a terminal, a server, and an emotion engine.

[0128] Users first use a device such as a smartphone or tablet to take pictures of the menu or input text information about the food. The device then transmits this information to the server, which analyzes the information using image analysis and natural language processing technologies. Specifically, the server uses image analysis tools to obtain text information from the images and extracts it as text data.

[0129] Subsequently, the server uses natural language processing to identify allergen components from the extracted text. These identified allergen components are then compared and matched with the user's registered allergy information to determine the allergy risk.

[0130] A distinctive feature of this invention is the combination of this determination process with an emotion engine. The emotion engine is used to recognize emotions from text information and voice data entered by the user. Based on this information, the user's emotional state is analyzed, and if the user is in a situation where they are likely to feel stressed, a warning message that draws particular attention to them can be generated.

[0131] For example, if a user feels anxious at a restaurant abroad, the emotion engine detects that anxiety and adjusts its settings to provide more detailed and thorough allergy information. In this way, users can enjoy their meal with peace of mind while reducing their mental burden.

[0132] The device ultimately presents the user with information received from the server, providing risk information and warning messages in an optimal format tailored to their emotional state. This facilitates safer food choices and alleviates anxiety about food.

[0133] This system incorporates a new approach that takes user emotions into account, providing a higher level of reassurance than traditional methods could achieve. This makes support for users with food allergies even more effective.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users take photos of restaurant menus or food packaging using their smartphone or tablet camera. Alternatively, they can directly input food names or descriptions of dishes as text on their device.

[0137] Step 2:

[0138] The device transmits captured image data or entered text information to a server via the internet.

[0139] Step 3:

[0140] The server processes the image data received from the terminal using image analysis tools. This tool extracts text information from the image data. Optical character recognition (OCR) technology is used to recognize characters within the image and convert them into text format.

[0141] Step 4:

[0142] The server analyzes the extracted text data using natural language processing to identify allergen components. This process identifies specific words or phrases within the text and determines whether they are components that may cause allergies.

[0143] Step 5:

[0144] The server compares the identified allergen components with the user's registered allergy information. This allows the server to assess the user's allergy risk.

[0145] Step 6:

[0146] Simultaneously, the system uses an emotion engine to analyze the user's emotional state based on their recent text input and voice data. This engine determines the degree of stress and anxiety based on the user's input.

[0147] Step 7:

[0148] The server integrates the allergy risk assessment results with the user's emotional state and adjusts the warning message as needed. For example, if the user is in an anxious emotional state, the message will be made more detailed and polite.

[0149] Step 8:

[0150] The device displays generated warning messages and allergy information to the user. This allows the user to receive information in a format best suited to their emotional state, enabling them to make safe food choices.

[0151] This process allows us to support users in making food choices that take their feelings into consideration and ensure safety.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] To ensure that users with food allergies can safely enjoy meals in different cultural and linguistic environments, a system is needed that can reliably and quickly verify allergen information. Furthermore, methods are required to consider the user's emotional state and alleviate their anxiety about food.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting character data from the image information, natural language processing means for identifying allergen components from the character data, emotion analysis means for analyzing emotional information from the user to identify the emotional state, and warning generation means for generating a warning message based on the identified emotional state. This makes it possible to provide emotionally sensitive information along with allergy risk analysis simply by the user inputting image or text information.

[0157] A "user" is an individual who uses the system to check the risk of food allergies.

[0158] "Image information" refers to visual data captured using cameras or electronic devices, including menus and food labels.

[0159] "Text information" refers to string data entered by the user, such as information about food ingredients or menu details.

[0160] "Image analysis means" refers to techniques used to extract character data from image information, and includes computer vision algorithms.

[0161] "Natural language processing means" refers to techniques for analyzing and extracting useful information from text data, and is used for identifying allergen components.

[0162] "Allergens" refer to substances that can cause allergic reactions and are specific components found in food.

[0163] A "comparative determination method" refers to a method for comparing identified allergen components with the user's registered allergy information, and plays a role in evaluating allergy risk.

[0164] "Emotional analysis methods" refer to technologies that analyze text and audio data to evaluate a user's emotional state.

[0165] "Warning generation method" refers to a method of creating messages based on emotional state or allergy risk in order to provide alerts to users.

[0166] This food allergy detection system is designed to help users with food allergies safely enjoy meals. The specific implementation method is described below.

[0167] Users use devices such as smartphones or tablets to take pictures of meal menus or food packaging, or to input text information about the food into their devices. The devices then transmit this image and text information to a server via the internet. Wi-Fi or mobile data communication is used for this transmission.

[0168] The server processes the received image information using image analysis technology. This process utilizes common image analysis software such as OpenCV to extract text data from the images. Furthermore, natural language processing, using Python's NLTK library, is applied to the extracted text data to specifically identify allergen components.

[0169] Furthermore, based on the allergy information registered by the user in advance, the server matches it with identified allergen components and determines the allergy risk. In this process, the system prepares to generate and provide allergy warning messages to the user.

[0170] A distinctive feature of the system is emotion analysis. The server utilizes a generative AI model to analyze text and voice data entered by the user, identifying the user's emotional state, such as "anxiety." Based on this emotional information, it is possible to adjust the content and presentation method of warning messages.

[0171] As a concrete example, in response to a prompt message entered by a user such as, "I'm looking for a restaurant abroad that doesn't serve dishes without nuts, but I'm worried," the system will detect the presence or absence of nuts and, taking the user's concerns into account, provide detailed safety information.

[0172] In this way, this system uses advanced image analysis and natural language processing technologies to provide users with necessary and reassuring food allergy information tailored to their individual emotional state.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] Users use devices such as smartphones or tablets to take pictures of menus or food packaging, or to directly input text information about the food. The entered data is sent to the device.

[0176] Step 2:

[0177] The terminal transmits image or text information provided by the user to the server via the internet. In this case, image data is sent to the server in JPEG format, and text data in UTF-8 format.

[0178] Step 3:

[0179] The server processes the received image data through an image analysis tool to extract text data from the image. This process uses image analysis software such as OpenCV, and the extracted text data is obtained as output.

[0180] Step 4:

[0181] The server sends the extracted string data to a natural language processing system to identify allergen components. This process uses the Python natural language processing library NLTK to obtain words that may be allergens as output.

[0182] Step 5:

[0183] The server compares the identified allergen components with the user's registration information to determine the allergy risk. Based on the comparison results, the allergy risk assessment is output, and a warning message is prepared if necessary.

[0184] Step 6:

[0185] The server uses a generative AI model to analyze emotional information from the user's input text data and identify the user's emotional state. The emotional state derived from the emotional analysis is then output. This process is based on emotional expressions such as "anxiety" that the user inputs.

[0186] Step 7:

[0187] The server generates the most appropriate warning message based on the allergy risk assessment and the user's emotional state. For example, if the emotional state is "anxious," the message will be polite and detailed. This message is then generated as the final output.

[0188] Step 8:

[0189] The terminal receives the final output from the server and presents the information to the user in the most optimal format. For example, a message might appear as a pop-up on the terminal's display. This allows the user to obtain information to make safe food choices.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] There is a need for a system that can quickly and accurately assess the health risks that allergens in food pose to users with allergies, and further provide information that reduces the user's emotional burden based on their emotional state. In particular, there is a need to strengthen safety and psychological support so that users with food allergies can enjoy meals with peace of mind in situations where they feel anxious or stressed.

[0193] The identification processing performed 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 receiving image information or text information input from a user, image analysis means for extracting text data from the image information, and natural language processing means for identifying allergen components from the text data. This makes it possible to identify allergens contained in food, appropriately evaluate the user's allergy risk, and further provide information according to the user's emotional state through emotion recognition.

[0194] A "user" is a person who uses the system to evaluate the allergy risk of food.

[0195] "Image information" refers to visual data about food that users input via their devices.

[0196] "Text information" refers to character data about food that users input via their devices.

[0197] "Image analysis means" refers to technical means that perform a process to extract text data from received image information.

[0198] "Natural language processing means" refers to technical means for identifying allergen components from extracted text data.

[0199] "Allergens" are components in food that can potentially cause allergic reactions.

[0200] A "comparative determination means" is a technical means for determining allergy risk by comparing identified allergen components with the user's allergy information.

[0201] "Emotion recognition means" refers to technical means for recognizing a user's emotional state and providing information to alleviate their mental burden.

[0202] "Allergy risk" refers to the potential health risks that identified allergen components pose to the user.

[0203] A "server" is a central computer system that receives image and text information and performs analysis and judgment.

[0204] This invention is a system for users to identify allergens in food and assess their risks. The process begins with the user taking a picture of a menu item using a smartphone or other device, or entering text information about the food. The device then transmits this image or text information to a server.

[0205] The server processes the received information as follows: First, it extracts text data from the image information using image analysis tools. This utilizes machine learning frameworks such as TENSORFLOW® to identify text information within images. Subsequently, it identifies allergen components from the extracted text data using natural language processing tools. In this step, natural language processing tools such as SpaCy are used to analyze food components within the text.

[0206] Furthermore, the server uses emotion recognition tools to analyze the user's emotional state. For example, it utilizes IBM Watson®'s emotion analysis API to recognize emotions from the user's input text and voice. Based on this information, the server generates detailed warning messages if the user is feeling anxious. This helps reduce the user's mental burden while supporting them in making safe food choices.

[0207] Finally, the server sends the analysis results to the terminal and presents the allergy risk assessment to the user in an optimal format based on sentiment analysis. This allows the user to choose their meals with confidence.

[0208] For example, when a user orders Chinese food and takes a picture of the menu and uploads it to the app, "peanuts" are identified as an allergen. If the user is concerned, the app will use this information to warn them, "This dish contains an allergen. Please consider other safer options."

[0209] Examples of prompt statements for a generative AI model are as follows:

[0210] Use a food allergy detection system to identify allergens from image / text information and generate messages that provide information tailored to the user's emotional state. Include points to consider especially when the user is feeling anxious.

[0211] This system configuration allows us to provide users with food allergies with a high level of safety and peace of mind.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] Users either take a picture of the food menu via a smartphone or other device, or input text information. The entered data is saved on the device as image or text information. This constitutes the initial input.

[0215] Step 2:

[0216] The terminal sends the input image or text information to the server. At this stage, the user's input data reaches the server and is ready to be analyzed in the next step.

[0217] Step 3:

[0218] The server extracts text data from received image information using image analysis tools. The input here is image data, and the output is the extracted text information. This process utilizes character recognition algorithms based on technologies such as TensorFlow.

[0219] Step 4:

[0220] The server uses natural language processing to identify allergen components from extracted text data. The input is text data, and the output is the identified allergen components. Natural language processing tools such as SpaCy are used to analyze the components within the text and extract allergen candidates.

[0221] Step 5:

[0222] The server analyzes the user's emotional state through emotion recognition mechanisms. It uses text and other information entered by the user as analysis material and identifies the user's emotional patterns using IBM Watson's emotion analysis API. The input is digital information about the user's emotions, and the output is the recognized emotional state.

[0223] Step 6:

[0224] The server integrates information on allergen components and the user's emotional state to generate detailed warning messages as needed. The input is the identified allergen components and the user's emotional state, while the output is a customized warning message presented to the user.

[0225] Step 7:

[0226] The server presents the generated warning messages and allergy risk information to the user via the terminal. The final output allows the user to receive information that enables them to make informed food choices.

[0227] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0243] The food allergy determination system according to the present invention helps users accurately determine the allergy risk of food. This system is realized through the collaborative operation of the user, terminal, and server.

[0244] Users can first take pictures of restaurant menus, food packaging, etc., using a smartphone or tablet. Users can also provide data by directly entering text information into their device.

[0245] The terminal is responsible for transmitting received image or text information to the server. The transmitted information is then analyzed on the server using advanced image analysis and natural language processing technologies. Specifically, the server extracts text data from the image information using image analysis tools. This is achieved through optical character recognition (OCR) technology.

[0246] Next, the server performs natural language processing on the extracted text data to identify allergen components. For example, it identifies words like "shrimp" listed on the menu and determines whether or not they are allergens.

[0247] Next, the server uses a comparison and determination mechanism to match the identified allergen component with the allergy information provided by the user. If an allergy risk is detected, the server generates a warning message to inform the user.

[0248] Users can receive this warning message through their device. For example, they might receive a notification stating, "This dish contains shrimp, so there is a risk of shellfish allergy." This allows users to make safer food choices.

[0249] Because it supports multiple languages, this system will function the same way even if the user encounters menus in a different language while abroad. Thus, the present invention aims to enable users to safely choose meals in any environment and to alleviate anxieties related to food allergies.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users take pictures of menus or food packaging using their smartphone or tablet camera, or they directly input the menu contents as text into their device.

[0253] Step 2:

[0254] The device sends captured image data or entered text information to the server. This transmission is securely performed via an internet connection.

[0255] Step 3:

[0256] The server receives image data transmitted from the terminal and extracts text data using image analysis tools. Specifically, it utilizes optical character recognition (OCR) technology to recognize characters within the image and converts them into a readable text format.

[0257] Step 4:

[0258] The server performs natural language processing (NLP) on the extracted text data. This identifies allergen components in the analyzed text. For example, the word "shrimp" is identified as an indicator of allergy risk.

[0259] Step 5:

[0260] The server compares the identified allergen components with the user's registered allergy information. This allows for a comparative assessment to evaluate the presence or absence of allergy risk.

[0261] Step 6:

[0262] Based on the assessment results, the server generates a warning message if an allergy risk is detected. For example, it might say, "This dish contains shrimp, therefore there is an allergy risk."

[0263] Step 7:

[0264] The terminal displays warning messages received from the server to the user. This allows the user to check the safety of the food and make appropriate food choices.

[0265] Through this process, users will be able to choose food with confidence and enjoy meals safely.

[0266] (Example 1)

[0267] Next, we will describe Example 1. 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."

[0268] Modern consumers are required to quickly and accurately understand information about food ingredients, but many consumers do not have sufficient access to this information when traveling abroad or dining in different cultural environments, often facing allergies and health risks. This invention aims to solve this problem and provide a system that enables users to safely select food despite language barriers.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0270] In this invention, the server includes means for receiving digital information input from a user via a receiving terminal, analysis means utilizing optical character recognition technology for extracting character information from the digital information, and natural language processing means for identifying food components from the character information. This enables users to properly understand food information written in different languages ​​and compare it with their own health information, thereby preventing allergies and other health risks.

[0271] "Digital information" refers to image data and text data that users can input through their devices.

[0272] "Optical character recognition technology" is a technology for extracting character information from image data, and it performs the process of identifying text from digital images and converting it into a string of characters.

[0273] "Analysis means" refers to a set of technical functions for processing input digital information and extracting necessary text data.

[0274] "Natural language processing means" refers to methods for identifying and analyzing specific information from extracted text information, and is particularly a language understanding technology used to identify food components.

[0275] A "comparison and determination means" is a technical means that performs a process to determine health risks by comparing identified food components with the user's health information.

[0276] An "information presentation means" is a system component that has the function of notifying users of analysis results and information regarding risks.

[0277] "International language support" refers to the ability to recognize and process multiple languages, and to accurately understand and process text information written in different languages.

[0278] "Warning information" refers to advisory messages provided to users based on risk assessment results, intended to inform them of potential health risks.

[0279] This invention relates to a food information analysis system for evaluating a user's food allergies and health risks. This system is realized through the collaborative operation of a user, a terminal, and a server.

[0280] First, users can take pictures of food packaging or restaurant menus using devices such as smartphones or tablets. Users can also directly input text information into their devices. The device then sends the captured image data or the entered text data to the server.

[0281] When the server receives the transmitted digital information, it uses optical character recognition technology (OCR) to extract character information from the image data. Through this extraction, information about food and ingredients is obtained in text form. Subsequently, the server uses natural language processing means on the extracted character information to identify food ingredients. In this process, specific keywords and phrases in the text are identified and their meanings are analyzed.

[0282] Specifically, based on the food ingredient list, the server identifies allergy-related words such as "shrimp" and "nuts". Also, for information written in different languages, appropriate processing is carried out by the international language support function.

[0283] After that, the server compares the identified food ingredients with the allergy and health information previously registered by the user to determine the health risk. Based on the determination result, the server generates warning information and notifies the user. This warning information serves as an important guideline for the user to safely select meals.

[0284] As a specific example, if the user has an image of a French menu taken overseas, the server identifies the word "crevettes (shrimp)" and warns that there is a risk of shrimp allergy.

[0285] Examples of prompt sentences include "Please identify the allergen components included in the French restaurant menu and compare them with the user's crustacean allergy information to determine the risk."

[0286] In this way, the system of the present invention helps the user to evaluate the allergy risk of food across language barriers and select a safe meal.

[0287] The flow of the specific processing in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] Users take pictures of food packaging or restaurant menus using their smartphones or tablets. The captured images are either saved on the device or sent directly to the server via the application. At this stage, users can also manually input text information, which is then used for analysis in subsequent processing steps.

[0290] Step 2:

[0291] The terminal receives captured or inputted digital information and transmits it to the server using a configured network communication module. Specifically, the data is packaged according to a particular protocol and uploaded to the server's dedicated API via the internet. This output becomes the input data for analysis by the server.

[0292] Step 3:

[0293] The server acquires the received digital information and extracts character information from the image data using optical character recognition (OCR) technology. The image input to the OCR engine undergoes character identification and text conversion processes, and is output as a string of characters. This output character information is then passed on to the subsequent natural language processing step.

[0294] Step 4:

[0295] The server, upon receiving the textual information, uses natural language processing to identify specific food components from this information. Specifically, it utilizes a generative AI model to analyze specific keywords within the text, identify allergen components, and output them. This output is then used in the next comparison and judgment step.

[0296] Step 5:

[0297] The server compares the identified allergen component with the user's pre-registered allergy information. It searches the user's health database for relevant information and, if a matching allergen is found, identifies it as an allergy risk and outputs it. The results of this determination are used to generate a warning message.

[0298] Step 6:

[0299] The server that obtains the judgment results generates a warning message as needed. This message is intended to inform the user of potential health risks, and a specific example might be, "This dish contains certain allergens." This output is then notified to the user.

[0300] Step 7:

[0301] The user receives a warning message generated by the server through their device. This message pops up via the device's notification system, allowing the user to choose safer meals based on it. Risk information is displayed on the device's screen, serving as output for the user.

[0302] (Application Example 1)

[0303] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0304] When users with food allergies choose food products, there is a need to address the challenge of ensuring safe consumption by quickly and accurately identifying the allergens contained in the food and providing visual warnings. Furthermore, information must be available in multiple languages.

[0305] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for receiving image information or text information input from a user, image analysis means for extracting text data from the image information, natural language processing means for identifying allergen components from the text data, comparison and determination means for collating the allergen components with the user's allergy information to determine an allergy risk, and output means for presenting the determination result of the allergy risk to a visual display device. As a result, it becomes possible for the user to easily confirm allergen information and safely select food by making an appropriate judgment.

[0307] The "user" refers to an individual who uses the system to confirm allergen information contained in food.

[0308] The "image information" refers to visual data of a food package or menu obtained by the user using a device such as smart glasses.

[0309] The "text information" refers to character information about food manually or automatically input by the user.

[0310] The "image analysis means" refers to a function for extracting text data from the acquired image information using technologies such as optical character recognition technology.

[0311] The "natural language processing means" refers to a technology for analyzing the meaning of text information in order to identify allergen components from text data.

[0312] The "comparison and determination means" refers to a function for collating the extracted allergen components with the user's pre-registered allergy information and evaluating the allergy risk.

[0313] The "visual display device" refers to a device that visually presents information to the user using smart glasses or other display devices.

[0314] "Output means" refers to a function that displays warnings or information to notify the user of the determined allergy risk.

[0315] This invention provides a system that quickly and accurately identifies allergen information contained in food and displays a warning on a visual display device used by the user. The system primarily utilizes the following hardware and software.

[0316] The user wears smart glasses and takes pictures of food packaging or restaurant menus with the camera. The acquired image information is sent to a server via the terminal. The server has OpenCV and Tesseract OCR installed for image analysis, and uses them to extract text data from the images. The extracted text data is analyzed using SpaCy, a natural language processing software, to identify allergen components.

[0317] The server is equipped with a comparison mechanism to match identified allergen components with the user's pre-registered allergy information. This comparison assesses the user's allergy risk, and if a risk exists, a warning message is immediately generated. The generated warning is displayed on the smart glasses' HUD, allowing the user to visually receive the information.

[0318] For example, if a user visits an Italian restaurant while out and about and takes a picture of a menu item called "Shrimp Risotto," the system will identify shrimp as an allergen and display a warning on the smart glasses saying, "You have a shrimp allergy. Please be careful." In this way, users can make food choices safely.

[0319] An example of a prompt message is: "There is a shrimp risotto on the menu. Identify the allergens and generate a risk warning."

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The user uses the camera on their smart glasses to take pictures of food packaging or restaurant menus. The camera captures the image information and sends that data to the device as input.

[0323] Step 2:

[0324] The terminal uploads the received image information to the cloud server. The server receives this image information as input and prepares to proceed to the next analysis step.

[0325] Step 3:

[0326] The server uses OpenCV and Tesseract OCR to extract text data from the received image information. At this stage, the input is image information, and the output is the corresponding text data. The text data includes the names of the food items and ingredient information listed on the menu.

[0327] Step 4:

[0328] The server analyzes the extracted text data using SpaCy, a natural language processing framework, to identify allergen components. The input is text data, and a list of allergens is output. In this process, the meaning and context of words are considered to determine whether a food component is an allergen.

[0329] Step 5:

[0330] The server matches the identified allergen components against the user's pre-registered allergy information. The input is a list of allergens and the user's allergy information, and the output is an assessment of the allergy risk. During the matching process, comparative calculations are performed to determine whether or not a risk exists.

[0331] Step 6:

[0332] The server generates a warning message based on the determined allergy risk and sends that message to the smart glasses via the terminal. The input is the result of the risk assessment, and the output is the warning message. The system visually informs the user of the risk.

[0333] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0334] The food allergy detection system according to the present invention helps users evaluate the allergy risk of food and enjoy meals safely. This system consists of a user, a terminal, a server, and an emotion engine.

[0335] Users first use a device such as a smartphone or tablet to take pictures of the menu or input text information about the food. The device then transmits this information to the server, which analyzes the information using image analysis and natural language processing technologies. Specifically, the server uses image analysis tools to obtain text information from the images and extracts it as text data.

[0336] Subsequently, the server uses natural language processing to identify allergen components from the extracted text. These identified allergen components are then compared and matched with the user's registered allergy information to determine the allergy risk.

[0337] A distinctive feature of this invention is the combination of this determination process with an emotion engine. The emotion engine is used to recognize emotions from text information and voice data entered by the user. Based on this information, the user's emotional state is analyzed, and if the user is in a situation where they are likely to feel stressed, a warning message that draws particular attention to them can be generated.

[0338] For example, if a user feels anxious at a restaurant abroad, the emotion engine detects that anxiety and adjusts its settings to provide more detailed and thorough allergy information. In this way, users can enjoy their meal with peace of mind while reducing their mental burden.

[0339] The device ultimately presents the user with information received from the server, providing risk information and warning messages in an optimal format tailored to their emotional state. This facilitates safer food choices and alleviates anxiety about food.

[0340] This system incorporates a new approach that takes user emotions into account, providing a higher level of reassurance than traditional methods could achieve. This makes support for users with food allergies even more effective.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] Users take photos of restaurant menus or food packaging using their smartphone or tablet camera. Alternatively, they can directly input food names or descriptions of dishes as text on their device.

[0344] Step 2:

[0345] The device transmits captured image data or entered text information to a server via the internet.

[0346] Step 3:

[0347] The server processes the image data received from the terminal using image analysis tools. This tool extracts text information from the image data. Optical character recognition (OCR) technology is used to recognize characters within the image and convert them into text format.

[0348] Step 4:

[0349] The server analyzes the extracted text data using natural language processing to identify allergen components. This process identifies specific words or phrases within the text and determines whether they are components that may cause allergies.

[0350] Step 5:

[0351] The server compares the identified allergen components with the user's registered allergy information. This allows the server to assess the user's allergy risk.

[0352] Step 6:

[0353] Simultaneously, the system uses an emotion engine to analyze the user's emotional state based on their recent text input and voice data. This engine determines the degree of stress and anxiety based on the user's input.

[0354] Step 7:

[0355] The server integrates the allergy risk assessment results with the user's emotional state and adjusts the warning message as needed. For example, if the user is in an anxious emotional state, the message will be made more detailed and polite.

[0356] Step 8:

[0357] The device displays generated warning messages and allergy information to the user. This allows the user to receive information in a format best suited to their emotional state, enabling them to make safe food choices.

[0358] This process allows us to support users in making food choices that take their feelings into consideration and ensure safety.

[0359] (Example 2)

[0360] Next, we will describe Example 2. 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".

[0361] To ensure that users with food allergies can safely enjoy meals in different cultural and linguistic environments, a system is needed that can reliably and quickly verify allergen information. Furthermore, methods are required to consider the user's emotional state and alleviate their anxiety about food.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting character data from the image information, natural language processing means for identifying allergen components from the character data, emotion analysis means for analyzing emotional information from the user to identify the emotional state, and warning generation means for generating a warning message based on the identified emotional state. This makes it possible to provide emotionally sensitive information along with allergy risk analysis simply by the user inputting image or text information.

[0364] A "user" is an individual who uses the system to check the risk of food allergies.

[0365] "Image information" refers to visual data captured using cameras or electronic devices, including menus and food labels.

[0366] "Text information" refers to string data entered by the user, such as information about food ingredients or menu details.

[0367] "Image analysis means" refers to techniques used to extract character data from image information, and includes computer vision algorithms.

[0368] "Natural language processing means" refers to techniques for analyzing and extracting useful information from text data, and is used for identifying allergen components.

[0369] "Allergens" refer to substances that can cause allergic reactions and are specific components found in food.

[0370] A "comparative determination method" refers to a method for comparing identified allergen components with the user's registered allergy information, and plays a role in evaluating allergy risk.

[0371] "Emotional analysis methods" refer to technologies that analyze text and audio data to evaluate a user's emotional state.

[0372] "Warning generation method" refers to a method of creating messages based on emotional state or allergy risk in order to provide alerts to users.

[0373] This food allergy detection system is designed to help users with food allergies safely enjoy meals. The specific implementation method is described below.

[0374] Users use devices such as smartphones or tablets to take pictures of meal menus or food packaging, or to input text information about the food into their devices. The devices then transmit this image and text information to a server via the internet. Wi-Fi or mobile data communication is used for this transmission.

[0375] The server processes the received image information using image analysis technology. This process utilizes common image analysis software such as OpenCV to extract text data from the images. Furthermore, natural language processing, using Python's NLTK library, is applied to the extracted text data to specifically identify allergen components.

[0376] Furthermore, based on the allergy information registered by the user in advance, the server matches it with identified allergen components and determines the allergy risk. In this process, the system prepares to generate and provide allergy warning messages to the user.

[0377] A distinctive feature of the system is emotion analysis. The server utilizes a generative AI model to analyze text and voice data entered by the user, identifying the user's emotional state, such as "anxiety." Based on this emotional information, it is possible to adjust the content and presentation method of warning messages.

[0378] As a concrete example, in response to a prompt message entered by a user such as, "I'm looking for a restaurant abroad that doesn't serve dishes without nuts, but I'm worried," the system will detect the presence or absence of nuts and, taking the user's concerns into account, provide detailed safety information.

[0379] In this way, this system uses advanced image analysis and natural language processing technologies to provide users with necessary and reassuring food allergy information tailored to their individual emotional state.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] Users use devices such as smartphones or tablets to take pictures of menus or food packaging, or to directly input text information about the food. The entered data is sent to the device.

[0383] Step 2:

[0384] The terminal transmits image or text information provided by the user to the server via the internet. In this case, image data is sent to the server in JPEG format, and text data in UTF-8 format.

[0385] Step 3:

[0386] The server processes the received image data through an image analysis tool to extract text data from the image. This process uses image analysis software such as OpenCV, and the extracted text data is obtained as output.

[0387] Step 4:

[0388] The server sends the extracted string data to a natural language processing system to identify allergen components. This process uses the Python natural language processing library NLTK to obtain words that may be allergens as output.

[0389] Step 5:

[0390] The server compares the identified allergen components with the user's registration information to determine the allergy risk. Based on the comparison results, the allergy risk assessment is output, and a warning message is prepared if necessary.

[0391] Step 6:

[0392] The server uses a generative AI model to analyze emotional information from the user's input text data and identify the user's emotional state. The emotional state derived from the emotional analysis is then output. This process is based on emotional expressions such as "anxiety" that the user inputs.

[0393] Step 7:

[0394] The server generates the most appropriate warning message based on the allergy risk assessment and the user's emotional state. For example, if the emotional state is "anxious," the message will be polite and detailed. This message is then generated as the final output.

[0395] Step 8:

[0396] The terminal receives the final output from the server and presents the information to the user in the most optimal format. For example, a message might appear as a pop-up on the terminal's display. This allows the user to obtain information to make safe food choices.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] There is a need for a system that can quickly and accurately assess the health risks that allergens in food pose to users with allergies, and further provide information that reduces the user's emotional burden based on their emotional state. In particular, there is a need to strengthen safety and psychological support so that users with food allergies can enjoy meals with peace of mind in situations where they feel anxious or stressed.

[0400] The identification processing performed 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 receiving image information or text information input from a user, image analysis means for extracting text data from the image information, and natural language processing means for identifying allergen components from the text data. This makes it possible to identify allergens contained in food, appropriately evaluate the user's allergy risk, and further provide information according to the user's emotional state through emotion recognition.

[0401] A "user" is a person who uses the system to evaluate the allergy risk of food.

[0402] "Image information" refers to visual data about food that users input via their devices.

[0403] "Text information" refers to character data about food that users input via their devices.

[0404] "Image analysis means" refers to technical means that perform a process to extract text data from received image information.

[0405] "Natural language processing means" refers to technical means for identifying allergen components from extracted text data.

[0406] "Allergens" are components in food that can potentially cause allergic reactions.

[0407] A "comparative determination means" is a technical means for determining allergy risk by comparing identified allergen components with the user's allergy information.

[0408] "Emotion recognition means" refers to technical means for recognizing a user's emotional state and providing information to alleviate their mental burden.

[0409] "Allergy risk" refers to the potential health risks that identified allergen components pose to the user.

[0410] A "server" is a central computer system that receives image and text information and performs analysis and judgment.

[0411] This invention is a system for users to identify allergens in food and assess their risks. The process begins with the user taking a picture of a menu item using a smartphone or other device, or entering text information about the food. The device then transmits this image or text information to a server.

[0412] The server processes the received information as follows: First, it extracts text data from the image information using image analysis tools. This utilizes techniques that identify text information within images using machine learning frameworks such as TensorFlow. Then, it identifies allergen components from the extracted text data using natural language processing tools. In this step, it analyzes food components in the text using natural language processing tools such as SpaCy.

[0413] Furthermore, the server uses emotion recognition tools to analyze the user's emotional state. For example, it leverages IBM Watson's emotion analysis API to recognize emotions from the user's input text and voice. Based on this information, the server generates detailed warning messages if the user is feeling anxious. This helps reduce the user's mental burden while supporting safe food choices.

[0414] Finally, the server sends the analysis results to the terminal and presents the allergy risk assessment to the user in an optimal format based on sentiment analysis. This allows the user to choose their meals with confidence.

[0415] For example, when a user orders Chinese food and takes a picture of the menu and uploads it to the app, "peanuts" are identified as an allergen. If the user is concerned, the app will use this information to warn them, "This dish contains an allergen. Please consider other safer options."

[0416] Examples of prompt statements for a generative AI model are as follows:

[0417] Use a food allergy detection system to identify allergens from image / text information and generate messages that provide information tailored to the user's emotional state. Include points to consider especially when the user is feeling anxious.

[0418] This system configuration allows us to provide users with food allergies with a high level of safety and peace of mind.

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] Users either take a picture of the food menu via a smartphone or other device, or input text information. The entered data is saved on the device as image or text information. This constitutes the initial input.

[0422] Step 2:

[0423] The terminal sends the input image or text information to the server. At this stage, the user's input data reaches the server and is ready to be analyzed in the next step.

[0424] Step 3:

[0425] The server extracts text data from received image information using image analysis tools. The input here is image data, and the output is the extracted text information. This process utilizes character recognition algorithms based on technologies such as TensorFlow.

[0426] Step 4:

[0427] The server uses natural language processing to identify allergen components from extracted text data. The input is text data, and the output is the identified allergen components. Natural language processing tools such as SpaCy are used to analyze the components within the text and extract allergen candidates.

[0428] Step 5:

[0429] The server analyzes the user's emotional state through emotion recognition mechanisms. It uses text and other information entered by the user as analysis material and identifies the user's emotional patterns using IBM Watson's emotion analysis API. The input is digital information about the user's emotions, and the output is the recognized emotional state.

[0430] Step 6:

[0431] The server integrates information on allergen components and the user's emotional state to generate detailed warning messages as needed. The input is the identified allergen components and the user's emotional state, while the output is a customized warning message presented to the user.

[0432] Step 7:

[0433] The server presents the generated warning messages and allergy risk information to the user via the terminal. The final output allows the user to receive information that enables them to make informed food choices.

[0434] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] The food allergy determination system according to the present invention helps users accurately determine the allergy risk of food. This system is realized through the collaborative operation of the user, terminal, and server.

[0451] Users can first take pictures of restaurant menus, food packaging, etc., using a smartphone or tablet. Users can also provide data by directly entering text information into their device.

[0452] The terminal is responsible for transmitting received image or text information to the server. The transmitted information is then analyzed on the server using advanced image analysis and natural language processing technologies. Specifically, the server extracts text data from the image information using image analysis tools. This is achieved through optical character recognition (OCR) technology.

[0453] Next, the server performs natural language processing on the extracted text data to identify allergen components. For example, it identifies words like "shrimp" listed on the menu and determines whether or not they are allergens.

[0454] Next, the server uses a comparison and determination mechanism to match the identified allergen component with the allergy information provided by the user. If an allergy risk is detected, the server generates a warning message to inform the user.

[0455] Users can receive this warning message through their device. For example, they might receive a notification stating, "This dish contains shrimp, so there is a risk of shellfish allergy." This allows users to make safer food choices.

[0456] Because it supports multiple languages, this system will function the same way even if the user encounters menus in a different language while abroad. Thus, the present invention aims to enable users to safely choose meals in any environment and to alleviate anxieties related to food allergies.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] Users take pictures of menus or food packaging using their smartphone or tablet camera, or they directly input the menu contents as text into their device.

[0460] Step 2:

[0461] The device sends captured image data or entered text information to the server. This transmission is securely performed via an internet connection.

[0462] Step 3:

[0463] The server receives image data transmitted from the terminal and extracts text data using image analysis tools. Specifically, it utilizes optical character recognition (OCR) technology to recognize characters within the image and converts them into a readable text format.

[0464] Step 4:

[0465] The server performs natural language processing (NLP) on the extracted text data. This identifies allergen components in the analyzed text. For example, the word "shrimp" is identified as an indicator of allergy risk.

[0466] Step 5:

[0467] The server compares the identified allergen components with the user's registered allergy information. This allows for a comparative assessment to evaluate the presence or absence of allergy risk.

[0468] Step 6:

[0469] Based on the assessment results, the server generates a warning message if an allergy risk is detected. For example, it might say, "This dish contains shrimp, therefore there is an allergy risk."

[0470] Step 7:

[0471] The terminal displays warning messages received from the server to the user. This allows the user to check the safety of the food and make appropriate food choices.

[0472] Through this process, users will be able to choose food with confidence and enjoy meals safely.

[0473] (Example 1)

[0474] Next, we will describe Example 1. 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."

[0475] Modern consumers are required to quickly and accurately understand information about food ingredients, but many consumers do not have sufficient access to this information when traveling abroad or dining in different cultural environments, often facing allergies and health risks. This invention aims to solve this problem and provide a system that enables users to safely select food despite language barriers.

[0476] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0477] In this invention, the server includes means for receiving digital information input from a user via a receiving terminal, analysis means utilizing optical character recognition technology for extracting character information from the digital information, and natural language processing means for identifying food components from the character information. This enables users to properly understand food information written in different languages ​​and compare it with their own health information, thereby preventing allergies and other health risks.

[0478] "Digital information" refers to image data and text data that users can input through their devices.

[0479] "Optical character recognition technology" is a technology for extracting character information from image data, and it performs the process of identifying text from digital images and converting it into a string of characters.

[0480] "Analysis means" refers to a set of technical functions for processing input digital information and extracting necessary text data.

[0481] "Natural language processing means" refers to methods for identifying and analyzing specific information from extracted text information, and is particularly a language understanding technology used to identify food components.

[0482] A "comparison and determination means" is a technical means that performs a process to determine health risks by comparing identified food components with the user's health information.

[0483] An "information presentation means" is a system component that has the function of notifying users of analysis results and information regarding risks.

[0484] "International language support" refers to the ability to recognize and process multiple languages, and to accurately understand and process text information written in different languages.

[0485] "Warning information" refers to advisory messages provided to users based on risk assessment results, intended to inform them of potential health risks.

[0486] This invention relates to a food information analysis system for evaluating a user's food allergies and health risks. This system is realized through the collaborative operation of a user, a terminal, and a server.

[0487] First, users can take pictures of food packaging or restaurant menus using devices such as smartphones or tablets. Users can also directly input text information into their devices. The device then sends the captured image data or the entered text data to the server.

[0488] Upon receiving the transmitted digital information, the server uses optical character recognition (OCR) technology to extract text information from the image data. This extraction provides information about the food and its ingredients in text format. Subsequently, the server uses natural language processing techniques on the extracted text information to identify the food ingredients. This process identifies specific keywords and phrases within the text and analyzes their meaning.

[0489] Specifically, the server identifies allergen-related words such as "shrimp" and "nuts" based on a list of food ingredients. It also appropriately processes information written in different languages ​​using its international language support function.

[0490] The server then compares the allergy and health information previously registered by the user with the identified food components to determine the health risk. Based on the determination, the server generates warning information and notifies the user. This warning information serves as an important guide for the user to make safe food choices.

[0491] For example, if a user has an image of a French menu taken overseas, the server will identify the word "crevettes" (shrimp) and warn of the risk of a shrimp allergy.

[0492] An example of a prompt message might be: "Identify the allergens in the French restaurant menu and compare them with the user's shellfish allergy information to determine the risk."

[0493] Thus, the system of the present invention helps users assess the risk of food allergies and make safe food choices, overcoming language barriers.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] Users take pictures of food packaging or restaurant menus using their smartphones or tablets. The captured images are either saved on the device or sent directly to the server via the application. At this stage, users can also manually input text information, which is then used for analysis in subsequent processing steps.

[0497] Step 2:

[0498] The terminal receives captured or inputted digital information and transmits it to the server using a configured network communication module. Specifically, the data is packaged according to a particular protocol and uploaded to the server's dedicated API via the internet. This output becomes the input data for analysis by the server.

[0499] Step 3:

[0500] The server acquires the received digital information and extracts character information from the image data using optical character recognition (OCR) technology. The image input to the OCR engine undergoes character identification and text conversion processes, and is output as a string of characters. This output character information is then passed on to the subsequent natural language processing step.

[0501] Step 4:

[0502] The server, upon receiving the textual information, uses natural language processing to identify specific food components from this information. Specifically, it utilizes a generative AI model to analyze specific keywords within the text, identify allergen components, and output them. This output is then used in the next comparison and judgment step.

[0503] Step 5:

[0504] The server compares the identified allergen component with the user's pre-registered allergy information. It searches the user's health database for relevant information and, if a matching allergen is found, identifies it as an allergy risk and outputs it. The results of this determination are used to generate a warning message.

[0505] Step 6:

[0506] The server that obtains the judgment results generates a warning message as needed. This message is intended to inform the user of potential health risks, and a specific example might be, "This dish contains certain allergens." This output is then notified to the user.

[0507] Step 7:

[0508] The user receives a warning message generated by the server through their device. This message pops up via the device's notification system, allowing the user to choose safer meals based on it. Risk information is displayed on the device's screen, serving as output for the user.

[0509] (Application Example 1)

[0510] Next, we will explain Application Example 1. In the following explanation, 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."

[0511] When users with food allergies choose food products, there is a need to address the challenge of ensuring safe consumption by quickly and accurately identifying the allergens contained in the food and providing visual warnings. Furthermore, information must be available in multiple languages.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0513] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting text data from the image information, natural language processing means for identifying allergen components from the text data, comparison and determination means for determining allergy risk by comparing the allergen components with the user's allergy information, and output means for presenting the determination result of the allergy risk on a visual display device. This makes it possible for users to easily check allergen information and safely select food based on appropriate judgment.

[0514] A "user" refers to an individual who uses the system to check allergen information contained in food products.

[0515] "Image information" refers to visual data of food packaging and menus acquired by users using devices such as smart glasses.

[0516] "Text information" refers to written information about food products that users manually or automatically enter.

[0517] "Image analysis means" refers to a function that extracts text data from acquired image information using optical character recognition technology or the like.

[0518] "Natural language processing means" refers to techniques that analyze the meaning of text information in order to identify allergen components from text data.

[0519] The "comparison and determination means" refers to a function that compares extracted allergen components with the user's pre-registered allergy information to evaluate the allergy risk.

[0520] A "visual display device" refers to a device that uses smart glasses or other display devices to visually present information to the user.

[0521] "Output means" refers to a function that displays warnings or information to notify the user of the determined allergy risk.

[0522] This invention provides a system that quickly and accurately identifies allergen information contained in food and displays a warning on a visual display device used by the user. The system primarily utilizes the following hardware and software.

[0523] The user wears smart glasses and takes pictures of food packaging or restaurant menus with the camera. The acquired image information is sent to a server via the terminal. The server has OpenCV and Tesseract OCR installed for image analysis, and uses them to extract text data from the images. The extracted text data is analyzed using SpaCy, a natural language processing software, to identify allergen components.

[0524] The server is equipped with a comparison mechanism to match identified allergen components with the user's pre-registered allergy information. This comparison assesses the user's allergy risk, and if a risk exists, a warning message is immediately generated. The generated warning is displayed on the smart glasses' HUD, allowing the user to visually receive the information.

[0525] For example, if a user visits an Italian restaurant while out and about and takes a picture of a menu item called "Shrimp Risotto," the system will identify shrimp as an allergen and display a warning on the smart glasses saying, "You have a shrimp allergy. Please be careful." In this way, users can make food choices safely.

[0526] An example of a prompt message is: "There is a shrimp risotto on the menu. Identify the allergens and generate a risk warning."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The user uses the camera on their smart glasses to take pictures of food packaging or restaurant menus. The camera captures the image information and sends that data to the device as input.

[0530] Step 2:

[0531] The terminal uploads the received image information to the cloud server. The server receives this image information as input and prepares to proceed to the next analysis step.

[0532] Step 3:

[0533] The server uses OpenCV and Tesseract OCR to extract text data from the received image information. At this stage, the input is image information, and the output is the corresponding text data. The text data includes the names of the food items and ingredient information listed on the menu.

[0534] Step 4:

[0535] The server analyzes the extracted text data using SpaCy, a natural language processing framework, to identify allergen components. The input is text data, and a list of allergens is output. In this process, the meaning and context of words are considered to determine whether a food component is an allergen.

[0536] Step 5:

[0537] The server matches the identified allergen components against the user's pre-registered allergy information. The input is a list of allergens and the user's allergy information, and the output is an assessment of the allergy risk. During the matching process, comparative calculations are performed to determine whether or not a risk exists.

[0538] Step 6:

[0539] The server generates a warning message based on the determined allergy risk and sends that message to the smart glasses via the terminal. The input is the result of the risk assessment, and the output is the warning message. The system visually informs the user of the risk.

[0540] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0541] The food allergy detection system according to the present invention helps users evaluate the allergy risk of food and enjoy meals safely. This system consists of a user, a terminal, a server, and an emotion engine.

[0542] Users first use a device such as a smartphone or tablet to take pictures of the menu or input text information about the food. The device then transmits this information to the server, which analyzes the information using image analysis and natural language processing technologies. Specifically, the server uses image analysis tools to obtain text information from the images and extracts it as text data.

[0543] Subsequently, the server uses natural language processing to identify allergen components from the extracted text. These identified allergen components are then compared and matched with the user's registered allergy information to determine the allergy risk.

[0544] A distinctive feature of this invention is the combination of this determination process with an emotion engine. The emotion engine is used to recognize emotions from text information and voice data entered by the user. Based on this information, the user's emotional state is analyzed, and if the user is in a situation where they are likely to feel stressed, a warning message that draws particular attention to them can be generated.

[0545] For example, if a user feels anxious at a restaurant abroad, the emotion engine detects that anxiety and adjusts its settings to provide more detailed and thorough allergy information. In this way, users can enjoy their meal with peace of mind while reducing their mental burden.

[0546] The device ultimately presents the user with information received from the server, providing risk information and warning messages in an optimal format tailored to their emotional state. This facilitates safer food choices and alleviates anxiety about food.

[0547] This system incorporates a new approach that takes user emotions into account, providing a higher level of reassurance than traditional methods could achieve. This makes support for users with food allergies even more effective.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] Users take photos of restaurant menus or food packaging using their smartphone or tablet camera. Alternatively, they can directly input food names or descriptions of dishes as text on their device.

[0551] Step 2:

[0552] The device transmits captured image data or entered text information to a server via the internet.

[0553] Step 3:

[0554] The server processes the image data received from the terminal using image analysis tools. This tool extracts text information from the image data. Optical character recognition (OCR) technology is used to recognize characters within the image and convert them into text format.

[0555] Step 4:

[0556] The server analyzes the extracted text data using natural language processing to identify allergen components. This process identifies specific words or phrases within the text and determines whether they are components that may cause allergies.

[0557] Step 5:

[0558] The server compares the identified allergen components with the user's registered allergy information. This allows the server to assess the user's allergy risk.

[0559] Step 6:

[0560] Simultaneously, the system uses an emotion engine to analyze the user's emotional state based on their recent text input and voice data. This engine determines the degree of stress and anxiety based on the user's input.

[0561] Step 7:

[0562] The server integrates the allergy risk assessment results with the user's emotional state and adjusts the warning message as needed. For example, if the user is in an anxious emotional state, the message will be made more detailed and polite.

[0563] Step 8:

[0564] The device displays generated warning messages and allergy information to the user. This allows the user to receive information in a format best suited to their emotional state, enabling them to make safe food choices.

[0565] This process allows us to support users in making food choices that take their feelings into consideration and ensure safety.

[0566] (Example 2)

[0567] Next, we will describe Example 2. 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."

[0568] To ensure that users with food allergies can safely enjoy meals in different cultural and linguistic environments, a system is needed that can reliably and quickly verify allergen information. Furthermore, methods are required to consider the user's emotional state and alleviate their anxiety about food.

[0569] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0570] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting character data from the image information, natural language processing means for identifying allergen components from the character data, emotion analysis means for analyzing emotional information from the user to identify the emotional state, and warning generation means for generating a warning message based on the identified emotional state. This makes it possible to provide emotionally sensitive information along with allergy risk analysis simply by the user inputting image or text information.

[0571] A "user" is an individual who uses the system to check the risk of food allergies.

[0572] "Image information" refers to visual data captured using cameras or electronic devices, including menus and food labels.

[0573] "Text information" refers to string data entered by the user, such as information about food ingredients or menu details.

[0574] "Image analysis means" refers to techniques used to extract character data from image information, and includes computer vision algorithms.

[0575] "Natural language processing means" refers to techniques for analyzing and extracting useful information from text data, and is used for identifying allergen components.

[0576] "Allergens" refer to substances that can cause allergic reactions and are specific components found in food.

[0577] A "comparative determination method" refers to a method for comparing identified allergen components with the user's registered allergy information, and plays a role in evaluating allergy risk.

[0578] "Emotional analysis methods" refer to technologies that analyze text and audio data to evaluate a user's emotional state.

[0579] "Warning generation method" refers to a method of creating messages based on emotional state or allergy risk in order to provide alerts to users.

[0580] This food allergy detection system is designed to help users with food allergies safely enjoy meals. The specific implementation method is described below.

[0581] Users use devices such as smartphones or tablets to take pictures of meal menus or food packaging, or to input text information about the food into their devices. The devices then transmit this image and text information to a server via the internet. Wi-Fi or mobile data communication is used for this transmission.

[0582] The server processes the received image information using image analysis technology. This process utilizes common image analysis software such as OpenCV to extract text data from the images. Furthermore, natural language processing, using Python's NLTK library, is applied to the extracted text data to specifically identify allergen components.

[0583] Furthermore, based on the allergy information registered by the user in advance, the server matches it with identified allergen components and determines the allergy risk. In this process, the system prepares to generate and provide allergy warning messages to the user.

[0584] A distinctive feature of the system is emotion analysis. The server utilizes a generative AI model to analyze text and voice data entered by the user, identifying the user's emotional state, such as "anxiety." Based on this emotional information, it is possible to adjust the content and presentation method of warning messages.

[0585] As a concrete example, in response to a prompt message entered by a user such as, "I'm looking for a restaurant abroad that doesn't serve dishes without nuts, but I'm worried," the system will detect the presence or absence of nuts and, taking the user's concerns into account, provide detailed safety information.

[0586] In this way, this system uses advanced image analysis and natural language processing technologies to provide users with necessary and reassuring food allergy information tailored to their individual emotional state.

[0587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0588] Step 1:

[0589] Users use devices such as smartphones or tablets to take pictures of menus or food packaging, or to directly input text information about the food. The entered data is sent to the device.

[0590] Step 2:

[0591] The terminal transmits image or text information provided by the user to the server via the internet. In this case, image data is sent to the server in JPEG format, and text data in UTF-8 format.

[0592] Step 3:

[0593] The server processes the received image data through an image analysis tool to extract text data from the image. This process uses image analysis software such as OpenCV, and the extracted text data is obtained as output.

[0594] Step 4:

[0595] The server sends the extracted string data to a natural language processing system to identify allergen components. This process uses the Python natural language processing library NLTK to obtain words that may be allergens as output.

[0596] Step 5:

[0597] The server compares the identified allergen components with the user's registration information to determine the allergy risk. Based on the comparison results, the allergy risk assessment is output, and a warning message is prepared if necessary.

[0598] Step 6:

[0599] The server uses a generative AI model to analyze emotional information from the user's input text data and identify the user's emotional state. The emotional state derived from the emotional analysis is then output. This process is based on emotional expressions such as "anxiety" that the user inputs.

[0600] Step 7:

[0601] The server generates the most appropriate warning message based on the allergy risk assessment and the user's emotional state. For example, if the emotional state is "anxious," the message will be polite and detailed. This message is then generated as the final output.

[0602] Step 8:

[0603] The terminal receives the final output from the server and presents the information to the user in the most optimal format. For example, a message might appear as a pop-up on the terminal's display. This allows the user to obtain information to make safe food choices.

[0604] (Application Example 2)

[0605] Next, we will explain application example 2. In the following explanation, 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."

[0606] There is a need for a system that can quickly and accurately assess the health risks that allergens in food pose to users with allergies, and further provide information that reduces the user's emotional burden based on their emotional state. In particular, there is a need to strengthen safety and psychological support so that users with food allergies can enjoy meals with peace of mind in situations where they feel anxious or stressed.

[0607] The identification processing performed 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 receiving image information or text information input from a user, image analysis means for extracting text data from the image information, and natural language processing means for identifying allergen components from the text data. This makes it possible to identify allergens contained in food, appropriately evaluate the user's allergy risk, and further provide information according to the user's emotional state through emotion recognition.

[0608] A "user" is a person who uses the system to evaluate the allergy risk of food.

[0609] "Image information" refers to visual data about food that users input via their devices.

[0610] "Text information" refers to character data about food that users input via their devices.

[0611] "Image analysis means" refers to technical means that perform a process to extract text data from received image information.

[0612] "Natural language processing means" refers to technical means for identifying allergen components from extracted text data.

[0613] "Allergens" are components in food that can potentially cause allergic reactions.

[0614] A "comparative determination means" is a technical means for determining allergy risk by comparing identified allergen components with the user's allergy information.

[0615] "Emotion recognition means" refers to technical means for recognizing a user's emotional state and providing information to alleviate their mental burden.

[0616] "Allergy risk" refers to the potential health risks that identified allergen components pose to the user.

[0617] A "server" is a central computer system that receives image and text information and performs analysis and judgment.

[0618] This invention is a system for users to identify allergens in food and assess their risks. The process begins with the user taking a picture of a menu item using a smartphone or other device, or entering text information about the food. The device then transmits this image or text information to a server.

[0619] The server processes the received information as follows: First, it extracts text data from the image information using image analysis tools. This utilizes techniques that identify text information within images using machine learning frameworks such as TensorFlow. Then, it identifies allergen components from the extracted text data using natural language processing tools. In this step, it analyzes food components in the text using natural language processing tools such as SpaCy.

[0620] Furthermore, the server uses emotion recognition tools to analyze the user's emotional state. For example, it leverages IBM Watson's emotion analysis API to recognize emotions from the user's input text and voice. Based on this information, the server generates detailed warning messages if the user is feeling anxious. This helps reduce the user's mental burden while supporting safe food choices.

[0621] Finally, the server sends the analysis results to the terminal and presents the allergy risk assessment to the user in an optimal format based on sentiment analysis. This allows the user to choose their meals with confidence.

[0622] For example, when a user orders Chinese food and takes a picture of the menu and uploads it to the app, "peanuts" are identified as an allergen. If the user is concerned, the app will use this information to warn them, "This dish contains an allergen. Please consider other safer options."

[0623] Examples of prompt statements for a generative AI model are as follows:

[0624] Use a food allergy detection system to identify allergens from image / text information and generate messages that provide information tailored to the user's emotional state. Include points to consider especially when the user is feeling anxious.

[0625] This system configuration allows us to provide users with food allergies with a high level of safety and peace of mind.

[0626] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0627] Step 1:

[0628] Users either take a picture of the food menu via a smartphone or other device, or input text information. The entered data is saved on the device as image or text information. This constitutes the initial input.

[0629] Step 2:

[0630] The terminal sends the input image or text information to the server. At this stage, the user's input data reaches the server and is ready to be analyzed in the next step.

[0631] Step 3:

[0632] The server extracts text data from received image information using image analysis tools. The input here is image data, and the output is the extracted text information. This process utilizes character recognition algorithms based on technologies such as TensorFlow.

[0633] Step 4:

[0634] The server uses natural language processing to identify allergen components from extracted text data. The input is text data, and the output is the identified allergen components. Natural language processing tools such as SpaCy are used to analyze the components within the text and extract allergen candidates.

[0635] Step 5:

[0636] The server analyzes the user's emotional state through emotion recognition mechanisms. It uses text and other information entered by the user as analysis material and identifies the user's emotional patterns using IBM Watson's emotion analysis API. The input is digital information about the user's emotions, and the output is the recognized emotional state.

[0637] Step 6:

[0638] The server integrates information on allergen components and the user's emotional state to generate detailed warning messages as needed. The input is the identified allergen components and the user's emotional state, while the output is a customized warning message presented to the user.

[0639] Step 7:

[0640] The server presents the generated warning messages and allergy risk information to the user via the terminal. The final output allows the user to receive information that enables them to make informed food choices.

[0641] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0642] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0647] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0648] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0649] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0650] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0651] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0652] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0653] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0654] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0655] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0656] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0657] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] The food allergy determination system according to the present invention helps users accurately determine the allergy risk of food. This system is realized through the collaborative operation of the user, terminal, and server.

[0659] Users can first take pictures of restaurant menus, food packaging, etc., using a smartphone or tablet. Users can also provide data by directly entering text information into their device.

[0660] The terminal is responsible for transmitting received image or text information to the server. The transmitted information is then analyzed on the server using advanced image analysis and natural language processing technologies. Specifically, the server extracts text data from the image information using image analysis tools. This is achieved through optical character recognition (OCR) technology.

[0661] Next, the server performs natural language processing on the extracted text data to identify allergen components. For example, it identifies words like "shrimp" listed on the menu and determines whether or not they are allergens.

[0662] Next, the server uses a comparison and determination mechanism to match the identified allergen component with the allergy information provided by the user. If an allergy risk is detected, the server generates a warning message to inform the user.

[0663] Users can receive this warning message through their device. For example, they might receive a notification stating, "This dish contains shrimp, so there is a risk of shellfish allergy." This allows users to make safer food choices.

[0664] Because it supports multiple languages, this system will function the same way even if the user encounters menus in a different language while abroad. Thus, the present invention aims to enable users to safely choose meals in any environment and to alleviate anxieties related to food allergies.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] Users take pictures of menus or food packaging using their smartphone or tablet camera, or they directly input the menu contents as text into their device.

[0668] Step 2:

[0669] The device sends captured image data or entered text information to the server. This transmission is securely performed via an internet connection.

[0670] Step 3:

[0671] The server receives image data transmitted from the terminal and extracts text data using image analysis tools. Specifically, it utilizes optical character recognition (OCR) technology to recognize characters within the image and converts them into a readable text format.

[0672] Step 4:

[0673] The server performs natural language processing (NLP) on the extracted text data. This identifies allergen components in the analyzed text. For example, the word "shrimp" is identified as an indicator of allergy risk.

[0674] Step 5:

[0675] The server compares the identified allergen components with the user's registered allergy information. This allows for a comparative assessment to evaluate the presence or absence of allergy risk.

[0676] Step 6:

[0677] Based on the assessment results, the server generates a warning message if an allergy risk is detected. For example, it might say, "This dish contains shrimp, therefore there is an allergy risk."

[0678] Step 7:

[0679] The terminal displays warning messages received from the server to the user. This allows the user to check the safety of the food and make appropriate food choices.

[0680] Through this process, users will be able to choose food with confidence and enjoy meals safely.

[0681] (Example 1)

[0682] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] Modern consumers are required to quickly and accurately understand information about food ingredients, but many consumers do not have sufficient access to this information when traveling abroad or dining in different cultural environments, often facing allergies and health risks. This invention aims to solve this problem and provide a system that enables users to safely select food despite language barriers.

[0684] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0685] In this invention, the server includes means for receiving digital information input from a user via a receiving terminal, analysis means utilizing optical character recognition technology for extracting character information from the digital information, and natural language processing means for identifying food components from the character information. This enables users to properly understand food information written in different languages ​​and compare it with their own health information, thereby preventing allergies and other health risks.

[0686] "Digital information" refers to image data and text data that users can input through their devices.

[0687] "Optical character recognition technology" is a technology for extracting character information from image data, and it performs the process of identifying text from digital images and converting it into a string of characters.

[0688] "Analysis means" refers to a set of technical functions for processing input digital information and extracting necessary text data.

[0689] "Natural language processing means" refers to methods for identifying and analyzing specific information from extracted text information, and is particularly a language understanding technology used to identify food components.

[0690] A "comparison and determination means" is a technical means that performs a process to determine health risks by comparing identified food components with the user's health information.

[0691] An "information presentation means" is a system component that has the function of notifying users of analysis results and information regarding risks.

[0692] "International language support" refers to the ability to recognize and process multiple languages, and to accurately understand and process text information written in different languages.

[0693] "Warning information" refers to advisory messages provided to users based on risk assessment results, intended to inform them of potential health risks.

[0694] This invention relates to a food information analysis system for evaluating a user's food allergies and health risks. This system is realized through the collaborative operation of a user, a terminal, and a server.

[0695] First, users can take pictures of food packaging or restaurant menus using devices such as smartphones or tablets. Users can also directly input text information into their devices. The device then sends the captured image data or the entered text data to the server.

[0696] Upon receiving the transmitted digital information, the server uses optical character recognition (OCR) technology to extract text information from the image data. This extraction provides information about the food and its ingredients in text format. Subsequently, the server uses natural language processing techniques on the extracted text information to identify the food ingredients. This process identifies specific keywords and phrases within the text and analyzes their meaning.

[0697] Specifically, the server identifies allergen-related words such as "shrimp" and "nuts" based on a list of food ingredients. It also appropriately processes information written in different languages ​​using its international language support function.

[0698] The server then compares the allergy and health information previously registered by the user with the identified food components to determine the health risk. Based on the determination, the server generates warning information and notifies the user. This warning information serves as an important guide for the user to make safe food choices.

[0699] For example, if a user has an image of a French menu taken overseas, the server will identify the word "crevettes" (shrimp) and warn of the risk of a shrimp allergy.

[0700] An example of a prompt message might be: "Identify the allergens in the French restaurant menu and compare them with the user's shellfish allergy information to determine the risk."

[0701] Thus, the system of the present invention helps users assess the risk of food allergies and make safe food choices, overcoming language barriers.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] Users take pictures of food packaging or restaurant menus using their smartphones or tablets. The captured images are either saved on the device or sent directly to the server via the application. At this stage, users can also manually input text information, which is then used for analysis in subsequent processing steps.

[0705] Step 2:

[0706] The terminal receives captured or inputted digital information and transmits it to the server using a configured network communication module. Specifically, the data is packaged according to a particular protocol and uploaded to the server's dedicated API via the internet. This output becomes the input data for analysis by the server.

[0707] Step 3:

[0708] The server acquires the received digital information and extracts character information from the image data using optical character recognition (OCR) technology. The image input to the OCR engine undergoes character identification and text conversion processes, and is output as a string of characters. This output character information is then passed on to the subsequent natural language processing step.

[0709] Step 4:

[0710] The server, upon receiving the textual information, uses natural language processing to identify specific food components from this information. Specifically, it utilizes a generative AI model to analyze specific keywords within the text, identify allergen components, and output them. This output is then used in the next comparison and judgment step.

[0711] Step 5:

[0712] The server compares the identified allergen component with the user's pre-registered allergy information. It searches the user's health database for relevant information and, if a matching allergen is found, identifies it as an allergy risk and outputs it. The results of this determination are used to generate a warning message.

[0713] Step 6:

[0714] The server that obtains the judgment results generates a warning message as needed. This message is intended to inform the user of potential health risks, and a specific example might be, "This dish contains certain allergens." This output is then notified to the user.

[0715] Step 7:

[0716] The user receives a warning message generated by the server through their device. This message pops up via the device's notification system, allowing the user to choose safer meals based on it. Risk information is displayed on the device's screen, serving as output for the user.

[0717] (Application Example 1)

[0718] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] When users with food allergies choose food products, there is a need to address the challenge of ensuring safe consumption by quickly and accurately identifying the allergens contained in the food and providing visual warnings. Furthermore, information must be available in multiple languages.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0721] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting text data from the image information, natural language processing means for identifying allergen components from the text data, comparison and determination means for determining allergy risk by comparing the allergen components with the user's allergy information, and output means for presenting the determination result of the allergy risk on a visual display device. This makes it possible for users to easily check allergen information and safely select food based on appropriate judgment.

[0722] A "user" refers to an individual who uses the system to check allergen information contained in food products.

[0723] "Image information" refers to visual data of food packaging and menus acquired by users using devices such as smart glasses.

[0724] "Text information" refers to written information about food products that users manually or automatically enter.

[0725] "Image analysis means" refers to a function that extracts text data from acquired image information using optical character recognition technology or the like.

[0726] "Natural language processing means" refers to techniques that analyze the meaning of text information in order to identify allergen components from text data.

[0727] The "comparison and determination means" refers to a function that compares extracted allergen components with the user's pre-registered allergy information to evaluate the allergy risk.

[0728] A "visual display device" refers to a device that uses smart glasses or other display devices to visually present information to the user.

[0729] "Output means" refers to a function that displays warnings or information to notify the user of the determined allergy risk.

[0730] This invention provides a system that quickly and accurately identifies allergen information contained in food and displays a warning on a visual display device used by the user. The system primarily utilizes the following hardware and software.

[0731] The user wears smart glasses and takes pictures of food packaging or restaurant menus with the camera. The acquired image information is sent to a server via the terminal. The server has OpenCV and Tesseract OCR installed for image analysis, and uses them to extract text data from the images. The extracted text data is analyzed using SpaCy, a natural language processing software, to identify allergen components.

[0732] The server is equipped with a comparison mechanism to match identified allergen components with the user's pre-registered allergy information. This comparison assesses the user's allergy risk, and if a risk exists, a warning message is immediately generated. The generated warning is displayed on the smart glasses' HUD, allowing the user to visually receive the information.

[0733] For example, if a user visits an Italian restaurant while out and about and takes a picture of a menu item called "Shrimp Risotto," the system will identify shrimp as an allergen and display a warning on the smart glasses saying, "You have a shrimp allergy. Please be careful." In this way, users can make food choices safely.

[0734] An example of a prompt message is: "There is a shrimp risotto on the menu. Identify the allergens and generate a risk warning."

[0735] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0736] Step 1:

[0737] The user uses the camera on their smart glasses to take pictures of food packaging or restaurant menus. The camera captures the image information and sends that data to the device as input.

[0738] Step 2:

[0739] The terminal uploads the received image information to the cloud server. The server receives this image information as input and prepares to proceed to the next analysis step.

[0740] Step 3:

[0741] The server uses OpenCV and Tesseract OCR to extract text data from the received image information. At this stage, the input is image information, and the output is the corresponding text data. The text data includes the names of the food items and ingredient information listed on the menu.

[0742] Step 4:

[0743] The server analyzes the extracted text data using SpaCy, a natural language processing framework, to identify allergen components. The input is text data, and a list of allergens is output. In this process, the meaning and context of words are considered to determine whether a food component is an allergen.

[0744] Step 5:

[0745] The server matches the identified allergen components against the user's pre-registered allergy information. The input is a list of allergens and the user's allergy information, and the output is an assessment of the allergy risk. During the matching process, comparative calculations are performed to determine whether or not a risk exists.

[0746] Step 6:

[0747] The server generates a warning message based on the determined allergy risk and sends that message to the smart glasses via the terminal. The input is the result of the risk assessment, and the output is the warning message. The system visually informs the user of the risk.

[0748] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0749] The food allergy detection system according to the present invention helps users evaluate the allergy risk of food and enjoy meals safely. This system consists of a user, a terminal, a server, and an emotion engine.

[0750] Users first use a device such as a smartphone or tablet to take pictures of the menu or input text information about the food. The device then transmits this information to the server, which analyzes the information using image analysis and natural language processing technologies. Specifically, the server uses image analysis tools to obtain text information from the images and extracts it as text data.

[0751] Subsequently, the server uses natural language processing to identify allergen components from the extracted text. These identified allergen components are then compared and matched with the user's registered allergy information to determine the allergy risk.

[0752] A distinctive feature of this invention is the combination of this determination process with an emotion engine. The emotion engine is used to recognize emotions from text information and voice data entered by the user. Based on this information, the user's emotional state is analyzed, and if the user is in a situation where they are likely to feel stressed, a warning message that draws particular attention to them can be generated.

[0753] For example, if a user feels anxious at a restaurant abroad, the emotion engine detects that anxiety and adjusts its settings to provide more detailed and thorough allergy information. In this way, users can enjoy their meal with peace of mind while reducing their mental burden.

[0754] The device ultimately presents the user with information received from the server, providing risk information and warning messages in an optimal format tailored to their emotional state. This facilitates safer food choices and alleviates anxiety about food.

[0755] This system incorporates a new approach that takes user emotions into account, providing a higher level of reassurance than traditional methods could achieve. This makes support for users with food allergies even more effective.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] Users take photos of restaurant menus or food packaging using their smartphone or tablet camera. Alternatively, they can directly input food names or descriptions of dishes as text on their device.

[0759] Step 2:

[0760] The device transmits captured image data or entered text information to a server via the internet.

[0761] Step 3:

[0762] The server processes the image data received from the terminal using image analysis tools. This tool extracts text information from the image data. Optical character recognition (OCR) technology is used to recognize characters within the image and convert them into text format.

[0763] Step 4:

[0764] The server analyzes the extracted text data using natural language processing to identify allergen components. This process identifies specific words or phrases within the text and determines whether they are components that may cause allergies.

[0765] Step 5:

[0766] The server compares the identified allergen components with the user's registered allergy information. This allows the server to assess the user's allergy risk.

[0767] Step 6:

[0768] Simultaneously, the system uses an emotion engine to analyze the user's emotional state based on their recent text input and voice data. This engine determines the degree of stress and anxiety based on the user's input.

[0769] Step 7:

[0770] The server integrates the allergy risk assessment results with the user's emotional state and adjusts the warning message as needed. For example, if the user is in an anxious emotional state, the message will be made more detailed and polite.

[0771] Step 8:

[0772] The device displays generated warning messages and allergy information to the user. This allows the user to receive information in a format best suited to their emotional state, enabling them to make safe food choices.

[0773] This process allows us to support users in making food choices that take their feelings into consideration and ensure safety.

[0774] (Example 2)

[0775] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] To ensure that users with food allergies can safely enjoy meals in different cultural and linguistic environments, a system is needed that can reliably and quickly verify allergen information. Furthermore, methods are required to consider the user's emotional state and alleviate their anxiety about food.

[0777] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0778] In this invention, the server includes means for receiving image information or text information input by a user, image analysis means for extracting character data from the image information, natural language processing means for identifying allergen components from the character data, emotion analysis means for analyzing emotional information from the user to identify the emotional state, and warning generation means for generating a warning message based on the identified emotional state. This makes it possible to provide emotionally sensitive information along with allergy risk analysis simply by the user inputting image or text information.

[0779] A "user" is an individual who uses the system to check the risk of food allergies.

[0780] "Image information" refers to visual data captured using cameras or electronic devices, including menus and food labels.

[0781] "Text information" refers to string data entered by the user, such as information about food ingredients or menu details.

[0782] "Image analysis means" refers to techniques used to extract character data from image information, and includes computer vision algorithms.

[0783] "Natural language processing means" refers to techniques for analyzing and extracting useful information from text data, and is used for identifying allergen components.

[0784] "Allergens" refer to substances that can cause allergic reactions and are specific components found in food.

[0785] A "comparative determination method" refers to a method for comparing identified allergen components with the user's registered allergy information, and plays a role in evaluating allergy risk.

[0786] "Emotional analysis methods" refer to technologies that analyze text and audio data to evaluate a user's emotional state.

[0787] "Warning generation method" refers to a method of creating messages based on emotional state or allergy risk in order to provide alerts to users.

[0788] This food allergy detection system is designed to help users with food allergies safely enjoy meals. The specific implementation method is described below.

[0789] Users use devices such as smartphones or tablets to take pictures of meal menus or food packaging, or to input text information about the food into their devices. The devices then transmit this image and text information to a server via the internet. Wi-Fi or mobile data communication is used for this transmission.

[0790] The server processes the received image information using image analysis technology. This process utilizes common image analysis software such as OpenCV to extract text data from the images. Furthermore, natural language processing, using Python's NLTK library, is applied to the extracted text data to specifically identify allergen components.

[0791] Furthermore, based on the allergy information registered by the user in advance, the server matches it with identified allergen components and determines the allergy risk. In this process, the system prepares to generate and provide allergy warning messages to the user.

[0792] A distinctive feature of the system is emotion analysis. The server utilizes a generative AI model to analyze text and voice data entered by the user, identifying the user's emotional state, such as "anxiety." Based on this emotional information, it is possible to adjust the content and presentation method of warning messages.

[0793] As a concrete example, in response to a prompt message entered by a user such as, "I'm looking for a restaurant abroad that doesn't serve dishes without nuts, but I'm worried," the system will detect the presence or absence of nuts and, taking the user's concerns into account, provide detailed safety information.

[0794] In this way, this system uses advanced image analysis and natural language processing technologies to provide users with necessary and reassuring food allergy information tailored to their individual emotional state.

[0795] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0796] Step 1:

[0797] Users use devices such as smartphones or tablets to take pictures of menus or food packaging, or to directly input text information about the food. The entered data is sent to the device.

[0798] Step 2:

[0799] The terminal transmits image or text information provided by the user to the server via the internet. In this case, image data is sent to the server in JPEG format, and text data in UTF-8 format.

[0800] Step 3:

[0801] The server processes the received image data through an image analysis tool to extract text data from the image. This process uses image analysis software such as OpenCV, and the extracted text data is obtained as output.

[0802] Step 4:

[0803] The server sends the extracted string data to a natural language processing system to identify allergen components. This process uses the Python natural language processing library NLTK to obtain words that may be allergens as output.

[0804] Step 5:

[0805] The server compares the identified allergen components with the user's registration information to determine the allergy risk. Based on the comparison results, the allergy risk assessment is output, and a warning message is prepared if necessary.

[0806] Step 6:

[0807] The server uses a generative AI model to analyze emotional information from the user's input text data and identify the user's emotional state. The emotional state derived from the emotional analysis is then output. This process is based on emotional expressions such as "anxiety" that the user inputs.

[0808] Step 7:

[0809] The server generates the most appropriate warning message based on the allergy risk assessment and the user's emotional state. For example, if the emotional state is "anxious," the message will be polite and detailed. This message is then generated as the final output.

[0810] Step 8:

[0811] The terminal receives the final output from the server and presents the information to the user in the most optimal format. For example, a message might appear as a pop-up on the terminal's display. This allows the user to obtain information to make safe food choices.

[0812] (Application Example 2)

[0813] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0814] There is a need for a system that can quickly and accurately assess the health risks that allergens in food pose to users with allergies, and further provide information that reduces the user's emotional burden based on their emotional state. In particular, there is a need to strengthen safety and psychological support so that users with food allergies can enjoy meals with peace of mind in situations where they feel anxious or stressed.

[0815] The identification processing performed 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 receiving image information or text information input from a user, image analysis means for extracting text data from the image information, and natural language processing means for identifying allergen components from the text data. This makes it possible to identify allergens contained in food, appropriately evaluate the user's allergy risk, and further provide information according to the user's emotional state through emotion recognition.

[0816] A "user" is a person who uses the system to evaluate the allergy risk of food.

[0817] "Image information" refers to visual data about food that users input via their devices.

[0818] "Text information" refers to character data about food that users input via their devices.

[0819] "Image analysis means" refers to technical means that perform a process to extract text data from received image information.

[0820] "Natural language processing means" refers to technical means for identifying allergen components from extracted text data.

[0821] "Allergens" are components in food that can potentially cause allergic reactions.

[0822] A "comparative determination means" is a technical means for determining allergy risk by comparing identified allergen components with the user's allergy information.

[0823] "Emotion recognition means" refers to technical means for recognizing a user's emotional state and providing information to alleviate their mental burden.

[0824] "Allergy risk" refers to the potential health risks that identified allergen components pose to the user.

[0825] A "server" is a central computer system that receives image and text information and performs analysis and judgment.

[0826] This invention is a system for users to identify allergens in food and assess their risks. The process begins with the user taking a picture of a menu item using a smartphone or other device, or entering text information about the food. The device then transmits this image or text information to a server.

[0827] The server processes the received information as follows: First, it extracts text data from the image information using image analysis tools. This utilizes techniques that identify text information within images using machine learning frameworks such as TensorFlow. Then, it identifies allergen components from the extracted text data using natural language processing tools. In this step, it analyzes food components in the text using natural language processing tools such as SpaCy.

[0828] Furthermore, the server uses emotion recognition tools to analyze the user's emotional state. For example, it leverages IBM Watson's emotion analysis API to recognize emotions from the user's input text and voice. Based on this information, the server generates detailed warning messages if the user is feeling anxious. This helps reduce the user's mental burden while supporting safe food choices.

[0829] Finally, the server sends the analysis results to the terminal and presents the allergy risk assessment to the user in an optimal format based on sentiment analysis. This allows the user to choose their meals with confidence.

[0830] For example, when a user orders Chinese food and takes a picture of the menu and uploads it to the app, "peanuts" are identified as an allergen. If the user is concerned, the app will use this information to warn them, "This dish contains an allergen. Please consider other safer options."

[0831] Examples of prompt statements for a generative AI model are as follows:

[0832] Use a food allergy detection system to identify allergens from image / text information and generate messages that provide information tailored to the user's emotional state. Include points to consider especially when the user is feeling anxious.

[0833] This system configuration allows us to provide users with food allergies with a high level of safety and peace of mind.

[0834] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0835] Step 1:

[0836] Users either take a picture of the food menu via a smartphone or other device, or input text information. The entered data is saved on the device as image or text information. This constitutes the initial input.

[0837] Step 2:

[0838] The terminal sends the input image or text information to the server. At this stage, the user's input data reaches the server and is ready to be analyzed in the next step.

[0839] Step 3:

[0840] The server extracts text data from received image information using image analysis tools. The input here is image data, and the output is the extracted text information. This process utilizes character recognition algorithms based on technologies such as TensorFlow.

[0841] Step 4:

[0842] The server uses natural language processing to identify allergen components from extracted text data. The input is text data, and the output is the identified allergen components. Natural language processing tools such as SpaCy are used to analyze the components within the text and extract allergen candidates.

[0843] Step 5:

[0844] The server analyzes the user's emotional state through emotion recognition mechanisms. It uses text and other information entered by the user as analysis material and identifies the user's emotional patterns using IBM Watson's emotion analysis API. The input is digital information about the user's emotions, and the output is the recognized emotional state.

[0845] Step 6:

[0846] The server integrates information on allergen components and the user's emotional state to generate detailed warning messages as needed. The input is the identified allergen components and the user's emotional state, while the output is a customized warning message presented to the user.

[0847] Step 7:

[0848] The server presents the generated warning messages and allergy risk information to the user via the terminal. The final output allows the user to receive information that enables them to make informed food choices.

[0849] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0850] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0852] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0853] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0854] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0855] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0856] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0857] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0858] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0859] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0860] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0861] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0862] 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.

[0863] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0864] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0865] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0866] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0867] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0868] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0869] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A means for receiving image information or text information entered by a user,

[0873] Image analysis means for extracting text data from the aforementioned image information,

[0874] A natural language processing means for identifying allergen components from the aforementioned text data,

[0875] A comparative determination means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information,

[0876] A means for presenting the results of the allergy risk assessment to the user,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the natural language processing means has a function for supporting multiple languages.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the result of determining the allergy risk has a function to generate a warning message to ensure the user's safety.

[0882] "Example 1"

[0883] (Claim 1)

[0884] A means for receiving digital information input from a user via a receiving terminal,

[0885] An analysis means that utilizes optical character recognition technology to extract character information from the aforementioned digital information,

[0886] A natural language processing means for identifying food components from the aforementioned textual information,

[0887] A comparison and determination means for comparing the aforementioned food components with the user's health information and determining the associated health risks,

[0888] Information presentation means for notifying the user of the results of the assessment of the health risk,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the natural language processing means enables support for international languages.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising a function for generating warning information based on the results of the health risk assessment.

[0894] "Application Example 1"

[0895] (Claim 1)

[0896] A means for receiving image information or text information entered by a user,

[0897] Image analysis means for extracting text data from the aforementioned image information,

[0898] A natural language processing means for identifying allergen components from the aforementioned text data,

[0899] A comparative determination means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information,

[0900] Output means for displaying the allergy risk determination result on a visual display device,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the natural language processing means has a function for supporting multiple languages ​​and presents information using a visual display device.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the result of the allergy risk assessment has a function to visually display a warning message to ensure the user's safety.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] A means for receiving image information or text information entered by a user,

[0909] Image analysis means for extracting character data from the aforementioned image information,

[0910] A natural language processing means for identifying allergen components from the aforementioned text data,

[0911] A matching means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information,

[0912] A means for presenting the results of the allergy risk assessment to the user,

[0913] A means for analyzing emotional information from users to identify their emotional state,

[0914] A warning generation means for generating a warning message based on an identified emotional state,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the natural language processing means has a function for supporting multiple languages.

[0918] (Claim 3)

[0919] The system according to claim 1, which has a function to generate a warning message based on the allergy risk assessment result and the user's emotional information.

[0920] "Application example 2 when combining with an emotional engine"

[0921] (Claim 1)

[0922] A means for receiving image information or text information entered by a user,

[0923] Image analysis means for extracting text data from the aforementioned image information,

[0924] A natural language processing means for identifying allergen components from the aforementioned text data,

[0925] A comparative determination means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information,

[0926] In the allergy risk determination process, an emotion recognition means analyzes input information to recognize the user's emotional state and provides information to reduce the user's mental burden,

[0927] A means for presenting the results of the allergy risk assessment and information based on emotional recognition in a format suitable for the user,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the natural language processing means and emotion recognition means have functions for multilingual support.

[0931] (Claim 3)

[0932] The system according to claim 1, further comprising a function to generate a warning message to further ensure the user's safety based on the allergy risk assessment result and the user's emotional state. [Explanation of symbols]

[0933] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving image information or text information entered by a user, Image analysis means for extracting text data from the aforementioned image information, A natural language processing means for identifying allergen components from the aforementioned text data, A comparative determination means for determining allergy risk by comparing the aforementioned allergen component with the user's allergy information, A means for presenting the results of the allergy risk assessment to the user, A system that includes this.

2. The system according to claim 1, wherein the natural language processing means has a function for supporting multiple languages.

3. The system according to claim 1, wherein the result of determining the allergy risk has a function to generate a warning message to ensure the user's safety.

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